Social Network Review Engine for Authenticating Consumer Feedback
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Solution Overview
Problem
Current review systems lack trustworthiness due to biased reviews from sellers or affiliates posing as buyers, making it difficult for consumers to make informed purchase decisions, as existing systems fail to distinguish between genuine and biased feedback.
Innovation Solution
A socially enabled review system that integrates social networks to prioritize reviews based on the reviewer's relationship to the user, using a review engine with a social network engine, rate and rank engine, credentials engine, and privacy engine to sort, filter, and authenticate reviews, ensuring legitimacy and authenticity while addressing privacy concerns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If reviews are collected from all users without verification, then the quantity of reviews increases, but the reliability of reviews deteriorates due to biased reviews from sellers posing as buyers
Solution Approach 1:
The patent introduces social network connections as an intermediary mechanism to verify reviewer authenticity. By requiring that reviewers be connected to the buyer through social networks (friends, acquaintances, family), the system mediates between collecting numerous reviews and ensuring their reliability. This social connection requirement acts as a filter that prevents sellers from posing as buyers while still allowing genuine customer feedback to be collected.
2Reliability
If social network integration is implemented to verify reviewer authenticity, then the reliability of reviews improves, but the device complexity increases due to multiple engines and databases required
Solution Approach 1:
The patent makes the social network database serve multiple functions: it stores social connection data for verification, provides the basis for ranking reviews by relationship closeness, and enables the system to identify and prioritize trusted reviewers. By making the social network infrastructure multi-functional, the patent reduces the need for separate verification mechanisms, thereby managing complexity while maintaining reliability.
Solution Approach 2:
The system uses the social network's existing infrastructure and data structures to perform verification and ranking functions. Rather than building entirely new verification systems, the patent leverages the social network's own connection data and relationship metrics to authenticate reviewers and rank their reviews, allowing the social network to effectively service its own verification needs.
3Reliability
If reviews are sorted by social relationship closeness, then the trustworthiness of reviews improves, but the loss of information occurs as some potentially valuable reviews from non-connected users are excluded
Solution Approach 1:
The patent applies different quality standards to different portions of the review set. Reviews from socially connected users are given higher priority and marked as more trustworthy, while reviews from non-connected users are still included but ranked lower. This local differentiation in quality assessment allows the system to highlight reliable reviews without completely excluding other potentially valuable feedback, thus maintaining review diversity while emphasizing trustworthiness.
Data Source
AI summary
The embodiments of the present system include a review engine that is connected to support modules and databases that receive, store and retrieve reviews, based upon the subject and the users' relationship to the authors of the reviews. The review engine comprises a social network engine, a rate and rank engine, a credentials engine and a privacy engine. These engines allow reviews to be sorted, filtered and ordered in terms of relevance when presented to the user. Numerous methods are also provided by the system that receive, store and retrieve reviews.