Abstract: Systems and methods herein describe receiving a target location from a computing device, using a machine learning model: determining a first access point and a second access point associated with the target location, causing presentation of the first access point as a first selectable user interface element and the second access point as a second selectable user interface within a graphical user interface on the computing device, receiving a first selection of the first selectable user interface element from the computing device; and in response to receiving the first selection, and initiating a trip request based on the refined map coordinates of the first access point.

Abstract: A system and method for dynamically adjusting attributes for a network service is described. A computing system can determine a change that is to be implemented to an attribute of the network service. For each of a plurality of computing devices, the computing system can receive information associated with that computing device from a designated application operating on that computing device. The designated application is associated with the network service. The computing system automatically implements the change to at least one but not all of the plurality of computing devices based on the information received from each of the plurality of computing devices.

Abstract: Systems and methods for controlling an autonomous vehicle to reduce idle data usage and vehicle downtime are provided. In one example embodiment, a computing system can obtain data associated with autonomous vehicle(s) that are online with a service entity. The computing system can obtain data indicative of the geographic area with an imbalance in a number of vehicles associated with the geographic area. The computing system can determine a first autonomous vehicle for re-positioning with respect to the geographic area based at least in part on the data associated with the one or more autonomous vehicles and the data indicative of the geographic. The computing system can communicating data indicative of a first re-positioning assignment associated with the first autonomous vehicle. In some implementations, the computing system can generate vehicle service incentive to entice a vehicle provider to re-position its autonomous vehicles with respect to the geographic area.

Abstract: 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. In examples, the network system includes a user computing device on which media is recorded and stored in an unrenderable state. The user can elect to make a media recording submission for a particular service activity (e.g., trip provided or received by user). In response to the media recording submission, the user computing device identifies one or more media files that contain media data which depict the service activity. The identified media files are transmitted to a service computing system where the media files can be rendered.

Abstract: A transport system can manage an on-demand transportation service to connect available vehicles with users, and can compile ride history data for each user. The ride history data can indicate the contextual usage of the on-demand transportation service by the user. Based on the ride history data, the transport system can determine demographic and personal interest information of the respective user. The transport system may then personalize one or more ride characteristics of any ride requested by the user based on the demographic and personal interest information determined from the ride history of the user.

Abstract: An inferential user matching system can determine a mutual interest between a first user and a second user. Based on determining the mutual interest, the system can transmit an expression of interest indication to a portable device(s) of the first and/or second user.

Abstract: Provided are systems, methods, and computer-program products for generating a personalized item list. In various examples, a server computer on a network can receive a request that includes a user identifier. The computer can use the user identifier to look up a data model associated with the user identifier. The computer can further determine a geolocation, and use the geolocation to determine a list of items associated with an eatery at or near the geolocation. The computer can input the item list into the data model, for the data model to output a probability for each item, the probability indicating a likelihood that the user will select the item. The probabilities can be used to generate a personalized item list, which can be output onto the network for receipt by a computing device.

Abstract: A computing system computes a timing interval between high-capacity vehicles (HCVs) for each HCV corridor within a geographic region to control rates of HCVs entering and exiting each of the HCV corridors. For each of the HCV corridors, the computing system schedules a first HCV to provide a transport service along the corridor beginning at a first starting time, transmits first schedule data for the corridor to the first HCV, schedules a second HCV to provide the transport service along the corridor beginning at a second starting time after the first starting time according to the timing interval for the corridor, and transmits second schedule data for the corridor to the second HCV.

Abstract: A system can receive, over one or more networks, 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 repeatedly determine, based at least in part on location data corresponding to a directional heading of a proximate transport provider in relation to the current position of the requesting user, an optimal 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, over the one or more networks, data corresponding to the optimal rendezvous location to the computing device of the requesting user.

Abstract: Neural Networks such as Deep Neural Networks (DNNs) output calibrated probabilities that substantially represent frequencies of occurrences of events. A DNN propagates uncertainty information of a unit of the DNN from an input to an output of the DNN. The uncertain information measures a degree of consistency of the test data with training data used to train a DNN. The uncertainty information of all units of the DNN can be propagated. Based on the uncertainty information, the DNN outputs probability scores that reflect received input data that is substantially different from the training data.

Abstract: Systems and methods for optimizing issue reporting in a navigation environment are provided. In example embodiments, a networked system causes presentation of a navigation user interface on a user device of a user, whereby the navigation user interface includes a route being navigated and navigation instructions. The networked system also causes presentation of one or more issue icons on the user device, whereby each issue icon represents a different type of issue to be reported. A selection of an issue icon from the one or more issue icons is received by the networked system. In response to receiving the selection of the issue icon, the networked system infers a location associated with the issue. The networked system updates a database with the issue and the inferred location. In some embodiments, the database is updated after the inferred location or the issue is verified.

Abstract: Computationally implemented methods and systems that are designed for receiving a request for transporting one or more end users towards a destination location; providing a travel plan for facilitating the one or more end users to travel to the destination location from a starting location, the travel plan identifying at least two route legs including at least one transport route leg that calls for at least one transportation vehicle unit to transport the one or more end users over the transport route leg; and directing the at least one transportation vehicle unit to rendezvous with the one or more end users at a rendezvous location in order to transport the one or more end users over the transport route leg. In addition to the foregoing, other aspects are described in the claims, drawings, and text.

Abstract: Systems and methods for controlling an autonomous vehicle to reduce idle data usage and vehicle downtime are provided. In one example embodiment, a computing system can obtain data associated with autonomous vehicle(s) that are online with a service entity. The computing system can obtain data indicative of the geographic area with an imbalance in a number of vehicles associated with the geographic area. The computing system can determine a first autonomous vehicle for re-positioning with respect to the geographic area based at least in part on the data associated with the one or more autonomous vehicles and the data indicative of the geographic. The computing system can communicating data indicative of a first re-positioning assignment associated with the first autonomous vehicle. In some implementations, the computing system can generate vehicle service incentive to entice a vehicle provider to re-position its autonomous vehicles with respect to the geographic area.

Abstract: Systems and methods for determining appropriate energy replenishment and controlling autonomous vehicles are provided. An example computer-implemented method can include obtaining one or more energy parameters associated with an autonomous vehicle. The method can include determining a refueling task for the autonomous vehicle based at least in part on the energy parameters associated with the autonomous vehicle. The refueling task comprises a first refueling task that is interruptible by a vehicle service assignment or a second refueling task that is not interruptible by the vehicle service assignment. The method can include communicating data indicative of the refueling task to the autonomous vehicle or to a second computing system that manages the autonomous vehicle. The method can include determining whether the refueling task for the autonomous vehicle has been accepted or rejected.

Abstract: 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.

Abstract: 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.

Abstract: A network system, such as a transport management system, efficiently allocates resources by monitoring the geospatial and topological locations of a rider responsive to receiving a trip request. A trip management module matches a rider with an available driver based in part on an comparison of the estimated times of arrival of the rider and the driver at the pickup location. A client positioning module monitors the rider's progress through nodes and edges in a topological graph associated with the origin location based on radio signatures received at the rider client device. A client ETA module calculates a rider ETA based on the rider's rate of travel through the origin location represented by nodes in the topological graph. Responsive to determining that the rider ETA and the driver ETA vary by over a threshold amount of time, the trip management module matches the rider with a second available driver.

Abstract: 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.

Abstract: This disclosure generally relates to a light electric vehicle. More specifically, this disclosure describes how to limit or restrict access to a light electric vehicle based on determined or anticipated environmental conditions. The disclosure also describes how to change one or more capabilities or operating parameters of the light electric vehicle based on determined and/or anticipated environmental conditions.

Abstract: Systems and methods for multi-autonomous vehicle servicing and control are provided. A method can include receiving a service request for a vehicle service. The method can include determining a vehicle route from the start location to the end location. The method can include identifying a plurality of candidate vehicles from a plurality of autonomous vehicles. The method can include obtaining data indicative of one or more operational capabilities for each candidate vehicle in the plurality of autonomous vehicles. The method can include segmenting the vehicle route into one or more route segments based on the one or more operational capabilities associated with each autonomous vehicle in the plurality of autonomous vehicles. The method can include assigning at least two candidate vehicles to perform the vehicle service by assigning at least one of the one or more route segments to each of the at least two candidate vehicle.