State Inference via Composite Indicators and Tree Visualization
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Solution Overview
Problem
Current methods for inferring the state of a system from heterogeneous data are time-consuming, prone to errors, and require human intervention due to the complexity and uncertainty in interpreting multidimensional data, especially in fields like medicine and manufacturing.
Innovation Solution
A method that defines composite indicators by combining first and second indicators, forming difference values and measures of goodness, and visualizing these using a tree structure with color, size, and pattern cues to facilitate quick and accurate inference of a system's state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computerized methods are used to analyze multidimensional data, then productivity is improved, but measurement precision deteriorates due to uncertainty and erroneous measurements
Solution Approach 1:
The patent segments the complex state inference process into distinct components: computerized analysis generates initial classifications, while uncertainty metrics and expert knowledge are separately evaluated. The final state determination segments the reliance between automated systems and human experts, allowing each to contribute their strengths without compromising overall accuracy.
Solution Approach 2:
The patent introduces an intermediary layer between computerized data analysis and final state determination. This intermediary comprises uncertainty metrics, confidence scores, and expert knowledge bases that mediate the transition from raw data to reliable state inference, resolving the precision problem while maintaining productivity benefits.
2Device complexity
If computerized methods provide only classification output, then device complexity is reduced, but loss of information increases due to inability to capture expert knowledge
Solution Approach 1:
The patent makes the system multi-functional by enabling it to both automatically process multidimensional data and integrate expert knowledge. The system universally handles multiple data types (measurements, images, test results) and multiple knowledge sources (computerized analysis, expert rules, uncertainty metrics), preventing information loss while maintaining manageable complexity through standardized processing frameworks.
3Measurement precision
If user inspects multiple heterogeneous data values, then measurement precision is improved, but loss of time increases due to time-consuming interpretation
Solution Approach 1:
The patent performs preliminary actions by pre-processing heterogeneous data into standardized formats, pre-calculating uncertainty metrics, and pre-organizing expert knowledge into accessible structures. This preliminary preparation reduces the time required for users to interpret data while maintaining precision, as the heavy lifting of data integration and uncertainty analysis is completed before user review.
4Adaptability or versatility
If heterogeneous data is combined heuristically, then adaptability is improved, but manufacturing precision deteriorates due to unreliability in combining data
Solution Approach 1:
The patent changes the parameters of data combination from heuristic to systematic by introducing quantitative uncertainty metrics, confidence scores, and standardized integration rules. These parameter changes transform the combination process from unreliable heuristic methods to precise, reproducible calculations that maintain adaptability across different data types while improving reliability through mathematically sound integration methods.
Data Source
AI summary
The invention relates to inferring the state of a system of interest having a plurality of indicator values and possibly being heterogeneous in nature. A number of indicator values from a control state and from a comparison state are gathered. From these indicator values, classification power between the control and comparison states (measure of goodness) is computed. Difference values are computed for the indicator values from the system of interest based on the difference to the indicator values from control and comparison states. From a number of these indicators, composite indicators are formed, and composite measures of goodness and composite difference values are computed. A plurality of composite indicators may be formed at different levels. These indicators may be represented as a tree and grouped according to content, and at the same time they may be arranged according to the measure of goodness or some other value. The indicators, measures of goodness, and difference values may be visualized and shown to a user, who may use such a representation for inferring the state of the system.