Contribution Allocation Based on Activity Data
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Solution Overview
Problem
In team-based projects, determining individual contributions is often subjective, leading to undervalued efforts and overvaluation of others, resulting in dissatisfaction and potential bad behavior within companies.
Innovation Solution
A computer-based system that monitors team members' contributions using application usage and electronic communication data, employing natural language processing to quantify both quantity and quality of contributions, and allocates awards based on accurate contribution scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subjective methods are used to determine individual contributions in team projects, then the allocation process is simple and quick, but the accuracy and fairness of contribution assessment deteriorates
Solution Approach 1:
The patent introduces an intermediary system comprising processors and data sources that objectively measure and quantify team members' contributions. This intermediary layer between the team and the award allocation process captures activity data, communication patterns, and task completion metrics to generate accurate contribution scores, eliminating the need for subjective human judgment while maintaining system manageability.
Solution Approach 2:
The patent replaces the mechanical system of subjective human assessment with an automated computational system. Processors analyze electronic data sources including communication logs, task management systems, and performance metrics to objectively calculate contribution scores, substituting human subjectivity with algorithmic precision and consistency.
2Measurement precision
If automated systems are introduced to measure contributions objectively, then contribution assessment accuracy improves, but the complexity of the system increases
Solution Approach 1:
The patent creates a universal system that performs multiple functions: collecting data from various electronic sources, analyzing communication patterns, tracking task completion, calculating contribution scores, and facilitating award allocation. This multi-functional approach consolidates what would otherwise require multiple separate systems into a single integrated platform, managing complexity through functional consolidation.
Solution Approach 2:
The system automatically collects and processes its own operational data without requiring external intervention. By monitoring its own usage within the collaborative environment and self-generating contribution metrics, the system reduces the need for external configuration and management overhead, effectively managing its own complexity.
3Measurement precision
If detailed monitoring of team members' activities is implemented, then contribution quantification accuracy improves, but the intrusiveness and potential privacy concerns increase
Solution Approach 1:
The patent extracts only the specific data elements necessary for measuring contribution accuracy from the broader context of team members' digital activities. Rather than monitoring all communications and actions, the system selectively extracts relevant metrics such as task completion data, communication frequency related to project work, and contribution to specific deliverables, minimizing privacy intrusion while maintaining measurement precision.
Solution Approach 2:
The patent applies different levels of monitoring intensity to different aspects of team member activities based on their relevance to contribution measurement. High-detail monitoring is applied to work-related tasks and deliverables where precision is critical, while less intrusive methods are used for general communications, creating a localized approach that balances accuracy with privacy respect.
Data Source
AI summary
A method includes: receiving, by a processor set, user input defining parameters of a project; receiving, by the processor set, opt-in input from team members defined in the parameters of the project; retrieving, by the processor set and from electronic data sources, data that corresponds to performance of tasks of the project by each of the team members; generating, by the processor set and based on the retrieved data, a respective contribution score of each of the team members; generating, by the processor set and based on the respective contribution scores of each of the team members, a respective allocation score of each of the team members; allocating, by the processor set, an award to the team members based on the respective allocation scores of each of the team members.