Model Management System for Reusing Existing Models
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Solution Overview
Problem
Current model development processes are inefficient as they often require creating new data sets, which can be time-consuming and resource-intensive, especially when dealing with large 'big data' sets, and there is a need to leverage existing models that closely represent new problems to improve model management and development.
Innovation Solution
A system and method for model management that evaluates newly acquired data against a database of existing models using probabilistic statistical analysis, featuring specialized language technology components like NLP, NLU, and machine learning algorithms to determine if existing models can be reused, modified, or combined to develop new models efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If new data sets are created to model new problems, then model accuracy can be optimized for specific problems, but model development time and resource consumption increase significantly
Solution Approach 1:
The system performs preliminary evaluation by comparing new data against existing models before committing resources to create new models. The model controller automatically assesses whether existing models can be reused, modified, or combined, preventing unnecessary model creation and reducing development time while maintaining accuracy requirements
Solution Approach 2:
The patent enables existing models to serve multiple purposes through automatic evaluation and adaptation. By determining whether existing models can be reused, modified, or combined for new problems, the system makes models universally applicable across different data sets and problems, reducing the need to create new models from scratch
2Reliability
If new data sets are created for each new problem, then models can be highly specialized, but resource consumption and complexity increase
Solution Approach 1:
The model controller acts as an intermediary between new data and existing models, automatically evaluating whether existing models can be reused, modified, or combined. This intermediary layer manages the complexity of model selection and adaptation, reducing manual intervention and simplifying model management while maintaining specialization through targeted model modifications
Solution Approach 2:
The system merges existing models through automatic evaluation and combination processes. By determining whether existing models can be combined to address new problems, the system reduces the number of separate specialized models needed, thereby reducing overall system complexity while maintaining the ability to handle specialized problems through model combinations
3Productivity
If existing models are reused and modified, then model development efficiency improves, but model adaptability to new problems may be compromised
Solution Approach 1:
The system dynamically determines the appropriate approach for each new problem by automatically evaluating whether existing models should be reused, modified, or combined. This dynamic decision-making process ensures that models are adapted appropriately to new problems while maintaining development efficiency, avoiding both unnecessary model creation and inappropriate model reuse
Solution Approach 2:
The patent applies parameter changes by automatically modifying existing models when reuse is determined to be appropriate. The model controller adjusts model parameters and configurations to adapt existing models to new problems, maintaining both development efficiency through reuse and adaptability through systematic parameter modification
Data Source
AI summary
A method for model management includes receiving data on which to base a model, evaluating the received data against a plurality of existing models and data associated with each of the plurality of existing models, determining whether any of the plurality of existing models can be used as the model or as a basis to develop the model for the received data, and providing a user with the existing models that can be used as the model or as a basis to develop the model for the received data.