Semantic Object Model Automation via Self-Service Maintenance
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Solution Overview
Problem
Extensible object models require significant resources and technical expertise to create custom object models, leading to potential errors and incorrect data introduction, which can result in erroneous operations and rework, especially when users lack domain knowledge.
Innovation Solution
A computer-implemented method for maintaining a seeded semantic object model by identifying similar object types across independent semantic object models and updating the seeded model based on these similarities, using machine learning or rules-based engines, thereby automating the standardization process and reducing manual engineering needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users create custom object models manually, then the object model can be tailored to specific needs, but it requires significant resources and technical expertise leading to potential errors
Solution Approach 1:
The system automatically maintains the seeded semantic object model by analyzing independent semantic object models and updating the seeded model without requiring manual user configuration. The machine learning model autonomously identifies similar object types and performs updates, allowing the system to serve itself rather than requiring continuous human intervention for maintenance.
Solution Approach 2:
The patent replaces manual mechanical processes of model creation and maintenance with automated machine learning-based processes. Instead of users manually defining object models, the system uses machine learning algorithms to automatically identify patterns, determine similarities, and update models, substituting human expertise with automated intelligent systems.
2Adaptability or versatility
If users create custom object models manually, then specific domain needs can be met, but human errors and incorrect data introduction occur
Solution Approach 1:
The system performs self-maintenance by automatically analyzing independent semantic object models and updating the seeded model without human intervention. This eliminates human errors in model maintenance while preserving the ability to meet domain-specific needs through automated pattern recognition and similarity matching.
Solution Approach 2:
The system continuously monitors and analyzes independent semantic object models to identify similarities with the seeded model. This feedback loop allows the system to automatically detect when updates are needed and applies them consistently, ensuring data accuracy while adapting to domain-specific requirements.
3Reliability
If the seeded semantic object model is manually maintained, then updates can be controlled, but it requires continuous user configuration and time
Solution Approach 1:
The system automatically maintains itself by continuously analyzing independent semantic object models and updating the seeded model without requiring user configuration time. The machine learning model autonomously determines when updates are needed and executes them, eliminating the time loss associated with manual maintenance while preserving update control through automated decision-making.
4Adaptability or versatility
If extensive manual engineering is performed for object model creation, then detailed customization is achieved, but it increases engineering costs and complexity
Solution Approach 1:
The patent replaces manual engineering processes with automated machine learning-based processes. The system automatically identifies similar object types, determines updates needed, and maintains the seeded model without requiring extensive manual engineering, thereby improving productivity while maintaining customization capabilities through intelligent automation.
Solution Approach 2:
The system performs self-maintenance and self-updating without requiring continuous human engineering intervention. The machine learning model autonomously analyzes patterns in independent semantic object models and automatically updates the seeded model, eliminating the need for ongoing manual engineering while preserving detailed customization through automated pattern recognition.
Data Source
AI summary
Embodiments of the disclosure provide for maintaining a seeded semantic object model based on independent semantic object models. Such embodiments enable automatically standardizing a seeded semantic object model across one or more domains. Some embodiments identify a plurality of independent semantic object models having a plurality of object types, process the plurality of object types to determine at least one similar object type, determine that the at least one similar object type satisfies model updating criteria, and in response to determining that the at least one similar object type satisfies the model updating criteria, update the seeded semantic object model based at least in part on the at least one similar object type.