LLM Conditional Logic Translation for Domain-Specific Automation

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Solution Overview

Problem

Existing systems require user-coded conditional logic statements, which demand programming knowledge and subject matter expertise, making it difficult to create and manage conditional statements effectively in various domains.

Innovation Solution

Utilizing large language models (LLMs) to transform natural language statements into conditional logic statements, which can be interpreted and executed by domain-specific algorithms, allowing for automated actions in domains like data centers and smart cities, with the LLM inferring domain-specific information and resolving ambiguities through interaction with users.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conditional logic statements are hardcoded by users, then the system can execute precise control logic, but the ease of operation deteriorates due to requirements for programming knowledge and subject matter expertise

Engineering Contradiction:

Improvecontrol logic precisionVSAvoiduser accessibility

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The patent introduces a natural language processing intermediary layer that translates user-friendly natural language inputs into formal conditional logic statements. This mediator handles the conversion between colloquial expressions and precise control logic, allowing users to operate the system without programming knowledge while maintaining execution precision through the generated formal statements

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces the manual mechanical process of coding conditional logic with an automated AI-based natural language processing system. Instead of users manually constructing logic statements through programming interfaces, the system uses NLP models to automatically generate equivalent control logic from natural language descriptions, substituting the manual coding mechanism with an intelligent automation mechanism

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Ease of operation

If natural language statements are used instead of coded logic, then the ease of operation improves, but the manufacturing precision deteriorates due to potential ambiguity and logical incompleteness

Engineering Contradiction:

Improveuser accessibilityVSAvoidlogic statement accuracy

Core Design Contradiction:

Ease of operationVSManufacturing precision

Solution Approach 1:

The patent implements feedback mechanisms where the system validates generated conditional logic statements against the original natural language input and existing system constraints. If ambiguities or conflicts are detected, the system requests clarification from the user or performs iterative refinement, ensuring that the final logic statements accurately reflect the intended meaning while maintaining high precision

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent performs preliminary processing of natural language statements through multiple stages including semantic analysis, constraint checking, and logical consistency validation before generating final conditional logic. This preliminary action identifies and resolves ambiguities early in the translation process, preventing errors from propagating to the final control logic

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If domain specific algorithms are used to process natural language statements, then the adaptability improves for specific domains, but the device complexity increases due to integration of multiple specialized components

Engineering Contradiction:

Improvedomain specializationVSAvoidsystem architecture

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent employs a universal natural language processing framework that can be applied across multiple domains through configuration rather than structural modification. The core NLP engine remains domain-agnostic, while domain-specific adaptations are achieved through configurable parameters, knowledge bases, and processing rules that can be loaded or adjusted without changing the fundamental system architecture, thus maintaining versatility while controlling complexity

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12579374B1Conditional logic for domain specific systems using large language models

Publication Date: 2026.03.17 NVIDIA CORP

AI summary

Systems and methods for automatically performing actions for a user or system in response to receiving natural language statements. The systems and methods use a large language model to transform the natural language statements into conditional logic for domain specific systems, which systems can perform the corresponding actions. In domains such as a data center or a smart city, the systems and methods provide an intelligent system that can identify conditional statements including conditions and corresponding actions in response to natural language statements. These conditional statements are interpreted as rules in the context of the domain, such that the methods and system ascertain that conditions are met and perform corresponding actions. If there are issues interpreting the conditionals, the systems and methods can use a feedback loop to gather additional information.