Data Grouping Platform for Flexible Aggregation
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Solution Overview
Problem
Existing data grouping methods require complex and inflexible hard-coded logic to aggregate disparate data sets, limiting their ability to adapt to user-defined criteria and triggers, especially in applications like healthcare where multiple data sources need to be summarized for analysis.
Innovation Solution
A data grouping platform that allows users to define aggregates, triggers, and criteria, using a processor to identify and validate data sets and create aggregates based on user-defined formats, enabling flexible and adaptable data aggregation across multiple sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hard-coded logic is used to group disparate data sets, then data aggregation can be accomplished, but flexibility and adaptability to user-defined criteria are limited
Solution Approach 1:
The patent implements dynamic configuration of data grouping logic through user-defined triggers, criteria, and aggregates. Instead of static hard-coded logic, the system allows runtime modification of grouping parameters, enabling flexible adaptation to different data sources and analysis requirements while maintaining manageable complexity through a structured configuration approach.
Solution Approach 2:
The system enables parameter changes by allowing users to define and modify triggers, criteria, and aggregate formats dynamically. This permits the data grouping logic to adapt to different scenarios by changing parameters such as trigger conditions, grouping criteria, and output formats without requiring fundamental changes to the system architecture.
2Loss of information
If multiple disparate data sources are aggregated, then comprehensive analysis is enabled, but the process requires diverse and complicated logic rules
Solution Approach 1:
The patent implements a universal data grouping framework that can handle multiple disparate data sources through a single configurable system. The trigger-criteria-aggregate structure serves as a multi-functional template that adapts to different data types and sources, eliminating the need for separate complex logic rules for each data source while ensuring comprehensive information aggregation.
Solution Approach 2:
The system introduces an intermediary configuration layer (triggers and criteria) that mediates between diverse data sources and the aggregation process. This intermediary structure standardizes the interface to various data sources, simplifying the logic required to handle multiple disparate sources while maintaining complete information gathering.
3Ease of manufacture
If hard-coded processes are used for data grouping, then implementation is straightforward, but built-in flexibility is limited
Solution Approach 1:
The patent applies preliminary action by pre-defining the structural framework of triggers, criteria, and aggregates before actual data grouping occurs. This preliminary configuration establishes a flexible template that guides the data grouping process, making implementation straightforward while retaining the ability to adapt to specific requirements through parameter customization.
Data Source
AI summary
A method and system for creating a data grouping platform for grouping data from disparate locations and sources, according to a user-defined aggregate. The method and system includes a method for receiving a user-defined aggregate, a user-defined trigger event, and a plurality of user-defined grouping criteria and parameters for creating the user-defined aggregate. The system and method also allows for a user to identify input data sets and sources for creating the grouped aggregates. Once the method and system receives the user-defined parameters, the method and system builds a platform to group data and build data aggregates based on the user-defined criteria.