Location reconciliation based on multiple computing device signals

Patent metadata

Field	Value
Publication number	US-12112292-B2
Application number	US-202217976286-A
Country	US
Kind code	B2
Filing date	Oct 28, 2022
Priority date	Oct 28, 2022
Publication date	Oct 8, 2024
Grant date	Oct 8, 2024

Abstract

Systems and methods for reconciling location based on multiple computing device signals. For example, the computing system can obtain location datasets associated with freight carrier services from computing sources. The computing system can determine an expected signal pattern for a location associated with a freight transportation service. The computing system can determine, for each computing source, a confidence score. The confidence score can represent the probability that the respective location dataset is associated with a load being transported for a freight transportation service. The computing system can determine a primary location dataset based on the confidence scores. The computing system can perform actions based on the primary location dataset.

First claim

What is claimed is:

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.

2. The computing system of claim 1 , wherein generating, for a respective computing source, the respective confidence score based on the expected signal pattern and the respective location dataset comprises:
generating the respective confidence score using a machine-learned model trained to output the probability that the respective location dataset of the respective computing source is representative of the load location associated with a load being transported for the first freight transportation service.

3. The computing system of claim 2 , wherein the machine-learned model comprises a plurality of input layers, each input layer being associated with a different location dataset of a computing source.

4. The computing system of claim 2 , wherein the machine-learned model is a gradient boosted tree model.

5. The computing system of claim 1 , wherein the one or more actions associated with the first freight transportation service comprises:
determining, based on the primary location dataset for the load, at least one of an in-time value or an out-time value for the location associated with the first freight transportation service.

6. The computing system of claim 1 , wherein the one or more actions associated with the first freight transportation service comprises at least one of: (i) tracking a progress of the load for the first freight transportation service; (ii) updating an estimated time of arrival for the load; (iii) adjusting a risk model; (iv) updating a status associated with the first freight transportation service; (v) outputting a notification indicative of the status for display via a user device; (vi) determining a performance of the freight carrier; (vii) determining a compensation value for the freight carrier; or (viii) retraining a machine-learned model trained to determine the respective confidence score.

7. The computing system of claim 1 , wherein the location associated with the first freight transportation service comprises a facility for pick-up or delivery of the load, and wherein determining the expected signal pattern comprises:
filtering the plurality of location datasets to a limited time period associated with an appointment time associated with the first freight transportation service;
filtering the plurality of location datasets to a limited geographical area associated with the facility; and
determining the expected signal pattern based on the limited time period and the limited geographical area.

8. The computing system of claim 1 , wherein the expected signal pattern is indicative of a distance and time relationship for at least one of: (i) approaching the location associated with the first freight transportation service; or (ii) leaving the location associated with the first freight transportation service.

9. The computing system of claim 1 , wherein
computing the respective confidence score comprises:
generating a comparison of the respective location dataset of the respective computing source associated with the freight carrier at the different instances of time with the expected signal pattern of the freight carrier; and
determining the respective confidence score based on the comparison of the respective location dataset of the respective computing source associated with the freight carrier at the different instances of time with the expected signal pattern of the freight carrier.

10. The computing system of claim 1 , wherein the computing sources comprise at least one of: (i) a mobile application, (ii) a freight carrier device, (iii) a third-party location service, or (iv) a carrier system.

11. The computing system of claim 1 , wherein at least one respective location dataset comprises a plurality of GPS signals.

12. The computing system of claim 1 , wherein the expected signal pattern comprises a plurality of data points associated with an expected travel path indicative of a road traversed to arrive at the location associated with the first freight transportation service.

13. The computing system of claim 1 , wherein automatically generating the geofence for the location associated with the first freight transportation service comprises:
determining that the density of a second location of the plurality of locations is above a threshold density; and
generating, in response to determining that the density of the second location of the plurality of locations is above the threshold density, the geofence encompassing at least the first location and the second location.

14. A computer-implemented method, performed by one or more processors, 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

Assignees

• Uber Technologies Inc

Inventors

• Deshpande Ajinkya Manoj
• Gupta Mudit
• Rane Siddharth
• Tromblee Martin Alan
• Wang Jianing
• Wang Yu
• Wee David
• Wei Cheng

Classifications

• H04W64/006

  with additional information processing, e.g. for direction or speed determination · CPC title

• G06Q10/0833 Primary

  Tracking · CPC title