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SYSTEM AND METHOD FOR AUTOMATIC GENERATION OF A TARGET MACHINE LEARNING ALGORITHM

Resumen de: US2025077971A1

Method and system for automatic generation of a target machine learning (ML) pipeline for executing a pre-determined task. The method includes accessing candidate ML pipelines, accessing a plurality of variation operators associated with probability distribution functions, accessing training data and iteratively updating the candidate ML pipelines. An update iteration of a given candidate ML pipelines includes applying, in parallel, one or more of the variation operators to the candidate ML pipeline, training the candidate ML pipeline based on the training data, executing the candidate ML pipeline to determine a performance score, dynamically adjusting the probability distribution functions of the one or more variation operators based on the performance scores and selecting a given variation operator based on the probability distribution functions thereof for a current iteration. The method also includes identifying a given candidate ML pipeline having a highest performance score for executing the pre-determined task.

METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT FOR OPTIMIZATION OF DECISION TREE MODELS BASED ON SENSITIVITY COEFFICIENT

Resumen de: WO2025043713A1

Provided is a method for optimization of a decision tree machine learning model based on a sensitivity coefficient that includes receiving a dataset for a population, where the dataset includes a plurality of data instances associated with a plurality of features, generating the decision tree model based on a set of parameters associated with the decision tree model and the dataset, where generating the decision tree model includes determining a measure of a sensitivity coefficient associated with a split of the population for each feature of the plurality of features, determining a plurality of nodes of the decision tree model based on the measure of the sensitivity coefficient associated with the split for each feature of the plurality of features, and performing an action based on an output of the decision tree model that resulted from an input associated with an account. Systems and computer program products are also provided.

An integrated machine-learning framework to estimate homologous recombination deficiency

Resumen de: AU2025201049A1

Abstract Methods, systems, and software are provided for determining a homologous recombination pathway status of a cancer in a test subject, e.g., to improve cancer treatment predictions and outcomes. In some embodiments, classifiers using one or more of (i) a heterozygosity status for DNA damage repair genes in a cancerous tissue, (ii) a measure of the loss of heterozygosity across the genome of the cancerous tissue, (iii) a measure of variant alleles detected in a second plurality of DNA damage repair genes in the genome of the cancerous tissue, (iv) a measure of variant alleles detected in the second plurality of DNA damage repair genes in the genome of a non-cancerous tissue, and (v) tumor sample purity are provided

MEDIA DEVICE ON/OFF DETECTION USING RETURN PATH DATA

Resumen de: US2025077920A1

Example methods disclosed herein include accessing common homes data for a group of common homes, the common homes data including return path data and panel meter data. Disclosed example methods also include accessing common homes data for a group of common homes, the common homes data including first return path data and corresponding panel meter data associated with respective ones of the common homes, grouping the common homes data into view segments, classifying the view segments based on whether the return path data in respective ones of the view segments has matching panel meter data to determine labeled view segments, generating features from the labeled view segments, training a machine learning algorithm based on the features, and applying second return path data to the trained machine learning algorithm to determine whether a media device associated with the second return path data is on or off.

A CHATBOT FOR DEFINING A MACHINE LEARNING (ML) SOLUTION

Resumen de: US2025077915A1

The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.

DEVICE FOR PREDICTING AN OPTIMAL TREATMENT TYPE FOR THE PERCUTANEOUS ABLATION OF A LESION

Resumen de: US2025078979A1

The invention relates to a device for carrying out a method for selecting an optimal treatment for the percutaneous ablation of a lesion within an anatomical structure of interest of a patient. The method uses a machine learning algorithm trained to calculate, from a medical image on which the lesion can be seen, and for each of a plurality of available treatments, a confidence score, the value of which represents a likelihood of success of said treatment for the ablation of the lesion. The machine learning algorithm is trained beforehand using a set of training elements each comprising a medical image on which a lesion can be seen within the anatomical structure of interest of another patient, a treatment selected from the various available treatments for treating said other patient, and a confidence score for the selected treatment on said other patient. The optimal treatment is then selected on the basis of the confidence scores calculated for the different available treatments.

PROVIDING PRIORITIZED PRECISION TREATMENT RECOMMENDATIONS

Resumen de: US2025078974A1

A machine learning-based system, and corresponding methods of use, prioritize therapeutic regimens based on genetic variations to provide ranked treatment recommendations.

SPATIOTEMPORAL TRANSFER MACHINE LEARNING

Resumen de: WO2025046310A1

A computer-implemented, machine learning method for spatiotemporal transfer learning. Sectors of an area are aggregated using preprocessed data from one or more data sources. The sectors are clustered based on different representations obtained for each context feature associated with each of the sectors. One or more context features that have a higher impact on a target feature to be predicted than other context features are identified from a plurality of context features and aggregated to obtain a representation of the area. Using the representation of the area, a particular sector within each of the clustered sectors is selected based on similarity to a respective centroid of the cluster to generate a set of particular sectors. A model associated with a source sector of the set of particular sectors is trained. The method has applications including, but not limited to smart cities, public safety and energy optimization.

INTERNODAL BEHAVIOR PREDICTION IN MACHINE LEARNING MODELS

Resumen de: WO2025048807A1

A method includes accessing a machine learning model, including a plurality of nodes. Each node includes parameters configured to control generation of outputs of the node based on inputs of the node. A first input to the first node corresponding to a first data source is accessed. The parameters of a first node of the network are adjusted in accordance with an objective function configured to decrease an expected difference between a future first output and a future first input to the first node. The first output is generated based on the first input and the parameters of the first node. The first output is used as an input to the first data source. An inference is generated using the machine learning model, based on the first output.

DISCRETE INTERNODAL INPUT/OUTPUT FOR MACHINE LEARNING

Resumen de: WO2025048805A1

A method includes obtaining one or more inputs of a node of a network of a machine learning model, each input of the node having a respective input value, each input value being a member of a finite set of discrete values. The one or more inputs of the node are processed to generate one or more outputs of the node, each output having a respective output value, each output value being generated as a member of the finite set of discrete values by quantization. The machine learning model is used to generate inference data based at least in part on the one or more outputs of the node.

QUERY PERFORMANCE DISCOVERY AND IMPROVEMENT

Resumen de: US2025077515A1

Embodiments analyze a query pattern of an incoming query on a database, perform a semantic analysis of the query pattern of the incoming query, generate a re-write query that has an improved query performance in comparison to a query performance of the incoming query based on the analyzed query pattern and the semantic analysis; build a query model using machine learning based on at least one of the query pattern and the semantic analysis; and apply the re-write query by performing the re-write query on the database to provide the improved query performance.

TRAINING MACHINE LEARNING MODELS FOR AUTOMATED COMPOSITION GENERATION

Resumen de: US2025077831A1

A process for automated story generation can comprise receiving, via at least one computing device, interaction data associated with an entity and a physical environment. Based on the interaction data, the at least one computing device can determine that at least one event occurred based on the interaction data. The at least one computing device can execute a trained machine learning model on the interaction data to generate an output comprising one or more interests. The at least one computing device can generate a composition comprising an audio element and a visual element based on the output.

SYSTEM AND METHOD FOR MENTAL HEALTH DISORDER DETECTION SYSTEM BASED ON WEARABLE SENSORS AND ARTIFICIAL NEURAL NETWORKS

Resumen de: US2025078998A1

According to various embodiments, a machine-learning based system for mental health disorder identification and monitoring is disclosed. The system includes one or more processors configured to interact with a plurality of wearable medical sensors (WMSs). The processors are configured to receive physiological data from the WMSs. The processors are further configured to train at least one neural network based on raw physiological data augmented with synthetic data and subjected to a grow-and-prune paradigm to generate at least one mental health disorder inference model. The processors are also configured to output a mental health disorder-based decision by inputting the received physiological data into the generated mental health disorder inference model.

Unsupervised Machine Learning Methods

Resumen de: US2025078957A1

The present disclosure provides systems and methods for classifying lupus disease state of a patient is disclosed. The method can include analyzing a patient data set comprising or derived from gene expression measurements data of at least 2 genes, from a biological sample obtained or derived from the patient, to classify the lupus disease state of the patient. The at least 2 genes can be selected from Tables 17-1 to 17-30, and/or Tables 24-1 to 24-30.

MACHINE-LEARNING-BASED MEAL DETECTION AND SIZE ESTIMATION USING CONTINUOUS GLUCOSE MONITORING (CGM) AND INSULIN DATA

Resumen de: WO2023212738A1

Disclosed is a meal detection and meal size estimation machine learning technology. In some embodiments, the techniques entail applying to a trained multioutput neural network model a set of input features, the set of input features representing glucoregulatory management data, insulin on board, and time of day, the trained multioutput neural network model representing multiple fully connected layers and an output layer formed from first and second branches, the first branch providing a meal detection output and the second branch providing a carbohydrate estimation output; receiving from the meal detection output a meal detection indication; and receiving from the carbohydrate estimation output a meal size estimation.

SYSTEM AND METHOD FOR AUTOMATIC GENERATION OF A TARGET MACHINE LEARNING ALGORITHM

Resumen de: EP4517595A1

Method and system for automatic generation of a target machine learning (ML) pipeline for executing a pre-determined task. The method includes accessing candidate ML pipelines, accessing a plurality of variation operators associated with probability distribution functions, accessing training data and iteratively updating the candidate ML pipelines. An update iteration of a given candidate ML pipelines includes applying, in parallel, one or more of the variation operators to the candidate ML pipeline, training the candidate ML pipeline based on the training data, executing the candidate ML pipeline to determine a performance score, dynamically adjusting the probability distribution functions of the one or more variation operators based on the performance scores and selecting a given variation operator based on the probability distribution functions thereof for a current iteration. The method also includes identifying a given candidate ML pipeline having a highest performance score for executing the pre-determined task.

METHOD FOR ESTIMATING TIME PERIOD UNTIL EMPTY FOR MATERIAL IN A TANK OF VEHICLE

Resumen de: EP4517611A1

During an initialization period of a machine learning model, an electronic data processor is configured to estimate an initial depletion estimate of time period until empty for a material in the tank or container of a machine based on summing initial weighted inputs to the machine learning model in accordance with an initial equation set being applicable to the initialization period that is defined by an initial sub-operation period. After the initialization period of the machine learning model, an electronic data processor is configured to estimate a revised depletion time, where the revised depletion time comprises a time duration until empty or near empty.

MACHINE LEARNING MODEL POSITIONING PERFORMANCE MONITORING AND REPORTING

Resumen de: CN119054306A

Techniques for wireless communication are disclosed. In an aspect, a network entity receives, from a user equipment (UE), a provision location information message including one or more location estimates derived by the UE during one or more location inference occasions of a machine learning model, wherein the machine learning model applies to one or more measurements of a wireless channel between the UE and a network node during each of the one or more positioning inference occasions, and transmitting a performance report, the performance report indicates that the machine learning model derives performance of the one or more positioning estimates at least during the one or more positioning inference opportunities.

A METHOD AND ELECTRONIC DEVICE FOR SECURE ON-DEVICE STORAGE FOR MACHINE LEARNING MODELS

Resumen de: US2024152283A1

A method for performing an inference includes: detecting a context among at least one context associated with at least one application; triggering a model execution command to a smart agent of an electronic device, based on the detected context; loading a machine learning (ML) model into a secure storage of the electronic device, based on the detected context and the triggered model execution command; generating, using the loaded ML model, an inference, based on data associated with the detected context; and sharing the generated inference with each application of the at least one application that is registered for the detected context.

Deep Learning Methods For Biosynthetic Gene Cluster Discovery

Resumen de: US2025069701A1

The present disclosure relates to computer-implemented methods and systems for identifying biosynthetic gene clusters (BGCs) that encode pathways for the production of secondary metabolites. Secondary metabolites that target genes or gene products that are homologous to, e.g., human genes or gene products may have utility as potential drug compounds.

SYSTEM AND METHOD FOR CONTROLLING RESOURCE MANAGEMENT USING MACHINE LEARNING

Resumen de: WO2025042741A1

In some aspects, a computing system can use a machine learning model for resource management. For example, the system can receive a request for a set of steps associated with a target model output of a machine learning model. The request can include a starting input feature set and a number of steps. For each of the number of steps, the system can calculate a change to one or more features from the starting input feature set to arrive at the target model output based on a current position in feature space of the machine learning model. The system can update a feature vector by applying the change to the features of the starting input feature set and transmitting the set of steps. The system can then cause a resource of the external computing system to transition toward a position defined by the target model output.

Methods and Systems for Discovery of Embedded Target Genes in Biosynthetic Gene Clusters

Resumen de: US2025069699A1

The present disclosure relates to computer-based methods and systems for identifying genes associated with biosynthetic gene clusters (BGCs), including embedded target genes (ETaGs) that are homologs of potential therapeutic targets, using comparative genomics techniques and machine learning models.

GUI for Interacting with Analytics Provided by Machine-Learning Services

Resumen de: US2025068978A1

A data pipeline tool provides a machine-learning design interface that a user can utilize (e.g., via an electronic device such as a personal computer, tablet, or smart phone) to design or configure data pipelines or workflows defining the manner in which ML models are developed, trained, tested, validated, or deployed. Once deployed, a designed ML model may generate predictive results based on input data fed to the ML model. The tool may present the predictive results via a GUI, and may enable a user to mark-up or otherwise interact with those predictive results. The tool may enable the user to share the results (which may include a mark-up or annotation provided by a user).

FIELD PUMP EQUIPMENT SYSTEM

Resumen de: US2025067164A1

A method can include, receiving by a computational device at a wellsite, real-time, time series data from pump equipment at the wellsite, where the wellsite includes a wellbore in contact with a fluid reservoir; using the computational device, processing the time series data as input to a trained machine learning model to detect a performance issue of the pump equipment; and issuing a signal responsive to detection of the performance issue.

TRAINING OR USING SETS OF EXPLAINABLE MACHINE-LEARNING MODELING ALGORITHMS FOR PREDICTING TIMING OF EVENTS

Nº publicación: US2025068944A1 27/02/2025

Solicitante:

EQUIFAX INC [US]
EQUIFAX INC

Resumen de: US2025068944A1

Certain aspects involve building timing-prediction models for predicting timing of events that can impact one or more operations of machine-implemented environments. For instance, a computing system can generate program code executable by a host system for modifying host system operations based on the timing of a target event. The program code, when executed, can cause processing hardware to a compute set of probabilities for the target event by applying a set of trained timing-prediction models to predictor variable data. A time of the target event can be computed from the set of probabilities. To generate the program code, the computing system can build the set of timing-prediction models from training data. Building each timing-prediction model can include training the timing-prediction model to predict one or more target events for a different time bin within the training window. The computing system can generate and output program code implementing the models' functionality.