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METHOD AND SYSTEM FOR DETECTING A HARMFUL SHIFT IN A MACHINE LEARNING MODEL

Resumen de: US20260050504A1

A method and system for detecting harmful shift in a machine learning (ML) model associated with unlabeled data utilized by the ML model. The method includes implementing an error estimator model with regressor algorithm and training the error estimator model with a first portion of a labeled calibration dataset. The method further includes computing, by the trained error estimator model, an error estimation threshold based on a second portion of the labeled calibration dataset; predicting a performance of the ML model by detecting the harmful shift via the trained error estimator model analyzing the unlabeled data over a predetermined time period and determining a proportion of estimated errors associated with the unlabeled data over the predetermined time period that exceeds the error estimation threshold; and generate an alert when the proportion of estimated errors exceeds the error estimation threshold.

APPARATUS AND METHOD OF TRANSPORT DATA AGGREGATION

Resumen de: US20260049833A1

An apparatus and method for transport management is presented. The apparatus includes a memory communicatively connected to a processor to output routing data of transport entities as a function of aggregated transport data, wherein the outputting comprises: receive transport data and bound parameters of a transport from a carrier device; iteratively train an aggregation machine-learning model to combine the transport data, wherein the training comprises generating an aggregation training data correlating the transport data as inputs and aggregated transport data as outputs; modify a characteristic of the transport; update the aggregated transport data based on the modification of the characteristic of the transport; retrain the aggregation machine-learning model as a function of the updated aggregated transport data; generate the routing data, wherein the routing data comprises instructions to further modify the characteristic of the transport; and automatically change the characteristic of the transport based on the routing data.

METHODS AND SYSTEMS FOR REAL-TIME RESOLUTION OF ERRORS THAT ARE DETECTED BY MULTIVARIATE MACHINE LEARNING MODELS BETWEEN USERS USING UNIVARIATE RESPONSES

Resumen de: US20260050503A1

Methods and systems are for generating real-time resolutions of errors arising from user submissions, computer processing tasks, etc. For example, the methods and systems described herein recite improvements for detecting errors in one or more user submissions and providing resolutions in real-time. To provide these improvements, the methods and systems use a machine learning model that is trained to return probability error scores based on a plurality of variables. By using the multivariate approach, the methods and systems may produce a highly accurate detection.

SYSTEMS AND METHODS FOR MACHINE LEARNING ASSISTED SYSLOG PARSER GENERATORS

Nº publicación: US20260050582A1 19/02/2026

Solicitante:

SECUREWORKS CORP [US]
SecureWorks Corp

Resumen de: US20260050582A1

Systems and methods for generating a parser from a log file including: receiving a log file, wherein the log file is a structured text file of a plurality of data elements; invoking a machine learning model to: process the log file to identify name-value-pairs from the data elements; classify the log file as being associated with a schema based in part on the name-value pairs; map a first name-value pair to a first input field of the schema based on characteristics of the first name-value pair; determine a confidence level associated with mapping the first name-value pair to the first input field; and when the confidence level for mapping the first name-value pair exceeds a threshold, provide the first name-value pair to the first input field; and generating a parser from the plurality of input fields of the schema.