AI Metadata Extraction from Audio Text Using Timeline Context
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Solution Overview
Problem
Generating accurate and sufficient metadata for media content items is difficult, costly, and time-consuming, especially when text data such as closed captions lacks context and is arranged in a way that makes it difficult to understand and process.
Innovation Solution
Utilizing AI/ML models to preprocess audio-related text data, such as closed captions, by adding relevant information, grouping it based on topics and timelines, and arranging it meaningfully to generate context-aware metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to generate metadata for media content items, then the process is simple, but the metadata accuracy and completeness are insufficient
Solution Approach 1:
The patent segments the metadata generation process into multiple stages: obtaining text data from media content, determining modifications based on playback timeline and topics, generating representations, and creating final metadata. This segmentation allows each stage to be optimized independently, improving overall metadata accuracy while managing complexity through structured processing steps.
Solution Approach 2:
The patent applies preliminary action by pre-processing text data before final metadata generation. It determines modifications based on playback timeline deviations, topics, and chronological sequences in advance, creating a refined representation that enhances the accuracy and completeness of the resulting metadata without increasing final processing complexity.
2Loss of information
If text data is processed without considering context and timeline, then the processing is fast, but the metadata completeness and relevance are poor
Solution Approach 1:
The patent performs preliminary analysis of text data to determine modifications based on playback timeline, topics, and chronological sequences before generating the final representation. This advance processing ensures context information is preserved and organized, reducing information loss while managing processing time through efficient pre-computation.
Solution Approach 2:
The patent changes the parameters of text data processing by incorporating temporal parameters (playback timeline, chronological sequence) and contextual parameters (topics, events) into the processing framework. This transforms raw text data into context-enriched representations, minimizing information loss about the original media content's temporal and thematic structure.
3Reliability
If AI/ML models are used to preprocess text data with context and timeline, then the metadata quality improves, but the computational cost and processing time increase
Solution Approach 1:
The patent segments the AI/ML processing into distinct functional modules: text data acquisition, modification determination based on timeline and topics, representation generation, and metadata creation. This segmentation allows computational resources to be allocated efficiently to each stage, improving metadata reliability while managing energy consumption through targeted processing at each step.
Solution Approach 2:
The patent extracts only the essential contextual information (playback timeline deviations, topics, chronological sequences) needed for metadata generation, rather than processing all possible text data attributes. This extraction approach maintains high metadata reliability by focusing on critical context while reducing overall computational energy requirements.
4Ease of operation
If text data is arranged in original sequence, then the processing is straightforward, but the understandability and usability of metadata are reduced
Solution Approach 1:
The patent organizes text data in advance by determining modifications based on playback timeline, topics, and chronological sequences before generating the final representation. This preliminary organization enhances metadata usability by structuring information in a meaningful way, while managing data organization complexity through systematic categorization rather than random arrangement.
Solution Approach 2:
The patent adds temporal and thematic dimensions to the text data organization by incorporating playback timeline, chronological sequences, and topic classifications. This multi-dimensional organization improves metadata usability by providing structured context, while managing complexity through clear dimensional categories rather than unstructured data arrangements.
Data Source
AI summary
System, apparatus, article of manufacture, method and/or computer program product embodiments (and/or combinations and sub-combinations thereof) are provided for using AI/ML models to generate context-aware metadata for a media content item based on audio-related text data associated with the media content item. An example method can include obtaining text data associated with a content item, the text data including a transcription/translation of audio associated with the content item; determining a modified version of the text data based on a deviation in a playback timeline of the content item, topics associated with the text data, a chronological timeline associated with the content item, and/or a sequence of events associated with the content item and/or content of the content item; generating a representation of the modified version of the text data; and generating metadata associated with the content item based on the representation of the modified version of the text data.