Microsoft technology licensing, llc (20240121311). MACHINE LEARNING FOR IDENTIFYING IDLE SESSIONS simplified abstract

MACHINE LEARNING FOR IDENTIFYING IDLE SESSIONS

Organization Name

microsoft technology licensing, llc

Inventor(s)

Prerana Dharmesh Gambhir of San Jose CA (US)

Sharena Meena Pari-monasch of Union City CA (US)

Khoa Dang Nguyen of Murphy TX (US)

Yiming Shi of Milpitas CA (US)

Yongchang Dong of San Jose CA (US)

MACHINE LEARNING FOR IDENTIFYING IDLE SESSIONS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240121311 titled 'MACHINE LEARNING FOR IDENTIFYING IDLE SESSIONS

Simplified Explanation

The abstract describes systems and methods for identifying and evicting idle sessions in a cloud-based service using a machine learning model trained to classify active sessions based on parameters associated with the session documents.

• The machine learning model is trained to classify active sessions between clients and the cloud-based service.
• The model receives parameters related to the session document as input and applies rules to determine the classification of the active session.
• The classified sessions are then used to identify and evict idle sessions from the cloud-based service.

Potential Applications

This technology can be applied in cloud-based services, online platforms, and network management systems to improve efficiency by automatically identifying and removing idle sessions.

Problems Solved

1. Efficient resource management: By evicting idle sessions, resources can be allocated more effectively to active sessions, improving overall system performance. 2. Enhanced security: Removing idle sessions reduces the risk of unauthorized access or security breaches in the cloud-based service.

Benefits

1. Improved system performance: By evicting idle sessions, system resources are optimized for active sessions, leading to better performance. 2. Automated session management: The machine learning model automates the process of identifying and evicting idle sessions, reducing manual intervention.

Potential Commercial Applications

"Automated Idle Session Eviction in Cloud-Based Services"

Possible Prior Art

There may be prior art related to session management systems in cloud services or network environments that address similar issues of identifying and managing idle sessions.

How does the machine learning model handle different types of session parameters?

The abstract mentions a plurality of parameters related to the session document, but it does not specify how the model processes and prioritizes these parameters for classification.

What impact does evicting idle sessions have on overall system performance?

While the benefits of evicting idle sessions are mentioned, the abstract does not provide specific data or metrics on how this action improves system performance.

Original Abstract Submitted

systems and methods for identifying and evicting idle sessions include training a machine learning model as a session classifying model to learn rules for classifying active sessions between clients and the cloud-based service. the session classifying model is trained to receive a plurality of parameters pertaining to the document associated with an active session as input and to apply the rules to the plurality of parameters to determine a classification for the active session and to provide an output indicative of the classification for the active session. the session classifying model is then utilized in the cloud-based service to classify the active sessions. the active sessions classified as idle sessions may then be evicted from the cloud-based service.