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SYSTEM AND METHOD FOR DETERMINING ONE OR MORE PERFORMANCE INDICATOR VALUES FOR A PLURALITY OF PHYSICAL COMPONENTS IN A TECHNICAL INSTALLATION

Resumen de: WO2025045782A1

Disclosed is an engineering system (102) and a method (400) for determining one or more performance indicator values for a plurality of physical components (108A-108N) in a technical installation (106) to be visualized in a multi-layered manner. The method comprises receiving, by a processing unit (202), a request to determine one or more performance indicator values associated with one or more physical components in the technical installation (106), determining one or more physical components associated with the one or more performance indicators based on analysis of an engineering design and corresponding engineering project of the technical installation, determining a first knowledge graph from a second knowledge graph, and determining, the requested one or more performance indicator values using a trained machine learning model on the first knowledge graph.

ADAPTIVE DATA COLLECTION OPTIMIZATION

Resumen de: US2025077604A1

Systems and methods to intelligently optimize data collection requests are disclosed. In one embodiment, systems are configured to identify and select a complete set of suitable parameters to execute the data collection requests. In another embodiment, systems are configured to identify and select a partial set of suitable parameters to execute the data collection requests. The present embodiments can implement machine learning algorithms to identify and select the suitable parameters according to the nature of the data collection requests and the targets. Moreover, the embodiments provide systems and methods to generate feedback data based upon the effectiveness of the data collection parameters. Furthermore, the embodiments provide systems and methods to score the set of suitable parameters based on the feedback data and the overall cost, which are then stored in an internal database.

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.

Continuous Integration and Automated Testing of Machine Learning Models

Resumen de: US2025077986A1

A system and method are disclosed to generate, modify, and deploy machine learning models. Embodiments include a database comprising historical sales data and a server comprising a processor and memory. Embodiments receive historical sales data comprising aggregated sales data for one or more items sold in one or more stores over one or more past time periods. Embodiments train a first machine learning model to learn model parameters and generate sales predictions by identifying one or more causal factors that influence the sale of one or more items. Embodiments train a second machine learning model, based on the first machine learning model, to generate second predictions. Embodiments evaluate the predictions of the first and second machine learning models as compared to the historical sales data, and deploy the machine learning model that generated the predictions that are closer to the historical sales data to generate one or more subsequent predictions.

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.

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

CELL-FREE DNA SEQUENCE DATA ANALYSIS TECHNIQUES FOR ESTIMATING FETAL FRACTION AND PREDICTING PREECLAMPSIA

Resumen de: AU2023329418A1

In some embodiments, a computer-implemented method of enhancing sequence read data from a cell-free DNA (cfDNA) sample from a subject for predicting a pregnancy-related condition is provided. A computing system determines a coverage profile based on sequence read data for a plurality of informative sites associated with specific tissue types, cell types, or cell states. The computing system generates a prediction of a presence of or an absence of the pregnancy-related condition by providing at least features from a set of features based on a predicted fetal fraction and a set of features based on the coverage profile as input to at least one machine learning model trained to predict a probability of future onset of the pregnancy-related condition based on the features. In some embodiments, a computer-implemented method of enhancing sequence read data for predicting fetal fraction is 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.

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.

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.

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.

SHOPPING BASKET MONITORING USING COMPUTER VISION

Resumen de: US2025078632A1

A system for monitoring shopping carts uses cameras to generate images of the carts moving in a store. In some implementations, cameras may additionally or alternatively be mounted to the shopping carts and configured to image cart contents. The system may use the collected image data, and/or other types of sensor data (such as the store location at which an item was added to the basket), to classify items detected in the shopping carts. For example, a trained machine learning model may classify item in a cart as “non-merchandise,” “high theft risk merchandise,” “electronics merchandise,” etc. When a shopping cart approaches a store exit without any indication of an associated payment transaction, the system may use the associated item classification data, optionally in combination with other data such as cart path data, to determine whether to execute an anti-theft action, such as locking a cart wheel or activating a store alarm. The system may also compare the classifications of cart contents to payment transaction records (or summaries thereof) to, e.g., detect underpayment events.

MACHINE-LEARNED MODEL FOR OPTMIZING SELECTION SEQUENCE FOR ITEMS IN A WAREHOUSE

Resumen de: US2025078025A1

An online shopping concierge system sorts a list of items to be picked in a warehouse by receiving data identifying a warehouse and items to be picked by a picker in the warehouse. The system retrieves a machine-learned model that predicts a next item of a picking sequence of items. The model was trained, using machine-learning, based on sets of data that each include a list of picked items, an identification of a warehouse from which the items were picked, and a sequence in which the items were picked. The system identifies an item to pick first and a plurality of remaining items. The system predicts, using the model, a next item to be picked based on the remaining items, the first item, and the warehouse. The system transmits data identifying the first item and the predicted next item to be picked to the picker in the warehouse.

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.

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.

VIRTUAL INTELLIGENCE AND OPTIMIZATION THROUGH MULTI-SOURCE, REAL-TIME, AND CONTEXT-AWARE REAL-WORLD DATA

Resumen de: US2025068981A1

A system for managing and optimizing real world entities with machine learning algorithms are described including a server computer system configured to store and process input data, the server computer system comprising a memory and a processor; and wherein the memory of the server computer system stores a persistent virtual world system comprising virtual replicas of the real world entities, and wherein the server computer system is configured to generate explicit data sets representing functioning and behavior of the real world entities; train the machine learning algorithms with the explicit data sets to generate trained machine learning data sets; and apply the trained machine learning data sets in an artificial intelligence application to manage operation of the real world entities and generate real behavior data of the real world entities during the operation of the real world entities. Corresponding methods are also described.

SYSTEMS AND METHODS FOR OPTIMAL DEEP LEARNING SIGNAL CLASSIFICATION WITH WAVELET COMPRESSIVE SENSING

Resumen de: US2025071801A1

Systems and methods for operating a quantum processor. The methods comprise: training one or more quantum neural networks using modulation class data to make decisions as to a modulation classification for a signal based on one or more feature inputs for the signal; obtaining, by the quantum processor, principle components of real and imaginary components of a signal received by a communication device; and performing first quantum neural network operations by the quantum processor using the principle components as inputs to the trained one or more quantum neural networks to generate a plurality of scores, wherein each said score represents a likelihood that the received signal was modulated using a given modulation type of a plurality of different modulation types.

SYSTEM AND METHOD FOR CONTROLLING RESOURCE MANAGEMENT USING MACHINE LEARNING

Nº publicación: WO2025042741A1 27/02/2025

Solicitante:

EQUIFAX INC [US]
EQUIFAX INC

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.