JSON Parsing Layer for Accurate LLM Quality Record Analysis
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Solution Overview
Problem
Existing methods for analyzing quality records, which are stored as JSON objects and attached files, are inefficient and inaccurate due to the language-independent nature of JSON objects and the requirement for clear English context by large language models (LLMs).
Innovation Solution
A system and method that converts JSON objects and attached files into English language-based textual data using APIs and applicable rules, enabling accurate analysis and summary generation through large language models (LLMs).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual analysis of quality records is performed, then human judgment can be applied, but the process is difficult and time-consuming due to JSON files not being easily interpretable
Solution Approach 1:
The patent introduces an intermediary system comprising a parser and LLM wrapper that converts JSON objects into natural language text. This intermediary layer bridges the gap between machine-storable JSON format and human-interpretable natural language, enabling both automated processing and accurate analysis without manual intervention.
Solution Approach 2:
The patent replaces manual mechanical analysis with an automated system combining parsing algorithms and large language models. The system automatically converts JSON structures to natural language and performs analysis, substituting human manual work with automated computational processes that are both faster and equally accurate.
2Productivity
If LLMs are used to analyze quality records, then automation is achieved, but accuracy decreases because LLMs require clearly defined English context which JSON objects do not provide
Solution Approach 1:
The patent applies preliminary action by converting JSON objects to natural language text before feeding them to the LLM. The parser and template application occur first, creating properly contextualized English text that provides the LLM with the clear definitions and context it needs for accurate analysis, rather than attempting direct JSON-to-LLM processing.
Solution Approach 2:
The system introduces a natural language text as an intermediary between the JSON data structure and the LLM. This intermediary layer translates machine-oriented JSON format into human-oriented natural language with proper context, enabling the LLM to process the data accurately while maintaining automation.
3Adaptability or versatility
If hybrid quality records with attached files are processed, then comprehensive analysis is possible, but LLMs become ineffective when provided with files in formats including PDFs
Solution Approach 1:
The patent extracts content from attached files (PDFs, images, etc.) and converts it to natural language text separately before combining it with the parsed JSON data. This extraction approach removes the problematic file formats from the LLM input pipeline while preserving their informational content in a form the LLM can process accurately.
Solution Approach 2:
The system merges the converted file content (extracted and transformed to natural language) with the parsed JSON object data into a unified text input for the LLM. This combination integrates information from multiple sources (JSON structure and attached files) into a coherent natural language narrative that the LLM can analyze effectively.
Data Source
AI summary
The present disclosure discloses a system and method for handling of JSON objects by a large language model (LLM). The proposed system and method involves receiving a user input requesting an analysis of one or more quality records, and extracting the one or more of quality records, wherein each quality record comprises one or more JSON objects. The system and method further involve identifying an applicable rule for parsing each of the one or more JSON objects, parsing each of the one or more JSON objects by an API corresponding to the identified applicable rule, feeding the parsed JSON objects to the LLM to obtain the analysis of the one or more quality records, and displaying the obtained analysis of the one or more quality records to the user.