Blockchain Document Review Scoring via Multi-Modal Feedback
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Solution Overview
Problem
Organizations face challenges in evaluating and selecting the most valuable ideas from numerous submissions due to reliance on insufficient textual feedback, which lacks reliability and effectiveness in assessing the credibility and quality of ideas.
Innovation Solution
A blockchain-based distributed architecture system for electronic document review and scoring that incorporates crowdsourced expertise validation, utilizing both textual and visual feedback from certified experts, including a credentialing engine, expert scoring module, and document scoring engine with natural language processing and visual scoring capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If only textual feedback is used for document evaluation, then the evaluation process is simple, but the reliability and credibility of the evaluation results are insufficient
Solution Approach 1:
The patent combines multiple feedback types (textual, visual, eye tracking, micro expressions) into a unified evaluation system. The document scoring engine integrates scores from NLP-based textual analysis and visual scoring engine to produce a comprehensive aggregate score, thereby improving reliability through multi-modal feedback aggregation.
Solution Approach 2:
The evaluation system is designed to handle multiple types of feedback uniformly through a universal scoring framework. The document scoring engine can process textual feedback via NLP analysis and visual feedback via eye tracking and micro expression analysis, with each contributing to the overall evaluation in a standardized manner.
2Measurement precision
If multiple attributes and feedback types are aggregated for scoring, then the assessment becomes more comprehensive, but the system complexity increases
Solution Approach 1:
The evaluation system is segmented into distinct functional modules: NLP-based analysis engine for textual feedback, visual scoring engine for visual feedback, eye tracks processor for eye movement analysis, and micro expressions processor for facial expression analysis. Each module independently processes its specific feedback type and contributes to the overall document score through the document scoring engine.
Solution Approach 2:
The document scoring engine acts as an intermediary that receives and integrates scores from multiple independent processors (NLP-based analysis, visual scoring, eye tracking, micro expressions). It aggregates these diverse feedback types into a unified document score, managing complexity through standardized integration interfaces.
3Reliability
If visual feedback processing is added to textual feedback, then the evaluation credibility improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of visual feedback by capturing eye tracking data and micro expression data during the document review process itself, rather than requiring separate post-processing sessions. This allows visual feedback to be collected and processed concurrently with textual feedback analysis.
Solution Approach 2:
The evaluation process maintains continuity by processing multiple feedback types simultaneously through parallel computational paths. The NLP-based analysis engine, visual scoring engine, eye tracks processor, and micro expressions processor operate concurrently, with results aggregated by the document scoring engine to produce the final score without sequential delays.
Data Source
AI summary
A blockchain configured system and a method for facilitating an expertise driven review and scoring of electronic documents in a crowdsourced environment. The system includes a server computer, a memory circuit and a processing circuit. The processing circuit is coupled to the memory circuit and includes or is coupled to a credentialing engine. The system further includes an expert scoring module. The system further includes a document reviewing and scoring engine coupled to the processing circuit. The document review and scoring module associates an aggregate score to the electronic document based on aggregation of the review ratings by crowdsourced experts and aggregate scores of each of the crowdsourced experts based on the set of attributes including one or more of the credentialed expertise, reputation of the expert, and the officiality.