Framework for Learning Based Recommendation and Scoring for Operator Access to Infrastructure
Summary
Oracle International Corporation filed US Patent Application US20260111564A1 for a learning-based recommendation system that evaluates operator access requests and recommends appropriate duration and privilege levels for accessing customer infrastructure resources. The system uses machine learning to assess and score access requests when new operator access is sought. The application (No. 18924925) was filed on October 23, 2024 and published on April 23, 2026.
“Disclosed is an improved approach to implement a learning-based recommendation system which provides a recommendation for the operator access, e.g., for the proper duration and privilege required when a new operator access request is raised for accessing the customer resource.”
About this source
USPTO classification G06N covers computer systems based on specific computational models: neural networks, knowledge representation, fuzzy logic, expert systems, evolutionary algorithms. With the AI patent boom, this is one of the most-filed application classes in the office. Every newly published application in G06N lands in this feed, around 230 a month. Patent applications publish 18 months after filing, so this feed reveals what AI labs and companies were working on in the prior year and a half. Watch this if you compete in machine learning, file freedom-to-operate analyses, scout acquisition targets in AI infrastructure, or track which research groups are converting publications to patents. GovPing pulls each application with the filing number, title, applicant, and abstract.
What changed
Oracle International Corporation has filed a patent application for a learning-based recommendation system that evaluates operator access requests to customer infrastructure resources. The system provides recommendations for the proper duration and privilege level required when new operator access requests are raised. This is a routine patent filing publication with no immediate regulatory implications.
Technology companies and software developers may find this patent relevant if their systems handle operator or administrative access management for customer resources. The patent describes machine learning techniques for scoring and evaluating access duration recommendations, which may influence future access management system designs.
Archived snapshot
Apr 24, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
FRAMEWORK FOR LEARNING BASED RECOMMENDATION AND SCORING FOR OPERATOR ACCESS TO INFRASTRUCTURE
Application US20260111564A1 Kind: A1 Apr 23, 2026
Assignee
Oracle International Corporation
Inventors
Joydip Kundu, Anindya Patthak
Abstract
Disclosed is an improved approach to implement a learning-based recommendation system which provides a recommendation for the operator access, e.g., for the proper duration and privilege required when a new operator access request is raised for accessing the customer resource.
CPC Classifications
G06F 21/577 G06N 20/00 G06F 2221/034
Filing Date
2024-10-23
Application No.
18924925
Parties
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