Machine-Learned Data Transfer Between Devices
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Solution Overview
Problem
Current machine-learning technologies lack efficient methods for managing and transferring machine-learned data between devices, which limits the sharing and enhancement of machine-learned skills, especially across different hardware configurations.
Innovation Solution
A computer-implemented method that obtains a machine-learned data set from a first device, determines the hardware requirements for the data set, and transfers it to a second device only if the second device meets those requirements, enabling the sharing of categorized machine-learned information and enhancing the second device's skills.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If machine-learned data sets are transferred between devices without hardware requirement verification, then data transfer speed is improved, but system reliability deteriorates due to incompatibility issues
Solution Approach 1:
The patent applies preliminary action by determining hardware requirements and verifying device compatibility before transferring machine-learned data sets. The system checks whether the target device meets necessary hardware specifications (processor type, memory, storage) prior to initiating data transfer, preventing incompatibility issues while maintaining efficient transfer processes
2Reliability
If hardware requirement checks are performed before data transfer, then device compatibility is improved, but processing time increases
Solution Approach 1:
The system performs hardware requirement determination and compatibility verification as preliminary checks before data transfer initiation. By establishing clear hardware criteria in advance and implementing efficient verification logic, the system ensures compatibility while minimizing the time overhead of prerequisite checks
3Adaptability or versatility
If machine-learned data sets are shared across devices with different hardware configurations, then adaptability is improved, but device complexity increases due to compatibility management
Solution Approach 1:
The patent applies parameter changes by establishing and evaluating specific hardware parameters (processor type, memory capacity, storage space) against predefined requirements for each machine-learned data set. The system dynamically assesses whether the target device's hardware parameters meet the necessary thresholds, enabling adaptable cross-device sharing while managing complexity through structured parameter validation
Data Source
AI summary
Management of machine-learned data between machine-learning devices is facilitated by a processor(s) obtaining a machine-learned data set of a first device, with the machine-learned data set of the first device being categorized machine-learned information. The processor(s) determines one or more device hardware requirements to use the machine-learned data set, and based on receiving a request to provide the machine-learned data set to a second device, determines whether the second device meets the one or more device hardware requirements to use the machine-learned data set of the first device. Based on determining that the second device meets the one or more device hardware requirements, the processor(s) provides the machine-learned data set of the first device to the second device to provide the categorized machine-learned information of the first device to the second device for use by the second device.