Consumer Product Rating System with Dynamic Weighting
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Solution Overview
Problem
Current e-commerce rating systems fail to effectively combine lay and expert opinions, leading to biased ratings due to lack of standardization and weighting mechanisms, resulting in unreliable product and service evaluations.
Innovation Solution
A computer-implemented method and system that calculates a weighted average of lay and expert ratings, allowing users to select the weight given to each group, using a standardized five-parameter rating system (enjoyment, quality, suitability, contribution, and cost-benefit) with predefined weights, and displaying the total rating on a GUI.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If only customer ratings are used, then the system is simple and easy to operate, but the rating reliability is low due to potential bias from lay users
Solution Approach 1:
The patent combines two separate rating sources (customer ratings and expert ratings) into a unified rating system. The composite rating merges lay user opinions with expert opinions, where expert opinions are weighted more heavily to improve overall rating reliability while maintaining system operability through automated calculation algorithms.
Solution Approach 2:
The patent introduces a weighting parameter system where different sources of ratings are assigned different weights. Expert ratings are given higher weight (e.g., 70-90%) compared to customer ratings (e.g., 10-30%), allowing the system to adjust the influence of each source to optimize reliability while managing complexity through predefined weight configurations.
2Measurement precision
If expert opinions are included, then the rating precision improves, but the system complexity increases due to needing multiple rating sources
Solution Approach 1:
The patent segments the rating system into distinct components: customer rating module, expert rating module, and composite rating calculation module. Each segment handles specific functions independently, with clear separation between data collection, processing, and aggregation, which manages complexity through modular design while maintaining high measurement precision.
Solution Approach 2:
The patent introduces an intermediary computational layer (the weighted average algorithm) that mediates between raw customer ratings and expert ratings to produce the composite rating. This intermediary processing layer standardizes different rating sources and combines them systematically, improving precision while containing complexity through automated calculation.
3Measurement precision
If a standardized rating system with multiple parameters is implemented, then the measurement precision improves, but the ease of operation decreases
Solution Approach 1:
The patent implements a universal rating framework with five standardized parameters (quality, value, enjoyment, suitability, contribution) that can be applied across different product categories. This multi-functional parameter system provides precise measurement while maintaining ease of operation through consistent, pre-defined categories that users can evaluate without needing product-specific knowledge.
Solution Approach 2:
The patent transforms complex product evaluations into five standardized parameters with predefined weightings. By converting diverse product attributes into uniform parameter categories (each with standard weightings like quality=30%, value=25%, etc.), the system achieves high measurement precision while keeping the user interface simple and consistent across different products.
4Adaptability or versatility
If user-selected weighting is allowed, then the adaptability improves, but the device complexity increases due to additional controls
Solution Approach 1:
The patent introduces dynamic weighting capabilities where users can adjust the weight distribution between customer ratings and expert ratings according to their preferences. The system adapts to individual user needs by allowing weight adjustment (e.g., more weight to experts for technical products, more to customers for lifestyle products) while maintaining a relatively simple interface through slider controls or preset options.
Data Source
AI summary
A computer-implemented method for rating a consumer product or service, the method including: calculating, by a computer system comprising at least one computer in a network comprising at least one processor, a lay rating from at least one rating score built from several weighted parameters, provided by a respective lay consumer; calculating, by the computer system, an expert rating calculated from at least one rating score built from several weighted parameters provided by a respective expert; receiving, by the end user, using the computer system, a selection of a respective weight for each of the lay rating and the expert rating by an end-user via a graphic user interface (GUI); calculating, by the computer system, a total rating as a result of a weighted average biased according to the respective weights selected by the end-user; and displaying, by the computer system, the total rating on the GUI.