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:
Feb. 25, 2025

Filed:

Mar. 16, 2018
Applicant:

Ford Motor Company, Dearborn, MI (US);

Inventors:

Jamel Seagraves, Campbell, CA (US);

Chih-wei Tang, Mountain View, CA (US);

Sudipto Aich, Palo Alto, CA (US);

Beaudry Kock, San Francisco, CA (US);

Assignee:

Ford Global Technologies, LLC, Dearborn, MI (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06N 5/04 (2023.01); B62H 3/00 (2006.01); B62H 5/00 (2006.01); G01S 19/01 (2010.01); G06F 16/29 (2019.01); G06F 16/9537 (2019.01); G06N 20/00 (2019.01); G06Q 10/0631 (2023.01); G06Q 30/0645 (2023.01); G06Q 50/40 (2024.01);
U.S. Cl.
CPC ...
G06N 5/04 (2013.01); B62H 3/00 (2013.01); B62H 5/00 (2013.01); G01S 19/01 (2013.01); G06F 16/29 (2019.01); G06F 16/9537 (2019.01); G06N 20/00 (2019.01); G06Q 10/06315 (2013.01); G06Q 50/40 (2024.01); G06Q 30/0645 (2013.01); G06Q 2240/00 (2013.01);
Abstract

An intelligent bicycle sharing system, or other vehicle sharing system, is able to provide helpful bicycle availability predictions based on historical data, including various utilization statistics. Historical data can be collected over time as users use the bicycle sharing system. For example, the historical data may include the number of available bicycles at various locations and times, as well as contextual data associated with the locations and times. Contextual data may include data regarding the weather, local events, season, day of the week or year, news events, among other environmental factors that may or may not influence bicycle utilization. In some embodiments, a model, such as a machine learning model (e.g., neural network) may be trained using the historical data as training data such that the model can predict bicycle availability for a certain future time and location.


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