Provided herein are methods that utilize a CRISPR/Cas complex having collateral activity, one or more nucleases, one or more oligonucleotides and a fluorescent reporter. The methods disclosed herein can amplify a fluorescent signal when a target nucleic acid is present in a sample.
C12N 15/11 - DNA or RNA fragments; Modified forms thereof
C12Q 1/70 - Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving virus or bacteriophage
Systems and methods are described for providing a risk assessment for a software application based on evidence obtained from one or more sources. In some aspects, security and compliance evidence may be obtained from one or more evidence sources, for an application offered through a service provider, where the evidence sources include operational data from the application executing within a runtime environment provided by the service provider. The obtained evidence may be mapped to risk assessment criteria to generate a risk assessment. In some cases, the risk assessment criteria includes a plurality of attributes of the application, with the attributes indicating potential vulnerabilities of the application. A representation of the risk assessment may be generated across at least some of the attributes based on the risk assessment comparison. The risk assessment representation may then be updated based on monitoring of the security and compliance evidence for the application.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
A distributed voice controlled system has a primary assistant and at least one secondary assistant. The primary assistant has a housing to hold one or more microphones, one or more speakers, and various computing components. The secondary assistant is similar in structure, but is void of speakers. The voice controlled assistants perform transactions and other functions primarily based on verbal interactions with a user. The assistants within the system are coordinated and synchronized to perform acoustic echo cancellation, selection of a best audio input from among the assistants, and distributed processing.
A content distribution environment enables acquisition of a video feed in a first format, conversion into a second format, and transmission to a cloud service using on-premises equipment managed and operated by the content provider. A gateway or bridge may facilitate operation with one or more managed services, such as a content distribution service, that provides a reliable way to ingest a video stream from a source, replicate the video stream to one or more destinations, and permit sharing of video streams to affiliates and partners. The on-premises equipment may execute a software package to provide end-to-end operations that leverage operations of a cloud service while executing on content provider owned or managed hardware.
Vulnerabilities to physical memory, such as server dynamic random access memory (DRAM) with error correction code (ECC) capability, can be mitigated though the use of guard pages allocated in that physical memory. Physical memory pages can be mapped to virtual memory pages of a contiguous virtual address space. When an error such as a bit flip is detected in a physical memory page, the data from that physical memory page can be copied to a protected page, such as a guard page or page isolated from other sensitive data. Information such as an error correction code (ECC) can be used to determine and correct the erroneous bit. The mappings in a related page table can be updated such that the same virtual pages or addresses are then mapped to the guard page that now includes the relevant data.
Systems and methods are described for service coordination using a PartiQL query. For instance, a method may include selecting a recipe from a plurality of recipes in response to a request from a user device, wherein the recipe includes a plurality of operations to provide a complex function, each of the plurality of operations corresponding to a service of a plurality of services; determining a PartiQL query for an operation of the plurality of operations; executing the PartiQL query to obtain a result; transmitting a call to a service of the plurality of services that corresponds to the operation based on the result; receiving a response from the service; and after all operations of the plurality of operations are called, transmitting a response to the request to the user device.
This disclosure describes a data dependency and monitoring service that includes a database module (data dictionary) that contains information about database entities, for example, reports, tables, views, dashboards, etc. The information may include, for example, description, ownership, location, refresh cadence, etc. The data dependency and monitoring system provides a data dependency module that lists upstream data sources behind the data entities and also illustrates other entities and table dashboards that use the data entities downstream. The data dependency monitoring system provides a data monitoring and alert module that triggers alerts when data within a data entity is not refreshed by a predefined refresh cadence, e.g., a predetermined amount of time, thereby causing the corresponding data to be stale or out of date.
Systems, devices, and methods are provided for generating machine code for a second execution environment based on bytecode generated for a first execution environment. A method may comprise steps to obtain a set of bytecode instructions executable in a first execution environment, parse the set of bytecode instructions to determine at least one stack-based class object, determine an intermediate representation (IR) of the set of bytecode instructions, wherein the intermediate representation comprising at least one single static assignment (SSA)-based representation that corresponds to the at least one stack-based class object, and translate the intermediate representation into machine code that is executable in the second execution environment. For example, Java bytecode generated to run on a Java Virtual Machine (JVM) may be translated to machine code that runs in an execution environment that lacks or otherwise restricts the use of a JVM.
Disclosed are various embodiments for an external housing assembly of a sensing device externally mounted on a skewed panel of a vehicle. The external housing assembly can provide ingress protection from fluids and foreign objects. In one example, the external housing assembly includes an external housing, a cylindrical conduit, a spacer, a retention nut, and a retention ring. The external housing being configured to surround an aperture of a vehicle. The cylindrical conduit attaches to the external housing, and the sensing device having a lens that is positioned within the cylindrical conduit. The spacer is positioned adjacent to the skewed panel. The retention nut is positioned adjacent to the spacer and is attached to the cylindrical conduit. A portion of the sensing device is positioned between the retention nut and the retention ring. The retention ring is attached to the retention nut.
Techniques for generating customized synthetic voices personalized to a user, based on user-provided feedback, are described. A system may determine embedding data representing a user-provided description of a desired synthetic voice and profile data associated with the user, and generate synthetic voice embedding data using synthetic voice embedding data corresponding a profile associated with a user determined to be similar to the current user. Based on user-provided feedback with respect to a customized synthetic voice, generated using synthetic voice characteristics corresponding to the synthetic voice embedding data and presented to the user, and the synthetic voice embedding data, the system may generate new synthetic voice embedding data, corresponding to a new customized synthetic voice. The system may be configured to assign the customized synthetic voice to the user, such that a subsequent user may not be presented with the same customized synthetic voice.
G10L 13/10 - Prosody rules derived from text; Stress or intonation
G10L 15/06 - Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
G10L 25/30 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the analysis technique using neural networks
11.
Capacitive sensor for item identifying mobile apparatus
This disclosure describes, in part, a mobile apparatus for identifying items within a facility using an omnidirectional imaging system. For instance, the mobile apparatus may include a main frame, a chassis attached to the main frame, a basket that attaches to the chassis in order to prevent the basket from contacting the main frame, and an omnidirectional imaging system providing image data in and around the mobile apparatus. A user may place item(s) within a receptacle of the basket. The mobile apparatus may further include a handlebar module attached to the main frame, the handlebar module including the omnidirectional imaging system and a computing system for identifying items and events in and around the cart.
G08B 13/14 - Mechanical actuation by lifting or attempted removal of hand-portable articles
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
G06Q 10/087 - Inventory or stock management, e.g. order filling, procurement or balancing against orders
12.
Controller device management of peripheral devices
Controller devices may be communicatively coupled to distributed devices in a workspace. The controller devices may be positioned in various geographic locations relative to locations of the distributed devices within the workspace. Each of the controller devices may be utilized to maintain an account of, and locally control, one or more of the distributed devices. Portal devices may utilize the controller devices to manage the distributed devices by configuring, controlling, and updating the controller devices. The control devices may be utilized to establish communication channels between the portal devices and the distributed devices to provide access for a user to the distributed devices. The communication channels may be accessible to user devices based on security credentials that are modified in real-time or near real-time.
Techniques are described for monitoring and analyzing input/output (I/O) messages for patterns indicative of ransomware attacks affecting computer systems of a cloud provider, and for performing various remediation actions to mitigate data loss once a potential ransomware attack is detected. The monitoring of I/O activity for such patterns is performed at least in part by I/O proxy devices coupled to computer systems of a cloud provider network, where an I/O proxy device is interposed in the I/O path between guest operating systems running on a computer system and storage devices to which I/O messages are destined. An I/O proxy device can analyze I/O messages for patterns indicative of potential ransomware attacks by monitoring for anomalous I/O patterns which may, e.g., be indicative of a malicious process attempting to encrypt or otherwise render in accessible a significant portion of one or more storage volumes as part of a ransomware attack.
An image of at least a portion of a user during enrollment to a biometric identification system is acquired and processed with a first model to determine a first embedding that is representative of features in that image in a first embedding space. The first embedding may be stored for later comparison to identify the user, while the image is not stored. A second model that uses a second embedding space may be later developed. A transformer is trained to accept as input an embedding from the first model and produce as output an embedding consistent with the second embedding space. The previously stored first embedding may be converted to a second embedding in a second embedding space using the transformer. As a result, new embedding models may be implemented without requiring storage of user images for later reprocessing with the new models or requiring re-enrollment by users.
A system may be configured to detect custom acoustic events, where the system generates an acoustic event profile for the custom acoustic event based on a natural language description provided by a user and using an audio sample of the described acoustic event. For example, the user may describe the custom acoustic event as “dog bark.” The system may ask the user questions to refine the description (e.g., dog breed, dog gender, age, etc.). Using an audio sample of the refined description, the system may then determine that audio captured in the user's environment is a potential sample of the custom acoustic event. Such captured audio may be presented to the user for confirmation, and then may be used to detect future occurrences of the custom acoustic event in the user's environment.
G10L 25/51 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination
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 15/30 - Distributed recognition, e.g. in client-server systems, for mobile phones or network applications
16.
Maintaining partition-level parity data for improved volume durability
A data storage system stores a plurality of partitions for a volume and at least one parity partition for the volume. The parity partition includes erasure encoded data that enables any one of the partitions to be reconstructed using the erasure encoded data of the parity partition. Additionally, the data storage system is configured to generate parity data updates in response to modifications to the volume and store updated parity data in the parity partition, such that a current state of any of the partitions of the volume can be re-created in response to a loss of one of the partitions.
G06F 3/06 - Digital input from, or digital output to, record carriers
G06F 11/10 - Adding special bits or symbols to the coded information, e.g. parity check, casting out nines or elevens
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
A system receives a request to retrieve a data object from a data storage device associated with an asynchronous-access data storage service. For data object retrieval, the system identifies the data storage device and creates a job corresponding to the data object retrieval. Once the job is executed, the data object is retrieved to satisfy the request without having to restore the data object and further provided to a data storage device associated with a synchronous-access data storage service to satisfy subsequent requests synchronously.
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
Techniques for generating and utilizing investigative playbooks for cloud security events are described. Activity is detected indicative of a potential compromise in association with a resource of a multi-tenant cloud provider network. API calls originated by a client are determined to utilize API methods that exist within a set of known API methods included in a formal model of attack tactics. Responsive to both the detection and the determination, an investigative playbook is executed, based on the activity, that includes multiple logical tests to generate an attack report that can be presented to a user such as a security analyst for use in investigating cloud security events.
Techniques for a dynamic encoder-time scaling service for live and on-demand adaptive streaming are described. As one example, a computer-implemented method includes determining a first resolution for a first fragment of a video file based on a first encoding complexity of the first fragment at a bitrate, encoding the first fragment at the first resolution for the bitrate to generate an encoded first fragment, determining a second different resolution for a second fragment of the video file based on a second different encoding complexity of the second fragment at the bitrate, encoding the second fragment at the second different resolution for the bitrate to generate an encoded second fragment, receiving a request for a manifest for the video file from a client device, generating the manifest for the client device that identifies a single video representation for the bitrate that comprises the encoded first fragment and the encoded second fragment, and sending the manifest to the client device.
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
20.
Similarity detection based on token distinctiveness
Methods, systems, and computer-readable media for similarity detection based on token distinctiveness are disclosed. A similarity detection system determines candidate items for a seed item based on a comparison of tokens in textual descriptions of the candidate items to tokens in a textual description of the seed item. The system uses machine learning to determine importance scores for the tokens of the seed item. An importance score is determined based on the frequency of the individual token and the frequency of the most commonly occurring token in the candidate items. Importance scores for the same token differ from the seed item to another seed item. Based on the importance scores, the system determines similarity scores for the candidate items to the seed item. The system selects, from the candidate items, a set of similar items to the seed item based (at least in part) on the similarity scores.
Aspects related to a resource-constrained system are described herein that can provide object storage services after a service interruption is resolved, even if all of the transactions that were pending and incomplete prior to the service interruption have not yet been recovered and/or executed. For example, file systems implemented by computing systems of the resource-constrained system may treat each file or directory as a separate object. Thus, a transaction directed to one file may not affect the file's directory or other files in the directory. As a result, the resource-constrained system can achieve read-after-write consistency without first recovering and executing the pending, incomplete transactions. Instead, read-after-write consistency for an object can be achieved simply by completing any pending, incomplete transaction directed to that object. Accordingly, the resource-constrained system can provide object storage services to user devices immediately after the service interruption is resolved, thereby resulting in fast crash recovery times.
Systems, devices, and methods are provided for training and/or inferencing using machine-learning models. In at least one embodiment, a user selects a source media (e.g., video or audio file) and a target identity. A content embedding may be extracted from the source media, and an identity embedding may be obtained for the target identity. The content embedding of the source media and the identity embedding of the target identity may be provided to a transfer model that generates synthesized media. For example, a user may select a song that is sung by a first artist and then select a second artist as the target identity to produce a cover of the song in the voice of the second artist.
When a client requests a data import job, a remote storage service provider provisions a shippable storage device that will be used to transfer client data from the client to the service provider for import. The service provider generates security information for the data import job, provisions the shippable storage device with the security information, and sends the shippable storage device to the client. The service provider also sends client-keys to the client, separate from the shippable storage device (e.g., via a network). The client receives the device, encrypts the client data and keys, transfers the encrypted data and keys onto the device, and ships it back to the service provider. The remote storage service provider authenticates the storage device, decrypts client-generated keys using the client-keys stored at the storage service provider, decrypts the data using the decrypted client-side generated keys, and imports the decrypted data.
G06F 21/80 - Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer to assure secure storage of data in storage media based on magnetic or optical technology, e.g. disks with sectors
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
Techniques for updating a machine learning (ML) model are described. A device or system may receive input data corresponding to a natural or non-natural language (e.g., gesture) input. Using a first ML model, the device or system may determine the input data corresponds to a data category of a plurality of data categories. Based on the data category, the device or system may select a ML training type from among a plurality of ML training types. Using the input data, the device or system may perform the selected ML training type with respect to a runtime ML model to generate an updated ML model.
Methods and apparatus for providing persistent execution environments for computation systems including but not limited to interactive computation systems. A service is provided that extends the notion of static containers to dynamically changing execution environments into which users can install code, add files, etc. The execution environments are monitored, and changes to an execution environment are automatically persisted to environment versions(s) so that code run in the execution environment can be run later or elsewhere simply by referring to the environment. There is no explicit build step for the user. Instead, incremental changes are added to environment versions which are stored and are ready to be used to instantiate respective execution environments on other compute instances.
A transcoding service is described that is capable optimizing media transcoding jobs according to the licensing model associated with a particular transcoder being utilized. The service can receive a request to transcode the media content from a user and inspect an SLA to determine the parameters for the transcoding job, such as the time interval to complete the job or the price of performing the job. The service can then identify a licensing cost associated with transcoding the media content. For example, the licensing cost being based at least in part on a number of running instances of the transcoder. The transcoding service may apply a weight to the licensing cost when prioritizing the transcoding jobs. For example, the service may determine an optimal number of concurrently executing transcoder instances to utilize to reduce the licensing costs associated with the transcoding jobs.
A system and method for associating motion data with utterance audio data for use with a speech processing system. A device, such as a wearable device, may be capable of capturing utterance audio data and sending it to a remote server for speech processing, for example for execution of a command represented in the utterance. The device may also capture motion data using motion sensors of the device. The motion data may correspond to gestures, such as head gestures, that may be interpreted by the speech processing system to determine and execute commands. The device may associate the motion data with the audio data so the remote server knows what motion data corresponds to what portion of audio data for purposes of interpreting and executing commands. Metadata sent with the audio data and/or motion data may include association data such as timestamps, session identifiers, message identifiers, etc.
During text-to-speech processing, a speech model creates output audio data, including speech, that corresponds to input text data that includes a representation of the speech. A spectrogram estimator estimates a frequency spectrogram of the speech; the corresponding frequency-spectrogram data is used to condition the speech model. A plurality of acoustic features corresponding to different segments of the input text data, such as phonemes, syllable-level features, and/or word-level features, may be separately encoded into context vectors; the spectrogram estimator uses these separate context vectors to create the frequency spectrogram.
G10L 13/10 - Prosody rules derived from text; Stress or intonation
G10L 13/06 - Elementary speech units used in speech synthesisers; Concatenation rules
G10L 25/18 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band
29.
Systems and methods for visualizing analytics tags associated with page elements of a web page
Disclosed herein is a tag viewer that can be implemented to overlay content on a web page, such as a web page of a web-based console associated with a service of a service provider network. The tag viewer may determine which page elements of a web page are tagged based on data, such as a data structure, associated with the web page, and may overlay content on the web page, the content including: (i) one or more containers presented around one or more tagged page elements, and (ii) one or more annotations presented in association with the tagged page element(s). Individual annotations that are overlaid on the web page may include a tag type and/or a tag value corresponding to the analytics tag associated with the tagged page element.
Devices and techniques are generally described for evaluation of text data using large n-grams. In various examples, a first vector may be generated for first text data, wherein each element of the vector comprises a value indicating whether the first text data includes a respective n-gram included in a corpus of text data. First label data indicating that a user associated with the first text data has connected to a first computer-implemented service more than a threshold number of times during a past time period may be determined. A first machine learning model may be trained based at least in part on the first vector and the first label data. The first machine learning model may be used to determine a first probability associated with a first n-gram of the first vector. In some examples, at least a first user associated with the first n-gram may be determined.
Techniques for reducing a compilation time for compiling a neural network are disclosed. A description of a neural network is received by a compiler. A plurality of operators are identified based on the description of the neural network. A plurality of subgraphs are formed, each including one or more operators. For each subgraph, a performance factor is calculated based on a compute usage and a memory usage associated with the operators included in the subgraph. The performance factor is compared to a threshold. Based on the comparison, either the subgraph is classified as a compute bound subgraph and a set of memory optimizations are suppressed or the subgraph is classified as a memory bound subgraph and a set of compute optimizations are suppressed.
A system is provided for determining customized notification output based on user preferences and event information. A user may request a system to provide a notification when a future event occurs. The system may determine a custom notification/output template for the notification, where a portion of the template is determined based on a user preference. When the event occurs, the system may generate a notification using the template, where portions of the notification are determined using event information and user preferences.
Technologies directed to correcting terrestrial interference using narrowband adaptive filtering and beamforming technology are described. One method includes receiving a first and second radio frequency (RF) signal. The method includes generating first digital samples corresponding to the first RF signal using a first sample rate and generating second digital samples corresponding to the first RF signal using a second sample rate that is lower than the first sample rate. The method further includes generating third digital samples corresponding to the second RF signal using the second sample rate. The method further includes determining parameters associated with a filtering process using the second digital samples and the third digital samples. The method further includes generating fourth digital samples using the parameters of the filtering process. The method further includes removing a first portion from the first RF signal using the first digital samples and the fourth digital samples.
H04B 1/10 - Means associated with receiver for limiting or suppressing noise or interference
H04B 7/06 - Diversity systems; Multi-antenna systems, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
H04B 7/08 - Diversity systems; Multi-antenna systems, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
34.
Measuring input receiver thresholds using automated test equipment digital pins in a single shot manner
Techniques for determining the input threshold voltage level of an integrated circuit device may include configuring a voltage drive level and a load current for a tester channel of automated test equipment (ATE) connected to an input pin of an integrated circuit device, and executing a test pattern. The test pattern may include test vectors for generating a set of voltage ramps at the input pin by the tester channel using the load current and the voltage drive level. The set of voltage ramps can be characterized by different lengths and different amplitudes. The test pattern may also include test vectors for checking output values on an output pin of the integrated circuit device after each voltage ramp of the set of voltage ramps. The input threshold voltage level of the input pin can be determined based on results of executing the test pattern.
Techniques for determining buggy code are described. An encoder/decoder-based (e.g., transformer-based) model approach is described. In some embodiments, a service receives request to perform transformer-based bug fixing on code, performs bug fixing inference to the code by applying a trained encoder/decoder-based model, and reports out a result of the inference, wherein the output includes an indication of a location of a potential edit to be made in the code and the potential edit in the code.
Described is a multiple-camera system and process for determining an item involved in an event. For example, when a user picks an item or places an item at an inventory location, image information for the item may be obtained and processed to identify the item involved in the event and associate that item with the user.
Explanation jobs may be performed for computer vision tasks. A request to execute an explanation job for a computer vision machine learning model may be received. The execution job may be performed, including extracting different features from the image, determining the respective relative importance values of the different features on inferences generated by the computer vision machine learning model. The result of the explanation job, including the generated heat maps may be provided.
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/77 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
Systems and methods for event-detection confirmation are disclosed. For example, a sensing device may generate sensor data, such as from radar, and the sensor data may be utilized to determine if a predefined event as occurred. Event-confirmation operations may then be performed, such as by utilizing acoustic-event detection techniques and/or natural language understanding techniques. When occurrence of an event is confirmed, such as to a certain confidence level, one or more actions may be taken, such as sending a notification to another device and/or establishing a communication channel with another device, such as a device associated with emergency services, family members, friends, and/or neighbors.
Provided is a system for facilitating recovery of deleted computing resources in a cloud network environment. A centralized resource recovery service may communicate with a plurality of resource management services that are each configured to create, modify, or delete their respective computing resources such as storage volumes, databases, and compute instances. The resource recovery service may allow configuration of resource group policies such that deletion of grouped resources can be managed more effectively and efficiently. For example, in the event that a deleted resources matches multiple resource retention rules, the resource retention rule that encompasses multiple resource types may be used to place the deleted resource in a recoverable state so that resources of such multiple resource types can be managed according to the same resource retention.
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
H04L 41/0654 - Management of faults, events, alarms or notifications using network fault recovery
Implementations for metering features of media conferences on the client-side are described. Initially, a participant joins a media conference. An identifier of a feature and an allotment associated with the feature is then received by the participant. The feature is used during the media conference and the allotment associated with the feature is decremented based on the use of the feature. It is then determined that an allotment refresh is needed. In response, additional allotment associated with the feature is requested and received by the participant until the maximum number of additional allotment requests or the maximum allotment is reached.
Systems and methods for account association with voice-enabled devices are disclosed. For example, a voice-enabled device situated in a managed environment, such as a hotel room, may be taken by a temporary resident or guest of the environment. Upon determining that the device has been removed from the environment, a device identifier associated with the device may be dissociated from components and/or services associated with environment and/or systems related thereto, and the device identifier may be associated with a user account of the user.
Machine learning automatic pipeline management for automated software deployment is described. An adjustment to computing capacity for a region of a multi-region computing network is identified. A service to be deployed in the region of the multi-region computing network is further identified. Configuration settings for deployment of the service in the region is generated using past deployment data for the service in other regions of the multi-region computing network. A continuous code delivery service is directed to add a stage to a software deployment pipeline for the region. The stage may be configured using the at least one configuration setting.
Methods, systems, and computer-readable media for automated threat modeling using application relationships are disclosed. A graph is determined that includes of nodes and edges. At least a portion of the nodes represent software components, and at least a portion of the edges represent relationships between software components. An event is received, and a sub-graph associated with the event is determined. The event is indicative of a change to one or more of the nodes or edges in the graph. Threat modeling is performed on the sub-graph using one or more analyzers. The one or more analyzers determine whether the sub-graph is in compliance with one or more policies.
G06F 21/55 - Detecting local intrusion or implementing counter-measures
G06F 16/25 - Integrating or interfacing systems involving database management systems
G06F 16/901 - Indexing; Data structures therefor; Storage structures
G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
Techniques for intelligent multi-carrier network edge application deployment are described. Traffic that is destined for an application implemented in multiple edge locations of a cloud provider network is originated by a mobile user equipment device via use of a communications network of a first communications service provider (CSP). An edge location hosting the application, from multiple such candidates, can be selected as a destination for the traffic. The edge location may be deployed in a facility of a different CSP. The traffic can be sent into the edge location using a network address of the different CSP to securely allow for its entry thereto.
Techniques for intelligent multi-carrier network edge application deployment are described. Traffic that is destined for an application implemented in multiple edge locations of a cloud provider network is originated by a mobile user equipment device via use of a communications network of a first communications service provider (CSP). An edge location hosting the application, from multiple such candidates, can be selected as a destination for the traffic. The edge location may be deployed in a facility of a different CSP. The traffic can be sent into the edge location using a network address of the different CSP to securely allow for its entry thereto.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Monitoring applications, services, infrastructure, systems, and data for business purposes; analyzing and compiling business data; business data analysis. Downloadable computer software for monitoring, colleting operational data, detecting anomalous behavior, setting alarms, visualizing logs and metrics, automating actions, troubleshooting issues, and discovering insights. Computer services, namely, monitoring the web sites of others to improve their scalability and performance; Computer monitoring service which tracks application software performance, performs periodic maintenance and provides reports and alerts concerning such performance; Technical support services, namely, remote and on-site infrastructure management services for monitoring, administration and management of public and private cloud computing IT and application systems; Technical support, namely, monitoring technological functions of computer network systems; Monitoring of computer systems by remote access to ensure proper functioning; Providing temporary use of non-downloadable cloud-based software for collecting, aggregating, analyzing, and summarizing compute utilization information, and diagnostic information to help isolate and resolve issues; Computer services, namely, monitoring, testing, analyzing, and reporting on the Internet traffic control and content control of the web sites of others; Computer systems analysis; Data automation and collection service using proprietary software to evaluate, analyze and collect service data; Software as a service (SAAS) services featuring software for monitoring, colleting operational data, detecting anomalous behavior, setting alarms, visualizing logs and metrics, automating actions, troubleshooting issues, and discovering insights.
09 - Scientific and electric apparatus and instruments
35 - Advertising and business services
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Computer software for creating, accessing, managing, sharing, and utilizing, containerized software applications and data containers; Downloadable cloud-computing software for software development and deployment; Computer software for analyzing and managing server space usage and capacity; Computer software for use in management of data on cloud-based servers; Computer software for management of data containers or containerized software applications; Computer software for optimization of cluster space usage; Computer software for managing software application deployments, managing load balancing, performing diagnostics checks, and scaling based on usage. (1) Database management services; updating and maintenance of data in computer databases
(2) Business data analysis; Data processing services; Management of on-line databases for others; Management of containerized software applications and data.
(3) Software-as-a-service (SaaS) featuring software for creating, accessing, managing, sharing, and utilizing, containerized software applications and data containers; Software-as-a-service (SaaS) featuring software for cloud computing; Software-as-a-service (SaaS) featuring software for developing cloud-based software applications and programs; Software-as-a-service (SaaS) featuring software for management of server capacity and usage; Software-as-a-service (SaaS) featuring software for management of clusters and server space; Software-as-a-service (SaaS) featuring software for data management; Software-as-a-service (SaaS) featuring software for management of data containers or containerized software applications; Software-as-a-service (SaaS) featuring software for management of data containers, containerized software applications, and data clusters based on usage; Software-as-a-service (SaaS) featuring software for managing software application deployments, managing load balancing, performing diagnostics checks and scaling based on usage; Platform-as-a-service (PaaS) for cloud computing; Platform-as-a-service (PaaS) featuring software for creating, accessing, managing, sharing, and utilizing, containerized software applications; Platform-as-a-service (PaaS) featuring cloud-based data servers, software development tools, software for data management, software for cluster management, software for management of server capacity and usage, and software for managing software application deployments, managing load balancing, performing health checks, service discovery, and scaling based on usage; Technical support services, namely, infrastructure management services for containerized software applications or data containers; Platform-as-a-service (PaaS), Infrastructure-as-a-service (IaaS) and Software-as-a-service (SaaS) services featuring computer software for analyzing and managing containerized software applications, data, or containers containing data on servers; Electronic data storage services in the nature of technical management of data clusters.
09 - Scientific and electric apparatus and instruments
35 - Advertising and business services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer software for creating, accessing, managing, sharing, and utilizing, containerized software applications and data containers; Downloadable cloud-computing software for software development and deployment; Computer software for analyzing and managing server space usage and capacity; Computer software for use in management of data on cloud-based servers; Computer software for management of data containers or containerized software applications; Computer software for optimization of cluster space usage; Computer software for managing software application deployments, managing load balancing, performing diagnostics checks, and scaling based on usage. Business data analysis; Data processing services; Database management services; Updating and maintenance of data in computer databases; Management of on-line databases for others; Management of data. Software-as-a-service (SaaS) featuring software for creating, accessing, managing, sharing, and utilizing, containerized software applications and data containers; Software-as-a-service (SaaS) featuring software for cloud computing; Software-as-a-service (SaaS) featuring software for developing cloud-based software applications and programs; Software-as-a-service (SaaS) featuring software for management of server capacity and usage; Software-as-a-service (SaaS) featuring software for management of clusters and server space; Software-as-a-service (SaaS) featuring software for data management; Software-as-a-service (SaaS) featuring software for management of data containers or containerized software applications; Software-as-a-service (SaaS) featuring software for management of data containers, containerized software applications, and data clusters based on usage; Software-as-a-service (SaaS) featuring software for managing software application deployments, managing load balancing, performing diagnostics checks and scaling based on usage; Platform-as-a-service (PaaS) for cloud computing; Platform-as-a-service (PaaS) featuring software for creating, accessing, managing, sharing, and utilizing, containerized software applications; Platform-as-a-service (PaaS) featuring cloud-based data servers, software development tools, software for data management, software for cluster management, software for management of server capacity and usage, and software for managing software application deployments, managing load balancing, performing health checks, service discovery, and scaling based on usage; Consulting in the field of information technology and cloud computing; Technical support services, namely, infrastructure management services for containerized software applications or data containers; Platform-as-a-service (PaaS), Infrastructure-as-a-service (IaaS) and Software-as-a-service (SaaS) services featuring computer software for analyzing and managing containerized software applications, data, or containers containing data on servers; Electronic data storage services in the nature of technical management of data clusters.
49.
Custom garment pattern blending based on body data
Systems and methods are provided for generating custom garment patterns for producing clothing sized to fit any newly input human body representation. A set of reference sample bodies may be selected for which custom designed garment patterns are then obtained. Once reference patterns are obtained, a custom garment pattern for a particular new body may be automatically created by blending two or more of the reference patterns. For example, the neighboring reference bodies to the new body may be identified within a low-dimensional embedding space, and interpolation of garment parameters for the previously designed garment patterns for these reference bodies may be performed to produce a custom garment pattern.
A vacuum-gripper end effector conforms to and grasps items upon application of vacuum force and/or actuation of one or more actuators. The vacuum-gripper assembly can be round in top view or can be rectangular. At least three linear actuators deform the vacuum-gripper assembly. Four linear actuators may be spaced equidistantly apart on a square or rectangular vacuum-gripper assembly to enhance functionality of the vacuum-gripper, such as being capable of simultaneously engaging three sides formed at a corner of a box. A one-way actuation type air cylinder may be used. A low friction air cylinder may be returned to its retracted position by the resilience of the vacuum-gripper assembly.
Described implementations monitor potential voltage at a location to determine device usage at the location. The implementations utilize a plug-in energy sensor that is plugged directly into any electrical outlet at the location and measures deviation in voltage at the location. Once plugged into an electrical outlet, the plug-in energy sensor monitors one or more of the positive line and ground and/or the neutral line and ground for changes in potential voltage at the location. The plug-in energy sensor may also inject a load (resistive load, inductive load, capacitive load) into the electrical circuit at the location and then measure the signal or response to the injected load.
G01R 21/133 - Arrangements for measuring electric power or power factor by using digital technique
G01R 23/00 - Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
H02J 13/00 - Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
52.
Data retention management for partitioned datasets
Systems and methods are disclosed to implement a data storage system that manages data retention for partitioned datasets. A received data retention policy specifies to selectively delete data from a dataset based on a set of data retention attributes. If the data retention attributes are part of the dataset's partition key, a first type of data deletion job is configured to selectively delete entire partitions of the dataset. Otherwise, the system will generate a retention attribute index for the dataset, which will be used by a second type of data deletion job to selectively delete individual records within the partitions. In embodiments, the retention attribute index is implemented as Bloom filters that track retention attribute values in each partition. Advantageously, the disclosed system is able to automatically configure deletion jobs for any dataset schema that avoids full scans of the dataset partitions.
Systems and methods for multilingual keyword generation for low-resource language passages are disclosed. For example, high-resource language passages describing items in a catalog or other such passages may include numerous keywords for classifying and searching for the item while a low-resource language description of the item may not include the same rich keyword environment due to lower usage and traffic in the low-resource language. The systems and methods herein provide for leveraging the high-resource language through a multilingual natural language processing algorithm to identify similar passages across language barriers and identify keywords in the high-resource language for input into a decoder to generate keywords in the low-resource language.
Devices and techniques are generally described for machine learning hardware optimization. In some examples, a first computing device may receive first data describing a first machine learning model. A first operator type and a first input size for a first layer of the first machine learning model may be determined from the first data. First executable code may be generated that defines a first operator for the first layer of the first machine learning model. The first operator may be specific to the first input size and the first operator type. The first executable code may be stored in non-transitory computer-readable memory. In some examples, second data may be input into the first machine learning model. The first machine learning model may process the second data to generate first output data based at least in part on execution of the first code.
Accurate individual queue level metric forecasting for virtual contact center queues with insufficient data, using models trained at higher granularity level
Methods, systems, and computer-readable media for accurate usage forecasting for virtual contact centers are disclosed. A contact center management system configures a contact center instance associated with a client. The contact center instance comprises a plurality of queues configured to store contacts. At least a portion of the contacts are routed to a plurality of agents. The contact center management system determines, using one or more machine learning models associated with the contact center instance, a plurality of predictions for a plurality of metrics for the contact center instance for a plurality of time horizons. At least a portion of the predictions are generated for individual queues of the contact center instance.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
A device, such as an autonomous mobile device (AMD), includes sensors such as a camera. Multiple applications executing on the device may concurrently use sensor data from a single shared sensor. For example, a first application may use image data to localize the AMD while a second application uses the image data to recognize users. The first application and the second application may have different parameters for operation of the sensor or resulting sensor data, such as different image resolutions and frame rates. An arbitrator system manages these different parameters, and may also provide sensor data to the respective application that is consistent with the application's parameters. For example, different resolutions of image data may be provided to different applications based on their parameters.
Systems and methods are provided herein for generating optimized video segments. A derivative video segment (e.g., a scene) can be identified from derivative video content (e.g., a movie trailer). The segment may be used a query to search video content (e.g., the movie) for the segment. Once found, an optimized video segment may be generated from the video content. The optimized video segment may have a different start time and/or end time than those corresponding to the original segment. Once optimized, the video segment may be presented to a user or stored for subsequent content recommendations.
Described are systems and techniques for identifying users arriving at a facility based at least in part on a vehicle in which they arrive. In one implementation, vehicles are identified as they arrive at the facility. A candidate set of users previously associated with the identified vehicle is generated. The recognition system may then detect and identify the occupants of the vehicle using the candidate set. The identity of the vehicle may improve the accuracy of the user identification, reduce time to identify the user, or both.
A security device has user-configurable motion detection settings. The security device includes at least one camera configured to capture sequential frames of image data within a field of view; at least one processor; memory communicatively coupled with the processor(s); a location setting, stored in the memory, defining whether or not the security device is located at a common access area; and machine readable instructions stored in the memory. The machine readable instructions are executable by the processor(s) to determine the image data indicates motion; and determine that the motion indicates lingering presence at the common access area. In certain embodiments, the machine readable instructions are executable by the processor(s) to determine, from the image data, that the average ambient light over a daily period varies less than a light variation threshold, and configure the location setting to indicate that the security device is located at the common access area.
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
G06T 7/246 - Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
A speech-processing system may provide access to multiple virtual assistants via one or more voice-controlled devices. Each assistant may leverage language processing and language generation features of the speech-processing system, while handling different commands and/or providing access to different back applications. Each assistant may be associated with its own voice and/or speech style, and thus be perceived as having a particular “personality.” Different assistants may be available for use with a particular voice-controlled device based on time, location, the particular user, etc. In some situations, language processing may be improved by leveraging data such as intent data, grammars, lexicons, entities, etc., associated with assistants available for use with the particular voice-controlled device.
A connection management device may be used to with a common API to allow a host device of a client to securely connect to a remote provider network. The host device may only be able to use a connection management device by using a defined set of commands of a common API. A hardware root of trust may be pre-provisioned with security data (e.g., client certificate, encryption keys). A connection command may be used that is not specific to any particular communication protocol (e.g., WiFi, cellular, wired protocol). In response to receiving the connection command from a host device, the connection management device may perform commands specific to the communication protocol of the connection management device to connect to a remote provider network, use the security data for authentication, and establish a connection in accordance with the communication protocol based on the authentication.
09 - Scientific and electric apparatus and instruments
35 - Advertising and business services
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Computer software for creating, accessing, managing, sharing, and utilizing, containerized software applications and data containers; Downloadable cloud-computing software for software development and deployment; Computer software for analyzing and managing server space usage and capacity; Computer software for use in management of data on cloud-based servers; Computer software for management of data containers or containerized software applications; Computer software for optimization of cluster space usage; Computer software for managing software application deployments, managing load balancing, performing diagnostics checks, and scaling based on usage. (1) Updating and maintenance of data in computer databases
(2) Business data analysis; Data processing services; Database management services; Management of on-line databases for others; Management of data.
(3) Software-as-a-service (SaaS) featuring software for creating, accessing, managing, sharing, and utilizing, containerized software applications and data containers; Software-as-a-service (SaaS) featuring software for cloud computing; Software-as-a-service (SaaS) featuring software for developing cloud-based software applications and programs; Software-as-a-service (SaaS) featuring software for management of server capacity and usage; Software-as-a-service (SaaS) featuring software for management of clusters and server space; Software-as-a-service (SaaS) featuring software for data management; Software-as-a-service (SaaS) featuring software for management of data containers or containerized software applications; Software-as-a-service (SaaS) featuring software for management of data containers, containerized software applications, and data clusters based on usage; Software-as-a-service (SaaS) featuring software for managing software application deployments, managing load balancing, performing diagnostics checks and scaling based on usage; Platform-as-a-service (PaaS) for cloud computing; Platform-as-a-service (PaaS) featuring software for creating, accessing, managing, sharing, and utilizing, containerized software applications; Platform-as-a-service (PaaS) featuring cloud-based data servers, software development tools, software for data management, software for cluster management, software for management of server capacity and usage, and software for managing software application deployments, managing load balancing, performing health checks, service discovery, and scaling based on usage; Consulting in the field of information technology and cloud computing; Technical support services, namely, infrastructure management services for containerized software applications or data containers; Platform-as-a-service (PaaS), Infrastructure-as-a-service (IaaS) and Software-as-a-service (SaaS) services featuring computer software for analyzing and managing containerized software applications, data, or containers containing data on servers; Electronic data storage services in the nature of technical management of data clusters.
09 - Scientific and electric apparatus and instruments
35 - Advertising and business services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer software for creating, accessing, managing, sharing, and utilizing, containerized software applications and data containers; Downloadable cloud-computing software for software development and deployment; Computer software for analyzing and managing server space usage and capacity; Computer software for use in management of data on cloud-based servers; Computer software for management of data containers or containerized software applications; Computer software for optimization of cluster space usage; Computer software for managing software application deployments, managing load balancing, performing diagnostics checks, and scaling based on usage. Business data analysis; Data processing services; Database management services; Updating and maintenance of data in computer databases; Management of on-line databases for others; Management of containerized software applications and data. Software-as-a-service (SaaS) featuring software for creating, accessing, managing, sharing, and utilizing, containerized software applications and data containers; Software-as-a-service (SaaS) featuring software for cloud computing; Software-as-a-service (SaaS) featuring software for developing cloud-based software applications and programs; Software-as-a-service (SaaS) featuring software for management of server capacity and usage; Software-as-a-service (SaaS) featuring software for management of clusters and server space; Software-as-a-service (SaaS) featuring software for data management; Software-as-a-service (SaaS) featuring software for management of data containers or containerized software applications; Software-as-a-service (SaaS) featuring software for management of data containers, containerized software applications, and data clusters based on usage; Software-as-a-service (SaaS) featuring software for managing software application deployments, managing load balancing, performing diagnostics checks and scaling based on usage; Platform-as-a-service (PaaS) for cloud computing; Platform-as-a-service (PaaS) featuring software for creating, accessing, managing, sharing, and utilizing, containerized software applications; Platform-as-a-service (PaaS) featuring cloud-based data servers, software development tools, software for data management, software for cluster management, software for management of server capacity and usage, and software for managing software application deployments, managing load balancing, performing health checks, service discovery, and scaling based on usage; Technical support services, namely, infrastructure management services for containerized software applications or data containers; Platform-as-a-service (PaaS), Infrastructure-as-a-service (IaaS) and Software-as-a-service (SaaS) services featuring computer software for analyzing and managing containerized software applications, data, or containers containing data on servers; Electronic data storage services in the nature of technical management of data clusters.
A system that performs wall detection and localization to determine a position of a device relative to acoustically reflective surfaces. The device emits an audible sound including a frequency modulated signal and captures reflections of the audible sound. The frequency modulated signal enables the device to determine an amplitude of the reflections at different time-of-arrivals, which corresponds to a direction of the reflection. The device then performs beamforming to generate a 2D intensity map that represents an intensity of the reflections at each spatial location around the device. The device detects wall(s) in proximity to the device by identifying peak intensities represented in the 2D intensity map. In some examples, instead of performing beamforming, the device can perform directional wall detection by physically rotating the device and emitting the audible sound in multiple directions. The device may perform ultrasonic wall detection using ultrasonic sound frequencies.
Bitmaps for managing computing resources are described. Example bitmaps described in this application use less memory space by varying the sizes of the nodes in the bitmap's tree structure, and/or by limiting the number of nodes in the bitmap's leaf layer. Other example bitmaps described in this application reduce the time needed to traverse the bitmap by tailoring the search direction according to the bitmap's configuration.
The systems and methods are provided for the dynamic processing of API requests received by an API execution service. The API execution service may map out a dependency graph based on dependency relationships between all the actions involved in processing the API requests, some of which can be determined from the API definitions and configurations. From the dependency graph, an execution plan can be generated that represents a request processing pipeline conveying the optimal order and arrangement to perform the actions (e.g., serially, in parallel). The execution plan can be followed to process API requests and its performance monitored. The API execution service may dynamically modify this execution plan used to process API requests as it becomes apparent that the execution plan is no longer optimal.
The motion of objects within a scene may be detected and tracked using digital (e.g., visual and depth) cameras aligned with fields of view that overlap at least in part. Objects may be identified within visual images captured from the scene using a tracking algorithm and correlated to point clouds or other depth models generated based on depth images captured from the scene. Once visual aspects (e.g., colors or other features) of objects are correlated to the point clouds, shapes and/or positions of the objects may be determined and used to further train the tracking algorithms to recognize the objects in subsequently captured frames. Moreover, a Kalman filter or other motion modeling technique may be used to enhance the prediction of a location of an object within subsequently captured frames.
G06F 18/2113 - Selection of the most significant subset of features by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
G06F 18/2415 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
G06T 7/55 - Depth or shape recovery from multiple images
G06T 11/60 - Editing figures and text; Combining figures or text
H04N 7/18 - Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
H04N 23/90 - Arrangement of cameras or camera modules, e.g. multiple cameras in TV studios or sports stadiums
70.
Label recognition and notification for streaming video from non-overlapping cameras
Techniques for label recognition and notification for streaming video from non-overlapping cameras. A stream processing service of a provider network receives a first video stream from a first camera-equipped electronic device via an API endpoint of the stream processing service. The stream processing service also receives a second video stream from a second camera-equipped electronic device an API endpoint of the stream processing service. Meanwhile, a request for label recognition and notification is received at a computer vision service of the provider network via an API endpoint of the computer vision service. In response, the computer vision service recognizes a label in a video fragment of the first camera video stream and recognizes a label in a video fragment of the second camera video stream, and then identifies whether the two labels are the same label. If so, a notification service of the provider network sends a notification indicating that the label was recognized across non-overlapping cameras.
This disclosure describes, in part, techniques for sharing content associated with network applications. For instance, a user may want to share content for a network application, such as a game stream for a gaming application. As such, system(s) may launch a broadcasting session on a first virtual server and launch the network application on a second virtual server. The first virtual server may then receive content data representing states of the network application from the second virtual server. Additionally, the first virtual server may receive video data representing the user and/or audio data representing user speech from a user device. The first virtual server may then generate broadcasting data using the content data, the video data, and the audio data. After generating the broadcasting data, the system(s) may send the broadcasting data to one or more computing devices associated with a user account.
H04L 65/1069 - Session establishment or de-establishment
A63F 13/35 - Interconnection arrangements between game servers and game devices; Interconnection arrangements between game devices; Interconnection arrangements between game servers - Details of game servers
A63F 13/86 - Watching games played by other players
H04L 67/10 - Protocols in which an application is distributed across nodes in the network
72.
Configurable security policies for radio-based networks
Disclosed are various embodiments for configurable security policies in radio-based networks. In one embodiment, a security event detection rule or a security event mitigation rule for a radio-based network is accessed. The radio-based network includes a radio access network and an associated core network. At least a portion of the radio-based network is operated by a cloud provider on behalf of an organization. A security event is detected based at least in part on the security event detection rule. At least one action is performed in response to the security event based at least in part on the security event mitigation rule.
41 - Education, entertainment, sporting and cultural services
Goods & Services
Development of marketing concepts; providing commercial and business contact information; commercial intermediation services; providing business information via a website; administration of consumer loyalty programs; market intelligence services; promotion of goods through influencers; influencer marketing; sales promotion for others; marketing; targeted marketing; business intermediary services relating to the matching of various professionals with clients; personnel management consultancy; data processing services; compilation of information into computer databases; systemization of information into computer databases; data search in computer files for others; search engine optimization for sales promotion; website traffic optimization; updating and maintenance of data in computer databases; compiling indexes of information for commercial or advertising purposes; business auditing; rental of cash registers; rental of vending machines; sponsorship search; rental of sales stands; retail services for pharmaceutical, veterinary and sanitary preparations and medical supplies; advertising services to create brand identity for others; publication of publicity texts; advertising; publicity; writing of publicity texts; advertising design; production of advertising films; development of advertising concepts; scriptwriting for advertising purposes; consultancy regarding advertising communication strategies; promotion of goods and services through sponsorship of sports events; business consulting services for digital transformation; conducting of commercial events; lead generation services; business management assistance; business research; public relations; marketing research. Production of podcasts; arranging and conducting of conferences; entertainment services; film production, other than advertising films; production of radio and television programmes; production of shows; providing museum facilities; scriptwriting, other than for advertising purposes; photographic reporting; providing online virtual guided tours; providing online music, not downloadable; providing online videos, not downloadable; screenplay writing; film distribution; film directing, other than advertising films; providing user reviews for entertainment or cultural purposes; providing user rankings for entertainment or cultural purposes; animal training; educational services; instruction services; practical training; vocational guidance; modelling for artists; organization of lotteries; rental of indoor aquaria; transfer of business knowledge and know-how; vocational retraining; know-how transfer; arranging and conducting of entertainment events; photography; news reporters services; writing of texts; conducting fitness classes; toy rental; providing facilities for playing Live Action Role Playing games; organization of competitions; arranging and conducting of seminars; arranging and conducting of workshops; arranging and conducting of in-person educational forums; multimedia library services; publication of texts, other than publicity texts; online publication of electronic books and journals; providing online electronic publications, not downloadable; circulation of video tapes; directing of shows.
75.
CLIENT-DIRECTED PLACEMENT OF REMOTELY-CONFIGURED SERVICE INSTANCES
Methods and apparatus for client-directed placement of remotely configured service instances are described. One or more placement target options are selected for a client of a network-accessible service based on criteria such as service characteristics of the placement targets. The selected options, including a particular placement target that includes instance hosts configurable from remote control servers, are indicated programmatically to the client. A determination is made that a service instance is to be configured at the particular placement target on behalf of the client. A remote control server is configured to issue administrative commands to an instance host at the particular placement target to configure the service instance.
H04L 41/0893 - Assignment of logical groups to network elements
H04L 41/5041 - Network service management, e.g. ensuring proper service fulfilment according to agreements characterised by the time relationship between creation and deployment of a service
H04L 67/52 - Network services specially adapted for the location of the user terminal
Techniques for recognizing phonemes from a spoken input and providing pronunciation feedback as part of a language learning experience are described. Some embodiments use a machine learning model configured to recognize phonemes spoken in a user's native language and spoken in the language to be learned. The model is trained with the native language's lexicon and the learning language's lexicon. The system can provide feedback at a word level, a syllable level and/or phoneme level. The system can also provide feedback with respect to phoneme stress.
G10L 15/02 - Feature extraction for speech recognition; Selection of recognition unit
G10L 13/08 - Text analysis or generation of parameters for speech synthesis out of text, e.g. grapheme to phoneme translation, prosody generation or stress or intonation determination
G10L 13/10 - Prosody rules derived from text; Stress or intonation
A system includes a rack with multiple hardware acceleration devices and multiple modular controllers coupled together into a single system implementing one or more servers. Each modular hardware acceleration device includes multiple hardware accelerators, such as graphical processing units, field programmable gate arrays or other specialized processing circuits. In each modular hardware acceleration device, hardware accelerators are communicatively coupled to a multi-port connection device, such as a switch, and also communicatively coupled to at least two external ports. A modular controller of a particular server coordinates operation of hardware accelerators of multiple hardware acceleration devices included in the particular server to provide advanced processing capabilities. Hardware accelerators may be dynamically assigned to particular processing servers to adjust processing capabilities of those servers. A particular server may be assigned one or more standby controller to enhance availability of the server.
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
G06F 11/07 - Responding to the occurrence of a fault, e.g. fault tolerance
Data storage techniques for services in a network are described herein. In an example, a computer system determines a mapping between a first data schema associated with first data storage by a first service in a first data store and a second data schema associated with second data storage by a second service in a second data store. The computer system receives an event associated with an element and determines, based on the mapping and the event, an operation to be performed on a first attribute of the element that is stored in the first data store the second data store. The computer system generates, based on the mapping, notifications indicating a time for the operation associated with the first attribute to be performed by the first service and the second service. The computer system sends the notifications to the first service and to the second service.
Techniques are described for enabling users of cloud provider services to verify, via cryptographic attestation, that trusted “enclaves” are used to process user data during limited points in time at which user data may be unencrypted or otherwise vulnerable. A cloud provider service processes requests involving user data at least in part using an enclave, where an enclave includes a virtual machine running on isolated computing resources of a host computing device managed by the cloud provider. The enclave, for example, can include an application that performs operations such as decrypting user data included in requests sent to a service (e.g., user data encrypted as part of a Transport Layer Security (TLS) connection established between the service and a client computing device), obtaining user-specific encryption keys from a key management service or other source, encrypting the user data using the encryption keys, and forwarding the encrypted data for further processing.
G06F 21/00 - Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
G06F 9/455 - Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
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
80.
Contrastive learning of scene representation guided by video similarities
A plurality of similar video pairs may be determined based on one or more similarity information types. Each video pair of the plurality of similar video pairs may include a first respective video and a second respective video. For each video pair, one or more similar scene pairs may be determined. Each of the one or more similar scene pairs may include a respective first scene from the first respective video and a second respective scene from the second respective video. An encoder may be trained using a contrastive learning model that contrasts a plurality of similar scene pairs with a plurality of random scenes. The plurality of similar scene pairs may include the one or more scene pairs for each video pair. One or more scene features of one or more other scenes of one or more other videos may be determined using the encoder.
Technologies are disclosed for interacting with a virtual assistant to coordinate, recommend and perform actions. According to some examples, a user may use their voice to interact with a virtual assistant to receive recommendations relating to determining when to perform one or more actions. For example, a user may interact with a virtual assistant to request a recommendation as to when they should leave for the office, leave the office for the day, perform a task, and the like. The recommendation system accesses selected data sources (e.g., calendars, task lists, traffic, transportation schedules, maps, . . . ) to obtain data used in generating the recommendation. In addition, to providing a recommended time, the virtual assistant may also recommend actions to perform. The virtual assistant may also provide notifications to one or more other users that includes information relating to the user leaving.
This disclosure describes, in part, techniques for reducing pulsating currents of internal power sources, such as batteries. For example, a device may include a power source, a load, and a control device located between the power source and the load. The control device may include a power converter that is configured to maintain a constant input current from the power source and output a pulsating current to the load. While regulating the power, the control device may determine whether an average output power is different than a reference power. If the average output power is equal to the reference power, then the control device may cause the power converter to maintain the constant input current. However, if the average output power is different than the reference power, then the control device may cause the power converter to alter (e.g., decrease/increase) the input current being received from the power source.
A head-mounted wearable device (HMWD) uses temples as housing for a transducer, maximizing the available space within the temples for other components, such as a battery. The exteriors of the temples act as housings so that separate transducer housings do not consume space within the temples. The temples may include openings that direct emitted sound toward the ear of a user of the HMWD. Sound generated by the transducer exits the openings and provides audio to the user's ear having a suitable volume. Each temple may include two microphones to facilitate use of a beamforming algorithm. When voice input is received, the HMWD may determine a particular set of microphones for use based on a charge level of power storage devices in each temple, a noise level associated with the microphones, or other factors.
An item identification and pose determination system is described for use in purposeful selection of items from a collection. The system may be configured to determine a pose of one or more items in a collection of items based on bounding box information associated with the item as well as depth data of the present location of the item. The bounding box is aligned to the depth data using a machine learning algorithm and randomized search algorithm and the pose of the item enables determination of grasp locations for removing the item from the collection and intentionally placing at a destination in a particular pose.
At a lock-less data management service, a multi-phase commit of a transaction is performed. The multi-phase commit includes a read set validation phase (in which a first set of timestamp-based conditions is used to determine whether the transaction has a read-write conflict), a pre-commit timestamps update phase (in which respective pre-commit timestamps associated with data items of the transaction's write set are set to a proposed commit time after verifying that the proposed commit time satisfies a second set of timestamp-based conditions), and a write initialization phase (in which respective new versions of individual data items of the write set are stored, without storing data item values indicated in the write set).
Data being identified includes a first portion of data and a second portion of data. Based on identifying the data, a data structure is generated. The data structure can include a first section having a first symbol associated with the first portion of data and a second symbol associated with the second portion of data. Further, the first section can include a first offset value corresponding to the first portion of data and a second offset value corresponding to the second portion of data. The data structure can include a second section with a plurality of pointers that reference at least a plurality of symbols including at least the first and second symbol. The data structure can be referenced to process one or more queries against the data.
Techniques for managing certificate metrics are described. A method of managing certificate metrics can include adding certificate data associated with one or more certificates to a plurality of slots of a metric certificate data store, reading, by a metric publisher, the certificate data associated with a first slot of the metric certificate data store in response to an event, determining a metric associated with each certificate associated with a subset of the certificate data associated with the first slot of the metric certificate data store, and providing the metric associated with each certificate to a resource monitoring service.
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
Techniques for input adaptation from disparate data sources for heterogeneous machine learning model execution are described. A preprocessing adapter can perform preprocessing of data obtained from edge devices to suit the input data characteristic requirements of one or more machine learning (ML) models. The preprocessing adapter can determine the input data characteristic requirements in a variety of ways, such as via analysis of the input layer of a ML model or through data variation testing and associated feedback resulting from output data generated by the ML model.
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
An extendable augmented reality (AR) system for recognizing user-selected objects in different contexts. A user may select certain entities (text, objects, etc.) that are viewed on an electronic device and create notes or additional content associated with the selected entities. The AR system may remember those entities and indicate to the user when those entities are encountered by the user in a different context, such as in a different application, on a different device, etc. The AR system offers the user the ability to access the user created note or content when the entities are encountered in the new context.
Systems and methods for personalized device routines include determining devices associated with user account data and generating device usage data indicating aspects of smart device usage over a period of time. The usage data may then be utilized to identify candidate smart devices, time indicators, and trigger event types to associate with candidate routines. One or more of the candidate routines may be recommended to a user based at least in part on the usage data and the trigger event types at issue to personalize the device routine recommendations.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
Techniques for establishing communication channels between user devices experiencing network connectivity issues and remote communication systems are described herein. The techniques include the use of a secondary device to act as a proxy, or a “middle man,” to facilitate the communications with the user device. A user device may detect lack of network connectivity, and begin broadcasting advertisement messages that indicate the lack of connectivity. A secondary device may detect the advertisement message, and send a discovery message to a connectivity system indicating that it detected the advertisement message. The connectivity system can provide this information to a remote communication system, and the remote communication system can establish a connection with the secondary device and instruct the secondary device to establish a connection with the user device. The remote communication system then has a communication channel with the user device, using the secondary device, to troubleshoot the user device.
H04L 67/51 - Discovery or management thereof, e.g. service location protocol [SLP] or web services
H04L 69/16 - Implementation or adaptation of Internet protocol [IP], of transmission control protocol [TCP] or of user datagram protocol [UDP]
H04W 4/20 - Services signalling; Auxiliary data signalling, i.e. transmitting data via a non-traffic channel
H04W 4/80 - Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
09 - Scientific and electric apparatus and instruments
35 - Advertising and business services
38 - Telecommunications services
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Computer software for cloud computing; computer software for virtualization; computer software for managing and deploying virtual machines to a cloud computing platform; computer software for use in cloud infrastructure management and automation; computer software for running cloud computing based applications; computer software platforms for cloud computing networks and applications; computer software for monitoring cloud and application performance; computer software for collecting, editing, modifying, organizing, synchronizing, integrating, monitoring, transmitting, storage and sharing of data and information; computer software for data backup, recovery and archiving; computer software for data protection and data security; computer software for database management; computer software for creating, configuring, provisioning and scaling databases; computer software for storing, retrieving, caching, extracting, formatting, structuring, systematizing, organizing, indexing, processing, querying, analyzing, replicating and controlling access to data; computer user authentication software. (1) Database management services; business data analysis; data processing services.
(2) Electronic data transmission; streaming of data; streaming of software applications; providing data streaming capacity to others; providing multiple user access to global computer information networks for the transfer and dissemination of a wide range of information; providing user access to computer software in data networks; providing access to remotely hosted operating systems and computer applications through the internet; providing access to cloud based computing resources and storage; providing access to databases; providing virtual private network (VPN) services.
(3) Cloud computing services; cloud computing featuring software for use in providing virtual computer environments for software-as-a-service (SaaS), infrastructure-as-a-service (IaaS), platform-as-a-service (PaaS) and desktop-as-a-service (DaaS) functions; cloud computing featuring software for use in providing a virtual desktop environment; computer services, namely, cloud hosting provider services; computer services, namely, providing desktop-as-a-service servers to others; cloud hosting of electronic databases and virtual computing environments; server hosting; providing virtual computer systems and virtual computer environments through cloud computing; computer services, namely, providing virtual application, web, file, database and storage servers of variable capacity to others; administering and maintaining databases and virtual computing environments for others; electronic data storage; rental of a database server (to third parties); rental of web servers; application service provider (ASP), namely, hosting computer software applications and databases of others; computer software rental; consulting and providing information in the fields of information technology, cloud computing, web services, software, software as a service (SaaS), data processing and analytics, data storage, data warehousing, data archiving, data and information security, networking, mobile computing, and the Internet of Things (IoT); design and development of software, databases, web services and cloud computing infrastructure; data and application migration services; data backup and data restoration services; remote online backup of computer data; data encryption and decryption services; technical support services, namely, troubleshooting of computer software problems; technical support services, namely, monitoring of network systems, servers and web and database applications and notification of related events and alerts; computer services, namely, providing virtual data storage and caching to others; computer services, namely, providing desktop and application streaming; software as a service (SaaS) featuring software for cloud computing; software as a service (SaaS) featuring software for virtualization; software as a service (SaaS) services featuring software for a virtual desktop environment; software as a service (SaaS) featuring software for managing and deploying virtual machines to a cloud computing platform; software as a service (SaaS) featuring software for use in cloud infrastructure management and automation; software as a service (SaaS) featuring software for monitoring cloud and application performance; software as a service (SaaS) featuring software for data, desktop and application streaming; software as a service (SaaS) featuring software for data protection and data security; software as a service (SaaS) featuring database management software; software as a service (SaaS) featuring user authentication software.
42 - Scientific, technological and industrial services, research and design
Goods & Services
Technological services, namely, data warehousing services; technological services, namely, hosting, managing, provisioning, scaling, administering, maintaining, monitoring, securing, encrypting, decrypting, replicating and backing up databases for others; technological services, namely, software as a service (SAAS) services featuring software for use in database management, data access, data extraction and reporting and query functionality for management of data; technological services, namely, cloud computing featuring software for use in database management and data warehousing; technological services, namely, cloud hosting of electronic databases; technological services, namely, providing a website featuring non-downloadable software for database management and data warehousing; technological services, namely, electronic data storage; technological services, namely, providing database servers of variable capacity to others; technological services, namely, rental of computing and data storage facilities of variable capacity to third parties; technological services, namely, data encryption and decoding services; technological services, namely, management of on-line databases for others; technological services, namely, data processing services; technological services, namely, systematization of data in computer databases; technological services, namely, updating and maintenance of data in computer databases; technological services, namely, providing access to databases; technological services, namely, transmission of database information via telecommunications networks; technological services, namely, electronic data transmission; technological services, namely, providing access to remotely hosted computer applications through the Internet; technological services, namely, computer security services in the nature of maintaining security and integrity of databases and digital information; design and development of computer software.
94.
ARTIFICIAL INTELLIGENCE SYSTEM PROVIDING INTERACTIVE MODEL INTERPRETATION AND ENHANCEMENT TOOLS
An interactive interpretation session with respect to a first version of a machine learning model is initiated. In the session, indications of factors contributing to a prediction decision are provided, as well indications of candidate model enhancement actions. In response to received input, an enhancement action is implemented to obtain a second version of the model. The second version of the model is stored.
Techniques are described for providing virtual networking functionality for managed computer networks. In some situations, a user may configure or otherwise specify a logical network topology for a managed computer network with multiple computing nodes that includes one or more virtual networking devices each associated with a specified group of the multiple computing nodes. Corresponding networking functionality may be provided for communications between the multiple computing nodes by emulating functionality that would be provided by the networking devices if they were physically present and configured to support the specified network topology. In some situations, the managed computer network is a virtual computer network overlaid on a substrate network, and the networking device functionality emulating includes receiving routing communications directed to the networking devices and using included routing information to update the specified network topology for the managed computer network.
H04L 41/12 - Discovery or management of network topologies
G06F 9/455 - Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
H04L 41/0816 - Configuration setting characterised by the conditions triggering a change of settings the condition being an adaptation, e.g. in response to network events
H04L 41/50 - Network service management, e.g. ensuring proper service fulfilment according to agreements
41 - Education, entertainment, sporting and cultural services
Goods & Services
Development of marketing concepts; providing commercial and business contact information; commercial intermediation services; providing business information via a website; administration of consumer loyalty programs; market intelligence services; promotion of goods through influencers; influencer marketing; sales promotion for others; marketing; targeted marketing; business intermediary services relating to the matching of various professionals with clients; personnel management consultancy; data processing services; compilation of information into computer databases; systemization of information into computer databases; data search in computer files for others; search engine optimization for sales promotion; website traffic optimization; updating and maintenance of data in computer databases; compiling indexes of information for commercial or advertising purposes; business auditing; rental of cash registers; rental of vending machines; sponsorship search; rental of sales stands; retail services for pharmaceutical, veterinary and sanitary preparations and medical supplies; advertising services to create brand identity for others; publication of publicity texts; advertising; publicity; writing of publicity texts; advertising design; production of advertising films; development of advertising concepts; scriptwriting for advertising purposes; consultancy regarding advertising communication strategies; promotion of goods and services through sponsorship of sports events; business consulting services for digital transformation; conducting of commercial events; lead generation services; business management assistance; business research; public relations; marketing research Production of podcasts; arranging and conducting of conferences; entertainment services; film production, other than advertising films; production of radio and television programmes; production of shows; providing museum facilities; scriptwriting, other than for advertising purposes; photographic reporting; providing online virtual guided tours; providing online music, not downloadable; providing online videos, not downloadable; screenplay writing; film distribution; film directing, other than advertising films; providing user reviews for entertainment or cultural purposes; providing user rankings for entertainment or cultural purposes; animal training; educational services; instruction services; practical training; vocational guidance; modelling for artists; organization of lotteries; rental of indoor aquaria; transfer of business knowledge and know-how; vocational retraining; know-how transfer; arranging and conducting of entertainment events; photography; news reporters services; writing of texts; conducting fitness classes; toy rental; providing facilities for playing Live Action Role Playing games; organization of competitions; arranging and conducting of seminars; arranging and conducting of workshops; arranging and conducting of in-person educational forums; multimedia library services; publication of texts, other than publicity texts; online publication of electronic books and journals; providing online electronic publications, not downloadable; circulation of video tapes; directing of shows
97.
CLIENT-CONFIGURABLE RETENTION PERIODS FOR MACHINE LEARNING SERVICE-MANAGED RESOURCES
A post-task-completion retention period for which a computing resource is to be retained, without de-activating the resource, on behalf of a set of requesters of machine learning tasks is determined at a machine learning service. A first task, identified at the service prior to expiration of the retention period at a first computing resource at which a second task has completed, is initiated at the first computing resource. In response to obtaining an indication of a third task and determining that a threshold criterion associated with the retention period satisfies a criterion, the third task is initiated at an additional computing resource. The additional computing resource is de-activated after the third task completes, without waiting for the retention period to expire.
A connectivity monitor of a vehicle determines current and/or future states of antennas. A workload monitor of the vehicle receives execution criteria for different workloads to be executed. An intelligent connectivity engine at the vehicle receives the current and/or future states of the antennas and the execution criteria for the respective workloads. Based on the current and/or future states of the antennas and the execution criteria for the respective workloads, the intelligent connectivity engine assigns at least one of the respective workloads for current execution and at least another of the respective workloads for future execution. A client may use an intelligent connectivity service to configure various aspects of the vehicle connectivity. For example, the client can provide workload recommendation code for the intelligent connectivity engine to assign workloads for current or future execution.
H04W 76/11 - Allocation or use of connection identifiers
H04W 84/02 - Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
Components of a public certificate authority (CA) generate respective cryptographic assertions during performance of respective tasks of a certificate issuance workflow and a workflow approval component approves/rejects certificate issuance, based upon verification of the cryptographic assertions. For example, a workflow manager may assign tasks of a certificate workflow process to a number of components that process the tasks. The components generate responses and sign the respective responses with keys particular to each component. The workflow manager gathers the cryptographic assertions and sends them to a workflow approval component that validates the assertions, verifies the assertions indicate successful completion of the workflow and approves or rejects certificate issuance.
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 9/14 - Arrangements for secret or secure communications; Network security protocols using a plurality of keys or algorithms
Techniques for recognizing phonemes from a spoken input and providing pronunciation feedback as part of a language learning experience are described. Some embodiments use a machine learning model configured to recognize phonemes spoken in a user's native language and spoken in the language to be learned. The model is trained with the native language's lexicon and the learning language's lexicon. The system can provide feedback at a word level, a syllable level and/or phoneme level. The system can also provide feedback with respect to phoneme stress.
