Automated Statistical Model Builder for AI Efficiency
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Solution Overview
Problem
Current statistical inference methods require humans or AI systems to have knowledge of mathematical derivation and computation, limiting their ability to design statistical models independently and leading to potential errors due to lack of interpretable model designs and proper statistical analysis.
Innovation Solution
A system that allows users or AI to define unknown parameters through equations, providing standardized building blocks and simple rules for designing statistical models without requiring mathematical expertise, automating mathematical derivation and implementation, and quantifying uncertainty in model predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional statistical inference methods are used, then mathematical accuracy is maintained, but accessibility and ease of use deteriorate due to requiring specialized mathematical knowledge
Solution Approach 1:
The patent introduces an automated system that acts as an intermediary between users and mathematical derivations. The system automatically performs mathematical derivations, model building, and statistical analysis based on user-defined parameters, eliminating the need for users to directly engage with complex mathematics while maintaining accuracy.
Solution Approach 2:
The system enables self-service statistical modeling by automatically generating models, performing derivations, and conducting analysis without requiring users to have specialized mathematical training. The automated platform handles all mathematical complexities internally while providing user-friendly interfaces.
2Productivity
If manual mathematical derivation and implementation are performed, then model precision is maintained, but productivity and efficiency deteriorate due to time-consuming processes
Solution Approach 1:
The patent replaces manual mechanical processes of mathematical derivation and model implementation with an automated computational system. The system automatically performs symbolic mathematics, derives models, and implements statistical analysis, dramatically increasing productivity while maintaining precision through systematic automated procedures.
3Measurement precision
If specialized statistical tools are used, then analysis accuracy is improved, but device complexity increases requiring multiple software packages
Solution Approach 1:
The patent merges multiple specialized statistical tools and functions into a single integrated automated system. The platform combines model building, mathematical derivation, statistical analysis, and visualization capabilities in one unified system, maintaining the precision of specialized tools while eliminating the need to switch between multiple software packages.
Solution Approach 2:
The automated system provides universal functionality by handling diverse statistical modeling tasks within a single platform. It can perform various types of statistical analysis, model building, and derivation tasks that previously required different specialized software packages, making the system multi-functional and broadly applicable.
4Reliability
If extensive mathematical knowledge is required, then model quality is ensured, but loss of time occurs due to hiring statisticians and software engineers
Solution Approach 1:
The system enables self-service model development by automatically performing tasks that previously required hiring specialized statisticians and software engineers. The automated platform handles mathematical derivations, model building, and validation internally, eliminating the need to outsource these tasks to experts while maintaining model quality through systematic automated procedures.
Data Source
AI summary
Presented herein are systems and methods for modeling and increasing accuracy of statistical models by artificial intelligence systems for increased efficiency in computing in such AI systems. The models may be designed and tested by a single (or small number of) AI system(s) and shared to multiple AI systems for further efficiency. The design and analysis may include considering desired level of precision; applying artificial intelligence techniques to design an equation for use in development of a statistical model, including selecting parameters; calculating and reporting precision for the developed model; recording any models that achieve the precision level or have the highest calculated precision; and providing models to a plurality of artificial intelligence systems to increase efficiency in statistical analysis in such systems.