The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 27, 2023

Filed:

Dec. 17, 2019
Applicant:

Mapbox, Inc., San Francisco, CA (US);

Inventors:

Camilo Sanin Riano, Oakland, CA (US);

Ruggero Altair Tacchi, San Francisco, CA (US);

David Ariel Gold, Columbia, CA (US);

Assignee:

Mapbox, Inc., Washington, DC (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01C 21/34 (2006.01); G01C 21/36 (2006.01); G06N 20/00 (2019.01); G01C 21/32 (2006.01); G06F 16/29 (2019.01); G01C 21/16 (2006.01);
U.S. Cl.
CPC ...
G01C 21/3492 (2013.01); G01C 21/16 (2013.01); G01C 21/32 (2013.01); G01C 21/3694 (2013.01); G06F 16/29 (2019.01); G06N 20/00 (2019.01);
Abstract

A method for correcting speed estimates for route planning using a machine-learned speed correction model trained on aggregated road data. Location and movement data collected from a plurality of mobile computing devices is aggregated on a server computer and used to train a speed correction model to correct estimated speeds corresponding to roads in one or more geographic regions. Speeds estimates for a road segment in a geographic region are corrected using a speed correction model trained on road data describing road segments in the same geographic region. In some embodiments, road data corresponding to one or more geographic regions is assigned to groups in training the speed correction model. The road data may be anonymized or segmented such that an originating device or route is unidentifiable. More fine-grained speed correction models may also be trained for different or additional factors than geographic region, such as day and/or time.


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