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:
Jun. 30, 2026

Filed:

Sep. 18, 2023
Applicant:

Dropbox, Inc., San Francisco, CA (US);

Inventors:

Eric Cunningham, Kirkland, WA (US);

Bradley Crossen, Texas City, TX (US);

Tejas Patel, Bellevue, WA (US);

Royce Ausburn, Melbourne, AU;

Brett Bergeron, Portland, OR (US);

Assignee:

Dropbox, Inc., San Francisco, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/284 (2020.01); G06F 16/907 (2019.01); G06F 40/205 (2020.01); G06F 40/40 (2020.01);
U.S. Cl.
CPC ...
G06F 40/284 (2020.01); G06F 16/907 (2019.01); G06F 40/205 (2020.01); G06F 40/40 (2020.01);
Abstract

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating content-item-specific large language model responses from content items by segmenting a content item and selecting relevant sections of the content item to provide to a large language model to generate a corresponding output. In particular, in one or more embodiments, the disclosed systems can generate a text representation that includes a plurality of text segments each comprising a number of tokens of the text representation. Further, the systems can extract, from the plurality of text segments, segment-specific text embeddings that correspond to respective portions of the text representation of the content item. Additionally, the systems can determine a segment-specific text embedding corresponding to a model output request. Moreover, the systems can generate a model output by passing a text segment corresponding to the segment-specific text embedding to a large language model together with the model output request.


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