AI Semantic Search for Media Supporting Material
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Solution Overview
Problem
Media editors face challenges in efficiently locating relevant and semantically related supporting material for media projects due to the vast amount of available information, often resulting in irrelevant search results and missed contextual material, especially when dealing with unscripted content like news stories.
Innovation Solution
Utilizing large language models (LLMs) and machine-learning-based encoders to generate embeddings for supporting material types and source material, enabling a search engine to identify the most similar vectors and provide relevant search results across various media modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If editors perform manual searches through vast amounts of available information, then they can locate supporting material, but the time required increases significantly and relevance decreases
Solution Approach 1:
The patent introduces an intermediary system consisting of LLMs and machine learning encoders that mediate between the editor's search intent and the vast repository of available material. The LLM generates semantic queries from project context, the encoder transforms these into embedding vectors, and the search engine matches these vectors against encoded source material, automatically filtering and ranking results by relevance without manual browsing
Solution Approach 2:
The system performs preliminary actions by pre-encoding all source material into embedding vectors and storing them in the search engine database before actual searches are needed. When an editor initiates a search, the heavy computational work of encoding and semantic analysis has already been completed, allowing rapid retrieval and matching without time-consuming processing during the search itself
2Productivity
If editors narrow down search queries to focus on specific material, then search efficiency improves, but relevant semantically related material may be missed
Solution Approach 1:
The system changes the parameter space of search by transforming textual queries into a different representation space (embedding vectors) that captures semantic meaning rather than literal keyword matches. This allows the search to operate on semantic similarity rather than exact phrase matching, broadening the scope to include semantically related material while maintaining efficiency through vector space operations
Solution Approach 2:
The patent adds another dimension to the search process by introducing semantic embedding space as an intermediate layer between the query and the material repository. Instead of searching in the original text space, the system projects both queries and materials into a high-dimensional embedding space where semantic relationships are preserved, enabling discovery of related material across different modalities and formats
3Adaptability or versatility
If the system searches across multiple media modalities, then the scope and variety of results increases, but the complexity of encoding and searching increases
Solution Approach 1:
The patent implements a universal encoding approach where a single machine learning encoder model is trained to handle multiple media modalities (text, images, audio, video) through a unified embedding space. This multi-functional encoder transforms diverse input types into a common vector representation, allowing the same search infrastructure to operate across all modalities without requiring separate encoding pipelines for each type
Data Source
AI summary
A large language model (LLM) is used to broaden a search for supporting material for a media project. The LLM is provided with contextual material from the media project and optional grounding material and generates a list of types of supporting material. The list is provided to a machine-learning-based encoder, which encodes the list items into embedding space vectors. A body of source material, which may multimodal, is also encoded into the embedding space. A search engine is used to locate items of source material having embedded space vectors closest to vectors corresponding to the list of types of supporting material. Located items are provided to a media editing application as suggested supporting material. The body of source material may include live external sources. A small language model fine-tuned with a training data set generated by a LLM may be used instead of the LLM.