Natural Language Control Interface for Reliable Infrastructure Prediction
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Solution Overview
Problem
Existing management systems for devices, machines, and infrastructures have complex user interfaces that require specific training, and they lack reliable predictive capabilities for infrastructure states, such as production levels or energy consumption.
Innovation Solution
A simplified control management system that allows users to interact using natural language, providing reliable predictions and allowing users to assess and improve prediction reliability through simple confirmation systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex decision-making engines with sophisticated algorithms are used to provide reliable predictive information, then prediction reliability is improved, but system complexity and user training requirements increase
Solution Approach 1:
The patent introduces a natural language processing intermediary layer between the user and the complex predictive analytics system. Users interact through simple natural language queries rather than directly with complex algorithms, while the system maintains sophisticated decision-making engines in the background to provide reliable predictions. This intermediary simplifies the user interface without compromising prediction reliability.
Solution Approach 2:
The system automatically performs complex data analysis and predictive computations without requiring user intervention in the algorithmic processes. The decision-making engines self-manage the sophisticated algorithms, data processing, and predictive modeling, freeing users from needing to understand or configure complex parameters while still delivering reliable predictions.
2Measurement precision
If sophisticated algorithms and multiple parameters are used for reliable predictions, then prediction accuracy is improved, but ease of operation deteriorates due to complex tuning requirements
Solution Approach 1:
The system autonomously handles parameter selection, data processing, and algorithm configuration without requiring user input. The decision-making engines automatically tune parameters and select appropriate algorithms based on the data available, eliminating the need for users to have analytical or engineering skills while maintaining high prediction accuracy.
Solution Approach 2:
A natural language interface acts as an intermediary that translates simple user questions into complex analytical requests. Users can ask questions in everyday language without needing to understand the underlying parameters or algorithms, while the system's intermediary layer handles the sophisticated data processing and parameter tuning required for accurate predictions.
3Loss of information
If complex control panels and dashboards are used for system management, then information processing capability is improved, but ease of operation worsens due to training requirements
Solution Approach 1:
The patent replaces traditional mechanical-style control panels and dashboards with a natural language processing system. Instead of requiring users to navigate complex graphical interfaces, click through menus, and understand dashboard metrics, users simply type or speak their questions in natural language, and the system provides direct textual responses, maintaining full information processing capability while dramatically improving ease of use.
Solution Approach 2:
The natural language interface serves as an intermediary that handles all information processing tasks without requiring users to directly interact with complex control panels. The intermediary translates user intent into data processing operations and converts system responses into natural language answers, preserving full information processing capability while eliminating the need for complex interface navigation and user training.
Data Source
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AI summary
A simplified control management system, assigned for the management of at least one work system (S) connected to first memory means (Z) for computer data (D), comprises: - a computer (3) assigned to perform operations on computer data (D); - a user interface (7) assigned for interaction between the system (1) and a user (U) of the system (1); - a management interface (5) assigned for interaction between the system (1) and the at least one work system (S) and the first memory means (Z); the computer (3), the user interface (7) and the management interface (5) being connected to each other. The system implements at least one generative artificial intelligence model (10) adapted at least to: - receiving from the user interface (7) natural language messages (M) provided by the user (U) to the user interface (7) and including intents and/or information; - producing computer data (D) including data, requests and/or commands for the system (1), the at least one work system (S) and/or the first memory means (Z) on the basis of said intents and/or information; - forwarding said computer data (D) including data, requests and/or commands to the system (1), to at least one work system (S) and/or to the first memory means (Z); - collecting computer data (D) including data, responses and/or signals from the system (1), from at least one work system (S) and/or from the first memory means (Z); - generating natural language messages (M) for the user (U) including responses and/or information based on said data, responses and/or signals; - providing the user interface (7) with said natural language messages (M) for the user (U) including responses and/or information. The at least one generative artificial intelligence model (10) is based on one or more between Large Language Models (LLM), Large Multimodal Models (LMM), transformer-based models, Small Language Models (SLM).