Conceptual Calculator Workflow for Cited, Hallucination-Resistant Retrieval
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques for converting human-readable workflows to machine-usable format and back suffer from limitations such as failing to return relevant documents, hallucinating answers, failing to cite sources, returning incorrect information, and difficulty handling unstructured data, among others, leading to subpar performance and inability to fulfill requests properly.
Innovation Solution
The conceptual calculator system employs processes like Content-Handling and Extraction Workflow (CHEW), Verified Referenced Encoder (VRE), Directed Retrieval-Augmented Generation (DRAG), Directed Recursive Organizing Placement (DROP), and Document Interpolation from Graphs by Extracting Structured Text (DIGEST) to address these limitations, ensuring accurate, sourced information retrieval and synthesis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional indexing, machine learning, or vector-embedding techniques are used to convert human-readable workflows to machine-usable format, then automation is improved, but reliability deteriorates due to hallucination, incorrect answers, and failure to cite sources
Solution Approach 1:
The patent segments the information processing workflow into distinct stages: document ingestion and chunking, embedding generation, vector storage, and query processing. Each stage is handled by specialized components (ingestion pipeline, embedding model, vector database, query processor) that can be independently optimized and validated, improving overall reliability while maintaining automation.
Solution Approach 2:
The patent introduces vector embeddings as an intermediary representation between human-readable text and machine processing. The embedding model converts text into numerical vectors that capture semantic meaning, enabling reliable automated retrieval without requiring direct text-matching. This intermediary layer resolves the contradiction by allowing automation while preserving semantic accuracy through mathematical transformations.
2Adaptability or versatility
If large language models with extended context windows are used to handle more information, then adaptability is improved, but device complexity increases due to computational requirements
Solution Approach 1:
The patent divides large documents into smaller chunks or segments before processing. Each chunk is independently embedded and stored in the vector database, allowing the system to handle extensive information without requiring the LLM to process everything at once. This segmentation reduces computational complexity while maintaining adaptability through modular processing.
Solution Approach 2:
The patent performs preliminary processing of documents during ingestion, including chunking, embedding generation, and storage in the vector database before queries are submitted. This preliminary action prepares the data in advance, reducing the computational burden on the LLM during actual query processing and enabling faster, simpler operations while maintaining high adaptability.
3Measurement precision
If vector-embedding and vector search techniques are used to improve retrieval accuracy, then measurement precision is improved, but loss of information occurs during dimensionality reduction
Solution Approach 1:
The patent transforms text data from one parameter space (human-readable text) into another parameter space (numerical vectors) through the embedding model. This parameter change enables precise mathematical operations and similarity searches while the model is designed to preserve semantic relationships. The transformation optimizes the data for its intended use (vector search) while maintaining the essential meaning through careful model selection and training.
Data Source
AI summary
A conceptual calculator system and method have a plurality of elements/processes that overcome and address the issues/limitations of the known techniques. In one example, the conceptual calculator may be used for research, but has a plurality of different use cases.