18497642. USING MACHINE LEARNING TO IDENTIFY MEMORY COMPATIBILITY simplified abstract (Micron Technology, Inc.)
Contents
- 1 USING MACHINE LEARNING TO IDENTIFY MEMORY COMPATIBILITY
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 USING MACHINE LEARNING TO IDENTIFY MEMORY COMPATIBILITY - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
USING MACHINE LEARNING TO IDENTIFY MEMORY COMPATIBILITY
Organization Name
Inventor(s)
USING MACHINE LEARNING TO IDENTIFY MEMORY COMPATIBILITY - A simplified explanation of the abstract
This abstract first appeared for US patent application 18497642 titled 'USING MACHINE LEARNING TO IDENTIFY MEMORY COMPATIBILITY
Simplified Explanation
The patent application describes a system where a device can recommend memory types based on the device type and configuration using machine learning models.
- The device obtains input identifying a device type and information indicating a configuration.
- Based on the input, the device determines compatibilities between memory types and the device type using machine learning models trained for each memory type.
- The device then recommends one or more memory types for the device type and transmits the recommendation.
Potential Applications
This technology could be applied in various industries such as electronics manufacturing, computer hardware, and mobile devices to optimize memory selection for different device types.
Problems Solved
This technology solves the problem of manual memory selection for different device types, reducing errors and improving overall performance by recommending compatible memory types.
Benefits
The benefits of this technology include improved efficiency in memory selection, increased performance of devices, and reduced compatibility issues between memory types and device types.
Potential Commercial Applications
A potential commercial application of this technology could be in the consumer electronics industry, where manufacturers can use this system to recommend memory types for their devices, enhancing user experience and reducing technical support issues.
Possible Prior Art
One possible prior art could be systems that recommend hardware components based on device specifications, but not specifically focusing on memory types and configurations as described in this patent application.
Unanswered Questions
How does the device determine the compatibility between memory types and device types?
The device determines compatibility using a plurality of machine learning models trained for each memory type, but the specific algorithms and methodologies used are not detailed in the abstract.
What types of memory configurations are considered in the recommendation process?
The abstract mentions configurations associated with the device type, but it does not specify the range or types of configurations that are taken into account during the recommendation process.
Original Abstract Submitted
In some implementations, a device may obtain an input that identifies a device type. The device may obtain, based on the input, information indicating a configuration associated with the device type. The device may determine, using a plurality of machine learning models respectively associated with a plurality of memory types, compatibilities between the plurality of memory types and the device type based on the configuration associated with the device type. Each of the plurality of machine learning models may be trained to determine a compatibility of a respective memory type, of the plurality of memory types, with a given configuration. The device may determine a recommendation of one or more memory types for the device type based on the compatibilities between the plurality of memory types and the device type. The device may transmit an indication of the recommendation of the one or more memory types.
