Domain-Specific Language Models for Video Content Relatedness
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Solution Overview
Problem
Current automated broadcasting systems fail to accurately identify and organize related video content due to differences in audio or video aspects, leading to redundant content selection and distribution.
Innovation Solution
A system and method utilizing domain-specific language models to analyze and compare time-coded keywords in audio content, determining relatedness based on keyword occurrence patterns and confidence models, ensuring accurate selection and compilation of video content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated broadcasting systems use traditional video or audio detection methods to identify content, then the system can operate automatically, but the system fails to accurately determine whether two pieces of content relate to the same subject matter or event
Solution Approach 1:
The patent introduces domain-specific language models as an intermediary between the audio content and the automated broadcasting system. These language models process audio transcripts and generate confidence scores that indicate whether two audio segments relate to the same subject matter or event, enabling accurate automated content identification without requiring manual review
Solution Approach 2:
The patent replaces traditional video or audio detection methods with a language-based analysis system. Instead of relying on mechanical comparison of video frames or audio waveforms, the system uses natural language processing of audio transcripts combined with domain-specific knowledge models to determine content relatedness, achieving both automation and accuracy
2Adaptability or versatility
If the system selects multiple content clips from different broadcasters covering the same event, then more content variety is available, but redundant clips are distributed to consumers
Solution Approach 1:
The patent implements a feedback mechanism where the domain-specific language model continuously evaluates audio transcripts and generates confidence scores. When a new content clip is considered for distribution, the system compares its audio transcript against already-selected clips using the language model, receiving feedback on whether the new clip relates to the same subject matter. This feedback loop prevents redundant content distribution while maintaining variety from multiple broadcasters
3Productivity
If traditional content detection methods are used, then the system can process content quickly, but the system cannot distinguish between different aspects of the same event versus completely different events
Solution Approach 1:
The patent performs preliminary processing of audio content by generating transcripts and analyzing them through domain-specific language models before final content selection and distribution. By pre-processing the audio data to extract meaningful linguistic information and event context, the system maintains high processing speed while avoiding loss of contextual understanding about what events are being covered
Data Source
AI summary
Systems and methods for controlling related video content based on domain specific language models are disclosed. Some embodiments include one or more processors configured to obtain a first audio information of first video content, identify first primary time codes based on the first audio information, obtain second audio information of second video content and identify a first secondary plurality of time codes based on the second audio information, determine an amount of differences between the first primary time codes and first secondary time codes, and generate information indicating whether the first video content and the second video content are related based on the determined amount of differences.