Peer-to-Peer Learning Engine for Mobile Device Intelligence Augmentation
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Solution Overview
Problem
Computing devices with limited resources face challenges in enhancing their capabilities using machine-learning techniques due to the resource-intensive nature of organic augmentation methods, which can be slow and limited in achieving full effectiveness.
Innovation Solution
A learning engine hosted on a computing device enables the augmentation of capabilities by identifying and incorporating learnable capabilities from peer devices, using adaptable computational models and parameters, and employing machine-learning and closed-loop feedback optimization to modify existing models and parameters, thereby reducing the reliance on human operators or cloud-based servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine-learning techniques are used to enhance capabilities of mobile computing devices, then device intelligence and adaptability are improved, but computing resources (processors, memory, energy) are consumed
Solution Approach 1:
The system segments the learning process into two distinct phases: (1) organic augmentation phase where the device learns from its own sensor data locally, and (2) peer learning phase where the device transfers and applies learned capabilities from peer devices. This segmentation allows resource-intensive learning to occur peer-to-peer while minimizing local resource consumption during deployment.
Solution Approach 2:
The patent introduces peer devices as intermediaries that facilitate capability transfer. Instead of relying solely on cloud-based servers or continuous human operation, peer devices act as mediators that share pre-learned capabilities through direct device-to-device communication, reducing the need for resource-intensive local training and external cloud dependency.
2Adaptability or versatility
If organic augmentation methods are used to learn capabilities, then device capabilities are enhanced, but the process is slow and resource-intensive
Solution Approach 1:
Peer devices perform the resource-intensive learning and capability development in advance during their own operation and sensor data collection phases. When a device needs a capability, it can acquire pre-learned models from peers that have already completed the time-consuming training process, significantly reducing local learning time and resource requirements.
Solution Approach 2:
The system enables devices to copy learned capabilities (machine learning models, parameters, and configurations) from peer devices rather than learning everything from scratch. This copying mechanism allows rapid acquisition of proven capabilities without repeating the time-intensive organic augmentation process locally.
3Adaptability or versatility
If cloud-based servers or human operators are used for capability augmentation, then device intelligence is improved, but device autonomy is reduced
Solution Approach 1:
The system enables devices to autonomously identify their own capability gaps, search for and select appropriate peer devices, negotiate capability transfer, and integrate learned capabilities without human intervention or cloud server coordination. Each device independently manages its own learning and augmentation needs through peer-to-peer interactions.
Solution Approach 2:
Peer devices serve as decentralized intermediaries that enable capability transfer without requiring centralized cloud server mediation or human operators. The peer-to-peer communication protocol allows devices to autonomously negotiate and exchange learned capabilities directly, enhancing device independence and reducing external dependencies.
Data Source
AI summary
Briefly, example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to enhance capabilities of peer devices. In an implementation, at least one agent to: identify one or more learnable capabilities enabled by one or more parameters that are accessible via receipt of one or more message at the one or more communication devices from one or more other computing devices; and determine a utility of augmenting at least one of the one or more learning engines with at least one of the one or more learnable capabilities.