Technology Information Attribution for Search-to-Purchase Commission Tracking
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Solution Overview
Problem
Existing systems fail to accurately identify which computer components contribute to long-term customer information provision leading to purchases, as the data processing and synthesizing required is too vast for manual methods, and current IT infrastructure does not enable connections between components that predict and reward technology evangelism opportunities.
Innovation Solution
Implementing a determining projected technology information effect component that proactively identifies overlaps between initial information provided and later purchases, using data science approaches to detect emerging technologies and allocate commissions to components that identify these opportunities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual data processing methods are used to identify contributing components, then system complexity is reduced, but measurement precision and productivity deteriorate due to the vast amount of data
Solution Approach 1:
The patent replaces manual data processing methods with an automated computer system that uses algorithms to process search data, generate tags, and identify contributing components. This substitution enables the system to handle vast amounts of data with high precision while maintaining manageable system complexity through automated workflows.
Solution Approach 2:
The patent introduces tags as intermediary elements that bridge search data and component identification. These tags serve as mediators that structure and organize the vast data, enabling accurate identification of contributing components without requiring direct manual analysis of the entire dataset.
2Measurement precision
If automated data processing is implemented to improve identification accuracy, then measurement precision improves, but device complexity increases due to additional components and infrastructure
Solution Approach 1:
The patent segments the data processing system into distinct functional components: search data collection, tag generation, tag association with user accounts, content selection, and commission tracking. This segmentation allows each component to perform its specific function with high precision while the overall system complexity is managed through modular architecture.
Solution Approach 2:
The patent creates a multi-functional system where the same infrastructure handles multiple tasks: processing search data, generating tags, selecting content, tracking user interactions, and calculating commissions. This universality reduces the need for separate specialized systems, thereby managing complexity while maintaining high identification accuracy.
3Productivity
If connections between predictive and reward components are established, then productivity improves through automated commission allocation, but device complexity increases due to integrated infrastructure
Solution Approach 1:
The patent merges the predictive component (tag generation from search data) with the reward component (commission allocation) into a single integrated system. This merging allows automated tracking of user journeys from search to purchase, enabling efficient commission allocation without requiring separate complex systems for prediction and reward distribution.
Solution Approach 2:
The patent implements feedback loops where purchase data is fed back into the system to validate and refine tag effectiveness. This feedback mechanism enables continuous improvement of the predictive model and automated commission allocation, increasing productivity while the feedback infrastructure is managed through existing system components.
Data Source
AI summary
A system can associate interests and responsibilities that correspond to a user account with a tag, based on search data originated by the user account. The system can determine content to send to the user account based on the tag. The system can determine that an offering is first offered after sending the content to the user account. The system can determine that the user account has purchased the offering. The system can determine that a portion of a commission associated with the user account purchasing the offering is credited to sending the content to the user account based on the tag. The system can store an indication that the portion of the commission associated with the user account purchasing the offering is credited to sending the content to the user account based on the tag.