AI-Generated IoT Automation for Multi-Device Intent Coordination
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Solution Overview
Problem
Conventional systems fail to support seamless and dynamic interactions among multiple IoT devices, particularly in creating complex, personalized, and immersive experiences that require coordination across real and virtual environments, lacking guidance for optimal device interactions and automation.
Innovation Solution
A system and method utilizing a pre-trained generative model to receive user inputs, identify required entities and automations, predict execution plans, and trigger sequences of actions across IoT devices based on user intents, employing AI-based assistance to generate comprehensive and context-aware activity plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional systems are used for IoT device coordination, then device complexity is reduced, but automation capability and interaction quality deteriorate
Solution Approach 1:
The patent introduces an AI-based automation system as an intermediary between users and IoT devices. This mediator receives user intents, generates coordinated action sequences, and manages device interactions automatically, thereby increasing automation capability without requiring users to directly handle complex coordination logic.
Solution Approach 2:
The patent replaces manual mechanical coordination of IoT devices with an AI-based automated system. Instead of users directly controlling multiple devices through complex interfaces, the system uses generative AI models to automatically generate and execute coordinated actions, substituting human cognitive processing with automated intelligent systems.
2Productivity
If manual planning and execution of automations is used, then device complexity is reduced, but productivity and time efficiency deteriorate
Solution Approach 1:
The patent implements preliminary action by pre-training AI models with extensive knowledge of IoT device capabilities, interaction patterns, and use cases. The system pre-generates action sequences and coordination strategies, so that when actual automation generation is needed, the AI can quickly retrieve and adapt pre-learned patterns rather than planning from scratch, significantly reducing time loss.
Solution Approach 2:
The system performs self-service by automatically generating, optimizing, and executing automation sequences without requiring manual intervention for planning or sequencing. The AI model independently analyzes user intents, selects appropriate devices, determines optimal action orders, and coordinates execution, thereby increasing productivity while eliminating the time users would otherwise spend on manual planning.
3Adaptability or versatility
If conventional smart speakers are used, then device complexity is reduced, but adaptability and context-awareness deteriorate
Solution Approach 1:
The patent implements universality by creating a multi-functional AI-based automation system that can handle diverse contexts, intents, and device types through a single unified platform. The generative AI model adapts to different scenarios (birthday parties, movie nights, gardening) and automatically selects appropriate devices and actions, providing versatile context-aware operations without requiring separate specialized systems for each function.
Solution Approach 2:
The system utilizes parameter changes by dynamically adjusting AI model outputs based on contextual parameters such as user preferences, environmental conditions, device availability, and event types. The generative model modifies automation sequences in real-time according to changing parameters, enabling high adaptability and context-awareness while maintaining a relatively simple underlying system architecture.
4Extent of automation
If complex coordination of multiple devices is manually performed, then ease of operation is improved, but productivity and automation extent deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where the AI system continuously monitors execution results, user responses, and environmental changes, using this feedback to refine and optimize future automation generations. This feedback loop enables the system to learn from actual usage patterns, improving autonomous activity planning while providing intuitive, adaptive guidance that enhances ease of operation rather than complicating it.
Data Source
AI summary
A method for creating automations for interactions of a plurality of electronic devices may include receiving a user input including user intents from a user, generating, using a generative model based on the user input, a list of activities to be executed in connection with the user intents, identifying, using the generative model, a plurality of entities that are required to perform the activities, predicting an execution plan including a plurality of automations to be carried out the activities, based on relations between the activities and the plurality of entities for triggering the plurality of automations via the plurality of electronic devices, mapping a corresponding electronic device among the plurality of electronic devices with a corresponding entity among the plurality of entities based on the execution plan, and triggering, based on the mapping, the plurality of automations in a sequence upon occurrence of events in connection with the activities.