Neural Network Document Analysis with Visual Explainability
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Solution Overview
Problem
Automated software tools for analyzing financial documents lack transparency in explaining how they determine the financial health of a business entity, making it inadequate for regulatory and legal requirements.
Innovation Solution
An artificial intelligence system that uses a trained machine learning module with neural networks to analyze financial documents, generating an output image that highlights the input data features influencing the analysis, providing insight into the reasoning behind the financial health score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated software tools are used to analyze financial documents, then productivity and efficiency are improved, but transparency and explainability of the analysis process deteriorate
Solution Approach 1:
The patent introduces an intermediary visualization layer that mediates between the black-box neural network and the user. This layer processes the network's internal representations and transforms them into interpretable visual formats (heatmaps, feature visualizations, attention maps) that reveal which document features influenced the analysis outcome, thereby restoring transparency without sacrificing automation efficiency
Solution Approach 2:
The patent employs color-based visualizations (such as heatmaps with varying intensity colors) to encode the importance or influence of different document regions on the analysis result. By mapping neural network attention weights or feature activations to color intensities, the system provides an intuitive visual representation of the analysis reasoning, allowing users to understand which parts of the financial document drove the automated conclusions
2Measurement precision
If neural networks are used to analyze documents, then measurement precision of financial health assessment is improved, but difficulty of detecting and measuring the analysis reasoning increases
Solution Approach 1:
The patent extracts specific interpretable signals from the complex neural network processing. By isolating and visualizing key elements such as attention weights, activation patterns, or feature importance scores, the system separates the measurable precision aspects (the what) from the complex reasoning aspects (the why), providing a simplified view of the analysis logic that maintains measurement accuracy while reducing analytical complexity
Solution Approach 2:
The patent transforms the high-dimensional, abstract internal representations of the neural network into lower-dimensional visual spaces that are easier to interpret. By projecting complex feature vectors onto 2D heatmaps or graphical overlays on the original document, the system preserves the precision information while presenting it in a dimensionally reduced format that is more accessible to human analysis
Data Source
AI summary
Systems and methods for receiving a set of documents (e.g., financial documents) converting them into graphical images, performing image-based, artificial intelligence analysis to determine a score for the set of documents. In addition, the artificial intelligence system generates an image output that indicates how the artificial intelligence system arrived at the score be visually depicting the graphical features detected by the artificial intelligence system. This may allow insight as to the basis for the score.