AI Assistant Interaction State Model for Resumable Task Automation
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Solution Overview
Problem
Existing artificial intelligence tools struggle to autonomously navigate and interact seamlessly with structured software environments, requiring extensive manual guidance, which hampers their efficiency and effectiveness in performing tasks within these environments.
Innovation Solution
A computer system that integrates a large language model (LLM) to generate computer-readable code for tasks within a digital environment, stores interaction states, and resumes or modifies interactions based on context, enabling autonomous task performance and context preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing artificial intelligence tools are used to automate tasks, then productivity is improved, but the tools require extensive manual guidance and cannot autonomously navigate structured software environments
Solution Approach 1:
The system enables AI tools to autonomously navigate structured software environments by generating and executing their own navigation code, eliminating the need for extensive manual guidance. The AI assistant independently performs tasks such as environment exploration, code generation, and task execution without continuous human intervention.
Solution Approach 2:
The patent replaces manual operational control with an AI-based automated system that uses large language models to generate and execute code for navigating and interacting with software environments. This substitution transforms the mechanical process of manual guidance into an intelligent automated system.
2Adaptability or versatility
If artificial intelligence tools are integrated into structured software environments, then task performance capability is improved, but the tools lack inherent capacity to comprehend and navigate these environments autonomously
Solution Approach 1:
The system replaces the lack of autonomous navigation capacity with an AI-based code generation and execution mechanism. The large language model generates navigation code that enables the AI tool to independently comprehend and interact with structured software environments, transforming the limitation into an automated capability.
Solution Approach 2:
The patent introduces an intermediary code generation layer between the AI tool and the structured software environment. This intermediary translates high-level task intentions into executable navigation code, enabling autonomous environment comprehension and interaction without direct manual control.
3Measurement precision
If manual guidance is provided to AI tools for environment navigation, then task accuracy is improved, but productivity and efficiency are reduced
Solution Approach 1:
The system enables AI tools to autonomously perform environment navigation and task execution with high accuracy through self-generated code. The AI assistant independently handles code generation, validation, and execution, eliminating the need for manual guidance while maintaining task execution accuracy through intelligent error handling and validation mechanisms.
Solution Approach 2:
The patent implements feedback mechanisms where the AI assistant monitors task execution, validates generated code, and adjusts its navigation strategies based on environment responses. This feedback loop ensures high task execution accuracy while maintaining autonomous operation and productivity.
Data Source
AI summary
A computing system stores representations of states of user interactions with an artificial intelligence assistant to enable the interactions to be resumed at any point. The system inputs a series of instructions to a large language model (LLM) to generate computer-readable code for performing tasks within a digital environment, where the code is executable by the system to perform the tasks. The system generates a transcript including the instructions in the series of instructions and corresponding computer-readable code generated by the LLM. The system stores a representation of each of a plurality of states of the transcript, wherein each state includes a portion of the transcript that corresponds to a task. A stored representation of a first state is accessed, and computer-readable code associated with the first state is executed, using a context of the environment corresponding to the first state, to reperform a corresponding task in the digital environment.