Content Retrieval Using AI-Generated Metadata for Partial Memories
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Solution Overview
Problem
Users face difficulty in locating previously consumed content due to the vast amount of available content and challenges in remembering specific details like names or scenes, leading to inefficient content retrieval.
Innovation Solution
A metadata database is generated using artificial intelligence mechanisms to analyze user descriptions and content components, enabling efficient retrieval based on generalized user inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users rely on traditional content search methods using titles or specific details, then search accuracy may be high for known content, but users cannot locate content when they only remember partial information or scenes
Solution Approach 1:
The system performs preliminary analysis of content to generate comprehensive metadata including scene descriptions, character information, and contextual data before users need to search. This pre-processing enables users to search using any remembered detail without needing to recall specific titles or accurate information.
Solution Approach 2:
Metadata acts as an intermediary between user memory and content database. Instead of users directly querying content titles or details, their partial descriptions are matched against pre-generated metadata that bridges the gap between incomplete user recall and precise content identification.
2Adaptability or versatility
If the content library grows substantially, then content variety and selection improve, but the ability to find previously consumed content deteriorates
Solution Approach 1:
The content library is segmented into discrete metadata elements (scenes, characters, objects, actions) that can be independently searched and matched. This segmentation allows the system to handle large content volumes efficiently by breaking down complex content into searchable components rather than treating each content item as a monolithic unit.
Solution Approach 2:
The patent replaces manual browsing and memory-based search with an automated AI-driven metadata matching system. The mechanical process of users recalling and searching for content is substituted with an intelligent system that automatically matches user descriptions against pre-analyzed metadata, dramatically reducing search time regardless of library size.
3Adaptability or versatility
If users only see a portion of content such as a specific scene, then content consumption flexibility increases, but the ability to locate and re-experience that content becomes more difficult
Solution Approach 1:
The system pre-generates metadata that includes detailed scene descriptions, visual elements, and contextual information for every portion of content. This preliminary processing ensures that even when users consume only partial content, the metadata for those specific portions is already prepared and searchable, enabling easy re-location of any viewed scene.
Solution Approach 2:
The patent creates metadata copies of content portions that users have viewed. Instead of requiring users to remember or re-access original content sources, the system generates and stores descriptive copies in metadata format that can be searched and matched against user descriptions of scenes they remember watching.
Data Source
AI summary
Systems and methods for providing content to a user using metadata generated by employing at least one artificial intelligence mechanism. A metadata database is generated for a plurality of content. To generate the database, each corresponding content is analyzed, including: generating first metadata for the corresponding content by employing an artificial intelligence mechanism on user descriptions of the corresponding content; generating second metadata for the corresponding content by employing an artificial intelligence mechanism on the corresponding content; and storing the first metadata and the second metadata in the metadata database and mapped to the corresponding content. Input is received from a user requesting content using the generated metadata database. The metadata database is search for metadata matching the input. And in response to identifying a metadata match, target content is identified from the plurality of content mapped to matched metadata and provided to the user.