ML Confidence Score Visualization for Legal Invoice Analysis
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Solution Overview
Problem
Current machine learning systems lack intuitive visualization of confidence scores and reasoning, making it difficult for users to understand and provide feedback on machine learning decisions, particularly in complex text-based data like legal invoices, which requires significant user effort and resources.
Innovation Solution
A method and apparatus that process textual descriptions using natural language processing to derive a machine learning confidence score, display this score graphically as a variable icon, and allow user input to modify the model in real time, with formatting instructions highlighting key words impacting the score, enabling users to easily understand and correct the model's determinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning systems process complex text-based data like legal invoices, then the accuracy of determination is improved, but the complexity of user understanding and feedback provision worsens
Solution Approach 1:
The patent introduces a confidence score visualization as an intermediary element between the machine learning system and the user. This visual indicator (such as a light bulb with varying intensity or a progress bar) mediates the complex internal confidence calculations by translating them into an intuitive visual format that users can immediately comprehend, thereby resolving the contradiction between maintaining high determination accuracy and reducing user understanding complexity
Solution Approach 2:
The patent employs color changes in the visualization elements to represent different confidence levels. For example, the light bulb icon changes from dim to bright, or progress bars change color from red to green, providing an intuitive visual language that allows users to quickly grasp the machine's confidence without needing to understand the underlying complex text analysis processes
2Ease of operation
If machine learning systems provide detailed reasoning for determinations, then the ease of user feedback provision is improved, but the device complexity worsens
Solution Approach 1:
The patent extracts only the most relevant and impactful reasoning elements from the complex text analysis process and presents them to the user in a simplified format. Instead of displaying all processing details, the system identifies and highlights key phrases or concepts that most influenced the determination, allowing users to provide feedback based on essential information rather than overwhelming detail
Solution Approach 2:
The patent segments the reasoning process into discrete, manageable components that can be individually presented to the user. By breaking down the complex analysis into separate reasoning steps or key factors, the system makes the feedback process more approachable while maintaining the sophistication of the underlying analysis mechanism
3Adaptability or versatility
If machine learning systems require user feedback for training, then the adaptability is improved, but the loss of time worsens
Solution Approach 1:
The patent implements an efficient feedback mechanism where users can provide corrections or confirmations of machine determinations. This feedback is immediately processed and used to refine the model, creating a continuous improvement loop that enhances adaptability while minimizing time investment through streamlined interaction design
Solution Approach 2:
The patent employs preliminary actions by pre-processing and pre-presentation of information in a way that anticipates user needs. The confidence score visualization and key reasoning elements are prepared and displayed before user interaction, allowing users to quickly comprehend and provide feedback without unnecessary delays in the adaptation process
Data Source
AI summary
A unique user interface for improving machine learning algorithms is described herein. The user interface comprises an icon with multiple visual indicators displaying the machine learning confidence score. When a mouse hovers over the icon, a set of icons are displayed to accept the teaching user's input. In addition, the words that drove the machine learning confidence score are highlighted with formatting so that the teaching user can understand what drove the machine learning confidence score.