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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 11868851 B1

PDFFull Text
Date of Patent:
Jan. 09, 2024

Filed:

Mar. 11, 2016

Systems and methods for predicting outcomes using a prediction learning model

Applicant:

Symphonyai Sensa Llc, Menlo Park, CA (US);

Inventor:

Gunnar Carlsson, Stanford, CA (US);

Assignee:

SymphonyAI Sensa LLC, Palo Alto, CA (US);

Attorney:

Ahmann Kloke LLP

Primary Examiner:

Miranda M Huang

Assistant Examiner:

Chase P Hinckley

Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 11/30 (2006.01); G06F 16/35 (2019.01); G06N 7/01 (2023.01); G06N 3/04 (2023.01); G06N 5/025 (2023.01); G16H 50/50 (2018.01); G16H 50/20 (2018.01); G06F 16/75 (2019.01); G06F 16/28 (2019.01); G06F 16/45 (2019.01); G06F 16/906 (2019.01); G06F 16/55 (2019.01); G06F 16/65 (2019.01); G05B 23/02 (2006.01); G06F 18/23 (2023.01); G06F 18/2137 (2023.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 11/3048 (2013.01); G06F 16/35 (2019.01); G06N 3/04 (2013.01); G06N 7/01 (2023.01); G05B 23/0281 (2013.01); G06F 16/285 (2019.01); G06F 16/45 (2019.01); G06F 16/55 (2019.01); G06F 16/65 (2019.01); G06F 16/75 (2019.01); G06F 16/906 (2019.01); G06F 18/2137 (2023.01); G06F 18/23 (2023.01); G06N 5/025 (2013.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01);
Abstract

A method comprises receiving a network of a plurality of nodes and a plurality of edges, each of the nodes comprising members representative of at least one subset of training data points, each of the edges connecting nodes that share at least one data point, grouping the data points into a plurality of groups, each data point being a member of at least one group, creating a first transformation data set, the first transformation data set including the training data set as well as a plurality of feature subsets associated with at least one group, values of a particular data point for a particular feature subset for a particular group being based on values of the particular data point if the particular data point is a member of the particular group, and applying a machine learning model to the first transformation data set to generate a prediction model.

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