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:
Aug. 01, 2023

Filed:

Dec. 29, 2021
Applicant:

Yongcong Chen, Beijing, CN;

Inventors:

Yongcong Chen, Beijing, CN;

Jun Zhang, Sichuan, CN;

Ting Zeng, Beijing, CN;

Xingyue Chen, Beijing, CN;

Assignee:

Other;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/004 (2023.01); G06N 5/022 (2023.01); G06N 5/04 (2023.01); G06V 10/82 (2022.01); G06N 20/20 (2019.01); G06N 3/02 (2006.01); G06N 3/063 (2023.01); G06N 3/10 (2006.01); G06N 7/00 (2023.01); G06F 18/22 (2023.01); G06F 18/21 (2023.01);
U.S. Cl.
CPC ...
G06V 10/82 (2022.01); G06F 18/217 (2023.01); G06F 18/22 (2023.01); G06N 3/004 (2013.01); G06N 3/02 (2013.01); G06N 3/063 (2013.01); G06N 3/10 (2013.01); G06N 5/022 (2013.01); G06N 5/04 (2013.01); G06N 7/00 (2013.01); G06N 20/20 (2019.01);
Abstract

In establishment of a general-purpose artificial intelligence system, the machine simulates similarity, repeatability and adjacency of information, and stores the demand, award/penalty and emotion symbols together with related low-level features as a preset relation network. When the machine encounters input information, the machine iteratively identifies low-level features in the input information and stores them in a memory according to a simultaneous storage method. With the low-level features as nodes and the similarity, repeatability and adjacency relations between nodes as connection relations, the machine establish relations between the low-level features and the demand, award/penalty and emotion symbols to extend the relation network. The machine uses the low-level features and searches for related low-level features through a chain associative activation process, searches for imitable experiences through segmented simulation, reassembles the experiences according to a principle of benefit-seeking and harm-avoiding to form an optimal response path.


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