Abstract: A method includes receiving a series of video segments and providing the series of video segments as input to a first machine learning model to produce text data. The text data is provided as input to a second machine learning model to produce categorized text data that includes a classification indication. The classification indication is added to metadata of the video segment, and the categorized text data is provided as input to a third machine learning model to produce a semantic vector. The method also includes causing the video segment and the metadata that includes the classification indication to be stored at a location of a database based on the semantic vector, the database being configured to be searched based on a search query associated with the semantic vector.
Type:
Grant
Filed:
August 12, 2024
Date of Patent:
July 1, 2025
Assignee:
CIPIO Inc.
Inventors:
Sundeep Sanghavi, Jin Yu, Growson Edwards, Harshil Shah
Abstract: This application directs to methods and systems for visual content retrieval using semantic search. An embodiment provides a method for generating media feature vectors from media data segments using jointly trained machine learning models, and storing these with entity indicators in a vector-based search database. An input vector is generated from text or image data, and a processor calculates cosine similarities between the input vector and existing media feature vectors to retrieve and rank relevant media segments. The method also includes generating a mean feature vector from the retrieved set and comparing it with mean feature vectors of other entities for ranking. There are other embodiments as well.