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. 24, 2025

Filed:

Feb. 29, 2024
Applicant:

Gracenote, Inc., New York, NY (US);

Inventors:

Konstantinos Antonio Dimitriou, San Francisco, CA (US);

Amanmeet Garg, Santa Clara, CA (US);

Assignee:

Gracenote, Inc., New York, NY (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 20/00 (2022.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06N 3/045 (2023.01); G06V 10/426 (2022.01); G06V 20/40 (2022.01); H04N 21/234 (2011.01);
U.S. Cl.
CPC ...
G06V 20/49 (2022.01); G06F 18/2148 (2023.01); G06F 18/22 (2023.01); G06N 3/045 (2023.01); G06V 10/426 (2022.01); G06V 20/41 (2022.01); H04N 21/23418 (2013.01);
Abstract

Methods and systems for automated video segmentation are disclosed. A sequence of video frames having video segments of contextually-related sub-sequences may be received. Each frame may be labeled according to segment and segment class. A video graph may be constructed in which each node corresponds to a different frame, and each edge connects a different pair of nodes, and is associated with a time between video frames and a similarity metric of the connected frames. An artificial neural network (ANN) may be trained to predict both labels for the nodes and clusters of the nodes corresponding to predicted membership among the segments, using the video graph as input to the ANN, and ground-truth clusters of ground-truth labeled nodes. The ANN may be further trained to predict segment classes of the predicted clusters, using the segment classes as ground truths. The trained ANN may be configured for application runtime video sequences.


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