Machine Learning System for Legacy Resource Attribute Identification
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Solution Overview
Problem
Conventional systems lack the capability to efficiently identify, maintain, and distribute designated resources associated with legacy resources, such as trust agreements, within an entity system, which hinders effective management and administration.
Innovation Solution
A system utilizing machine learning models to extract, convert, process, and segment legacy resources, identify attributes, and implement actions for maintenance, monitoring, and distribution, incorporating techniques like NLP, vectorization, and cross-validation to analyze and manage designated resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used to process legacy resources, then the ability to identify attributes and perform maintenance is improved, but the device complexity increases
Solution Approach 1:
The patent segments the processing of legacy resources into distinct stages: extraction of resources, conversion to structured format, processing through machine learning models, and identification of attributes. This segmentation allows the system to handle complexity in manageable portions while improving overall productivity.
Solution Approach 2:
The patent introduces an intermediary processing layer that converts legacy resources into a standardized format before machine learning models process them. This intermediary conversion step simplifies the interaction between the machine learning models and legacy data, reducing the effective complexity of the system.
2Measurement precision
If multiple processing steps are implemented for legacy resources, then the accuracy of attribute identification is improved, but the processing time increases
Solution Approach 1:
The patent performs preliminary conversion of legacy resources into a standardized format before the main processing by machine learning models. This preliminary action prepares the data in advance, reducing the processing time required during the main attribute identification phase while maintaining accuracy.
Solution Approach 2:
The patent incorporates cross-validation as a feedback mechanism that verifies the accuracy of attribute identification. By validating the results and using the feedback to refine the processing, the system achieves high accuracy without requiring excessive processing time, as the feedback loop efficiently identifies and corrects errors.
Data Source
AI summary
Embodiments of the present invention provide a system for electronic identification of attributes for performing maintenance, monitoring, and distribution of designated resource assets. In particular, the system may be configured to extract one or more legacy resources from a data repository of an entity system associated with an entity, wherein the legacy resources are in a first format, convert the one or more legacy resources from the first format to a second format, process the one or more legacy resources, via one or more machine learning models, identify one or more attributes based on processing the one or more legacy resources via the one or machine learning models, and implement one or more actions based on the one or more attributes.