Automated Decision Process for Uncertainty Reduction
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Solution Overview
Problem
Existing decision processes require significant human intervention, leading to bottlenecks in efficiently addressing uncertainties, particularly in situations with large amounts of available information, which limits the throughput and effectiveness of decision-making in fields like product and service research and development.
Innovation Solution
An adaptive decision process that enables an automatic closed-loop approach to information gathering and evaluation, utilizing computer-based systems to automate the determination of optimal actions for resolving uncertainties, incorporating probabilistic inferencing and experimental design to maximize the value of information gathered.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual modeling and expert intervention are used to determine expected value of information and experimental design, then decision quality can be maintained through human judgment, but productivity and throughput are significantly limited by manual bottlenecks
Solution Approach 1:
The system enables automated self-service by implementing computer-based probabilistic inferencing and experimental design capabilities that automatically determine expected value of information and optimize experimental parameters without requiring manual statistician intervention, thereby resolving the contradiction between maintaining decision quality and increasing throughput
Solution Approach 2:
The patent replaces the mechanical system of manual human expert analysis with computer-based automated inferencing systems, using algorithms to perform probabilistic reasoning and experimental design optimization, thus eliminating manual bottlenecks while preserving decision quality through systematic computational methods
2Speed
If high throughput experimentation methods are implemented to rapidly acquire new information, then information gathering speed increases, but manual bottlenecks in interpreting results and adjusting experimentation limit the actually attainable throughput
Solution Approach 1:
The system implements automated feedback loops where computer-based inferencing continuously analyzes experimental results and automatically adjusts subsequent experimentation parameters, creating a closed-loop system that eliminates manual interpretation bottlenecks and maintains high throughput throughout the entire experimentation and analysis process
Solution Approach 2:
The system performs preliminary automated analysis and interpretation of experimental results in real-time as data becomes available, rather than waiting for manual review, thereby maintaining the high speed of information gathering while eliminating downstream bottlenecks in result interpretation
3Productivity
If automated experimental design and inferencing systems are implemented, then productivity and throughput are significantly improved, but system complexity increases due to integration of probabilistic inferencing and experimental design algorithms
Solution Approach 1:
The patent implements a universal computer-based system that integrates multiple functions including probabilistic inferencing, experimental design optimization, and result analysis into a single multi-functional platform, thereby achieving high throughput while managing complexity through functional integration rather than separate specialized systems
Data Source
AI summary
An optimizing data-to-learning-to-action method and system identifies uncertainties embodied as probability distributions that influence a sequence of decisions. The uncertainties are mapped to a simulation of a computer-based infrastructure that supports the execution of the decisions. Actions with respect to the infrastructure that are expected to reduce the uncertainties are simulated. The probability distributions are updated accordingly for each simulated action and an associated net value of information for each simulated action is generated. The action with the greatest net value of information is implemented and the simulated infrastructure is updated accordingly. The process may then be re-run based upon the updated simulated infrastructure.