Uber Technologies, Inc.

Relcy, Inc.

Vikram Saxena

Alvin AuYoung

Chandan Prakash Sheth

Sheng Yang

Shivendra Pratap Singh

Livia Zarnescu Yanez

Upamanyu Madhow

Kyle Elliot DeHovitz

Ted Douglas Herringshaw

Joshua Lodge Ross

Kapil Gupta

Chun-Chen Kuo

Berk Gurakan

Rohit Satapathy

Sapeksha Vemulapati

Gaurav Mishra

Jialin Jiao

Haider Ali Razvi

Minzhen Yi

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

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

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 maintaining a current database of point of interest (POI) by automatically detecting a change associated with a POI based on cohort analysis are provided. A networked system accesses trip data associated with the POI. Using the analyzed trip data, the networked system generates a cohort associated with the POI. The networked system monitors for, and detects a change in, trip behavior in the cohort associated with the POI. In response to detecting the change in trip behavior, the networked system updates a database in a data store to indicate a status update.

Automatic detection of point of interest change using cohort analysis

A network system evaluates candidate GPS routes with respect to map routes that are based on ground truth map data to identify defects in the ground truth map data. Defects in the ground truth map data may include inconsistencies between various attributes of the map data and the actual road network represented by the candidate GPS routes under evaluation. The network system corrects the identified feedback to ensure that the ground truth map data accurately reflects the attributes of the actual road network.

Detecting defects in map data

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

Systems and methods are disclosed to enable delivering a contextually relevant action for an underlying focal point of a communication (an 'entity') between users over computing devices. Delivery of a contextually relevant action entails identifying the entity and associated descriptors or amplifying words in the communication surrounding the entity, reviewing databases of actions taken with respect to the identified entity and associated descriptors, reviewing the functions and features of platforms and applications supported on users' computing devices, computing correlations between the actions taken and entity involved and computing devices' available functions and features, and selecting a contextually relevant action from the computed correlation. The selected contextually relevant action is displayed simply as an executable action for a user to take or as a description of the entity or as a series of possible executable actions to take.

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Cupertino, CA, United States of America

Vikram Saxena