17949106. MACHINE LEARNING BASED FIRMWARE VERSION RECOMMENDER simplified abstract (HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP)
Contents
- 1 MACHINE LEARNING BASED FIRMWARE VERSION RECOMMENDER
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MACHINE LEARNING BASED FIRMWARE VERSION RECOMMENDER - 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 How does the machine learning model handle outliers in the historical customer firmware update data?
- 1.11 What measures are in place to ensure the security and privacy of customer firmware update data?
- 1.12 Original Abstract Submitted
MACHINE LEARNING BASED FIRMWARE VERSION RECOMMENDER
Organization Name
HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor(s)
SARGAM Jain of Houston TX (US)
CHARLES Hogg of Santa Clara CA (US)
DAVID Fehling, Jr. of Houston TX (US)
BERND Bandemer of Santa Clara CA (US)
JOSE Tellado of Santa Clara CA (US)
MACHINE LEARNING BASED FIRMWARE VERSION RECOMMENDER - A simplified explanation of the abstract
This abstract first appeared for US patent application 17949106 titled 'MACHINE LEARNING BASED FIRMWARE VERSION RECOMMENDER
Simplified Explanation
The presently disclosed technology provides automated firmware recommendation systems that utilize machine learning to make tailored firmware recommendations for individual network device clusters based on historical customer firmware update data.
- Machine learning model trained on historical customer firmware update data
- Dynamic training on a weekly basis
- Predict/recommend optimal firmware version based on firmware-related features and customer preferences
- Highly tailored firmware recommendations for individual network device clusters
Potential Applications
The technology can be applied in the telecommunications industry to improve firmware update processes for network devices.
Problems Solved
This technology solves the problem of manual firmware recommendations, which can be time-consuming and less accurate compared to automated machine learning-based recommendations.
Benefits
The benefits of this technology include increased efficiency in firmware update processes, improved customer satisfaction through tailored recommendations, and potentially higher network device performance.
Potential Commercial Applications
A potential commercial application of this technology is in the provision of firmware update services for telecommunications companies, where accurate and timely recommendations are crucial for network performance.
Possible Prior Art
One possible prior art for this technology could be automated software update recommendation systems in other industries, such as computer software or mobile applications.
Unanswered Questions
How does the machine learning model handle outliers in the historical customer firmware update data?
The article does not mention how the machine learning model deals with outliers in the data that may skew the predictions or recommendations.
What measures are in place to ensure the security and privacy of customer firmware update data?
The article does not address the security and privacy concerns related to handling sensitive customer data for training the machine learning model.
Original Abstract Submitted
Examples of the presently disclosed technology provide automated firmware recommendation systems that inject the intelligence of machine learning into the firmware recommendation process. To accomplish this, examples train a machine learning model on troves of historical customer firmware update data on a dynamic basis (e.g., examples may train the machine learning model on weekly basis to predict accepted firmware updates made by a vendor's customers across the most recent 6 months). From this dynamic training, the machine learning model can learn to predict/recommend an optimal firmware version for a customer/network device cluster based on firmware-related features, recent customer preferences, and other customer-specific factors. Once trained, examples can deploy the machine learning model to make highly tailored firmware recommendations for individual network device clusters of individual customers taking the above described factors into account.
