LLM Feedback Aggregation With Conflict-Resolved Summary Visualization
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Solution Overview
Problem
Aggregating and visualizing user feedback from multiple sources is challenging due to reviewer diversity, non-representative samples, and iterative review cycles leading to conflicting information and reviewer fatigue, with latent information often remaining unused.
Innovation Solution
An aggregator system that ingests user inputs from various interfaces, applies machine learning models to generate a textual output summarizing user experiences, and uses weighting and sentiment analysis to resolve conflicts and modulate sentiments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If detailed surveys are used to capture comprehensive user feedback, then data completeness is improved, but respondent representativeness deteriorates due to friction
Solution Approach 1:
The patent introduces an intermediary processing layer (aggregator system with machine learning models) that mediates between user feedback inputs and analysis outputs. This intermediary automatically processes, standardizes, and aggregates feedback from multiple sources, reducing the friction that would otherwise prevent representative participation while maintaining comprehensive data capture through automated processing of diverse input formats
Solution Approach 2:
The system enables self-service feedback aggregation where the aggregator automatically collects, processes, and analyzes feedback from multiple users without requiring manual intervention. The machine learning models autonomously handle data standardization and conflict resolution, allowing the system to serve itself in managing the aggregation process while maintaining both completeness and representativeness
2Measurement precision
If iterative review cycles are used to resolve conflicts between reviewers, then information accuracy is improved, but reviewer fatigue increases
Solution Approach 1:
The patent replaces the mechanical iterative review process with an automated machine learning-based aggregation system. The ML models perform conflict resolution and information verification automatically, substituting human reviewers in the iterative cycle with computational processes that can handle conflicts without fatigue while maintaining or improving accuracy through systematic analysis
Solution Approach 2:
The aggregator system serves as an intermediary between conflicting reviewer inputs, automatically mediating disputes and resolving conflicts through ML-based analysis. This intermediary handles the iterative resolution process that would otherwise require repeated human review cycles, maintaining accuracy while eliminating reviewer fatigue
3Loss of information
If multiple reviewers are used to provide diverse perspectives, then information comprehensiveness is improved, but conflict resolution complexity increases
Solution Approach 1:
The patent transforms the complexity of conflict resolution by changing the parameters of the aggregation process. The machine learning models adjust weighting parameters, confidence scores, and aggregation algorithms dynamically based on input characteristics, allowing the system to handle diverse reviewer perspectives comprehensively while managing complexity through adaptive parameter optimization rather than fixed complex procedures
Data Source
AI summary
Systems and methods for information aggregation and visualization are provided. A method includes presenting a plurality of instances of a user interface to a corresponding plurality of users, each instance including a user prompt. The method includes receiving, via each of the instances of the user interface, a user response to the user prompt. The method includes generating, responsive to the receipt of the responses, an instruction for ingestion into a large language model configured to construct an output text corpus. The method includes presenting, via the user interface, the output text corpus.