US 12,462,091 B2
Similar information determination and text generation for auto-annotation of web documents
Sunil Pinnamaneni, Mineola, NY (US)
Assigned to ExactNote, Inc., Dover, DE (US)
Filed by Sunil Pinnamaneni, Mineola, NY (US)
Filed on Dec. 17, 2023, as Appl. No. 18/542,735.
Application 18/542,735 is a continuation of application No. 17/541,146, filed on Dec. 2, 2021, granted, now 11,886,791.
Application 17/541,146 is a continuation of application No. 16/679,278, filed on Nov. 10, 2019, granted, now 11,321,515, issued on May 3, 2022.
Prior Publication US 2024/0119217 A1, Apr. 11, 2024
Int. Cl. G06F 40/106 (2020.01); G06F 3/04817 (2022.01); G06F 3/0482 (2013.01); G06F 3/0483 (2013.01); G06F 3/04847 (2022.01); G06F 3/0489 (2022.01); G06F 16/483 (2019.01); G06F 16/58 (2019.01); G06F 16/81 (2019.01); G06F 16/84 (2019.01); G06F 16/93 (2019.01); G06F 16/953 (2019.01); G06F 16/955 (2019.01); G06F 16/957 (2019.01); G06F 40/169 (2020.01); G06F 40/20 (2020.01); G06F 40/30 (2020.01); G06N 20/00 (2019.01); G06Q 30/0241 (2023.01)
CPC G06F 40/106 (2020.01) [G06F 3/0483 (2013.01); G06F 3/04847 (2013.01); G06F 16/483 (2019.01); G06F 16/5866 (2019.01); G06F 16/81 (2019.01); G06F 16/86 (2019.01); G06F 16/94 (2019.01); G06F 16/953 (2019.01); G06F 16/9566 (2019.01); G06F 16/9577 (2019.01); G06F 40/169 (2020.01); G06Q 30/0276 (2013.01); G06Q 30/0277 (2013.01); G06F 3/04817 (2013.01); G06F 3/0482 (2013.01); G06F 3/0489 (2013.01); G06F 16/957 (2019.01); G06F 40/20 (2020.01); G06F 40/30 (2020.01); G06N 20/00 (2019.01)] 21 Claims
OG exemplary drawing
 
1. A computer-implemented method, for similar information determination and text generation for auto-annotations of potential interest to a user within a web document, comprising:
calculating a source set of sentence embedding vectors associated with sentences coming from a plurality of source content, wherein a type of an element of the plurality of source content is selected from a group comprised of web pages, documents, and annotations of web pages and documents, using a deep learning model for sentence embeddings;
loading a web document into a web browser when a user navigates the web browser to a corresponding URL for the web document;
calculating, using the deep learning model for sentence embedding vectors, a request set of sentence embedding vectors associated with sentences coming from a plurality of request content, wherein a type of an element of the plurality of request content is selected from a group comprised of the web document, annotations of the web document, replies to the annotations of the web document, and items associated to the web document that belong to a collection that contains the web document;
retrieving through APIs a retrieved subset of the plurality of source content using the request set of sentence embedding vectors and the source set of sentence embedding vectors;
auto-annotating a subset of the plurality of request content to create auto-annotations using the retrieved subset of the plurality of source content and the plurality of request content using a deep learning model for natural language processing, wherein the subset of the plurality of request content and auto-annotations are of potential interest to the user; and
displaying the auto-annotations to the user on the web document displayed in the web browser or the annotation capable web browser.