Incentive-Driven Data Collection for Labeled Work Machine Data
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Solution Overview
Problem
Current data collection systems face challenges in obtaining data that satisfy specific conditions required for analysis, such as metadata for supervised learning, due to the need for labeled training data, which is time-consuming and labor-intensive to prepare.
Innovation Solution
A data collection system that includes user apparatuses and a data requesting apparatus, where users input data that satisfy predetermined conditions, and the system provides incentives to suppliers for collecting and delivering relevant data, using a combination of hardware processors and communication networks to facilitate data collection and distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If labeled training data is prepared manually for supervised learning, then data quality and accuracy are improved, but time consumption and labor intensity increase significantly
Solution Approach 1:
The system pre-collects and stores unlabeled data in advance through various data collection devices. When labeling is needed, the pre-collected data can be quickly annotated without waiting for new data generation, significantly reducing the time required for preparing labeled training data while maintaining data quality standards.
Solution Approach 2:
The patent introduces an incentive mechanism as an intermediary that mediates between data collectors and data users. By providing incentives (such as points or rewards) to users who contribute labeled data, the system accelerates the data collection process without compromising quality, as the incentive structure encourages careful and accurate labeling.
2Reliability
If manual labeling is performed to ensure data accuracy, then data reliability is improved, but productivity decreases due to labor-intensive processes
Solution Approach 1:
The system performs preliminary data collection and pre-processing automatically before the labeling stage. This preliminary action filters and organizes data, so that only relevant data requires manual labeling, thereby maintaining reliability while improving overall productivity by reducing the volume of data that needs manual processing.
Solution Approach 2:
The patent enables data collectors to self-serve by allowing them to contribute and label data independently through the platform. The incentive mechanism motivates them to maintain high labeling quality autonomously, reducing the need for intensive manual oversight while preserving data reliability and significantly improving productivity through distributed labeling efforts.
3Quantity of substance
If comprehensive data collection is performed to meet various analysis conditions, then data completeness is improved, but system complexity increases due to multiple data requirements
Solution Approach 1:
The patent creates a universal data collection platform that can handle multiple types of data and various analysis conditions through a single unified system. The platform supports different data formats, collection methods, and incentive mechanisms, allowing comprehensive data collection without proportionally increasing system complexity, as the same infrastructure serves multiple purposes.
Solution Approach 2:
The system allows flexible adjustment of data collection parameters (such as data types, collection frequency, incentive levels) to match specific analysis requirements. This parametric approach enables comprehensive data collection for different conditions without redesigning the entire system, thereby maintaining data completeness while managing system complexity through configurable parameters.
Data Source
AI summary
A data collection system includes an input device configured to receive an input from a user, a first hardware processor configured to obtain data related to a work machine with a work attachment and satisfying a predetermined condition, according to the input received by the input device, and a second hardware processor configured to execute a process related to providing the user with an incentive when the first hardware processor obtains the data.