US 12,112,292 B2
Location reconciliation based on multiple computing device signals
Ajinkya Manoj Deshpande, San Jose, CA (US); Mudit Gupta, Berkeley, CA (US); Siddharth Rane, Lynnwood, WA (US); Martin Alan Tromblee, Camas, WA (US); Jianing Wang, Fremont, CA (US); Yu Wang, San Francisco, CA (US); David Wee, El Sobrante, CA (US); and Cheng Wei, Fremont, CA (US)
Assigned to UBER TECHNOLOGIES, INC., San Francisco, CA (US)
Filed by Uber Technologies, Inc., San Francisco, CA (US)
Filed on Oct. 28, 2022, as Appl. No. 17/976,286.
Prior Publication US 2024/0144160 A1, May 2, 2024
Int. Cl. G06Q 10/0833 (2023.01); H04W 64/00 (2009.01)
CPC G06Q 10/0833 (2013.01) [H04W 64/006 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computing system, comprising:
one or more processors; and
one or more non-transitory, computer-readable media storing instructions that are executable by the one or more processors to cause the computing system to perform operations, the operations comprising:
obtaining a plurality of location datasets from a plurality of computing sources, wherein a respective location dataset is indicative of a location of a freight carrier at different instances of time;
determining, for the freight carrier, an expected signal pattern for a location associated with a first freight transportation service;
for each respective computing source of the plurality of computing sources, generating a respective confidence score based on the expected signal pattern and the respective location dataset of the respective computing source, wherein the respective confidence score is indicative of a probability that the respective location dataset of the respective computing source is representative of a load location associated with a load being transported for the first freight transportation service;
determining, from among the plurality of location datasets, a primary location dataset for representation of the load location being transported for the first freight transportation service based on the respective confidence scores for the plurality of location datasets;
automatically generating a geofence for the location associated with the first freight transportation service by:
obtaining data indicative of a geographical radius associated with the location associated with the first freight transportation service;
determining a density of a plurality of locations within the geographical radius, wherein the density is indicative of a number of GPS pings at a respective location of the plurality of locations within the geographical radius during a time period;
determining that the density of a first location of the plurality of locations is above a threshold density; and
generating, in response to determining that the density of the first location of the plurality of locations is above the threshold density, the geofence encompassing at least the first location of the plurality of locations; and
performing one or more actions associated with the first freight transportation service based on the primary location dataset and the geofence.