Semantics Content Searching

Number: 20240220503
Published: 2024-07-04
Filed: 2023-01-03
Assignee: Disney Enterprises, Inc.
Inventors: Vilela; Danny et al.
CPC: G06N3/08; G06F16/7837; G06F16/2455; G06N3/045
Verdict: Set aside content search, business
Source: Google Patents · FreePatentsOnline

Abstract

A system includes a processor and a memory storing software code configured to support semantic content searching, one or more machine learning (ML) model(s) trained to translate between images and text, and a search engine populated with content representations output by the ML model(s). The software code is executed to receive a semantic content search query describing a searched content, generate, using the ML model(s) and the semantic content search query, a content representation corresponding to the searched content, and compare, using the search engine, the generated content representation with the content representations populating the search engine to identify one or more candidate matches for the searched content. The software code is further executed to identify one or more content unit(s) each corresponding respectively to one of the candidate matches, and output a query response identifying at least one of the identified content unit(s).

Background

BACKGROUND

Digital media content in the form of streaming movies and television (TV) content, for example, is consistently sought out and enjoyed by users. Nevertheless, the popularity of a particular item of content, for example, a particular movie, TV series, or even a specific TV episode can vary widely. In some instances, that variance in popularity may be due to fundamental differences in personal taste amongst users. However, in many instances, the lack of user interaction with content may be due less to its inherent undesirability to those users than to their lack of familiarity with that content, or even a lack of awareness that the content exists or is available.

Conventional approaches for enabling users to find content of interest include providing search functionality as part of the user interface, for example in the form of a search bar. Conventional search algorithms in streaming platforms use keywords to search for content, including: content titles, actor names, genres. That is to say, in order to use conventional search functionality effectively, users must have a priori knowledge about which titles, actors, or genres they are searching for. However, that reliance on a priori knowledge by a user regarding what content the user is searching for limits the utility of the “search” function by failing to facilitate the search for and discovery of new content. Thus conventional searching undesirably redirects users to familiar content while serving a

Claims

1: A system comprising: a hardware processor a system memory storing a software code configured to support semantic content searching, at least one machine learning (ML) model trained to translate between images and text, and a search engine populated with a plurality of content representations output by the at least one ML model; the hardware processor configured to execute the software code to: receive a semantic content search query describing a searched content; generate, using the at least one ML model and the semantic content search query, a content representation corresponding to the searched content; compare, using the search engine, the generated content representation with the plurality of content representations to identify one or more candidate matches for the searched content; identify one or more content units each corresponding respectively to one of the one or more candidate matches; and output a query response identifying at least one of the one or more content units. || 11: A method for use by a system including a hardware processor and a system memory storing a software code configured to support semantic content searching, at least one machine learning (ML) model trained to translate between images and text, and a search engine populated with a plurality of content representations output by the at least one ML model, the method comprising: receiving, by the software code executed by the hardware processor, a semantic content search query describing a searched content; generating, by the software code executed by the hardware processor and using the at least one ML model and the semantic content search query, a content representation corresponding to the searched content; comparing, by the software code executed by the hardware processor and using the search engine, the generated content representation with the plurality of content representations to identify one or more candidate matches for the searched content; identifying, by the software code executed by the hardware processor, one or more content units each corresponding respectively to one of the one or more candidate matches; and outputting, by the software code executed by the hardware processor, a query response identifying at least one of the one or more content units.