Digital Assistant Intent Routing for Natural-Language App Integration
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Solution Overview
Problem
Existing digital assistants struggle to effectively integrate with locally stored third-party applications, lacking a natural-language interface for task execution.
Innovation Solution
A method for receiving natural-language user inputs, identifying intents and parameters, and providing them to software applications, including the ability to handle tasks that cannot be fulfilled locally by obtaining a list of compatible applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If digital assistants integrate with third-party applications, then functionality and adaptability are improved, but device complexity and integration difficulty increase
Solution Approach 1:
The system is divided into distinct components: a digital assistant framework layer that handles natural language processing and intent recognition, and separate application layers that implement specific functionalities. This segmentation allows third-party applications to be added without modifying the core digital assistant architecture, thereby improving adaptability while maintaining manageable complexity.
Solution Approach 2:
The digital assistant framework is designed as a universal platform that can interface with multiple types of applications through standardized protocols. The framework provides common functions such as intent parsing, parameter extraction, and task execution that can be applied across different applications, enabling versatility without requiring separate integration logic for each application type.
2Ease of operation
If digital assistants provide natural-language interface with applications, then ease of operation is improved, but processing complexity increases
Solution Approach 1:
The system introduces an intermediary natural language processing layer that translates user speech or text into structured intent representations. This intermediary layer handles the complexity of language understanding separately from the application logic, allowing users to interact naturally while the system manages processing complexity through standardized intent schemas and parameter extraction mechanisms.
3Adaptability or versatility
If digital assistants handle remote applications, then adaptability is improved, but communication overhead and latency increase
Solution Approach 1:
The system performs preliminary actions by pre-loading application metadata, capabilities, and interface definitions into local caches. When users interact with remote applications, the framework can quickly retrieve pre-fetched information rather than making repeated network requests, thereby reducing communication overhead and latency while maintaining the ability to access remote applications.
Data Source
AI summary
Systems and processes for application integration with a digital assistant are provided. In accordance with one example, a method includes, at an electronic device having one or more processors and memory, receiving a natural-language user input; identifying, with the one or more processors, an intent object of a set of intent objects and a parameter associated with the intent, where the intent object and the parameter are derived from the natural-language user input. The method further includes identifying a software application associated with the intent object of the set of intent objects; and providing the intent object and the parameter to the software application.