Abstract

Incidents may be monitored using a camera in response to an activation of a trigger device. Trigger data may be received over a communication network in response to activation of a trigger device disposed within a first area of a vehicle. A camera disposed within a second area of the vehicle may be controlled over the communication network in response to receiving the trigger data, the controlling causing the camera to capture sensor information of a passenger of the vehicle. Passenger information of the passenger of the vehicle may be obtained in response to receiving the trigger data. An alert notification may be generated. The alert notification may comprise at least a portion of the sensor information of the passenger, and the alert notification may be capable of initiating one or more incident actions associated with the passenger.

IPC Classes  ?

• G06V 20/59 - Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
• H04W 4/48 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for in-vehicle communication
• B60R 25/30 - Detection related to theft or to other events relevant to anti-theft systems
• B60R 25/102 - Fittings or systems for preventing or indicating unauthorised use or theft of vehicles actuating a signalling device a signal being sent to a remote location, e.g. a radio signal being transmitted to a police station, a security company or the owner
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
• H04N 23/65 - Control of camera operation in relation to power supply

Abstract

Techniques for automatically detecting when a ride requester has requested a ride-share ride on behalf of a guest rider using some or all of the communications between the driver and ride requester are described herein. For example, a server can obtain chat logs between a ride requester and a driver and process the chat logs to identify whether the ride requester has requested a ride on behalf of a guest rider. In particular, the server can train an artificial intelligence model (e.g., a machine learning model) to predict potential guest rider behavior. Once trained, the server can obtain chat logs comprising chat messages sent between a driver and a ride requester, and apply a representation of the chat logs as an input to the trained artificial intelligence model to determine whether guest rider behavior is detected.

IPC Classes  ?

• G06Q 10/02 - Reservations, e.g. for tickets, services or events
• G06Q 50/30 - Transportation; Communications
• G10L 17/06 - Decision making techniques; Pattern matching strategies
• G06N 20/00 - Machine learning
• G06F 18/213 - Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
• G06F 18/214 - Generating training patterns; Bootstrap methods, e.g. bagging or boosting

Abstract

The present disclosure relates to systems and methods for displaying one or more points of interest (POIs) of a location-based service (LBS). The method may include receiving a request for an LBS sent by a user via a user terminal and obtaining a start location based on the request for the LBS. The method may also include determining one or more recommended POIs based on the start location and POI data associated with the start location. Further, the method may also include transmitting signals to the user terminal to display at least one of the one or more recommended POIs.

IPC Classes  ?

• G01C 21/34 - Route searching; Route guidance
• G01C 21/36 - Input/output arrangements for on-board computers

Abstract

The present disclosure relates to systems and methods for displaying vehicle information for an on-demand service. The method may include sending a request for on-demand service to a server. The method may further include obtaining information of a vehicle related to the request for on-demand service. The information of the vehicle may include color information of the vehicle. The method may further include generating, by a processor, a user interface based on the information of the vehicle. The user interface may include at least one user interface element corresponding to the color information of the vehicle.

IPC Classes  ?

• G06T 11/00 - 2D [Two Dimensional] image generation
• H04W 4/44 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
• G01C 21/34 - Route searching; Route guidance
• G06T 11/60 - Editing figures and text; Combining figures or text
• H04L 67/51 - Discovery or management thereof, e.g. service location protocol [SLP] or web services
• H04L 67/63 - Routing a service request depending on the request content or context

Abstract

The embodiments of the present disclosure provide a method, an apparatus, a storage medium, and an electronic device for information exchange. After receiving task information of a task, a task processing terminal may send its own position. A task issuing terminal may control a display device to render and display a position modification control corresponding to the task. In response to the position modification control is triggered, the task issuing terminal may display a modification page to detect a input operation. In response to that the input operation is detected, the task issuing terminal may determine corresponding position information and send position modification information determined based on the position information to a server. The server may obtain the task information and send, in response to that a task processing state of the task is unprocessed, a position modification prompt and the position modification information, so that the task processing terminal may modify a starting position of the task based on the position information. The method in the embodiments of the present disclosure may reduce a possibility of a task being cancelled and improve a utilization rate of task processing resources.

IPC Classes  ?

• G01C 21/34 - Route searching; Route guidance
• G01C 21/00 - Navigation; Navigational instruments not provided for in groups
• G06Q 50/30 - Transportation; Communications

Abstract

The present disclosure relates to a method and system for monitoring on route transportations. The method includes obtaining a driving route of a target vehicle; obtaining a reference position on the driving route away from a first current position of the target vehicle; determining a second current position of the target vehicle after a reference time; determining a distance between the second current position and the reference position is greater than a preset distance; and sending a signal to a target terminal indicating that the target vehicle is off-route.

IPC Classes  ?

• G08B 25/01 - Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium
• H04W 4/029 - Location-based management or tracking services
• G08G 1/005 - Traffic control systems for road vehicles including pedestrian guidance indicator
• G08G 1/0968 - Systems involving transmission of navigation instructions to the vehicle
• G08G 1/0969 - Systems involving transmission of navigation instructions to the vehicle having a display in the form of a map
• G08G 1/00 - Traffic control systems for road vehicles
• H04W 4/90 - Services for handling of emergency or hazardous situations, e.g. earthquake and tsunami warning systems [ETWS]
• G08B 25/08 - Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium using communication transmission lines
• H04W 4/024 - Guidance services
• H04W 4/44 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
• G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
• G06F 3/00 - Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
• G06Q 50/26 - Government or public services
• G08B 21/02 - Alarms for ensuring the safety of persons
• G08B 25/10 - Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium using wireless transmission systems

Abstract

The present disclosure may provide an information interaction method, apparatus, electronic device, and readable storage medium relating to the field of computer technology. The method may include: in response to monitoring that an expected pick-up location is within a preset coordinate range, displaying at least one pick-up area within the preset coordinate range, wherein the expected pick-up location is a coordinate point of a map; receiving a pick-up area selecting instruction, and determining a target pick-up area; determining a target pick-up location corresponding to the target pick-up area; determining a recommended route between a current location and the target pick-up location in response to receiving a submitting instruction for the target pick-up location; based on the recommended route, displaying route guidance information.

IPC Classes  ?

• G01C 21/34 - Route searching; Route guidance
• G01C 21/36 - Input/output arrangements for on-board computers

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media for acoustic echo cancellation and suppression are provided. An exemplary method comprises receiving a far-end acoustic signal and a corrupted near-end acoustic signal, wherein the corrupted near-end acoustic signal is generated based on (1) an echo of the far-end acoustic signal and (2) a near-end acoustic signal; feeding the far-end acoustic signal and the corrupted near-end acoustic signal into a neural network as an input to output a time-frequency (TF) mask that suppresses the echo and retains the near-end acoustic signal, and generating an enhanced version of the corrupted near-end acoustic signal by applying the obtained TF mask to the corrupted near-end acoustic signal.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks

Abstract

In accordance with one implementation of the present disclosure, a new approach for determining a movement orientation of a user is proposed in indoor navigation. Generally speaking, a device orientation of a terminal device is obtained based on at least one signal stream collected from the terminal device carried by a moving user. A deviation degree is determined based on the at least one signal stream, here the deviation degree represents a deviation between a movement orientation of the user and an actual device orientation of the terminal device. The movement orientation is determined based on the device orientation in accordance with a determination that the deviation degree is below a threshold degree. With the above implementation, the movement orientation of the user is determined in a more effective an accurate way, and thus accuracy of the indoor navigation is increased.

IPC Classes  ?

• G01C 21/20 - Instruments for performing navigational calculations
• G01C 21/16 - Navigation; Navigational instruments not provided for in groups by using measurement of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
• H04W 4/33 - Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
• H04W 4/02 - Services making use of location information

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media for evaluating and improving live translation captioning systems. An exemplary method includes: displaying a word in a first language; receiving a first audio sequence, the first audio sequence comprising a verbal description of the word; generating a first translated text in a second language; displaying the first translated text; receiving a second audio sequence, the second audio sequence comprising a guessed word based on the first translated text; generating a second translated text in the first language; determining a matching score between the word and the second translated text; determining a performance score of the live translation captioning system based on the matching score.

IPC Classes  ?

• G06F 40/00 - Handling natural language data
• G10L 15/32 - Multiple recognisers used in sequence or in parallel; Score combination systems therefor, e.g. voting systems
• G10L 15/08 - Speech classification or search
• G10L 15/06 - Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
• G06F 40/49 - Data-driven translation using very large corpora, e.g. the web
• G06F 40/51 - Translation evaluation
• G06F 40/58 - Use of machine translation, e.g. for multi-lingual retrieval, for server-side translation for client devices or for real-time translation
• G10L 25/27 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the analysis technique

Abstract

The present disclosure provides a method, a first service requester, a system, and an electronic device for information display. The method may include obtaining a service request location where a first service requester is located when a carpooling request of the first service requester is received; determining dynamic carpooling information of at least one second service requester within a preset distance from the service request location during a carpooling process; and displaying the dynamic carpooling information of the at least one second service requester on a display interface of the first service request terminal. Therefore, the dynamic carpooling information of the at least one second service requester within the preset range from the service request location of the first service requester may be displayed to the first service requester during the time period waiting for the carpooling request of the first service requester to be responded, which can effectively enrich the display content of the service requester terminal, improve the transparency of the relevant information, fill the idle time that the user is waiting, and reduce the probability of the order cancellation.

IPC Classes  ?

• G01C 21/34 - Route searching; Route guidance
• G06Q 10/02 - Reservations, e.g. for tickets, services or events

Abstract

In accordance with one implementation of the present disclosure, a new approach for identifying a stepping event is proposed in indoor navigation. Generally speaking, a first signal fragment and a second signal fragment respectively within a first time window and a second time window in an acceleration signal stream are obtained, here the acceleration signal stream is collected from an acceleration sensor associated with a moving user, the first time window being shorter than the second time window. A first amplitude feature and a second amplitude feature are determined for the first and second time windows based on the first and second signal fragments, respectively. A stepping event of the user is identified based on a deviation between the first and second amplitude features. With the above implementation, the stepping event is identified in a more effective an accurate way, and thus accuracy of the indoor navigation is increased.

IPC Classes  ?

• G01C 22/00 - Measuring distance traversed on the ground by vehicles, persons, animals or other moving solid bodies, e.g. using odometers or using pedometers
• G06K 9/62 - Methods or arrangements for recognition using electronic means

Abstract

Systems and methods for online to offline services are provided. The methods may include obtaining a first service request issued by a first service requester, the first service request including a request time point, a start location, and a destination. The methods may further include determining a target time point based on at least one of the request time point or an input by the first service requester, wherein the target time point is after the request time point. The methods may further include obtaining one or more candidate service requests, and determining a service request set and a target service provider that matches with the service request set by executing, starting at the target time point, a matching process based on the first service request and the one or more candidate service requests. The service request set may include the first service request.

IPC Classes  ?

• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

Embodiments of the disclosure provide systems and methods for verifying digital payments for a payment platform. The system includes a communication interface configured to receive service data associated with a transaction from a terminal device associated with a user of the payment platform. The system further includes at least one processor configured to automatically determine whether an authentication is waived for the transaction based on the received service data and a blacklist generated using a machine learning model. The blacklist includes a list of suspicious users and transaction behaviors. The at least one processor further configured to approve the transaction without requesting user authentication information from the terminal device when the authentication is waived. The at least one processor also configured to request the user authentication information from the terminal device and validate the user authentication information when the authentication is not waived.

IPC Classes  ?

• G06Q 30/00 - Commerce
• G06Q 10/10 - Office automation; Time management
• G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check of credit lines or negative lists
• G06Q 20/02 - Payment architectures, schemes or protocols involving a neutral third party, e.g. certification authority, notary or trusted third party [TTP]
• G06N 20/00 - Machine learning
• G06F 21/31 - User authentication
• G06Q 20/32 - Payment architectures, schemes or protocols characterised by the use of specific devices using wireless devices

Abstract

After determining a trip order has a high risk, verification information unavailable in a passenger information database system can be requested and provided for a passenger of the account. Validity of the verification information can be determined using a verification service and stored in a passenger information database. A decision for the trip order can be determined based on the validity of the verification information stored in the passenger information database.

IPC Classes  ?

• G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
• G06F 16/25 - Integrating or interfacing systems involving database management systems
• H04L 9/40 - Network security protocols
• G06Q 50/30 - Transportation; Communications

Abstract

The present disclosure relates to a system and a method for identifying a drunk passenger of a car hailing order. The system may perform the method to: obtain a plurality of samples from historical car hailing orders stored in a database; for each of the plurality of samples, using an application, extract a plurality of features including a passenger feature set, a driver feature set, and an order feature set, wherein the order feature set includes drunk-hotspot-relating features; and train a preliminary classification model based on the plurality of features and the plurality of samples to obtain a drunk model.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models

Abstract

Systems, methods, and non-transitory computer-readable media can construct a simulation framework for a ride sharing service. The simulation framework comprises a simulation environment and an agent comprising one or more algorithms including an order dispatching algorithm and a driver reposition algorithm. One or more states of the simulation environment include information about a plurality of drivers and a plurality of trip order requests, and can be provided to the agent. One or more actions from the agent can be obtained. The one or more actions comprises at least one of: a plurality of matches between the plurality of drivers and the plurality of trip order requests, or a plurality of reposition destinations for a subset of the plurality of drivers. The one or more states of the simulation environment can be updated based on the one or more actions.

IPC Classes  ?

• G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
• G06N 5/00 - Computing arrangements using knowledge-based models
• G06N 20/20 - Ensemble learning
• G06Q 50/30 - Transportation; Communications
• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

Method, system, and non-transitory computer-readable storage media for determining optimal resource allocation are described. An example method includes: obtaining historical data comprising a plurality of features of different system configurations, a plurality of resource consumptions of the different system configurations, and a plurality of rewards respectively generated from the different system configurations; training a plurality of neural network (NN)-based encoders respectively corresponding to the different system configurations; encoding, using the plurality of trained NN-based encoders, the plurality of features in the historical data to obtain a plurality of encoded features of the different system configurations; training a multi-armed bandit (MAB) model; and generating, using the trained MAB model, a matrix of predicted rewards, each of the predicted rewards corresponding to an estimated reward generated by allocating an amount of resources to one of the different system configurations.

IPC Classes  ?

• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• G06Q 10/02 - Reservations, e.g. for tickets, services or events
• G06N 3/08 - Learning methods

Abstract

The present disclosure provides methods and systems for vehicle verification. The method may include receiving vehicle verification information related to a vehicle to be verified from a client, wherein the vehicle verification information includes a plurality of images acquired by the client via an imaging device, and the vehicle verification information responds to at least one vehicle verification instruction. The method may further include determining a verification result of the vehicle based on the vehicle verification information.

IPC Classes  ?

• G06V 20/50 - Context or environment of the image
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks

Abstract

The application discloses Systems and methods for a data synchronization. The system may include a receiving module, an instruction generating module and a sending module. The receiving module may be configured to receive the first instruction. The first instruction may be used to instruct the start of data acquisition of the system. In response to receiving the first instruction, the instruction generating module may be configured to generate a second instruction. The second instruction may be used to trigger at least two sensors to acquire data. The sending module may be configured to send second instruction to at least two sensors respectively based on the first delay. The first delay causes the time difference between at least two sensors starting to acquire data less than the first preset threshold.

IPC Classes  ?

• H04L 12/70 - Packet switching systems
• H04L 67/133 - Protocols for remote procedure calls [RPC]
• H04L 7/00 - Arrangements for synchronising receiver with transmitter
• 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

Abstract

The embodiments of the present disclosure provide a visual positioning method, the method may include obtaining a positioning image collected by an imaging device; obtaining a three-dimensional (3D) point cloud map associated with an area where the imaging device is located; determining a target area associated with the positioning image from the 3D point cloud map based on the positioning image; and The embodiments of the present disclosure provide a visual positioning method, the method may include obtaining a positioning image collected by an imaging device; obtaining a three-dimensional (3D) point cloud map associated with an area where the imaging device is located; determining a target area associated with the positioning image from the 3D point cloud map based on the positioning image; and determining positioning information of the imaging device based on the positioning image and the target area.

IPC Classes  ?

• G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
• G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
• G06V 10/774 - Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
• G06V 10/74 - Image or video pattern matching; Proximity measures in feature spaces
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06V 20/70 - Labelling scene content, e.g. deriving syntactic or semantic representations
• G06T 7/10 - Segmentation; Edge detection
• G06V 10/52 - Scale-space analysis, e.g. wavelet analysis
• G01S 17/89 - Lidar systems, specially adapted for specific applications for mapping or imaging

Abstract

Systems and methods are provided for using encoded representations of numbers in various applications. The encoded representations of numbers, also referred to as number embeddings, may be multi-element data structures (e.g., multi-dimensional vectors) in which each element is a real-numbered value. The values of a given number embedding collectively encode information from which properties of the number represented by the number embedding may be derived. Number embeddings may be compared or otherwise analyzed with respect to each other to identify patterns or similarities in the numbers represented by each number embedding.

IPC Classes  ?

• G06F 17/16 - Matrix or vector computation
• G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
• G06N 5/04 - Inference or reasoning models
• G06N 20/00 - Machine learning

Abstract

Methods and systems for off-route identification may be provided. A first navigation route of an object that is traveling and one or more positioning location points during traveling may be obtained. Whether a first preset condition is satisfied may be determined based on a positional relationship between the one or more positioning location points and the first navigation route. In response to determining that the first preset condition is not satisfied, whether the object is off-route may be determined based on information associated with the traveling of the object.

IPC Classes  ?

• G01C 21/34 - Route searching; Route guidance
• G01C 21/36 - Input/output arrangements for on-board computers

Abstract

A method of echo path delay destination and echo cancellation is described in this disclosure. The method includes: obtaining a reference signal, a microphone signal, and a trained multi-task deep neural network, wherein the multi-task deep neural network comprises a first neural network and a second neural network; generating, using the first neural network of the multi-task deep neural network, an estimated echo path delay based on the reference signal and the microphone signal; updating the reference signal based on the estimated echo path delay; and generating, using the second neural network of the multi-task deep neural network, an enhanced microphone signal based on the microphone signal and the updated reference signal.

IPC Classes  ?

• H04B 3/20 - Reducing echo effects or singing; Opening or closing transmitting path; Conditioning for transmission in one direction or the other
• G10K 11/178 - Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/08 - Learning methods
• H03G 3/00 - Gain control in amplifiers or frequency changers

Abstract

This disclosure describes systems and methods for repositioning vehicles. An exemplary method includes obtaining a plurality of current features associated with a vehicle located in one of the plurality of grid cells; inputting the plurality current features associated with the vehicle into a neural network; obtaining, from the neural network, a plurality of conditional action values for repositioning the vehicle to a plurality of target grid cells conditioned upon the plurality current features associated with the vehicle, wherein the plurality of target grid cells comprise the one grid cell that the vehicle is currently located in and other grid cells in the plurality of grid cells that are within two or more layers surrounding the one grid cell; and sending one or more of the plurality of target grid cells with highest conditional action values to the vehicle for repositioning.

IPC Classes  ?

• G08G 1/00 - Traffic control systems for road vehicles
• G06N 3/02 - Neural networks
• G06F 9/30 - Arrangements for executing machine instructions, e.g. instruction decode
• G06K 9/62 - Methods or arrangements for recognition using electronic means

Abstract

A unified learning approach for large-scale ride-hailing is described, the approach includes obtaining an offline state value network for predicting a value of a vehicle state, the offline state value network being trained based on a plurality of historical vehicle trajectories; initializing an online state value network and dispatching a plurality of vehicles according to the online state value network for a period of time; training the online state value network based on vehicle states of the plurality of vehicles before and after the dispatching and rewards associated with the dispatching; ensembling the trained online state value network and the offline state value network to obtain an ensembled online state value network; and dispatching the plurality of vehicles according to the ensembled online state value network.

IPC Classes  ?

• G08G 1/00 - Traffic control systems for road vehicles
• G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
• H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
• G06K 9/62 - Methods or arrangements for recognition using electronic means

Abstract

A system and method of determining a policy to prevent fading drivers is described. The system and method creates virtual trajectories of incentives such as coupons offered to drivers in a transportation hailing system and corresponding states of drivers in response to the incentives. A joint policy simulator is created from an incentive policy, a confounding incentive policy, and an incentive object policy to generate the simulated actions of drivers in response to different incentives. The rewards of the simulated actions of the drivers is determined by a discriminator. The incentive policy for preventing fading drivers is optimized by reinforcement learning based on the virtual trajectories generated by the joint policy simulator and discriminator.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06Q 50/30 - Transportation; Communications
• G06Q 30/0211 - Determining the effectiveness of discounts or incentives
• G06Q 30/0208 - Trade or exchange of goods or services in exchange for incentives or rewards
• G06Q 30/0207 - Discounts or incentives, e.g. coupons or rebates

Abstract

A system for evaluating order dispatching policy includes a first computing device, at least one processor, and a memory. The first computing device is configured to generate historical driver data associated with a driver. The at least one processor is configured to store instructions. When executed by the at least one processor, the instructions cause the at least one processor to perform operations. The operations performed by the at least one processor includes obtaining the generated historical driver data associated with the driver. Based at least in part on the obtained historical driver data, a value function is estimated. The value function is associated with a plurality of order dispatching policies. An optimal order dispatching policy is then determined. The optimal order dispatching policy is associated with an estimated maximum value of the value function. The estimation of the value function applies a feed-forward neutral network

IPC Classes  ?

• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• G06Q 50/30 - Transportation; Communications
• G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"

Abstract

The disclosure relates to system and method for image quality detection. The method may include: obtaining an image acquired by a camera; obtaining an image quality detection model, the image quality detection model being provided by training a machine learning model using a plurality of training samples; determining a detection result of the image using the image quality detection model; and in response to a determination that the detection result includes a quality anomaly of the image, generating a strategy in response to the quality anomaly.

IPC Classes  ?

• G06T 7/00 - Image analysis
• G06V 10/422 - Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation for representing the structure of the pattern or shape of an object therefor
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks

Abstract

The present disclosure provides methods and systems for recommending pick-up points. The method may include obtaining information of a searched point of interest determined by a user; determining a first set of candidate pick-up points based at least on the information of the searched point of interest, the first set of candidate pick-up points including a plurality of candidate pick-up points; determining at least one recommended pick-up point through a preset algorithm based at least on the first set of candidate pick-up points, and displaying the at least one recommended pick-up point to the user.

IPC Classes  ?

• H04W 4/021 - Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• G01C 21/00 - Navigation; Navigational instruments not provided for in groups
• G06F 16/2457 - Query processing with adaptation to user needs
• H04W 4/02 - Services making use of location information

Abstract

A ride-sharing system can automatically and autonomously determine whether a rider is an account holder associated with an account of a ride-sharing service that requested a ride or a guest rider. A guest rider detection system can use geolocation data to determine whether a rider is a guest rider or an account holder. The system can obtain location data for an account holder that requests a ride using a ride sharing application. The location data of the account holder can be compared with one or more locations (e.g., pickup or drop off location) associated with the requested ride. Based at least in part on the comparisons between the location data of the account holder and the one or more locations associated with the requested ride, the system can determine with a particular degree of certainty whether the rider is the guest rider or the account holder.

IPC Classes  ?

• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

Systems and methods for registering point clouds are provided. An exemplary method includes parsing semantic information from source and target point clouds, segmenting points in the source point cloud into first and second groups based on the semantic information, segmenting points in the target point cloud into third and fourth groups based on the semantic information, determining an initial pose of the source point cloud by registering the first group of points in the source point cloud to the third group of points in the target point cloud according to a first resolution, and adjusting the initial pose of the source point cloud by registering the second group of points in the source point cloud to the fourth group of points in the target point cloud according to a second resolution, wherein the second resolution is different from the first resolution.

IPC Classes  ?

• G06T 7/30 - Determination of transform parameters for the alignment of images, i.e. image registration
• G06T 7/10 - Segmentation; Edge detection
• G06T 7/70 - Determining position or orientation of objects or cameras

Abstract

Embodiments of the disclosure provide systems and methods for detecting road markings from a laser intensity image. An exemplary method may include receiving, by a communication interface, the laser intensity image acquired by a sensor. The method may also include segmenting the laser intensity image into a plurality of road segments, and dividing a road segment into a plurality of sub-images. The method may further include generating a road marking image corresponding to each of the sub-images based on a semantic segmentation method using a learning model and generating an overall road marking image for the road segment by piecing together the road marking images corresponding to the sub-images of the road segment.

IPC Classes  ?

• G06V 20/10 - Terrestrial scenes
• G06V 10/26 - Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
• G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
• G06V 10/20 - Image preprocessing
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks

Abstract

A system for evaluating order dispatching policy includes a first computing device, at least one processor, and a memory. The first computing device is configured to generate historical driver data associated with a driver. The at least one processor is configured to store instructions. When executed by the at least one processor, the instructions cause the at least one processor to perform operations. The operations performed by the at least one processor includes obtaining the generated historical driver data associated with the driver. Based at least in part on the obtained historical driver data, a value function is estimated. The value function is associated with a plurality of order dispatching policies. An optimal order dispatching policy is then determined. The optimal order dispatching policy is associated with an estimated maximum value of the value function. The estimation of the value function applies a cerebellar model arithmetic controller.

IPC Classes  ?

• G01C 21/34 - Route searching; Route guidance
• B60W 40/09 - Driving style or behaviour

Abstract

Systems and methods for training an initial machine learning model is provided. The system may train an initial machine learning model using source domain training data with sample labels and target domain training data without sample labels. The initial machine learning model may include a feature extraction unit, a first processing unit, and an adversarial unit, wherein the first processing unit is associated with a first loss function, and the adversarial unit is associated with a second loss function. In some embodiments, the initial machine learning model may also include a second processing unit. A third loss function that reflects the consistency of the first processing unit and the second processing unit may be determined. The initial machine learning model may be trained based on the feature extraction unit, the first processing unit, the adversarial unit, and the second processing unit.

IPC Classes  ?

• G06N 20/20 - Ensemble learning
• G06N 3/08 - Learning methods
• G06K 9/62 - Methods or arrangements for recognition using electronic means

Abstract

The present disclosure provides a system for improving security. The system may identify a query associated with a user account, and access an ID graph database to obtain an ID graph relating to the user account by a database driver. The system may also determine whether the user account is a target account type based at least on the ID graph. The ID graph may include a plurality of nodes and a plurality of edges. Each of the plurality of edges may connect two nodes. Each of the plurality of nodes may include at least one of a register ID, a login ID, a payment ID, a background check ID, or a face ID. Each edge that connects two nodes may include at least one of a user type associated with the two nodes, a timestamp when the edge is connected, or source information of the edge.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• G06F 16/9535 - Search customisation based on user profiles and personalisation
• G06N 20/00 - Machine learning

Abstract

An automatic agent may be improved through feedback. A user input may be received through a user interface. A plurality of current utterance variables may be obtained by tokenizing the user input. The automatic agent may execute a machine learning policy to generate a reply to the user input based on the plurality of current utterance variables. A different reply may be obtained in response to an indication that the reply will lead to a breakdown, wherein the breakdown comprises an unhuman response from the automatic agent according to the machine learning policy. The machine learning policy may be adjusted based on the plurality of current utterance variables and the different reply.

IPC Classes  ?

• G10L 15/06 - Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
• G06F 3/16 - Sound input; Sound output
• G10L 15/01 - Assessment or evaluation of speech recognition systems
• G06F 40/56 - Natural language generation
• G06N 20/00 - Machine learning
• G06F 9/451 - Execution arrangements for user interfaces

Abstract

Systems and methods are disclosed configured to receive a ride request from an application hosted on a device of a user. A vehicle is routed to a pickup location. A route is generated configured to navigate the vehicle to a destination. Sensor data indicative of ride quality is monitored while the vehicle is navigating the generated route, wherein the sensor data comprises data indicating at least how bumpy the ride is and/or whether a sudden braking event occurred. When an adverse ride condition is detected, a determination is made that a content item is to be transmitted to a display device. A geofence is generated about the destination using a user content interaction history. The first item of content is selected based at least in part on an identification of an establishments within the geofence. The selected item of content is transmitted to the display device and the user may be transported to the pickup location within the geofence.

IPC Classes  ?

• G06Q 30/00 - Commerce
• G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
• G06N 3/06 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
• H04W 4/021 - Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• H04W 4/02 - Services making use of location information
• G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
• G01C 21/36 - Input/output arrangements for on-board computers

Abstract

Multi-objective distributional reinforcement learning may be applied to order dispatching on ride-hailing platforms. A set of historical driver trajectories and a set of driver-order pairs may be obtained. A weight vector between a first reward of the set of historical driver trajectories and a second reward of the set of historical driver trajectories may be determined using inverse reinforcement learning (IRL). A first value function and a second value function may be jointly learned using distributional reinforcement learning (DRL) based on the historical driver trajectories and the weight vector. A set of scores comprising a score of each driver-order pair in the set of driver-order pairs may be determined based on the weight vector, the first value function, and the second value function. A set of dispatch decisions may be determined based on the set of scores that maximizes a total reward of the set of dispatch decisions.

IPC Classes  ?

• G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
• G06Q 50/30 - Transportation; Communications
• G01C 21/34 - Route searching; Route guidance

Abstract

Systems and methods are disclosed configured to identify a location where current or predicted ride requests exceed a threshold within a time period. Driver location information for one or more drivers is used to identify a driver to satisfy the identified current or predicted ride requests at the location that exceed the threshold within the time period. In response to determining that a driver has at least a first likelihood of accepting a ride request at the location within the time period, item content is selected using a history of establishments frequented by the driver. The selected item of content, associated with a token, is provided for presentation on a display to the driver. In response to determining that the identified driver accepted the ride request at the location within the time period, the token is enabled to be utilized by the identified driver at an entity.

IPC Classes  ?

• G06Q 30/00 - Commerce
• G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
• G06Q 50/30 - Transportation; Communications
• G06Q 10/02 - Reservations, e.g. for tickets, services or events

Abstract

Deep reinforcement learning may be used for vehicle repositioning on mobility-on-demand platforms. Information may be obtained. The information may include a current location of a vehicle on a ride-sharing platform. A set of paths originated from the current location of the vehicle may be obtained. Each of the set of paths may have a length less than a preset maximum path length. A set of expected cumulative rewards along the set of paths may be obtained based on a trained deep value-network. A best path from the set of paths may be selected based on a heuristic tree search of the set of expected cumulative rewards. A next step along the best path may be recommended as a reposition action for the vehicle.

IPC Classes  ?

• G01C 21/34 - Route searching; Route guidance
• G06N 3/08 - Learning methods

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media for task control based on Bayesian Meta-Reinforcement learning. An exemplary method includes obtaining a base machine learning (ML) model trained based on historical data collected from historical tasks. The base ML model represents a prior distribution of model parameters in a neural network representing control policies. The exemplary method further includes receiving observed data from a new control task; training a task-level ML model based on the base ML model and the observed data, wherein the task-level ML model represents a posterior distribution of the model parameters; sampling, based on the posterior distribution of the model parameters, a set of the model parameters that represent a control policy; and applying the control policy in performing the new control task.

IPC Classes  ?

• G08G 1/081 - Plural intersections under common control
• G08G 1/01 - Detecting movement of traffic to be counted or controlled
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G06N 3/08 - Learning methods

Abstract

The present disclosure relates to a system and a method for performing instance segmentation based on semantic segmentation that is capable of (1) processing HD images in real-time given semantic segmentation; 2) delivering comparable performance with Mask R-CNN in terms of accuracy when combined with a widely-used semantic segmentation method (such as DPC), while consistently outperforms a state-of-the-art real-time solution; (3) working flexibly with any semantic segmentation model for instance segmentation; (4) outperforming Mask R-CNN if the given semantic segmentation is sufficiently good; and (5) being easily extended to panoptic segmentation.

IPC Classes  ?

• G06T 7/10 - Segmentation; Edge detection
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
• G06N 3/08 - Learning methods

Abstract

An automatic agent may be trained using reinforcement learning. A secret task may be obtained for a simulated user, and the secret task may be unknown to the automatic agent. At least one instruction to complete the secret task may be obtained from the simulated user according to at least one RL policy. At least one action may be generated by the automatic agent based on the at least one instruction and the at least one RL policy. Rewards may be determined for the simulated user and the automatic agent in response to determining that the at least one action successfully completes the secret task. The at least one RL policy may be adjusted based on the determined rewards.

IPC Classes  ?

• G06F 9/48 - Program initiating; Program switching, e.g. by interrupt
• G06N 20/00 - Machine learning
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
• G06F 9/38 - Concurrent instruction execution, e.g. pipeline, look ahead
• G06F 9/54 - Interprogram communication

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for constructing a virtual environment for a ride-hailing platform are disclosed. An exemplary method comprises: obtaining a plurality of historical interaction trajectories each comprising one or more interaction records between a driver and a ride-hailing platform, each interaction record comprising a program recommendation of the ride-hailing platform to the driver and a reaction of the driver in response to the program recommendation; training a simulator based on the plurality of historical interaction trajectories; and integrating a reward function with the simulator to construct the virtual environment, wherein the plurality of first program recommendations and the plurality of reactions form a plurality of simulated interactions, and a data distribution of the plurality of simulated interactions approximates a data distribution of a plurality of interaction records in the plurality of historical interaction trajectories.

IPC Classes  ?

• G06Q 50/30 - Transportation; Communications
• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
• G06N 20/00 - Machine learning

Abstract

Embodiments of the disclosure provide systems and methods for verifying digital payments for a payment platform. The system includes a communication interface configured to receive service data associated with a transaction from a terminal device associated with a user of the payment platform. The system further includes at least one processor configured to automatically determine whether an authentication is waived for the transaction based on the received service data and a blacklist generated using a machine learning model. The blacklist includes a list of suspicious users and transaction behaviors. The at least one processor further configured to approve the transaction without requesting user authentication information from the terminal device when the authentication is waived. The at least one processor also configured to request the user authentication information from the terminal device and validate the user authentication information when the authentication is not waived.

IPC Classes  ?

• G06Q 30/00 - Commerce
• G06Q 10/10 - Office automation; Time management
• G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check of credit lines or negative lists
• G06Q 20/02 - Payment architectures, schemes or protocols involving a neutral third party, e.g. certification authority, notary or trusted third party [TTP]
• G06N 20/00 - Machine learning
• G06F 21/31 - User authentication
• G06Q 20/32 - Payment architectures, schemes or protocols characterised by the use of specific devices using wireless devices
• G06Q 50/26 - Government or public services
• G06Q 40/02 - Banking, e.g. interest calculation or account maintenance

Abstract

A system and method for concurrent multi-path processing of audio signals for automatic speech recognition is presented. Audio information defining a set of audio signals may be obtained (502). The audio signals may convey mixed audio content produced by multiple audio sources. A set of source-specific audio signals may be determined by demixing the mixed audio content produced by the multiple audio sources. Determining the set of source-specific audio signals may comprises providing the set of audio signals to both a first signal processing path and a second signal processing path (504). The first signal processing path may determine a value of a demixing parameter for demixing the mixed audio content (506). The second signal processing path may apply the value of the demixing parameter to the individual audio signals of the set of audio signals (508) to generate the individual source-specific audio signals (510).

IPC Classes  ?

• G10L 15/00 - Speech recognition
• G10L 21/18 - Transformation of speech into a non-audible representation, e.g. speech visualisation or speech processing for tactile aids - Details of the transformation process
• G10L 19/02 - Speech or audio signal analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using spectral analysis, e.g. transform vocoders or subband vocoders
• 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

Abstract

Systems and methods involving obtaining point cloud data from one or more sources corresponding to one or more zones within a real-world space, the point cloud data representing surface features of structures detected within the one or more zones; defining a plurality of first-level nodes based on the point cloud data, individual first-level nodes corresponding to obtained point cloud data corresponding to individual zones of the one or more zones; identifying connections between two or more first-level nodes, the connections between the two or more first-level nodes based on connections between the point cloud data for the zones corresponding to the two or more first-level nodes; and defining a plurality of second-level nodes, individual second-level nodes corresponding to aggregated subsets of first-level nodes for which connections are identified.

IPC Classes  ?

• G01C 21/00 - Navigation; Navigational instruments not provided for in groups
• G01C 21/34 - Route searching; Route guidance

Abstract

A method includes: obtaining a plurality of first signals corresponding to a vehicle and a plurality of second signals corresponding to a plurality of candidate carpool combinations each comprising one or more unassigned transportation orders, wherein: the plurality of first signals comprise a current time, a location of the vehicle at the current time, and one or more static features corresponding to the vehicle, the plurality of second signals comprise timestamps, origins, and destinations of the unassigned transportation orders, and the vehicle has an on-going transportation order at the current time; inputting the plurality of first and second signals to a trained machine learning model; and obtaining, from an output of the trained machine learning model, a utility score of each of the plurality of candidate carpool combinations.

IPC Classes  ?

• G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
• G08G 1/00 - Traffic control systems for road vehicles
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/049 - Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
• G06Q 50/30 - Transportation; Communications

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for managing user intervention for a vehicle are provided. One of the methods includes: receiving an instruction to initiate an intervention session from a server, and providing, in response to receiving the instruction, a user interface associated with the intervention session for display on a terminal associated with the vehicle. The method further includes detecting, at the terminal, a user interaction corresponding to a command associated with operation of the vehicle, and generating a decision associated with the operation of the vehicle based at least in part on the command.

IPC Classes  ?

• B60W 50/10 - Interpretation of driver requests or demands
• B60W 50/14 - Means for informing the driver, warning the driver or prompting a driver intervention
• B60W 50/00 - CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
• G05D 1/00 - Control of position, course, altitude, or attitude of land, water, air, or space vehicles, e.g. automatic pilot
• G05D 1/02 - Control of position or course in two dimensions

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media for carpool dual-pricing in ridesharing are provided. An exemplary method comprises: obtaining a machine-learning model trained to generate a probability for a carpool request from a rider to convert to a trip based at least on a pair of price adjustment multipliers applied to the carpool request; determining one or more Key Performance Indicator (KPI) models based on the machine-learning model; constructing an optimization model based on the KPI models, the optimization model comprising a plurality of pairs of price adjustment multipliers as decision variables; and determining optimal values of the plurality of pairs of price adjustment multipliers for the plurality of pricing units by solving the optimization model based on a plurality of historical carpool requests.

IPC Classes  ?

• G06Q 10/02 - Reservations, e.g. for tickets, services or events
• G06N 7/00 - Computing arrangements based on specific mathematical models
• G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• G06Q 50/30 - Transportation; Communications
• G06N 20/00 - Machine learning
• G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
• G06Q 30/08 - Auctions

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media for carpool dual-pricing in ridesharing are provided. An exemplary method comprises: determining an expected trip count based on a plurality of carpool requests in a pricing unit and a pair of price adjustment multipliers applied to the pricing unit; for each of the plurality of carpool requests, generating a carpool matching probability of the carpool request by a second machine learning model based on the first expected trip count; constructing one or more Key Performance Indicator (KPI) models based on the plurality of carpool matching probabilities and the pair of price adjustment multipliers; and determining optimal values of the pair of price adjustment multipliers based on an optimization model maximizing an aggregated value of the one or more KPI models.

IPC Classes  ?

• G06Q 30/0283 - Price estimation or determination
• G06Q 30/0201 - Market modelling; Market analysis; Collecting market data
• G06Q 10/02 - Reservations, e.g. for tickets, services or events
• G06Q 10/0639 - Performance analysis of employees; Performance analysis of enterprise or organisation operations
• G06Q 50/30 - Transportation; Communications
• G06N 20/00 - Machine learning
• G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
• G06Q 30/08 - Auctions
• G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks

Abstract

The present disclosure relates to systems and methods for displaying vehicle information for an on-demand service. The method may include sending a request for on-demand service to a server. The method may further include obtaining information of a vehicle related to the request for on-demand service. The information of the vehicle may include color information of the vehicle. The method may further include generating, by a processor, a user interface based on the information of the vehicle. The user interface may include at least one user interface element corresponding to the color information of the vehicle.

IPC Classes  ?

• G06T 11/00 - 2D [Two Dimensional] image generation
• H04L 67/63 - Routing a service request depending on the request content or context
• G06T 7/90 - Determination of colour characteristics
• H04W 4/44 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
• G01C 21/34 - Route searching; Route guidance
• G06T 11/60 - Editing figures and text; Combining figures or text
• H04L 67/51 - Discovery or management thereof, e.g. service location protocol [SLP] or web services
• G06F 9/451 - Execution arrangements for user interfaces
• G06F 40/221 - Parsing markup language streams
• G06F 3/04817 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance using icons

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for exchange and synchronization of status information of a vehicle. One of the methods includes obtaining, by a terminal, status information of the vehicle from equipment on the vehicle and connected to the terminal, sending, by the terminal, the status information to a server, and receiving, by the terminal and from the server, an updated status identifier associated with the terminal, wherein the updated status identifier is determined based on the status information.

IPC Classes  ?

• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• G06Q 10/02 - Reservations, e.g. for tickets, services or events
• G06Q 50/30 - Transportation; Communications

Abstract

A method may comprise obtaining a machine-learning output generated by a computer system running a trained machine-learning model; obtaining characteristics associated with the generation of the output, the characteristics comprising at least one of an energy term or a power term; determining a precision term for the system based on a comparison of the output with a reference; and determining an overall score of the system based on the precision term and the characteristics.

IPC Classes  ?

• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G06N 20/00 - Machine learning
• G06F 40/58 - Use of machine translation, e.g. for multi-lingual retrieval, for server-side translation for client devices or for real-time translation

Abstract

An exemplary method includes: obtaining, at one or more cloud servers, endpoint data of an endpoint computing device; based on the endpoint data, determining, by the one or more cloud servers, a plurality of script-language rules, wherein: each of the plurality of script-language rules corresponds to an atomic operation of detecting and/or removing at least one rootkit, the at least one rootkit comprises a target rootkit, and the plurality of script-language rules comprise a set of one or more rootkit rules corresponding to the target rootkit; and transmitting, by the one or more cloud servers to the endpoint computing device, the plurality of script-language rules, wherein the set of rootkit rules is executable at the endpoint computing device to detect and/or remove the target rootkit by, for each of the set of rootkit rules, executing a corresponding atomic operation.

IPC Classes  ?

• G06F 21/00 - Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
• G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
• G06F 9/52 - Program synchronisation; Mutual exclusion, e.g. by means of semaphores
• H04L 12/40 - Bus networks

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining a plurality of context-based subscription products in a ride-hailing platform are described. An exemplary method may include: determining a plurality of transportation contexts to which a plurality of context-based subscription products are applicable; obtaining a plurality of historical trip-requesting sessions between a plurality of riders and the ride-hailing platform, and a plurality of historical subscription products offered to the plurality of riders; constructing at least one key performance indicator (KPI) model that predicts a reward for the ride-hailing platform offering the plurality of context-based subscription products to the plurality of riders; constructing an optimization model to maximize a weighted sum of the at least one KPI model; and determining optimal values of the plurality of parameter vectors corresponding to the plurality of context-based subscription products.

IPC Classes  ?

• G06Q 30/00 - Commerce
• G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
• G06N 20/20 - Ensemble learning
• G08G 1/00 - Traffic control systems for road vehicles
• G06Q 10/02 - Reservations, e.g. for tickets, services or events

Abstract

The present application is directed to a method and a system for processing pose data. The method and the system may be applied to a map generation device, the map generation device being coupled to a global positioning system and a pose sensing system, the global positioning system being configured for outputting positioning data, the pose sensing system being configured for outputting motion pose data, and the positioning data and the motion pose data being combined to generate pose estimation data. The method for processing pose data includes: determining, in response to generated positioning data, positioning accuracy information corresponding to the positioning data; determining a degree of confidence of the pose estimation data according to the positioning accuracy information; and generating optimized pose data by processing the pose estimation data according to the degree of the confidence of the pose estimation data.

IPC Classes  ?

• G06T 7/70 - Determining position or orientation of objects or cameras
• G06T 7/20 - Analysis of motion
• G06F 17/16 - Matrix or vector computation
• G06F 16/901 - Indexing; Data structures therefor; Storage structures

Abstract

Incidents may be monitored using a camera in response to an activation of a trigger device. Trigger data may be received over a communication network in response to activation of a trigger device disposed within a first area of a vehicle. A camera disposed within a second area of the vehicle may be controlled over the communication network in response to receiving the trigger data, the controlling causing the camera to capture sensor information of a passenger of the vehicle. Passenger information of the passenger of the vehicle may be obtained in response to receiving the trigger data. An alert notification may be generated. The alert notification may comprise at least a portion of the sensor information of the passenger, and the alert notification may be capable of initiating one or more incident actions associated with the passenger.

IPC Classes  ?

• G06V 20/59 - Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
• H04W 4/48 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for in-vehicle communication
• B60R 25/30 - Detection related to theft or to other events relevant to anti-theft systems
• B60R 25/102 - Fittings or systems for preventing or indicating unauthorised use or theft of vehicles actuating a signalling device a signal being sent to a remote location, e.g. a radio signal being transmitted to a police station, a security company or the owner
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
• H04N 23/65 - Control of camera operation in relation to power supply
• B60R 25/10 - Fittings or systems for preventing or indicating unauthorised use or theft of vehicles actuating a signalling device

Abstract

The present disclosure provides a method, an apparatus, an electronic device, and a computer-readable storage medium for distributing orders. The method may include obtaining attribute information of a service provider and order information of associated orders received by the service provider; obtaining a degree of association between the service provider and each of the associated orders by inputting the attribute information and the order information of the associated orders into an order distribution strategy network; and determining a distribution order for the service provider based on the degrees of association. According to the embodiments of the present disclosure, response rates of the service provider to the orders may be improved.

IPC Classes  ?

• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• G06Q 50/30 - Transportation; Communications

Abstract

The present disclosure is related to systems and methods for processing speech dialogue. The method includes obtaining target speech dialogue data. The method includes obtaining a text vector representation sequence, a phonetic symbol vector representation sequence, and a role vector representation sequence by performing a vector transformation on the target speech dialogue data based on a text embedding model, a phonetic symbol embedding model, and a role embedding model, respectively. The method includes determining a representation vector corresponding to the target speech dialogue data by inputting the text vector representation sequence, the phonetic symbol vector representation sequence, and the role vector representation sequence into a trained speech dialogue coding model. The method includes determining a summary of the target speech dialogue data by inputting the representation vector into a classification model.

IPC Classes  ?

• G10L 15/00 - Speech recognition
• G10L 15/06 - Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
• G06F 40/253 - Grammatical analysis; Style critique
• G10L 15/02 - Feature extraction for speech recognition; Selection of recognition unit
• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
• G06F 18/24 - Classification techniques

Abstract

The present disclosure discloses a method, a system, a device, and a computer-readable storage medium for information processing. The method may include: obtaining a help request transmitted by a user terminal, the help request being in response to an event associated with a user; determining a first service provider from a candidate service provider set based on the help request, the first service provider having an ability to arrive at a location where the event happens within a preset time period and handle the event; generating a first help order associated with the help request; and transmitting the first help order to a first terminal associated with the first service provider. The method of the present disclosure may dispatch a rescue order online, thereby improving the accuracy for dispatching the rescue order and the efficiency for rescue. Through dispatching serial rescue orders by obtaining feedback information of the service provider, the rescue order may be dispatched more accurately and a cost for human labor may be reduced.

IPC Classes  ?

• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

The present disclosure relates to an information providing method for an on-demand service. The method may include receiving service request information from a passenger of a passenger terminal device. The service request information may include a departure location of the passenger. The method may further include acquiring historical service request information related to the passenger; and determining travel-route-related information based at least in part on the departure location of the passenger and the historical service request information. Also disclosed is a system for implementing the method.

IPC Classes  ?

• G01C 21/36 - Input/output arrangements for on-board computers
• G06F 16/29 - Geographical information databases
• H04W 4/029 - Location-based management or tracking services
• G06Q 50/30 - Transportation; Communications
• H04W 4/02 - Services making use of location information
• H04W 4/40 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
• H04L 67/52 - Network services specially adapted for the location of the user terminal
• G06Q 30/04 - Billing or invoicing

Abstract

Desired vehicle destinations may be determined from spoken dialogs. A speech input may be received from a user through a voice user interface. Current utterance variables may be obtained by tokenizing the user speech input. One or more of a plurality of utterance templates for a reply to the user speech input may be determined by a trained automatic agent based on the plurality of current utterance variables. One of a plurality of Application Programming Interfaces (API) to call and one or more parameters for the API to call with may be determine by the trained automatic agent based on the plurality of current utterance variables. A response may be obtained from the API call. A context string for the reply to the user speech input by the trained automatic agent may be constructed based on the utterance templates and the response of the API call.

IPC Classes  ?

• G10L 15/06 - Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
• G06F 3/16 - Sound input; Sound output

Abstract

Methods, systems, and apparatus for detecting malicious activities in a ride-hailing platforms are described. An exemplary method comprises: identifying a set of trips from historical data to form training data; training a classifier based on a plurality of features of the set of trips in the training data to identify whether a given trip is malicious or benign; deploying the classifier to classify new trips in the ride-hailing platform for a first period of time to obtain a plurality of malicious trip candidates; storing the plurality of malicious trip candidates in a staging database for a second period of time for data cleansing based on supplementary data collected during the second period of time; fetching, from the staging database, a set of malicious trip candidates that have been stored in the staging database longer than the second period of time; and re-training the classifier.

IPC Classes  ?

• G01C 21/34 - Route searching; Route guidance
• G01C 21/36 - Input/output arrangements for on-board computers
• G06Q 50/30 - Transportation; Communications
• G06N 20/00 - Machine learning
• G06N 3/08 - Learning methods
• G06Q 10/02 - Reservations, e.g. for tickets, services or events
• G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
• G06Q 30/00 - Commerce
• G06F 21/44 - Program or device authentication

Abstract

Embodiments of the disclosure provide systems and methods for performing motion transfer using a learning model. An exemplary system may include a communication interface configured to receive a first image including a first movable object and a second image including a second movable object. The system may also include at least one processor coupled to the communication interface. The at least one processor may be configured to extract a first set of motion features of the first movable object from the first image using a first encoder of the learning model and extract a first set of static features of the second movable object from the second image using a second encoder of the learning model. The at least one processor may also be configured to generate a third image by synthesizing the first set of motion features and the first set of static features.

IPC Classes  ?

• G06T 7/246 - Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
• G06N 20/00 - Machine learning
• G06N 3/045 - Combinations of networks
• G06V 10/40 - Extraction of image or video features
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks

Abstract

The present disclosure relates to systems and methods for carpooling. The systems and methods may include obtaining a service request and a sharing instruction initiated by a first user. The service request may include a first departure location and a first destination. The systems and methods may also include generating an information sharing link based on the service request and the sharing instruction. The systems and methods may further include transmitting the information sharing link to one or more second users. The information sharing link may be associated with an information sharing interface. The information sharing interface may be configured to use the information sharing link and guide at least one of the one or more second users to input a second departure location and a second destination.

IPC Classes  ?

• G06Q 50/30 - Transportation; Communications
• G06Q 50/00 - Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
• G06K 19/06 - Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code
• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• G06F 16/955 - Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
• G01C 21/34 - Route searching; Route guidance

Abstract

Embodiments of the disclosure provide systems and methods for domain adaptation between a plurality of source domains and a target domain. The artificial intelligence method includes receiving labeled data from the plurality of source domains and unlabeled data from the target domain. The method further includes separately training, by a processor, a plurality of source classifiers each corresponding to a source domain using the labeled data received from the respective source domains. The method also includes selecting a subset of the labeled data received from each source domain based on a similarity between the selected labeled data and the unlabeled data of the target domain. The method additionally includes refining, by the processor, each source classifier using the selected subset of the labeled data, and predicting labels of the unlabeled data using the refined source classifiers.

IPC Classes  ?

• G06N 3/02 - Neural networks
• G06N 3/08 - Learning methods
• G06N 20/00 - Machine learning
• G06K 9/62 - Methods or arrangements for recognition using electronic means

Abstract

Systems and methods are disclosed related to generating an interactive user interface that enables a user to move, rotate or otherwise edit 3D point cloud data in virtual 3D space to align or match point clouds captured from LiDAR scans prior to generation of a high definition map. A system may obtain point cloud data for two or more point clouds, render the point clouds for display in a user interface, then receive a user selection of one of the point clouds and commands from the user to move and/or rotate the selected point cloud. The system may adjust the displayed position of the selected point cloud relative to the other simultaneously displayed point cloud(s) in real time in response to the user commands, and store the adjusted point cloud position data for use in generating a new high definition map.

IPC Classes  ?

• G01C 21/36 - Input/output arrangements for on-board computers
• G06F 3/0481 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
• G01S 17/89 - Lidar systems, specially adapted for specific applications for mapping or imaging

Abstract

A method for allocating a service request is provided. The method may include: receiving a service request from a terminal of a service requester, the service request being for a transport service and including a pick-up location and a drop-off location; determining a first service provider to provide the transport service; generating a first service order allocating the service request to the first service provider; receiving a request for navigation associated with the first service order at a second moment; generating route information based on a second location of the first service provider at the second moment and the pick-up location; determining whether to reallocate the service request to a second service provider based on the route information; further generating a second service order reallocating the service request to the second service provider; and transmitting signals to a terminal of the second service provider.

IPC Classes  ?

• G01C 21/34 - Route searching; Route guidance
• G06Q 50/30 - Transportation; Communications
• G08G 1/00 - Traffic control systems for road vehicles

Abstract

Systems and methods are provided for language-based service hailing. Such system may comprise one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the computing system to obtain a plurality of speech samples, each speech sample comprising one or more words spoken in a language, train a neural network model with the speech samples to obtain a trained model for determining languages of speeches, obtain a voice input, identify at least one language corresponding to the voice based at least on applying the trained model to the voice input, and communicate a message in the identified language.

IPC Classes  ?

• G10L 15/16 - Speech classification or search using artificial neural networks
• G10L 15/00 - Speech recognition
• G10L 15/06 - Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice

Abstract

Systems and methods are provided for estimating travel time and distance. Such method may comprise obtaining a vehicle trip dataset comprising an origin, a destination, a time-of-day, a trip time, and a trip distance associated with each of a plurality of trips, and training a neural network model with the vehicle trip dataset to obtain a trained model. The neural network model may comprise a first module and a second module, the first module may comprise a first number of neuron layers, the first module may be configured to obtain the origin and the destination as first inputs to estimate a travel distance, the second module may comprise a second number of neuron layers, and the second module may be configured to obtain the information of a last layer of the first module and the time-of-day as second inputs to estimate a travel time.

IPC Classes  ?

• G01C 21/36 - Input/output arrangements for on-board computers
• G01C 21/34 - Route searching; Route guidance
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
• G06N 3/08 - Learning methods

Abstract

Systems and methods are provided for ride order dispatching. Such method may comprise obtaining information on a location of a vehicle and a time to input into a trained neural network algorithm; and based on a policy generated from the trained neural network algorithm, obtaining action information for the vehicle, the action information comprising: staying at a current position of the vehicle, re-positioning the vehicle, or accepting a ride order.

IPC Classes  ?

• G06Q 50/30 - Transportation; Communications
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/08 - Learning methods
• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

The present disclosure relates to systems and methods for determining a pickup location. The systems may perform the methods to receive a request or an intention for a first transportation service that is allowed to combine with a second transportation service from a terminal association with a service requester, the request or the intention including an origin location of the first transportation service and a destination of the first transportation service; obtain a plurality of candidate locations; for each of the plurality of candidate locations, determine a matching index value of the candidate location based on the origin location or the destination, wherein the matching index value is a probability of becoming a pickup location of the first transportation service; select one or more pickup locations from the plurality of candidate locations based on the matching index value; and transmit the one or more pickup locations to the terminal.

IPC Classes  ?

• G08G 1/00 - Traffic control systems for road vehicles
• G06Q 10/02 - Reservations, e.g. for tickets, services or events

Abstract

The present disclosure relates to a system and a method of allocating orders. The system may include a non-transitory computer readable storage medium and a processor. The non-transitory computer may store an executable module. The processor may execute the executable module stored in the computer readable storage medium. The non-transitory computer readable storage medium may include a receiving unit (231) and an order allocation unit (361). The receiving unit (231) may receive order information and user information. The user information may include location information and/or time information. The order allocation unit (361) may allocate an order based on the location information and/or the time information. The method may include receiving order information and user information, wherein the order information and the user information may include location information and/or time information, and allocating an order based on the location information and/or the time information.

IPC Classes  ?

• G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
• G06Q 50/30 - Transportation; Communications

Abstract

A speech communication system for improving speech intelligibility may comprise one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the system to perform: determining a cutoff frequency based on an estimation of a spectrum of noise, wherein the cutoff frequency defines a noise dominant region of frequency; lifting a spectrum of a speech above the noise dominant region of frequency, wherein a frequency range of the spectrum of the speech increases by the cutoff frequency; and applying an adaptive filter to the speech to achieve echo cancelation, wherein the adaptive filter is controlled by a volume of the noise.

IPC Classes  ?

• G10L 21/0364 - Speech enhancement, e.g. noise reduction or echo cancellation by changing the amplitude for improving intelligibility
• G10L 15/08 - Speech classification or search
• G10L 21/0232 - Processing in the frequency domain

Abstract

The embodiments of the present disclosure may disclose methods and systems for information recommendation. The method for information recommendation method may include: determining a violation count corresponding to a violation behavior in historical driving behavior of a driver; determining, based on the violation behavior and its corresponding violation count, information to be recommended; and sending the information to be recommended to a driver terminal of the driver.

IPC Classes  ?

• B60W 40/09 - Driving style or behaviour
• B60W 50/14 - Means for informing the driver, warning the driver or prompting a driver intervention

Abstract

The present disclosure relates to systems and methods for recalling one or more POIs. The systems and methods may receive a query inputted by a user via a terminal and determine one or more terms by processing the received query. The systems and methods may further label the one or more terms using a trained tagging model. The systems and methods may determine one or more POs based on the labeled one or more terms, and display the one or more terms on the terminal.

IPC Classes  ?

• G06F 16/29 - Geographical information databases
• G06F 16/242 - Query formulation
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G06N 3/04 - Architecture, e.g. interconnection topology

Abstract

In some cases, a verbal harassment detection system may use machine learning models to detect verbal harassment in real-time or near real-time. The system may receive an audio segment comprising a portion of audio captured by a microphone located within a vehicle. Further, the system may convert the audio segment to a text segment. The system may provide at least the text segment to a prediction model associated with verbal harassment detection to obtain a harassment prediction. Further, the system may provide the audio segment to an emotion detector to obtain a detected emotion of a speaking user that made an utterance included in the audio segment. Based at least in part on the harassment prediction and the detected emotion, the system may automatically, and without user intervention, determine whether a user is being harassed.

IPC Classes  ?

• G10L 25/00 - Speech or voice analysis techniques not restricted to a single one of groups
• G10L 25/63 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for estimating an emotional state
• G10L 25/27 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the analysis technique
• B60R 11/02 - Arrangements for holding or mounting articles, not otherwise provided for for radio sets, television sets, telephones, or the like; Arrangement of controls thereof
• G06N 20/00 - Machine learning
• G10L 15/26 - Speech to text systems

Abstract

In some cases, one or more heuristics can be automatically generated using a small dataset of segments previously labeled by one or more domain experts. The generated one or more heuristics along with one or more patterns can be used to assign training labels to a large unlabeled dataset of segments. A subset of segments representing an occurrence of verbal harassment can be selected using the assigned training labels. Randomly selected segments can be used as being indicative of a non-occurrence of verbal harassment. The selected subset of segments and randomly selected segments can be used to train one or more machine learning models for verbal harassment detection.

IPC Classes  ?

• G10L 15/06 - Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
• G06N 20/00 - Machine learning
• G06N 3/08 - Learning methods
• G10L 25/63 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for estimating an emotional state
• G10L 15/26 - Speech to text systems
• G10L 15/08 - Speech classification or search

Abstract

In some cases, lower quality, large scale training data can be automatically generated by automatic labeling. The generated training data can be used to pre-train a machine learning model. For instance, the model can be a model for detection of verbal harassment. Parameters of the pre-trained model can be refined or updated using another one or more higher-quality sets of training data, with which the model can be subsequently trained.

IPC Classes  ?

• G10L 15/06 - Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
• G10L 15/08 - Speech classification or search
• G10L 15/26 - Speech to text systems
• G06N 20/00 - Machine learning
• G06N 3/08 - Learning methods
• G10L 25/63 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for estimating an emotional state

Abstract

Methods, systems, and apparatus for recommending alternative destinations in ride-sharing services are provided. A computing device implementing the method may start with receiving a trip request from a user device. The trip request may include an origin and a destination. Then the computing device classifies the trip request into one of a plurality of trip purpose categories based at least on the origin and the destination of the trip request, the rider's information, and a machine-learning classifier trained to predict the one trip purpose category of the trip request. In response to the one trip purpose category belonging to a preset group of trip purpose categories, the computing device determines one or more alternative destinations for the trip request, and sends to the user device, the one or more alternative destinations.

IPC Classes  ?

• G01C 21/34 - Route searching; Route guidance
• G06Q 50/30 - Transportation; Communications
• G06Q 10/02 - Reservations, e.g. for tickets, services or events
• G06Q 30/0202 - Market predictions or forecasting for commercial activities
• G06Q 30/0282 - Rating or review of business operators or products
• G06Q 30/0283 - Price estimation or determination
• H04W 4/021 - Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for ranking alternative destination recommendations are provided. One of the methods comprises: receiving a trip request comprising an origin and an initial destination; determining one or more alternative destinations based on one or more features of the initial destination; for each of the alternative destinations, determining, based on a machine-learning classifier, a probability for the rider to select a trip option from the origin to the alternative destination, where the machine-learning classifier is trained to accept input comprising one or more attributes of the trip option and generate output comprising the probability for the rider to select the alternative destination; generating a score for each of the alternative destinations based at least on the probability; and ranking, by the computing device of the ridesharing platform, the alternative destinations based on the scores.

IPC Classes  ?

• G01C 21/34 - Route searching; Route guidance
• G01C 21/36 - Input/output arrangements for on-board computers
• G06Q 50/30 - Transportation; Communications
• G06N 20/00 - Machine learning
• G06N 3/08 - Learning methods
• G06Q 10/02 - Reservations, e.g. for tickets, services or events
• G06Q 30/02 - Marketing; Price estimation or determination; Fundraising

Abstract

Embodiments of the disclosure provide a method for configuring indication information for transportation services. The method includes receiving a first transportation request which includes a first passenger pick-up point located in a preset geographical area, and the first transportation request matches a first vehicle. The method also includes receiving a second transportation request which includes a second passenger pick-up point located in the preset geographical area, and the second transportation request matches a second vehicle. The method further includes generating first indication information for the first transportation request and second indication information for the second transportation request based on a time difference between a first estimated time for the first vehicle to arrive in the preset geographical area and a second estimated time for the second vehicle to arrive in the preset geographical area.

IPC Classes  ?

• G06Q 50/30 - Transportation; Communications
• G01C 21/34 - Route searching; Route guidance
• G06Q 10/047 - Optimisation of routes or paths, e.g. travelling salesman problem
• G08G 1/00 - Traffic control systems for road vehicles
• H04W 4/02 - Services making use of location information
• G01C 21/36 - Input/output arrangements for on-board computers

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for decentralized data management are provided. One of the methods includes: instructing, by an operator, a client to obtain data from a data source, wherein the operator is not allowed to directly obtain data from the data source; receiving, by the operator, encrypted data from the client, wherein the encrypted data is generated by the client based on the obtained data from the data source and an encryption key of an authorized data consumer; and storing, by the operator, the encrypted data into a data store for the authorized data consumer to access and decrypt, wherein the operator is not allowed to read the saved encrypted data from the data store.

IPC Classes  ?

• G06F 21/00 - Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
• G06F 21/60 - Protecting data
• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for automatically adjusting strategies. One of the methods includes: determining one or more characteristics of a plurality of complaints, wherein each of the complaints corresponds to an order; classifying the plurality of complaints into a plurality of categories based on the one or more characteristics by using a trained classifier; selecting a category from the plurality of categories based on a number of complaints in the selected category; from a group of strategies each associated with one or more conditions and one or more actions, identifying a candidate strategy causing the complaints of the selected category, wherein the one or more actions are executed in response to the one or more conditions being satisfied; and optimizing the candidate strategy using a reinforcement learning model at least based on a plurality of historical orders.

IPC Classes  ?

• G06F 40/40 - Processing or translation of natural language
• G06F 16/9035 - Filtering based on additional data, e.g. user or group profiles
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G06N 20/00 - Machine learning

Abstract

Dynamic pricing may be applied in an online ride-hailing platform. Information may be obtained. The information may include a set of pricing candidates and an initial status of a ride-hailing platform. The set of pricing candidates may be updated based on the initial status of the ride-hailing platform to minimize a cross-entropy between the set of pricing candidates and a target pricing policy that maximizes a total income of the ride-hailing platform. A price for at least one current trip request on the ride-hailing platform may be generated based on the updated set of pricing candidates.

IPC Classes  ?

• G06Q 30/00 - Commerce
• G06N 3/00 - Computing arrangements based on biological models
• G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/08 - Learning methods

Abstract

Safety geofence zones may be created and deployed for a ride sharing service. At least one set of safety geofence zones may be created which are associated with a ride sharing service. A zone type may be set for each set of safety geofence zones in the at least one set of safety geofence zones. At least one trigger condition may be set for the at least one set of safety geofence zones. The at least one set of safety geofence zones may be deployed.

IPC Classes  ?

• H04W 4/021 - Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• H04W 4/12 - Messaging; Mailboxes; Announcements
• H04W 4/02 - Services making use of location information
• H04W 4/029 - Location-based management or tracking services

Abstract

Dynamic geofence zones may be deployed and updated for a ride sharing service. At least one set of dynamic geofence zones may be created which are associated with a ride sharing service. At least one time period may be set for the at least one set of dynamic geofence zones. The at least one set of dynamic geofence zones may be deployed. The at least one set of dynamic geofence zones may be updated.

IPC Classes  ?

• G06Q 50/30 - Transportation; Communications
• G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
• G06Q 10/02 - Reservations, e.g. for tickets, services or events
• G08G 1/00 - Traffic control systems for road vehicles

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for testing a plurality of variants among a plurality of users. One of the methods includes: determining a spatial granularity to divide an area into a plurality of grid cells; randomly splitting the plurality of grid cells into a plurality of testing groups, wherein a quantity of the plurality of testing groups is determined based on a quantity of the multiple versions of the feature to be tested; determining a temporal granularity to divide a testing period into a plurality of testing intervals; during each of the plurality of testing intervals, respectively assigning the multiple versions of the feature to the plurality of testing groups; and obtaining a treatment effect for each of the multiple versions of the feature and determining an optimal version of the feature.

IPC Classes  ?

• G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
• G06F 9/54 - Interprogram communication
• G06Q 30/0204 - Market segmentation

Abstract

Methods, apparatuses, and storage media storing instructions for scanning electronically-stored files are provided. A file stored in a computer-readable storage medium is scanned. Based on the scanning, a common analysis is performed on the file for two or more software functions. Based on the scanning, a software function-specific analysis is performed on the file for a respective software function. Two or more decisions on the file is made for the two or more software functions based on the common analysis and the software function-specific analysis.

IPC Classes  ?

• G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements

Abstract

Methods, apparatuses, and storage media storing instructions for classifying text documents are provided. A plurality of text documents is obtained. The plurality of text documents is classified into one or more document categories based on a plurality of classification rules. Each of the one or more document categories include one or more first text documents of the plurality of text documents. A second text document of the plurality of text documents is classified based on the plurality of classification rules as belonging to none of the one or more document categories. One or more document fingerprints are generated for respective first text documents in the one or more document categories. The second text document is classified into one of the one or more document categories based on the one or more document fingerprints.

IPC Classes  ?

• G06F 16/13 - File access structures, e.g. distributed indices
• G06V 30/418 - Document matching, e.g. of document images
• G06F 40/126 - Character encoding

Abstract

Incidents may be monitored using a camera in response to an activation of a trigger device. Trigger data may be received over a communication network in response to activation of a trigger device disposed within a first area of a vehicle. A camera disposed within a second area of the vehicle may be controlled over the communication network in response to receiving the trigger data, the controlling causing the camera to capture sensor information of a passenger of the vehicle. Passenger information of the passenger of the vehicle may be obtained in response to receiving the trigger data. An alert notification may be generated. The alert notification may comprise at least a portion of the sensor information of the passenger, and the alert notification may be capable of initiating one or more incident actions associated with the passenger.

IPC Classes  ?

• G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
• H04W 4/48 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for in-vehicle communication
• B60R 25/30 - Detection related to theft or to other events relevant to anti-theft systems
• B60R 25/102 - Fittings or systems for preventing or indicating unauthorised use or theft of vehicles actuating a signalling device a signal being sent to a remote location, e.g. a radio signal being transmitted to a police station, a security company or the owner
• H04N 5/232 - Devices for controlling television cameras, e.g. remote control
• B60R 25/10 - Fittings or systems for preventing or indicating unauthorised use or theft of vehicles actuating a signalling device

Abstract

After determining a trip order has a high risk, verification information unavailable in a passenger information database system can be requested and provided for a passenger of the account. Validity of the verification information can be determined using a verification service and stored in a passenger information database. A decision for the trip order can be determined based on the validity of the verification information stored in the passenger information database.

IPC Classes  ?

• G06Q 50/30 - Transportation; Communications
• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• G06F 16/25 - Integrating or interfacing systems involving database management systems
• H04L 9/40 - Network security protocols

Abstract

Identity and access management may update and remediation may be performed dynamically. Historical user behavior data may be obtained. An account specific baseline based on the historical user behavior data may be generated. An access request may be received from a current user. Current user behavior data associated with a current user may be obtained. The current user behavior data may be compared to the account specific baseline. It may be determined whether the current user behavior data satisfies the account specific baseline. If the current user behavior data satisfies the account specific baseline, an access may be granted to the current user. If the current user behavior data does not satisfy the account specific baseline, access may be denied to the current user.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• G06F 21/31 - User authentication
• G06N 20/00 - Machine learning

Abstract

Incidents may be detected and reported based on changes in sensor data from a device. Trigger data from a trigger device in a vehicle may be received over a communication network. An incident report may be generated based on the trigger data. The incident report may comprise driver information of a driver of the vehicle. A user account of the driver in a ride sharing application may be blocked.

IPC Classes  ?

• H04W 4/16 - Communication-related supplementary services, e.g. call-transfer or call-hold
• G06Q 10/02 - Reservations, e.g. for tickets, services or events
• H04W 4/029 - Location-based management or tracking services
• G06Q 50/30 - Transportation; Communications
• G01C 21/34 - Route searching; Route guidance
• H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
• H04W 4/40 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
• G07C 5/00 - Registering or indicating the working of vehicles

Abstract

A route may be recommended, and it may be determined if a vehicle detours from the recommended route. At least one recommend route may be obtained. A current route of a vehicle may be obtained. A variance value may be determined based on the current route and the at least one recommend route. The variance value may be compared to a threshold. It may be determined whether the variance value exceeds the threshold. If the variance value exceeds the threshold, it may be determined that the vehicle has detoured. If the variance value does not exceed the threshold, it may be determined that the vehicle has not detoured.

IPC Classes  ?

• G01C 21/34 - Route searching; Route guidance

Abstract

The present application discloses systems and methods for performing location-based actions. The methods may include obtaining, by an electronic device, location information associated with the electronic device with respect to a reference location. The methods may further include determining, by the electronic device, whether the location information changes from a first status to a second status. The methods may further include performing, by the electronic device, a predetermined action upon determining that the location information changes from the first status to the second status. The location information may relate to a speed, a direction, an acceleration, a geographic location of the electronic device, and/or a distance between the electronic device and the reference location.

IPC Classes  ?

• H04W 4/00 - Services specially adapted for wireless communication networks; Facilities therefor
• H04W 4/021 - Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• H04W 4/02 - Services making use of location information
• H04M 1/72457 - User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions according to geographic location
• G01C 21/34 - Route searching; Route guidance
• H04W 4/024 - Guidance services
• G01C 21/36 - Input/output arrangements for on-board computers

Abstract

Systems and methods are provided for data security grading. An exemplary method for data security grading, implementable by a computer, may comprise receiving a request to access a query data field, searching for the query data field from a security level table, in response to finding the query data field from the security level table, obtaining from the security level table a security level corresponding to the query data field, and in response to not finding the query data field from the security level table, determining a security level corresponding to the query data field based at least on a lineage tree and the security level table. The lineage tree may trace the query data field to one or more source data fields, and the security data level table may comprise one or more security levels corresponding to the one or more source data fields.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• G06F 16/22 - Indexing; Data structures therefor; Storage structures
• G06F 16/2455 - Query execution