Abrégé

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.

Classes IPC  ?

• G06V 20/59 - Contexte ou environnement de l’image à l’intérieur d’un véhicule, p.ex. concernant l’occupation des sièges, l’état du conducteur ou les conditions de l’éclairage intérieur
• H04W 4/48 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les véhicules, p.ex. communication véhicule-piétons pour la communication dans le véhicule
• B60R 25/30 - Détection relative au vol ou autres événements relatifs aux systèmes antivol
• B60R 25/102 - VÉHICULES, ÉQUIPEMENTS OU PARTIES DE VÉHICULES, NON PRÉVUS AILLEURS Équipements ou systèmes pour interdire ou signaler l’usage non autorisé ou le vol de véhicules actionnant un dispositif d’alarme un signal étant envoyé vers un lieu distant, p.ex. signal radio transmis à un poste de police, à une entreprise de sécurité ou au propriétaire du véhicule
• G06V 40/16 - Visages humains, p.ex. parties du visage, croquis ou expressions
• H04N 23/65 - Commande du fonctionnement de la caméra en fonction de l'alimentation électrique

Abrégé

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.

Classes IPC  ?

• G06Q 10/02 - Réservations, p.ex. pour billetterie, services ou manifestations
• G06Q 50/30 - Transport; Communications
• G10L 17/06 - Techniques de prise de décision; Stratégies d’alignement de motifs
• G06N 20/00 - Apprentissage automatique
• G06F 18/213 - Extraction de caractéristiques, p.ex. en transformant l'espace des caractéristiques; Synthétisations; Mappages, p.ex. procédés de sous-espace
• G06F 18/214 - Génération de motifs d'entraînement; Procédés de Bootstrapping, p.ex. ”bagging” ou ”boosting”

Abrégé

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.

Classes IPC  ?

• G01C 21/34 - Recherche d'itinéraire; Guidage en matière d'itinéraire
• G01C 21/36 - Dispositions d'entrée/sortie pour des calculateurs embarqués

Abrégé

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.

Classes IPC  ?

• G06T 11/00 - Génération d'images bidimensionnelles [2D]
• H04W 4/44 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les véhicules, p.ex. communication véhicule-piétons pour la communication entre véhicules et infrastructures, p.ex. véhicule à nuage ou véhicule à domicile
• G01C 21/34 - Recherche d'itinéraire; Guidage en matière d'itinéraire
• G06T 11/60 - Edition de figures et de texte; Combinaison de figures ou de texte
• H04L 67/51 - Découverte ou gestion de ceux-ci, p.ex. protocole de localisation de service [SLP] ou services du Web
• H04L 67/63 - Ordonnancement ou organisation du service des demandes d'application, p.ex. demandes de transmission de données d'application en utilisant l'analyse et l'optimisation des ressources réseau requises en acheminant une demande de service en fonction du contenu ou du contexte de la demande

Abrégé

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.

Classes IPC  ?

• G01C 21/34 - Recherche d'itinéraire; Guidage en matière d'itinéraire
• G01C 21/00 - Navigation; Instruments de navigation non prévus dans les groupes
• G06Q 50/30 - Transport; Communications

Abrégé

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.

Classes IPC  ?

• G08B 25/01 - Systèmes d'alarme dans lesquels l'emplacement du lieu où existe la condition déclenchant l'alarme est signalé à une station centrale, p.ex. systèmes télégraphiques d'incendie ou de police caractérisés par le moyen de transmission
• H04W 4/029 - Services de gestion ou de suivi basés sur la localisation
• G08G 1/005 - Systèmes de commande du trafic pour véhicules routiers comprenant un indicateur pour guider les piétons
• G08G 1/0968 - Systèmes impliquant la transmission d'indications de navigation au véhicule
• G08G 1/0969 - Systèmes impliquant la transmission d'indications de navigation au véhicule ayant un dispositif de visualisation sous forme de carte
• G08G 1/00 - Systèmes de commande du trafic pour véhicules routiers
• H04W 4/90 - Services pour gérer les situations d’urgence ou dangereuses, p.ex. systèmes d’alerte aux séismes et aux tsunamis
• G08B 25/08 - Systèmes d'alarme dans lesquels l'emplacement du lieu où existe la condition déclenchant l'alarme est signalé à une station centrale, p.ex. systèmes télégraphiques d'incendie ou de police caractérisés par le moyen de transmission utilisant les lignes de communication
• H04W 4/024 - Services d’orientation
• H04W 4/44 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les véhicules, p.ex. communication véhicule-piétons pour la communication entre véhicules et infrastructures, p.ex. véhicule à nuage ou véhicule à domicile
• G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p.ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
• G06F 3/00 - Dispositions d'entrée pour le transfert de données destinées à être traitées sous une forme maniable par le calculateur; Dispositions de sortie pour le transfert de données de l'unité de traitement à l'unité de sortie, p.ex. dispositions d'interface
• G06Q 50/26 - Services gouvernementaux ou services publics
• G08B 21/02 - Alarmes pour assurer la sécurité des personnes
• G08B 25/10 - Systèmes d'alarme dans lesquels l'emplacement du lieu où existe la condition déclenchant l'alarme est signalé à une station centrale, p.ex. systèmes télégraphiques d'incendie ou de police caractérisés par le moyen de transmission utilisant des systèmes de transmission sans fil

Abrégé

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.

Classes IPC  ?

• G01C 21/34 - Recherche d'itinéraire; Guidage en matière d'itinéraire
• G01C 21/36 - Dispositions d'entrée/sortie pour des calculateurs embarqués

Abrégé

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.

Classes IPC  ?

• G06N 3/08 - Méthodes d'apprentissage
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux

Abrégé

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.

Classes IPC  ?

• G01C 21/20 - Instruments pour effectuer des calculs de navigation
• G01C 21/16 - Navigation; Instruments de navigation non prévus dans les groupes en utilisant des mesures de la vitesse ou de l'accélération exécutées à bord de l'objet navigant; Navigation à l'estime en intégrant l'accélération ou la vitesse, c. à d. navigation par inertie
• H04W 4/33 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les environnements intérieurs, p.ex. les bâtiments
• H04W 4/02 - Services utilisant des informations de localisation

Abrégé

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.

Classes IPC  ?

• G06F 40/00 - Maniement de données en langage naturel
• G10L 15/32 - Reconnaisseurs multiples utilisés en séquence ou en parallèle; Systèmes de combinaison de score à cet effet, p.ex. systèmes de vote
• G10L 15/08 - Classement ou recherche de la parole
• G10L 15/06 - Création de gabarits de référence; Entraînement des systèmes de reconnaissance de la parole, p.ex. adaptation aux caractéristiques de la voix du locuteur
• G06F 40/49 - Traduction appuyée sur des données utilisant de très grands corpus, p.ex. le Web
• G06F 40/51 - Traitement ou traduction du langage naturel Évaluation de la traduction
• G06F 40/58 - Utilisation de traduction automatisée, p.ex. pour recherches multilingues, pour fournir aux dispositifs clients une traduction effectuée par le serveur ou pour la traduction en temps réel
• G10L 25/27 - Techniques d'analyses de la parole ou de la voix qui ne se limitent pas à un seul des groupes caractérisées par la technique d’analyse

Abrégé

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.

Classes IPC  ?

• G01C 21/34 - Recherche d'itinéraire; Guidage en matière d'itinéraire
• G06Q 10/02 - Réservations, p.ex. pour billetterie, services ou manifestations

Abrégé

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.

Classes IPC  ?

• G01C 22/00 - Mesure de la distance parcourue sur le sol par des véhicules, des personnes, des animaux ou autres corps solides en mouvement, p.ex. en utilisant des odomètres ou en utilisant des podomètres
• G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques

Abrégé

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.

Classes IPC  ?

• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation

Abrégé

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.

Classes IPC  ?

• G06Q 30/00 - Commerce
• G06Q 10/10 - Bureautique; Gestion du temps
• G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
• G06Q 20/02 - Architectures, schémas ou protocoles de paiement impliquant un tiers neutre, p.ex. une autorité de certification, un notaire ou un tiers de confiance
• G06N 20/00 - Apprentissage automatique
• G06F 21/31 - Authentification de l’utilisateur
• G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil

Abrégé

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.

Classes IPC  ?

• G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
• G06F 16/25 - Systèmes d’intégration ou d’interfaçage impliquant les systèmes de gestion de bases de données
• H04L 9/40 - Protocoles réseaux de sécurité
• G06Q 50/30 - Transport; Communications

Abrégé

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.

Classes IPC  ?

• G06N 20/00 - Apprentissage automatique
• G06F 16/28 - Bases de données caractérisées par leurs modèles, p.ex. des modèles relationnels ou objet

Abrégé

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.

Classes IPC  ?

• G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p.ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
• G06N 5/00 - Agencements informatiques utilisant des modèles fondés sur la connaissance
• G06N 20/20 - Techniques d’ensemble en apprentissage automatique
• G06Q 50/30 - Transport; Communications
• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation

Abrégé

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.

Classes IPC  ?

• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation
• G06Q 10/02 - Réservations, p.ex. pour billetterie, services ou manifestations
• G06N 3/08 - Méthodes d'apprentissage

Abrégé

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.

Classes IPC  ?

• G06V 20/50 - Contexte ou environnement de l’image
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux

Abrégé

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.

Classes IPC  ?

• H04L 12/70 - Systèmes de commutation par paquets
• H04L 67/133 - Protocoles pour les appels de procédure à distance [RPC]
• H04L 7/00 - Dispositions pour synchroniser le récepteur avec l'émetteur
• H04L 67/12 - Protocoles spécialement adaptés aux environnements propriétaires ou de mise en réseau pour un usage spécial, p.ex. les réseaux médicaux, les réseaux de capteurs, les réseaux dans les véhicules ou les réseaux de mesure à distance

Abrégé

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.

Classes IPC  ?

• G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
• G06V 10/22 - Prétraitement de l’image par la sélection d’une région spécifique contenant ou référençant une forme; Localisation ou traitement de régions spécifiques visant à guider la détection ou la reconnaissance
• G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
• G06V 10/74 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
• G06V 20/70 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène Étiquetage du contenu de scène, p.ex. en tirant des représentations syntaxiques ou sémantiques
• G06T 7/10 - Découpage; Détection de bords
• G06V 10/52 - Analyse spatiale multi-échelle, p.ex. analyse par ondelettes
• G01S 17/89 - Systèmes lidar, spécialement adaptés pour des applications spécifiques pour la cartographie ou l'imagerie

Abrégé

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.

Classes IPC  ?

• G06F 17/16 - Calcul de matrice ou de vecteur
• G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p.ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
• G06N 5/04 - Modèles d’inférence ou de raisonnement
• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• G01C 21/34 - Recherche d'itinéraire; Guidage en matière d'itinéraire
• G01C 21/36 - Dispositions d'entrée/sortie pour des calculateurs embarqués

Abrégé

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.

Classes IPC  ?

• G08G 1/00 - Systèmes de commande du trafic pour véhicules routiers
• G06N 3/02 - Réseaux neuronaux
• G06F 9/30 - Dispositions pour exécuter des instructions machines, p.ex. décodage d'instructions
• G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques

Abrégé

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.

Classes IPC  ?

• H04B 3/20 - Systèmes à ligne de transmission - Détails ouverture ou fermeture de la voie d'émission; Commande de la transmission dans une direction ou l'autre
• G10K 11/178 - Procédés ou dispositifs de protection contre le bruit ou les autres ondes acoustiques ou pour amortir ceux-ci, en général utilisant des effets d'interférence; Masquage du son par régénération électro-acoustique en opposition de phase des ondes acoustiques originales
• G06N 3/04 - Architecture, p.ex. topologie d'interconnexion
• G06N 3/08 - Méthodes d'apprentissage
• H03G 3/00 - Commande de gain dans les amplificateurs ou les changeurs de fréquence

Abrégé

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.

Classes IPC  ?

• G08G 1/00 - Systèmes de commande du trafic pour véhicules routiers
• G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
• H04L 29/08 - Procédure de commande de la transmission, p.ex. procédure de commande du niveau de la liaison
• G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques

Abrégé

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.

Classes IPC  ?

• G06N 3/08 - Méthodes d'apprentissage
• G06Q 50/30 - Transport; Communications
• G06Q 30/0211 - Détermination de l’efficacité des remises ou des incitations
• G06Q 30/0208 - Commerce ou échange de biens ou de services contre des incitations ou des récompenses
• G06Q 30/0207 - Remises ou incitations, p.ex. coupons ou rabais

Abrégé

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

Classes IPC  ?

• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation
• G06Q 50/30 - Transport; Communications
• G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"

Abrégé

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.

Classes IPC  ?

• G06T 7/00 - Analyse d'image
• G06V 10/422 - Extraction de caractéristiques globales par l’analyse du motif entier, p.ex. utilisant des transformations dans le domaine de fréquence ou d’autocorrélation pour représenter la structure du motif ou de la forme d’un objet à cet effet
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux

Abrégé

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.

Classes IPC  ?

• H04W 4/021 - Services concernant des domaines particuliers, p.ex. services de points d’intérêt, services sur place ou géorepères
• G01C 21/00 - Navigation; Instruments de navigation non prévus dans les groupes
• G06F 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur
• H04W 4/02 - Services utilisant des informations de localisation

Abrégé

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.

Classes IPC  ?

• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation

Abrégé

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.

Classes IPC  ?

• G06T 7/30 - Détermination des paramètres de transformation pour l'alignement des images, c. à d. recalage des images
• G06T 7/10 - Découpage; Détection de bords
• G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras

Abrégé

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.

Classes IPC  ?

• G06V 20/10 - Scènes terrestres
• G06V 10/26 - Segmentation de formes dans le champ d’image; Découpage ou fusion d’éléments d’image visant à établir la région de motif, p.ex. techniques de regroupement; Détection d’occlusion
• G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
• G06V 10/20 - Prétraitement de l’image
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux

Abrégé

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.

Classes IPC  ?

• G01C 21/34 - Recherche d'itinéraire; Guidage en matière d'itinéraire
• B60W 40/09 - Style ou comportement de conduite

Abrégé

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.

Classes IPC  ?

• G06N 20/20 - Techniques d’ensemble en apprentissage automatique
• G06N 3/08 - Méthodes d'apprentissage
• G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques

Abrégé

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.

Classes IPC  ?

• G06Q 30/02 - Marketing; Estimation ou détermination des prix; Collecte de fonds
• G06Q 50/30 - Transport; Communications
• G01C 21/34 - Recherche d'itinéraire; Guidage en matière d'itinéraire

Abrégé

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.

Classes IPC  ?

• H04L 9/40 - Protocoles réseaux de sécurité
• G06F 16/9535 - Adaptation de la recherche basée sur les profils des utilisateurs et la personnalisation
• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• G10L 15/06 - Création de gabarits de référence; Entraînement des systèmes de reconnaissance de la parole, p.ex. adaptation aux caractéristiques de la voix du locuteur
• G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p.ex. dialogue homme-machine 
• G06F 3/16 - Entrée acoustique; Sortie acoustique
• G10L 15/01 - Estimation ou évaluation des systèmes de reconnaissance de la parole
• G06F 40/56 - Génération de langage naturel
• G06N 20/00 - Apprentissage automatique
• G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur

Abrégé

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.

Classes IPC  ?

• G06Q 30/00 - Commerce
• G06Q 30/02 - Marketing; Estimation ou détermination des prix; Collecte de fonds
• G06N 3/06 - Réalisation physique, c. à d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone
• H04W 4/021 - Services concernant des domaines particuliers, p.ex. services de points d’intérêt, services sur place ou géorepères
• H04W 4/02 - Services utilisant des informations de localisation
• G07C 5/08 - Enregistrement ou indication de données de marche autres que le temps de circulation, de fonctionnement, d'arrêt ou d'attente, avec ou sans enregistrement des temps de circulation, de fonctionnement, d'arrêt ou d'attente
• G01C 21/36 - Dispositions d'entrée/sortie pour des calculateurs embarqués

Abrégé

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.

Classes IPC  ?

• G01C 21/34 - Recherche d'itinéraire; Guidage en matière d'itinéraire
• G06N 3/08 - Méthodes d'apprentissage

Abrégé

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.

Classes IPC  ?

• G06Q 30/00 - Commerce
• G06Q 30/02 - Marketing; Estimation ou détermination des prix; Collecte de fonds
• G06Q 50/30 - Transport; Communications
• G06Q 10/02 - Réservations, p.ex. pour billetterie, services ou manifestations

Abrégé

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.

Classes IPC  ?

• G08G 1/081 - Commande des signaux de trafic plusieurs carrefours dépendant d'une commande commune
• G08G 1/01 - Détection du mouvement du trafic pour le comptage ou la commande
• G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
• G06N 3/08 - Méthodes d'apprentissage

Abrégé

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.

Classes IPC  ?

• G06T 7/10 - Découpage; Détection de bords
• G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
• G06N 3/08 - Méthodes d'apprentissage

Abrégé

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.

Classes IPC  ?

• G06F 9/48 - Lancement de programmes; Commutation de programmes, p.ex. par interruption
• G06N 20/00 - Apprentissage automatique
• G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
• G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p.ex. dialogue homme-machine 
• G06F 9/38 - Exécution simultanée d'instructions
• G06F 9/54 - Communication interprogramme

Abrégé

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.

Classes IPC  ?

• G06Q 50/30 - Transport; Communications
• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation
• G06Q 30/02 - Marketing; Estimation ou détermination des prix; Collecte de fonds
• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• G06Q 30/00 - Commerce
• G06Q 10/10 - Bureautique; Gestion du temps
• G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
• G06Q 20/02 - Architectures, schémas ou protocoles de paiement impliquant un tiers neutre, p.ex. une autorité de certification, un notaire ou un tiers de confiance
• G06N 20/00 - Apprentissage automatique
• G06F 21/31 - Authentification de l’utilisateur
• G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
• G06Q 50/26 - Services gouvernementaux ou services publics
• G06Q 40/02 - Opérations bancaires, p.ex. calcul d'intérêts ou tenue de compte

Abrégé

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).

Classes IPC  ?

• G10L 15/00 - Reconnaissance de la parole
• G10L 21/18 - Transformation de la parole en une représentation non audible, p.ex. visualisation de la parole ou traitement de la parole pour les aides tactiles - Détails du procédé de transformation
• G10L 19/02 - Techniques d'analyse ou de synthèse de la parole ou des signaux audio pour la réduction de la redondance, p.ex. dans les vocodeurs; Codage ou décodage de la parole ou des signaux audio utilisant les modèles source-filtre ou l’analyse psychoacoustique utilisant l'analyse spectrale, p.ex. vocodeurs à transformée ou vocodeurs à sous-bandes
• G10L 25/18 - Techniques d'analyses de la parole ou de la voix qui ne se limitent pas à un seul des groupes caractérisées par le type de paramètres extraits les paramètres extraits étant l’information spectrale de chaque sous-bande

Abrégé

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.

Classes IPC  ?

• G01C 21/00 - Navigation; Instruments de navigation non prévus dans les groupes
• G01C 21/34 - Recherche d'itinéraire; Guidage en matière d'itinéraire

Abrégé

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.

Classes IPC  ?

• G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
• G08G 1/00 - Systèmes de commande du trafic pour véhicules routiers
• G06N 3/04 - Architecture, p.ex. topologie d'interconnexion
• G06N 3/049 - Réseaux neuronaux temporels, p.ex. éléments à retard, neurones oscillants ou entrées impulsionnelles
• G06Q 50/30 - Transport; Communications

Abrégé

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.

Classes IPC  ?

• G06Q 10/02 - Réservations, p.ex. pour billetterie, services ou manifestations
• G06N 7/00 - Agencements informatiques fondés sur des modèles mathématiques spécifiques
• G06Q 30/02 - Marketing; Estimation ou détermination des prix; Collecte de fonds
• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation
• G06Q 50/30 - Transport; Communications
• G06N 20/00 - Apprentissage automatique
• G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
• G06Q 30/08 - Ventes aux enchères

Abrégé

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.

Classes IPC  ?

• G06Q 30/0283 - Estimation ou détermination de prix
• G06Q 30/0201 - Modélisation du marché; Analyse du marché; Collecte de données du marché
• G06Q 10/02 - Réservations, p.ex. pour billetterie, services ou manifestations
• G06Q 10/0639 - Analyse des performances des employés; Analyse des performances des opérations d’une entreprise ou d’une organisation
• G06Q 50/30 - Transport; Communications
• G06N 20/00 - Apprentissage automatique
• G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
• G06Q 30/08 - Ventes aux enchères
• G06N 7/01 - Modèles graphiques probabilistes, p.ex. réseaux probabilistes

Abrégé

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.

Classes IPC  ?

• B60W 50/10 - Interprétation des requêtes ou demandes du conducteur
• B60W 50/14 - Moyens d'information du conducteur, pour l'avertir ou provoquer son intervention
• B60W 50/00 - COMMANDE CONJUGUÉE DE PLUSIEURS SOUS-ENSEMBLES D'UN VÉHICULE, DE FONCTION OU DE TYPE DIFFÉRENTS; SYSTÈMES DE COMMANDE SPÉCIALEMENT ADAPTÉS AUX VÉHICULES HYBRIDES; SYSTÈMES D'AIDE À LA CONDUITE DE VÉHICULES ROUTIERS, NON LIÉS À LA COMMANDE D'UN SOUS-ENSEMBLE PARTICULIER - Détails des systèmes d'aide à la conduite des véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier
• G05D 1/00 - Commande de la position, du cap, de l'altitude ou de l'attitude des véhicules terrestres, aquatiques, aériens ou spatiaux, p.ex. pilote automatique
• G05D 1/02 - Commande de la position ou du cap par référence à un système à deux dimensions

Abrégé

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.

Classes IPC  ?

• G06T 11/00 - Génération d'images bidimensionnelles [2D]
• H04L 67/63 - Ordonnancement ou organisation du service des demandes d'application, p.ex. demandes de transmission de données d'application en utilisant l'analyse et l'optimisation des ressources réseau requises en acheminant une demande de service en fonction du contenu ou du contexte de la demande
• G06T 7/90 - Détermination de caractéristiques de couleur
• H04W 4/44 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les véhicules, p.ex. communication véhicule-piétons pour la communication entre véhicules et infrastructures, p.ex. véhicule à nuage ou véhicule à domicile
• G01C 21/34 - Recherche d'itinéraire; Guidage en matière d'itinéraire
• G06T 11/60 - Edition de figures et de texte; Combinaison de figures ou de texte
• H04L 67/51 - Découverte ou gestion de ceux-ci, p.ex. protocole de localisation de service [SLP] ou services du Web
• G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
• G06F 40/221 - Analyse syntaxique de flux de langages de balisage
• G06F 3/04817 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] fondées sur des propriétés spécifiques de l’objet d’interaction affiché ou sur un environnement basé sur les métaphores, p.ex. interaction avec des éléments du bureau telles les fenêtres ou les icônes, ou avec l’aide d’un curseur changeant de comport utilisant des icônes

Abrégé

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.

Classes IPC  ?

• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation
• G06Q 10/02 - Réservations, p.ex. pour billetterie, services ou manifestations
• G06Q 50/30 - Transport; Communications

Abrégé

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.

Classes IPC  ?

• G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
• G06N 20/00 - Apprentissage automatique
• G06F 40/58 - Utilisation de traduction automatisée, p.ex. pour recherches multilingues, pour fournir aux dispositifs clients une traduction effectuée par le serveur ou pour la traduction en temps réel

Abrégé

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.

Classes IPC  ?

• G06F 21/00 - Dispositions de sécurité pour protéger les calculateurs, leurs composants, les programmes ou les données contre une activité non autorisée
• G06F 21/56 - Détection ou gestion de programmes malveillants, p.ex. dispositions anti-virus
• G06F 9/52 - Synchronisation de programmes; Exclusion mutuelle, p.ex. au moyen de sémaphores
• H04L 12/40 - Réseaux à ligne bus

Abrégé

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.

Classes IPC  ?

• G06Q 30/00 - Commerce
• G06Q 30/02 - Marketing; Estimation ou détermination des prix; Collecte de fonds
• G06N 20/20 - Techniques d’ensemble en apprentissage automatique
• G08G 1/00 - Systèmes de commande du trafic pour véhicules routiers
• G06Q 10/02 - Réservations, p.ex. pour billetterie, services ou manifestations

Abrégé

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.

Classes IPC  ?

• G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
• G06T 7/20 - Analyse du mouvement
• G06F 17/16 - Calcul de matrice ou de vecteur
• G06F 16/901 - Indexation; Structures de données à cet effet; Structures de stockage

Abrégé

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.

Classes IPC  ?

• G06V 20/59 - Contexte ou environnement de l’image à l’intérieur d’un véhicule, p.ex. concernant l’occupation des sièges, l’état du conducteur ou les conditions de l’éclairage intérieur
• H04W 4/48 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les véhicules, p.ex. communication véhicule-piétons pour la communication dans le véhicule
• B60R 25/30 - Détection relative au vol ou autres événements relatifs aux systèmes antivol
• B60R 25/102 - VÉHICULES, ÉQUIPEMENTS OU PARTIES DE VÉHICULES, NON PRÉVUS AILLEURS Équipements ou systèmes pour interdire ou signaler l’usage non autorisé ou le vol de véhicules actionnant un dispositif d’alarme un signal étant envoyé vers un lieu distant, p.ex. signal radio transmis à un poste de police, à une entreprise de sécurité ou au propriétaire du véhicule
• G06V 40/16 - Visages humains, p.ex. parties du visage, croquis ou expressions
• H04N 23/65 - Commande du fonctionnement de la caméra en fonction de l'alimentation électrique
• B60R 25/10 - VÉHICULES, ÉQUIPEMENTS OU PARTIES DE VÉHICULES, NON PRÉVUS AILLEURS Équipements ou systèmes pour interdire ou signaler l’usage non autorisé ou le vol de véhicules actionnant un dispositif d’alarme

Abrégé

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.

Classes IPC  ?

• G10L 15/00 - Reconnaissance de la parole
• G10L 15/06 - Création de gabarits de référence; Entraînement des systèmes de reconnaissance de la parole, p.ex. adaptation aux caractéristiques de la voix du locuteur
• G06F 40/253 - Analyse grammaticale; Corrigé du style
• G10L 15/02 - Extraction de caractéristiques pour la reconnaissance de la parole; Sélection d'unités de reconnaissance 
• G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p.ex. dialogue homme-machine 
• G06F 18/24 - Techniques de classification

Abrégé

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.

Classes IPC  ?

• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation
• G06Q 50/30 - Transport; Communications

Abrégé

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.

Classes IPC  ?

• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation

Abrégé

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.

Classes IPC  ?

• G10L 15/06 - Création de gabarits de référence; Entraînement des systèmes de reconnaissance de la parole, p.ex. adaptation aux caractéristiques de la voix du locuteur
• G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p.ex. dialogue homme-machine 
• G06F 3/16 - Entrée acoustique; Sortie acoustique

Abrégé

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.

Classes IPC  ?

• G01C 21/36 - Dispositions d'entrée/sortie pour des calculateurs embarqués
• G06F 16/29 - Bases de données d’informations géographiques
• H04W 4/029 - Services de gestion ou de suivi basés sur la localisation
• G06Q 50/30 - Transport; Communications
• H04W 4/02 - Services utilisant des informations de localisation
• H04W 4/40 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les véhicules, p.ex. communication véhicule-piétons
• H04L 67/52 - Services réseau spécialement adaptés à l'emplacement du terminal utilisateur
• G06Q 30/04 - Facturation

Abrégé

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.

Classes IPC  ?

• G01C 21/34 - Recherche d'itinéraire; Guidage en matière d'itinéraire
• G01C 21/36 - Dispositions d'entrée/sortie pour des calculateurs embarqués
• G06Q 50/30 - Transport; Communications
• G06N 20/00 - Apprentissage automatique
• G06N 3/08 - Méthodes d'apprentissage
• G06Q 10/02 - Réservations, p.ex. pour billetterie, services ou manifestations
• G06Q 30/02 - Marketing; Estimation ou détermination des prix; Collecte de fonds
• G06Q 30/00 - Commerce
• G06F 21/44 - Authentification de programme ou de dispositif

Abrégé

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.

Classes IPC  ?

• G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p.ex. le suivi des coins ou des segments
• G06N 20/00 - Apprentissage automatique
• G06N 3/045 - Combinaisons de réseaux
• G06V 10/40 - Extraction de caractéristiques d’images ou de vidéos
• G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux

Abrégé

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.

Classes IPC  ?

• G06Q 50/30 - Transport; Communications
• G06Q 50/00 - Systèmes ou procédés spécialement adaptés à un secteur particulier d’activité économique, p.ex. aux services d’utilité publique ou au tourisme
• G06K 19/06 - Supports d'enregistrement pour utilisation avec des machines et avec au moins une partie prévue pour supporter des marques numériques caractérisés par le genre de marque numérique, p.ex. forme, nature, code
• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation
• G06F 16/955 - Recherche dans le Web utilisant des identifiants d’information, p.ex. des localisateurs uniformisés de ressources [uniform resource locators - URL]
• G01C 21/34 - Recherche d'itinéraire; Guidage en matière d'itinéraire

Abrégé

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.

Classes IPC  ?

• G06N 3/02 - Réseaux neuronaux
• G06N 3/08 - Méthodes d'apprentissage
• G06N 20/00 - Apprentissage automatique
• G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques

Abrégé

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.

Classes IPC  ?

• G01C 21/36 - Dispositions d'entrée/sortie pour des calculateurs embarqués
• G06F 3/0481 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] fondées sur des propriétés spécifiques de l’objet d’interaction affiché ou sur un environnement basé sur les métaphores, p.ex. interaction avec des éléments du bureau telles les fenêtres ou les icônes, ou avec l’aide d’un curseur changeant de comport
• G01S 17/89 - Systèmes lidar, spécialement adaptés pour des applications spécifiques pour la cartographie ou l'imagerie

Abrégé

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.

Classes IPC  ?

• G01C 21/34 - Recherche d'itinéraire; Guidage en matière d'itinéraire
• G06Q 50/30 - Transport; Communications
• G08G 1/00 - Systèmes de commande du trafic pour véhicules routiers

Abrégé

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.

Classes IPC  ?

• G10L 15/16 - Classement ou recherche de la parole utilisant des réseaux neuronaux artificiels
• G10L 15/00 - Reconnaissance de la parole
• G10L 15/06 - Création de gabarits de référence; Entraînement des systèmes de reconnaissance de la parole, p.ex. adaptation aux caractéristiques de la voix du locuteur

Abrégé

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.

Classes IPC  ?

• G01C 21/36 - Dispositions d'entrée/sortie pour des calculateurs embarqués
• G01C 21/34 - Recherche d'itinéraire; Guidage en matière d'itinéraire
• G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
• G06N 3/04 - Architecture, p.ex. topologie d'interconnexion
• G06N 3/063 - Réalisation physique, c. à d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
• G06N 3/08 - Méthodes d'apprentissage

Abrégé

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.

Classes IPC  ?

• G06Q 50/30 - Transport; Communications
• G06N 3/04 - Architecture, p.ex. topologie d'interconnexion
• G06N 3/08 - Méthodes d'apprentissage
• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation

Abrégé

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.

Classes IPC  ?

• G08G 1/00 - Systèmes de commande du trafic pour véhicules routiers
• G06Q 10/02 - Réservations, p.ex. pour billetterie, services ou manifestations

Abrégé

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.

Classes IPC  ?

• G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
• G06Q 50/30 - Transport; Communications

Abrégé

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.

Classes IPC  ?

• G10L 21/0364 - Amélioration de l'intelligibilité de la parole, p.ex. réduction de bruit ou annulation d'écho en changeant l’amplitude pour améliorer l'intelligibilité
• G10L 15/08 - Classement ou recherche de la parole
• G10L 21/0232 - Traitement dans le domaine fréquentiel

Abrégé

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.

Classes IPC  ?

• B60W 40/09 - Style ou comportement de conduite
• B60W 50/14 - Moyens d'information du conducteur, pour l'avertir ou provoquer son intervention

Abrégé

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.

Classes IPC  ?

• G06F 16/29 - Bases de données d’informations géographiques
• G06F 16/242 - Formulation des requêtes
• G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
• G06N 3/04 - Architecture, p.ex. topologie d'interconnexion

Abrégé

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.

Classes IPC  ?

• G01C 21/34 - Recherche d'itinéraire; Guidage en matière d'itinéraire
• G06Q 50/30 - Transport; Communications
• G06Q 10/02 - Réservations, p.ex. pour billetterie, services ou manifestations
• G06Q 30/0202 - Prédictions ou prévisions du marché pour les activités commerciales
• G06Q 30/0282 - Notation ou évaluation d’opérateurs commerciaux ou de produits
• G06Q 30/0283 - Estimation ou détermination de prix
• H04W 4/021 - Services concernant des domaines particuliers, p.ex. services de points d’intérêt, services sur place ou géorepères

Abrégé

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.

Classes IPC  ?

• G01C 21/34 - Recherche d'itinéraire; Guidage en matière d'itinéraire
• G01C 21/36 - Dispositions d'entrée/sortie pour des calculateurs embarqués
• G06Q 50/30 - Transport; Communications
• G06N 20/00 - Apprentissage automatique
• G06N 3/08 - Méthodes d'apprentissage
• G06Q 10/02 - Réservations, p.ex. pour billetterie, services ou manifestations
• G06Q 30/02 - Marketing; Estimation ou détermination des prix; Collecte de fonds

Abrégé

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.

Classes IPC  ?

• G10L 25/00 - Techniques d'analyses de la parole ou de la voix qui ne se limitent pas à un seul des groupes
• G10L 25/63 - Techniques d'analyses de la parole ou de la voix qui ne se limitent pas à un seul des groupes spécialement adaptées pour un usage particulier pour comparaison ou différentiation pour estimer un état émotionnel
• G10L 25/27 - Techniques d'analyses de la parole ou de la voix qui ne se limitent pas à un seul des groupes caractérisées par la technique d’analyse
• B60R 11/02 - Autres aménagements pour tenir ou monter des objets pour postes radio, de télévision, téléphones, ou objets similaires; Disposition de leur commande
• G06N 20/00 - Apprentissage automatique
• G10L 15/26 - Systèmes de synthèse de texte à partir de la parole

Abrégé

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.

Classes IPC  ?

• G10L 15/06 - Création de gabarits de référence; Entraînement des systèmes de reconnaissance de la parole, p.ex. adaptation aux caractéristiques de la voix du locuteur
• G06N 20/00 - Apprentissage automatique
• G06N 3/08 - Méthodes d'apprentissage
• G10L 25/63 - Techniques d'analyses de la parole ou de la voix qui ne se limitent pas à un seul des groupes spécialement adaptées pour un usage particulier pour comparaison ou différentiation pour estimer un état émotionnel
• G10L 15/26 - Systèmes de synthèse de texte à partir de la parole
• G10L 15/08 - Classement ou recherche de la parole

Abrégé

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.

Classes IPC  ?

• G10L 15/06 - Création de gabarits de référence; Entraînement des systèmes de reconnaissance de la parole, p.ex. adaptation aux caractéristiques de la voix du locuteur
• G10L 15/08 - Classement ou recherche de la parole
• G10L 15/26 - Systèmes de synthèse de texte à partir de la parole
• G06N 20/00 - Apprentissage automatique
• G06N 3/08 - Méthodes d'apprentissage
• G10L 25/63 - Techniques d'analyses de la parole ou de la voix qui ne se limitent pas à un seul des groupes spécialement adaptées pour un usage particulier pour comparaison ou différentiation pour estimer un état émotionnel

Abrégé

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.

Classes IPC  ?

• G06F 21/00 - Dispositions de sécurité pour protéger les calculateurs, leurs composants, les programmes ou les données contre une activité non autorisée
• G06F 21/60 - Protection de données
• G06F 21/62 - Protection de l’accès à des données via une plate-forme, p.ex. par clés ou règles de contrôle de l’accès

Abrégé

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.

Classes IPC  ?

• G06F 40/40 - Traitement ou traduction du langage naturel
• G06F 16/9035 - Filtrage basé sur des données supplémentaires, p.ex. sur des profils d'utilisateurs ou de groupes
• G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• G06Q 30/00 - Commerce
• G06N 3/00 - Agencements informatiques fondés sur des modèles biologiques
• G06Q 30/02 - Marketing; Estimation ou détermination des prix; Collecte de fonds
• G06N 3/04 - Architecture, p.ex. topologie d'interconnexion
• G06N 3/08 - Méthodes d'apprentissage

Abrégé

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.

Classes IPC  ?

• H04W 4/021 - Services concernant des domaines particuliers, p.ex. services de points d’intérêt, services sur place ou géorepères
• H04W 4/12 - Messagerie; Boîtes aux lettres; Annonces
• H04W 4/02 - Services utilisant des informations de localisation
• H04W 4/029 - Services de gestion ou de suivi basés sur la localisation

Abrégé

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.

Classes IPC  ?

• G06Q 50/30 - Transport; Communications
• G06Q 30/02 - Marketing; Estimation ou détermination des prix; Collecte de fonds
• G06Q 10/02 - Réservations, p.ex. pour billetterie, services ou manifestations
• G08G 1/00 - Systèmes de commande du trafic pour véhicules routiers

Abrégé

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.

Classes IPC  ?

• G06Q 50/30 - Transport; Communications
• G01C 21/34 - Recherche d'itinéraire; Guidage en matière d'itinéraire
• G06Q 10/047 - Optimisation des itinéraires ou des chemins, p. ex. problème du voyageur de commerce
• G08G 1/00 - Systèmes de commande du trafic pour véhicules routiers
• H04W 4/02 - Services utilisant des informations de localisation
• G01C 21/36 - Dispositions d'entrée/sortie pour des calculateurs embarqués

Abrégé

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.

Classes IPC  ?

• G06Q 30/02 - Marketing; Estimation ou détermination des prix; Collecte de fonds
• G06F 9/54 - Communication interprogramme
• G06Q 30/0204 - Segmentation du marché

Abrégé

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.

Classes IPC  ?

• G06F 21/56 - Détection ou gestion de programmes malveillants, p.ex. dispositions anti-virus

Abrégé

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.

Classes IPC  ?

• G06F 16/13 - Structures d’accès aux fichiers, p.ex. indices distribués
• G06V 30/418 - Appariement de documents, p.ex. d’images de documents
• G06F 40/126 - Encodage de caractères

Abrégé

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.

Classes IPC  ?

• G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
• H04W 4/48 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les véhicules, p.ex. communication véhicule-piétons pour la communication dans le véhicule
• B60R 25/30 - Détection relative au vol ou autres événements relatifs aux systèmes antivol
• B60R 25/102 - VÉHICULES, ÉQUIPEMENTS OU PARTIES DE VÉHICULES, NON PRÉVUS AILLEURS Équipements ou systèmes pour interdire ou signaler l’usage non autorisé ou le vol de véhicules actionnant un dispositif d’alarme un signal étant envoyé vers un lieu distant, p.ex. signal radio transmis à un poste de police, à une entreprise de sécurité ou au propriétaire du véhicule
• H04N 5/232 - Dispositifs pour la commande des caméras de télévision, p.ex. commande à distance
• B60R 25/10 - VÉHICULES, ÉQUIPEMENTS OU PARTIES DE VÉHICULES, NON PRÉVUS AILLEURS Équipements ou systèmes pour interdire ou signaler l’usage non autorisé ou le vol de véhicules actionnant un dispositif d’alarme

Abrégé

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.

Classes IPC  ?

• G06Q 50/30 - Transport; Communications
• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation
• G06F 16/25 - Systèmes d’intégration ou d’interfaçage impliquant les systèmes de gestion de bases de données
• H04L 9/40 - Protocoles réseaux de sécurité

Abrégé

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.

Classes IPC  ?

• H04L 9/40 - Protocoles réseaux de sécurité
• G06F 21/31 - Authentification de l’utilisateur
• G06N 20/00 - Apprentissage automatique

Abrégé

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.

Classes IPC  ?

• H04W 4/16 - Services supplémentaires liés aux communications, p.ex. transfert ou mise en attente d'appels
• G06Q 10/02 - Réservations, p.ex. pour billetterie, services ou manifestations
• H04W 4/029 - Services de gestion ou de suivi basés sur la localisation
• G06Q 50/30 - Transport; Communications
• G01C 21/34 - Recherche d'itinéraire; Guidage en matière d'itinéraire
• H04L 29/08 - Procédure de commande de la transmission, p.ex. procédure de commande du niveau de la liaison
• H04W 4/40 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les véhicules, p.ex. communication véhicule-piétons
• G07C 5/00 - Enregistrement ou indication du fonctionnement de véhicules

Abrégé

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.

Classes IPC  ?

• G01C 21/34 - Recherche d'itinéraire; Guidage en matière d'itinéraire

Abrégé

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.

Classes IPC  ?

• H04W 4/00 - Services spécialement adaptés aux réseaux de télécommunications sans fil; Leurs installations
• H04W 4/021 - Services concernant des domaines particuliers, p.ex. services de points d’intérêt, services sur place ou géorepères
• H04W 4/02 - Services utilisant des informations de localisation
• H04M 1/72457 - Interfaces utilisateur spécialement adaptées aux téléphones sans fil ou mobiles avec des moyens permettant d’adapter la fonctionnalité du dispositif dans des circonstances spécifiques en s’appuyant sur la localisation géographique
• G01C 21/34 - Recherche d'itinéraire; Guidage en matière d'itinéraire
• H04W 4/024 - Services d’orientation
• G01C 21/36 - Dispositions d'entrée/sortie pour des calculateurs embarqués

Abrégé

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.

Classes IPC  ?

• G06F 21/62 - Protection de l’accès à des données via une plate-forme, p.ex. par clés ou règles de contrôle de l’accès
• G06F 16/22 - Indexation; Structures de données à cet effet; Structures de stockage
• G06F 16/2455 - Exécution des requêtes