Closed Caption Analysis for TV Program Recommendations
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Solution Overview
Problem
Existing television program suggestion techniques often fail to accurately recommend programs to users, as they rely on limited data sources such as program titles or synopses, which may not adequately represent the content, especially for news broadcasts or other programming with complex information.
Innovation Solution
The system utilizes closed caption or subtitle data from unwatched channels to analyze word frequencies and match user-designated keywords, providing real-time program suggestions by decoding and processing the text data from video content using inactive tuners, allowing for more comprehensive and relevant recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If program suggestion is based on limited data sources such as program titles or synopses, then the system complexity is reduced, but the accuracy of program recommendations deteriorates
Solution Approach 1:
The patent introduces closed caption data as an intermediary layer between the video content and the recommendation system. Instead of directly analyzing video content (which would be complex) or relying solely on titles/synopses (which are insufficient), the system uses closed captions as a mediator that provides rich textual information about program content, including specific topics, events, and details that are not captured in traditional metadata.
Solution Approach 2:
The patent replaces the mechanical approach of manual program selection or simple metadata-based recommendations with an automated text analysis system. The system automatically processes closed caption data, extracts meaningful information, and generates recommendations without requiring manual intervention or complex video analysis algorithms.
2Adaptability or versatility
If closed caption data from unwatched channels is analyzed in real-time, then the relevance of program suggestions is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary analysis of closed caption data by continuously monitoring and indexing keywords, topics, and themes from unwatched channels in the background. This preliminary processing allows the system to quickly generate relevant recommendations when users are browsing or when programming suggestions are requested, without requiring intensive real-time processing at the moment of recommendation generation.
Solution Approach 2:
The system uses inactive tuners and unused processing resources to autonomously analyze and process closed caption data from unwatched channels. This self-service approach allows the recommendation system to maintain up-to-date knowledge about program content without burdening the primary viewing or recording functions, effectively utilizing otherwise wasted resources.
3Quantity of substance
If multiple tuners are utilized for analyzing closed caption data, then the comprehensiveness of content coverage is improved, but the device complexity increases
Solution Approach 1:
The patent makes the tuner system universal by enabling inactive or unused tuners to serve dual purposes: their traditional function of receiving and processing video content, and an additional function of analyzing closed caption data from unwatched channels for recommendation generation. This multi-functionality allows the system to leverage existing hardware resources without adding dedicated analysis equipment.
Solution Approach 2:
The patent recovers value from otherwise discarded or idle tuner resources. Instead of leaving tuners inactive when they are not needed for primary viewing or recording, the system activates these unused resources to perform background analysis of closed caption data, effectively converting wasted capacity into useful recommendation capabilities.
Data Source
AI summary
Described are systems, methods and computer program products for suggesting television programs to a user by, for example, analyzing closed caption or subtitle content of unwatched or unrecorded video content to determine if the video content is of interest to the user. The user may indicate one or more keywords for use in the analysis and the analysis may scan the text of the closed caption or subtitle content to identify matches to the keywords. Beside user input keywords, the keywords may also be determined through analysis of video content watched or recorded by the user to determine frequently encountered words. These frequently encountered words may automatically be used in matching the text of the closed caption or subtitle content. The users may also or alternatively select particular words of the frequently encountered words to use for matching.