Pre-classified Data Sets for User Interface Activity Management
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Solution Overview
Problem
Users are overwhelmed by the sheer amount of digital data they interact with daily, leading to inefficiencies in data classification and presentation, causing them to abandon applications that could benefit from better back-end data classification processing and user interfaces.
Innovation Solution
A computer-implemented method and system that pre-classify larger data sets into manageable groups or 'buckets' using predictive models, allowing users to efficiently categorize activities through a user interface, reducing the need for manual sorting and improving data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually classify transactions from multiple financial accounts, then classification accuracy can be achieved, but user time and effort increase significantly leading to task abandonment
Solution Approach 1:
The system performs preliminary classification of transactions into pre-classified data sets using automated algorithms before presenting them to users. This preliminary action reduces the volume of transactions users must manually review while maintaining classification accuracy through subsequent user verification of the pre-grouped sets.
Solution Approach 2:
The system segments the large volume of transactions from multiple financial accounts into smaller, manageable pre-classified data sets based on automated classification criteria. This segmentation allows users to review and verify classifications in smaller batches rather than overwhelming them with the complete transaction list.
2Loss of information
If all digital data is presented to users through the user interface, then complete information availability is achieved, but user overwhelm increases causing application abandonment
Solution Approach 1:
The user interface presents data in segmented pre-classified data sets rather than displaying all raw transactions simultaneously. Each pre-classified set contains a subset of transactions grouped by automated classification, making the information more digestible while maintaining overall information availability through multiple navigable groups.
Solution Approach 2:
The system introduces pre-classified data sets as an intermediary layer between the complete transaction data and the user interface. This intermediary structure organizes and filters information before presentation, reducing user cognitive load while preserving access to all underlying data through the structured groups.
3Loss of information
If traditional data presentation methods are used, then data completeness is maintained, but data processing efficiency decreases due to lack of pre-classification
Solution Approach 1:
The system performs preliminary classification and grouping of transactions into pre-classified data sets before user interaction. This preliminary processing organizes data by relevant criteria (merchant category, amount ranges, frequency patterns) while maintaining complete data integrity, enabling users to process information more efficiently without losing any transaction details.
Solution Approach 2:
The system segments the complete transaction data into multiple pre-classified data sets organized by classification criteria. This segmentation improves processing efficiency by allowing users to work with organized subsets while the system maintains the ability to access and present the complete data set when needed, balancing efficiency with data completeness.
Data Source
Figure 1A
Figure 1B~1C
Figure 2A~2D
AI summary
Aspects of the present disclosure provide techniques for displaying reduced data sets based on pre-classification of a larger data set. Embodiments include receiving a plurality of activity records describing a plurality of activities associated with the user. Embodiments further include grouping the plurality of activities into one or more pre-classified data sets based on the plurality of activity records. Embodiments further include providing the user with a summary of a pre-classified data set of tire one or more pre-classified data sets via a user interface. Embodiments further include providing the user, via the user interface, with a user interface element that allows the user to categorize all activities in the pre-classified data set together based on the summary. Embodiments further include receiving input from the user via the user interface, the input assigning a category to all activities in the pre-classified data set together based on the summary.