Multi-Dimensional Account Coding for Temporal Change Analysis
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Solution Overview
Problem
Existing data structures struggle to efficiently analyze and compare changes to user and entity attributes over time, requiring substantial computational resources due to their focus on static data representation.
Innovation Solution
A multi-dimensional coded representation system is employed to efficiently track and analyze changes in account characteristics over time, using a time dimension, characteristic dimension, and category dimension, enabling rapid identification and comparison of changes across multiple categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional data structures are used to store and analyze user account data, then comprehensive data storage is achieved, but analysis efficiency deteriorates due to substantial computational resources required
Solution Approach 1:
The patent segments user account data into multiple dimensions including time dimension, characteristic dimension, and category dimension. Each dimension is independently coded and stored, allowing efficient retrieval and analysis without processing entire datasets. This segmentation enables the system to analyze specific aspects of user behavior over time without compiling large volumes of comprehensive data.
Solution Approach 2:
The patent introduces multiple dimensions (time, characteristic, category) to transform traditional flat data storage into a multi-dimensional coded representation. This dimensional transformation allows the system to efficiently query and analyze data along specific dimensions without requiring computational resources proportional to the total data volume, thereby resolving the contradiction between comprehensive storage and analysis efficiency.
2Device complexity
If static data structures are used to represent user state, then data simplicity is maintained, but ability to analyze changes over time deteriorates
Solution Approach 1:
The patent transforms static data structures into dynamic representations by incorporating a time dimension that tracks changes in user characteristics over time. The coded representation evolves as user behavior changes, allowing the system to maintain relatively simple data structures while capturing temporal dynamics and enabling change analysis without requiring complex historical data storage.
Data Source
AI summary
Methods and systems are presented for providing a framework that enables a computer system to analyze and compare changes to different account characteristics of different accounts that occurred over a time period. A code is generated for an account to represent changes to different account characteristics of the account within the time period. Changes to different account characteristics may be highlighted in the code using different colors or patterns. By analyzing the code, overlapping changes from different account characteristics that occurred within the same time frame may be detected. The different change patterns associated with the user account may then be used to assess a risk for the user account and/or a transaction involving the user account.