Script Editor for Automated Assistant Routines
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Solution Overview
Problem
Existing automated assistant routines require cumbersome processes for users to create custom sequences of actions, limiting user creativity and flexibility, as they must select from pre-populated actions paired with existing code scripts, lacking the ability to truly customize actions or conditions.
Innovation Solution
A system leveraging a pre-trained large language model (LLM) generates code scripts for automated assistant routines based on natural language prompts, identifying incomplete scripts and soliciting additional information from users to complete them, allowing for the creation of custom actions and conditions through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually select from pre-populated actions to create routines, then the system maintains reliability with existing code scripts, but the ease of operation and user flexibility deteriorate due to cumbersome processes
Solution Approach 1:
The system enables self-service by allowing users to create custom routines through natural language prompts without manually selecting from pre-populated actions. The LLM automatically generates code scripts based on user intent, eliminating the need for users to navigate complex menus or understand technical details about available actions and their associated code.
Solution Approach 2:
The patent replaces the mechanical interaction of manually selecting actions from lists and configuring parameters with a natural language processing system. Users simply describe their desired routine in plain English, and the LLM translates this into executable code, substituting the traditional point-and-click interface with an intelligent text-based system.
2Adaptability or versatility
If users are limited to pre-populated actions with existing code scripts, then the system maintains reliability, but the adaptability and user creativity deteriorate
Solution Approach 1:
The system performs preliminary validation by checking whether the LLM-generated code script contains required elements (such as action statements, device identifiers, and parameter assignments) before executing the routine creation. This preliminary check ensures that even though the code is generated dynamically, it meets the necessary structural requirements for reliability.
Solution Approach 2:
The LLM acts as an intermediary between user intent and executable code. It translates natural language descriptions into structured code scripts while maintaining the semantic meaning of the user's request. This intermediary layer allows for high adaptability in interpreting user needs while ensuring the generated code follows proper syntax and structure through validation checks.
3Ease of operation
If the system uses a pre-trained LLM to generate code scripts from natural language, then the ease of operation improves, but the code script may be incomplete requiring additional validation steps
Solution Approach 1:
The system implements feedback by validating the generated code script and identifying missing elements. When the LLM produces an incomplete script, the validation process provides feedback about what specific components are missing (such as required parameters or action statements), allowing the system to prompt the user for additional information or to automatically supplement the code to ensure completeness.
Data Source
AI summary
A method include receiving a natural language prompt from a user comprising a command to generate a code script for an automated assistant to perform a routine. The routine includes multiple discrete actions specified by the natural language prompt. The method further includes processing, by a pre-trained large language model (LLM), the natural language prompt to generate the code script as an LLM output, and processing the code script to determine the code script is incomplete, thereby rendering the code script unsuitable for the automated assistant to fulfill performance of the routine. Based on determining the code script is incomplete, the method includes issuing a user prompt soliciting the user to provide additional information needed to complete the code script and receiving user input of the additional information needed to complete the code script. The method includes supplementing the code script with the additional information to render completed code script.