Dynamic Data Processing Method Ranking via User History
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Solution Overview
Problem
Conventional data processing methods are presented in a static sequence, leading to prolonged user processing times and a non-user-friendly experience, as the first-choice method provided by the system may not be the user's most preferred option.
Innovation Solution
A system that determines a data processing fingerprint aggregate score for each method based on historical user data, ranking and prioritizing the most preferred method for presentation, thereby reducing the need for users to manually search through options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If data processing methods are presented in a static fixed sequence, then the system implementation is simple, but the user processing time is prolonged and user experience is degraded
Solution Approach 1:
The patent implements dynamic ordering of data processing methods based on user behavior history. Instead of a static fixed sequence, the system dynamically adjusts the presentation order of payment methods according to each user's historical selection patterns, making the interface adaptive and responsive to individual user preferences while maintaining simple system implementation.
Solution Approach 2:
The system performs preliminary analysis of user historical data before presenting the data processing method list. By pre-calculating the most likely preferred method based on past behavior, the system prepares the optimal presentation sequence in advance, allowing users to immediately see their preferred option without manual searching or interaction.
2Device complexity
If data processing methods are presented in a static fixed sequence, then the system is easy to implement, but the ease of operation is reduced as users may need to manually search through options
Solution Approach 1:
The system dynamically reorders data processing methods based on user-specific historical behavior, transforming the static presentation into an adaptive interface. This dynamic adjustment places each user's most likely preferred method at the top of the list, significantly improving ease of operation while keeping the underlying system implementation relatively simple.
Solution Approach 2:
The system automatically analyzes user historical data and self-adjusts the presentation order without requiring user configuration or input. The system serves itself by using its own collected historical data to optimize the user interface, eliminating the need for complex user setup while enhancing operational ease.
3Stability of the object's composition
If the first-choice data processing method provided by the system is not the user's most preferred option, then the system follows a fixed protocol, but the user experience is degraded and manual reselection is required
Solution Approach 1:
The system performs preliminary analysis of user historical behavior data before presenting the data processing method list. By pre-calculating the most likely preferred method based on past selections, the system ensures that the first-presented option aligns with user preferences, eliminating the need for manual reselection while maintaining stable system protocols.
Solution Approach 2:
The system incorporates feedback from user historical behavior to continuously optimize the presentation order. By analyzing past user selections and using this feedback to adjust the ordering algorithm, the system learns from user interactions and progressively improves accuracy in predicting user preferences, enhancing experience while maintaining protocol stability.
Data Source
AI summary
Providing data processing methods is disclosed, including: receiving a request to provide a plurality of data processing methods to a user; obtaining historical data associated with a plurality of historical user selections associated with the plurality of data processing methods, wherein the plurality of historical user selections is associated with the user; determining a plurality of data processing fingerprint aggregate scores corresponding to respective ones of the plurality of data processing methods based at least in part on the historical data; and providing the plurality of data processing methods based at least in part on the plurality of data processing fingerprint aggregate scores.