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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.

Patent No.:

US 12002068 B1

PDFFull Text
Date of Patent:
Jun. 04, 2024

Filed:

Sep. 25, 2019

Data processing system with machine learning engine to provide output generation functions

Applicant:

Allstate Insurance Company, Northbrook, IL (US);

Inventors:

Sunil Chintakindi, Menlo Park, CA (US);

Timothy W. Gibson, Barrington, IL (US);

Howard Hayes, Glencoe, IL (US);

Regina Madigan, Mountain View, CA (US);

Soton Ayodele Rosanwo, Chicago, IL (US);

Juan Ortiz-Zabala, Buffalo Grove, IL (US);

Assignee:

ALLSTATE INSURANCE COMPANY, Northbrook, IL (US);

Attorney:

Polsinelli PC

Primary Examiner:

Mohamed Barakat

Assistant Examiner:

Rufus C Point

Int. Cl.
CPC ...
G06Q 30/0207 (2023.01); A61B 5/024 (2006.01); G06F 16/335 (2019.01); G06F 21/31 (2013.01); G06F 21/32 (2013.01); G06N 20/00 (2019.01); G06Q 30/0251 (2023.01); G16H 10/60 (2018.01); H04W 4/029 (2018.01);
U.S. Cl.
CPC ...
G06Q 30/0239 (2013.01); A61B 5/024 (2013.01); G06F 16/337 (2019.01); G06F 21/31 (2013.01); G06F 21/32 (2013.01); G06N 20/00 (2019.01); G06Q 30/0222 (2013.01); G06Q 30/0236 (2013.01); G06Q 30/0269 (2013.01); G16H 10/60 (2018.01); H04W 4/029 (2018.02); G06Q 30/0255 (2013.01); G06Q 30/0261 (2013.01);
Abstract

Methods, computer-readable media, systems, and/or apparatuses for providing offer and output generation functions are provided. For instance, a request for an output may be received by a device. In response, user physical trait data, pre-stored data, location data, and the like, may be extracted and received from a plurality of sources. Machine learning may be used to analyze the data to predict one or more factors associated with the user and the output or offer may be generated based on the predicted factors.

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