Metadata-Enriched Text Chunk Routing for Accurate Data Retrieval
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Solution Overview
Problem
Conventional data analysis platforms are inefficient and inaccurate in ingesting, analyzing, and processing large datasets, particularly those with unlabeled data, leading to inaccurate or spurious data generation, which can result in ill-informed decisions.
Innovation Solution
The system processes raw data into text chunks, classifies them, augments with metadata, and distributes them to specialized data stores using machine learning models, embedding and sequencing to improve data management and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems process large datasets, then data processing capacity is maintained, but data analysis accuracy deteriorates leading to hallucinations and spurious data
Solution Approach 1:
The patent divides large datasets into smaller text chunks and processes them through specialized machine learning models. Each chunk is handled individually through embedding, classification, and routing to appropriate data stores, which improves processing accuracy while maintaining overall system capacity through parallel processing of multiple chunks.
2Productivity
If conventional systems ingest diverse data formats, then data coverage is maintained, but data comprehension efficiency deteriorates
Solution Approach 1:
The patent introduces an intermediary processing layer that converts diverse data formats into a standardized text chunk format. This intermediary step includes text extraction, cleaning, and normalization processes that transform various source formats into a uniform structure suitable for efficient machine learning model processing, thereby maintaining format versatility while improving comprehension efficiency.
3Measurement precision
If conventional systems retrieve data from large databases, then data availability is maintained, but retrieval accuracy deteriorates due to information overload
Solution Approach 1:
The patent segments large databases into multiple specialized data stores, each containing specific types of processed text chunks. When a query is received, the system routes it to the appropriate specialized data store based on the query type, thereby reducing the search space from the entire database to a relevant subset and significantly improving retrieval accuracy.
Solution Approach 2:
The patent transforms the traditional single-database structure into a multi-dimensional organized system with specialized data stores arranged by data type and classification. This dimensional reorganization allows the system to navigate and retrieve information more efficiently by moving from a flat, monolithic structure to a hierarchical, categorized structure.
Data Source
AI summary
Disclosed herein are systems, methods, and media for processing and distributing text data from a dataset. The disclosed embodiments include receiving raw data and converting the raw data into a set of text chunks. The disclosed embodiments include determining a set of classifications for the raw data. The disclosed embodiments include. augmenting a text chunk in the set of text chunks with metadata. The disclosed embodiments include generating a windowed chunk by appending context to the augmented chunk. The disclosed embodiments include embedding the windowed chunk and the extracted retrieval metadata into a chunk embedding. The disclosed embodiments include distributing the chunk embedding by determining a data store in the set of data stores corresponding to the assigned classification and assigning the chunk embedding to the determined data store.