Intelligent Report Configuration via ML Models
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Solution Overview
Problem
Existing report configuration processes are inefficient and cumbersome, requiring users to navigate through multiple user interfaces to complete configuration steps, leading to increased computing resources and network traffic.
Innovation Solution
A computer-implemented method for intelligent report configuration that aggregates historical configuration data, identifies configuration nodes in a hierarchical workflow, generates data objects using models based on historical and real-time data, and displays these data objects on a configuration user interface, thereby automating the configuration process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual navigation through multiple user interfaces is used for report configuration, then users can complete configuration steps, but computing resources and network traffic increase
Solution Approach 1:
The system performs self-service by automatically generating configuration data using trained machine learning models that process historical configuration data. The system configures report nodes without requiring manual user navigation, thereby reducing computing resource consumption while maintaining ease of operation.
Solution Approach 2:
The system performs preliminary action by pre-training machine learning models on historical configuration data before actual report configuration is needed. This preprocessing enables the system to quickly generate configuration data during runtime without requiring manual intervention or excessive computing resources.
2Ease of operation
If manual navigation through multiple user interfaces is used for report configuration, then users can complete configuration steps, but network traffic increases
Solution Approach 1:
The system performs self-service by automatically generating configuration data using trained machine learning models that process historical configuration data. The system configures report nodes without requiring manual user navigation, thereby reducing computing resource consumption while maintaining ease of operation.
Solution Approach 2:
The system extracts the essential configuration data generation function from the manual navigation process. By isolating the configuration generation task and handling it automatically through ML models, the system removes the need for users to navigate through multiple interfaces, thereby reducing network traffic while preserving operational ease.
3Productivity
If automated configuration using machine learning models is implemented, then manual navigation is reduced, but system complexity increases
Solution Approach 1:
The system introduces machine learning models as intermediary components between historical configuration data and current report configuration needs. These models act as mediators that automatically translate historical patterns into applicable configuration data, reducing manual navigation while managing system complexity through modular architecture.
Solution Approach 2:
The system changes parameters by transforming historical configuration data into predictive models that can generate configuration data for new reports. This parameter transformation approach enables automated configuration while managing complexity through standardized model interfaces and data structures.
Data Source
AI summary
Embodiments provide for intelligent report configuration. Some embodiments aggregate historical configuration data based at least in part on a plurality of historical reports, receive a signal indicative of a configuration request for a report having a hierarchical data structure, identify at least one configuration node of a report configuration workflow for configuring the report. The report configuration workflow may be defined by a hierarchical arrangement of a plurality of configuration nodes. Some embodiments generate, based at least in part on the historical configuration data and using one or more models, one or more data objects for the at least one configuration node. The one or more data objects may be indicative of at least a portion of the configuration data for one or more portions of the report. Some embodiments cause display of the one or more data objects on a configuration user interface rendered on a user device.