NETWORK ADJUSTMENT BASED ON MACHINE LEARNING END SYSTEM PERFORMANCE MONITORING FEEDBACK
Summary
Rochester Institute of Technology filed USPTO Patent Application US20260100880A1 on September 13, 2023. The invention discloses a system using machine learning to monitor end-system performance on a network and generate instructions to adjust network parameters based on comparing decision performance metrics to specifications. Patent application publications are informational documents that do not grant enforceable rights.
What changed
The USPTO published Patent Application US20260100880A1 by inventors Leon Reznik and Sergei Chuprov on behalf of Rochester Institute of Technology. The application discloses a system and method where a machine learning end-system receives data transmitted via a network facility, makes decisions using that data, determines decision performance metrics, compares those metrics to specifications, and generates instructions to adjust network parameters based on the comparison. The system enables adaptive network optimization through performance feedback loops.
Patent application publications are informational records and do not grant enforceable rights. Entities monitoring this space should note the technology for potential future licensing considerations or freedom-to-operate analysis once the application proceeds to grant.
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Apr 12, 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.
NETWORK ADJUSTMENT BASED ON MACHINE LEARNING END SYSTEM PERFORMANCE MONITORING FEEDBACK
Application US20260100880A1 Kind: A1 Apr 09, 2026
Assignee
Rochester Institute of Technology
Inventors
Leon Reznik, Sergei Chuprov
Abstract
A system, method, and computer readable storage medium for generating instructions to adjust a network parameter. The system, method, and computer readable storage medium include: i) receiving data transmitted from a data source to a machine learning end-system via a network facility, the data transmission being characterized by a network parameter; ii) making a decision with the machine learning end-system, using the data, iii) determining a decision performance metric for the decision, iv) comparing the decision performance metric to a decision performance specification; and v) generating instructions to adjust the network parameter based on the comparison.
CPC Classifications
H04L 41/0823 H04L 41/147 H04L 41/16
Filing Date
2023-09-13
Application No.
19112000
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