IoT Indirect Interaction System for Smart Home Task Coordination
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Solution Overview
Problem
Smart home environments lack the capability for seamless indirect interactions between devices and users, leading to inefficiencies and increased effort in completing tasks, as users often resort to less efficient direct interactions due to the limitations of current smart home technologies.
Innovation Solution
A system and method for enabling seamless indirect interactions in an IoT environment by predicting user contexts, identifying relevant users, and providing interactable interfaces for input and suggestions using deep learning, allowing for task-related inputs to be appended and processed efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If direct interactions are used between users and devices, then communication reliability is improved, but user effort and time consumption increase
Solution Approach 1:
The patent introduces an intermediary system that automatically translates user intents into device commands and coordinates interactions between multiple devices. This mediator handles the complex communication protocols and device coordination, allowing users to interact indirectly through natural language or simple inputs while the system manages the reliable transmission and coordination behind the scenes.
Solution Approach 2:
The system enables devices to autonomously understand and execute user intents without requiring direct user intervention in the communication process. Devices self-configure their response behaviors, and the system automatically routes communications, reducing the time users need to spend on manual device coordination while maintaining reliable interaction through automated service mechanisms.
2Ease of operation
If indirect interactions are enabled through automated systems, then user effort is reduced, but system complexity increases
Solution Approach 1:
The patent divides the complex interaction system into modular components: intent recognition modules, device discovery modules, command translation modules, and coordination engines. Each module handles a specific aspect of the interaction process, making the overall system more manageable and maintainable. This segmentation allows the system to provide sophisticated indirect interactions while keeping individual components relatively simple and well-defined.
3Measurement precision
If context prediction is used to identify relevant users, then interaction accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary context analysis and user identification in advance by continuously monitoring device usage patterns, user preferences, and environmental data. This pre-computed context information is stored and readily available when interactions occur, allowing the system to quickly identify relevant users without performing complex analysis in real-time, thus maintaining both accuracy and speed.
Data Source
AI summary
Methods, systems, and apparatuses for enabling indirect interactions between users in an Internet of Things (IoT) environment are provided. A method includes receiving, by a first device, an utterance from a first user, wherein the utterance relates to at least one task that is to be performed by the first user; based on receiving the utterance, identifying, by the first device, one or more second users related to the at least one task; providing, by the first device, an interactable interface to one or more second devices which are located closer to the one or more second users than the first device; receiving, by the first device, one or more inputs corresponding to the at least one task from the one or more second users through the interactable interface; and appending, by the first device, the received one or more inputs to the at least one task.