Automated Industrial Hygiene Assessment via ML Indexing
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Solution Overview
Problem
Current technological solutions are inadequate for automating the indexing, extraction, and identification of targeted and complex information from digitized documents, particularly in industrial hygiene assessments, due to the variability and structure of the data.
Innovation Solution
The development of automated systems and methods that utilize machine learning networks, such as visual ML and NLP, to index and extract information from digital records, generating health effect ratings, exposure ratings, and uncertainty ratings, and displaying these through an interactive user interface for industrial hygiene risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual indexing and extraction methods are used for digital records in industrial hygiene assessments, then accuracy can be maintained through human review, but productivity is significantly reduced due to time-consuming manual processing
Solution Approach 1:
The patent replaces manual mechanical indexing and extraction processes with automated machine learning systems. Visual ML networks process document images to identify and extract chemical information, while NLP ML networks analyze text to derive codes and generate assessments. This substitution dramatically increases productivity while maintaining accuracy through automated quality control mechanisms.
Solution Approach 2:
The system enables self-service automated assessment by having the machine learning networks independently process digital records, extract information, derive codes, and generate health effect ratings without requiring continuous human intervention. The automated system serves itself to complete the entire assessment workflow.
2Reliability
If comprehensive manual review of digital records is performed to ensure accurate code derivation and rating generation, then reliability is improved, but loss of time increases due to extensive human analysis required
Solution Approach 1:
The patent applies preliminary action by training machine learning networks in advance on extensive datasets of digital records and corresponding correct codes. This pre-training enables the networks to perform reliable assessments rapidly without requiring time-consuming manual review during actual assessment operations. The heavy lifting of learning patterns is done beforehand.
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning networks generate assessments that can be reviewed and corrected, with corrections fed back into the system to improve future performance. This feedback loop maintains reliability while reducing the need for extensive manual review of each individual assessment.
3Measurement precision
If simple extraction methods are used for chemical information from digital records, then device complexity is reduced, but measurement precision deteriorates due to inability to handle variable and structured data formats
Solution Approach 1:
The patent segments the information extraction task into distinct specialized components: visual ML networks for processing document images and identifying chemical information, NLP ML networks for text analysis and code derivation, and separate modules for generating different types of ratings. This segmentation allows each component to be optimized for its specific function, achieving high precision while managing complexity through modular design.
Solution Approach 2:
The machine learning networks are designed with universal capabilities to handle various digital record formats, structures, and variable data types. The same visual ML and NLP networks can process different document types (SDS, labels, specifications) and extract relevant chemical information regardless of the specific format, achieving precision across diverse inputs without requiring separate specialized systems for each format type.
Data Source
AI summary
Systems and methods are disclosed for automated industrial hygiene assessment and display comprising receiving sampling results for a stressor, such as a harmful environmental artifact in a physical environment; deriving one or more codes for the stressor from a digital record via an indexing module and/or from other data sources; generating a health effect rating (HER) based on the code; generating an exposure rating (ER) based on the sampling results; generating an uncertainty rating (UR) based on the sampling results; displaying, an interactive UIto facilitate approval or selection of at least one of the HER, the ER, or the UR; generating at least one of a risk rating (RR) or an information gathering priority rating (IGPR) based on a selection of the at least one of the HER, the ER, or the UR; and displaying via the interactive UI at least one of the RR or the IGPR.