Embedding-Based Innovation Intelligence Platform for Targeted Research
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Solution Overview
Problem
The rapid increase in data creation outpaces the development of effective tools for generating targeted intelligence, leading to inefficient and inaccurate research processes due to the need for manual Boolean queries, outdated relevance, and the challenges posed by generative AI hallucinations and biases.
Innovation Solution
A dynamically curated knowledge base, referred to as a Private Innovation Library (PIL), utilizes an embedding model and a Large Language Model (LLM) to index and prioritize data sets, ensuring relevance and context through user-defined project profiles and relevance indicators, thereby constructing targeted queries and minimizing hallucinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual Boolean search queries are used to search through data sets, then researchers can identify relevant information, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces manual Boolean search operations with an automated embedding model system. The embedding model automatically converts queries and documents into vector representations, enabling the system to perform relevance matching without manual intervention. This substitution of mechanical manual searching with automated computational processing directly resolves the contradiction by maintaining precision while eliminating time loss.
Solution Approach 2:
The system enables self-service through automated query processing. The embedding model automatically processes queries, retrieves relevant documents, and updates the knowledge base without requiring researcher intervention for each search operation. This self-service mechanism maintains measurement precision through consistent automated processing while dramatically reducing the time researchers spend on manual searching.
2Reliability
If data is continuously updated to remain current, then intelligence remains relevant, but the volume of data increases rapidly
Solution Approach 1:
The embedding model extracts only the essential semantic features from documents during indexing, representing entire documents or sections as compact vector embeddings. This extraction of key information in condensed form allows the system to maintain relevance through continuous updates while managing data volume efficiently, as the vector representations are far more compact than storing and processing full text documents.
Solution Approach 2:
The system changes the parameter representation of data from full text documents to vector embeddings. This parameter transformation allows the same information to be stored in a compressed format that scales more efficiently with continuous updates. The vector representations maintain the semantic content needed for relevance while dramatically reducing the storage and processing burden compared to raw text data.
3Productivity
If generative AI tools are used to process data faster, then productivity increases, but accuracy decreases due to hallucinations and biases
Solution Approach 1:
The embedding model serves as an intermediary between raw data and generative AI processing. Instead of feeding raw unprocessed data directly to generative AI models, the system first processes data through embedding models that create structured vector representations. This intermediary processing step maintains productivity by enabling fast vector operations while improving accuracy by providing the generative AI with pre-processed, semantically structured input that reduces hallucinations and biases.
Data Source
AI summary
A system and method of establishing and curating data. The method includes maintaining a knowledge base in a digital library to support an information-based decision-making process. The knowledge base is dynamically maintained by using an embedding model for indexing a plurality of data sets, and conducting at least one search over the plurality of data sets to identify one or more documents, the documents stored in the digital library.