Amazon Technologies, Inc.

United States of America

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H04L 29/06 - Communication control; Communication processing characterised by a protocol 2,378
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1.

Automated Generation and Presentation of Sign Language Avatars for Video Content

      
Application Number 18432623
Status Pending
Filing Date 2024-02-05
First Publication Date 2024-07-18
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Vajpayee, Avijit
  • Bhat, Vimal
  • Cholkar, Arjun
  • Barker, Louis Kirk
  • Jain, Abhinav

Abstract

Systems, methods, and computer-readable media are disclosed for systems and methods for automated generation and presentation of sign language avatars for video content. Example methods may include determining, by one or more computer processors coupled to memory, a first segment of video content, the first segment including a first set of frames, first audio content, and first subtitle data, where the first subtitle data comprises a first word and a second word. Methods may include determining, using a first machine learning model, a first sign gesture associated with the first word, determining first motion data associated with the first sign gesture, and determining first facial expression data. Methods may include generating an avatar configured to perform the first sign gesture using the first motion data, where a facial expression of the avatar while performing the first sign gesture is based on the first facial expression data.

IPC Classes  ?

  • G06T 13/40 - 3D [Three Dimensional] animation of characters, e.g. humans, animals or virtual beings
  • G06F 40/20 - Natural language analysis
  • G06N 3/08 - Learning methods
  • G06T 17/00 - 3D modelling for computer graphics
  • G06V 20/40 - Scenes; Scene-specific elements in video content
  • G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
  • G06V 40/20 - Movements or behaviour, e.g. gesture recognition
  • G09B 21/00 - Teaching, or communicating with, the blind, deaf or mute
  • G10L 25/63 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for estimating an emotional state
  • H04N 5/272 - Means for inserting a foreground image in a background image, i.e. inlay, outlay

2.

SYSTEM FOR BIOMETRIC IDENTIFICATION ENROLLMENT

      
Application Number US2024010284
Publication Number 2024/151469
Status In Force
Filing Date 2024-01-04
Publication Date 2024-07-18
Owner AMAZON TECHNOLOGIES, INC. (USA)
Inventor
  • Aggarwal, Manoj
  • Medioni, Gerard, Guy
  • Desjardins, Chad
  • Kumar, Dilip

Abstract

User enrollment to a biometric identification system begins with a pre-enrollment process on selected general input devices (GID) such as smartphones. The user may enter identification data such as their name and use a camera of the GID to acquire first image data, such as of their hand. The first image data is processed to determine a first representation. Upon presentation of a hand at a biometric input device, second image data is acquired. The second image data is processed to determine a second representation. If the second representation is deemed to be associated with the first representation, the enrollment process may be completed by storing the second representation for subsequent use.

IPC Classes  ?

3.

NETWORKING DEVICE THAT BRIDGES VIRTUAL AND PHYSICAL COMPUTER NETWORKS

      
Application Number 18407162
Status Pending
Filing Date 2024-01-08
First Publication Date 2024-07-18
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Cohn, Daniel Todd
  • Brandwine, Eric Jason
  • Doane, Andrew J.

Abstract

Techniques are described for providing logical networking functionality for managed computer networks, such as for virtual computer networks provided on behalf of users or other entities. In some situations, a user may configure or otherwise specify a network topology for a virtual computer network, such as a logical network topology that separates multiple computing nodes of the virtual computer network into multiple logical sub-networks and/or that specifies one or more logical networking devices for the virtual computer network. After a network topology is specified for a virtual computer network, logical networking functionality corresponding to the network topology may be provided in various manners, such as without physically implementing the network topology for the virtual computer network. In some situations, the computing nodes may include virtual machine nodes hosted on one or more physical computing machines or systems, such as by or on behalf of one or more users.

IPC Classes  ?

  • H04L 41/0803 - Configuration setting
  • G06F 9/455 - Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
  • G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
  • H04L 12/46 - Interconnection of networks
  • H04L 41/0806 - Configuration setting for initial configuration or provisioning, e.g. plug-and-play
  • H04L 41/0893 - Assignment of logical groups to network elements
  • H04L 41/12 - Discovery or management of network topologies
  • H04L 45/00 - Routing or path finding of packets in data switching networks
  • H04L 45/02 - Topology update or discovery
  • H04L 61/10 - Mapping addresses of different types
  • H04L 67/10 - Protocols in which an application is distributed across nodes in the network
  • H04L 41/0213 - Standardised network management protocols, e.g. simple network management protocol [SNMP]

4.

ACCOUNT ASSOCIATION WITH DEVICE

      
Application Number 18422561
Status Pending
Filing Date 2024-01-25
First Publication Date 2024-07-18
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Trapp, Szymon Grzegorz
  • Butler, Martin Patrick
  • Karanam, Manikanta Babu
  • Anderson, Nicholas Jeffrey
  • Hu, Boya
  • Wu, Zhengyang
  • Palak, Fnu
  • Marlotte, James

Abstract

Systems and methods for account data association with voice interface devices are disclosed. For example, when a host user/primary user and guest user have consented for account data to be associated with the primary user's devices, a request to associate the account data may be received. Voice and device-based authentication may be performed to confirm the identity of the guest user and the guest user's account data may be associated with the primary user's devices. During a guest session, voice recognition may be utilized to determine if a given user utterance is from the guest user or the primary user, and actions may be performed by the voice interface device accordingly.

IPC Classes  ?

  • G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
  • G10L 17/24 -  the user being prompted to utter a password or a predefined phrase
  • H04W 12/06 - Authentication
  • H04W 12/37 - Managing security policies for mobile devices or for controlling mobile applications
  • H04W 76/15 - Setup of multiple wireless link connections

5.

COMPUTER-IMPLEMENTED MULTI-SCALE MACHINE LEARNING MODEL FOR THE ENHANCEMENT OF COMPRESSED VIDEO

      
Application Number US2024010748
Publication Number 2024/151553
Status In Force
Filing Date 2024-01-08
Publication Date 2024-07-18
Owner AMAZON TECHNOLOGIES, INC. (USA)
Inventor
  • Misra, Kiran Mukesh
  • Segall, Christopher Andrew
  • Choi, Byeongdoo

Abstract

The present disclosure relates to methods, apparatus, systems, and non-transitory computer-readable storage media for training and using a multi-scale machine learning model for the enhancement of compressed video.

IPC Classes  ?

  • H04N 19/17 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object
  • H04N 19/176 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a block, e.g. a macroblock
  • H04N 19/186 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a colour or a chrominance component
  • H04N 19/46 - Embedding additional information in the video signal during the compression process
  • H04N 19/59 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving spatial sub-sampling or interpolation, e.g. alteration of picture size or resolution
  • H04N 19/82 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals - Details of filtering operations specially adapted for video compression, e.g. for pixel interpolation involving filtering within a prediction loop
  • H04N 19/85 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression

6.

Evaluating biometric authorization systems with synthesized images

      
Application Number 17709262
Grant Number 12039027
Status In Force
Filing Date 2022-03-30
First Publication Date 2024-07-16
Grant Date 2024-07-16
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Xu, Xiang
  • Zhou, Hao
  • Wu, Jonathan
  • Tighe, Joseph P

Abstract

A system for evaluating a biometric authorization system is described. The biometric authorization system is configured to apply a facial recognition model to image data to make an authorization determination based on detection of synthesized image data and based on matching a reference image to the image data. The system is also configured to execute one or more synthetic image data attack protocols to evaluate the biometric authorization system. The system also generates, according to one or more synthetic image data generation techniques, an evaluation set of image data comprising synthesized representations of a target and sends one or more authorization requests using the evaluation set of image data to the biometric authorization system. The system generates an evaluation of the biometric authorization system for synthetic image data attack analysis based on respective responses to the one or more authorization requests received from the biometric authorization system.

IPC Classes  ?

  • G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints

7.

Security sensor

      
Application Number 29821890
Grant Number D1035472
Status In Force
Filing Date 2022-01-04
First Publication Date 2024-07-16
Grant Date 2024-07-16
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Lu, Wen-Yo
  • Bowers, Alexsandra M.
  • Grant, Stephen James
  • Loew, Christopher
  • Sao, Vinay
  • Siminoff, James
  • Tsai, Yen-Chi

8.

Minimizing connection loss when changing database query engine versions

      
Application Number 18194579
Grant Number 12038946
Status In Force
Filing Date 2023-03-31
First Publication Date 2024-07-16
Grant Date 2024-07-16
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Shankar, Ramesh
  • Mittal, Raman

Abstract

Connection loss may be minimized for performing database query engine changes. A distributed database system may include different instances of the query engine that provide access to a database. When an event to change the version of the query engine is detected, a copy of the database may be created and a new instance of the query engine created. Read-only access to the database may be maintained using the different instances of the query engine while the new instance may be upgraded to the different version of the query engine. Upon successful installation of the different version of the query engine at the new instance, the new instance may be given read-write access to the database using the copy of the database and other database instances may be upgraded to the different version of the query engine.

IPC Classes  ?

  • G06F 16/23 - Updating
  • G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor

9.

Bandwidth estimation for video encoding

      
Application Number 15925498
Grant Number 12041303
Status In Force
Filing Date 2018-03-19
First Publication Date 2024-07-16
Grant Date 2024-07-16
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Brailovskiy, Ilya Vladimirovich
  • Carosi, Alessio
  • Brindis, Ulises

Abstract

Techniques are generally described for remote estimation of bandwidth. In various examples, a video stream may be received at a first bit rate over a first communication channel. A first value of a network condition of the video stream may be determined over a first time period. A determination may be made that the first value is less than a threshold value. A first bandwidth estimate of the communication channel may be determined. The first bandwidth estimate may comprise the first bit rate reduced by a first percentage. A second value of the network condition may be determined over a second time period. A determination may be made that the second value is greater than the threshold value. A second bandwidth estimate of the communication channel may be determined. The second bandwidth estimate may be less than the first bandwidth estimate.

IPC Classes  ?

  • H04W 24/00 - Supervisory, monitoring or testing arrangements
  • H04L 43/0829 - Packet loss
  • H04L 47/25 - Flow control; Congestion control with rate being modified by the source upon detecting a change of network conditions
  • H04N 19/166 - Feedback from the receiver or from the transmission channel concerning the amount of transmission errors, e.g. bit error rate [BER]
  • H04N 21/24 - Monitoring of processes or resources, e.g. monitoring of server load, available bandwidth or upstream requests
  • H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
  • H04L 43/0882 - Utilisation of link capacity

10.

Computer-implemented methods of an automated framework for virtual product placement in video frames

      
Application Number 17853720
Grant Number 12041278
Status In Force
Filing Date 2022-06-29
First Publication Date 2024-07-16
Grant Date 2024-07-16
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Bhargavi, V Divya
  • Sindwani, Karan
  • Gholami, Siavash
  • Nie, Xiaohan
  • Ahmed, Ahmed Aly Saad
  • Kuo, David
  • Chaturvedi, Yash
  • Ravipati, Vidya Sagar

Abstract

Techniques for a computer-implemented service for virtual product placement in video frames are described. According to some embodiments, a computer-implemented method includes receiving, at a virtual product placement service, a request to place a two-dimensional image of a virtual product into a video, identifying, by a machine learning model of the virtual product placement service, a surface depicted in the video for insertion of the two-dimensional image of the virtual product, inserting, by the virtual product placement service, of the two-dimensional image of the virtual product into one or more frames of the video onto the surface to generate a video including the virtual product, and transmitting the video including the virtual product to a viewer device or a storage location.

IPC Classes  ?

  • H04N 21/234 - Processing of video elementary streams, e.g. splicing of video streams or manipulating MPEG-4 scene graphs
  • H04N 21/25 - Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication or learning user preferences for recommending movies
  • H04N 21/81 - Monomedia components thereof

11.

Energy efficient phase shifting in digital beamforming circuits for phased array antennas

      
Application Number 17111397
Grant Number 12040557
Status In Force
Filing Date 2020-12-03
First Publication Date 2024-07-16
Grant Date 2024-07-16
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Sharma, Sunny
  • Lee, Jung Joo

Abstract

Technologies directed to energy efficient phase shifting in digital beamforming in phased array antennas in communication systems are described. Digital signal processing (DSP) circuitry includes a first phase shifter that generates second data by phase shifting first data according to a rotation-based operation without multiplication of the second data, a second phase shifter that generates fourth data by phase shifting third data according to the rotation-based operation without multiplication of the fourth data, a combiner that generates fifth data by adding the second data and the fourth data, and a multiplier that generates sixth data by multiplying the fifth data by a constant value.

IPC Classes  ?

  • H01Q 3/38 - Arrangements for changing or varying the orientation or the shape of the directional pattern of the waves radiated from an antenna or antenna system varying the distribution of energy across a radiating aperture varying the phase by electrical means with variable phase-shifters the phase-shifters being digital
  • H01Q 3/40 - Arrangements for changing or varying the orientation or the shape of the directional pattern of the waves radiated from an antenna or antenna system varying the distribution of energy across a radiating aperture varying the phase by electrical means with phasing matrix

12.

Programmable vector engine for efficient beam search

      
Application Number 17447677
Grant Number 12039330
Status In Force
Filing Date 2021-09-14
First Publication Date 2024-07-16
Grant Date 2024-07-16
Owner Amazon Technologies, Inc. (USA)
Inventor Meyer, Paul Gilbert

Abstract

To perform a beam search operation on an input tensor using a data processor with native hardware support, the data processor can be programmed with a set of instructions. The set of instructions can include a first machine instruction that operates on the input tensor to obtain N largest values in the input tensor, a second machine instruction that operates on the input tensor to obtain indices corresponding to the N largest values in the input tensor, and a third machine instruction that operates on the input tensor to replace the N largest values in the input tensor with a minimum value.

IPC Classes  ?

  • G06F 9/30 - Arrangements for executing machine instructions, e.g. instruction decode
  • G06N 3/02 - Neural networks

13.

Mitigating near-field-communication (NFC) antenna interference

      
Application Number 18132894
Grant Number 12039517
Status In Force
Filing Date 2023-04-10
First Publication Date 2024-07-16
Grant Date 2024-07-16
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Macy, Benjamin Blake
  • Kim, Sora
  • Acharya, Kaustuva
  • Garstki, Jacob Francis

Abstract

This disclosure describes systems and techniques for enabling a communication device to communicate wirelessly with a near-field-communication (NFC)-enabled payment terminal while avoiding interference between the NFC-enabled payment terminal and other NFC payment instruments. In some instances, the communication device may receive, via a non-NFC communication protocol, a payment token from an identification device and may send, over NFC, the payment token to the NFC-enabled payment terminal for satisfying the cost of a transaction.

IPC Classes  ?

  • G06Q 20/32 - Payment architectures, schemes or protocols characterised by the use of specific devices using wireless devices
  • H04B 5/48 - Transceivers

14.

Computer-implemented methods for synthetic accuracy measurement of a content recognition system

      
Application Number 17954005
Grant Number 12041138
Status In Force
Filing Date 2022-09-27
First Publication Date 2024-07-16
Grant Date 2024-07-16
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Mahajan, Nagaraj
  • Mcguire, David
  • Jin, Zhengping
  • Abdelal, Ahmed

Abstract

Techniques for synthetic accuracy measurement of a content recognition system are described. According to some examples, a computer-implemented method includes generating, by a provider network, a reference fingerprint for a secondary content (e.g., advertisement) media file; generating, by the provider network, a synthetic fingerprint for a transformed version of the secondary content media file; inserting, by the provider network, the synthetic fingerprint into a stream of fingerprints of a plurality of media files; comparing, by a comparison service of the provider network, the stream of fingerprints including the synthetic fingerprint to the reference fingerprint to generate an indication of a match between the synthetic fingerprint and the reference fingerprint in the stream; and sending the indication of the match to a storage location.

IPC Classes  ?

  • G06F 16/00 - Information retrieval; Database structures therefor; File system structures therefor
  • G06F 16/43 - Querying
  • H04L 67/51 - Discovery or management thereof, e.g. service location protocol [SLP] or web services

15.

Door mount

      
Application Number 29871747
Grant Number D1035417
Status In Force
Filing Date 2023-02-27
First Publication Date 2024-07-16
Grant Date 2024-07-16
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Lu, Wen-Yo
  • Tsai, Yen-Chi
  • Yemelin, Maksym
  • England, Matthew J.
  • Siminoff, James

16.

System to manipulate sliding obstacles with non-holonomic autonomous mobile device

      
Application Number 17659088
Grant Number 12038760
Status In Force
Filing Date 2022-04-13
First Publication Date 2024-07-16
Grant Date 2024-07-16
Owner AMAZON TECHNOLOGIES, INC. (USA)
Inventor
  • Thakar, Shantanu
  • Mishra, Pragyana K.
  • Majcherczyk, Nathalie

Abstract

A physical space may contain sliding doors in various places such as between rooms, as closet doors, on furniture, and so forth. An autonomous mobile device (AMD) capable of non-holonomic motion may selectively contact a portion of the sliding door and apply a force to open or close the door. The AMD moves to an initial pose relative to the door, with a portion of the AMD coming into contact with the door. The AMD then rotates and translates, relative to the door, applying a force to the door. Sensor data, such as linear acceleration, rotation, drive wheel torque, and so forth is used as input to determine the next movement in a series of motions, resulting in continued contact and force application. As the end of travel for this motion is reached, the AMD may separate from the door, reposition, and continue to apply a force to the door.

IPC Classes  ?

  • G05D 1/02 - Control of position or course in two dimensions
  • G05D 1/00 - Control of position, course, altitude, or attitude of land, water, air, or space vehicles, e.g. automatic pilot

17.

Wireless speaker

      
Application Number 29874063
Grant Number D1035610
Status In Force
Filing Date 2023-04-13
First Publication Date 2024-07-16
Grant Date 2024-07-16
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Lu, Wen-Yo
  • England, Matthew J.

18.

Efficient management of packet flow information at network function virtualization services

      
Application Number 17385778
Grant Number 12039358
Status In Force
Filing Date 2021-07-26
First Publication Date 2024-07-16
Grant Date 2024-07-16
Owner Amazon Technologies, Inc. (USA)
Inventor Addepalli, Meher Aditya Kumar

Abstract

A representation of an identifier of a packet flow is stored in a particular entry within a particular entry group of a first object maintained at a packet processing service, along with an expiration criterion for information pertaining to the packet flow, including a representation of an action to be performed. The action is performed after it is retrieved from an element identified within a second object based on an entry identifier of the particular entry and a group identifier of the particular group. In response to receiving a packet of another packet flow, respective indications that one or more in-use entries of the particular entry group (whose expiration criteria are met) are available for re-use are stored, without receiving an indication that the corresponding packet flows have terminated.

IPC Classes  ?

  • G06F 9/455 - Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
  • G06F 16/22 - Indexing; Data structures therefor; Storage structures
  • H04L 45/74 - Address processing for routing
  • H04L 47/41 - Flow control; Congestion control by acting on aggregated flows or links

19.

Secure query processing

      
Application Number 17988724
Grant Number 12038923
Status In Force
Filing Date 2022-11-16
First Publication Date 2024-07-16
Grant Date 2024-07-16
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Paduroiu, Andrei
  • Burd, Yaron
  • Yan, Yan

Abstract

A distributed database keeps user-defined functions separate from a query engine by using a frontend. The frontend allows a user-defined function to interact with a proxy application processing interface (API) that is based on an API of the query engine. The frontend sends serialized data to the query engine in order to interact with the API of the query engine. The user-defined function is executed in security environments separate from the frontend and the query engine.

IPC Classes  ?

  • G06F 16/2453 - Query optimisation
  • G06F 16/25 - Integrating or interfacing systems involving database management systems

20.

Privacy protecting transaction engine for a cloud provider network

      
Application Number 18165724
Grant Number 12041035
Status In Force
Filing Date 2023-02-07
First Publication Date 2024-07-16
Grant Date 2024-07-16
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Kotas, Paul A.
  • Sharma, Keerat Singh
  • Battles, Matthew H.

Abstract

A privacy protecting transaction engine for a cloud provider network is described. According to some embodiments, a computer-implemented method includes receiving a request from a customer of a cloud provider network to create a customer cloud in the cloud provider network, generating the customer cloud in the cloud provider network, receiving a first request at the cloud provider network for the customer cloud that includes private information of an end customer of the customer of the cloud provider network, removing the private information from the first request by a privacy protecting transaction engine of the cloud provider network to generate a second request, and sending the second request to the customer cloud for servicing.

IPC Classes  ?

21.

Automatic deployment of updates for edge device software components based on edge usage limits

      
Application Number 17710767
Grant Number 12039319
Status In Force
Filing Date 2022-03-31
First Publication Date 2024-07-16
Grant Date 2024-07-16
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Leung, Hok Peng
  • Battle, Robert

Abstract

An update deployment service performs automatic testing and deployment of a software update (e.g., security patches) for a software component on a fleet of IoT devices, without the need for the client to test the update and manually deploy the update to the IoT devices. A client configures the service by providing an upper usage limit for certain IoT hardware components (e.g., memory, hard disk) and/or code components (e.g., threads) that may be caused by any future updates. When a new update is available for deployment, the update deployment service obtains usage test data that indicates the amount of usage (or increase in usage) of the IoT hardware and/or code for the new update. If the usage (or increase in usage) is within the client-provided usage limits, then the service automatically deploys the update to the fleet of edge devices.

IPC Classes  ?

22.

Peripheral antenna placement for calibration for a phased array antenna

      
Application Number 17884868
Grant Number 12040555
Status In Force
Filing Date 2022-08-10
First Publication Date 2024-07-16
Grant Date 2024-07-16
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Yousefi, Tara
  • Ramachandran, Iyappan
  • Veysoglu, Murat
  • Hetzel, Peter James
  • Kao, Billy Pingli
  • Mahanfar, Alireza

Abstract

Technologies directed to a directional calibration antenna for calibration of an array antenna of a communication system are described. The communication system includes the array antenna which includes a number of antenna elements. The number of antenna elements are located in an area on a first side of a support structure. A directional antenna is located at a first height above a plane of the array antenna and at a periphery of the area. The directional antenna is pointed towards the array antenna and is located within a near field of the array antenna.

IPC Classes  ?

  • H04B 1/00 - TRANSMISSION - Details of transmission systems not characterised by the medium used for transmission
  • H01Q 1/28 - Adaptation for use in or on aircraft, missiles, satellites, or balloons
  • H01Q 3/36 - Arrangements for changing or varying the orientation or the shape of the directional pattern of the waves radiated from an antenna or antenna system varying the distribution of energy across a radiating aperture varying the phase by electrical means with variable phase-shifters
  • H01Q 9/04 - Resonant antennas
  • H01Q 13/02 - Waveguide horns
  • H04B 1/16 - Circuits

23.

System configuration control through locking of control registers

      
Application Number 17809461
Grant Number 12039339
Status In Force
Filing Date 2022-06-28
First Publication Date 2024-07-16
Grant Date 2024-07-16
Owner Amazon Technologies, Inc. (USA)
Inventor Chong, Nathan Yong Seng

Abstract

Disclosed herein are methods and corresponding apparatus and systems for controlling the configuration of a computer system through locking one or more control registers. In some embodiments, a write-enable controller is configured to permit writing to a control register by a software application when the value of a lock bit has been set to indicate that the control register is unlocked. The control register can be locked by setting the lock bit after the control register has been written to, e.g., as part of a system initialization process that places the computer system into a target configuration. After the control register has been locked, the write-enable controller may prevent further writes to the control register, e.g., a write request from the same application that wrote to the control register earlier or a different software application. The locking of the control register can be maintained until system reset.

IPC Classes  ?

  • G06F 9/00 - Arrangements for program control, e.g. control units
  • G06F 3/06 - Digital input from, or digital output to, record carriers
  • G06F 9/4401 - Bootstrapping

24.

Self-supervised federated learning

      
Application Number 17665129
Grant Number 12039998
Status In Force
Filing Date 2022-02-04
First Publication Date 2024-07-16
Grant Date 2024-07-16
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Kao, Chieh-Chi
  • Tang, Qingming
  • Sun, Ming
  • Rozgic, Viktor
  • Matsoukas, Spyridon
  • Wang, Chao

Abstract

An acoustic event detection system may employ self-supervised federated learning to update encoder and/or classifier machine learning models. In an example operation, an encoder may be pre-trained to extract audio feature data from an audio signal. A decoder may be pre-trained to predict a subsequent portion of audio data (e.g., a subsequent frame of audio data represented by log filterbank energies). The encoder and decoder may be trained using self-supervised learning to improve the decoder's predictions and, by extension, the quality of the audio feature data generated by the encoder. The system may apply federated learning to share encoder updates across user devices. The system may fine-tune the classifier to improve inferences based on the improved audio feature data. The system may distribute classifier updates to the user device(s) to update the on-device classifier.

IPC Classes  ?

  • G10L 25/78 - Detection of presence or absence of voice signals
  • G06N 3/045 - Combinations of networks
  • G06N 3/08 - Learning methods
  • G10L 25/21 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the type of extracted parameters the extracted parameters being power information

25.

Distributed system for efficient entity recognition

      
Application Number 17219715
Grant Number 12039770
Status In Force
Filing Date 2021-03-31
First Publication Date 2024-07-16
Grant Date 2024-07-16
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Deep, Aakash
  • Zhang, Jia Bi
  • Hedley, Jonathan

Abstract

A first encoding representing a set of detected signals is obtained at a sensor-proximity resource of an object recognition application which also includes resources of an analytics service of a provider network. In response to a determination that a cache at the sensor-proximity resource does not include a second encoding which satisfies a similarity criterion with respect to the first encoding, at least a portion of a partition of a spatial index is obtained from another resource selected using an index partition map. A recognition-based action is initiated based on determining that the partition includes an encoding which satisfies the similarity criterion.

IPC Classes  ?

  • G06V 10/75 - Image or video pattern matching; Proximity measures in feature spaces using context analysis; Selection of dictionaries
  • G06F 18/22 - Matching criteria, e.g. proximity measures
  • H04L 67/10 - Protocols in which an application is distributed across nodes in the network

26.

AMAZON RDS

      
Application Number 233838300
Status Pending
Filing Date 2024-07-15
Owner Amazon Technologies, Inc. (USA)
NICE Classes  ? 00 - No classifiable goods/services

Goods & Services

(1) Management of on-line databases for others; data processing services; systematization of data in computer databases; updating and maintenance of data in computer databases. (2) Providing access to databases; transmission of database information via telecommunications networks. (3) Electronic data storage; Software as a Service (SAAS) services featuring software using artificial intelligence for database setup, management, and maintenance; Software as a Service (SAAS) services featuring software for cloud database administration, provisioning, configuration, management, development, deployment, monitoring, hosting and operations; Software as a Service (SAAS) services featuring computer software for sharing and scaling database computing capacity; hosting of databases for others; cloud computing featuring software for use in database management, backups, recovery, encryption, performance optimization; computer services, namely, providing database servers of variable capacity to others; rental of database computing and electronic storage facilities of variable capacity to third parties.

27.

RING

      
Serial Number 98645714
Status Pending
Filing Date 2024-07-12
Owner Amazon Technologies, Inc. ()
NICE Classes  ?
  • 35 - Advertising and business services
  • 38 - Telecommunications services
  • 09 - Scientific and electric apparatus and instruments
  • 12 - Land, air and water vehicles; parts of land vehicles
  • 42 - Scientific, technological and industrial services, research and design
  • 45 - Legal and security services; personal services for individuals.

Goods & Services

Wholesale and retail services featuring alarms for vehicles, cameras for vehicles, dashboard cameras mounts for dashboard cameras, smart devices for vehicle safety and security, and parts, accessories, and fittings for the aforesaid goods; Online wholesale and retail services featuring alarms for vehicles, cameras for vehicles, dashboard cameras mounts for dashboard cameras, smart devices for vehicle safety and security, and parts, accessories, and fittings for the aforesaid goods; Promoting awareness of public safety and crime prevention and the benefits of using electronic security monitoring systems Transmission of motor vehicle status information via computer and communications networks, namely, electronic transmission of streamed and downloadable audio, visual and multimedia content files via computer and other communications networks; Communication services for transmission of information relating to tracking and monitoring of vehicles; Electronic transmission of geolocation data from mobile devices; Peer-to-peer photo sharing and video sharing services, namely, electronic transmission of digital photo files, videos, and audio visual content among users; Providing online forums for transmission of messages among computer users on topics of local interest and home and commercial security, safety, and surveillance; Electronic exchange of voice, data, audio, video, text and graphics accessible via computer and telecommunications networks; Instant messaging and web messaging services Cameras for vehicles; Frontview, rearview, reversing, and sideview cameras for vehicles; Safety cameras; Dashboard cameras; 360 degree cameras; Software and hardware for processing digital images for vehicle mounted cameras; Video monitors for vehicles; Mounting devices for cameras and monitors; Vehicle safety equipment, namely, an on-board vehicular surveillance system comprised of cameras and monitors for exposing and eliminating the blind spots on both sides of the vehicle; Apparatus for recording, transmission, and processing images for surround view for vehicles; Remote controls for operating vehicle alarms; Digital readers, sensors, magnetically encoded key fobs and tags, and multifunctional electronic devices for transmitting, storing, displaying, and uploading information via a global communications network; Parts, accessories, and fittings for the aforesaid goods; Downloadable and downloadable mobile application software for securing, controlling, or automating locking and closing of vehicles, locking vehicle doors, and closing vehicle windows; Roadside assistance and emergency crash software; Downloadable and downloadable mobile application software for vehicle communications; Downloadable software and computer application software for mobile phones for tracking of vehicles and drivers; Vehicle and driver tagging and tracking devices and apparatuses for interfacing and communicating with networks; Electronic navigational, positioning, and geolocation apparatus and instruments; Downloadable application programming interface (API) software for monitoring, controlling, capturing, storing, viewing, and transmitting videos and security data from vehicle cameras and from vehicle security systems; Downloadable software development kits (SDKs) for creating software and applications related to vehicle safety, security, automation, and surveillance; Downloadable computer software and downloadable mobile application software using artificial intelligence (AI) for searching videos; Recorded computer software using artificial intelligence (AI) for object recognition, interpretation, analysis, reporting, and alerting, as well as for controlling networked devices Anti-theft alarms for vehicles; Security alarms for vehicles Providing temporary use of non-downloadable computer software for roadside and emergency crash assistance; Providing temporary use of non-downloadable computer software for securing, controlling, or automating locking and closing of vehicles, locking vehicle doors, and closing vehicle windows; Providing temporary use of non-downloadable computer software for vehicle communications; Providing temporary use of non-downloadable computer software using artificial intelligence (AI) for searching videos; Providing temporary use of non-downloadable computer software using artificial intelligence (AI) for object recognition, interpretation, analysis, reporting, and alerting, as well as for controlling networked devices; Computer services, namely, creating an on-line community for registered users to participate in discussions, get feedback from their peers, form virtual communities, and engage in social networking services in the field of home and commercial security, safety, and surveillance Online social networking services

28.

FABI FUCHS

      
Serial Number 98643478
Status Pending
Filing Date 2024-07-11
Owner Amazon Technologies, Inc. ()
NICE Classes  ?
  • 25 - Clothing; footwear; headgear
  • 09 - Scientific and electric apparatus and instruments
  • 16 - Paper, cardboard and goods made from these materials
  • 41 - Education, entertainment, sporting and cultural services

Goods & Services

Athletic shoes; bandanas; baseball caps; beach cover-ups; beachwear; belts; bikinis; blazers; boots; bow ties; caps being headwear; cloaks; cloth bibs; coats; costumes for use in role-playing games; dresses; ear muffs; footwear; gloves; golf shirts; Halloween costumes; hats; head bands; headwear; hosiery; infantwear; jackets; jeans; jerseys; kerchiefs; leotards; leg warmers; lingerie; loungewear; mittens; neckties; night shirts; night gowns; overalls; pajamas; pants; polo shirts; ponchos; rainwear; robes; sandals; scarves; shirts; shoes; skirts; shorts; slacks; slippers; sleepwear; socks; stockings; sweaters; sweat pants; sweat shirts; swimsuits; t-shirts; tank tops; tights; underwear; vests; and wrist bands; all of the foregoing only for children pre-recorded CD-ROMs featuring computer games and activities for children; prerecorded audio and visual recordings in pre-recorded optical discs, DVD and CD format featuring educational and/or entertainment programming for children and teens; portable digital audio recorders; portable digital audio players; binoculars; calculators; camcorders; cameras cellular telephones; cellular telephone accessories, namely, batteries for cellular telephones, headsets for cellular telephones and earphones for cellular telephones; cellular telephone cases; face plates for cellular telephones; compact disc players; compact disc recorders; computer game programs; computer game cartridges and discs; computers; computer hardware; computer keyboards; computer monitors; computer mouse; computer disc drives; cordless telephones; decorative magnets; digital cameras; DVD players; DVD recorders; electronic personal organizers; headphones; karaoke machines; microphones; MP3 players; mouse pads; personal stereos; personal digital assistants; document printers for computers; radios; telephones; television sets; video cameras; video cassette recorders; video cassette players; video game cartridges; video game discs; videophones; walkie-talkies; and consumer electronics and accessories therefor, namely, audio speakers; measuring rulers motion picture films featuring animated entertainment, action-adventure, live-action, comedy, musicals, drama and documentaries Series of books on a variety of topics related to audiovisual entertainment, namely, a series of fiction and non-fiction books on a variety of topics in the nature of children's entertainment; fiction and non-fiction books on a variety of topics in the nature of children's entertainment; magazines in the field of stories, games, and activities for children and teens; comic books; graphic novels; printed stories in illustrated form and comic book stories, printed storyboards, and artwork; printed periodicals in the field of comic book stories, printed storyboards, and artwork; magazines featuring children's entertainment; newspapers and photographs of general interest; journals, printed periodicals, and newsletters featuring stories, games, and activities for children and teens; photographs; stationery; catalogues in the field of children's entertainment; address books; almanacs; appointment books; art prints; arts and craft paint kits; autograph books; baby books; ball point pens; baseball cards; binders; bookends; bookmarks; books, magazines and printed periodicals, featuring stories, games and activities for children and teens; bumper stickers; calendars; cartoon strips; Christmas cards; chalk; children's activity books; coasters made of paper; coin albums; coloring books; color pencils; coupon books; decals; decorative paper centerpieces; diaries; drawing rulers; envelopes; erasers; felt pens; flash cards; paper gift cards; gift wrapping paper; globes; greeting cards; guest books; maps; memo pads; modeling clay; newsletters about educational and entertainment programs for children and teens; newspapers; note paper; notebooks; notebook paper; paintings; paper flags; paper cake decorations; paper party decorations; paper napkins; paper party bags; paperweights; paper gift wrap bows; paper pennants; paper place mats; paper table cloths; pen or pencil holders; pencils; pencil sharpeners; pen and pencil cases and boxes; pens; printed periodicals in the field of children's entertainment; photograph albums; photographs; photo engravings; pictorial prints; picture books; portraits; postcards; posters; printed awards; printed certificates; printed invitations; printed menus; recipe books; rubber stamps; drawing rulers; score cards; stamp albums; stationery; staplers; stickers; trading cards, other than for games; writing paper; and writing implements providing online non-downloadable comic books and graphic novels; providing a website featuring blogs and non-downloadable publications in the nature of books, graphic novels, comics and screenplays in the field of children's entertainment; providing a website featuring entertainment information, audio, video and prose presentations, and online-non-downloadable publications in the nature of fiction and non-fiction books, graphic novels and comics all in the field of entertainment; entertainment services, namely, arranging and conducting contests; providing current event news and information in the field of entertainment relating to contests, video, audio and prose presentations and publications all in the field of entertainment; providing on-line reviews of television shows and movies; providing a video-on-demand website featuring non-downloadable movies and films; providing a website featuring non-downloadable videos in the field of movies, television shows, and film trailers; and providing information on entertainment, movies and television shows via social networks

29.

SYSTEM FOR BIOMETRIC IDENTIFICATION ENROLLMENT

      
Application Number 18152403
Status Pending
Filing Date 2023-01-10
First Publication Date 2024-07-11
Owner AMAZON TECHNOLOGIES, INC. (USA)
Inventor
  • Aggarwal, Manoj
  • Medioni, Gerard Guy
  • Desjardins, Chad
  • Kumar, Dilip

Abstract

User enrollment to a biometric identification system begins with a pre-enrollment process on selected general input devices (GID) such as smartphones. The user may enter identification data such as their name and use a camera of the GID to acquire first image data, such as of their hand. The first image data is processed to determine a first representation. Upon presentation of a hand at a biometric input device, second image data is acquired. The second image data is processed to determine a second representation. If the second representation is deemed to be associated with the first representation, the enrollment process may be completed by storing the second representation for subsequent use.

IPC Classes  ?

  • G06V 40/50 - Maintenance of biometric data or enrolment thereof
  • G06V 10/774 - Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
  • G06V 40/70 - Multimodal biometrics, e.g. combining information from different biometric modalities

30.

COMPUTER-IMPLEMENTED METHOD AND APPARATUS FOR VIDEO CODING USING SUPER-RESOLUTION RESTORATION WITH RESIDUAL FRAME CODING

      
Application Number 18186006
Status Pending
Filing Date 2023-03-17
First Publication Date 2024-07-11
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Choi, Byeongdoo
  • Segall, Christopher Andrew
  • Misra, Kiran Mukesh

Abstract

The present disclosure relates to methods, apparatus, systems, and non-transitory computer-readable storage media for video coding using super-resolution restoration with residual frame coding. According to some examples, a computer-implemented method includes receiving a coded frame of a video; performing a video coding on the coded frame of the video to generate a resultant for the coded frame at a second lower resolution than a first resolution; upsampling the resultant in at least a vertical direction to a higher resolution than the second lower resolution to generate an upsampled resultant; generating a decoded frame based on at least the upsampled resultant; and transmitting the decoded frame to a frame buffer or to a display device.

IPC Classes  ?

  • H04N 19/59 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving spatial sub-sampling or interpolation, e.g. alteration of picture size or resolution
  • G06T 3/40 - Scaling of a whole image or part thereof
  • H04N 19/139 - Analysis of motion vectors, e.g. their magnitude, direction, variance or reliability
  • H04N 19/91 - Entropy coding, e.g. variable length coding [VLC] or arithmetic coding

31.

COMPUTER-IMPLEMENTED MULTI-SCALE MACHINE LEARNING MODEL FOR THE ENHANCEMENT OF COMPRESSED VIDEO

      
Application Number 18186084
Status Pending
Filing Date 2023-03-17
First Publication Date 2024-07-11
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Misra, Kiran Mukesh
  • Segall, Christopher Andrew
  • Choi, Byeongdoo

Abstract

The present disclosure relates to methods, apparatus, systems, and non-transitory computer-readable storage media for training and using a multi-scale machine learning model for the enhancement of compressed video. According to some examples, a computer-implemented method includes receiving a video at a content delivery service; performing an encode on a frame of the video by the content delivery service that coverts the frame from a pixel domain to a transform domain and back to the pixel domain to generate first pixel values and a first residual for a block of the frame at a first resolution; generating a first set of features, by a machine learning model of the content delivery service, for an input, at a first resolution, of the first pixel values and the first residual of the block; generating a second set of features, by the machine learning model of the content delivery service, for an input, at a second lower resolution, of second pixel values and a second residual of the block; upsampling the second set of features to the first resolution to generate an upsampled second set of features; generating a modified version of the frame based on the first set of features and the upsampled second set of features; and transmitting the modified version of the frame to a frame buffer or from the content delivery service to a viewer device.

IPC Classes  ?

  • H04N 19/42 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals - characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation
  • H04N 19/117 - Filters, e.g. for pre-processing or post-processing
  • H04N 19/12 - Selection from among a plurality of transforms or standards, e.g. selection between discrete cosine transform [DCT] and sub-band transform or selection between H.263 and H.264
  • H04N 19/136 - Incoming video signal characteristics or properties
  • H04N 19/172 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a picture, frame or field
  • H04N 19/176 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a block, e.g. a macroblock
  • H04N 19/60 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding
  • H04N 19/82 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals - Details of filtering operations specially adapted for video compression, e.g. for pixel interpolation involving filtering within a prediction loop

32.

AUTOMATIC CONFIGURATION CHANGE OF VIRTUAL MACHINES IN A COMPUTING NODE GROUP

      
Application Number 18403626
Status Pending
Filing Date 2024-01-03
First Publication Date 2024-07-11
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Brandwine, Eric Jason
  • Miller, Kevin Christopher
  • Doane, Andrew J.

Abstract

Techniques are described for providing managed computer networks, such as for managed virtual computer networks overlaid on one or more other underlying computer networks. In some situations, the techniques include facilitating replication of a primary computing node that is actively participating in a managed computer network, such as by maintaining one or more other computing nodes in the managed computer network as replicas, and using such replica computing nodes in various manners. For example, a particular managed virtual computer network may span multiple broadcast domains of an underlying computer network, and a particular primary computing node and a corresponding remote replica computing node of the managed virtual computer network may be implemented in distinct broadcast domains of the underlying computer network, with the replica computing node being used to transparently replace the primary computing node in the virtual computer network if the primary computing node becomes unavailable.

IPC Classes  ?

  • H04L 67/1029 - Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers using data related to the state of servers by a load balancer
  • G06F 9/455 - Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
  • G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
  • G06F 11/20 - Error detection or correction of the data by redundancy in hardware using active fault-masking, e.g. by switching out faulty elements or by switching in spare elements
  • H04L 61/2503 - Translation of Internet protocol [IP] addresses
  • H04L 61/5007 - Internet protocol [IP] addresses
  • H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
  • H04L 101/668 - Internet protocol [IP] address subnets

33.

NETWORK TRAFFIC MANAGEMENT AT RADIO-BASED APPLICATION PIPELINE PROCESSING SERVERS

      
Application Number 18413879
Status Pending
Filing Date 2024-01-16
First Publication Date 2024-07-11
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Gupta, Diwakar
  • Wojtowicz, Benjamin
  • Shevade, Upendra Bhalchandra
  • Yang, Ximeng Simon

Abstract

At a radio-based application pipeline processing server at which a portion of a distributed unit (DU) of a radio-based application is implemented, a particular networking hardware device is selected from among several devices (which include least one device incorporated within a network function accelerator card and at least one device which is not part of an accelerator card) for transmission of at least a portion of mid-haul traffic to a centralized unit (CU). The mid-haul traffic is transmitted to the CU via the selected device. At least a portion of front-haul traffic is transmitted to a radio unit (RU) via a networking hardware device incorporated within a network function accelerator card of the server.

IPC Classes  ?

  • H04L 67/1008 - Server selection for load balancing based on parameters of servers, e.g. available memory or workload
  • H04L 67/60 - Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
  • H04W 28/08 - Load balancing or load distribution
  • H04W 28/16 - Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]

34.

MULTI-DOMAIN CONFIGURABLE DATA COMPRESSOR/DE-COMPRESSOR

      
Application Number 18608674
Status Pending
Filing Date 2024-03-18
First Publication Date 2024-07-11
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Pavlichin, Dmitri
  • Chandak, Shubham
  • Weissman, Itschak
  • Burgess, Christopher George

Abstract

A data service implements a configurable data compressor/decompressor using a recipe generated for a particular data set type and using compression operators of a common registry (e.g., pantry) that are referenced by the recipe, wherein the recipe indicates at which nodes of a compression graph respective ones of the compression operators of the registry are to be implemented. The configurable data compressor/decompressor provides a customizable framework for compressing data sets of different types (e.g., belonging to different data domains) using a common compressor/decompressor implemented using a common set of compression operators.

IPC Classes  ?

  • G06F 3/06 - Digital input from, or digital output to, record carriers

35.

GLOBAL EXPLANATIONS OF MACHINE LEARNING MODEL PREDICTIONS FOR INPUT CONTAINING TEXT ATTRIBUTES

      
Application Number 18610140
Status Pending
Filing Date 2024-03-19
First Publication Date 2024-07-11
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Archambeau, Cedric Philippe
  • Das, Sanjiv Ranjan
  • Donini, Michele
  • Hardt, Michaela
  • Hill, Tyler Stephen
  • Kenthapadi, Krishnaram
  • Larroy, Pedro L
  • Liu, Xinyu
  • Vasist, Keerthan Harish
  • Yilmaz, Pinar Altin
  • Zafar, Muhammad Bilal

Abstract

A determination is made that an explanatory data set for a common set of predictions generated by a machine learning model for records containing text tokens is to be provided. Respective groups of related tokens are identified from the text attributes of the records, and record-level prediction influence scores are generated for the token groups. An aggregate prediction influence score is generated for at least some of the token groups from the record-level scores, and an explanatory data set based on the aggregate scores is presented.

IPC Classes  ?

  • G06F 40/20 - Natural language analysis
  • G06F 16/22 - Indexing; Data structures therefor; Storage structures
  • G06N 5/01 - Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound

36.

NEURAL NETWORK TRAINING IN A DISTRIBUTED SYSTEM

      
Application Number 18221454
Status Pending
Filing Date 2023-07-13
First Publication Date 2024-07-11
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Vivekraja, Vignesh
  • Hah, Thiam Khean
  • Huang, Randy Renfu
  • Diamant, Ron
  • Heaton, Richard John

Abstract

Methods and systems for performing a training operation of a neural network are provided. In one example, a method comprises: performing backward propagation computations for a second layer of a neural network to generate second weight gradients; splitting the second weight gradients into portions; causing a hardware interface to exchange a first portion of the second weight gradients with the second computer system; performing backward propagation computations for a first layer of the neural network to generate first weight gradients when the exchange of the first portion of the second weight gradients is underway, the first layer being a lower layer than the second layer in the neural network; causing the hardware interface to transmit the first weight gradients to the second computer system; and causing the hardware interface to transmit the remaining portions of the second weight gradients to the second computer system.

IPC Classes  ?

  • G06N 3/084 - Backpropagation, e.g. using gradient descent
  • G06N 3/045 - Combinations of networks
  • G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
  • G06N 3/10 - Interfaces, programming languages or software development kits, e.g. for simulating neural networks

37.

CONFIGURABLE LOGIC PLATFORM

      
Application Number 18383833
Status Pending
Filing Date 2023-10-25
First Publication Date 2024-07-11
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Atta, Islam
  • Pettey, Christopher Joseph
  • Khan, Asif
  • Johnson, Robert Michael
  • Davis, Mark Bradley
  • Izenberg, Erez
  • Bshara, Nafea
  • Constantinides, Kypros

Abstract

The following description is directed to a configurable logic platform. In one example, a configurable logic platform includes host logic and a reconfigurable logic region. The reconfigurable logic region can include logic blocks that are configurable to implement application logic. The host logic can be used for encapsulating the reconfigurable logic region. The host logic can include a host interface for communicating with a processor. The host logic can include a management function accessible via the host interface. The management function can be adapted to cause the reconfigurable logic region to be configured with the application logic in response to an authorized request from the host interface. The host logic can include a data path function accessible via the host interface. The data path function can include a layer for formatting data transfers between the host interface and the application logic.

IPC Classes  ?

  • G06F 13/40 - Bus structure
  • G06F 9/445 - Program loading or initiating
  • G06F 13/42 - Bus transfer protocol, e.g. handshake; Synchronisation
  • G06F 15/78 - Architectures of general purpose stored program computers comprising a single central processing unit

38.

NAMING DEVICES VIA VOICE COMMANDS

      
Application Number 18615766
Status Pending
Filing Date 2024-03-25
First Publication Date 2024-07-11
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Mutagi, Rohan
  • Taylor, Isaac Michael

Abstract

Techniques for naming devices via voice commands are described herein. For instance, a user may issue a voice command to a voice-controlled device stating, “you are the kitchen device”. Thereafter, the device may respond to voice commands directed, by name, to this device. For instance, the user may issue a voice command requesting to “play music on my kitchen device”. Given that the user has configured the device to respond to this name, the device may respond to the command by outputting the requested music.

IPC Classes  ?

  • G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
  • G06F 3/16 - Sound input; Sound output

39.

ITEM IDENTIFYING MOBILE APPARATUS

      
Application Number 18615810
Status Pending
Filing Date 2024-03-25
First Publication Date 2024-07-11
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Burch, Wade
  • Serra, Robert David
  • Webster, Matthew Clark
  • Siegel, Jacob A.
  • Mcleod, Brendan Kyle
  • Warren, Jacob Paul
  • Vasu, Sridharan Thirumalai

Abstract

This disclosure describes, in part, a mobile apparatus for identifying items. For instance, the mobile apparatus may include a frame, a basket that attaches to the frame, a tray that attaches to the frame, and a user-facing module that attaches to the frame. Weight sensors may be located beneath the basket and the tray in order to determine the weights of items added to the mobile apparatus. The user-facing module may include imaging devices, such as cameras, that the mobile apparatus uses to perform one or more functions. For example, the mobile apparatus may use the imaging devices to identify items placed within the basket, determine locations of the mobile apparatus within a facility, and/or the like. By including these components, the mobile apparatus is able to generate data that represents the items added to the mobile apparatus, the weights of the items, and the costs of the items.

IPC Classes  ?

  • B62B 5/00 - Accessories or details specially adapted for hand carts
  • B62B 3/14 - Hand carts having more than one axis carrying transport wheels; Steering devices therefor; Equipment therefor characterised by provisions for nesting or stacking, e.g. shopping trolleys
  • G01G 19/12 - Weighing apparatus or methods adapted for special purposes not provided for in groups for incorporation in vehicles having electrical weight-sensitive devices
  • G06Q 30/0601 - Electronic shopping [e-shopping]
  • H04N 23/90 - Arrangement of cameras or camera modules, e.g. multiple cameras in TV studios or sports stadiums

40.

AMAZON SAVER

      
Serial Number 98642009
Status Pending
Filing Date 2024-07-10
Owner Amazon Technologies, Inc. ()
NICE Classes  ?
  • 29 - Meat, dairy products, prepared or preserved foods
  • 30 - Basic staples, tea, coffee, baked goods and confectionery
  • 31 - Agricultural products; live animals
  • 32 - Beers; non-alcoholic beverages
  • 33 - Alcoholic beverages other than beer

Goods & Services

UNCOOKED HAMBURGER PATTIES; UNCOOKED FOODS, NAMELY, BEEF; PACKAGED MEATS; BUTTER; CANNED AND BOTTLED FRUITS AND VEGETABLES; CHEESE; CHICKEN; COOKED FRUITS AND VEGETABLES; COOKING OIL; CREAM; DIPS; DRIED FRUITS; EGGS; FROZEN, PREPARED AND PACKAGED ENTREES, MEALS, APPETIZERS, AND SIDE DISHES CONSISTING PRIMARILY OF MEAT, SEAFOOD, POULTRY, VEGETABLES, OR CHEESE; FRUIT-BASED SNACK FOOD; FRUIT CHIPS; VEGETABLE CHIPS; FRUIT AND VEGETABLE PUREES; FRUIT SPREADS; JELLIES, JAMS, COMPOTES; MEAT; MILK; MILK-BASED PRODUCTS, EXCLUDING ICE CREAM, ICE MILK AND FROZEN YOGURT; PROCESSED MIXED NUTS; NUT-BASED AND DRIED FRUIT-BASED SNACK BARS; OLIVE OIL FOR FOOD; PEANUT BUTTER; POTATO CHIPS; RAISINS; SALADS EXCEPT MACARONI, RICE AND PASTA SALAD; SEAFOOD, NOT LIVE; SNACK MIX CONSISTING OF DEHYDRATED FRUIT AND PROCESSED NUTS; SOUP; SOY-BASED FOOD BEVERAGE USED AS A MILK SUBSTITUTE, SOY MILK; TOMATO PASTE; TRAIL MIX CONSISTING PRIMARILY OF PROCESSED NUTS OR GRANOLA; FROZEN VEGETABLES; VEGETABLE-BASED SNACK FOOD; WHIPPED CREAM; YOGURT; FRUIT JELLIES; MILK OF ALMONDS; PEANUT MILK; NUT-BASED SNACK BARS; PICKLED VEGETABLES, NAMELY, PICKLES; CONDIMENTS, NAMELY, DIPS BEING DAIRY-BASED DIPS; CONDIMENTS, NAMELY, JELLIES BAKING POWDER; BAKING SODA; BREAD; BREAD CRUMBS; BREAD STICKS; BISCUITS; BURGERS CONTAINED IN BREAD ROLLS; CAKE MIXES; COOKIE MIXES; CAKES; CARAMELS; CHEWING GUM; CORN-BASED CHIPS; GRAIN-BASED CHIPS; PRETZEL CHIPS; PITA CHIPS; BAGEL CHIPS; TORTILLA CHIPS; RICE CHIPS; CHOCOLATE; CHOCOLATE-BASED BEVERAGES; CHOCOLATE MOUSSES; CINNAMON; COCOA-BASED CONDIMENTS, INGREDIENTS, MIXES, POWDERS, AND SPREADS; COFFEE; COFFEE-BASED BEVERAGES; COFFEE BEANS; COFFEE AND TEA PODS; CONDIMENTS, NAMELY, SAVORY SAUCES, MINCED GARLIC, SOYA BEAN PASTE, CHILI PEPPER PASTE, PEPPER SAUCE, PIMIENTO, HORSERADISH, KETCHUP, RELISH; CONFECTIONERY MADE OF SUGAR; CONFECTIONERY MADE OF SUGAR-SUBSTITUTES; COOKIES; CRACKERS; DIPPING SAUCE; DOUGH; DRESSINGS FOR SALAD; FLAVORINGS FOR FOODS AND BEVERAGES, OTHER THAN ESSENTIAL OILS; FROZEN YOGURT; FLOUR; FROZEN, PREPARED, AND PACKAGED MEALS OR ENTREES CONSISTING PRIMARILY OF PASTA OR RICE; FROZEN FOODS, NAMELY, GRAIN AND BREAD BASED APPETIZERS, HORS D'OEUVRES, AND CANAPÉS; FOOD PACKAGE COMBINATIONS CONSISTING PRIMARILY OF BREAD, CRACKERS OR COOKIES; FROZEN CONFECTIONS; GRAVY; GRITS; HONEY; ICE CREAM; ICE CREAM CONES; ICED TEA; ICES; ICE; ICING; JELLY BEANS; NON-MEDICATED LOZENGES; MARINADES; MAYONNAISE; MIXES FOR MAKING BREADING; MUSTARD; NATURAL SWEETENERS; NOODLES; NOODLES, SAUCE, AND TOPPING COMBINED IN UNITARY PACKAGES; PACKAGED MEAL KITS CONSISTING PRIMARILY OF PASTA OR RICE; PANCAKES; PASTA; PASTA SALAD; PASTRIES; PASTRY MIXES, CREAM, DOUGH, AND SHELLS; PEPPER; PIES; PIZZAS; POPCORN; PUDDINGS; RICE; RICE CAKES; RICE-BASED SNACK FOOD; RICE AND SEASONING MIX COMBINED IN A UNITARY PACKAGE; SALT; SANDWICHES; SAUCES; SEASONINGS; SPICES; STUFFING MIXES CONTAINING BREAD; SUGAR; SUSHI; SYRUP FOR FLAVORING FOOD AND BEVERAGES; TACOS; TEA; TEA BAGS; TEA-BASED BEVERAGES; TEA-BASED SNACK FOODS; TORTILLAS; VANILLA; VINEGAR; WAFFLES; YEAST; FROZEN, PREPARED AND PACKAGED ENTREES, MEALS, APPETIZERS, AND SIDE DISHES CONSISTING PRIMARILY OF PASTA, RICE, BREAD, CRACKERS, COOKIES, SAUCES, SEASONING OR BEANS; MILK-BASED PRODUCTS, NAMELY, ICE CREAM, ICE MILK AND FROZEN YOGURT; PROCESSED GRAINS; MALT FOR FOOD PURPOSES; PROCESSED WHEAT; SORBETS; SAUCE; CONDIMENTS, NAMELY, DIPS BEING DIPPING SAUCES BEANS, FRESH; BEANS, UNPROCESSED; FRESH COCONUTS; FRESH CORN; FRESH FRUITS; FRESH VEGETABLES; FRESH HERBS; UNPROCESSED GRAIN; LETTUCE, FRESH; LOBSTERS, LIVE; MALT FOR BREWING AND DISTILLING; FRESH OATS; RICE, UNPROCESSED; SESAME, EDIBLE; SHELLFISH, LIVE; UNPROCESSED NUTS; FRESH WHEAT BEER; COCKTAILS, NON-ALCOHOLIC; ENERGY DRINKS; FLAVORED WATER; FRUIT-BASED BEVERAGES; FRUIT JUICES; GINGER ALE; ISOTONIC BEVERAGES; LEMONADES; MALT WORT; MINERAL AND AERATED WATERS; NON-ALCOHOLIC BEVERAGES, NAMELY, CARBONATED BEVERAGES; NON-ALCOHOLIC MALT BEVERAGES; NON-ALCOHOLIC FRUIT EXTRACTS USED IN THE PREPARATION OF BEVERAGES; NON-ALCOHOLIC COCKTAIL MIXES; PREPARATIONS FOR MAKING BEVERAGES, NAMELY, FRUIT DRINKS; SELTZER WATER; SMOOTHIES; SOFT DRINKS; VEGETABLE JUICES; WATER BEVERAGES; WHEY BEVERAGES; DISTILLED BEVERAGES, NAMELY, DISTILLED DRINKING WATER; SWEET CIDER; MALT SYRUP FOR BEVERAGES ALCOHOLIC BEVERAGES, EXCEPT BEER; ALCOHOLIC COCKTAIL MIXES; ALCOHOLIC ESSENCES; ALCOHOLIC EXTRACTS; BRANDY; BOURBON; DISTILLED SPIRITS; GIN; LIQUEURS; PRE-MIXED ALCOHOLIC BEVERAGES, OTHER THAN BEER-BASED; PREPARED ALCOHOLIC COCKTAIL; RUM; SAKE; SPIRITS; HARD CIDER; DISTILLED BLUE AGAVE LIQUOR; VODKA; WHISKY; WINE

41.

SEASON'S GRAZINGS

      
Serial Number 98640831
Status Pending
Filing Date 2024-07-10
Owner AMAZON TECHNOLOGIES, INC. ()
NICE Classes  ?
  • 29 - Meat, dairy products, prepared or preserved foods
  • 30 - Basic staples, tea, coffee, baked goods and confectionery

Goods & Services

Roasted nuts, namely, mixed nuts; Snack mix consisting of dehydrated fruit and processed nuts; Snack mix consisting primarily of processed fruits, processed nuts and/or raisins; Snack mix consisting primarily of processed nuts, and also including chocolate; Trail mix consisting primarily of processed nuts, and also including granola, seeds, dried fruit, pretzels, and/or chocolate; Trail mix consisting primarily of processed nuts, seeds, dried fruit and also including chocolate Snack mix consisting primarily of crackers, pretzels and/or popped popcorn; Snack mix consisting primarily of popcorn and also including dried fruit, seeds, chocolate, and/or processed nuts; Trail mix consisting primarily of granola, and also including dried fruit, seeds, chocolate, and/or processed nuts; Trail mix consisting primarily of pretzels, popcorn, and crackers, and also including dried fruit, seeds, chocolate, and/or processed nuts

42.

Deformable item stabilizer tray

      
Application Number 17488843
Grant Number 12030689
Status In Force
Filing Date 2021-09-29
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Yancho, Benjamin
  • Varghese, George M.

Abstract

A deformable item stabilizer tray is described for stabilizing an item during conveyance and transport. The deformable item stabilizer tray includes a base adapted to support an article and a plurality of sidewalls surrounding and extending at an angle from the base. A first notched-out region in a first one of the sidewalls defines a first lip support region having at least one indent configured to control a deformation of the lip above the first lip support region. A second notched-out region in a second one of the sidewalls defines a second lip support region having at least one indent configured to control a deformation of the lip above the second lip support region. The first notched-out region and the second notched-out region may be on opposite ones of the sidewalls.

IPC Classes  ?

  • B65D 1/40 - Rigid or semi-rigid containers having bodies formed in one piece, e.g. by casting metallic material, by moulding plastics, by blowing vitreous material, by throwing ceramic material, by moulding pulped fibrous material or by deep-drawing operations p - Details of walls
  • B65B 5/04 - Packaging single articles
  • B65D 1/34 - Trays or like shallow containers
  • B65D 81/05 - Containers, packaging elements, or packages, for contents presenting particular transport or storage problems, or adapted to be used for non-packaging purposes after removal of contents specially adapted to protect contents from mechanical damage maintaining contents at spaced relation from package walls, or from other contents
  • B65G 47/00 - Article or material-handling devices associated with conveyors; Methods employing such devices

43.

Optical detector system with multiple path lengths

      
Application Number 17114923
Grant Number 12032079
Status In Force
Filing Date 2020-12-08
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner AMAZON TECHNOLOGIES, INC. (USA)
Inventor
  • Zhang, Rui
  • Adams, Jeff Clark
  • Duelli, Markus Stefan
  • Cornwell, Donald Mitchell
  • Masalkar, Prafulla

Abstract

An optical detector system provides positioning data to facilitate tracking in optical communications. The system provides first and second path lengths to direct light onto an array of photodetectors. Incoming first light with a first polarization is reflected by a polarizing beam splitter (PBS) to the array, resulting in a first path length and a relatively wide field of view (FOV). Incoming second light with a second polarization passes through the PBS, interacts with a first quarter wave retarder (QWR) and a convex mirror, is reflected by the PBS, passes through a second QWR and is reflected by a flat mirror to pass through the PBS again and onto the array. The second light experiences a second path length greater than the first path length, exhibiting a relatively narrow FOV. The resulting spots of light on the array provide information about a position of the incoming beam.

IPC Classes  ?

  • G01S 3/78 - Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received using electromagnetic waves other than radio waves
  • G01S 3/785 - Systems for determining direction or deviation from predetermined direction using adjustment of orientation of directivity characteristics of a detector or detector system to give a desired condition of signal derived from that detector or detector system
  • G01S 3/789 - Systems for determining direction or deviation from predetermined direction using rotating or oscillating beam systems, e.g. using mirrors, prisms
  • H04B 10/40 - Transceivers

44.

Data event management for monotonic read consistency in a distributed storage system

      
Application Number 17491029
Grant Number 12032562
Status In Force
Filing Date 2021-09-30
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Zuber, James
  • Kannan, Abhishek
  • Narendra, Vishwas
  • Cohen, Ernest S.
  • Wilkinson, Bryan T.
  • Choudhary, Sameer
  • Pruett, Phillip H.
  • Shah, Nikhil
  • Li, Wilson

Abstract

Systems and methods are provided for efficiently maintaining a transaction sequence witness service to ensure that requests to read data provide consistent results across all storage nodes in a distributed system. Each storage node that stores data in response to a particular update event may first update the transaction sequence witness service with the sequence number for the update event. Thus, any other storage node that services a subsequent request for the data will be able to see, via the transaction sequence witness service, the sequence number that must be available on the storage node to respond to the request.

IPC Classes  ?

  • G06F 16/23 - Updating
  • G06F 16/22 - Indexing; Data structures therefor; Storage structures
  • H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]

45.

Responding with unresponsive content

      
Application Number 18091037
Grant Number 12032611
Status In Force
Filing Date 2022-12-29
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor
  • George, Michael Martin
  • Uffelman, David Garfield
  • Maini, Deepak
  • Beyer, Kyle
  • Sandhu, Amarpaul Singh

Abstract

This disclosure describes systems and techniques receiving a request for information from a user and, in response, outputting the requested information along with unsolicited, interesting content that is related to, yet nonresponsive to, the requested information. In some instances, if the requested information is unknown, the techniques may output an indication that the information is unknown, followed by the additional, unsolicited, interesting content.

IPC Classes  ?

46.

Inventory status determination with fleet management

      
Application Number 17948035
Grant Number 12033434
Status In Force
Filing Date 2022-09-19
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Doke, Abhay
  • Santhanam, Venkataraman
  • Anor, Tomer
  • Kuzhinjedathu, Kamal Ramachandran

Abstract

This disclosure describes, in part, techniques for identifying facility status updates using opportunistic data gathering from independently controlled devices operated by users within the facility. For instance, system(s) may determine first status information for inventory and/or locations within the facility and determine associated freshness scores. In response to a freshness score being below a threshold level, the system may determine imaging locations for the independently controlled devices to be positioned at to capture image data to update information on the inventory or locations. The system may receive uploaded image data from a fleet device after it opportunistically reaches the imaging location and captures image data. The system may then update the facility status information and the freshness score for the particular location.

IPC Classes  ?

  • G06V 40/20 - Movements or behaviour, e.g. gesture recognition
  • G06Q 10/087 - Inventory or stock management, e.g. order filling, procurement or balancing against orders
  • G06Q 30/0201 - Market modelling; Market analysis; Collecting market data
  • G06V 10/26 - Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
  • G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
  • G08B 13/196 - Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
  • G08B 13/24 - Electrical actuation by interference with electromagnetic field distribution
  • H04N 23/61 - Control of cameras or camera modules based on recognised objects

47.

Techniques for implementing customized intrusion zones

      
Application Number 17951749
Grant Number 12033482
Status In Force
Filing Date 2022-09-23
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Tsyba, Yevhen
  • Bondariev, Illia
  • Moshin, Roman
  • Savelyev, Oleg
  • Sytyi, Mykyta
  • Pikozh, Nataliia

Abstract

This disclosure describes, in part, techniques for implementing customized intrusion zones for security monitoring. For instance, a user may provide (e.g., via a GUI) an indication of a physical space to be monitored. An electronic device may detect one or more objects and its respective location from radar data collected by a radar sensor. The electronic device may additionally detect one or more objects within image data captured via a camera. The electronic device may then analyze the radar data and the image data in order to determine that the object detected by the radar sensor includes the same object represented by the image data. As such, the electronic device may send an alert to one or more computing devices that may include the radar data and/or the image data.

IPC Classes  ?

  • G08B 13/196 - Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
  • G01S 13/88 - Radar or analogous systems, specially adapted for specific applications

48.

Relevant context determination

      
Application Number 17546502
Grant Number 12033618
Status In Force
Filing Date 2021-12-09
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Wei, Kai
  • Tran, Thanh Dac
  • Strimel, Grant

Abstract

Techniques for determining and storing relevant context information for a user input, such as a spoken input, are described. In some embodiments, context information is determined to be relevant on an audio frame basis. Context scores for different types of context data (e.g., prior dialog turn data, user profile data, device information, etc.) are determined for individual audio frames corresponding to a spoken input. Based on the corresponding context scores, the most relevant context is stored in a local context cache. The local context cache is updated as subsequent audio frames, of the user input, are processed. The data stored in the context cache is provided to downstream components to perform tasks such as ASR, NLU and SLU.

IPC Classes  ?

  • G10L 15/18 - Speech classification or search using natural language modelling
  • G06N 3/08 - Learning methods
  • G10L 15/06 - Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
  • G10L 15/16 - Speech classification or search using artificial neural networks
  • G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
  • G10L 15/28 - Constructional details of speech recognition systems

49.

Ambient device state content display

      
Application Number 17980262
Grant Number 12033633
Status In Force
Filing Date 2022-11-03
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Hu, Guoning
  • Hart, Michael
  • Brickman, Seth
  • Gupta, Ashok
  • Jones, David Jason
  • Cook, Sandy Huang

Abstract

Devices and techniques are generally described for sending a first instruction for a device to output first content while the speech-processing device is in an ambient state during a first time period. First feedback data is received indicating that a first action associated with the first content was requested at a first time. A determination is made that the first time is during the first time period. Timing data related to a current time of the device is determined. Second content is determined based at least in part on the first action being requested during the first time period and the timing data. A second instruction is sent effective to cause the device to output second content while in the ambient state during a second time period.

IPC Classes  ?

  • G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
  • G10L 15/06 - Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
  • G10L 15/30 - Distributed recognition, e.g. in client-server systems, for mobile phones or network applications

50.

Disambiguating contacts using relationship data

      
Application Number 18126025
Grant Number 12033634
Status In Force
Filing Date 2023-03-24
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Shivalingaiah, Inchara
  • Oostergo, Milo
  • Zhong, Gary
  • Nair, Aakarsh
  • Bhatia, Sushant

Abstract

Technologies are disclosed for disambiguating contact information using relationship data using a virtual assistant. A user interacts with a virtual assistant to obtain and utilize contact information. For example, a virtual assistant may allow users to perform an action that utilizes contact information (e.g., make a call to a contact). The virtual assistant utilizes a contact service to identify candidate contacts that are related to the requesting user. The contact service identifies candidate contacts based on relationship data between the requesting user and the stored contacts. For example, the relationship data may indicate that the requesting user is on the same project or team as another contact, that the requesting user has the same role as another contact, that the requesting user is a manager of another contact, as well as other attributes. In some examples, the contact service limits the number of candidate contacts provided to requesting user.

IPC Classes  ?

  • G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
  • G06F 9/54 - Interprogram communication
  • G06F 16/901 - Indexing; Data structures therefor; Storage structures
  • G06F 16/9535 - Search customisation based on user profiles and personalisation
  • G10L 13/00 - Speech synthesis; Text to speech systems
  • G10L 15/18 - Speech classification or search using natural language modelling
  • G10L 15/30 - Distributed recognition, e.g. in client-server systems, for mobile phones or network applications

51.

High-density microbump and probe pad arrangement for semiconductor components

      
Application Number 17546359
Grant Number 12033903
Status In Force
Filing Date 2021-12-09
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor Jayakumar, Nikhil

Abstract

A semiconductor package component (such as a die or interposer) can include a body having a top surface and a bottom surface. The component can further include an interface array arranged along the top surface or the bottom surface. The interface array can include a first set of microbumps arranged in a first row. The interface array can further include a second set of microbumps arranged in a second row adjacent the first row. The interface array can also include a probe pad extending into both the first row and the second row.

IPC Classes  ?

  • H01L 23/50 - Arrangements for conducting electric current to or from the solid state body in operation, e.g. leads or terminal arrangements for integrated circuit devices
  • H01L 21/66 - Testing or measuring during manufacture or treatment
  • H01L 23/00 - SEMICONDUCTOR DEVICES NOT COVERED BY CLASS - Details of semiconductor or other solid state devices
  • H01L 23/498 - Leads on insulating substrates

52.

Semiconductor package with stiffener ring having elevated opening

      
Application Number 17548325
Grant Number 12033960
Status In Force
Filing Date 2021-12-10
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor Abdel-Dayem, Bassam

Abstract

A stiffener ring can include a body sized for attachment to a substrate in a semi-conductor package. The stiffener ring body can have a top surface and a bottom surface. A through-hole may penetrate the body so as to extend through the top surface and the bottom surface and so that the body is formed as ring around the through-hole. An anchor surface can form a portion of the bottom surface and be configured for engaging the substrate. An elevated surface can also form a portion of the bottom surface and be elevated above the anchor surface.

IPC Classes  ?

  • H01L 21/50 - Assembly of semiconductor devices using processes or apparatus not provided for in a single one of the groups
  • H01L 23/00 - SEMICONDUCTOR DEVICES NOT COVERED BY CLASS - Details of semiconductor or other solid state devices
  • H01L 23/34 - Arrangements for cooling, heating, ventilating or temperature compensation
  • H01L 23/367 - Cooling facilitated by shape of device
  • H01L 23/498 - Leads on insulating substrates

53.

Techniques for transformative and derivative metering

      
Application Number 16168563
Grant Number 12034612
Status In Force
Filing Date 2018-10-23
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Xia, Zhihan
  • Wang, Bo
  • Abraham, Matthias
  • Dahmen, Brian
  • Dhulekar, Anket
  • Ehrhart, Timon Edward
  • Feng, Kai
  • Fort, Michael
  • Kovtun, Dmytro
  • Macadar, Diego Sebastian
  • Martinez Maqueda, Daniel
  • Qian, Zhi
  • Fernandes Queiroz, Iuri

Abstract

A client of a metering service defines mapping data and derivation instructions comprising schema metadata and predicate metadata. The metering service provides usage information for a set of computing resources provided by one or more services of a computing resource service provider. As computing resources are utilized, usage records are generated. A derivation system may obtain one or more usage records and determine whether a predicate applies. If the predicate applies, a derivation function may be executed. For example, the derivation function may generate, from an original usage record, a derived usage record using mapping data provided by a client.

IPC Classes  ?

  • H04L 41/50 - Network service management, e.g. ensuring proper service fulfilment according to agreements
  • G06F 16/22 - Indexing; Data structures therefor; Storage structures
  • G06F 16/2455 - Query execution
  • G06F 16/84 - Mapping; Conversion
  • H04L 12/14 - Charging arrangements

54.

Network devices for stateful transmission of network traffic

      
Application Number 17643799
Grant Number 12034637
Status In Force
Filing Date 2021-12-10
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Papirakis, Emmanuel
  • Bytheway, Cameron Jared
  • Einwag, Matthias
  • Yadavalli, Yashwanth
  • Li, Yuchao
  • Vasquez, Jorge Peixoto

Abstract

Systems and methods are provided to use a signed connection identifier to route packets of network traffic. Each network host can include a network device that independently routes the packets of network traffic without sharing state information with other network devices. A network device can receive a packet of network traffic and determine if the packet of network traffic includes a signed connection identifier. If the packet does not include a signed connection identifier, the network device can perform a load balancing operation to select a network host for the packet and generate a signed connection identifier for the packet identifying the selected network host. If the packet does include a signed connection identifier, the network device can encapsulate the packet and route the packet to a particular network host based on the signed connection identifier.

IPC Classes  ?

  • H04L 45/74 - Address processing for routing
  • H04L 9/00 - Arrangements for secret or secure communications; Network security protocols
  • H04L 9/40 - Network security protocols
  • H04L 47/125 - Avoiding congestion; Recovering from congestion by balancing the load, e.g. traffic engineering
  • H04L 67/1004 - Server selection for load balancing
  • H04L 69/22 - Parsing or analysis of headers

55.

Techniques for performing compound operations on security modules

      
Application Number 17543501
Grant Number 12034844
Status In Force
Filing Date 2021-12-06
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Grubin, Benjamin
  • Norum, Steve Preston Lightner

Abstract

Systems, devices, and methods are provided for performing compound operations on a security module. In some embodiments, a hardware security module (HSM) comprises executable code that, as a result of execution by one or more processors of the HSM, causes the HSM to obtain a request to perform a compound operation, parse the compound operation to determine a sequence of operations, perform the sequence of operations within a protected execution environment, wherein one or more intermediate results of the sequence of operations are programmatically unexportable from the protected execution environment, determine, based on complete execution of the sequence of operations, an output, and export the final output from the protected execution environment, thereby making it available to external devices.

IPC Classes  ?

  • H04L 9/08 - Key distribution
  • H04L 9/14 - Arrangements for secret or secure communications; Network security protocols using a plurality of keys or algorithms

56.

Color filter array interpolation for RGB-IR image sensors

      
Application Number 17547104
Grant Number 12035054
Status In Force
Filing Date 2021-12-09
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor Pawlik, Bartlomiej

Abstract

Techniques are generally described for color filter array interpolation of image data. A first frame of image data representing a plurality of pixels arranged in a grid may be received. A low pass filter may be used to generate a first luminance value for a first non-green pixel of the plurality of pixels. A first chrominance value of the first non-green pixel may be determined based at least in part on a combination of at least a chrominance value of a first green pixel located adjacent to the first non-green pixel and a chrominance value of a second green pixel located adjacent to the first non-green pixel. A second luminance value for the first non-green pixel may be determined based on a combination of the first chrominance value and the first luminance value. Output values for the first non-green pixel may be determined based at least in part on the second luminance value.

IPC Classes  ?

  • G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
  • H04N 9/69 - Circuits for processing colour signals for controlling the amplitude of colour signals, e.g. automatic chroma control circuits for modifying the colour signals by gamma correction
  • H04N 23/84 - Camera processing pipelines; Components thereof for processing colour signals
  • H04N 25/13 - Arrangement of colour filter arrays [CFA]; Filter mosaics characterised by the spectral characteristics of the filter elements
  • H04N 25/131 - Arrangement of colour filter arrays [CFA]; Filter mosaics characterised by the spectral characteristics of the filter elements including elements passing infrared wavelengths

57.

Protected power management transitions in wireless networks

      
Application Number 17892547
Grant Number 12035234
Status In Force
Filing Date 2022-08-22
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Shukla, Ashish Kumar
  • Joshi, Avinash

Abstract

Techniques for increasing the security and reliability of frame transmission are described. In an example, a network access device transmits a first frame indicating that a protected frame is to be used for a power mode change. The network access device receives a second frame that includes an identifier of a device and a change to a power mode of the device. The network access device determines whether the second frame is protected. In addition, the network access device determines whether data received for the device is to be stored prior to transmission to the device based at least in part on whether the second frame is protected.

IPC Classes  ?

  • H04W 52/02 - Power saving arrangements
  • G06F 1/3206 - Monitoring of events, devices or parameters that trigger a change in power modality
  • H04L 61/103 - Mapping addresses of different types across network layers, e.g. resolution of network layer into physical layer addresses or address resolution protocol [ARP]
  • H04W 12/06 - Authentication
  • H04W 48/10 - Access restriction or access information delivery, e.g. discovery data delivery using broadcasted information
  • H04W 48/20 - Selecting an access point
  • H04L 101/622 - Layer-2 addresses, e.g. medium access control [MAC] addresses
  • H04W 84/12 - WLAN [Wireless Local Area Networks]

58.

Wearable device

      
Application Number 29865601
Grant Number D1034586
Status In Force
Filing Date 2022-08-03
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Matsumoto, Ippei
  • Wildner, Bernhard

59.

Wearable device

      
Application Number 29865603
Grant Number D1034587
Status In Force
Filing Date 2022-08-03
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Matsumoto, Ippei
  • Wildner, Bernhard

60.

PERFORMANCE+

      
Serial Number 98639014
Status Pending
Filing Date 2024-07-09
Owner Amazon Technologies, Inc. ()
NICE Classes  ?
  • 35 - Advertising and business services
  • 09 - Scientific and electric apparatus and instruments
  • 42 - Scientific, technological and industrial services, research and design

Goods & Services

Digital and video advertising services provided via electronic media, specifically the Internet, wireless networks, and mobile and portable devices; advertising services, namely, creating, composing, updating, compiling, disseminating, supporting, tracking, reporting on and analyzing advertisements for distribution on internet web pages and wireless networks; semantic advertising services, namely, advertising analysis of the meaning of website content in order to optimize and provide targeted advertising to consumers while protecting advertiser brands in real-time; Providing advertising, marketing, and promotional services, namely, the management and development of advertising campaigns through electronic media, specifically the Internet, wireless networks, and mobile and portable devices; Publishing of advertising texts; outdoor advertising; pay per click advertising management services; advertising agency services; digital advertising services; online advertising services for others; production of advertising matter and commercials; business management consulting with relation to strategy, marketing, sales, operation, product design particularly specializing in the use of analytic and statistic models for the understanding and predicting of consumers, businesses, and market trends and actions; consultancy regarding advertising communications strategy; marketing consulting services; advertising, marketing, and publicity services; Providing television home shopping services in the field of general consumer merchandise; provide business information in the social media field; provide marketing consulting in the social media field; provide business information via the internet, wired networks or other forms of data transmission; advertising research; marketing research; market research; business strategic planning services; analysis of business data; market analysis and research services; computerized market research service; business advisory, consultancy and information services; statistical evaluation of marketing data; market research data and statistical analysis; economic forecasting and analysis; consumer profiling for commercial or marketing purposes; consumer research; Providing an online searchable database featuring information related to conducting market surveys; business management consultancy; business management consulting online; Creation and updating advertising material; Development of advertising concepts; Editing, production, and distribution of advertising materials; publication of advertising and publicity materials; distribution of advertising materials; distribution of advertising and advertising materials in the nature of flyers, prospectuses, brochures, leaflets and samples; scriptwriting for advertising purposes; production of advertising films; Copy writing for advertising purposes; rental of advertising space; provision and rental of an online space for advertising; provision of advertising space in periodicals, newspapers and magazines; provide advertising space through electronic channels and global information networks; rental of advertising time on communication media; advertising intermediation; online advertising for others by electronic communication network; rental advertising space and advertising materials; public relations; consultancy regarding public relations communications strategy; public relations consultancy; provision of an on-line marketplace for buyers and sellers of goods and services; development of advertising concepts Downloadable computer software for the creation and delivery of digital content and advertising via global communication networks; downloadable computer software for tracking, monitoring, and reporting on trends for advertising, marketing, and promotional purposes; downloadable computer software for evaluating and analyzing customer behavior in the field of advertising and marketing; downloadable computer software for media buying; downloadable computer software for the purpose of development of advertising-focused technology, data, and analytic tools; downloadable computer software for the purpose of evaluating customer behavior; downloadable computer software for customizing the subject matter and appearance of digital content and advertising; Recorded computer software, downloadable computer programs and downloadable software applications for use in providing retail and ordering services for a wide variety of consumer goods; downloadable computer software for use as an application programming interface (API); Recorded computer software, downloadable computer programs and downloadable software applications for enabling the distribution of digital and video advertising through an electronic media, specifically the Internet, wireless networks, and mobile and portable devices; Recorded computer software, downloadable computer programs and downloadable software applications for use in creating and transmitting via the Internet messages and advertisements, in connection with digital advertising campaign management services; Recorded computer software, downloadable computer programs and downloadable software applications that analyzes the meaning of website content in order to optimize and provide targeted advertising to consumers while protecting advertiser brands in real-time; Recorded and downloadable computer software, namely, software using artificial intelligence and machine learning to automate campaign setup, audience creation, and optimization; recorded and downloadable computer software using artificial intelligence for allowing users to integrate human judgment and review into artificial intelligence and machine learning software applications for complex decision-making; recorded and downloadable computer software using artificial intelligence and machine learning for auditing and providing analytic capabilities on source code and application software; recorded and downloadable computer software using artificial intelligence and machine learning for creating and integrating source code and application software into artificial intelligence and machine learning software applications; recorded and downloadable computer software using artificial intelligence and machine learning with human judgment for reviewing source code, application software, data, and operational metrics Digital advertising technology provider featuring software and services to enable the distribution of digital and video advertising through an electronic media, specifically the Internet, wireless networks, and mobile and portable devices, namely, providing temporary use of an online, nondownloadable Internet software platform for use in creating and transmitting via the Internet messages and advertisements, in connection with digital advertising campaign management services; Providing online non-downloadable software that analyzes the meaning of website content in order to optimize and provide targeted advertising to consumers while protecting advertiser brands in real-time; Software as a service services featuring software to enable the distribution of digital and video advertising through an electronic media, namely, providing temporary use of an online, non-downloadable Internet software platform for use in creating and transmitting via the Internet messages and advertisements, in connection with digital advertising campaign management services; rental of computer software and programs for use in creating and transmitting via the Internet messages and advertisements, in connection with digital advertising campaign management services design and development of computer software; Computer programming services for business analysis and reporting; monitoring of computer systems by remote access; development and creation of computer programs for data processing; creating and designing website-based indexes of information for others; updating of data processing software; Designing websites for advertising purposes; cloud storage services for electronic data; digital compression of computer data; Graphic design of advertising materials; nondownloadable computer software, namely, software using artificial intelligence and machine learning to automate campaign setup, audience creation, and optimization; nondownloadable computer software using artificial intelligence for allowing users to integrate human judgment and review into artificial intelligence and machine learning software applications for complex decision-making; nondownloadable computer software using artificial intelligence and machine learning for auditing and providing analytic capabilities on source code and application software; nondownloadable computer software using artificial intelligence and machine learning for creating and integrating source code and application software into artificial intelligence and machine learning software applications; nondownloadable computer software using artificial intelligence and machine learning with human judgment for reviewing source code, application software, data, and operational metrics

61.

AMAZON SAVER

      
Serial Number 98640228
Status Pending
Filing Date 2024-07-09
Owner Amazon Technologies, Inc. ()
NICE Classes  ?
  • 29 - Meat, dairy products, prepared or preserved foods
  • 30 - Basic staples, tea, coffee, baked goods and confectionery
  • 31 - Agricultural products; live animals
  • 32 - Beers; non-alcoholic beverages
  • 33 - Alcoholic beverages other than beer

Goods & Services

UNCOOKED HAMBURGER PATTIES; UNCOOKED FOODS, NAMELY, BEEF; PACKAGED MEATS; BUTTER; CANNED AND BOTTLED FRUITS AND VEGETABLES; CHEESE; CHICKEN; COOKED FRUITS AND VEGETABLES; COOKING OIL; CREAM; DIPS; DRIED FRUITS; EGGS; FROZEN, PREPARED AND PACKAGED ENTREES, MEALS, APPETIZERS, AND SIDE DISHES CONSISTING PRIMARILY OF MEAT, SEAFOOD, POULTRY, VEGETABLES, OR CHEESE; FRUIT-BASED SNACK FOOD; FRUIT CHIPS; VEGETABLE CHIPS; FRUIT AND VEGETABLE PUREES; FRUIT SPREADS; JELLIES, JAMS, COMPOTES; MEAT; MILK; MILK-BASED PRODUCTS, EXCLUDING ICE CREAM, ICE MILK AND FROZEN YOGURT; PROCESSED MIXED NUTS; NUT-BASED AND DRIED FRUIT-BASED SNACK BARS; OLIVE OIL FOR FOOD; PEANUT BUTTER; POTATO CHIPS; RAISINS; SALADS EXCEPT MACARONI, RICE AND PASTA SALAD; SEAFOOD, NOT LIVE; SNACK MIX CONSISTING OF DEHYDRATED FRUIT AND PROCESSED NUTS; SOUP; SOY-BASED FOOD BEVERAGE USED AS A MILK SUBSTITUTE, SOY MILK; TOMATO PASTE; TRAIL MIX CONSISTING PRIMARILY OF PROCESSED NUTS OR GRANOLA; FROZEN VEGETABLES; VEGETABLE-BASED SNACK FOOD; WHIPPED CREAM; YOGURT; FRUIT JELLIES; MILK OF ALMONDS; PEANUT MILK; NUT-BASED SNACK BARS; PICKLED VEGETABLES, NAMELY, PICKLES; CONDIMENTS, NAMELY, DIPS BEING DAIRY-BASED DIPS; CONDIMENTS, NAMELY, JELLIES BAKING POWDER; BAKING SODA; BREAD; BREAD CRUMBS; BREAD STICKS; BISCUITS; BURGERS CONTAINED IN BREAD ROLLS; CAKE MIXES; COOKIE MIXES; CAKES; CARAMELS; CHEWING GUM; CORN-BASED CHIPS; GRAIN-BASED CHIPS; PRETZEL CHIPS; PITA CHIPS; BAGEL CHIPS; TORTILLA CHIPS; RICE CHIPS; CHOCOLATE; CHOCOLATE-BASED BEVERAGES; CHOCOLATE MOUSSES; CINNAMON; COCOA-BASED CONDIMENTS, INGREDIENTS, MIXES, POWDERS, AND SPREADS; COFFEE; COFFEE-BASED BEVERAGES; COFFEE BEANS; COFFEE AND TEA PODS; CONDIMENTS, NAMELY, SAVORY SAUCES, MINCED GARLIC, SOYA BEAN PASTE, CHILI PEPPER PASTE, PEPPER SAUCE, PIMIENTO, HORSERADISH, KETCHUP, RELISH; CONFECTIONERY MADE OF SUGAR; CONFECTIONERY MADE OF SUGAR-SUBSTITUTES; COOKIES; CRACKERS; DIPPING SAUCE; DOUGH; DRESSINGS FOR SALAD; FLAVORINGS FOR FOODS AND BEVERAGES, OTHER THAN ESSENTIAL OILS; FROZEN YOGURT; FLOUR; FROZEN, PREPARED, AND PACKAGED MEALS OR ENTREES CONSISTING PRIMARILY OF PASTA OR RICE; FROZEN FOODS, NAMELY, GRAIN AND BREAD BASED APPETIZERS, HORS D'OEUVRES, AND CANAPÉS; FOOD PACKAGE COMBINATIONS CONSISTING PRIMARILY OF BREAD, CRACKERS OR COOKIES; FROZEN CONFECTIONS; GRAVY; GRITS; HONEY; ICE CREAM; ICE CREAM CONES; ICED TEA; ICES; ICE; ICING; JELLY BEANS; NON-MEDICATED LOZENGES; MARINADES; MAYONNAISE; MIXES FOR MAKING BREADING; MUSTARD; NATURAL SWEETENERS; NOODLES; NOODLES, SAUCE, AND TOPPING COMBINED IN UNITARY PACKAGES; PACKAGED MEAL KITS CONSISTING PRIMARILY OF PASTA OR RICE; PANCAKES; PASTA; PASTA SALAD; PASTRIES; PASTRY MIXES, CREAM, DOUGH, AND SHELLS; PEPPER; PIES; PIZZAS; POPCORN; PUDDINGS; RICE; RICE CAKES; RICE-BASED SNACK FOOD; RICE AND SEASONING MIX COMBINED IN A UNITARY PACKAGE; SALT; SANDWICHES; SAUCES; SEASONINGS; SPICES; STUFFING MIXES CONTAINING BREAD; SUGAR; SUSHI; SYRUP FOR FLAVORING FOOD AND BEVERAGES; TACOS; TEA; TEA BAGS; TEA-BASED BEVERAGES; TEA-BASED SNACK FOODS; TORTILLAS; VANILLA; VINEGAR; WAFFLES; YEAST; FROZEN, PREPARED AND PACKAGED ENTREES, MEALS, APPETIZERS, AND SIDE DISHES CONSISTING PRIMARILY OF PASTA, RICE, BREAD, CRACKERS, COOKIES, SAUCES, SEASONING OR BEANS; MILK-BASED PRODUCTS, NAMELY, ICE CREAM, ICE MILK AND FROZEN YOGURT; PROCESSED GRAINS; MALT FOR FOOD PURPOSES; PROCESSED WHEAT; SORBETS; SAUCE; CONDIMENTS, NAMELY, DIPS BEING DIPPING SAUCES BEANS, FRESH; BEANS, UNPROCESSED; FRESH COCONUTS; FRESH CORN; FRESH FRUITS; FRESH VEGETABLES; FRESH HERBS; UNPROCESSED GRAIN; LETTUCE, FRESH; LOBSTERS, LIVE; MALT FOR BREWING AND DISTILLING; FRESH OATS; RICE, UNPROCESSED; SESAME, EDIBLE; SHELLFISH, LIVE; UNPROCESSED NUTS; FRESH WHEAT BEER; COCKTAILS, NON-ALCOHOLIC; ENERGY DRINKS; FLAVORED WATER; FRUIT-BASED BEVERAGES; FRUIT JUICES; GINGER ALE; ISOTONIC BEVERAGES; LEMONADES; MALT WORT; MINERAL AND AERATED WATERS; NON-ALCOHOLIC BEVERAGES, NAMELY, CARBONATED BEVERAGES; NON-ALCOHOLIC MALT BEVERAGES; NON-ALCOHOLIC FRUIT EXTRACTS USED IN THE PREPARATION OF BEVERAGES; NON-ALCOHOLIC COCKTAIL MIXES; PREPARATIONS FOR MAKING BEVERAGES, NAMELY, FRUIT DRINKS; SELTZER WATER; SMOOTHIES; SOFT DRINKS; VEGETABLE JUICES; WATER BEVERAGES; WHEY BEVERAGES; DISTILLED BEVERAGES, NAMELY, DISTILLED DRINKING WATER; SWEET CIDER; MALT SYRUP FOR BEVERAGES ALCOHOLIC BEVERAGES, EXCEPT BEER; ALCOHOLIC COCKTAIL MIXES; ALCOHOLIC ESSENCES; ALCOHOLIC EXTRACTS; BRANDY; BOURBON; DISTILLED SPIRITS; GIN; LIQUEURS; PRE-MIXED ALCOHOLIC BEVERAGES, OTHER THAN BEER-BASED; PREPARED ALCOHOLIC COCKTAIL; RUM; SAKE; SPIRITS; HARD CIDER; DISTILLED BLUE AGAVE LIQUOR; VODKA; WHISKY; WINE

62.

Radio frequency antenna for wearable device

      
Application Number 17116368
Grant Number 12029557
Status In Force
Filing Date 2020-12-09
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner AMAZON TECHNOLOGIES, INC. (USA)
Inventor
  • Kenny, Justin Christopher
  • Napoles, Adrian
  • Caduff, Andreas
  • Grajewski, Christopher Raymond
  • Heckerman, David

Abstract

Data about concentration of one or more types of molecules present within a human body are determined noninvasively using radio frequency signals. Signals at several different frequencies at very low power levels are emitted using an antenna mounted to a wearable device. The antenna includes nested elements of different sizes. Operating values, such as changes to the impedance of the antenna, are associated with the concentration of one or more types molecules within the user. Concentrations at different depths may be measured by using different sets of the nested elements at different times. Operating values at those times are measured and used to determine information indicative of concentration of the molecules. During use, a long axis of the antenna may be aligned with a long axis of a user's arm.

IPC Classes  ?

  • A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
  • A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
  • A61B 5/053 - Measuring electrical impedance or conductance of a portion of the body
  • H01Q 1/27 - Adaptation for use in or on movable bodies
  • H04B 1/3827 - Portable transceivers
  • H04B 17/10 - Monitoring; Testing of transmitters

63.

Flexible input/output (I/O) allocation for integrated circuit scan testing

      
Application Number 18055762
Grant Number 12032015
Status In Force
Filing Date 2022-11-15
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor Strulovici, Ilan

Abstract

Flexible input/output (I/O) allocation techniques for integrated circuit scan testing are disclosed. The techniques implement configurable definition of the core circuitry blocks comprising the scan domain under test and their mapping to the scan test pads. Scan testing of the scan domain is performed by receiving a test pattern on the set of one or more scan-in test pads, transmitting the test pattern to the scan domain via the set of one or more scan input channels, receiving a test response on the set of one or more scan output channels, and transmitting the test response to the set of one or more scan-out test pads.

IPC Classes  ?

  • G01R 31/28 - Testing of electronic circuits, e.g. by signal tracer
  • H01L 21/66 - Testing or measuring during manufacture or treatment

64.

Resource recovery service

      
Application Number 17449629
Grant Number 12032450
Status In Force
Filing Date 2021-09-30
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Kumar, Sandeep
  • Gathala, Anil
  • Nagapudi, Venkatesh
  • Khunger, Vaibhav

Abstract

Provided is a system for facilitating recovery of deleted computing resources in a cloud network environment. A centralized resource recovery service may be in network communication with a plurality of resource management services that are each configured to create, modify, or delete their respective computing resources such as data storage volumes, databases, compute instances, and the like. The resource recovery service may be configured to receive a delete request associated with a resource managed by one of the resource management services, and cause the resource to be retained in a recovery bin based on the resource satisfying one of a plurality of resource recovery conditions used to manage resource recovery across the resource management services.

IPC Classes  ?

  • G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
  • G06F 16/11 - File system administration, e.g. details of archiving or snapshots

65.

File-level snapshot access service

      
Application Number 17217957
Grant Number 12032516
Status In Force
Filing Date 2021-03-30
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Gathala, Anil
  • Kumar, Sandeep
  • Dalvi, Kiran Shantaram
  • Valicherla, Chakravarthi Kalyana
  • Verma, Shailendra
  • Park, Adonijah

Abstract

A file-level snapshot access service provides direct access to individual files included in a snapshot for virtual volume of a block-storage service without requiring a volume to be re-created from the snapshot, attached to a computing device, or mounted in a file system. For example, a user/client may directly retrieve individual files from specified snapshots via a user interface/API of the file-level snapshot access service. Additionally, the file-level snapshot access service is configured to provide a listing of files included in a given snapshot. In some embodiments, a file-level snapshot access service may provide direct access to individual files included in snapshots generated for other types of storage systems, such as an object-based storage system.

IPC Classes  ?

  • G06F 16/11 - File system administration, e.g. details of archiving or snapshots
  • G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
  • G06F 16/172 - Caching, prefetching or hoarding of files
  • G06F 9/54 - Interprogram communication

66.

Anomaly detection using feedback

      
Application Number 17107820
Grant Number 12033048
Status In Force
Filing Date 2020-11-30
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Callot, Laurent
  • Chhabra, Jasmeet
  • Chen, Lifan
  • Chen, Ming
  • Januschowski, Tim
  • Kan, Andrey
  • Kong, Luyang
  • Kurt, Baris
  • Perera, Pramuditha
  • Rahmani, Mostafa
  • Bhatia, Parminder

Abstract

Techniques for performing anomaly detection are described. An exemplary method includes receiving a request to detect potential anomalies using an anomaly detection system having at least one anomaly scoring model; processing the received data using the anomaly detection system to score the data to determine when the data is potentially anomalous based on one or more thresholds; requesting feedback of at least one determined potential anomaly; receiving feedback on the least one determined potential anomaly; and adjusting at least one of one or more of thresholds used to determine potential anomalies and what is considered an anomaly without adjusting the at least one anomaly scoring model.

IPC Classes  ?

  • H04L 29/06 - Communication control; Communication processing characterised by a protocol
  • G06F 18/214 - Generating training patterns; Bootstrap methods, e.g. bagging or boosting
  • G06N 20/20 - Ensemble learning

67.

E-commerce document framework with cross-source and cross-document validation

      
Application Number 17216500
Grant Number 12033195
Status In Force
Filing Date 2021-03-29
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Constantin, Catalin
  • Chen, Miao
  • Yuan, Fei
  • Das, Sreeja
  • Li, Qingyun
  • Iyer, Abhishek H
  • Singh, Avinash Vinodkumar
  • Shanmugam, Nathan P.

Abstract

Methods, systems, and computer-readable media for an e-commerce document framework with cross-source and cross-document validation are disclosed. A document framework system determines a document template associated with a request to generate a document comprising a record of one or more transactions. The document template indicates a plurality of data sources that store a plurality of data elements for the document and a plurality of validation tasks. The document framework system receives the plurality of data elements for the document from the plurality of data sources. The document framework system performs the validation tasks to determine the correctness of the data elements. The validation comprises cross-source validation and cross-document validation. The document framework system generates the document based (at least in part) on the data elements if the validation is successful.

IPC Classes  ?

68.

Systems and methods for causal detection and diagnosis of vehicle faults

      
Application Number 17113707
Grant Number 12033445
Status In Force
Filing Date 2020-12-07
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor Mishra, Pragyana K.

Abstract

Described are systems and methods relating to the causal detection and diagnosing of faults and anomalous operation of autonomous vehicles, such as unmanned aerial vehicles (UAVs), using machine learning. Embodiments of the present disclosure can provide systems and methods for detecting and diagnosing faults based on comparisons between the measured operation and/or behavior of a vehicle to the vehicle's expected nominal operation and/or behavior. Accordingly, the systems and methods according to embodiments of the present disclosure do not require prior knowledge of faults or modeling of the vehicle, the vehicle's operation, and/or environmental uncertainties. Further, embodiments of the present disclosure can facilitate sequencing of a vehicle's faults and/or anomalous operation and/or behavior, identify dependencies between a vehicle's faults and/or anomalous operation and/or behavior, and can detect and diagnose faults and/or anomalous operation and/or behavior in a contextual manner.

IPC Classes  ?

  • G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
  • B64C 39/02 - Aircraft not otherwise provided for characterised by special use
  • B64U 101/00 - UAVs specially adapted for particular uses or applications
  • G06F 18/214 - Generating training patterns; Bootstrap methods, e.g. bagging or boosting
  • G06F 18/23 - Clustering techniques
  • G06N 20/00 - Machine learning

69.

Target-device resolution

      
Application Number 17163695
Grant Number 12033622
Status In Force
Filing Date 2021-02-01
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Peng, Lizhen
  • Eberhardt, Sven
  • Kumar, Akshay
  • Brett, Charles Edwin Ashton
  • Hillenmeyer, Sara Parker
  • Welbourne, William Evan

Abstract

Systems and methods for target-device resolution are disclosed. A user may speak a user utterance requesting an action to be performed with respect to an accessory device, such as a smart-home device. The user utterance may include an identifier for the accessory device, but that identifier may not correspond to a naming indicator of an accessory device and/or may correspond to multiple naming indicators. In these examples, one or more models are utilized to identify which accessory device is most likely to correspond to the accessory device targeted by the user utterance for operation.

IPC Classes  ?

  • G10L 21/00 - Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
  • G10L 15/18 - Speech classification or search using natural language modelling
  • G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
  • G10L 25/00 - Speech or voice analysis techniques not restricted to a single one of groups

70.

Self-trigger prevention

      
Application Number 17671724
Grant Number 12033631
Status In Force
Filing Date 2022-02-15
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor Joshi, Aditya Sharadchandra

Abstract

A system configured to perform self-trigger prevention to avoid a device waking itself up when a wakeword is output by the device's own output audio. For example, during active playback the device may perform double-talk detection and suppress wakewords or other device-directed utterances when near-end speech is not present. To detect whether near-end speech is present, an Audio Front End (AFE) of the device may perform echo cancellation and generate correlation data indicating an amount of correlation between an output of the echo canceller and an estimated reference signal. When the correlation is high in certain frequency ranges, near-end speech is not present and the device may suppress the utterance. When the correlation is low, indicating that near-end speech could be present, the device does not suppress the utterance and sends the utterance to a remote system for speech processing.

IPC Classes  ?

  • G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
  • G10L 15/08 - Speech classification or search
  • G10L 21/0208 - Noise filtering
  • G10L 25/06 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the type of extracted parameters the extracted parameters being correlation coefficients

71.

Distributed denial of service mitigation in a container based framework

      
Application Number 15083098
Grant Number 12034740
Status In Force
Filing Date 2016-03-28
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Carmack, Scott Gerard
  • Rao Lakkakula, Narasimha
  • Sharifi Mehr, Nima

Abstract

In response to a process being triggered, at least in part by receipt of information regarding communication directed to a first application by a second application, a threat level is computed based at least in part on the information. As a result of the threat level being of a first severity, the second application is migrated to a destination zone that allows for improved communications with the first application. As a result of the threat level being of a second severity, migration of the second application to the destination zone is delayed. As a result of the threat level being of a third severity, a mitigation action is performed.

IPC Classes  ?

  • H04L 9/00 - Arrangements for secret or secure communications; Network security protocols
  • H04L 9/40 - Network security protocols

72.

Highly available certificate issuance using specialized certificate authorities

      
Application Number 17411740
Grant Number 12034872
Status In Force
Filing Date 2021-08-25
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Sharma, Param
  • Cignetti, Todd

Abstract

Techniques for providing specialized certificate authorities are described. A method of providing specialized certificate authorities may include receiving a request to generate a private certificate at a specialized certificate authority, the specialized certificate authority configured to generate only one type of digital certificate using a user-specified template, generating a certificate based on the customer-specified template, and returning the certificate.

IPC Classes  ?

  • H04L 9/32 - Arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system
  • H04L 9/08 - Key distribution

73.

Board physical placement verification in a server chassis

      
Application Number 17361031
Grant Number 12035495
Status In Force
Filing Date 2021-06-28
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Madar, Shay
  • Admon, Yotam
  • Elashri, Ali
  • Harel, Ziv
  • Bshara, Nafea
  • Navon, Noam

Abstract

A computer system includes a chassis comprising location indicating elements, such as springs, magnets, standoffs, or other types of location indicating elements physically attached to the chassis in a configuration that indicates locations in the chassis. Also, the computer system includes multiple printed circuit boards comprising pads configured to interface with the location indicating elements, wherein combinations of sensed or not sensed location indicating elements at the pads of the printed circuit boards provide a physically verified placement location for the printed circuit boards.

IPC Classes  ?

  • H05K 5/02 - Casings, cabinets or drawers for electric apparatus - Details
  • G01R 33/02 - Measuring direction or magnitude of magnetic fields or magnetic flux
  • H05K 5/00 - Casings, cabinets or drawers for electric apparatus

74.

Camera with floodlights

      
Application Number 29866703
Grant Number D1034750
Status In Force
Filing Date 2022-09-23
First Publication Date 2024-07-09
Grant Date 2024-07-09
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Bowers, Alexsandra M.
  • Hruska, Ryan David
  • Klein, Kit William
  • Lan, Chang-Feng
  • Parkman, Jon-Christopher
  • Siminoff, James
  • Takhchi, Youssef

75.

DYNAMIC VOICE SEARCH TRANSITIONING

      
Application Number 18402896
Status Pending
Filing Date 2024-01-03
First Publication Date 2024-07-04
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Prasad, Rohit
  • Santos, Anna
  • Sanchez, David
  • Strawderman, Jared
  • Castle, Sarah
  • Hammil, Kerry
  • Schindler, Christopher
  • Twerdahl, Timothy
  • Tavares, Joseph
  • Gulik, Bartek

Abstract

Systems, methods, and computer-readable media are disclosed for dynamic voice search transitioning. Example methods may include receiving, by a computer system in communication with a display, a first incoming voice data indication, initiating a first user interface theme for presentation at a display, wherein the first user interface theme is a default user interface theme, and receiving first voice data. Example methods may include sending the first voice data to a remote server for processing, receiving an indication from the remote server to initiate a second user interface theme, and initiating the second user interface theme for presentation at the display.

IPC Classes  ?

  • G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
  • H04N 21/422 - Input-only peripherals, e.g. global positioning system [GPS]
  • H04N 21/478 - Supplemental services, e.g. displaying phone caller identification or shopping application
  • H04N 21/482 - End-user interface for program selection

76.

EXECUTION OF AUXILIARY FUNCTIONS IN AN ON-DEMAND NETWORK CODE EXECUTION SYSTEM

      
Application Number 18412105
Status Pending
Filing Date 2024-01-12
First Publication Date 2024-07-04
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Mullen, Niall
  • Piwonka, Philip
  • Wagner, Timothy Allen
  • Brooker, Marc

Abstract

Systems and methods are described for providing auxiliary functions in an on-demand code execution system in a manner that enables efficient execution of code. A user may generate a task on the system by submitting code. The system may determine the auxiliary functions that the submitted code may require when executed on the system, and may provide these auxiliary functions by provisioning or configuring sidecar virtualized execution environments that work in conjunction with the main virtualized execution environment executing the submitted code. Sidecar virtualized execution environments may be identified and obtained from a library of preconfigured sidecar virtualized execution environments, or a sidecar agent that provides the auxiliary function may be identified from a library, and then a virtualized execution environment may be provisioned with the agent and/or configured to work in conjunction with the main virtualized execution environment.

IPC Classes  ?

  • G06F 9/455 - Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
  • G06F 9/48 - Program initiating; Program switching, e.g. by interrupt
  • G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]

77.

MULTI-DEVICE SPEECH PROCESSING

      
Application Number 18420937
Status Pending
Filing Date 2024-01-24
First Publication Date 2024-07-04
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Gupta, Rahul
  • Dupuy, Christophe
  • Stolee, Jacob Ryan
  • Chung, Clement

Abstract

Techniques for partially processing an input on a device and completing processing at a remote system are provided. The device may process an input using an on-device machine learning (ML) model, and determine to cease processing at an intermediary node of the (ML) model based on the output of the intermediary node. Based on the output of the intermediary node satisfying a condition, the device may use the output of the intermediary node to generate an output responsive to the input. Conversely, if the output of the intermediary node does not satisfy a condition, the device may send the output of the intermediary node to the remote system, so the remote system can use another machine learning model to complete processing with respect to the input.

IPC Classes  ?

  • G10L 15/16 - Speech classification or search using artificial neural networks
  • G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog

78.

AUTO-TUNING PERMISSIONS USING A LEARNING MODE

      
Application Number 18604379
Status Pending
Filing Date 2024-03-13
First Publication Date 2024-07-04
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Kjelstrup, Jacob A.
  • Mukkati Prakash, Bharath
  • Johnson, Brigid Ann
  • Pugalia, Ujjwal Rajkumar

Abstract

Methods, systems, and computer-readable media for auto-tuning permissions using a learning mode are disclosed. A plurality of access requests to a plurality of services and resources by an application are determined during execution of the application in a learning mode in a pre-production environment. The plurality of services and resources are hosted in a multi-tenant provider network. A subset of the services and resources that were used by the application during the learning mode are determined. An access control policy is generated that permits access to the subset of the services and resources used by the application during the learning mode. The access control policy is attached to a role associated with the application to permit access to the subset of the services and resources in a production environment.

IPC Classes  ?

79.

Data Streaming Service with Virtualized Broker Clusters

      
Application Number 18609987
Status Pending
Filing Date 2024-03-19
First Publication Date 2024-07-04
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Chakravorty, Sayantan
  • Koduru, Nagarjuna
  • Maji, Nabanita
  • Kistampalli, Vijaya Rama Reddy
  • Bhatia, Sankalp
  • Dorwat, Sahil

Abstract

Various embodiments of systems and methods for providing virtualized (e.g., serverless) broker clusters for a data streaming service are disclosed. A data streaming service uses a front-end proxy layer and a back-end broker layer to provide virtualized broker clusters, for example in a Kafka-based streaming service. Resources included in a virtualized broker cluster are monitored and automatically scaled-up, scaled-down, or re-balanced in a way that is transparent to data producing and/or data consuming clients of the data streaming service.

IPC Classes  ?

  • H04L 67/562 - Brokering proxy services
  • H04L 9/40 - Network security protocols
  • H04L 41/50 - Network service management, e.g. ensuring proper service fulfilment according to agreements
  • H04L 65/60 - Network streaming of media packets

80.

AUTOMATED PREVIEW GENERATION FOR VIDEO ENTERTAINMENT CONTENT

      
Application Number 18411720
Status Pending
Filing Date 2024-01-12
First Publication Date 2024-07-04
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Sharma, Mayank
  • Gupta, Prabhakar
  • Gupta, Honey
  • Keshav, Kumar

Abstract

A respective set of features, including emotion-related features, are extracted from segments of a video for which a preview is to be generated. A subset of the segments is chosen using the features and filtering criteria including at least one emotion-based filtering criterion. Respective weighted preview-suitability scores are assigned to the segments of the subset using at least a metric of similarity between individual segments and a plot summary of the video. The scores are used to select and combine segments to form a preview for the video.

IPC Classes  ?

  • H04N 21/8549 - Creating video summaries, e.g. movie trailer
  • H04N 21/466 - Learning process for intelligent management, e.g. learning user preferences for recommending movies
  • H04N 21/472 - End-user interface for requesting content, additional data or services; End-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification or for manipulating displayed content

81.

REMOTE DURABLE LOGGING FOR JOURNALING FILE SYSTEMS

      
Application Number 18518176
Status Pending
Filing Date 2023-11-22
First Publication Date 2024-07-04
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Kinney, Jr., James Edward
  • Shams, Khawaja Salman

Abstract

A journaling file system may implement remote durable logging. Updates to a file system may be received, and log records describing the updates may be stored in a locally-accessible file system change log. The update may then be acknowledged as committed. The log records may then be sent to be stored in a network-based data store in a remote version of the file system change log. Once it may be determined that the log records are stored in the remote version, storage space for the log records in the local file system change log may be reclaimed. Various types of restoration and duplication techniques may be implemented based on the remote version of the change log to restore a file system at an originating device or to duplicate the file system at a different device.

IPC Classes  ?

82.

TIME-BASED CONTEXT FOR VOICE USER INTERFACE

      
Application Number 18603683
Status Pending
Filing Date 2024-03-13
First Publication Date 2024-07-04
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Sepasi Ahoei, Arash
  • Tomlinson, Jack Andrew
  • Urtnowski, Matthew Brian
  • Aginlar, Volkan
  • Lu, Lei Raymond
  • Chen, Song
  • Ramaswamy, Arun

Abstract

A media marker mechanism may be used a cloud service to determine up-to-date context regarding playback of a media content stream on a user device. The cloud service may insert a media content item into a media content stream and/or combine media content items into a media content stream. The cloud service may implement the media marker mechanism to tag a content item with metadata that can be read by the user device. The user device can play the streaming media content and, when the tagged content item plays, read the metadata and send it to the cloud service. The cloud service can use the metadata to enrich the media content delivery by, for example, sending the user device a companion image to display, providing a link to make the companion image clickable, handling requests referring to the media content, etc.

IPC Classes  ?

  • G06F 3/16 - Sound input; Sound output
  • G06F 3/04842 - Selection of displayed objects or displayed text elements
  • G06F 40/106 - Display of layout of documents; Previewing
  • G06F 40/134 - Hyperlinking
  • G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog

83.

Automated container provisioning systems and methods

      
Application Number 17592858
Grant Number 12024367
Status In Force
Filing Date 2022-02-04
First Publication Date 2024-07-02
Grant Date 2024-07-02
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Day, John Daryl
  • Mohammed, Raashid

Abstract

Automated container provisioning systems described herein may include one or more pairs of transport belts, one or more guide surfaces, and one or more sensors. The transport belts may engage flaps or other portions of containers, and the guide surfaces may support surfaces or other portions of containers, in order to transport containers to downstream stations within a facility. In addition, the sensors may detect containers during transport, responsive to which various additional operations may be performed with respect to containers, such as sortation, printing, labeling, tracking, packing, accumulation, or others.

IPC Classes  ?

  • B65G 15/14 - Conveyors having endless load-conveying surfaces, i.e. belts and like continuous members, to which tractive effort is transmitted by means other than endless driving elements of similar configuration comprising two or more co-operating endless surfaces with parallel longitudinal axes, or a multiplicity of parallel elements, e.g. ropes defining an endless surface with two or more endless belts the load being conveyed between the belts
  • B31B 50/92 - Delivering
  • B31B 50/98 - Delivering in stacks or bundles
  • B65G 15/44 - Belts or like endless load-carriers made of rubber or plastics having ribs, ridges, or other surface projections for impelling the loads
  • B65G 21/20 - Means incorporated in, or attached to, framework or housings for guiding load-carriers, traction elements or loads supported on moving surfaces
  • B65G 43/08 - Control devices operated by article or material being fed, conveyed, or discharged
  • B31B 50/74 - Auxiliary operations
  • B65G 15/02 - Conveyors having endless load-conveying surfaces, i.e. belts and like continuous members, to which tractive effort is transmitted by means other than endless driving elements of similar configuration for conveying in a circular arc
  • B65G 15/58 - Belts or like endless load-carriers with means for holding or retaining the loads in fixed position, e.g. magnetic

84.

Dynamic resource management of network device

      
Application Number 17444352
Grant Number 12026103
Status In Force
Filing Date 2021-08-03
First Publication Date 2024-07-02
Grant Date 2024-07-02
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Machulsky, Georgy
  • Bshara, Nafea
  • Belgazal, Netanel Israel
  • Schmeilin, Evgeny
  • Bshara, Said
  • Matushevsky, Alexander

Abstract

A resource request is received by a peripheral device from host processing logic. The resource request includes a requested resource size. The peripheral device allocates resource of the peripheral device in response to the resource request. A resource response is sent by the peripheral device to the host processing logic. The resource response includes a location of the allocated resource.

IPC Classes  ?

  • G06F 13/00 - Interconnection of, or transfer of information or other signals between, memories, input/output devices or central processing units
  • G06F 3/06 - Digital input from, or digital output to, record carriers
  • G06F 9/455 - Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
  • G06F 13/16 - Handling requests for interconnection or transfer for access to memory bus
  • G06F 13/28 - Handling requests for interconnection or transfer for access to input/output bus using burst mode transfer, e.g. direct memory access, cycle steal
  • G06F 13/40 - Bus structure
  • G06F 13/42 - Bus transfer protocol, e.g. handshake; Synchronisation

85.

Knowledge graph alignment

      
Application Number 17935442
Grant Number 12026203
Status In Force
Filing Date 2022-09-26
First Publication Date 2024-07-02
Grant Date 2024-07-02
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Anubhai, Rishita Rajal
  • Bhargavi, V. Divya
  • Ravipati, Vidya Sagar

Abstract

Techniques for aligning (merging) knowledge graphs is described. Graph entity alignment is the problem of “joining” two knowledge graphs based on common entities. Most approaches in literature solve this using some annotated seed entity pairs and train a supervised model to rank entities in one knowledge base against another. In some examples, unsupervised alignment approach that uses graph-to-text summaries to encode entities in two or more distinct graphs into the same representation space, encodes those summaries into a common space, and then uses similarity analysis to determine when graph entities should be aligned (merged).

IPC Classes  ?

  • G06F 17/00 - Digital computing or data processing equipment or methods, specially adapted for specific functions
  • G06F 16/901 - Indexing; Data structures therefor; Storage structures

86.

Memory operation for systolic array

      
Application Number 17964291
Grant Number 12026607
Status In Force
Filing Date 2022-10-12
First Publication Date 2024-07-02
Grant Date 2024-07-02
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Huynh, Jeffrey T.
  • Diamant, Ron

Abstract

A neural network accelerator executes instructions to: load a first weight data element of an array of weight data elements from a memory into a systolic array; extract, from the instructions, information indicating a first number of input data elements to be obtained from a first address of the memory and a second number of input data elements to be skipped between adjacent input data elements to be obtained, the first address being based on first coordinates of the first weight data element, and the first and second numbers being based on a stride of a convolution operation; based on the information, obtain first input data elements from the first address of the memory; and control the systolic array to perform first computations based on the first weight data element and the first input data elements to generate first output data elements of an output data array.

IPC Classes  ?

  • G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
  • G06F 15/80 - Architectures of general purpose stored program computers comprising an array of processing units with common control, e.g. single instruction multiple data processors
  • G06N 3/02 - Neural networks

87.

Slimmable neural network architecture search optimization

      
Application Number 16710951
Grant Number 12026619
Status In Force
Filing Date 2019-12-11
First Publication Date 2024-07-02
Grant Date 2024-07-02
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Berman, Maxim
  • Pishchulin, Leonid
  • Xu, Ning
  • Medioni, Gerard Guy

Abstract

Disclosed are systems and methods to perform neural architecture search (“NAS”) that automatically optimizes for the number of channels to allocate to each layer of a deep neural network. Some implementations include a pairwise slimming that includes a global optimization step. Likewise, in some implementations, a bias toward a region of interest may be applied to channel path selection during training.

IPC Classes  ?

  • G06N 3/082 - Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
  • G06F 11/30 - Monitoring
  • G06N 3/04 - Architecture, e.g. interconnection topology

88.

Automated sequencing and dispensing in final segment of delivery

      
Application Number 17472183
Grant Number 12026663
Status In Force
Filing Date 2021-09-10
First Publication Date 2024-07-02
Grant Date 2024-07-02
Owner Amazon Technologies, Inc. (USA)
Inventor Kalm, William Scott

Abstract

An item delivery system can include a carrier positioned on a rail. The rail can be positioned in a delivery vehicle, for example. The carrier can include rigid or semi-rigid sidewall and dividers to form item slots. The item slots can receive one or more items. The carrier can be positioned on the rail and moved around the rail by an advancement device. The advancement device can engage with a portion of the carrier to move the carrier. The carrier can be moved to a position for removal of items from the carrier. The removal position may correspond to an area accessible from an operator area of the delivery vehicle, for example.

IPC Classes  ?

  • B65G 1/137 - Storage devices mechanical with arrangements or automatic control means for selecting which articles are to be removed
  • B65G 1/04 - Storage devices mechanical
  • G06Q 10/087 - Inventory or stock management, e.g. order filling, procurement or balancing against orders

89.

Network namespace monitoring system

      
Application Number 17809464
Grant Number 12028312
Status In Force
Filing Date 2022-06-28
First Publication Date 2024-07-02
Grant Date 2024-07-02
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Bailey, Jr., Donald Lee
  • Fuller, Abigail
  • Torres, John Paul
  • Luz, Giulian Dalton

Abstract

A namespace monitoring service may track released namespaces such as internet protocol (IP) addresses and manage namespace cooldown pools, available namespace pools, and a registry of released namespaces to detect and mitigate security vulnerabilities that arise from reassignment of namespaces. The namespace monitoring service provides access to the released namespace registry and/or sends a data stream of namespace registry updates. The namespace monitoring service may manage namespace reassignment process and extend the cooldown period of released namespaces or place a hold on available name spaces.

IPC Classes  ?

90.

Enhanced video streaming and reference frame synchronization

      
Application Number 17830628
Grant Number 12028549
Status In Force
Filing Date 2022-06-02
First Publication Date 2024-07-02
Grant Date 2024-07-02
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Wang, Qi Keith
  • Ghorashi, Ramin
  • Bannister, Stephen John
  • Brailovskiy, Ilya

Abstract

Methods of video streaming are generally described. In some examples, a camera device periodically captures an image, communicates encoded frame data representing that image to a server, and decodes and stores the previously encoded frame data as a background picture. The server receives the encoded frame data, decodes it, and stores the decoded frame in a buffer for future use. Subsequently, upon initiation of a streaming session, the camera device captures another image and encodes a predicted key frame based on differences between the captured image and the background picture. The camera device sends the predicted key frame to the server, which receives it and reconstructs a facsimile of the captured image utilizing the previously decoded frame stored in the buffer. Methods of acknowledging successfully decoded frames for use in selecting background pictures is also described.

IPC Classes  ?

  • H04N 19/593 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving spatial prediction techniques
  • H04N 19/124 - Quantisation
  • H04N 19/139 - Analysis of motion vectors, e.g. their magnitude, direction, variance or reliability
  • H04N 19/176 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a block, e.g. a macroblock
  • H04N 19/625 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding using discrete cosine transform [DCT]
  • H04N 19/70 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by syntax aspects related to video coding, e.g. related to compression standards

91.

Remote scheduling of recorded content for digital video recorders

      
Application Number 16289254
Grant Number 12028576
Status In Force
Filing Date 2019-02-28
First Publication Date 2024-07-02
Grant Date 2024-07-02
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Subramaniam, Muthukumar
  • Rangasamy, Surendran
  • Kumar, Tarun
  • Prasanna, Shenbaga
  • Bhatta, Anantha Krishna Hodrali Srinivasa

Abstract

Devices and methods are provided for remote scheduling of recorded content for digital video recorders. The device may determine a first channel, a first date, and a first time at which a first program will be presented. The device may determine a first recording schedule for a remote digital video recording (DVR) device, wherein the first recording schedule includes the first program at the first channel, the first date, and the first time. The device may receive a synchronization notification that the first recording schedule has been modified at the remote DVR device to include a second program to be recorded. The device may determine a second recording schedule, wherein the second recording schedule includes the second program at the second time. The device may send the second recording schedule to the remote DVR device.

IPC Classes  ?

  • H04N 21/2747 - Remote storage of video programs received via the downstream path, e.g. from the server
  • H04N 21/426 - Internal components of the client
  • H04N 21/458 - Scheduling content for creating a personalised stream, e.g. by combining a locally stored advertisement with an incoming stream; Updating operations, e.g. for OS modules

92.

Earbud

      
Application Number 29828138
Grant Number D1033395
Status In Force
Filing Date 2022-02-24
First Publication Date 2024-07-02
Grant Date 2024-07-02
Owner AMAZON TECHNOLOGIES, INC. (USA)
Inventor
  • Laffon De Mazieres, Emmanuel
  • Hameed, Shameem

93.

Metric data processing and storage

      
Application Number 17548423
Grant Number 12026163
Status In Force
Filing Date 2021-12-10
First Publication Date 2024-07-02
Grant Date 2024-07-02
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Giuliano, Andrea
  • Taylor, Gary
  • Bramhill, Gavin

Abstract

A query including a time range can be received. The query can be processed to determine data associated with the time range of the query. Cached data can be analyzed to identify a first block of data including metadata identifying a first timestamp associated with the time range of the query. A communication can be generated to include the first timestamp associated with the first block of data. Based on the communication, a second block of data can be received with metadata identifying a second timestamp associated with the time range of the query. A query result can be generated to include the first block of data and the second block of data.

IPC Classes  ?

94.

Generating description pages for media entities

      
Application Number 17690931
Grant Number 12026199
Status In Force
Filing Date 2022-03-09
First Publication Date 2024-07-02
Grant Date 2024-07-02
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Siagian, Christian Garcia
  • Shete, Vedant Ulhas
  • Stephani, Timothy William
  • Vecino, Jobel Kyle Petallana
  • Zheng, Gordon

Abstract

Pages describing episodes of podcasts or other media entities are constructed by interpreting content of the media entities. A transcript of an episode is determined by one or more natural language understanding techniques and divided into chapters. For each of the chapters, a summary sentence of the chapter and one or more key phrases are determined from the transcript, and participants in the chapter are identified. A summary of the episode is determined from the summary sentences of each of the chapters. A page that describes the episode of the podcast including the summary of the episode, as well as one or more of the key phrases and identities of the participants is generated and provided to prospective listeners to the episode.

IPC Classes  ?

  • G06F 16/683 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
  • G06F 16/34 - Browsing; Visualisation therefor
  • G06F 16/68 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
  • G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
  • G06F 40/295 - Named entity recognition

95.

Tensor network decoder with accounting for correlated noise

      
Application Number 17486503
Grant Number 12026585
Status In Force
Filing Date 2021-09-27
First Publication Date 2024-07-02
Grant Date 2024-07-02
Owner Amazon Technologies, Inc. (USA)
Inventor Kubica, Aleksander Marek

Abstract

A tensor network decoder accounts for correlated noise between qubits of a two-dimensional code. The tensor network decoder is generated using a graphical noise model for a quantum device that is used (or to be used) to implement the two-dimensional code. For example, an input graphical noise model, such as a hypergraph, may be used to generate a tensor network decoder. Whereas other decoders assume noise is independent and identically distributed (e.g. iid noise), a tensor network decoder accounts for correlated noise not considered in such decoders that assume iid noise.

IPC Classes  ?

  • G06N 10/00 - Quantum computing, i.e. information processing based on quantum-mechanical phenomena
  • G06F 16/901 - Indexing; Data structures therefor; Storage structures
  • G06N 3/045 - Combinations of networks

96.

Event image information retention

      
Application Number 17340817
Grant Number 12026635
Status In Force
Filing Date 2021-06-07
First Publication Date 2024-07-02
Grant Date 2024-07-02
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Camp, Wyatt David
  • Kumar, Dilip
  • Taylor, Amber Autrey
  • Famularo, Jason Michael
  • Mathiesen, Thomas Meilandt
  • Frank, Jared Joseph

Abstract

Described is a system and method for proactively determining and resolving events having low confidence scores and/or resolving disputed events. For example, when an event, such as a pick of an item from an inventory location within a materials handling facility occurs, the event aspects (e.g., user involved in the event, item involved in the event, action performed) are determined based on information provided from one or more input components (e.g., camera, weight sensor). If each event aspect cannot be determined with a high degree of confidence, the event information is provided to an associate for resolution.

IPC Classes  ?

  • G06N 5/04 - Inference or reasoning models
  • G06F 16/11 - File system administration, e.g. details of archiving or snapshots
  • G06N 5/048 - Fuzzy inferencing

97.

Low latency anomaly detection and related mitigation action

      
Application Number 17532142
Grant Number 12026718
Status In Force
Filing Date 2021-11-22
First Publication Date 2024-07-02
Grant Date 2024-07-02
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Rahmaniani, Ragheb
  • Deewan, Kalpit
  • Dey, Subhas Chandra
  • Kothari, Shubhankar Ajit

Abstract

Anomalies can be detected and related mitigation actions can be executed. For example, a computing device receives, from a first source, first data associated with a first process of a payment service in support of transactions of a first plurality of users. The computing device generates first aggregated metrics based on the first data. The computing device determines, by using the first aggregated metrics as first input to a machine learning engine, an anomaly associated with the execution of the first process. The computing device determines, based on the anomaly and a history of anomalies associated with the execution of the first process, an indication of an outage associated with the first process. The computing device causes an execution of a mitigation action based on the indication of the outage. The execution of the mitigation action is in support of transactions of a second plurality of users.

IPC Classes  ?

  • G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check of credit lines or negative lists
  • G06N 20/00 - Machine learning

98.

Fallback capacity providers for capacity management of workload infrastructure

      
Application Number 18082779
Grant Number 12028268
Status In Force
Filing Date 2022-12-16
First Publication Date 2024-07-02
Grant Date 2024-07-02
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Featonby, Malcolm
  • Petersen, Derek J
  • Nautiyal, Abhishek
  • Young, Li Chieh

Abstract

Techniques implemented by a cloud computing system for providing fallback capacity providers to ensure that infrastructure capacity required to run containerized services is available despite primary capacity providers experiencing a failure. Cloud providers offer container-management services that automate the management and scaling of containerized services of users. The container-management services are supported by capacity providers that manage the computing infrastructure on which the containerized services run (e.g., servers, VMs, etc.). However, there are times when a capacity provider is unable to provision capacity for containerized services, such as due to a large scale failure. Rather than leaving capacity requests unserved, the container-management service designates a fallback capacity provider that acts as a failover for provisioning requested infrastructure capacity. The fallback capacity providers have different provisioning paths than the primary capacity providers, and thus different availability postures and failure modes, to ensure resiliency in provisioning capacity for containerized services.

IPC Classes  ?

  • H04L 47/78 - Architectures of resource allocation
  • H04L 47/74 - Admission control; Resource allocation measures in reaction to resource unavailability

99.

Detecting anomalous storage service events using autoencoders

      
Application Number 17115107
Grant Number 12028362
Status In Force
Filing Date 2020-12-08
First Publication Date 2024-07-02
Grant Date 2024-07-02
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Cui, Qian
  • Ding, Wei
  • Polyakov, Oleg Yurievich
  • Coskun, Baris

Abstract

Techniques for enabling the identification of anomalous events associated with an object storage service of a cloud provider network using a variational autoencoder model including a pre-trained embedding for selected features of events are described. A variational autoencoder, for example, encodes data into a latent space and reconstructs approximations of the data from an encoding in the latent space. In this context, for example, anomalous events of interest might represent unauthorized or abusive behavior associated with storage resources provided by an object storage service (or in association with other types of computing resources provided by other services of a cloud provider network). Legitimate (or benign) access patterns to an object storage service can be modeled by utilizing observed data plane events stored by an account activity monitoring service. Once trained, the model can be used to identify anomalous events.

IPC Classes  ?

  • H04L 9/40 - Network security protocols
  • G06F 18/214 - Generating training patterns; Bootstrap methods, e.g. bagging or boosting
  • G06N 3/045 - Combinations of networks
  • G06N 3/047 - Probabilistic or stochastic networks
  • G06N 3/088 - Non-supervised learning, e.g. competitive learning
  • G06V 10/75 - Image or video pattern matching; Proximity measures in feature spaces using context analysis; Selection of dictionaries
  • H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]

100.

Combined wireless speaker and range extender device

      
Application Number 29847322
Grant Number D1033383
Status In Force
Filing Date 2022-07-22
First Publication Date 2024-07-02
Grant Date 2024-07-02
Owner Amazon Technologies, Inc. (USA)
Inventor
  • Lu, Wen-Yo
  • England, Matthew J.
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