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. 03, 2025

Filed:

Dec. 30, 2022
Applicant:

Nielsen Consumer Llc, Chicago, IL (US);

Inventors:

David Montero, Valladolid, ES;

Javier Martinez Cebrian, Madrid, ES;

Jose Javier Yebes Torres, Valladolid, ES;

Assignee:

Nielsen Consumer LLC, Chicago, IL (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 30/19 (2022.01); G06F 16/901 (2019.01); G06V 10/44 (2022.01); G06V 10/82 (2022.01); G06V 30/14 (2022.01); G06V 30/148 (2022.01);
U.S. Cl.
CPC ...
G06V 30/19187 (2022.01); G06F 16/9024 (2019.01); G06V 10/44 (2022.01); G06V 10/82 (2022.01); G06V 30/1448 (2022.01); G06V 30/153 (2022.01); G06V 30/19107 (2022.01);
Abstract

Methods, apparatus, systems, and articles of manufacture are disclosed that determine related content. An example apparatus includes processor circuitry to generate a segment-level graph by sampling segment-level edges among segment nodes representing text segments, the segment-level graph including segment node embeddings representing features of the segment nodes; cluster the text segments to form entities by applying a first GAN based model to the segment-level graph to update the segment node embeddings; generate a multi-level graph by (a) generating an entity-level graph including hypernodes representing the entities and sampled entity edges connecting ones of the hypernodes, and (b) connecting the segment nodes to respective ones of the hypernodes using relation edges; generate hypernode embeddings by propagating the updated segment node embeddings using a relation graph; and cluster the entities by product by applying a second GAN based model to the multi-level graph, the multi-level graph to generate updated hypernode embeddings.


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