Neural Indexing for Semantic Document Retrieval
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Solution Overview
Problem
Current information retrieval systems, particularly search engines and question answering systems, face challenges in efficiently processing and retrieving multimedia data and natural language queries due to the complexity of encoding information across various formats and the lack of human-like understanding of natural language.
Innovation Solution
The implementation of a neural indexing system that transforms queries and documents into n-dimensional semantic vectors, allowing for efficient comparison and retrieval of relevant information using deep learning models and neural networks, which enables accurate and scalable information retrieval across diverse datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional keyword searching is used, then the system is simple to implement, but it cannot effectively retrieve information from multimedia data in various formats
Solution Approach 1:
The patent introduces an intermediary component (encoder/model) that transforms multimedia data into a common representation format. This encoder acts as a mediator between diverse multimedia inputs and the retrieval system, enabling unified processing without requiring complex format-specific handling for each data type.
Solution Approach 2:
The system changes the representation parameters of multimedia data by encoding it into vector embeddings. Instead of working with raw multimedia formats directly, the system transforms them into numerical parameter representations that can be uniformly processed and compared, enabling versatile retrieval across different formats.
2Measurement precision
If vector-based searching is used, then information retrieval accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing encoded representations (embeddings) of multimedia data during an indexing phase. When retrieval is needed, the system only needs to compare query embeddings against pre-computed data embeddings, avoiding repeated encoding and reducing processing time during actual search operations.
3Ease of operation
If deep learning models are used for semantic understanding, then natural language query comprehension improves, but computational complexity and resource requirements increase
Solution Approach 1:
The system uses copying by creating simplified numerical representations (embeddings) of semantic meaning. Instead of performing complex deep learning inference during each query operation, the system copies pre-computed semantic representations and performs simpler similarity comparisons, reducing computational complexity while maintaining understanding capability.
4Adaptability or versatility
If traditional indexing methods are used, then storage efficiency is maintained, but the system cannot handle diverse multimedia formats effectively
Solution Approach 1:
The patent applies extraction by separating the semantic content representation from the original multimedia data. The system extracts essential meaning into compact vector embeddings for storage and processing, while the original large-volume multimedia files can be stored separately or on-demand, reducing the storage burden for the retrieval system while maintaining format versatility.
Data Source
AI summary
A question and answer (Q&A) system is enhanced to support natural language queries into any document format regardless of where the underlying documents are stored. The Q&A system may be implemented “as-a-service,” e.g., a network-accessible information retrieval platform. Preferably, the techniques herein enable a user to quickly and reliably locate a document, page, chart, or data point that he or she is looking for across many different datasets. This provides for a unified view of all of the user's (or, more generally, an enterprise's) information assets (such as Adobe® PDFs, Microsoft® Word documents, Microsoft Excel spreadsheets, Microsoft PowerPoint presentations, Google Docs, scanned materials, etc.), and to be able to deeply search all of these sources for the right document, page, sheet, chart, or even answer to a question.