Cognitive Inspection System for Multi-Modal Data Fusion
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Solution Overview
Problem
Current inspection systems for medical and industrial applications require multiple devices and highly skilled operators to interpret data from various frequency ranges and modalities, leading to increased complexity, cost, and a high risk of false negative and false positive diagnostic results due to reliance on manual data fusion and rule-based models.
Innovation Solution
A cognitive inspection system that integrates acoustical and non-acoustical data using cognitive artificial intelligence, emulating human cognitive processing through symbolic architectures and inference algebras to fuse data across multiple frequency ranges and modalities, enabling the system to anticipate abnormal conditions and communicate results to operators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple independent inspection devices and highly skilled operators are used, then measurement precision and diagnostic reliability are improved, but device complexity and operational cost increase
Solution Approach 1:
The patent combines multiple independent inspection devices (acoustical sensors for infrasound, audible sound, and ultrasound, plus non-acoustical inspection devices) into a single integrated system with a common housing. This merging eliminates the need for multiple separate devices while maintaining comprehensive inspection capabilities across all frequency ranges and modalities.
Solution Approach 2:
The integrated inspection system performs multiple functions simultaneously: it collects data across infrasound, audible, and ultrasound frequency ranges; processes acoustical and non-acoustical data; fuses multi-modal data; and provides cognitive analysis. This universal system replaces multiple specialized devices while maintaining or improving diagnostic accuracy.
2Reliability
If multiple highly skilled operators are used for data interpretation, then diagnostic reliability is improved, but operational cost and time consumption increase
Solution Approach 1:
The system incorporates automated cognitive processing capabilities that perform data fusion, analysis, and interpretation without requiring multiple human operators. The cognitive processing module autonomously fuses data from multiple sensors, compares results against known signatures, and generates diagnostic conclusions, enabling the system to serve itself rather than relying on extensive human expertise.
Solution Approach 2:
The patent replaces the mechanical process of manual data interpretation by multiple skilled operators with an automated cognitive processing system. This substitution uses electronic data fusion and cognitive analysis algorithms to perform functions that previously required human expertise, significantly reducing time consumption while maintaining diagnostic reliability.
3Ease of operation
If manual data fusion and rule-based models are used, then ease of operation is improved, but diagnostic reliability deteriorates due to false negative and false positive results
Solution Approach 1:
The patent replaces simple rule-based models with advanced cognitive processing capabilities. The system uses data fusion techniques to integrate information from multiple sensors and modalities, then applies cognitive analysis to interpret the fused data against known signatures. This substitution maintains operational simplicity while dramatically improving diagnostic accuracy by reducing false negative and false positive results.
Data Source
AI summary
The present invention relates to inspection of medical patients including, but not limited to, phonocardiography, auscultation and ultrasound medical imaging and other non-acoustical inspection techniques; and industrial non-destructive testing and evaluation of materials, structural components and machinery; and more particularly to the incorporation of cognitive artificial intelligence into an inspection system and method that utilizes cognitive mathematical techniques which emulate the cognitive processing abilities of the human brain including, but not limited to, symbolic cognitive architectures and inference process algebras, to analyze data collected from infrasound acoustical sensors (0.1 Hz-20 Hz), audible acoustical sensors (20 Hz to 20 kHz), ultrasound acoustical sensors and transmitters above 20 kHz, data collected from other non-acoustical inspection devices and systems including, but not limited to electrocardiography (EKG), computed-tomography (CT), single photon emission computed tomography (SPECT), positron emission tomography (PET), magnetic resonance imaging (MRI), electromagnetic testing (ET), magnetic particle inspection (MT or MPI), magnetic flux leakage testing (MFL), liquid penetrant, radiographic (x-ray and gamma ray), eddy-current testing, low coherence interferometry, and combinations thereof (i.e., multi-modality inspection data); fuse this data resulting in the generation of new metadata; and then utilize cognitive mathematical techniques to interpret this data against inspection signatures that characterize conditions being diagnosed. The present invention has the ability to also identify and anticipate abnormal conditions that fall outside known inspection signature patterns; and communicate the inspection results to an operator thereby simplifying the initial inspection and diagnosis for medical patients and industrial objects; minimizing false negative and false positive initial inspection results and lowering costs.