Natural-Language Task Automation Across Contexts Using Action Embeddings
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Solution Overview
Problem
Existing computer applications require users to repeatedly perform semantically-similar tasks across different contexts, which can be cumbersome, prone to error, and inefficient, and are often constrained to specific contexts with complex scripts that are difficult to manipulate.
Innovation Solution
Implementing a method to capture sequences of actions in a generalized 'action embedding' space, using natural language processing to generate 'task' or 'policy' embeddings, allowing seamless extension across domains without programming knowledge, and utilizing domain models to translate actions between different contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional macros or scripting languages are used to automate tasks, then task automation capability is improved, but the complexity of the system increases and it becomes constrained to specific applications
Solution Approach 1:
The patent introduces an intermediary system that translates natural language instructions into application-specific actions. This intermediary layer allows users to automate tasks using simple natural language without needing to learn complex scripting languages, while still achieving the desired automation effect in the target application.
Solution Approach 2:
The system creates a universal natural language interface that can automate tasks across multiple different applications and contexts. Instead of requiring separate macros for each application, a single natural language command can be adapted to perform semantically similar tasks in different applications, making the automation system universally applicable.
2Extent of automation
If traditional macros are used to automate tasks, then task automation capability is improved, but adaptability to different contexts deteriorates
Solution Approach 1:
The system enables a single natural language automation to be adapted across multiple applications and contexts. The natural language interface is designed to be universally applicable, allowing the same command structure to perform semantically similar tasks in different applications without requiring context-specific programming.
Solution Approach 2:
The system dynamically adjusts parameters such as application context, target objects, and action sequences based on the user's natural language input and the current application state. This allows the automation to adapt to different contexts by changing its operational parameters while maintaining the core automation logic.
3Extent of automation
If traditional macros are used to automate tasks, then task automation capability is improved, but ease of operation deteriorates
Solution Approach 1:
The patent introduces an intermediary natural language interface that translates user-friendly commands into application-specific actions. This intermediary layer shields users from the complexity of scripting languages while still enabling powerful automation capabilities, making the system accessible to non-programmers.
Solution Approach 2:
The system replaces the mechanical process of learning and writing complex scripts with a natural language interaction model. Instead of requiring users to manually program automation sequences, they can simply describe what they want to accomplish in natural language, which the system then translates into executable actions.
Data Source
AI summary
Disclosed implementations relate to automating semantically-similar computing tasks across multiple contexts. In various implementations, an initial natural language input and a first plurality of actions performed using a first computer application may be used to generate a first task embedding and a first action embedding in action embedding space. An association between the first task embedding and first action embedding may be stored. Later, subsequent natural language input may be used to generate a second task embedding that is then matched to the first task embedding. Based on the stored association, the first action embedding may be identified and processed using a selected domain model to select actions to be performed using a second computer application. The selected domain model may be trained to translate between an action space of the second computer application and the action embedding space.