ML Text Condensation for Legislative Feedback Automation
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Solution Overview
Problem
The overwhelming volume of legislative text generated by Congress makes it impossible for constituents and representatives to understand and provide feedback in a timely manner, as the sheer amount of text exceeds human absorption capacity.
Innovation Solution
A system using machine learning models to condense large-scale text objects into concise summaries and generate survey questions, allowing for remote feedback collection from constituents, which are then aggregated to provide actionable metrics for representatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual reading and understanding of legislative text is used, then constituents can comprehend the content, but the volume of text exceeds human absorption capacity making it impossible to process in a timely manner
Solution Approach 1:
The patent replaces the mechanical human reading and comprehension process with an automated machine learning system. The ML model ingests large-scale text objects (legislative documents), processes them computationally, and generates condensed summaries with classification metadata, thereby substituting human cognitive processing with automated computational processing to handle the volume and complexity of legislative text.
2Productivity
If manual solicitation of feedback from constituents is used, then representatives can gather constituent views, but the increasing population and text volume make it physically impossible to obtain timely feedback
Solution Approach 1:
The system enables constituents to self-serve by automatically receiving personalized condensed summaries of legislative text relevant to their district and interests, along with embedded feedback mechanisms. This eliminates the need for representatives to manually solicit feedback from each constituent, as constituents can independently review the condensed content and submit feedback through the provided interface.
Solution Approach 2:
The patent implements a closed-loop feedback system where constituent responses to condensed summaries are automatically collected, aggregated, and transmitted back to representatives in real-time. This continuous feedback mechanism allows representatives to rapidly understand constituent positions on specific legislative provisions without manual intervention.
3Extent of automation
If condensed summaries are generated using machine learning, then text processing becomes automated and scalable, but the complexity of the machine learning system increases
Solution Approach 1:
The patent segments the complex task of legislative text analysis into distinct functional components: text ingestion, ML-based condensation and classification, metadata generation, personalized delivery, and feedback collection. This modular segmentation allows each component to be developed and optimized independently, managing the overall system complexity while achieving high automation.
Data Source
AI summary
A system to quantify aggregate alignment of segmented text with an evaluator population, with a data processing system comprising memory and one or more processors, can segment a first extended text object into one or more evaluation text objects associated with a population reference, identify one or more text frame objects corresponding to the evaluation text objects, the text frame objects being associated with a second extended text object, generate, based on the text frame objects, one or more context identifier objects corresponding to the evaluation text objects, and generate a condensed text object including one or more of the evaluation text objects, the evaluation text objects being positioned in the condensed text object in response to output of a first machine learning model trained with input including at least one of the first extended text objects, the evaluation text objects, the context identifier objects, and the text frame objects.