Video Search Snippet Generation With Query-Grounded Descriptions
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Solution Overview
Problem
Current search engines fail to provide concise and relevant snippets for video search results, as transcripts of video content are verbose and processing them is resource-intensive, leading to inefficiencies in generating query-specific summaries.
Innovation Solution
A system utilizing trained generative models to generate concise, query-specific snippets by combining video content with other information, employing OCR, entity recognition, and image understanding, and verifying the generated snippets for relevance before indexing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If video transcripts are used to generate search result snippets, then relevant information can be extracted from video content, but the transcripts are verbose and processing them is resource-intensive
Solution Approach 1:
The patent extracts only the essential visual elements from video content using OCR technology, rather than processing entire transcripts. This selective extraction of text from video frames eliminates the verbosity of full transcripts while maintaining key information for search relevance
Solution Approach 2:
The video content is segmented into discrete frames that are processed individually through OCR, entity recognition, and image understanding. This segmentation allows the system to process only relevant portions of video content rather than entire transcripts, reducing computational resources while preserving important information
2Reliability
If comprehensive video content processing is performed to generate accurate snippets, then snippet quality improves, but processing time and system complexity increase
Solution Approach 1:
The patent combines multiple processing techniques—OCR, entity recognition, and image understanding—into an integrated pipeline that works synergistically. This merging allows the system to achieve high snippet relevance through comprehensive analysis while managing complexity through unified architecture and shared processing components
3Adaptability or versatility
If video content is processed in real-time to provide query-specific snippets, then snippet relevance to search queries improves, but processing latency increases
Solution Approach 1:
The system performs preliminary processing of video content by extracting visual text and identifying key entities during indexing, before search queries are submitted. This preliminary action creates a pre-processed foundation that enables rapid generation of query-specific snippets without requiring intensive real-time processing, thus reducing latency while maintaining adaptability
Data Source
AI summary
Disclosed implementations for providing a description in a search result for a resource providing video content. A resource providing video content is received. A query having the resource as a relevant result is generated. A description of the resource is generated by processing a prompt through a generative model. The prompt includes the query and descriptive content related to the video content. The query and the description are stored based on a groundedness score meeting a groundedness threshold. The groundedness score indicates an alignment between the description and the prompt. The description is provided in a search result that includes the resource.