Crowdsourced Data Compensation via Blockchain Micro-Transaction Batching
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Solution Overview
Problem
Current methods for compensating crowd-sourced data contributors do not accurately attribute value to the data provided, leading to inefficient incentivization and potential de-incentivization of valuable data contributors, as traditional payment systems are costly and inefficient for small transactions, and existing reward schemes often misattribute data relevance and quality.
Innovation Solution
A system that identifies and compensates data contributors proportionally to the value of their data contributions using micro-transactions via cryptocurrency, aggregating small payments when they reach a predetermined value, and records compensation using a blockchain ledger, ensuring fair and proper incentive allocation based on the data's impact on the machine learning model's utility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional payment systems are used to compensate data contributors, then compensation can be provided, but transaction costs are high and efficiency is low for small transactions
Solution Approach 1:
The patent combines multiple small compensation transactions into a single batched transaction. The system accumulates micro-compensations for multiple data contributors and processes them together, reducing the number of individual transactions and associated fees while maintaining fair compensation distribution.
Solution Approach 2:
The patent changes the parameter of transaction size by aggregating multiple small payments into a larger batched transaction. This transformation allows the system to benefit from reduced transaction fees that typically apply to larger transaction amounts, thereby reducing overall compensation costs.
2Ease of operation
If data contributors are compensated based on volume of data provided, then simplicity is maintained, but accuracy of value attribution is poor
Solution Approach 1:
The system implements feedback by analyzing the actual impact and utility of each data contributor's input on the machine learning model's performance and outputs. This feedback mechanism allows the system to attribute compensation based on measured value contribution rather than simple volume metrics, improving accuracy while maintaining automated processing.
Solution Approach 2:
The patent replaces the mechanical counting method (simple volume-based attribution) with an intelligent system that uses machine learning analysis to evaluate data quality and impact. This substitution enables accurate value attribution by leveraging computational intelligence to assess contributor value beyond mere data quantity.
3Reliability
If individual micro-transactions are processed for each data contributor, then fair compensation is provided, but transaction costs become prohibitive
Solution Approach 1:
The system merges multiple individual micro-transactions into a single batched transaction that compensates multiple data contributors simultaneously. This approach preserves the fairness of individual compensation by maintaining separate attribution of value to each contributor while reducing overall transaction costs through consolidation.
Solution Approach 2:
The system takes a partial action approach by processing only the necessary batched transactions rather than every possible individual transaction. By aggregating compensations and processing them in batches, the system achieves sufficient compensation fairness without the excessive cost of individual micro-transactions for each data contributor.
Data Source
AI summary
A method, apparatus and computer program product are provided to incentivize crowd sourcing of data by identifying and compensating content contributors based on a value of the content to training a neural network. Methods may include: receiving a request for a machine learning model trained from training data received from a plurality of data contributors, where the training data identified a contributor having provided the respective training data; processing the request for the machine learning model to infer a result based on a subset of training data relevant to the request; identifying one or more data contributors that provided the subset of training data relevant to the request; and providing compensation to the one or more data contributors that provided the subset of training data.