Microsoft technology licensing, llc (20240338193). REINFORCEMENT LEARNING FOR CONTROLLING SOFTWARE UPDATE TIMING simplified abstract

REINFORCEMENT LEARNING FOR CONTROLLING SOFTWARE UPDATE TIMING

Organization Name

microsoft technology licensing, llc

Inventor(s)

Dhirendra Kumar Bhupati of Sammamish WA (US)

Johnny Sterling Campbell of Woodinville WA (US)

REINFORCEMENT LEARNING FOR CONTROLLING SOFTWARE UPDATE TIMING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240338193 titled 'REINFORCEMENT LEARNING FOR CONTROLLING SOFTWARE UPDATE TIMING

The abstract describes a software update distribution service that utilizes reinforcement learning to determine optimal times for downloading and installing software updates on client computing devices within an enterprise.

• The software update distribution service leverages reinforcement learning, a type of machine learning algorithm, to learn the best times to perform software update activities.
• The service aims to minimize penalties associated with software update timing, considering factors such as network traffic and power consumption.
• By using reinforcement learning, the software-based agent can adapt and improve its update scheduling over time.
• The service is designed to achieve predefined goals or objectives set by the enterprise, enhancing efficiency and reducing potential disruptions caused by software updates.

Potential Applications: - Enterprise software management - IT infrastructure optimization - Network traffic control

Problems Solved: - Inefficient software update scheduling - High network traffic and power consumption due to simultaneous updates - Disruption of user productivity during software updates

Benefits: - Improved efficiency in software update distribution - Reduced network congestion and power consumption - Enhanced user experience with minimized disruptions

Commercial Applications: Optimizing software update distribution for large enterprises can lead to cost savings, improved productivity, and better resource management.

Questions about the technology: 1. How does reinforcement learning improve software update distribution efficiency? Reinforcement learning allows the software agent to learn from experience and adjust its update scheduling to minimize penalties.

2. What are the key metrics used to determine optimal software update times? Metrics such as network traffic impact and power consumption are considered in calculating penalties for update timing.

Original Abstract Submitted

described herein is a software update distribution service that leverages reinforcement learning—a specific type machine learning algorithm—to discover or learn optimal times (e.g., a schedule) to download software updates and to install software updates for software applications installed on a group of client computing devices of a specific enterprise, in order to achieve one of several predefined goals or objectives selected for the specific enterprise, or for the specific group of client computing devices. using reinforcement learning, a software-based agent learns to perform activities relating to software updates at specific times that minimize a penalty, wherein the penalty is derived based on a weighted combination of metrics, some of which relate to the impact of software update timing on network traffic and power consumption.