AI Dialogue Context Control for Computing Process Flow Automation
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Solution Overview
Problem
Current AI systems lack effective methods for task-oriented dialogue with in-context learning, particularly in automating computing process flows, as they struggle to interpret user intentions within the context of ongoing conversations and manage conversations that deviate from predefined paths.
Innovation Solution
The system employs a large language model (LLM) to understand user inputs and generate commands aligned with predefined computational logic, using a dialogue understanding process that considers the conversation context and branching logic, enabling the AI assistant to navigate and manage conversations through defined computing process flows, including automatic conversation repair mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional NLU systems are used to interpret user input, then the system can process individual messages, but it cannot understand the greater context of ongoing conversations
Solution Approach 1:
The patent transitions from processing single messages in isolation to processing multi-turn conversations by adding the temporal dimension of conversation history. The LLM receives not just the current user input but also the entire conversation context, enabling it to understand references, pronouns, and intentions that depend on previous turns.
Solution Approach 2:
The patent introduces an intermediary layer (the LLM with system prompts and conversation history) between the user input and the computational logic execution. This intermediary processes the raw input in context and generates structured commands that bridge natural language and predefined computational flows.
2Adaptability or versatility
If fixed intent lists are used in NLU systems, then the system has clear boundaries, but it cannot handle user requests outside predefined intents
Solution Approach 1:
The patent makes the system dynamically adaptable by using an LLM that can interpret any user input within the conversation context, rather than relying on a static list of predefined intents. The system adapts to new types of requests while maintaining reliability through the constraint of predefined computational flows that the LLM must map to.
Solution Approach 2:
The patent changes the fundamental parameter of intent recognition from a discrete classification task (matching to fixed categories) to a generative task where the LLM produces commands in a controlled language. This allows the system to handle diverse requests while maintaining reliability through the structured command format and predefined flow mappings.
3Adaptability or versatility
If the AI assistant follows strict predefined computational logic, then task accuracy is high, but it cannot handle conversations that deviate from the script
Solution Approach 1:
The patent segments the conversation handling into two distinct layers: (1) a flexible LLM-based dialogue understanding layer that interprets user input and generates commands, and (2) a strict computational flow execution layer that ensures accurate task completion. This segmentation allows each layer to specialize - the LLM handles adaptability while the computational flows ensure precision.
Solution Approach 2:
The patent introduces structured commands as an intermediary representation between the flexible LLM output and the strict computational flow execution. The LLM generates commands in a controlled language that maps to predefined flows, acting as a mediator that translates natural language intent into reliable executable actions while allowing for conversational flexibility.
4Adaptability or versatility
If the system uses generative language models for dialogue understanding, then it can handle diverse inputs, but it may produce hallucinated or incorrect commands
Solution Approach 1:
The patent changes the output parameter of the LLM from free-form generative text to structured commands in a controlled language with specific grammar and vocabulary. This constraint on the output format, combined with system prompts that guide the LLM to produce only valid commands, significantly reduces hallucinations while maintaining the adaptability of generative models.
Solution Approach 2:
The patent implements a feedback loop where the system validates LLM-generated commands against the predefined computational flows and conversation state. If a command is invalid or inconsistent with the current context, the system can request clarification or correction from the LLM, ensuring that only accurate and appropriate commands are executed.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media for computing process flow control via determination of dialogue context between a user and an artificial intelligence assistant. Multiple defined computing process flows are received by the system. Each of the defined computing process flows describe one or more instructions to be performed. Information about the defined computing process flows and dialogue between the user and the artificial intelligence assistant are provided as a prompt to a large language model (LLM). The system receives from the LLM one or more selected commands for a defined computing process flow relating to the context of the received input from the user. The system performs the command as to the computing process flow.