User Interface Customization via Disparate Data Aggregation
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Solution Overview
Problem
The challenge of efficiently aggregating and utilizing data from disparate sources to generate customized user interfaces is hindered by the inefficiencies in sorting through large volumes of data with varying formats and storage approaches, leading to difficulties in decision-making processes.
Innovation Solution
A system and method for retrieving and categorizing data from multiple databases to generate user interface elements based on user behavior and exchange conditions, using transformative processing engines to integrate and transform data from various formats for presentation on user devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored in disparate sources with varying formats, then data storage flexibility is improved, but data aggregation efficiency deteriorates
Solution Approach 1:
The patent introduces an intermediary processing layer that sits between disparate data sources and the aggregation system. This intermediary standardizes data formats and protocols, enabling efficient aggregation while preserving the flexibility of storing data in various native formats across different sources. The intermediary acts as a translator that converts diverse data representations into a unified format suitable for aggregation.
Solution Approach 2:
The system dynamically changes data parameters such as format, structure, and representation during the aggregation process. By transforming data from its original disparate formats into standardized parameters, the system maintains storage flexibility at the source while achieving efficient aggregation through parameter uniformity during processing.
2Quantity of substance
If large volumes of data are stored across multiple sources, then data completeness is improved, but sorting and processing time increases
Solution Approach 1:
The patent divides the large volume of data into manageable segments or chunks that can be processed independently and in parallel. By segmenting data from multiple sources into discrete units, the system maintains complete data representation while reducing the time required for sorting and processing through distributed and parallel operations on smaller data subsets.
Solution Approach 2:
The system performs preliminary actions such as data validation, formatting, and preliminary sorting at the data source level before aggregation. This preliminary processing reduces the workload during the main aggregation phase, thereby maintaining data completeness while minimizing the time required for final sorting and processing.
3Loss of information
If data from multiple disparate sources is aggregated, then decision-making information quality is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal aggregation framework that can handle multiple data sources, formats, and types through a single unified system. This multi-functional approach consolidates what would otherwise require multiple separate processing systems, thereby improving decision-making information quality by aggregating diverse data while actually reducing overall system complexity through consolidation and standardization.
Data Source
AI summary
In some examples, there may be provided systems, devices, and methods for using data from disparate databases to determine characteristics of a set of users within an organizational unit and generate customized user interface elements for display within a user interface.