LOGICAL WIDTH-BASED PROMPT CONDITIONING FOR GENERATIVE AI AND LLM

Number: 20260252620
Published: 2026-08-27
Filed: 2025-02-25
Assignee: DISNEY ENTERPRISES, INC.
Inventors: ACCARDO; Anthony M.
CPC: G06F16/367; G06F16/383
Verdict: Set aside streaming, recommendation, search/metadata
In edition: 2026-W36
Source: Google Patents · FreePatentsOnline

The keeper's note

Abstract

The present invention sets forth a technique for performing automated generation of descriptive metadata, the computer-implemented method comprising receiving one or more taxonomies associated with a media content ontology domain, a description of the one or more taxonomies, and one or more hierarchical relationships associated with the one or more taxonomies. The method also includes generating an ontology based on the one or more descriptions and the one or more hierarchical relationships and generating a prompt that includes a representation of the ontology, contextual information associated with a media content item, and a textual instruction to a machine learning model. The method further includes generating, via the machine learning model and based at least on the prompt, one or more descriptive metadata tags associated with the media content item.

Background

BACKGROUND Field of the Various Embodiments

Embodiments of the present disclosure relate generally to generative machine learning and, more specifically, to techniques for performing logical width-based prompt conditioning for generative machine learning models. Description of the Related Art

Generating descriptive metadata associated with a media content item is a common task in the field of machine learning. Media content items may include (but are not limited to) films, television or streaming episodes, podcasts, or other audiovisual content. Generated descriptive metadata may be used to organize media content items by classifying individual media content items into one or more categories based on characteristics associated with the media content item. The characteristics may include a genre associated with the media content item, a setting associated with the media content item, story elements included in the media content item, and/or characters or actors included in the media content item. Descriptive metadata may also serve as an input in a recommendation system that suggests one or more media content items to a consumer based on the descriptive metadata associated with the media content item.

Existing techniques for generating descriptive metadata may include manual annotation of a media content item by a human reviewer. These techniques may be time-consuming, as they may require that the human reviewer watch or listen to all or part of a media co

Claims

1. A computer-implemented method for performing automated generation of descriptive metadata, the computer-implemented method comprising: receiving (i) one or more taxonomies associated with a media content ontology domain, (ii) a description of the one or more taxonomies, and (iii) one or more hierarchical relationships associated with the one or more taxonomies; generating an ontology based on the one or more descriptions and the one or more hierarchical relationships; generating a prompt that includes (i) a representation of the ontology, (ii) contextual information associated with a media content item, and (iii) a textual instruction to a machine learning model; and generating, via the machine learning model and based at least on the prompt, one or more descriptive metadata tags associated with the media content item. || 11. One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of: receiving (i) one or more taxonomies associated with a media content ontology domain, (ii) a description of the one or more taxonomies, and (iii) one or more hierarchical relationships associated with the one or more taxonomies; generating an ontology based on the one or more descriptions and the one or more hierarchical relationships; generating a prompt that includes (i) a representation of the ontology, (ii) contextual information associated with a media content item, and (iii) a textual instruction to a machine learning model; and generating, via the machine learning model and based at least on the prompt, one or more descriptive metadata tags associated with the media content item. || 19. A system comprising: one or more memories storing instructions; and one or more processors for executing the instructions to: receive (i) one or more taxonomies associated with a media content ontology domain, (ii) a description of the one or more taxonomies, and (iii) one or more hierarchical relationships associated with the one or more taxonomies; generate an ontology based on the one or more descriptions and the one or more hierarchical relationships; generate a prompt that includes (i) a representation of the ontology, (ii) contextual information associated with a media content item, and (iii) a textual instruction to a machine learning model; and generate, via the machine learning model and based at least on the prompt, one or more descriptive metadata tags associated with the media content item.