Robotics & Machine Learning Daily News2024,Issue(Jul.25) :145-149.

'Predicting Access Revocation For Applications Using Machine Learning Models' in Patent Application Approval Process (USPTO 20240232393)

Robotics & Machine Learning Daily News2024,Issue(Jul.25) :145-149.

'Predicting Access Revocation For Applications Using Machine Learning Models' in Patent Application Approval Process (USPTO 20240232393)

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Abstract

The following quote was obtained by the news editors from the background informa tion supplied by theinventors: “Application access requirements within an organ ization are often difficult to assess, as differentindividuals in different rol es require varying access to applications. For example, different functions of agiven application may be used by each member of the organization. Currently, sy stems rely on manualassessment of access requirements, which is difficult to de termine accurately, especially when there aremany users and many applications w ithin an organization, and as a result, user access is often inaccurate.Too lit tle access to application functions may hinder a user’s ability to perform their duties while too muchaccess is an opportunity for malfeasance. Thus, systems a nd methods are needed for accurately predictinguser access requirements for app lications.”

Key words

Business/Capital One Services LLC/Cybo rgs/Emerging Technologies/Machine Learning

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
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