Event-Driven Data Set Population with Distributed Validation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data set population methods are inefficient and error-prone, relying on static interfaces that fail to provide multiple action pathways for high-confidence data validation, leading to suboptimal data retrieval and processing.
Innovation Solution
An event-listener architecture with a feedback loop is implemented to dynamically update and distribute conditions across distributed services, triggering events for incremental data set completion, allowing for multiple action pathways to verify and populate missing data points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single large user interface is used to collect all data in a single session, then data collection is completed in one go, but the interface becomes static and inefficient in retrieving required information
Solution Approach 1:
The patent divides the data collection process into multiple progressive web forms that incrementally populate the data set. Each form focuses on specific data points, making the interface less complex while maintaining efficient data retrieval through targeted, incremental collection rather than attempting to collect all data in a single overwhelming interface.
Solution Approach 2:
The system dynamically generates and updates web forms based on the current state of data collection. The interface adapts to user progress and data availability, transforming from a static single-page form into a dynamic multi-step process that adjusts its complexity and content based on real-time conditions, thereby improving efficiency without requiring a permanently complex interface.
2Ease of operation
If progressive web forms are used to incrementally populate the data set, then data collection is broken into manageable steps, but the system becomes error-prone and lacks multiple action pathways for high-confidence data validation
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors the data set completion status and triggers events when data points are added or conditions change. This feedback loop enables the system to dynamically update and distribute conditions across distributed services, providing multiple action pathways for validating data points with high confidence scores and reducing errors through continuous verification.
Solution Approach 2:
The system introduces distributed services as intermediaries between data collection and validation. These services receive conditions, execute appropriate action pathways, and return validation results, thereby enhancing data reliability without increasing operational complexity for the user. The intermediary layer handles the complexity of multiple validation pathways behind a simple progressive form interface.
3Ease of manufacture
If static interfaces are used for data collection, then the system is simple to implement, but it fails to provide multiple action pathways for retrieving data from sources with high confidence scores
Solution Approach 1:
The patent creates a universal event-listener architecture that can handle multiple types of data retrieval actions through a single unified system. The distributed services framework provides multi-functionality by executing different action pathways (user input, external data services, document extraction, etc.) through the same event-driven mechanism, thereby achieving high adaptability without requiring separate static interfaces for each data retrieval method.
Solution Approach 2:
The system automatically determines and executes the most efficient action pathways based on the current data set state and confidence scores. Rather than requiring manual configuration of different retrieval methods, the event-listener architecture self-adapts by listening for events, evaluating conditions, and autonomously selecting appropriate data retrieval strategies from multiple available pathways, maintaining ease of implementation while achieving high versatility.
Data Source
AI summary
A method of updating a data set using event-based conditions includes adding a first data point to the data set and triggering a first event indicating that the data set has changed. Adding the first data point to the data set triggers the first event. The method also includes, in response to the first event, updating conditions for completing the data set. The conditions are associated with action pathways for receiving or verifying missing data points in the data set. The method further includes triggering a second event indicating that the conditions for completing the data set have changed and, in response to the second event, distributing the conditions to a plurality of distributed services. Each of the plurality of distributed services is configured to execute one or more of the action pathways for receiving or verifying the missing data points in the data set.