Multi-angle Data Valuation for Fair Incentive Allocation

The patent segments the data valuation process into multiple independent modules: data quality assessment module, data relevance assessment module, and incentive allocation module. Each module handles a specific aspect of valuation (quality metrics, relevance to tasks, fair distribution), breaking down the complex computation into manageable segments that can be processed separately and efficiently.

The patent introduces a computing system acting as an intermediary between data providers and data consumers. This intermediary performs the computationally intensive valuation calculations using machine learning models, then provides simplified valuation results and incentive allocations to the parties involved, shielding them from the underlying computational complexity while maintaining high accuracy.

The patent divides the valuation perspective into distinct segments: individual data consumer needs assessment, global data market value assessment, and task-specific relevance assessment. Each segment evaluates data from a different angle and contributes to the overall fair incentive allocation, allowing comprehensive multi-angle valuation while maintaining manageable system complexity through modular design.

The patent creates a universal valuation framework that can assess data from multiple perspectives (individual consumer needs, global market value, task-specific relevance) using a single integrated system. This multi-functional approach allows the system to handle diverse valuation requirements across different scenarios while maintaining consistent fairness criteria through the unified computing system.

The computing system serves as an intermediary that handles all complex machine learning operations internally. Data providers and data consumers interact with a simplified interface that automatically performs advanced valuation calculations using machine learning models, extracting features, assessing quality and relevance, and determining fair incentives without requiring users to understand or configure the underlying complex algorithms.

The system implements self-service automation where the machine learning models automatically perform data feature extraction, quality assessment, relevance evaluation, and incentive calculation without manual intervention. The computing system autonomously processes valuation requests, applies trained models, and generates results, eliminating the need for operators to manually configure or manage the complex machine learning processes.