Multi-stage Tool Execution Plan Generation via ML Culling
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Solution Overview
Problem
Software tools are underutilized due to lack of education, awareness, non-intuitive user interfaces, and difficulty in finding appropriate tools among a vast selection, leading to inefficiencies in scientific research and product design.
Innovation Solution
A machine learning model generates a multi-stage plan to invoke a sequence of tools in response to a user request, culled from available tools based on similarity to pre-generated plans and user input, converting the plan to executable code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a large selection of software tools is provided, then tool functionality and versatility are improved, but tool discoverability and ease of operation deteriorate
Solution Approach 1:
The patent introduces an intermediary system consisting of machine learning models, tool databases, and plan generation algorithms that mediate between the user and the large selection of software tools. This intermediary automatically matches user needs with appropriate tools and sequences, eliminating the need for users to manually search and select from vast tool collections while preserving full access to all tool functionalities.
Solution Approach 2:
The system enables self-service by allowing the automated plan generation system to independently perform tool selection, sequence determination, and execution planning without requiring user expertise in tool discovery or configuration. The machine learning models autonomously analyze user requests, match them with suitable tools from the database, and generate executable plans, making the system self-sufficient in bridging the gap between user needs and tool capabilities.
2Ease of operation
If manual tool selection and configuration is required, then user control is improved, but productivity and ease of operation deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-processing user requests through machine learning models that automatically analyze requirements, identify suitable tools from the database, and generate execution plans before actual tool execution. This preliminary automated planning and configuration eliminates the need for manual tool selection and setup, maintaining user control over the overall process while significantly improving productivity by removing time-consuming manual configuration steps.
3Productivity
If automated plan generation is implemented, then productivity is improved, but system complexity deteriorates
Solution Approach 1:
The patent segments the automated plan generation system into distinct functional modules: a tool database storing tool metadata and capabilities, machine learning models for request analysis and tool matching, a plan generation component that sequences tools, and an execution engine. This segmentation allows each component to specialize in specific tasks, improving overall automation efficiency while managing system complexity through modular design that enables independent development, testing, and maintenance of each segment.
Data Source
AI summary
Machine learning models are used to invoke a sequence of tools in response to a user request. A list of available tools is culled before a machine learning model selects from the remaining tools to generate a plan. The plan, which describes tool invocations in natural language, is then converted to code that can programmatically invoke the tools. In some configurations, the list of available tools is first culled by removing tools that do not appear in similarly described plans. The list may further be culled by removing tools that do not appear in plans generated by machine learning models. Then, a machine learning model is prompted to generate a final plan from the culled list and the user request. In some configurations, the final plan is converted to code by prompting a machine learning model to extrapolate from the code of similar plans.