Cross-Device Learning and Inference for Ambient Sensor Collaboration
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Solution Overview
Problem
Smart devices in ambient computing environments operate independently, leading to inefficiencies in training models and performing inference tasks, resulting in wasted time and resources, as well as missed opportunities for collaboration among devices with diverse sensing capabilities.
Innovation Solution
Implement cross-device learning and multi-device inference methods where new devices are trained using supervision signals from existing devices, and models are adapted to include or exclude inputs based on device presence and power consumption, enabling collaborative perception and inference across multiple devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If each smart device operates independently to perform its designated tasks, then each device can autonomously detect and respond to events in its environment, but this leads to wasted time for training models in new environments and excessive aggregate power and computing cycles needed for inference tasks
Solution Approach 1:
The patent merges the inference operations of multiple smart devices into a coordinated system. Instead of each device independently running its own model, the system combines inputs from multiple devices (cameras, microphones, sensors) and performs unified inference using shared models. This reduces redundant computing cycles and power consumption while maintaining autonomous detection capabilities across the device ecosystem.
Solution Approach 2:
The patent implements universal models that can perform multiple inference tasks across different device types. A single trained model can process inputs from various sensors and devices, eliminating the need for each device to maintain separate specialized models. This multi-functionality reduces the overall computational burden and energy consumption across the device network.
2Adaptability or versatility
If each smart device independently trains its own models in a new environment, then each device can adapt to local conditions, but this results in redundant training time and computational resources being wasted across multiple devices
Solution Approach 1:
The patent performs model training in advance using aggregated data from multiple devices before deployment. By pre-training models with diverse environmental data collected from the device ecosystem, the system eliminates the need for each individual device to undergo lengthy training periods when deployed in new environments. The pre-trained models are then ready for immediate use, significantly reducing adaptation time.
Solution Approach 2:
The patent creates and distributes copies of trained models across multiple devices. Instead of each device independently training its own model, a master model is trained centrally using aggregated data, then replicated and deployed to multiple devices. This copying approach maintains environmental adaptability while eliminating redundant training computations and time delays.
3Speed
If multiple devices independently perform inference tasks simultaneously, then each device can respond to events in real-time, but this consumes excessive aggregate power and computing cycles
Solution Approach 1:
The patent merges inference operations by coordinating multiple devices to work together on shared inference tasks. Instead of each device independently processing the same environmental data, the system combines sensor inputs from multiple devices and performs unified inference. This maintains real-time response capability while dramatically reducing the aggregate power and computing cycle consumption across the device network.
4Reliability
If each device independently monitors and perceives its environment, then each device can autonomously detect events, but this misses opportunities for devices to benefit from different perspectives and sensing capabilities of other smart devices
Solution Approach 1:
The patent implements universal inference models that can process diverse input types from different device sensors. These multi-functional models are designed to handle inputs from cameras, microphones, temperature sensors, and other devices, allowing the system to leverage different perspectives and sensing capabilities without requiring separate specialized models for each device type.
Data Source
AI summary
Systems and methods for multi device learning and inference in an ambient computing environment. In some aspects, the present technology discloses systems and methods for performing cross-device learning in which new devices may be trained based on supervision signals from existing devices in the ambient computing environment. In some aspects, the present technology discloses systems and methods for performing multi-device inference across two or more devices in the ambient computing environment. Likewise, in some aspects, the present technology discloses systems and methods for training models that are robust to the addition or removal of one or more devices from an ambient computing environment.