Example aspects of the present disclosure relate to demand shaping by adjusting signals to shift system resource utilization. An example method includes accessing data associated with batched service instances for a batch delivery window. The method includes determining a probability that a user initiates a service instance outside of the batch delivery window is greater than a probability for initiating the service instance within the window. Responsive to determining the first probability is greater than the second probability, determining an incentive predicted to increase the probability of the service instance within the batch delivery window. The method includes generating a selectable user interface element including an indication of the incentive associated with the batch delivery window. The method includes automatically updating the user interface responsive to selection of the user interface to provide the incentive for display alongside a number of available items associated with the batch delivery window.
This disclosure presents a system for monitoring trips by detecting one or more anomalies for each of a plurality of trips. The system updates route data and utilizes anomaly detectors to identify one or more anomalies, such as detours, low-speed movement, and unexpected stops. The system generates a health status of the trip based on any identified anomalies. In one example, the system disseminates the health status to relevant entities.
Systems and methods for centralized testing of software. The system can access change data indicative of at least one change to an implemented version of software, wherein: (i) the change is associated with a first computing environment and the implemented version of software is associated with a second computing environment, and (ii) the change data is associated with a request to test the change against the implemented version of software. The method can include generating an isolated computing environment to test the change against the implemented version of software, wherein generating an isolated computing environment includes determining one or more datasets that are relevant to the at least one change. The method can include testing, within the isolated computing environment, the change against the implemented version of software. The method can include migrating the change from the first computing environment to the second computing environment.
Example implementations are directed to systems and methods for improving navigation accuracy for a last segment of a delivery route. A client application on a user device is configured to display user interfaces that allow users to provide user-generated content (UGC) to refine last segment data, such as parking locations, building entrances, and drop-off points. The UGC is collected via interactive map-based tools, where users can adjust pins and provide metadata including entry codes and images. The system integrates the UGC with historical trip data and inference data to generate updated last segment data, which is presented to couriers. Conflicts between the UGC and the inference data can be resolved by analyzing courier behavior and prioritizing the data source most frequently followed. A machine learning model can also be retrained using the UGC, inference data, and courier behavior to improve future predictions of the last segment data.
Various examples are directed to systems and methods for generating improved autonomous vehicle (AV) navigation route data and estimated time of arrival (ETA) data. A system may receive delivery data from an AV delivering a passenger or product, where the delivery data includes a delivery origin, a delivery destination, and an ETA and navigation route generated by the AV. The AV may have accurate short-term information, such as the next few navigation maneuvers planned by the AV. The system may be able to provide more accurate longer-term information, such as generating an overall calculated route and calculated completion time based a current location and the delivery destination. The system may receive updated short-term information and combine this information with the calculated route and calculated completion to generate a more accurate set of route data and ETA data.
A network system can receive a first request for a transport service and a second request for the transport service. The system can identify, from a plurality of service providers, a first set of service providers for the first request, and a second set of service providers for the second request. Based on a first set of predictive parameters for the first set of service providers, the system implements a multi-invite mode by transmitting a first invitation data set to service the first request to a plurality of provider devices of the first set of service providers. Based on a second set of predictive parameters for the second set of service providers, the system implements an exclusive-invite mode by transmitting a second invitation data set to a provider device of a selected service provider of the second set of service providers.
A system can monitor event data corresponding to a current user experience of a requesting user during a current application session with a network service. Based on the event data, the system generates one or more representations corresponding to the current user experience of the requesting user, and executes a machine learning model to process the one or more representations in order to predict a negative user experience for the requesting user within a future time frame during the current application session. In response to predicting the negative user experience, the system implements one or more corrective actions during the current application session through the service application to prevent or mitigate the predicted negative user experience.
A system and method for managing a network service is described. A system can provide a map interface for a user that includes a location pin. The system can detect a user input that sets the location pin at a selected pickup location on the map interface. In response to detecting the user input, the system can (i) determine an alternative pickup location based at least in part on historical information corresponding to clustered trip entries of the network service, and (ii) cause the map interface to dynamically and visually relocate the location pin to the alternative pickup location. The system may then receive a transport request from the mobile computing device of the user, and in response to receiving the transport request, select a transport provider to rendezvous with the user at the alternative pickup location.
Systems and methods for providing an AI assistant to users of a food delivery system. The method includes receiving a user query, wherein the user query is associated with a food delivery system. The method further includes accessing contextual data for the user query. The method further includes generating model input, the model input including the user query and the contextual data for the user query. The method further includes providing model input as input to a machine-learned large language model. The method further includes receiving a query response as an output of the machine-learned large language model processing the model input. The method further includes outputting the query response to the user for display, the query response comprising a carousel of selectable options available through the food delivery system.
Systems and methods for providing an AI assistant to users of a food delivery system. The method includes receiving a user query, wherein the user query is associated with a food delivery system. The method further includes accessing contextual data for the user query. The method further includes generating model input, the model input including the user query and the contextual data for the user query. The method further includes providing model input as input to a machine-learned large language model. The method further includes receiving a query response as an output of the machine-learned large language model processing the model input. The method further includes outputting the query response to the user for display, the query response comprising a carousel of selectable options available through the food delivery system.
A network system operates to enable a user to specify a campaign configuration for a new or existing campaign. Based on the specified campaign configuration, the network system determines one or more campaign execution parameters for configuring an execution of the new or existing campaign on an external content delivery channel, using a data set that is representative of an environment of the external content delivery channel.
Advertising; business management, organization and administration; career management services; executive search and placement services; marketing and advertising services to promote career opportunities for corporate positions; matching job candidates with recruiters for job placement purposes; online interactive career counseling services and resume preparations for others; online recruiting services, namely, providing searchable job postings and resume postings; personnel recruitment and placement; placement of full-time staff; professional staffing, placement, recruiting, and career networking services; providing an online resume database featuring information relating to job seekers; providing an online searchable database featuring career and job opportunities; providing an online searchable database featuring career and job opportunities and content about careers and jobs; providing business information in the field of job opportunities, personnel staffing, careers, and freelance work; providing career information via social media mobile applications; providing career information via social media websites; providing information about recruitment, hiring, and onboarding via a website; providing information about, career and job opportunities for corporate positions via a website; providing online information in the field of recruitment, hiring, and careers; providing recruitment, hiring, and onboarding services for personnel staffing and corporate positions; providing talent recruitment and hiring information; talent hiring, recruiting, placement, staffing and career services; talent recruiting services.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable artificial intelligence personal assistant software for performing tasks or services on behalf of a user that is activated by user input, location awareness, and online information; downloadable application programming interface (API) software for use in developing AI (artificial intelligence) platforms, namely, bots, virtual agents and virtual assistants; downloadable computer software, downloadable mobile application software, and downloadable computer application software for mobile devices and computers, all for using artificial intelligence for use as a digital companion; downloadable computer chatbot software for simulating conversations; downloadable software for using artificial intelligence for conversational query; downloadable computer software, downloadable mobile application software, and downloadable computer application software for facilitating interaction and communication between humans and AI (artificial intelligence) platforms, namely, bots, virtual agents and virtual personal assistants; downloadable computer virtual assistant software, downloadable mobile application virtual assistant software, and downloadable computer application virtual assistant software that can perform tasks or services on behalf of a user that is activated by user input, location awareness, and online information; downloadable software for use in processing and generating natural language queries; downloadable software using artificial intelligence (AI) for the production of speech and text; downloadable software for multi-modal machine-learning based language, text, and speech processing software; downloadable software for facilitating multi-modal natural language, speech, text, image, video, and sound input; downloadable chatbot software for simulating conversations, analyzing images, summarizing text, creating content, generating code, brainstorming, trip planning, and answering queries Providing online non-downloadable software, software as a service (SaaS) services, and platform as a service (PAAS) services featuring artificial intelligence personal assistant software for performing tasks or services on behalf of a user that is activated by user input, location awareness, and online information; providing online non-downloadable software, software as a service (SaaS) services, and platform as a service (PAAS) services featuring software for use in developing AI (artificial intelligence) platforms, namely, bots, virtual agents and virtual assistants; providing online non-downloadable software, software as a service (SaaS) services, and platform as a service (PAAS) services featuring software for mobile devices and computers, all for using artificial intelligence for use as a digital companion; providing online non-downloadable software, software as a service (SaaS) services, and platform as a service (PAAS) services featuring computer chatbot software for providing information or simulating conversations; providing online non-downloadable software, software as a service (SaaS) services, and platform as a service (PAAS) services featuring software for using artificial intelligence for conversational query; providing online non-downloadable software, software as a service (SaaS) services, and platform as a service (PAAS) services featuring software for facilitating interaction and communication between humans and AI (artificial intelligence) platforms, namely, bots, virtual agents and virtual personal assistants; providing online non-downloadable software, software as a service (SaaS) services, and platform as a service (PAAS) services featuring software that can perform tasks or services on behalf of a user that is activated by user input, location awareness, and online information; providing online non-downloadable software for use in processing and generating natural language queries; providing online non-downloadable software using artificial intelligence (AI) for the production of speech and text; providing online non-downloadable software for multi-modal machine-learning based language, text, and speech processing software; providing temporary use of online non-downloadable software for facilitating interaction and communication between humans and artificial intelligence (AI) chatbots in the fields of transportation; providing temporary use of online non-downloadable software for facilitating multi-modal natural language, speech, text, image, video, and sound input; providing temporary use of online non-downloadable chatbot software for simulating conversations, analyzing images, summarizing text, creating content, generating code, brainstorming, trip planning, and answering queries
The disclosed examples are directed to systems and methods for computing copresence of devices using GPS signals. The systems and methods access a plurality of GPS signals comprising a first set of GPS signals associated with a first device and a second set of GPS signals associated with a second device. The systems and methods align the first set of GPS signals with the second set of GPS signals and compute copresence probability for each pair of GPS signals in the aligned first and second sets of GPS signals. The systems and methods smooth the copresence probability for each pair of GPS signals and determine one or more copresence events based on the smoothed copresence probability for each pair of GPS signals.
Systems and methods for coordinating point of interest pickups in a transportation service are provided. In example embodiments, the system detects a location of a device of a user. Responsive to detecting the location of the device of the user, the system automatically determines one or more potential pickup points based on the detected location. A pickup point user interface (UI) that displays one or more potential pickup points based on the detected location is presented on the device of the user without displaying a map. The system receives confirmation of a pickup point from the one or more potential pickup points and receives an indication of a destination. The system then establishes the transportation service based on the confirmed pickup point and the destination. The system can provide user interfaces that display progress of a driver to the pickup point and progress to the destination without displaying a map.
A computing system can receive utilization data from computing devices of requesting users. Based on the utilization data, the system can determine, for each requesting user, an intent of the requesting user, the intent corresponding to a probability that the requesting user will utilize the transport service upon arrival at an arrival location of a transit vehicle. The system may determine a destination for the requesting user that requires additional transport from the arrival location of the transit vehicle. Based on the destination of the requesting user, the system can transmit a set of transport requests to computing devices of a set of the transport providers to facilitate transport for the requesting users at the arrival location of the transit vehicle.
Systems and methods for displaying corresponding content for vehicle services using a distributed set of electronic devices are provided. For example, a computer-implemented method includes obtaining data associated with a vehicle service instance. The vehicle service instance is associated with a request for a vehicle service for a user. The method includes determining, based on the data associated with the vehicle service instance, a first advertisement content item for a display device positioned on an exterior of a vehicle assigned to the vehicle service instance and a second advertisement content item for a user device associated with the vehicle service instance. The method includes communicating data that initiates the display of the first advertisement content item for the display device positioned on the exterior of the vehicle and data that initiates the display of the second advertisement content item for the user device.
Lighting apparatus and installations, namely, luminous tubes
for lighting, searchlights, vehicle reflectors, lights for
vehicles; safety lamps; vehicle lights for assisting
rideshare passengers to locate vehicles; spot lights; lights
for vehicles; directional lights for bicycles; light bulbs
for directional signals for vehicles; solar-powered
all-weather lights; lights for use in illuminating signs and
displays; LED and HID light assemblies for vehicles; LED
lighting assemblies for illuminated signs; light reflectors;
lighting apparatus and installations for vehicles; light
panels for vehicles, namely, cars, motorcars, automobiles,
trucks, vans, SUVs, bicycles, motor scooters, two wheeled
motorized scooters, self-balancing scooters, hovering
boards, mopeds, skateboards, self-balancing boards, electric
bicycles, electric assist bicycles, electric pedal assist
bicycles, electric motor scooters, electrically-powered
motor scooters, electric two wheeled motorized scooters,
electric self-balancing scooters, self-balancing two-wheeled
electric scooters, self-balancing one-wheeled electric
scooters, electric hovering boards, electric mopeds,
electric motorized skateboards, electric motorized
self-balancing boards, semi-tractor trailer trucks and
semi-tractor trailers, boats, watercraft, tractors and
tractor trailers, helicopters, airplanes, aircraft, drones,
vertical takeoff and landing aircraft (VTOL); light bars for
vehicles, namely, cars, motorcars, automobiles, trucks,
vans, SUVs, bicycles, motor scooters, two wheeled motorized
scooters, self-balancing scooters, hovering boards, mopeds,
skateboards, self-balancing boards, electric bicycles,
electric assist bicycles, electric pedal assist bicycles,
electric motor scooters, electrically-powered motor
scooters, electric two wheeled motorized scooters, electric
self-balancing scooters, self-balancing two-wheeled electric
scooters, self-balancing one-wheeled electric scooters,
electric hovering boards, electric mopeds, electric
motorized skateboards, electric motorized self-balancing
boards, semi-tractor trailer trucks and semi-tractor
trailers, boats, watercraft, tractors and tractor trailers,
helicopters, airplanes, aircraft, drones, vertical takeoff
and landing aircraft (VTOL).
19.
Priority-Based Load Shedding for Computing Systems
A system and method for dynamic load shedding in computing environments experiencing high request volumes. The system monitors a request queue storing unprocessed requests from client devices and determines both a current queue size and an aggregate historical queue size. During a defined time interval, the system identifies the number of requests enqueued and dequeued. Based on these metrics, the system determines a percentage or number of requests to be rejected to mitigate overload. Each request is ranked according to its type and the time it was received. A subset of unprocessed requests is then selected for rejection based on their rankings and the determined rejection threshold. This targeted rejection strategy enables prioritized load shedding that maintains system responsiveness while minimizing user impact.
Systems and methods for ephemeral processing of high cardinality data. The system can receive log data indicative of metrics associated with a computing system, wherein the log data is received by an aggregation layer. The method includes aggregating the log data by deduplicating the plurality of logs using one or more aggregation parameters, wherein the one or more aggregation parameters are configurable to increase or decrease a level of deduplication. The method includes, in response to aggregating the log data, determining deduplicated log data including one or more unique logs indicative of unique metrics associated with the computing system. The method includes transmitting the deduplicated log data to a storage system.
Systems and method for dynamic data object distribution based on historical performance. The method includes accessing a centralized data structure with a number of order requests. Computing a shopping list including data objects for items and characteristic data for each item. Accessing data indicative of selection of the first shopping list by a first device, updating the order status of the first shopping list. Accessing data indicative of selection of the first shopping list by a second device. Computing a first subset of items and second subset of items for each respective computing device based on (i) features associated with the first computing device, (ii) features associated with the second computing device, and (iii) the characteristic data of each respective item of a plurality of items. Transmitting data to cause an interactive user interface of the first computing device to display the first subset of items.
Example embodiments are directed to systems and methods for providing popular route inference and reconstruction. The system captures trip data of a plurality of users traversing routes by monitoring user devices of the plurality of users. The system then analyzes the trip data between an origin/destination (O/D) pair to determine candidate popular trips between the O/D pair comprising detours. Top-ranking waypoints of segments of the candidate popular trips are determined. The top-ranking waypoints of the O/D pair are stored in a waypoint data storage. In response to receiving a request for a transportation service between the O/D pair from a user, the system reconstructs a popular route using the top-ranking waypoints and causes presentation on a user device of the user of a plurality of route options for selection by the user including the reconstructed popular route.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable computer programs for user interface design;
downloadable software development kits (SDKs) and
application programming interfaces (APIs) for use in
connection with machine learning systems; downloadable
computer software for designing, building, training,
deploying, evaluating, managing, and operating machine
learning models; downloadable computer software for
generative AI for developers to combine model prompts,
generated text, and functions for text-based applications;
downloadable computer software for integrating machine
learning models into production environments; downloadable
computer software for managing and accessing shared data
features used in machine learning applications; downloadable
computer software for model versioning, model monitoring,
model visualization, and automated machine learning
(AutoML); downloadable computer software for processing
data, training machine learning models, and performing
automated analytics; downloadable computer software for use
by data scientists, engineers, and researchers in training,
testing, and validating machine learning algorithms;
downloadable computer software for use in data processing,
data labeling, feature engineering, predictive analytics,
time series forecasting, and deep learning; downloadable
computer software for use in predictive analytics,
artificial intelligence, data processing, and automated
decision-making; downloadable computer software using
artificial intelligence (AI) for introducing developers to
large language model fundamentals, and generative AI to
build applications; downloadable computer software using
artificial intelligence (AI) for use in enabling others to
build their own generative AI models using their own data
and criteria. User interface (UI) design; technical consulting services in
the field of machine learning infrastructure, machine
learning operations, and artificial intelligence; providing
information in the field of machine learning platform design
and deployment; providing online non-downloadable computer
software platforms for use in data analysis in the field of
machine learning, discovering, organizing, and synthesizing
data for use in research and data analysis via machine
learning; providing temporary use of non-downloadable
software for designing, building, training, deploying,
evaluating, managing, and operating machine learning models;
providing temporary use of non-downloadable software for
generative AI for developers to combine model prompts,
generated text, and functions for text-based applications;
providing temporary use of non-downloadable software for
integrating machine learning models into production
environments; providing temporary use of non-downloadable
software for managing and accessing shared data features
used in machine learning applications; providing temporary
use of non-downloadable software for model versioning, model
monitoring, model visualization, and automated machine
learning (automl); providing temporary use of
non-downloadable software for processing data, training
machine learning models, and performing automated analytics;
providing temporary use of non-downloadable software for use
by data scientists, engineers, and researchers in training,
testing, and validating machine learning algorithms;
providing temporary use of non-downloadable software for use
in data processing, data labeling, feature engineering,
predictive analytics, time series forecasting, and deep
learning; providing temporary use of non-downloadable
software for use in predictive analytics, artificial
intelligence, data processing, and automated
decision-making; providing temporary use of non-downloadable
software using artificial intelligence (AI) for introducing
developers to large language model fundamentals, and
generative AI to build applications; providing temporary use
of non-downloadable software using artificial intelligence
(AI) for use in enabling others to build their own
generative AI models using their own data and criteria;
platform as a service (PaaS) and software as a service
(SaaS) featuring a machine learning operations (MLOps)
platform for managing the end-to-end machine learning
lifecycle, including data ingestion, feature engineering,
model training, evaluation, deployment, monitoring, and
visualization; platform as a service (PaaS) featuring a
cloud-based platform for designing, deploying, and managing
machine learning models and artificial intelligence systems;
Platform-as-a-Service (PaaS) services featuring computer
software platforms for machine learning;
Platform-as-a-Service (PaaS) services featuring computer
software platforms using artificial intelligence for machine
learning; Platform-as-a-Service (PaaS) services featuring
computer software platforms for discovering, organizing, and
synthesizing data for use in research and data analysis via
machine learning; software as a service (SaaS) featuring
software for use in the development, testing, deployment,
and management of machine learning systems;
Software-as-a-Service (SaaS) featuring software for
providing machine learning model quality metrics and
analytics, namely, assessing, monitoring, and reporting the
performance, accuracy, and reliability of machine learning
models; Software-as-a-Service (SaaS) services featuring
software for use in data analysis in the field of machine
learning, discovering, organizing, and synthesizing data for
use in research and data analysis via machine learning;
Software-as-a-Service (SaaS) services featuring software
using artificial intelligence for machine learning;
Software-as-a-Service (SaaS) services featuring software
using artificial intelligence for creating text-based
experiences by connecting prompts, data, and models in a
user interface.
Various embodiments pertain to techniques for proactively delivering navigation options to a user via a mobile device. In various embodiments, one or more navigation options can be determined for the user and delivered to the user's mobile device at a relevant time. Navigation options can be selected based on the user's current location, the user's future plans, the time, and other locally relevant information, such as friends nearby or a nearby favorite location of the user. The navigation options can be delivered to the user's mobile device at a time that the navigation options are relevant.
H04W 4/21 - Signalisation de servicesSignalisation de données auxiliaires, c.-à-d. transmission de données par un canal non destiné au trafic pour applications de réseaux sociaux
09 - Appareils et instruments scientifiques et électriques
35 - Publicité; Affaires commerciales
39 - Services de transport, emballage et entreposage; organisation de voyages
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Scientific, research, navigation, surveying, photographic, cinematographic, audiovisual, optical, weighing, measuring, signalling, detecting, testing, inspecting, life-saving and teaching apparatus and instruments; apparatus and instruments for conducting, switching, transforming, accumulating, regulating or controlling the distribution or use of electricity; apparatus and instruments for recording, transmitting, reproducing or processing sound, images or data; recorded and downloadable media, computer software, blank digital or analogue recording and storage media; mechanisms for coin-operated apparatus; cash registers, calculating devices; computers and computer peripheral devices; diving suits, divers’ masks, ear plugs for divers, nose clips for divers and swimmers, gloves for divers, breathing apparatus for underwater swimming; fire-extinguishing apparatus; downloadable software for transportation bookings and reservations; downloadable software for vehicle rental comparison and rentals; downloadable software for tracking and monitoring prices of vehicle rental reservations; downloadable software for booking, canceling, modifying, and managing vehicle rental reservations; downloadable software for procuring, engaging, and coordinating transportation; downloadable software for renting vehicles; downloadable software for accessing rental services; downloadable software for renting conveyances Advertising; business management, organization and administration; office functions; brokering the sale of travel, lodging, and transportation services; business consulting and management services in the field of transportation; business management services in the field of transport and delivery; business administration in the field of transport and delivery; business management services, namely, booking, canceling, modifying, and managing vehicle rental reservations; providing a website featuring information about transportation management services, namely, procuring, engaging, and coordinating transportation and monitoring prices of vehicle rental reservation; providing a website featuring information and tracking information regarding prices of vehicle rental reservation; transportation management services, namely, booking, canceling, modifying, and managing vehicle rental reservations and monitoring prices of vehicle rental reservations; transportation management services for others, namely, planning, coordinating, and tracking the transportation of people and conveyances; providing a website featuring information about transportation management services, namely, about planning, coordinating, and tracking the transportation of people and conveyances; comparison shopping services; on-line price monitoring and adjusting for customers of travel related purchases Transport; packaging and storage of goods; travel arrangement; rental car reservation services; arranging of passenger transportation services for others; making reservations and bookings for transportation; transportation reservation services; transportation reservation services, namely, providing reservation management services; providing information and online reservations relating to transport and vehicle rental services; travel agency services; travel agency services, namely, reserving and managing reservations and bookings for rental vehicles Scientific and technological services and research and design relating thereto; industrial analysis, industrial research and industrial design services; quality control and authentication services; design and development of computer hardware and software; software as a service (SAAS) featuring software for transportation bookings and reservations; software as a service (SAAS) featuring software for vehicle rental comparison and rentals; software as a service (SAAS) featuring software for tracking and monitoring prices of vehicle rental reservations; software as a service (SAAS) featuring software for booking, canceling, modifying, and managing vehicle rental reservations; providing online non-downloadable software for procuring, engaging, and coordinating transportation; providing online non-downloadable software for renting vehicles; providing online non-downloadable software for accessing rental services; providing online non-downloadable software for renting conveyances
A network computer system can implement a hierarchical selection process to fulfill a scheduled transport request. The hierarchical selection process can include a first selection process and a second selection process. The first selection process can include assigning a first transport provider to fulfill the scheduled transport request. The second selection process can be implemented at a specified time prior to the scheduled time and can include selecting a backup transport provider to service the scheduled transport request.
A method for user-initiated end of delivery confirmation. A method and technique comprises causing display of a first interactive element on a user device in response to a vehicle carrying a payload approaching a destination; receiving a first user input comprising a first user interaction with the first interactive element; in response to receiving the first user input, causing a vehicle management component communicatively coupled to the vehicle to enable access to the payload in the vehicle and causing display of a second interactive element on the user device; receiving a second user input from the user device, the second user input comprising a second user interaction with the second interactive element; determining a retrieval status associated with the payload based on the second user interaction; and causing display of a confirmation on the user device based on retrieval status, the confirmation indicating that the payload has been retrieved.
A network computing system can coordinate on-demand transport serviced by transport providers operating throughout a transport service region. The transport providers can comprise a set of internal autonomous vehicles (AVs) and a set of third-party AVs. The system can receive a transport request from a requesting user of the transport service region, where the transport request indicates a pick-up location and a destination. The system can determine a subset of the transport providers to service the respective transport request, and executing a selection process among the subset of the transport providers to select a transport provider to service the transport request. The system may then transmit a transport assignment to the selected transport provider to cause the selected transport provider to service the transport request.
G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
G01C 21/28 - NavigationInstruments de navigation non prévus dans les groupes spécialement adaptés pour la navigation dans un réseau routier avec corrélation de données de plusieurs instruments de navigation
G01C 21/34 - Recherche d'itinéraireGuidage en matière d'itinéraire
G06Q 30/0283 - Estimation ou détermination de prix
29.
Route coordination and navigation based on user proximity to points of interest
A system receives sensor data from computing devices of passengers riding in an autonomous vehicle (AV). Based on the sensor data, the system can determine a position of each of the passengers within the AV. The system determines a next passenger to be picked up by the AV. Based at least in part on the position of each of the passengers within the AV, the system can (i) select a pickup location for the next passenger, and (ii) determine a route for the AV based on the pickup location such that an open seat within the AV is adjacent to the next passenger when the AV arrives at the pickup location for the next passenger. The system can transmit data corresponding to the route to enable the AV to update a current route in order to facilitate a rendezvous with the passenger at the pickup location.
Computationally implemented methods and systems that are designed for receiving one or more first directives that direct a transportation vehicle unit to transport a first end user; receiving, while the transportation vehicle unit is en route to or is transporting the first end user, one or more second directives that direct the transportation vehicle unit to transport a second end user while transporting the first end user, the transportation vehicle unit having been determined to be able to accommodate transport of the second end user while transporting the first end user; and verifying that compliance with the one or more second directives will not conflict with one or more obligations to transport the first end user by the transportation vehicle unit. In addition to the foregoing, other aspects are described in the claims, drawings, and text.
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
G06Q 50/40 - Procédés d’affaires s’appliquant à l’industrie du transport
31.
METHODS AND SYSTEMS FOR PROVIDING ALERTS TO DRIVERS
A monitoring system can receive sensor data from one or more sensors of a vehicle to monitor an exterior of the vehicle. Based on monitoring the exterior of the vehicle, the system can detect movement of one or more pedestrians in the exterior of the vehicle. Based at least in part on detecting the movement of the one or more pedestrians in the exterior of the vehicle, the system can output an alert to a driver of the vehicle via one or more output devices of the vehicle.
B60Q 9/00 - Agencement ou adaptation des dispositifs de signalisation non prévus dans l'un des groupes principaux
B60R 11/04 - Montage des caméras pour fonctionner pendant la marcheDisposition de leur commande par rapport au véhicule
B60W 50/14 - Moyens d'information du conducteur, pour l'avertir ou provoquer son intervention
G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p. ex. véhicules ou piétonsReconnaissance des objets de la circulation, p. ex. signalisation routière, feux de signalisation ou routes
G06V 40/20 - Mouvements ou comportement, p. ex. reconnaissance des gestes
G08B 21/22 - Alarmes de situation réagissant à la présence ou à l'absence de personnes
G08B 21/24 - Alarmes aide-mémoire, p. ex. alarmes contre la perte
G08G 1/0962 - Dispositions pour donner des instructions variables pour le trafic avec un indicateur monté à l'intérieur du véhicule, p. ex. délivrant des messages vocaux
G08G 1/0967 - Systèmes impliquant la transmission d'informations pour les grands axes de circulation, p. ex. conditions météorologiques, limites de vitesse
H04N 7/18 - Systèmes de télévision en circuit fermé [CCTV], c.-à-d. systèmes dans lesquels le signal vidéo n'est pas diffusé
H04W 4/46 - 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 de véhicule à véhicule
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
H04W 4/90 - Services pour gérer les situations d’urgence ou dangereuses, p. ex. systèmes d’alerte aux séismes et aux tsunamis
A computing system determines that a transportation vehicle is transporting a first end user to a first destination location. Based on a set of factors, the system determines that a driver of the transportation vehicle is able to travel to a rendezvous location to rendezvous with a second end user while the transportation vehicle is progressing to the first destination location along an original route. The system determines an alternate route for the transportation vehicle to travel to the rendezvous location that satisfies the set of factors, and directs the driver of the transportation vehicle to the rendezvous location to rendezvous with the second end user along the alternate route.
A proximity alert system tracks geographic locations of riders and drivers using global navigation satellite system receivers in their mobile devices or in a device such as a beacon or dashcam. The proximity alert system compares the location data received from the riders' and drivers' devices and determines whether a service-requesting user is within a threshold distance of one of the driver devices that does not belong to the driver assigned to provide transport service for the rider. If so, the proximity alert system can communicate a notification message to the rider to confirm whether the rider is in the correct car. The proximity alert system can also communicate a message to the driver asking the driver to double-check the identity of the rider.
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
34.
SYSTEMS AND METHODS FOR TRAVEL PLANNING THAT CALLS FOR AT LEAST ONE TRANSPORTATION VEHICLE UNIT
Computationally implemented methods and systems that are designed for transmitting a travel plan to an end user device to facilitate an end user in travelling to a destination location from a starting location, monitoring a location of the end user device to determine whether the end user has deviated from the travel plan, and in response to determining that the end user has deviated from the travel plan, determining an adjustment to a remainder of the travel plan. The methods and system further transmit the adjustment to the end user device as the end user travels to the destination location.
A computer system operates to receive a plurality of service requests from computing devices of requesters within a geographic region. The system may further receive service information from a plurality of computing devices within the geographic region, each computing device being associated with a respective service provider. The system may then determine, for the respective service provider, (i) a current location of the respective service provider based on the service information received from the computing device of the respective service provider, and (ii) one or more preferred subregions of the respective service provider. The system can then match the respective service provider to a first service request of the plurality of service requests based at least in part on (i) the one or more preferred subregions of the respective service provider, and (ii) a destination of the first service request.
A system can receive a request for transport from a computing device of a user and select a service provider to provide transport for the user to a destination location. The system can receive location data from the computing device of the service provider. After the service provider picks up the user, the system can determine whether the service provider progresses toward the destination location in accordance with a set of progress conditions. Based at least in part on determining that the service provider has not progressed toward the destination location in accordance with the set of progress conditions, the system can adjust a fare for providing transport for the user to the destination location.
A computing system can receive a transport request from a computing device of a requesting user, the transport request indicating a pickup location, and initiate a matching process to identify a transport provider for the requesting user. The system can receive data indicating a cancelation request from the computing device of the requesting user, and determine one or more alternative options for fulfilling the transport request. The system may then transmit a set of data to the computing device of the requesting user to display contextual information associated with the matching process, and provide the one or more alternative options for fulfilling the transport request.
G06Q 50/40 - Procédés d’affaires s’appliquant à l’industrie du transport
G01C 21/34 - Recherche d'itinéraireGuidage en matière d'itinéraire
H04L 67/52 - Services réseau spécialement adaptés à l'emplacement du terminal utilisateur
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
A system can receive user data from a computing device of a user. Based on the user data, the system can determine that the user will utilize a transport service to arrive at a destination location at a specified time. Prior to the specified time, the system can monitor transport service conditions within a region that includes a current location of the user, and determine a service request time for the user based at least in part on the transport service conditions. The system then automatically generates the service request for the user at the service request time to match the user to a transport provider.
H04L 67/62 - 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 établissant un calendrier pour servir les requêtes
A computing system can wirelessly connect with a computing device of a driver of a vehicle while the driver is within the vehicle. The system can determine, based on being wirelessly connected with the computing device of the driver, that the driver of the vehicle is using the computing device, and using a vehicle monitoring system of the vehicle while the vehicle is moving, the system can process sensor data from at least one sensor of the vehicle monitoring system to detect an object ahead of the vehicle. Based on detecting the object and determining that the driver is using the computing device, the system can output an alert to the computing device of the driver.
B60Q 9/00 - Agencement ou adaptation des dispositifs de signalisation non prévus dans l'un des groupes principaux
B60C 9/00 - Armatures des pneumatiques ou disposition des nappes dans ces derniers
B60W 50/14 - Moyens d'information du conducteur, pour l'avertir ou provoquer son intervention
G08G 1/0962 - Dispositions pour donner des instructions variables pour le trafic avec un indicateur monté à l'intérieur du véhicule, p. ex. délivrant des messages vocaux
G08G 1/0967 - Systèmes impliquant la transmission d'informations pour les grands axes de circulation, p. ex. conditions météorologiques, limites de vitesse
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
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
H04W 4/90 - Services pour gérer les situations d’urgence ou dangereuses, p. ex. systèmes d’alerte aux séismes et aux tsunamis
Systems and methods of using satellite signal strength to determine indoor/outdoor transition points for places are disclosed herein. In some example embodiments, a computer system accesses service data and sensor data for a plurality of requests for a transportation service associated with a place, with the service data comprising pick-up data indicating a pick-up location and drop-off data indicating a drop-off location, and the sensor data comprising satellite signals indicating a pick-up path or a drop-off path, with the satellite signals each having a corresponding signal strength. The computer system determines a transition geographic location for the place based on the signal strengths of the satellite signals.
A computing system can receive service requests from computing devices of requesting users, with each respective service request including a start location and a destination location. For a respective service request, the system can transmit a transport invitation to a plurality of provider computing devices in accordance with a multi-invitation mode. The transport invitation is displayed on a respective provider computing device and selectable by the respective transport provider to accept the respective service request. The system can receive an acceptance of the transport invitation from two or more provider computing devices. Based on the received data from the two or more provider computing devices, the system can select a transport provider from the respective two or more transport providers associated with the two or more provider computing devices to service the respective service request.
H04L 67/1004 - Sélection du serveur pour la répartition de charge
G06Q 50/40 - Procédés d’affaires s’appliquant à l’industrie du transport
H04L 67/51 - Découverte ou gestion de ceux-ci, p. ex. protocole de localisation de service [SLP] ou services du Web
H04L 67/54 - Gestion de la présence, p. ex. surveillance ou enregistrement pour la réception des informations de connexion des utilisateurs ou état de connexion des utilisateurs
42.
NETWORK SYSTEM TO FILTER REQUESTS BY DESTINATION AND DEADLINE
A method and system for filtering service requests by destination and deadline are described. A network computer system receives provider data corresponding to a specified destination and a deadline from a service provider. The network computer system tracks a current location of the service provider through a device equipped with one or more location-based resources and receives request data corresponding to requests for service from users. The network computer system analyzes the request data for each of the requests for service to identify a subset of the requests that are assignable to the service provider based on whether the service provider is able to fulfill the request and travel to the desired destination before the deadline. The network computer system transmits a message to the service provider's device requesting that the service provider fulfill one of the requests for service from the identified subset.
A network system can receive location data from a provider device of a service provider. Using at least the location data in an optimization model, the network system can determine one or more actions for the service provider to optimize one or more metrics. The one or more metrics correspond to at least one of (i) an expected wait time for the service provider over a future period of time, (ii) an expected travel distance between providing services over a future period of time, or (iii) an expected amount of earnings for the service provider over a future period of time. The network system may then transmit a dataset to the provider device to display information corresponding to the one or more actions for the service provider.
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
H04L 41/0823 - Réglages de configuration caractérisés par les objectifs d’un changement de paramètres, p. ex. l’optimisation de la configuration pour améliorer la fiabilité
H04L 41/147 - Analyse ou conception de réseau pour prédire le comportement du réseau
H04L 41/16 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets en utilisant l'apprentissage automatique ou l'intelligence artificielle
H04L 41/22 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets comprenant des interfaces utilisateur graphiques spécialement adaptées [GUI]
H04L 41/40 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets en utilisant la virtualisation des fonctions réseau ou ressources, p. ex. entités SDN ou NFV
H04L 41/5025 - Pratiques de respect de l’accord du niveau de service en réagissant de manière proactive aux changements de qualité du service, p. ex. par reconfiguration après dégradation ou mise à niveau de la qualité du service
H04L 41/5054 - Déploiement automatique des services déclenchés par le gestionnaire de service, p. ex. la mise en œuvre du service par configuration automatique des composants réseau
H04L 67/51 - Découverte ou gestion de ceux-ci, p. ex. protocole de localisation de service [SLP] ou services du Web
H04L 67/61 - 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 tenant compte de la qualité de service [QoS] ou des exigences de priorité
H04L 67/62 - 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 établissant un calendrier pour servir les requêtes
H04L 67/75 - Services réseau en affichant sur l'écran de l'utilisateur les conditions du réseau ou d'utilisation
44.
COMPUTING SYSTEM IMPLEMENTING A DRIVER SELECTION PROCESS BASED ON DEVICE LOCATION
A computing system establishes a geofence associated with a particular service area. The system monitors a location of a computing device of a driver and detects when the driver enters the geofence. The system then places the driver into a queue for the particular service area.
G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
G06Q 50/40 - Procédés d’affaires s’appliquant à l’industrie du transport
H04B 1/3822 - Émetteurs-récepteurs, c.-à-d. dispositifs dans lesquels l'émetteur et le récepteur forment un ensemble structural et dans lesquels au moins une partie est utilisée pour des fonctions d'émission et de réception spécialement adaptés à l'utilisation dans des véhicules
H04W 4/021 - Services concernant des domaines particuliers, p. ex. services de points d’intérêt, services sur place ou géorepères
A computing system can receive a service request from a computing device of a given user. The system can select an entrance from multiple entrances for a geographic area associated with the service request, and determine a sequence of instructions for a driver of a vehicle to fulfill the service request.
Systems and methods for real-time multi-order batching using multiple couriers. The method includes determining that a walker courier can pick up a walking portion of a first order request and a second order request. The method includes determining that the walker courier can pick up the second order items based on (i) a current location of a computing device associated with the first walker courier, (ii) estimated time of preparation for the second item associated with the second order request, and (iii) an estimated item of arrival for the vehicle courier to a merchant area associated with the first order request and the second order request. Based on progress of assigned vehicle couriers, the transition point for the walker courier to met the vehicle courier can be computed and transmitted to the respective computing devices.
Systems and methods for real-time multi-order batching using multiple couriers. The method includes determining that a walker courier can pick up a walking portion of a first order request and a second order request. The method includes determining that the walker courier can pick up the second order items based on (i) a current location of a computing device associated with the first walker courier, (ii) estimated time of preparation for the second item associated with the second order request, and (iii) an estimated item of arrival for the vehicle courier to a merchant area associated with the first order request and the second order request. Based on progress of assigned vehicle couriers, the transition point for the walker courier to met the vehicle courier can be computed and transmitted to the respective computing devices.
A network system operates to receive, over one or more networks, a transport request from a computing device of a service requester. The network system generates and stores, in a data store, a job order for the transport request. Further, the job order is assigned to a first service provider based on a current location of the first service provider, where the current location is determined based on location information received from a computing device of the first service provider. The network system monitors an activity of the first service provider during a time interval that follows when the first service provider is assigned to the job order. Based on the monitored activity, the network system determines an intent of the first service provider and based on the determination, reassigns the job order.
Example embodiments are directed to systems and methods for providing end of route navigation. In example embodiments, a network system identifies a destination of a route and retrieves a display template based on the destination. The display template provides guidelines for display of end of route content, whereby the display of the end of the route content is different than display of content during a middle of the route. The network system identifies, based on the display template, a display time to trigger the display of the end of the route content. The display time may be associated with a threshold distance to the destination. The network system monitors a location of a vehicle along the route and accesses end of route content. Responsive to detecting that the location of the vehicle is at the threshold distance to the destination, the network system causes presentation of the end of the route content on a device associated with the vehicle.
The present disclosure is directed to state-based autonomous-vehicle operations. In particular, the methods, devices, and systems of the present disclosure can: determine, based at least in part on one or more actions of a passenger associated with a trip of an autonomous vehicle, a current state of the trip from amongst a plurality of different predefined states of the trip; identify, based at least in part on the current state of the trip, one or more computing devices associated with the passenger; generate, based at least in part on the current state of the trip, data describing one or more interfaces for display by the computing device(s) associated with the passenger; and communicate, to the computing device(s) associated with the passenger, the data describing the interface(s) for display.
G01C 21/34 - Recherche d'itinéraireGuidage en matière d'itinéraire
B60K 35/10 - Dispositions d'entrée, c.-à-d. de l'utilisateur au véhicule, associées aux fonctions du véhicule ou spécialement adaptées à celles-ci
B60K 35/28 - Dispositions de sortie, c.-à-d. du véhicule à l'utilisateur, associées aux fonctions du véhicule ou spécialement adaptées à celles-ci caractérisées par le type d’informations de sortie, p. ex. divertissement vidéo ou informations sur la dynamique du véhiculeDispositions de sortie, c.-à-d. du véhicule à l'utilisateur, associées aux fonctions du véhicule ou spécialement adaptées à celles-ci caractérisées par la finalité des informations de sortie, p. ex. pour attirer l'attention du conducteur
B60K 35/80 - Dispositions pour la commande des instruments
B60W 50/00 - Détails des systèmes d'aide à la conduite des véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier
B60W 50/14 - Moyens d'information du conducteur, pour l'avertir ou provoquer son intervention
G08G 1/00 - Systèmes de commande du trafic pour véhicules routiers
G08G 1/123 - Systèmes de commande du trafic pour véhicules routiers indiquant la position de véhicules, p. ex. de véhicules à horaire déterminé
H04W 4/029 - Services de gestion ou de suivi basés sur la localisation
51.
NETWORK SYSTEM FOR PRESELECTING A SERVICE PROVIDER BASED ON PREDICTIVE INFORMATION
A computing system detects activation of a service application on a computing device of a user and performs a selection process to select a service provider to provide service for the user before receiving a request for service from the computing device of the user. Before receiving the request for service from the computing device of the user, the system transmits service provider information corresponding to the selected service provider to the computing device of the user. Subsequent to performing the selection process, the system receives the request for service, and transmits an invitation for providing service for the user to a provider device of the selected service provider.
Systems and methods for controlling an autonomous vehicle and the service selection for an autonomous vehicle are provided. In one example embodiment, a computing system can obtain data indicative of a first vehicle service assignment for an autonomous vehicle. The first vehicle service assignment can be associated with a first service entity and indicative of a first vehicle service. The computing system can determine that the autonomous vehicle is available to perform a second vehicle service concurrently with the first vehicle service. The computing system can obtain data indicative of a second vehicle service assignment for the autonomous vehicle. The second vehicle service assignment can be associated with a second service entity that is different than the first service entity and is indicative of the second vehicle service. The computing system can cause the autonomous vehicle to concurrently perform the first vehicle service with the second vehicle service.
A system, non-transitory computer-readable medium and computing device to communicate, over one or more networks, with a network system to receive job order data for a plurality of job orders. The job order data includes each of service information for each of the plurality of job orders, and one of multiple priority designations associated with each of the plurality of job orders. A user interface is provided to display the service information for each of the job orders. The user interface can be provided in one of multiple modes, including (i) a focus mode in which service information for one job order is pre-selected and displayed based on the priority designation of that job order; and (ii) a browse mode in which the service information for multiple job orders are displayed at one time or made available to the user.
A system, non-transitory computer-readable medium and computing device to communicate, over one or more networks, with a network system to receive job order data for a plurality of job orders. The job order data includes each of service information for each of the plurality of job orders, and one of multiple priority designations associated with each of the plurality of job orders. A user interface is provided to display the service information for each of the job orders. The user interface can be provided in one of multiple modes, including (i) a focus mode in which service information for one job order is pre-selected and displayed based on the priority designation of that job order; and (ii) a browse mode in which the service information for multiple job orders are displayed at one time or made available to the user.
G06F 3/048 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI]
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06Q 50/40 - Procédés d’affaires s’appliquant à l’industrie du transport
55.
Computing system configuring destination accelerators based on usage patterns of users of a transport service
A computing system can detect the launch of a rider application on computing devices of users of a transport service. The computing system can receive location data indicating the current location of each user, and determine a usage pattern for each user based on historical data corresponding to historical utilization of the transport service by the user. Based on the current location and the usage pattern of the user, the computing system can determine one or more suggested destination locations for the user, and transmit, over the one or more networks, display data to cause the rider application to display a destination accelerator for each of the one or more suggested destination locations. The destination accelerator can be selectable by the user to automatically input a destination location into a transport request for the transport service.
G06Q 10/1093 - Ordonnancement basé sur un agenda pour des personnes ou des groupes
H04L 67/1095 - Réplication ou mise en miroir des données, p. ex. l’ordonnancement ou le transport pour la synchronisation des données entre les nœuds du réseau
H04L 67/52 - Services réseau spécialement adaptés à l'emplacement du terminal utilisateur
H04L 67/60 - 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
56.
COMPUTER SYSTEM AND DEVICE FOR CONTROLLING USE OF SECURE MEDIA RECORDINGS
A network system is provided that enables a user to record media in connection with a user operating a service application to participate in a transport service.
Systems and methods for generating and presenting an optimized path using sequential location trace clustering is provided. The system receives a request for a transportation service from a client device of a user. The request indicates a destination point (i.e., a pickup location) and is associated with a start point (i.e., a location at a time the user requested the transportation service). Based on the start point and destination point, the system identifies one or more paths between the start point and the destination point, whereby the one or more paths are generated using sequential location trace clustering from previous transportation services involving the start point and the destination point. The system then causes presentation of a path of the one or more paths on a user interface on the client device of the user with which the user can use to navigate to the destination point.
Advertising services; business management; business organization consulting; business administration; career management planning services; executive search and placement services; marketing and advertising services to promote career opportunities for corporate positions; job placement, namely, matching job candidates with recruiters; online interactive resume preparation; online recruiting services, namely, providing an online searchable database featuring job postings and resume postings; personnel recruitment and placement; placement of full-time staff; professional staffing, placement, recruiting, and career networking services; providing an online resume database featuring information relating to job seekers; providing an online searchable database featuring career and job opportunities; providing an online searchable database featuring career and job opportunities and content about careers and jobs; providing business information in the field of job opportunities, personnel staffing, careers, and freelance work; providing career information via social media mobile applications; providing career information via social media websites; providing information about recruitment, hiring, and onboarding via a website; providing information about career and job opportunities for corporate positions via a website; providing online information in the field of recruitment, hiring, and careers; providing recruitment, hiring, and onboarding services for personnel staffing and corporate positions; providing talent recruitment and hiring information; talent hiring, recruiting, placement, staffing and career services
59.
NETWORK SYSTEM TO UTILIZE MATCHING OF BACKUPS FOR JOB ORDERS
The network system generates a job order for a received transport request. During a first time interval, the network system performs a matching process to match the job order to multiple service providers, including a first and second service provider. The network system transmits a first communication that specifies the job order to a computing device of the first service provider to enable the first service provider to exclusively accept or not accept the job order. In response to the first service provider not accepting the job order, the network system transmits, without reperforming the matching process, a second communication that specifies the job order to a computing device of the second service provider to enable the second service provider to exclusively accept or not accept the job order using the computing device of the second service provider.
A network system can communicate with user and provider devices to facilitate the provision of a network-based service. The network system can identify optimal service providers to provide services requested by users. The network can utilize context data in matching service providers with users. In particular, the network system can determine, based on context data associated with a user, whether to perform pre-request matching for that user. A service provider who is pre-request matched with the user can be directed by the network system to relocate via a pre-request relocation direction. When the user submits the service request after the pre-request match, the network system can either automatically transmit an invitation to the pre-request matched service provider or can perform post-request matching to identify an optimal service provider for the user.
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
G01C 5/06 - Mesure des hauteursMesure des distances transversales par rapport à la ligne de viséeNivellement entre des points séparésNiveaux à lunette en utilisant des moyens barométriques
G01S 13/88 - Radar ou systèmes analogues, spécialement adaptés pour des applications spécifiques
H04L 67/51 - Découverte ou gestion de ceux-ci, p. ex. protocole de localisation de service [SLP] ou services du Web
H04L 67/62 - 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 établissant un calendrier pour servir les requêtes
09 - Appareils et instruments scientifiques et électriques
35 - Publicité; Affaires commerciales
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable computer software, namely, software for connecting companies with independent contractors; downloadable computer software, namely, software for coordinating independent contractors with gig opportunities; downloadable software for posting and listing gig opportunities, and recruiting and hiring personnel. Advertising; business management, organization and administration; office functions; online business brokerage services in the nature of matching companies with independent contractors for project-based work; online business brokerage services in the nature of matching companies with independent contractors for project-based work in the fields of data labeling, localization, software testing, and data collection; providing an online marketplace for buyers and sellers of services, namely, data labeling, localization, software testing, and data collection; business services, namely, providing an online platform for matching independent contractors with companies in need of artificial intelligence training data services; outsourcing services in the nature of arranging service contracts for others in the fields of artificial intelligence data preparation and quality assurance; personnel recruitment and placement. Design and development of computer hardware and software; providing temporary use of online non-downloadable software for posting, managing, and fulfilling task-based work in the fields of data labeling, localization, software testing, and data collection; providing temporary use of online non-downloadable software for posting, managing, and fulfilling task-based work in the fields artificial intelligence data preparation and quality assurance; providing temporary use of online non-downloadable software for connecting companies with independent contractors; providing temporary use of online non-downloadable software for coordinating independent contractors with gig opportunities; providing temporary use of online non-downloadable software for use by recruiters to recruit and refer independent contractors for freelance work.
A system can receive location data from a computing device of a requesting user, where the location data indicates a current position of the requesting user. The system can determine a rendezvous location for the requesting user prior to the requesting user transmitting a service request to the network computer system. The system may then transmit data corresponding to the rendezvous location to the computing device of the requesting user. The system may further periodically receive an update request from the computing device of the user, and for each update request, (i) determine a second plurality of transport providers with the predetermined distance or time from the current position of the user, and (ii) based on respective locations of these transport providers, transmit updated map data to the computing device to indicate an updated rendezvous location on the map interface.
H04W 84/02 - Réseaux pré-organisés hiérarchiquement, p. ex. réseaux de messagerie, réseaux cellulaires, réseaux locaux sans fil [WLAN Wireless Local Area Network] ou boucles locales sans fil [WLL Wireless Local Loop]
H04W 24/02 - Dispositions pour optimiser l'état de fonctionnement
H04W 48/18 - Sélection d'un réseau ou d'un service de télécommunications
H04W 64/00 - Localisation d'utilisateurs ou de terminaux pour la gestion du réseau, p. ex. gestion de la mobilité
H04W 88/06 - Dispositifs terminaux adapté au fonctionnement dans des réseaux multiples, p. ex. terminaux multi-mode
A system can receive a request for transport from a computing device of a user while the user is riding a transit vehicle of a transit service, the request specifying a start location and a destination for the user. The system can determine an estimated time of arrival (ETA) of the transit vehicle to an arrival location that corresponds to the start location. The system can determine an ETA of a vehicle to the start location based on location data of the vehicle, and determine that the ETA of the vehicle to the start location is within a threshold amount of time of the ETA of the transit vehicle to the arrival location. The system can select the vehicle to service the request for the user, and transmit a transport invitation indicating the start location to the computing device associated with the vehicle.
G06Q 50/40 - Procédés d’affaires s’appliquant à l’industrie du transport
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"
64.
SYSTEM AND METHOD FOR PERFORMING MULTIVARIATE OPTIMIZATIONS BASED ON LOCATION DATA
An optimization and recommendation engine can receive user location data to programmatically generate customized recommendations regarding the user's operation as a potential service provider for a network service. The optimization and recommendation engine can determine potential service routes based on the user location data indicating a frequent route of the user and on service data associated with the network service. The optimization and recommendation engine can also perform multivariate optimizations to select one or more optimal service routes from the potential service routes. The recommendations can include the one or more optimal service routes as well as parameters associated with such routes. The recommendations can further include a comparison of the parameters against characteristics of the user's frequent route.
09 - Appareils et instruments scientifiques et électriques
35 - Publicité; Affaires commerciales
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable computer software, namely, software for connecting companies with independent contractors; downloadable computer software, namely, software for coordinating independent contractors with gig opportunities; downloadable software for posting and listing gig opportunities, and recruiting and hiring personnel Advertising; business management, organization and administration; providing office functions; online business brokerage services in the nature of matching companies with independent contractors for project-based work; online business brokerage services in the nature of matching companies with independent contractors for project-based work in the fields of data labeling, localization, software testing, and data collection; providing an online marketplace for buyers and sellers of services, namely, data labeling, localization, software testing, and data collection; business networking services, namely, providing an online platform for matching independent contractors with companies in need of artificial intelligence training data services; outsourcing services in the nature of arranging service contracts for others in the fields of artificial intelligence data preparation and quality assurance; personnel recruitment and placement Design and development of computer hardware and software; providing temporary use of online non-downloadable software for posting, managing, and fulfilling task-based work in the fields of data labeling, localization, software testing, and data collection; providing temporary use of online non-downloadable software for posting, managing, and fulfilling task-based work in the fields artificial intelligence data preparation and quality assurance; providing temporary use of online non-downloadable software for connecting companies with independent contractors; providing temporary use of online non-downloadable software for coordinating independent contractors with gig opportunities; providing temporary use of online non-downloadable software for use by recruiters to recruit and refer independent contractors for freelance work
66.
Network Computing System for Providing Interactive Menus and Group Recommendations
A network computing system provides an interactive menu to enable a consumer to initiate an order session. The network computing system can detect the order session is for placement of a group order, and can further provide a recommendation to the requesting consumer with respect to personalizing the group order for individual members of the group.
A network system can receive, from a user device of a requesting user, a query related to a first service. If the network system determines that a first service provider is in progress of providing a second service for the requesting user, the network system can identify, based on a service location of the second service, a plurality of entities that provide items available for selection in association with the first service. The network system can further determine whether to select the first service provider to fulfill the request for the first service based on an estimated first service duration associated with the first service and an estimated duration remaining for the second service. The first service duration can be estimated based on respective timing information associated one or more items selected by the requesting user. The network system can update a route for the first service provider.
A computer system can estimate preparation times associated with items offered by a plurality of entities to manage a service over a given geographic region. The computer system can receive, from a user device of a user, a request that indicates a user selection of a first set of one or more items to be provided by a first entity and a second set of one or more items to be provided by a second entity. The network system can determine a route of travel for a service provider to navigate in fulfilling the request. The route of navigation can be determined based at least in part on a first set of preparation timing information associated with the first set of one or more items and a second set of preparation timing information associated with the second set of one or more items.
Systems and methods for displaying corresponding content for vehicle services using a distributed set of electronic devices are provided. For example, a computer-implemented method includes obtaining data associated with a vehicle service instance. The vehicle service instance is associated with a request for a vehicle service for a user. The method includes determining, based on the data associated with the vehicle service instance, a first advertisement content item for a display device positioned on an exterior of a vehicle assigned to the vehicle service instance and a second advertisement content item for a user device associated with the vehicle service instance. The method includes communicating data that initiates the display of the first advertisement content item for the display device positioned on the exterior of the vehicle and data that initiates the display of the second advertisement content item for the user device.
Systems and method for data ingestion pipeline for bucketized search and selection. The method includes accessing a data structure including a number of delivery zones, merchants, and associated travel durations. A bucketized index data structure can be generated including travel duration buckets and associated merchants. The method includes obtaining a service order request including a drop-off location. The method includes determining a cross-bucket subset of merchants based at least in part on the index data structure and drop-off location. The method includes transmitting data including instructions that, when executed by a client device, cause the cross-bucket subset of merchants to be provided for display via an interface of the client device.
A system processes images of documents, for example, identification documents. The system transforms an image of a document to generate an image that represent the document in a canonical form. For example, if the input image has a document that is tilted at an angle with respect to the sides of the image, the system modifies the orientation of the document to show the document having sides aligned with the sides of the image. The system stores user accounts that include user information including images. The system generates a graph of nodes that represent user accounts with edges determined based on similarity scores between user accounts. The system determines connected components of user accounts, such that each connected component represents user accounts that have a high likelihood of being duplicates.
G06Q 50/26 - Services gouvernementaux ou services publics
G06F 18/21 - Conception ou mise en place de systèmes ou de techniquesExtraction de caractéristiques dans l'espace des caractéristiquesSéparation aveugle de sources
G06F 18/22 - Critères d'appariement, p. ex. mesures de proximité
G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
G06V 10/24 - Alignement, centrage, détection de l’orientation ou correction 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
G06V 30/19 - Reconnaissance utilisant des moyens électroniques
G06V 30/413 - Classification de contenu, p. ex. de textes, de photographies ou de tableaux
G06V 40/16 - Visages humains, p. ex. parties du visage, croquis ou expressions
A network system facilitates management of the operational states of transportation vehicles. Within a system environment, the network system also coordinates transport service between service providers operating the transportation vehicles and service requestors operating client devices. A transportation vehicle includes a processor or computing device that can determine and change operational states of the transportation vehicle. The transportation vehicle communicates operational states to one or more devices in the environment. Operational states can be communicated as vehicle datasets using a communication port of the transportation vehicle. The network system and client devices can act to enhance transport service using vehicle datasets. For example, the network system and/or client devices can manage an operational state by changing the current operational state off the transportation vehicle to a different operational state. Transport service can be enhanced at various points during the transport service coordination.
G07C 5/00 - Enregistrement ou indication du fonctionnement de véhicules
G01C 21/34 - Recherche d'itinéraireGuidage en matière d'itinéraire
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
The disclosed examples are directed to systems and methods for performing co-presence estimation. The systems and methods detect, by a first device associated with a first user, a beacon signal generated by a second device associated with a second user and generate co-presence information representing proximity between the first device and the second device based on detecting the beacon signal. The systems and methods select a graphical indicator from a plurality of graphical indicators of co-presence based on the generated co-presence information and present a proximity graphical user interface (GUI) comprising a map portion and a co-presence portion using the selected graphical indicator.
H04W 4/02 - Services utilisant des informations de localisation
G06Q 50/40 - Procédés d’affaires s’appliquant à l’industrie du transport
H04W 4/80 - Services utilisant la communication de courte portée, p. ex. la communication en champ proche, l'identification par radiofréquence ou la communication à faible consommation d’énergie
H04W 8/00 - Gestion de données relatives au réseau
The disclosed examples are directed to systems and methods for performing co-presence estimation. The systems and methods detect, by a first device associated with a first user, a beacon signal generated by a second device associated with a second user and generate co-presence information representing proximity between the first device and the second device based on detecting the beacon signal. The systems and methods select a graphical indicator from a plurality of graphical indicators of co-presence based on the generated co-presence information and present a proximity graphical user interface (GUI) comprising a map portion and a co-presence portion using the selected graphical indicator.
G01C 21/34 - Recherche d'itinéraireGuidage en matière d'itinéraire
G01S 5/02 - Localisation par coordination de plusieurs déterminations de direction ou de ligne de positionLocalisation par coordination de plusieurs déterminations de distance utilisant les ondes radioélectriques
G01S 13/02 - Systèmes utilisant la réflexion d'ondes radio, p. ex. systèmes du type radar primaireSystèmes analogues
G06Q 50/47 - Requêtes de disponibilité de places de passager pour effectuer un trajet, p. ex. sollicitation d’une course
H04W 4/02 - Services utilisant des informations de localisation
H04W 4/80 - Services utilisant la communication de courte portée, p. ex. la communication en champ proche, l'identification par radiofréquence ou la communication à faible consommation d’énergie
75.
Automated Detection and Propagation of Multi-System Launches
Systems and methods for detecting and orchestrating the deployment of software changes. The system can access schema data indicative of a change associated with a first system, wherein executable code associated with the change is executable within a first computing environment. The system can determine a potential impact to a second system. The system can generate one or more computing tasks to notify the second system of the change and an acknowledgment placeholder with a unique identifier associated with the second system. The system can access data indicative of an execution status of the one or more computing tasks. The system can generate an update indicating that the one or more computing tasks have been executed by the second system. The system can transmit to the first system, data acknowledging the potential impact to the second system, and command instructions to deploy the executable code to a second computing environment.
Systems and methods for query cancellation. The system can receive a query, the query may be associated with one or more requests executable by a computing system. The method includes generating, using a machine-learned model, a query cost for the query, wherein the query cost is indicative of an estimated performance of the computing system. The method includes, based on the query cost, cancelling the query before completing execution of the one or more requests by the computing system.
A system or method for training neural networks using an indirect network. The system receives a set of direct inputs and provides them to a direct network with a set of weights. An indirect network generates a distribution of expected weights for each weight based on indirect parameters. If a direct input includes missing data, the indirect network modifies the distribution to reduce reliance on the incomplete input. Initial weight values are set using the modified distributions, and training input is processed to generate training output. The system determines an error between the expected and training outputs, updating the indirect network's parameters and generating updated distributions of expected weights. The direct network's weights are further updated based on the error and updated distributions. Moreover, the trained direct network generates outputs using the updated weights.
Systems and methods for advertisement-based vehicle matching a routing. For example, a computer-implemented method includes obtaining data associated with a vehicle that is online with a service entity associated with providing vehicle services. The method includes obtaining data associated with a vehicle that is online with a service entity associated with providing one or more vehicle services. The method includes determining that the vehicle is not currently associated with a vehicle service instance for performing the vehicle services and determining a selected advertisement content item for the vehicle. The method includes determining a selected route for the vehicle based on the selected advertisement content item. The method includes communicating data that initiates display of the selected advertisement content item via a display device positioned on an exterior of the vehicle and data indicative of route information to a computing device associated with the vehicle.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Downloadable computer programs for user interface design; downloadable software development kits (SDKs) and application programming interfaces (APIs) for use in connection with machine learning systems; downloadable computer software for designing, building, training, deploying, evaluating, managing, and operating machine learning models; downloadable computer software for generative AI for developers to combine model prompts, generated text, and functions for text-based applications; downloadable computer software for integrating machine learning models into production environments; downloadable computer software for managing and accessing shared data features used in machine learning applications; downloadable computer software for model versioning, model monitoring, model visualization, and automated machine learning (AutoML); downloadable computer software for processing data, training machine learning models, and performing automated analytics; downloadable computer software for use by data scientists, engineers, and researchers in training, testing, and validating machine learning algorithms; downloadable computer software for use in data processing, data labeling, feature engineering, predictive analytics, time series forecasting, and deep learning; downloadable computer software for use in predictive analytics, artificial intelligence, data processing, and automated decision-making; downloadable computer software using artificial intelligence (AI) for introducing developers to large language model fundamentals, and generative AI to build applications; downloadable computer software using artificial intelligence (AI) for use in enabling others to build their own generative AI models using their own data and criteria. (1) User interface (UI) design; technical consulting services in the field of machine learning infrastructure, machine learning operations, and artificial intelligence; providing information in the field of machine learning platform design and deployment; providing online non-downloadable computer software platforms for use in data analysis in the field of machine learning, discovering, organizing, and synthesizing data for use in research and data analysis via machine learning; providing temporary use of non-downloadable software for designing, building, training, deploying, evaluating, managing, and operating machine learning models; providing temporary use of non-downloadable software for generative AI for developers to combine model prompts, generated text, and functions for text-based applications; providing temporary use of non-downloadable software for integrating machine learning models into production environments; providing temporary use of non-downloadable software for managing and accessing shared data features used in machine learning applications; providing temporary use of non-downloadable software for model versioning, model monitoring, model visualization, and automated machine learning (automl); providing temporary use of non-downloadable software for processing data, training machine learning models, and performing automated analytics; providing temporary use of non-downloadable software for use by data scientists, engineers, and researchers in training, testing, and validating machine learning algorithms; providing temporary use of non-downloadable software for use in data processing, data labeling, feature engineering, predictive analytics, time series forecasting, and deep learning; providing temporary use of non-downloadable software for use in predictive analytics, artificial intelligence, data processing, and automated decision-making; providing temporary use of non-downloadable software using artificial intelligence (AI) for introducing developers to large language model fundamentals, and generative AI to build applications; providing temporary use of non-downloadable software using artificial intelligence (AI) for use in enabling others to build their own generative AI models using their own data and criteria; platform as a service (PaaS) and software as a service (SaaS) featuring a machine learning operations (MLOps) platform for managing the end-to-end machine learning lifecycle, including data ingestion, feature engineering, model training, evaluation, deployment, monitoring, and visualization; platform as a service (PaaS) featuring a cloud-based platform for designing, deploying, and managing machine learning models and artificial intelligence systems; Platform-as-a-Service (PaaS) services featuring computer software platforms for machine learning; Platform-as-a-Service (PaaS) services featuring computer software platforms using artificial intelligence for machine learning; Platform-as-a-Service (PaaS) services featuring computer software platforms for discovering, organizing, and synthesizing data for use in research and data analysis via machine learning; software as a service (SaaS) featuring software for use in the development, testing, deployment, and management of machine learning systems; Software-as-a-Service (SaaS) featuring software for providing machine learning model quality metrics and analytics, namely, assessing, monitoring, and reporting the performance, accuracy, and reliability of machine learning models; Software-as-a-Service (SaaS) services featuring software for use in data analysis in the field of machine learning, discovering, organizing, and synthesizing data for use in research and data analysis via machine learning; Software-as-a-Service (SaaS) services featuring software using artificial intelligence for machine learning; Software-as-a-Service (SaaS) services featuring software using artificial intelligence for creating text-based experiences by connecting prompts, data, and models in a user interface.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable computer programs for user interface design; downloadable software development kits (SDKs) and application programming interfaces (APIs) software for use in connection with machine learning systems; downloadable computer software for designing, building, training, deploying, evaluating, managing, and operating machine learning models; downloadable computer software for generative AI enabling software developers to combine model prompts, generated text, and functions for text-based applications; downloadable computer software for integrating machine learning models into production environments; downloadable computer software for managing and accessing shared data features used in machine learning applications; downloadable computer software for model versioning, model monitoring, model visualization, and automated machine learning; downloadable computer software for processing data, training machine learning models, and performing automated analytics; downloadable computer software for use by data scientists, engineers, and researchers in training, testing, and validating machine learning algorithms; downloadable computer software for use in data processing, data labeling, feature engineering, predictive analytics, time series forecasting, and deep learning; downloadable computer software for use in predictive analytics, artificial intelligence, data processing, and automated decision-making; downloadable computer software using artificial intelligence (AI) for introducing software developers to large language model fundamentals and utilizing generative AI to build software applications; downloadable computer software using artificial intelligence (AI) for use in enabling others to build their own generative AI models using their own data and criteria User interface (UI) design; technical consulting services in the field of machine learning infrastructure, machine learning operations, and artificial intelligence; providing technology information in the field of machine learning platform design and deployment; providing online non-downloadable computer software platforms for use in data analysis in the field of machine learning, discovering, organizing, and synthesizing data for use in research and data analysis via machine learning; providing temporary use of non-downloadable software for designing, building, training, deploying, evaluating, managing, and operating machine learning models; providing temporary use of non-downloadable software for generative AI enabling software developers to combine model prompts, generated text, and functions for text-based applications; providing temporary use of non-downloadable software for integrating machine learning models into production environments; providing temporary use of non-downloadable software for managing and accessing shared data features used in machine learning applications; providing temporary use of non-downloadable software for model versioning, model monitoring, model visualization, and automated machine learning; providing temporary use of non-downloadable software for processing data, training machine learning models, and performing automated analytics; providing temporary use of non-downloadable software for use by data scientists, engineers, and researchers in training, testing, and validating machine learning algorithms; providing temporary use of non-downloadable software for use in data processing, data labeling, feature engineering, predictive analytics, time series forecasting, and deep learning; providing temporary use of non-downloadable software for use in predictive analytics, artificial intelligence, data processing, and automated decision-making; providing temporary use of non-downloadable software using artificial intelligence (AI) for introducing software developers to large language model fundamentals, and utilizing generative AI to build software applications; providing temporary use of non-downloadable software using artificial intelligence (AI) for use in enabling others to build their own generative AI models using their own data and criteria; platform as a service (paas) and software as a service (saas) featuring a machine learning operations platform for managing the end-to-end machine learning lifecycle, including data ingestion, feature engineering, model training, evaluation, deployment, monitoring, and visualization; platform as a service (paas) featuring a cloud-based platform for designing, deploying, and managing machine learning models and artificial intelligence systems; platform-as-a-service (paas) services featuring computer software platforms for machine learning; platform-as-a-service (paas) services featuring computer software platforms using artificial intelligence for machine learning; platform-as-a-service (paas) services featuring computer software platforms using machine learning for discovering, organizing, and synthesizing data for use in research and data analysis via machine learning; software as a service (saas) featuring software for use in the development, testing, deployment, and management of machine learning systems; software-as-a-service (saas) featuring software for providing machine learning model quality metrics and analytics, namely, assessing, monitoring, and reporting the performance, accuracy, and reliability of machine learning models; software-as-a-service (saas) services featuring software for use in data analysis in the field of machine learning for discovering, organizing, and synthesizing data for use in research and data analysis via machine learning; software-as-a-service (saas) services featuring software using artificial intelligence for machine learning; software-as-a-service (saas) services featuring software using artificial intelligence for creating text-based experiences by connecting prompts, data, and models in a user interface
81.
OUT-OF-STOCK PREDICTION MODEL AND INTERVENTION METHOD
Systems and methods for determining out-of-stock predictions for merchant items and performing intervention actions based on the out-of-stuck predictions using machine-learned models. The method includes obtaining feature data including at least (i) aggregations of historical found rate data, (ii) item level metadata, and (iii) merchant signal data into an out-of-stock model. The method includes determining a number of output scores for a number of items. The output scores can be indicative of a likelihood of an item being out-of-stock. The method includes determining a first output score for a first item of the plurality of items satisfies an intervention criterion. The method includes responsive to determining the first output score satisfies the intervention criterion, performing an intervention action.
Systems and methods for determining out-of-stock predictions for merchant items and performing intervention actions based on the out-of-stuck predictions using machine-learned models. The method includes obtaining feature data including at least (i) aggregations of historical found rate data, (ii) item level metadata, and (iii) merchant signal data into an out-of-stock model. The method includes determining a number of output scores for a number of items. The output scores can be indicative of a likelihood of an item being out-of-stock. The method includes determining a first output score for a first item of the plurality of items satisfies an intervention criterion. The method includes responsive to determining the first output score satisfies the intervention criterion, performing an intervention action.
A system receives a service request sent from a computing device of a user, specifying an origin location, a destination location, and a service request time. Based on the origin location, service request time, and historical service data for users, a plurality of candidate locations is identified. A location is selected from the candidate locations using criteria including proximity to the origin location and the number of successful service requests at each candidate location during a specified time window. In response to receiving user acceptance of the selected location, the system generates navigation instructions for a provider to travel from their current location to the selected location.
Systems and methods for discovering caller paths in a micro-service graph are disclosed. The system can receive a first request to interact with a first node, wherein the first node is associated with a software application. The system can generate a first bit encoding indicative of the first node. The system can transmit, from the first node, a second request to a second node associated with the software application, wherein the second request is associated with the first request. The system can generate, based on the first bit encoding, a second bit encoding indicative of the first node and the second node. The system can determine, based on the second bit encoding, a sequence of requests.
Various examples are directed to systems and methods for generating improved autonomous vehicle (AV) navigation route data and estimated time of arrival (ETA) data. A system may receive delivery data from an AV delivering a passenger or product, where the delivery data includes a delivery origin, a delivery destination, and an ETA and navigation route generated by the AV. The AV may have accurate short-term information, such as the next few navigation maneuvers planned by the AV. The system may be able to provide more accurate longer-term information, such as generating an overall calculated route and calculated completion time based a current location and the delivery destination. The system may receive updated short-term information and combine this information with the calculated route and calculated completion to generate a more accurate set of route data and ETA data.
An online system receives a trip request including a location of the user requesting the trip. The online system identifies buildings or geographies based on the received location and determines location boundaries associated with the identified buildings or geographies. The online system identifies a set of hotspots representing locations that are frequently used for pickup or drop off. The online system additionally identifies a set of points of interest. The points of interest are, for example, businesses, landmarks, building names, or other visible information related to the location. The online system scores the set of points of interest based on a relative value of displaying the point of interest for orientation or navigation purposes. The online system modifies a user interface to display a map of the area including the identified location boundaries, hotspots, and one or more points of interest based on the scoring.
G06F 16/9538 - Présentation des résultats des requêtes
G01C 21/36 - Dispositions d'entrée/sortie pour des calculateurs embarqués
G06F 3/04845 - 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 pour la transformation d’images, p. ex. glissement, rotation, agrandissement ou changement de couleur
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
G06F 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur
G06F 16/29 - Bases de données d’informations géographiques
G06F 16/9535 - Adaptation de la recherche basée sur les profils des utilisateurs et la personnalisation
G06F 16/9537 - Recherche à dépendance spatiale ou temporelle, p. ex. requêtes spatio-temporelles
G06Q 50/40 - Procédés d’affaires s’appliquant à l’industrie du transport
Example embodiments are directed to systems and methods for generating and providing elevation-aware hotspots. In example embodiments, a network system detects an initiation of a request for a transportation service at a client device of a user and receives an indication of a location of the client device and corresponding signal strengths associated with the client device. The network system then determines a telematics vector based on the signal strengths associated with the client device. Based on the location of the client device and the telematics vector associated with the client device, the network system identifies one or more top ranked elevation-aware hotspots. A pickup point recommendation is then presented, by the network system on a user interface on the client device of the user, whereby the pickup point recommendation includes the one or more top ranked elevation-aware hotspots.
H04W 4/02 - Services utilisant des informations de localisation
H04W 4/029 - Services de gestion ou de suivi basés sur la localisation
H04W 4/80 - Services utilisant la communication de courte portée, p. ex. la communication en champ proche, l'identification par radiofréquence ou la communication à faible consommation d’énergie
A key management system can store, in a tag repository, information associating policy tags with column datasets. The system can receive a client request for an encrypted encryption key (EEK) to access a column dataset, where the client request includes a column name for the column dataset. Based on the client request, the system can perform a lookup in the tag repository to identify one or more tags associated with the column dataset and determine whether the client device is authorized to access the column dataset. Based on determining that the client device is authorized to access the column dataset, the system can generate an original encryption key, and generate the EEK using a shared master key, the original encryption key, and at least the column name for the column dataset. The system may then provide the EEK to the client device over the one or more networks.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
A simulation system accesses historical data generated from a real-world execution of a content delivery platform for a particular market that is defined at least in part by a prior time interval. A set of simulation configurations are identified for the simulation execution, where each simulation configuration represents a deviation to the real-world execution of the content delivery platform. A simulation execution of the content delivery platform for the particular market is generated using the set of simulation configurations. The outcomes of the simulation execution are then recorded and analyzed.
A computer system can estimate preparation times associated with items offered by a plurality of entities to manage a service over a given geographic region. The computer system can receive, from a user device of a user, a request that indicates a user selection of a first set of one or more items to be provided by a first entity and a second set of one or more items to be provided by a second entity. The network system can determine a route of travel for a service provider to navigate in fulfilling the request. The route of navigation can be determined based at least in part on a first set of preparation timing information associated with the first set of one or more items and a second set of preparation timing information associated with the second set of one or more items.
Various examples are directed to systems and methods for operating an autonomous vehicle. A matching cycle may be executed to match respective job requests of a plurality of job requests to at least one respective vehicle of a plurality of vehicles comprising the autonomous vehicle. A set of job requests may be selected from job requests that were matched to the autonomous vehicle during the matching cycle. Verification data may be received indicating that the autonomous vehicle is suitable for the set of job requests. A first offer message may be sent to a first user device associated with a first user making a first job request of the set of job requests. A reply message responsive to the first offer message may be received from the first user device. An instruction may be sent to the autonomous vehicle to begin executing a selected job from the set of job requests. The identity of the selected job may be based at least in part on the first reply message.
Various examples are directed to systems and methods for operating an autonomous vehicle. A matching cycle may be executed to match respective job requests of a plurality of job requests to at least one respective vehicle of a plurality of vehicles comprising the autonomous vehicle. A set of job requests may be selected from job requests that were matched to the autonomous vehicle during the matching cycle. Verification data may be received indicating that the autonomous vehicle is suitable for the set of job requests. A first offer message may be sent to a first user device associated with a first user making a first job request of the set of job requests. A reply message responsive to the first offer message may be received from the first user device. An instruction may be sent to the autonomous vehicle to begin executing a selected job from the set of job requests. The identity of the selected job may be based at least in part on the first reply message.
Example implementations are directed to systems and methods for providing transportation service using an autonomous vehicle (AV) of a third-party system based on multiple stopping locations. A service assignment system receives, from a user, a service request from the user for a transportation service. The system can detect a location of the user and determine whether to share the user location with the third-party system. Based on the determining, the system provides either the user location or a selected pickup location, whereby the selected pickup location is a pickup location determined by the service assignment system or the user. In response to providing the user location or the selected pickup location, the system receives multiple AV stopping locations from the third-party system. An AV stopping location is selected from the multiple AV stopping locations. The AV is then triggered to travel to the selected AV stopping location.
An autonomous vehicle (AV) system is described. The AV system receives a request for a transportation service associated with pickup and drop off (PUDO) locations. The AV system, in response to receiving the request, transmits data representing the PUDO locations to an AV service provider and receives a proposed navigation plan, for an AV, that includes the PUDO locations. The AV system determines whether the proposed navigation plan received from the AV service provider satisfies one or more trip quality criteria. The AV system selectively transmits an offer to the AV service provider to perform the transportation service in response to determining whether the proposed navigation plan received from the AV service provider satisfies the one or more trip quality criteria, the offer comprising the PUDO locations and service quality information corresponding to the one or more trip quality criteria.
Systems and methods for improving inbound/outbound prioritization and processing for large blob workloads. The system can receive latency profiles and determine parameters that control movement of data across data tiers. The method includes receiving a plurality of commands to interact with data. The method includes detecting conditions that fail to satisfy at least one latency profile. The method includes adjusting parameters based on detecting the conditions. Adjusting the parameters adjusts a percentage of data being classified in a first tier to being reclassified in a second tier to satisfy the latency profiles. The method includes processing the plurality of commands based on the adjusted percentage of data.
A computing system can maximize throughput for a common rendezvous location by determining estimated times of arrival (ETAs) to the common rendezvous location for matched users and/or transport providers. Based on the ETAs of each of the transport providers, the computing system can generate a dynamic queue comprising the transport providers for the common rendezvous location and manage the dynamic queue by routing the transport providers through the common rendezvous location. The computing system can further dynamically adjust the queue based on changes to the ETAs by transmitting updated navigation-related data to one or more of the matched transport providers.
A computing system can wirelessly connect with a computing device of a driver of a vehicle while the driver is within the vehicle. The system can determine, based on being wirelessly connected with the computing device of the driver, that the driver of the vehicle is using the computing device, and using a vehicle monitoring system of the vehicle while the vehicle is moving, the system can process sensor data from at least one sensor of the vehicle monitoring system to detect an object ahead of the vehicle. Based on detecting the object and determining that the driver is using the computing device, the system can output an alert to the computing device of the driver.
B60Q 9/00 - Agencement ou adaptation des dispositifs de signalisation non prévus dans l'un des groupes principaux
B60C 9/00 - Armatures des pneumatiques ou disposition des nappes dans ces derniers
B60W 50/14 - Moyens d'information du conducteur, pour l'avertir ou provoquer son intervention
G08G 1/0962 - Dispositions pour donner des instructions variables pour le trafic avec un indicateur monté à l'intérieur du véhicule, p. ex. délivrant des messages vocaux
G08G 1/0967 - Systèmes impliquant la transmission d'informations pour les grands axes de circulation, p. ex. conditions météorologiques, limites de vitesse
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
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
H04W 4/90 - Services pour gérer les situations d’urgence ou dangereuses, p. ex. systèmes d’alerte aux séismes et aux tsunamis
A network system can receive location data from a provider device of a service provider. Using at least the location data in an optimization model, the network system can determine one or more actions for the service provider to optimize one or more metrics. The one or more metrics correspond to at least one of (i) an expected wait time for the service provider over a future period of time, (ii) an expected travel distance between providing services over a future period of time, or (iii) an expected amount of earnings for the service provider over a future period of time. The network system may then transmit a dataset to the provider device to display information corresponding to the one or more actions for the service provider.
H04L 67/61 - 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 tenant compte de la qualité de service [QoS] ou des exigences de priorité
H04L 41/0823 - Réglages de configuration caractérisés par les objectifs d’un changement de paramètres, p. ex. l’optimisation de la configuration pour améliorer la fiabilité
H04L 41/147 - Analyse ou conception de réseau pour prédire le comportement du réseau
H04L 41/16 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets en utilisant l'apprentissage automatique ou l'intelligence artificielle
H04L 41/22 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets comprenant des interfaces utilisateur graphiques spécialement adaptées [GUI]
H04L 41/5025 - Pratiques de respect de l’accord du niveau de service en réagissant de manière proactive aux changements de qualité du service, p. ex. par reconfiguration après dégradation ou mise à niveau de la qualité du service
H04L 41/5054 - Déploiement automatique des services déclenchés par le gestionnaire de service, p. ex. la mise en œuvre du service par configuration automatique des composants réseau
H04L 67/51 - Découverte ou gestion de ceux-ci, p. ex. protocole de localisation de service [SLP] ou services du Web
H04L 67/62 - 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 établissant un calendrier pour servir les requêtes
H04L 67/75 - Services réseau en affichant sur l'écran de l'utilisateur les conditions du réseau ou d'utilisation
Systems and methods herein describe a navigation system for reducing complex maneuvers in navigation instructions. The navigation system receives a transportation request comprising an origin and a destination, determines a first shortest path from the origin to the destination location, and identifies a subset of maneuvers in the first shortest path that are associated with a penalty value. The navigation system further generates a modified path value by applying the penalty value to the identified subset of maneuvers, and in response to identifying that the modified total path value exceeds a threshold value, determines a second shortest path from the origin to the destination. The navigation system generates navigation instructions comprising a second plurality of maneuvers associated with the second shortest path, and transmits the navigation instructions to a computing device.
A computing system generates recommendations for users within the context of a network service. To account for objectives of various users associated with the network service, some of which may not reach optimality at the same time, the computing system generates values associated with each of the objectives separately. For example, for each objective, the system may train a computer model to produce a representative value. To generate a recommendation of an entity for a user, the system uses the generated objective values as inputs to an optimization algorithm. The optimization step may use linear programming or quadratic programming to generate a recommendation score, for example. This two-step process allows the system to account for multiple objectives and makes the system easily adaptable to change when the set of objectives is updated.
