AI Prediction Engine for NAICS Classification and Model Deployment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current business classification methods, such as the North American Industry Classification System (NAICS), face inaccuracies and inefficiencies due to limited classification codes, cross-referencing, and lack of a single source of truth, leading to delays and monetary losses in industries like insurance and lending, while machine learning model deployment lacks standardization, resulting in technical debt and inefficiencies.
Innovation Solution
A processor-driven prediction engine using an ensemble of machine learning models, including a gateway model and classification models, predicts accurate NAICS codes with high precision by employing concepts matrices and natural probability models, and a model core deployment framework standardizes model deployment, debugging, and monitoring across enterprises.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional classification codes (NAICS) are used for business classification, then a standardized classification system is provided, but classification accuracy deteriorates due to limited codes and cross-referencing issues
Solution Approach 1:
The patent replaces the manual mechanical classification system (human reviewers assigning NAICS codes) with an automated machine learning classification engine that uses natural language processing and probabilistic models to automatically classify businesses into NAICS categories, thereby improving accuracy while reducing operational complexity
Solution Approach 2:
The patent transforms the classification approach by changing from deterministic code assignment to probabilistic classification using machine learning models that calculate likelihoods of different NAICS codes based on business data, enabling more accurate classification while managing complexity through algorithmic processing
2Productivity
If manual classification processes are used, then flexibility in handling complex cases is maintained, but processing time increases leading to delays and monetary loss
Solution Approach 1:
The patent implements self-service classification where the machine learning engine autonomously classifies businesses without requiring manual human intervention, enabling rapid automated processing that significantly reduces turnaround time while maintaining consistent classification standards across all submissions
Solution Approach 2:
The patent substitutes manual human classification processes with automated machine learning systems that process business data and assign NAICS codes algorithmically, dramatically increasing processing speed and eliminating delays associated with manual review while reducing monetary losses from prolonged processing times
3Reliability
If multiple classification codes are used for the same business, then comprehensive coverage is achieved, but consistency deteriorates due to lack of single source of truth
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning classification engine continuously learns from classification outcomes and performance metrics, refining its models to improve consistency and reliability of NAICS code assignments while maintaining the ability to adapt to new business types and classification requirements
Solution Approach 2:
The patent creates a universal machine learning classification engine that handles multiple classification scenarios and business types through a single standardized system, providing consistent and reliable NAICS code assignments across diverse industries while maintaining adaptability to emerging business models
4Ease of manufacture
If custom deployment procedures are used for each model, then specific model requirements are met, but maintenance complexity increases creating technical debt
Solution Approach 1:
The patent establishes a universal model deployment framework that provides standardized procedures and tools for deploying, monitoring, and maintaining machine learning models across the enterprise, enabling easy model deployment while reducing complexity through consistent standardized processes that eliminate technical debt from custom procedures
Solution Approach 2:
The patent segments the deployment system into modular standardized components that can be independently configured and maintained, making deployment easier through clear separation of concerns while reducing overall system complexity through reusable standardized modules that can be applied across multiple models
Data Source
AI summary
An artificial intelligence (AI) prediction engine is used to correctly classify an entity based on a predetermined classification taxonomy, e.g., NAICS. The engine and process for using takes as inputs an entity's social presence (e.g., name, web address, etc.) and address. The AI prediction engine employs various machine learning models to make a classification prediction.