Alerta

MACHINE-LEARNING MODEL(S) FOR ESTIMATING RAN FUNCTIONALITY MACHINE LEARNING MODEL IMPACT ON PERFORMANCE MEASUREMENT COUNTERS

Absstract of: WO2025210109A1

This specification relates to the execution of machine-learning models on user devices. According to a first aspect of this specification, there is described apparatus comprising: means for receiving a network configuration derived from a plurality of machine-learning models, each machine-learning model directed towards a respective one or more radio access network functionalities; means for receiving a plurality of predicted performance measurement counters output from a plurality of machine-learning performance measurement models, each machine-learning prediction measurement model corresponding to one of the plurality of machine-learning models; and means for processing, using a common machine-learning performance measurement counter model, the network configuration and the plurality of predicted performance measurement counters to determine a model output comprising, for one or more performance measurement counters, a respective plurality of impact scores. Each impact score is indicative of a predicted impact of a corresponding machine-learning model in the plurality of machine-learning models on the respective performance measurement counter of said impact score for the network configuration.

System and Method for Performing Fault and Event Analysis in Electrical Substations

Absstract of: US2025315339A1

A system and method performing fault and event analysis in electrical substations comprises receiving a disturbance record triggered by an intelligent electronic device (IED) at an electrical substation, pre-processing the received disturbance record to extract at least one variable time series data of plurality of electrical parameters, generating a causality matrix based on the extracted at least one variable time series data by applying causal analysis, predicting, using a Machine learning (ML) module, a fault type at least based on the causality matrix, retrieving, from a knowledge database, a plurality of probable causes corresponding to the predicted fault type, determining at least one exact cause from the plurality of probable causes based on the causal pattern, and providing the fault type, the plurality of probable causes, and the at least one exact cause to a user.

SYSTEM AND METHOD FOR GENERATING A PATH LOSS PROPAGATION MODEL THROUGH MACHINE LEARNING

Absstract of: US2025317224A1

The present disclosure provides a system and a method for generating a path loss propagation model through machine learning. The system generates a path loss propagation model for fifth generation (5G) networks for network planning. The path loss model predicts a reference signal received power/signal to noise interference ratio (RSRP/SINR) by leveraging a fourth generation (4G) user data.

PREDICTING ALBUMINURIA USING MACHINE LEARNING

Absstract of: US2025316377A1

An example embodiment may involve obtaining, by a computing system, an observation of demographic values of an individual, vital sign values of the individual, and blood test values of the individual: applying, by the computing system, a machine learning model to the observation, wherein the machine learning model was trained with a training data set, wherein the training data set contained observations of corresponding demographic values, vital sign values, blood test values, and either urine albumin-to-creatinine ratio (UACR) values or urine protein-to-creatinine ratio (UPR) values for a plurality of individuals, and wherein the machine learning model is configured to provide predictions of whether further observations are indicative of undiagnosed albuminuria or proteinuria; and providing, by the computing system, a prediction of whether the individual exhibits undiagnosed albuminuria or proteinuria based on the observation.

MACHINE LEARNING NETWORKS, ARCHITECTURES AND TECHNIQUES FOR DETERMINING OR PREDICTING DEMAND METRICS IN ONE OR MORE CHANNELS

Absstract of: US2025315681A1

This disclosure relates to artificial intelligence (AI) and machine learning networks for predicting or determining demand metrics across multiple channels. An analytics platform can receive channel events from multiple channels corresponding to geographic areas, and channel features related to demand conditions in the channels can be extracted from the channel events. During a training phase, the channel features can be accumulated into one or more training datasets for training one or more demand prediction models. The one or more demand prediction models can be trained to predict or determine demand metrics for each of the channels. The demand metrics can indicate or predict demand conditions based on the current conditions in the channels and/or based on future, predicted conditions in the channels. Other embodiments are disclosed herein as well.

PROACTIVE DEFENSE OF UNTRUSTWORTHY MACHINE LEARNING SYSTEM

Absstract of: US2025315674A1

Methods and systems for inducing model shift in a malicious computer's machine learning model is disclosed. A data processor can determine that a malicious computer uses a machine learning model with a boundary function to determine outcomes. The data processor can then generate transition data intended to shift the boundary function and then provide the transition data to the malicious computer. The data processor can repeat generating and providing the transition data, thereby causing the boundary function to shift over time.

METHOD AND SYSTEM FOR IMPROVING MACHINE LEARNING OPERATION BY REDUCING MACHINE LEARNING BIAS

Absstract of: US2025315738A1

A network operation system and method accesses a training dataset for a network operation predictive model including historical network operation records and historical decision records, generates an inferred protected class dataset by executing a protected class demographic model, executes an algorithmic bias model using as input the historical decision records and the inferred protected class dataset to generate one or more fairness metrics, executes, based on the fairness metrics, a bias adjustment model using as input the historical decision records and the inferred protected class dataset to generate an adjusted training dataset, trains the network operation predictive model using as input the adjusted training dataset, receives an electronic request for a network operation, executes the network operation predictive model using as input at least one attribute of the electronic request for the network operation, and executes the network operation based on a prediction of the network operation predictive model.

MACHINE LEARNING POWERED ANOMALY DETECTION FOR MAINTENANCE WORK ORDERS

Absstract of: US2025315798A1

An industrial work order analysis system applies statistical and machine learning analytics to both open and closed work orders to identify problems and abnormalities that could impact manufacturing and maintenance operations. The analysis system applies algorithms to learn normal maintenance behaviors or characteristics for different types of maintenance tasks and to flag abnormal maintenance behaviors that deviate significantly from normal maintenance procedures. Based on this analysis, embodiments of the work order analysis system can identify unnecessarily costly maintenance procedures or practices, as well as predict asset failures and offer enterprise-specific recommendations intended to reduce machine downtime and optimize the maintenance process.

PROCESSING SYSTEM HAVING A MACHINE LEARNING ENGINE FOR PROVIDING A SURFACE DIMENSION OUTPUT

Absstract of: US2025315868A1

Systems and apparatuses for generating surface dimension outputs are provided. The system may collect an image from a mobile device. The system may analyze the image to determine whether they comprise one or more standardized reference objects. Based on analysis of the image and the one or more standardized reference objects, the system may determine a surface dimension output. The system may determine one or more settlement outputs and one or more repair outputs for the driver based on the surface dimension output.

TECHNIQUES FOR PROVIDING SYNCHRONOUS AND ASYNCHRONOUS DATA PROCESSING

Absstract of: US2025315437A1

Techniques discussed herein include dynamically providing synchronous and/or asynchronous data processing by a machine-learning model service. The machine-learning model service (“the service”) executes a stream manager application, a web interface, and a machine-learning model via a common container. The stream manager application can obtain input data (e.g., from an input data stream, a partition of an input data stream, etc.) and provide the data to the machine-learning model through the web interface using a local communication channel (e.g., a loopback interface that bypasses local network interface hardware of the computing device on which the model executes). Prediction results from the model may be provided as output data (e.g., to an output data stream, to a partition of an output data stream, etc.).

Topic Identification Based on Virtual Space Machine Learning Models

Absstract of: US2025315628A1

Techniques for displaying workflow responses based on determining topics associated with user requests are discussed herein. In some examples, a user may post a request (e.g., question) to a virtual space (e.g., a channel, thread, board, etc.) of a communication platform. The communication platform may input the request into a machine learning model trained to identify topics associated with the request and confidence levels associated with topics. In such examples, the communication platform may associate a topic with the user request based on the confidence level of the topic. In some examples, the communication platform may determine that the topic is associated with a graphical identifier (e.g., emoji). The communication platform may cause the graphical identifier to be displayed to the virtual space within which the user request was posted. In response to displaying the graphical identifier, the communication platform may display a workflow response to the virtual space.

AI-POWERED ADAPTIVE PERFORMANCE

Absstract of: US2025315723A1

Methods and systems for federated caching with intelligent content delivery network (CDN) optimization are disclosed. A caching system collects data relating to one or more user's interactions with an application. Machine learning (M/L) models analyze and train on the usage data to predict user behavior patterns, application performance trends and potential data roadblocks. The predicted outputs may be used to generate an adaptive performance policy configured to enable proactive caching decisions and system performance optimizations.

CANCER CLASSIFIER MODELS, MACHINE LEARNING SYSTEMS AND METHODS OF USE

Absstract of: US2025316339A1

Disclosed herein are classifier models, computer implemented systems, machine learning systems and methods thereof for classifying asymptomatic patients into a risk category for having or developing cancer and/or classifying a patient with an increased risk of having or developing cancer into an organ system-based malignancy class membership and/or into a specific cancer class membership.

Method, System, and Computer Program Product for Ensemble Learning With Rejection

Absstract of: US2025315740A1

Methods, systems, and computer program products are provided for ensemble learning. An example system includes at least one processor configured to: (i) generate a rejection region for each baseline model of a set of baseline models (ii) generate a global rejection region based on the rejection regions of each baseline model; (iii) train an ensemble machine learning model; (iv) update, based on a baseline model predictive performance metric for each baseline machine learning model, the set of baseline machine learning models; and (iv) repeat (i)-(iv) until there is a single baseline model in the set of baseline models or a predictive performance or global acceptance ratio of the ensemble model satisfies a threshold.

AN INTELLIGENT CENTRALIZED AGENT FOR AUTONOMOUSLY ORCHESTRATING MULTIPLE DATA TOOLS

Absstract of: WO2025212608A1

An intelligent centralized agent comprising: a dynamic planner; a context short-term memory specific to an interaction session; and at least one data tool that enables the intelligent centralized agent to interact with the application programming interface, the external long-term memory, the machine learning model, and the user interface; wherein the dynamic planner receives input from the application programming interface to make decisions regarding subsequent actions based on the interaction session from the context short term memory, the data tool, and the machine learning model. This unique system provides the intelligent centralized agent with vast access to externally stored data which enables users to resolve questions or queries quickly and reliably in milliseconds.

SIMULATION-DATA BASED PREDICTIVE MODELS FOR GAS SEPARATION PLANTS

Absstract of: WO2025209965A1

The invention concerns a computer-implemented method for predicting performance parameter values of at least one individual gas separation stage, the method comprising: - receiving (162) a set of data points (21), each data point comprising operating parameter values indicative of a configuration or state of a separation stage of a gas separation plant (1800) and comprising performance parameter values obtained by simulating the operation of said separation stage given the operating parameter values of said data point; - using (164) the received set of data points as a training data set in a machine learning process for generating a trained predictive model (1704-1708, 2304); - receiving (166) input parameter values being indicative of one or more operation parameter values for the at least one separation stage; - using (168) the trained model for predicting performance parameter values of the at least one individual separation stage as a function of the input parameter values; and - outputting (170) the predicted performance parameter values for use in the design or control of the single separation stage or of the plant comprising the same.

SYSTEMS AND METHODS FOR MACHINE LEARNING FEATURE GENERATION

Absstract of: WO2024119010A1

A method and apparatus for generating an ML model may include: generating an ML feature template comprising a first grouping of first ML feature variables and a second grouping of second ML feature variables; generating ML features by combining a respective one of each of the first ML feature variables with a respective one of each of the second ML feature variables; training a first ML model utilizing the ML features and first training data to generate an ML output; analyzing the ML output to determine a prediction accuracy of the ML features; based on the prediction accuracy of the ML features, selecting a subset of the ML features; training a second ML model based on the subset of the ML features and the first training data; and providing a network transaction to the second ML model to generate a classification of the network transaction.

CHANNEL RECOMMENDATIONS USING MACHINE LEARNING

Absstract of: CN120283235A

Techniques are discussed herein for generating user profile data, including one or more frequent channels, related users, and/or related topics within a communication platform. In some examples, a machine learning model may receive user interaction data (sent messages, read messages, channel publication, shared documents, frequent keywords used, etc.) associated with a communication platform, and output one or more frequent channels, related users, and/or related topics. The communication platform may then associate the one or more frequent channels, related users, and/or related topics with profile data for the users. In some examples, a communication platform may present different frequent channels, related users, and/or related topics associated with a profile page based on interaction actions associated with a user account viewing the profile page.

SYSTEM AND METHOD FOR PERFORMING FAULT AND EVENT ANALYSIS IN ELECTRICAL SUBSTATIONS

Absstract of: EP4629009A1

The present disclosure describes a system and method performing fault and event analysis in electrical substations is disclosed. The method comprises the step of receiving a disturbance record triggered by an intelligent electronic device (IED) at an electrical substation, preprocessing the received disturbance record to extract at least one variable time series data of plurality of electrical parameters, generating a causality matrix based on the extracted at least one variable time series data by applying causal analysis, predicting, using a Machine learning (ML) module, a fault type at least based on the causality matrix, retrieving, from a knowledge database, a plurality of probable causes corresponding to the predicted fault type, determining at least one exact cause from the plurality of probable causes based on the causal pattern, and providing the fault type, the plurality of probable causes, and the at least one exact cause to a user.

PREDICTION DEVICE, PREDICTION METHOD, AND PREDICTION PROGRAM

Absstract of: EP4629144A1

A prediction device that accurately and efficiently predicts drug discovery of desired drugs as well as efficacy and side effects of drugs by integrating chemical substance information of compounds, information acquired at the time of administration to the cells, and biological or clinical information. The prediction device has an acquisition unit that acquires chemical substance information and pharmacological information of the drug; an estimation unit that estimates estimated information of the drug by performing machine learning using the chemical substance information and pharmacological information; and an output unit that predicts and output both efficacy and side effects of the drug on an organism by retraining a model of the machine learning on the basis of the estimated information.

TEMPORALLY DYNAMIC LOCATION-BASED PREDICTIVE DATA ANALYSIS

Absstract of: US2025307662A1

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing temporally dynamic location-based predictive data analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform temporally dynamic location-based predictive data analysis by using at least one of cohort generation machine learning models and cohort-based growth forecast machine learning models.

COMPUTER-AUTOMATED PROCESSING WITH RULE-SUPPLEMENTED MACHINE LEARNING

Absstract of: US2025307673A1

Various examples are directed to systems and methods for executing a computer-automated process using trained machine learning (ML) models. A computing system may access first event data describing a first event. The computing system may execute a first ML model to determine an ML characterization of the first event using the first event data. The computing system may also apply a first rule set to the first event data to generate a rule characterization of the first event. The computing system may determine an output characterization of the first event based at least in part on the rule characterization of the first event and determine to deactivate the first rule set based at least in part on the ML characterization of the first event.

IDENTIFYING CONTENT ITEMS IN RESPONSE TO A TEXT-BASED REQUEST

Absstract of: US2025307306A1

Systems and methods for responding to a subscriber's text-based request for content items are presented. In response to a request from a subscriber, word pieces are generated from the text-based terms of the request. A request embedding vector of the word pieces is obtained from a trained machine learning model. Using the request embedding vector, a set of content items, from a corpus of content items, is identified. At least some content items of the set of content items are returned to the subscriber in response to the text-based request for content items.

Selectively Providing Machine Learning Model-Based Services

Absstract of: US2025307758A1

An online concierge system provides arrival prediction services for a user placing an order to be retrieved by a shopper. An order may have a predicted arrival time predicted by a model that may err under some conditions. To reduce the likelihood of providing the predicted arrival time (and related services) when the arrival time may be incorrect, the prediction model and related services are throttled (e.g., selectively provided) based on one or more predicted delivery metrics, which may include a time to accept the order by a shopper and a predicted portion of late orders that will be delivered past the respective predicted arrival times. The predicted delivery metrics are compared with thresholds and the result of the comparison used to selectively provide, or not provide, the predicted delivery services.

MACHINE LEARNING-BASED PREDICTION OF MIGRAINE ATTACKS

Nº publicación: WO2025207584A1 02/10/2025

Applicant:

MAYO FOUNDATION FOR MEDICAL EDUCATION AND RES [US]
MAYO FOUNDATION FOR MEDICAL EDUCATION AND RESEARCH