LLM Data Mapping for Structured Object Creation From Unstructured Files
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Solution Overview
Problem
Organizations face challenges in capturing and structuring legacy data stored in unstructured formats, which is resource-consuming, manual, and prone to human errors, necessitating integration into a structured format for effective data processing.
Innovation Solution
A data extraction and object creation system (DQS) that automates the process of converting unstructured data into a structured format, using a large language model (LLM) to generate mappings between raw data and data objects, allowing user review and approval before storage, and enabling efficient data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processes are used to structure unstructured data, then human judgment and flexibility are applied, but the process becomes resource-consuming and prone to human errors
Solution Approach 1:
The patent introduces an intermediary system comprising a processor and memory that automatically matches unstructured data to structured data objects using machine learning models. This intermediary automates the matching process, eliminating manual human judgment while maintaining high accuracy through trained algorithms, thus resolving the contradiction between reliability and productivity.
Solution Approach 2:
The patent replaces the mechanical manual process of data structuring with an automated electronic system using machine learning and pattern recognition algorithms. This substitution eliminates human errors and significantly increases processing speed and productivity while maintaining or improving accuracy through consistent algorithmic application.
2Productivity
If automated systems are used to structure unstructured data, then productivity and consistency are improved, but the system complexity and initial resource requirements increase
Solution Approach 1:
The patent creates a universal data matching system that can handle multiple types of unstructured data and map them to various structured data objects using the same core architecture. This multi-functional approach reduces overall system complexity by avoiding the need for separate specialized systems for different data types, while maintaining high productivity across diverse data processing tasks.
3Adaptability or versatility
If manual data structuring is performed, then flexibility in handling diverse data formats is maintained, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent employs preliminary action by pre-training machine learning models with diverse data formats and structures before actual data processing. This preliminary training enables the system to rapidly adapt to new data formats during operation without manual intervention, maintaining flexibility while dramatically reducing processing time compared to manual methods.
Data Source
AI summary
Disclosed herein are various embodiments for a sensitive data management system. An embodiment operates by receiving a request to generate a new data object based on a file comprising unstructured data. Raw data is extracted from the file, the raw data comprising a string comprising the unstructured data. A prompt for a large language model (LLM) is generated, the prompt corresponding to creating a mapping between the raw data and fields for the data object. A mapping between at least a subset of the fields and the raw data is received from the LLM. The mapping is for display via a user interface, an approval of the mapping is received, and the data object is generated in a data storage system responsive to receiving the approval.