Uber Technologies, Inc.

Shivendra Pratap Singh

Chandan Prakash Sheth

Sheng Yang

Alvin AuYoung

Vikram Saxena

Livia Zarnescu Yanez

Upamanyu Madhow

Vasudev Parameswaran

Jane Alam Jan

Daniel Wolf

Neil Chagas Fernandes

Aditya Somani

Krishna Aditya Gabbita

Kyle Elliot DeHovitz

Ted Douglas Herringshaw

Joshua Lodge Ross

Satyendra Kumar Nainwal

Dineshkumar Karuppanna Gounder Ramasamy

Kaivalya Bachubhai Parikh

Gaurang Ramakant Khetan

Chun-Chen Kuo

Minzhen Yi

A system receives a service request sent from a computing device of a user, which identifies a service to be provided. The system then identifies potential locations based on the service request and historical data. These locations are assessed based on predetermined criteria, including an amount of successful and unsuccessful service requests at each location according to the historical data. After a location is chosen, the user's acceptance is obtained. Upon receiving this acceptance, the system generates and sends navigation instructions from the provider's current position to the selected location to a second computing device associated with the provider.

Location determination based on historical service data

Systems and methods of using sensor data for coordinate prediction are disclosed herein. In some example embodiments, for a place, a computer system accesses corresponding service data comprising pick-up data and drop-off data for requests, and accesses corresponding sensor data indicating at least one path of mobile devices of the requesters of the requests, with the at least one path comprising at least one of a pick-up path ending at the pick-up location indicated by the pick-up data and a drop-off path beginning at the drop-off location indicated by the drop-off data. In some example embodiments, the computer system generates at least one predicted geographic location using the paths indicated by the sensor data, and stores the at least one predicted geographic location in a database in association with an identification of the place.

Using sensor data for coordinate prediction

A network system receives a service request sent from a computing device of a user. The service request identifies a service to be provided by a provider and a service request location. In response to receiving the service request, the network system identifies a plurality of candidate locations. The network system selects the location from a plurality of candidate locations according to predetermined criteria including a frequency measurement of each candidate location. The network system replaces the service request location with the selected location. The network system sends the selected location to the computing device. Responsive to receiving an acceptance of the selected location from the computing device, the network system generates navigation instructions for the provider from a current location of the provider to the selected location.

In some example embodiments, a computer system performs operations comprising: receiving a request for a transportation service associated with a place; determining a type of the transportation service from among a plurality of types of transportation services based on the request; retrieving an entrance geographic location for the place from a database based on the type of the transportation service, the entrance geographic location being stored in association with the place in the database, and the entrance geographic location representing an entrance for accessing the place; generating route information based on the retrieved entrance geographic location, the route information indicating a route from an origin geographic location of a computing device of a user to the entrance geographic location of the place; and causing the generated route information to be displayed within a user interface on a computing device of the user.

Determining navigation data based on service type

Systems and methods for detecting and verifying closed places (e.g., claims no longer in business) from trip data are provided. A networked system accesses trip data associated with the POI. The networked system processes the trip data to generate at least two time buckets based on timestamps from the trip data associated with the POI, and calculates trip counts associated with the POI for each of the time buckets. Using a machine learning algorithm and based on the at least two time buckets, the networked system determines that the trip counts show a decline over time that indicates that the POI is likely closed. In response to the determining, the networked system updates a database to indicate the POI is closed.

Detecting attribute change from trip data

An authoritative candidate is selected for determining a location of a point of interest (POI). Source data including name, address, and location for POIs is received from multiple data sources. The received data is normalized for ease of comparison, and coordinates for each candidate are compared to coordinates of other candidates to determine which candidate if any is an authoritative location for the POI. The candidate locations are compared using two models a metric-based scoring system and a machine learning model that may utilize a gradient boosted decision tree. The authoritative candidate can be used to render digital maps that include the POI. In addition, the authoritative candidate's location can be used to provide vehicle route guidance to the POI.

Vehicle routing guidance to an authoritative location for a point of interest

Systems and methods for improving attribute data for a point of interest (POI) are provided. A networked system accesses trip data associated with the POI. The networked system generates, using a processor-implemented clustering algorithm, a first spatial cluster and a second spatial cluster using coordinates corresponding to the POI indicated in the trip data. A centroid for the first spatial cluster and a centroid for the second spatial cluster are identified by the networked system. The networked system determines that a difference in distance between the centroid for the first spatial cluster and the centroid for the second spatial cluster meets or transgresses a centroid distance threshold. In response to the determining, a database is updated to indicate a new attribute for the POI, the new attribute corresponds to an attribute associated with either the first spatial cluster or the second spatial cluster.

Systems and methods for improving accuracy of geographic position data are provided. A networked system mines ticket data from content of the ticket. Based on the ticket data, a determination is made that the ticket indicates an issue with a trip involving a point of interest (POI). The networked system extracts trip data from a trip log corresponding to the trip involving the POI, and identifies, from a data storage, stored attributes of the POI. The networked system analyzes the ticket data, trip data, and attributes to determine a workflow to improve accuracy of the POI, whereby the analyzing comprises determining a priority level to verify accuracy of the POI. The workflow is triggered based on the priority level to verify accuracy of the POI.

Point of interest accuracy using tickets

Systems and methods of deep learning coordinate prediction using satellite and service data are disclosed herein. In some example embodiments, for each one of a plurality of places, a computer system trains a deep learning model based on training data of the plurality of places. The deep leaning model is configured to generate a predicted geographical location of a place based on satellite image data and service data associated with the place. The training data for each place comprises satellite image data of the place, service data, and a ground truth geographical location of the place. The service data comprises at least one of pick-up data indicating a geographical location at which a provider started transporting a requester in servicing a request associated with the place or drop-off data indicating a geographical location at which the provider completed transporting the requester in servicing the request associated with the place.

Deep learning coordinate prediction using satellite and service data

An authoritative candidate is selected for determining a location of a point of interest (POI). Source data including name and location for POIs is received from multiple data sources. The received data is normalized for ease of comparison, and coordinates for each candidate are compared to coordinates of other candidates to determine which candidates lie outside of a consensus set of candidates. A consensus set of candidates are those that are each located on a same side of a road segment. Candidates that are not part of the consensus set are eliminated from consideration. Further criteria are then applied to the consensus set of candidates. The authoritative candidate can be used to render digital maps that include the POI. In addition, the authoritative candidate's location can be used to provide vehicle route guidance to the POI.

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Redwood City, CA, United States of America

Shivendra Pratap Singh