Computing Product Review Accreditation With Multi-Factor Credibility Scoring
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Solution Overview
Problem
Existing systems lack an effective method to accurately determine the accreditation of review entities for computing products based on electronic documents, which are crucial for assessing credibility and reliability.
Innovation Solution
A method involving calculating anonymity, content specificity, perspective view, usage context, reliability, and acceptance scores of electronic documents, followed by a credibility score, to update a computing product's profile, using natural language processing and machine learning to analyze and normalize data from various sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple scoring dimensions (anonymity, content specificity, perspective view, usage context) are integrated to assess document credibility, then the reliability of accreditation determination is improved, but the system complexity increases
Solution Approach 1:
The patent segments the credibility assessment into five distinct scoring dimensions: anonymity score, content specificity score, perspective view score, usage context score, and user interaction score. Each dimension is calculated independently through specific analysis methods, allowing comprehensive evaluation while maintaining manageable complexity through modular processing of each aspect separately.
Solution Approach 2:
The patent merges multiple independent scoring dimensions into a unified credibility score through a weighted combination formula. This integration combines the anonymity score, content specificity score, perspective view score, usage context score, and user interaction score into a single comprehensive accreditation determination, achieving reliable holistic assessment while leveraging the structured breakdown of individual components.
2Measurement precision
If comprehensive text analysis including noun, pronoun, and adjective analysis is performed to calculate content specificity score, then the measurement precision of document evaluation is improved, but the processing time increases
Solution Approach 1:
The patent performs preliminary text analysis by extracting and analyzing specific linguistic elements (nouns, pronouns, adjectives) before computing the content specificity score. This preliminary processing of text components enables precise measurement of how specifically a document discusses the computing product, establishing a foundation for accurate credibility assessment while structuring the analysis to efficiency.
Data Source
AI summary
A method of determining an accreditation of a review entity of a computing product, including calculating an anonymity score of the electronic document based on publicly available data of the entity; calculating a content specificity score of the electronic document based on a text analysis of the electronic document; calculating a perspective view score of the electronic document based on a sentiment of the electronic document; calculating a usage context score of the electronic document based on workload mentions within the electronic document; calculating a reliability score of the electronic document based on i) the anonymity score, ii) the content specificity score, iii) the perspective view score, iv) the usage context score; calculating an acceptance score of the electronic document based on user-interaction data of the electronic document; calculating a credibility score of the electronic document based on a weighted average of the reliability score and the acceptance score.