Industrial Virtual Assistant for Knowledge Retention
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Solution Overview
Problem
Industrial facilities face challenges due to a decline in knowledgeable operational staff, loss of operational knowledge with retiring employees, and outdated technology, leading to complex user interfaces and hard-coded error detection rules that are difficult to adapt to changing conditions.
Innovation Solution
An Industrial Virtual Assistant (IVA) platform with Robotic Process Automation (RPA) that allows natural language conversations for information retrieval and control of operations, using a knowledge graph and machine learning algorithms to provide intuitive responses and automate processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional control systems and documentation are used, then operational knowledge is retained in hard-coded rules and manuals, but the systems become difficult to navigate and adapt to changing conditions
Solution Approach 1:
The patent introduces a virtual assistant as an intermediary layer between the user and the complex control systems. This virtual assistant processes natural language queries and translates them into appropriate system commands or information requests, eliminating the need for users to navigate complex menus while still accessing operational knowledge stored in the system
Solution Approach 2:
The patent replaces traditional mechanical navigation through menus and interfaces with an AI-based natural language processing system. Instead of requiring users to manually navigate through hierarchical menu structures, the system uses machine learning models to interpret and respond to natural language queries directly
2Measurement precision
If domain experts interpret hard-coded rules for error detection, then accurate incident detection is achieved, but the knowledge is difficult to transfer to less experienced staff
Solution Approach 1:
The system enables self-service by encoding operational knowledge into an AI model that automatically interprets hard-coded rules and provides incident detection without requiring domain expert intervention. The virtual assistant learns from documented expert knowledge and applies it autonomously to detect and diagnose incidents, making the expertise accessible to all users regardless of their experience level
Solution Approach 2:
The patent transforms static hard-coded rules into dynamic AI model parameters that can be trained and updated. By converting rigid if-then logic into learnable model parameters, the system maintains detection accuracy while gaining the ability to adapt to new situations and transfer knowledge more effectively across different users and contexts
3Productivity
If modern software interfaces are implemented, then functionality is enhanced, but the interfaces require detailed knowledge to operate
Solution Approach 1:
The virtual assistant serves as an intermediary that handles the complexity of modern software interfaces. Users can query the system in natural language about complex operations or parameters, and the virtual assistant translates these queries into appropriate system calls or explanations, maintaining full system functionality while eliminating the need for users to understand complex interface structures
Data Source
AI summary
An Industrial Virtual Assistant (IVA) platform with Robotic Process Automation that operates like a Digital Knowledge Companion and allows operational staff at industrial facilities to have natural language conversations with the IVA to obtain information about, and to control operations of, industrial facilities, and which automates certain processes based in part on those natural language conversations. In an embodiment, the platform uses a Robotic Process Automater (RPA) to ingest information from documentation, human inputs, and operational data from the facility, organize that information into a knowledge graph containing comprehensive facility information, and apply machine learning algorithms to the knowledge graph to provide natural language responses to human queries and to automate certain processes of the facility.