US 12,488,792 B2
Real-time video conference chat filtering using machine learning models
Amy Rose, Chapel Hill, NC (US); Andrew James Woodard, Buckinghamshire (GB); and Benjemin Thomas Waine, Cheshunt (GB)
Assigned to NVIDIA Corporation, Santa Clara, CA (US)
Filed by NVIDIA Corporation, Santa Clara, CA (US)
Filed on Jun. 21, 2023, as Appl. No. 18/339,138.
Application 18/339,138 is a continuation of application No. 17/085,618, filed on Oct. 30, 2020, granted, now 11,741,949.
Prior Publication US 2023/0335121 A1, Oct. 19, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G10L 15/18 (2013.01); G06N 3/08 (2023.01); G10L 15/08 (2006.01); G10L 15/16 (2006.01); G10L 15/22 (2006.01); H04L 51/046 (2022.01); H04N 7/15 (2006.01)
CPC G10L 15/1815 (2013.01) [G06N 3/08 (2013.01); G10L 15/16 (2013.01); G10L 15/22 (2013.01); H04L 51/046 (2013.01); G10L 2015/088 (2013.01); H04N 7/15 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
determining a context corresponding to audio data from a data stream;
selecting, based at least on the determined context, at least one natural language processing (NLP) machine learning model from at least two NLP machine learning models, each of the at least two NLP machine learning models having a different level of breadth to evaluate a relevance of textual data to the context;
computing, using the at least one NLP machine learning model and based at least on the textual data, data indicative of the relevance of the textual data to the determined context evaluated at the different level of breadth that corresponds to the at least one NLP machine learning model; and
filtering, based at least on the relevance, the textual data for a display of the textual data in an interface of an application associated with the data stream.