Evaluator Selection Model for Provider Compatibility
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Solution Overview
Problem
Conventional methods for selecting evaluators to assess products or services often fail to choose the most suitable evaluator due to reliance on single attributes, leading to ineffective evaluations.
Innovation Solution
An information processing apparatus that trains a user model using data about providers and evaluators, including attributes and past evaluation usefulness, to predict the effectiveness of evaluations and select suitable evaluators based on compatibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If evaluator selection is based on single attributes (such as specialty or taste), then the selection process is simple and fast, but the accuracy of evaluator selection deteriorates
Solution Approach 1:
The patent changes the parameters used for evaluator selection from single attributes (specialty, taste) to multiple attributes including age, gender, occupation, education level, and evaluation history. This parameter expansion allows the system to capture more comprehensive evaluator characteristics, thereby improving selection accuracy while maintaining efficiency through automated multi-parameter processing
Solution Approach 2:
The patent introduces a new dimension to evaluator selection by incorporating evaluation history and usefulness metrics alongside traditional attributes. This dimensional expansion enables the system to evaluate evaluators based on their actual performance and compatibility with providers, transforming the selection process from static attribute matching to dynamic performance-based selection
2Measurement precision
If multiple attributes are used for evaluator selection, then the accuracy of evaluator selection is improved, but the complexity of the selection process increases
Solution Approach 1:
The patent replaces manual, mechanical evaluation processes with an automated information processing system that uses algorithms to process multiple attributes and select evaluators. This substitution of mechanical decision-making with computational methods reduces the complexity burden on users while maintaining high selection accuracy through systematic multi-parameter analysis
Solution Approach 2:
The patent introduces an intermediary information processing apparatus that mediates between the provider's selection needs and the available evaluator data. This intermediary system automatically handles the complexity of multi-attribute processing and matching, shielding users from the underlying complexity while delivering accurate selection results
3Ease of operation
If conventional evaluator selection methods are used, then the system is simple to operate, but the evaluation effectiveness deteriorates
Solution Approach 1:
The patent incorporates feedback mechanisms by utilizing evaluation history and usefulness data from past evaluations to inform future selections. The system learns from previous evaluation outcomes and adjusts its selection criteria accordingly, creating a feedback loop that continuously improves evaluation effectiveness while maintaining user-friendly operation through automated learning processes
Data Source
AI summary
An information processing apparatus for determining an evaluator to evaluate a product or service provided by one of providers from among a plurality of evaluators, the information processing apparatus comprises a controller configured to execute: training a user model, with first data about the providers and second data about the plurality of evaluators who have made evaluations of products or services provided by the providers in past as input data, and third data indicating degrees of usefulness of the evaluations made by the plurality of evaluators in the past as output data; and selecting the evaluator suitable for the one of the providers from among the plurality of evaluators by using the user model.