Procedure Analysis Using Category-Specific Models for Faster Validation
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Solution Overview
Problem
Organizations face challenges in accurately assessing procedures due to the diverse and complex requirements of multiple business functions, leading to inefficiencies and inconsistencies in manual review processes, which hinder the adoption of new procedures and may result in errors.
Innovation Solution
Implementing a computing system that trains category-specific models using machine learning to analyze procedures from the perspective of individual business functions, generating tailored validation reports, and combining these reports to provide an overall assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single omnibus model is trained to assess procedures for all business functions, then comprehensive coverage is achieved, but parameter size increases leading to higher memory usage and slower inference speeds
Solution Approach 1:
The patent divides a single omnibus model into multiple category-specific models, each trained to assess procedures for a specific business function. This segmentation reduces the parameter size of each individual model, thereby decreasing memory usage and increasing inference speeds while maintaining comprehensive coverage across all business functions through the collective capability of multiple specialized models.
2Productivity
If multiple category-specific models are trained separately, then training speed and accuracy improve, but system complexity increases
Solution Approach 1:
The system segments the procedure assessment task into multiple independent category-specific models, each trained separately on domain-specific data. This segmentation enables faster and more accurate training for each category while managing complexity through modular architecture and automated orchestration of the multiple models.
Solution Approach 2:
The framework provides a universal multi-functionality solution where a standardized system architecture handles multiple business functions through category-specific models. The system maintains a unified interface and assessment framework that works across different categories, reducing the operational complexity despite having multiple specialized models.
3Ease of operation
If manual review processes are used for procedure assessment, then flexibility is maintained, but efficiency and consistency deteriorate
Solution Approach 1:
The system implements self-service through automated category-specific models that independently assess procedures for their respective business functions. Each model autonomously evaluates procedures against domain-specific criteria, eliminating the need for manual review while maintaining consistency and efficiency. The system serves itself by generating comprehensive assessments without human intervention.
Data Source
AI summary
A computing system may generate, using a plurality of category-specific models, a plurality of validation reports assessing a procedure associated with an organization, wherein each category-specific model of the plurality of category-specific models is associated with a corresponding business function of the organization and is trained via machine learning to produce a corresponding validation report that is a written assessment of whether the procedure is satisfactory for the corresponding business function. The computing system may generate, based on the plurality of validation reports, a combined validation report that is a written assessment of whether the procedure is satisfactory for the organization.