Language Model Task Assistant for Natural Language Query Processing
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Solution Overview
Problem
Conventional methods for managing computer tasks on user devices require extensive user interaction and resource-intensive updates, limiting efficiency and usability, especially for users with limited dexterity and for implementing new tasks.
Innovation Solution
A task assistant that receives natural language queries, generates machine-readable instructions using a language model, and executes these instructions to perform computer tasks, minimizing user interaction and resource usage by automating task management and eliminating the need for costly updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional interface controls are used to execute computer actions, then users can perform tasks on user devices, but extensive user interaction and manual manipulation are required
Solution Approach 1:
The patent replaces manual mechanical interaction with interface controls by introducing a language model that processes natural language queries. Instead of manually manipulating input devices to execute computer actions, users simply type or speak their intent, and the language model translates it into executable instructions, thereby substituting the mechanical interaction system with an intelligent processing system.
Solution Approach 2:
The system enables self-service by allowing the language model to autonomously generate and execute machine-readable instructions based on user queries. The task assistant automatically manages the workflow from query interpretation to task execution without requiring users to manually navigate through interface controls or understand system operations.
2Adaptability or versatility
If new computer tasks are implemented using conventional methods, then functionality is added, but resource-intensive updates are required
Solution Approach 1:
The patent uses copying by leveraging the language model's existing capabilities to generate instructions for new tasks without requiring system updates. Instead of installing new software components or updating the operating system to add functionality, the system copies the language model's instruction-generation capability to handle new task types dynamically, thereby adding adaptability without resource-intensive updates.
Solution Approach 2:
The system implements dynamic adaptability where the language model can dynamically generate instructions for new computer tasks on-demand. This dynamic approach allows the system to adapt to new task requirements without static updates, enabling versatility while avoiding the resource cost of implementing and distributing software updates for each new functionality.
3Ease of operation
If users with limited dexterity manually manipulate input devices, then computer tasks can be executed, but the process becomes difficult and time-consuming
Solution Approach 1:
The patent replaces the mechanical system of manual input device manipulation with an intelligent language processing system. Users with limited dexterity can simply type or speak their task intent, and the language model automatically translates it into executable instructions, eliminating the need for complex manual navigation through interface controls and significantly reducing task completion time.
Solution Approach 2:
The language model serves as an intermediary between the user's natural language query and the system's executable instructions. This intermediary automatically handles the complex translation and coordination required to execute tasks, removing the burden of manual manipulation from users and enabling efficient task completion despite limited dexterity.
Data Source
AI summary
According to an aspect, a method includes receiving, via an interface, a natural language query about a request for a user device to perform a computer task, generating a prompt including the natural language query and a list of functions, transmitting the prompt to a language model, and receiving a response from the language model, where the response includes machine-readable instructions executable by the user device to perform the computer task. The machine-readable instructions use at least one function from the list of functions. The method includes executing the machine-readable instructions to perform the computer task.