ML Decision Engine Combining Historical, Experimental, and Simulation Insights
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Solution Overview
Problem
Manual decision-making in organizations is prone to individual biases, errors, and inefficiencies, particularly when dealing with large volumes of data or complex scenarios, leading to inconsistent and suboptimal outcomes.
Innovation Solution
A machine learning-based computing system that includes subsystems for data analysis, insight generation, synthesis, and decision-making, utilizing both machine learning models and simulation-based models to generate and optimize decisions based on historical and real-time data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual decision-making is used in organizations, then human judgement and flexibility are maintained, but individual biases, errors, and inconsistencies occur leading to suboptimal outcomes
Solution Approach 1:
The patent replaces manual human decision-making processes with an automated machine learning-based computing system. The system uses trained ML models to analyze data and generate decisions, substituting human cognitive processes with computational algorithms that eliminate biases, errors, and inconsistencies while maintaining reliability across all decisions.
Solution Approach 2:
The computing system is designed to autonomously make decisions without requiring continuous human intervention. The ML models self-service by automatically processing input data, generating decisions, and providing outputs, thereby maintaining consistent and reliable decision-making while reducing dependence on human judgment.
2Productivity
If manual decision-making is used, then human judgement is applied, but processing capacity is limited when dealing with large volumes of data
Solution Approach 1:
The patent replaces manual human data processing with automated machine learning models that can handle large volumes of data efficiently. The computing system processes vast datasets without the cognitive limitations of human decision-makers, significantly increasing productivity while the modular architecture manages system complexity.
Solution Approach 2:
The computing system is divided into distinct modules including data reception, preprocessing, ML model processing, and output generation. This segmentation allows each component to handle specific tasks efficiently, increasing overall data processing capacity while keeping individual module complexity manageable.
3Loss of time
If manual decision-making is used, then human expertise is leveraged, but timeliness and accessibility of information are delayed
Solution Approach 1:
The system performs preliminary actions by pre-processing data and having trained ML models ready to generate decisions immediately when input data is received. This eliminates delays associated with manual data gathering and analysis, significantly reducing decision time while the automated preprocessing manages complexity.
Solution Approach 2:
The patent replaces manual information gathering and processing with automated computational processes. The system instantly accesses and processes relevant information without human delays, reducing decision time while the structured automated approach manages system complexity.
4Adaptability or versatility
If manual decision-making is used, then human judgement is applied, but scalability is limited when organizational complexity increases
Solution Approach 1:
The computing system is designed with universal ML models that can handle multiple types of decisions across different organizational contexts. The same system architecture scales from small to large organizations, adapting to increasing complexity through configurable parameters rather than requiring fundamentally different systems.
Solution Approach 2:
The system dynamically adapts to organizational complexity by adjusting processing depth, model selection, and data requirements based on the specific decision context. This dynamic behavior allows the system to scale efficiently as organizational complexity increases without requiring proportional increases in system complexity.
Data Source
AI summary
A machine learning based computing system for generating decisions corresponding to processes in organizations. The ML-based computing system is configured to: receive data associated with experiments, from electronic devices associated with users; analyze first data associated with first experiments, second data associated with second experiments, and third data associated with third experiments; generate second insights associated with second experiments, based on the analyzed second data associated with the second experiments by ML models; generate third insights associated with the third experiments, based on analyzed third data associated with the third experiments by simulation based models; synthesize first insights retrieved from historical data, the second insights generated from the second experiments by the ML models, and the third insights generated from the third experiments by the simulation based models; generate the decisions based on synthesization of first, second insights, and third insights; provide an output of decisions to user interfaces.