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:
May. 12, 2026

Filed:

Sep. 05, 2023
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Tuan Nguyen, San Jose, CA (US);

Sergei Volnov, London, GB;

Yunfan Ye, Sunnyvale, CA (US);

Alexey Galata, San Jose, CA (US);

William A. Truong, San Jose, CA (US);

Tzu-Chan Chuang, San Francisco, CA (US);

Liang-Yu Chen, Sunnyvale, CA (US);

Qiong Huang, San Jose, CA (US);

Krunal Shah, Mountain View, CA (US);

Sai Aditya Chitturu, Sunnyvale, CA (US);

Sana Mithani, Plantation, FL (US);

Assignee:

GOOGLE LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/80 (2022.01); G06V 40/16 (2022.01); G10L 15/183 (2013.01); G10L 15/30 (2013.01);
U.S. Cl.
CPC ...
G06V 10/80 (2022.01); G06V 40/174 (2022.01); G10L 15/183 (2013.01); G10L 15/30 (2013.01);
Abstract

Implementations relate to generating and using multimodal embeddings. In various implementations, first modality data may be obtained and encoded into first modality embedding(s) using a trained first modality encoder that is stored in memory of edge-based client device(s). Second modality data may be obtained and encoded into second modality embedding(s) using a trained second modality encoder that is also stored in the memory of the edge-based client device(s). The first and second modality embeddings may be processed using an edge-based multimodal LLM that is also stored locally in memory of the edge-based client device(s) to generate a multimodal contextual embedding, which may be provided to a remote server that hosts a central LLM, e.g., in conjunction with a natural language input provided by the user. Information generated using the central LLM, responsive to the natural language input, may be received from the remote server.


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