Application Classification Using Natural Language Manufacturing Data
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Solution Overview
Problem
There is a need for effective control and management of manufacturing processes using machine learning tools such as generative AI and large language models.
Innovation Solution
Converting manufacturing data into near natural language representations and utilizing inference engines and large language models to generate analysis, recommendations, and solutions based on domain expertise, integrating various data sources and control systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manufacturing data is converted into near natural language representations and processed through large language models, then analysis quality and domain expertise utilization are improved, but system complexity and computational resources increase
Solution Approach 1:
The patent introduces natural language representations as an intermediary layer between manufacturing data and the analysis system. Data from multiple sources (sensor data, metadata, process parameters) is converted into natural language descriptions that the large language model can process. This intermediary transformation enables the system to leverage domain expertise embedded in natural language corpora while managing the complexity of integrating heterogeneous data sources.
2Productivity
If multiple data sources and control systems are integrated through natural language processing, then comprehensive analysis capability is improved, but data processing time and computational load increase
Solution Approach 1:
The patent applies preliminary action by converting manufacturing data into near natural language representations before processing through the large language model. This pre-processing step organizes and structures data from multiple sources (sensor data, explicit metadata, implicit metadata, process parameters) into a format that leverages the model's pre-trained domain expertise, reducing the computational burden during the actual analysis phase and enabling comprehensive multi-source integration.
Data Source
AI summary
In modern industrial environments, there may be numerous independent applications deployed throughout a facility, all performing different tasks, gathering different data, and communicating data and control information among one another. For example, this may include industrial control, quality control, work instructions, training, oversight, and so forth. Against this backdrop, an AI system is trained with a large language model to assist in characterization of new applications, in order to support administration and management of the software infrastructure for a facility.