Pseudo-Image Data Representation for AI Explainability
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Solution Overview
Problem
Current data processing applications are inadequate for handling complex, voluminous big data sets, particularly in image analysis, which requires sophisticated tools for storing, querying, analyzing, and processing, and lacks human-interpretable explanations for machine learning and deep learning operations.
Innovation Solution
Transforming complex heterogeneous data into image-like representations to enable image processing tasks and generate interpretable explanations using machine learning or deep learning techniques, allowing for the management and processing of ordered datasets through pseudo-image representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current data processing applications are used for handling complex voluminous big data sets, then the processing can be performed with existing tools, but the applications are inadequate for image analysis and lack human-interpretable explanations
Solution Approach 1:
The patent introduces an intermediary explanation generation component that sits between the image processing task and the final output. This component takes the processed image data and generates human-interpretable explanations that bridge the gap between machine processing and human understanding, thereby resolving the contradiction between advanced image analysis capability and lack of interpretability
Solution Approach 2:
The system segments the data processing pipeline into distinct components: data transformation to image-like representations, image processing task execution, and explanation generation. This segmentation allows each component to be optimized independently, enabling sophisticated image analysis while separately addressing the interpretability requirement through dedicated explanation mechanisms
2Adaptability or versatility
If complex heterogeneous data is transformed into image-like representations, then image processing tasks can be enabled, but the data structure becomes more complex
Solution Approach 1:
The patent applies parameter changes by transforming heterogeneous data into a standardized image-like representation format. This transformation changes the structural parameters of the data to match image processing requirements, enabling the use of成熟的image processing algorithms while managing complexity through format standardization
Solution Approach 2:
The image-like representation serves multiple functions: it enables image processing tasks, maintains the underlying data structure for further processing, and provides a basis for generating human-interpretable explanations. This multi-functionality reduces the need for separate processing pipelines for different data types
Data Source
AI summary
Embodiments for intelligent interpretation of image processing results using machine learning in a computing environment by a processor. One or more data sets may be transformed into one or more pseudo-image representations to enable one or more image processing tasks for image processing. An interpretation of an image processing task result from applying the one or more image processing tasks on the one or more pseudo-image representations generated from one or more data sets.