AI Formulation Database Extraction and Optimization
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Solution Overview
Problem
Existing electronic devices struggle to accurately extract and process data needed by users from source documents, particularly in building an artificial intelligence-based formulation database.
Innovation Solution
An electronic device and its operating method that involve extracting data from documents to build a formulation database, generating composite formulation data, predicting compound properties using a formulation property prediction model, and optimizing formulations using a formulation optimization model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If natural language processing technology is used to analyze documents, then information extraction capability is improved, but data extraction accuracy from source data deteriorates
Solution Approach 1:
The patent segments the data extraction process into multiple specialized components: a formulation data extractor that identifies formulation-related information, a property data extractor that extracts property information, and a data processor that integrates and validates the extracted data. This segmentation allows each component to specialize in specific extraction tasks, improving overall accuracy while maintaining comprehensive information extraction capability.
Solution Approach 2:
The patent introduces an intermediary data processing layer between the natural language processing stage and the final data output. This intermediary layer includes validation rules, formatting standards, and cross-referencing mechanisms that verify extracted data against known formulation patterns and property relationships, thereby enhancing extraction accuracy without losing information.
2Quantity of substance
If comprehensive data extraction from documents is performed, then formulation database completeness is improved, but processing complexity deteriorates
Solution Approach 1:
The extraction system is divided into modular components: document parser, formulation data extractor, property data extractor, data validator, and database integrator. Each module handles a specific aspect of the extraction process, allowing comprehensive data collection while keeping individual processing steps manageable and maintainable.
Solution Approach 2:
The patent creates a universal data processing framework that can handle multiple document formats (PDF, Word, text files) and various formulation types through a single integrated system. The extractor uses standardized patterns and rules that apply across different data sources, reducing processing complexity while achieving comprehensive database population.
3Measurement precision
If manual data extraction methods are used, then data accuracy is improved, but processing efficiency deteriorates
Solution Approach 1:
The system implements automated self-validation mechanisms where the extraction process automatically checks extracted data against formulation rules, property ranges, and consistency criteria. The system can identify and flag potential errors without human intervention, maintaining high data accuracy while operating at automated processing speeds.
Solution Approach 2:
The patent incorporates feedback loops where extraction results are validated against known formulation patterns and property relationships. When inconsistencies are detected, the system automatically adjusts extraction parameters or flags data for review, ensuring high accuracy while maintaining efficient automated processing throughput.
Data Source
AI summary
According to the present disclosure, the present disclosure provides an electronic device for building an artificial intelligence-based formulation database and an operating method thereof, the method including: extracting data from a document to build a formulation database, generating composite formulation data based on the formulation database, predicting, using a formulation property prediction model that infers a property change according to a formulation ratio, the property of a compound from the formulation information of the compound based on the composite formulation data, and optimizing a formulation using a formulation optimization model that generates a new compound formulation suitable for a target property in a compound formulation.