AI Contribution Estimation for Project Skill Evaluation
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Solution Overview
Problem
It is challenging for project managers to objectively and appropriately evaluate the contribution degrees of members with different skills participating in a project, as existing technologies lack the capability to estimate these contributions effectively.
Innovation Solution
A contribution degree estimation system that includes an information reception unit, an estimation unit using a trained model, and a presenting unit to receive and present the contribution degrees of members based on project, task, and member information, allowing for the estimation of contribution degrees before, during, or after a project.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If project managers manually evaluate contribution degrees of members with different skills, then evaluation can be performed, but objectivity and appropriateness of evaluation deteriorates
Solution Approach 1:
The patent replaces manual mechanical evaluation processes with an automated AI-based estimation system. The estimation unit uses machine learning models to automatically calculate contribution degrees based on input data about member tasks, skills, and project information, eliminating subjective manual evaluation while maintaining high accuracy.
Solution Approach 2:
The patent introduces an intermediary AI estimation system between the raw project data and the final evaluation results. This intermediary layer processes member information, task data, and project context through trained models to produce objective contribution degree estimates, mediating between available data and evaluation outcomes.
2Adaptability or versatility
If existing evaluation mechanisms are used, then some evaluation capability is provided, but ability to estimate contribution degrees of members with different skills deteriorates
Solution Approach 1:
The patent changes the evaluation parameters from simple task completion metrics to a multi-dimensional parameter set including member skills, task difficulty levels, market values, and project context. This parameter transformation enables the system to accurately estimate contribution degrees for members with diverse skill sets by weighing different factors appropriately.
Solution Approach 2:
The patent creates a universal estimation system that can handle members with various skill types and project contexts through a single AI model framework. The model is designed to process different combinations of member information, task data, and project parameters, making it adaptable to any skill combination while maintaining consistent evaluation accuracy.
3Measurement precision
If detailed analysis of member contributions is performed, then evaluation accuracy improves, but time required for evaluation increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing and structuring member information, task data, and project context before the actual evaluation. The system prepares and validates input data in advance, ensuring that when contribution degree estimation is needed, the processed information is ready for immediate analysis by the AI model, reducing overall evaluation time.
Solution Approach 2:
The patent substitutes time-consuming manual analysis with automated AI processing. The estimation unit rapidly processes detailed member and task information through machine learning models, achieving high accuracy evaluation in a fraction of the time required for manual review, thus resolving the contradiction between accuracy and time consumption.
Data Source
AI summary
A contribution degree estimation system includes: an information reception unit configured to receive project information including at least one of a type, a goal, budget, or outcomes of a project, task information including at least one of levels of difficulties or market values of a plurality of required tasks that are required to carry out the project, and member information including at least one of tasks, skills, roles, or knowledge of the respective members participating in the project; an estimation unit configured to estimate the contribution degrees of the respective members using a trained model that has performed learning so as to receive, as input, the project information, the task information, and the member information and output contribution degrees of the respective members in the project; and a presenting unit configured to cause a display device to present the estimated contribution degrees of the respective member.