Biological Data Reward Allocation System
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Solution Overview
Problem
Existing reward determination systems for biological data lack an efficient mechanism to distribute rewards among multiple stakeholders based on their contributions and reliability, leading to inefficiencies and potential fraud.
Innovation Solution
A reward determination system that calculates reliability and contribution parameters for development, data provision, and data processing tasks, and uses these parameters to determine the allocation of rewards to various stakeholders, including platform developers, data processing operators, and data providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a cryptocurrency system is used to pay rewards to data providers, then data providers receive rewards when predetermined conditions are satisfied, but there is no efficient mechanism to distribute rewards among multiple stakeholders based on their contributions and reliability
Solution Approach 1:
The patent segments the reward distribution system into multiple independent modules: a reward determination unit that calculates distribution amounts, a reliability evaluation unit that assesses stakeholder trustworthiness, a contribution evaluation unit that measures actual contributions, and a distribution execution unit that implements the distribution. This segmentation allows each module to handle specific tasks independently, improving reliability through specialized functions while managing system complexity through modular design.
Solution Approach 2:
The patent implements feedback mechanisms where the reliability evaluation unit continuously monitors stakeholder behavior and updates reliability scores, and the contribution evaluation unit tracks actual contributions and feeds this information back to the reward determination unit. This feedback loop ensures that reward distribution remains fair and reliable by dynamically adjusting based on observed performance and contribution levels, while the automated feedback process manages complexity through systematic data collection and analysis.
2Productivity
If rewards are distributed based on predetermined conditions, then distribution is automated, but there is no consideration of long-term reliability and short-term contributions of stakeholders
Solution Approach 1:
The patent performs preliminary actions by establishing evaluation models before reward distribution occurs. The reliability evaluation unit pre-assesses stakeholder reliability scores based on historical data, and the contribution evaluation unit pre-defines contribution metrics and weighting factors. These preliminary evaluations enable the reward determination unit to quickly calculate fair distributions without complex real-time analysis, improving efficiency while maintaining fairness through pre-established objective criteria.
Solution Approach 2:
The patent changes parameters by dynamically adjusting reliability scores and contribution weights based on observed stakeholder behavior. The system transitions from static predetermined conditions to dynamic parameter adjustment, where reliability parameters are updated based on long-term performance and contribution parameters are adjusted based on short-term actions. This parameter changes approach maintains automation efficiency while improving distribution fairness through adaptive parameter tuning.
Data Source
AI summary
A reward determination system determines distribution of a reward received from a data user to reward distribution targets in a platform for distribution and utilization of biological data provided by data providers. Each reward distribution target has: development task information obtained by quantifying a workload of a task required for developing the platform into numbers; data provision task information obtained by quantifying a workload of a task required for providing the biological data into numbers; and data processing task information obtained by quantifying a workload of a task required for processing the biological data into numbers. A reliability parameter and a contribution parameter of each task are calculated based on the development task information, the data provision task information, and the data processing task information on each task, and a reward to be allocated is determined for each reward distribution target based on the reliability parameter and the contribution parameter.