Natural Language Device Control via Social Graph Authorization
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Solution Overview
Problem
Current social networking systems lack the ability to seamlessly control and manage smart devices using natural language commands, requiring precise device identification which can be cumbersome and inefficient.
Innovation Solution
Implementing a messaging system with human-like intelligence that allows users to control and manage smart devices using natural language commands, where the system infers the correct device based on context, such as location and previous interactions, enabling users to control devices without specifying their exact identity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users specify precise device identification to control smart devices, then device control accuracy is improved, but operation complexity increases
Solution Approach 1:
The system performs automatic device identification and selection based on contextual information without requiring users to manually specify device identifiers. The messaging system infers the intended device from location data, conversation history, and device proximity, allowing the system to serve itself in resolving device ambiguity.
Solution Approach 2:
The system changes the parameters used for device identification from explicit user-specified identifiers to implicit contextual parameters such as GPS location, device proximity signals, and conversation history. This parameter transformation enables more natural and easier operation while maintaining accurate device identification.
2Measurement precision
If users manually identify each device before control, then device control precision is improved, but time consumption increases
Solution Approach 1:
The system performs preliminary device identification and contextual analysis before the user issues control commands. By pre-processing location data, device proximity information, and conversation history, the system prepares the identified device list in advance, eliminating the need for users to spend time on device identification during the control moment.
Solution Approach 2:
The system uses feedback from conversation history and location data to continuously refine device identification. The messaging system analyzes previous interactions and contextual information to predict and pre-identify the intended device, reducing the time required for device selection in subsequent control actions.
3Measurement precision
If the system requires explicit device specification, then device control accuracy is improved, but user experience deteriorates
Solution Approach 1:
The messaging system acts as an intermediary between the user and the device control system. It translates natural language user intent into precise device identification by using contextual information as a mediator, thereby maintaining high control accuracy while preserving natural and pleasant user experience.
Solution Approach 2:
The system automatically performs device identification and selection without requiring users to engage in complex device specification processes. This self-service approach maintains accurate device control while significantly improving user experience by making the interaction more natural and less cumbersome.
4Ease of operation
If the system processes contextual information for device inference, then operation simplicity is improved, but computational complexity increases
Solution Approach 1:
The system segments the contextual information processing into distinct modules: location data processing, conversation history analysis, device proximity detection, and device identification inference. This segmentation distributes computational complexity across multiple specialized components, making the overall system more manageable and efficient.
Data Source
AI summary
In one embodiment, a method includes receiving a natural-language message including an authorization request to authorize a first user access to one or more of a number of connected devices associated with a second user; parsing the natural-language message; identifying, based on the parsed natural-language message, the first user and one or more of a number of connected devices; implicitly determining that the first user is authorized to access the identified one or more of the number of connected devices based on a calculated strength of a relationship between a node representing the first user in a social graph and a node representing the second user in the social graph satisfying a pre-determined threshold; and providing, based on the implicit authorization, access to the identified one or more of the number of connected devices.