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SYSTEM AND METHOD FOR PREDICTING INSULIN RESISTANCE OR PANCREATIC BETA-CELL FUNCTION AND COMPUTER READABLE MEDIUM THEREOF

Resumen de: US2025308709A1

A system and a method for predicting insulin resistance and/or pancreatic β-cell function are provided, where a machine learning model is utilized to predict insulin resistance and/or pancreatic a decline of β-cell function of a subject in need thereof based on a feature set extracted from a database. Therefore, clinicians or the subject can be warned to take necessary actions on, and adjust related medical treatment or lifestyle before the subject is diagnosed with diabetes mellitus. In addition, a computer readable medium thereof is also provided.

TRAINING DATA GENERATING PROGRAM, MACHINE LEARNING PROGRAM, ESTIMATION PROGRAM, METHOD, AND DEVICE

Resumen de: WO2025204495A1

An information processing device according to the present invention extracts, from combinations of words included in each of a plurality of sentences to which a teacher label is assigned, a combination of words in which an index value indicating a relation between the combination of words and the teacher label satisfies a predetermined condition, determines whether or not the extracted combination of words is included in the same context in each of the plurality of sentences, and generates training data associated with the teacher label by using, as a feature, presence/absence of the combination of words determined to be included in the same context, in each of the plurality of sentences.

MACHINE LEARNING ACCELERATED SEMANTIC EQUIVALENCE DETECTION

Resumen de: US2025307245A1

Examples detect equivalent subexpressions within a computational workload. Examples include converting a query plan tree associated with a first subexpression into a matrix. The first subexpression is a portion of a database query from the computational workload. Each node in the query plan tree is represented as a row of the matrix. The matrix is converted into a first vector. The first subexpression is determined to be equivalent to a second subexpression by comparing the first vector to a second vector associated with the second subexpression. The comparison includes computing a distance between the first and second vectors that is lower than a distance threshold. The computational workload is modified, based on the determining, to perform the first subexpression and exclude performance of the second subexpression as duplicative.

MACHINE LEARNING-BASED PREDICTION OF MIGRAINE ATTACKS

Resumen de: WO2025207584A1

Onset and/or continuation of a migraine attack is predicted in a subject using a machine learning model. Subject health data are accessed with a computer system, where the subject health data include clinical test or measurement data received from the subject and/or subject symptom data received from the subject. A trained machine learning model is accessed with the computer system, where the trained machine learning model has been trained on training data to predict a likelihood of migraine attack occurring within a specified timeframe based on features in subject health data. The subject health data are input to the trained machine learning model using the computer system, generating classified feature data as an output. The classified feature data indicate a likelihood of the subject having a migraine attack within the specified timeframe.

Selectively Providing Machine Learning Model-Based Services

Resumen de: 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.

BATTERY LIFESPAN ESTIMATION DEVICE AND METHOD

Resumen de: EP4624960A1

A battery life estimation apparatus includes a charging/discharging unit configured to charge/discharge a battery and a controller configured to calculate a partial accumulative capacity corresponding to a partial voltage period defined as a period from a first voltage to a second voltage by charging/discharging the battery in the partial voltage period, and estimate a life corresponding to an entire voltage period of the battery by inputting the first voltage, the second voltage, and the partial accumulative capacity to an estimation model trained based on machine learning.

Method and apparatus for support of AI/ML-based positioning

Resumen de: GB2639745A

According to various examples of the present disclosure, there is provided a location management function (LMF) entity configured to: subscribe to or request artificial intelligence/machine learning (AI/ML) -related services from a network data analytics function (NWDAF) entity in relation to an AI/ML model for determining positioning of a user equipment (UE); and receive, from the NWDAF entity, an indication that training has been performed for the AI/ML model. According to various examples of the present disclosure, there is provided a network data analytics function (NWDAF) entity configured to: receive, from a location management function (LMF) entity, a subscription to or request for artificial intelligence/machine learning (AI/ML) -related services in relation to an AI/ML model for determining positioning of a UE; obtain data for training the AI/ML model from at least one other entity; train the AI/ML model based on the obtained data; and transmit, to the LMF entity, an indication that training has been performed for the AI/ML model; wherein the NWDAF entity includes a model training logical function (MTLF).

MACHINE LEARNING ACCELERATED SEMANTIC EQUIVALENCE DETECTION

Resumen de: EP4625199A1

Examples detect equivalent subexpressions within a computational workload. Examples include converting a query plan tree associated with a first subexpression into a matrix. The first subexpression is a portion of a database query from the computational workload. Each node in the query plan tree is represented as a row of the matrix. The matrix is converted into a first vector. The first subexpression is determined to be equivalent to a second subexpression by comparing the first vector to a second vector associated with the second subexpression. The comparison includes computing a distance between the first and second vectors that is lower than a distance threshold. The computational workload is modified, based on the determining, to perform the first subexpression and exclude performance of the second subexpression as duplicative.

GERIATRIC FUNCTIONAL ASSESSMENT SYSTEM USING PASSIVE WEARABL SENSING AND DEEP LEARNING

Resumen de: WO2025199137A1

A system may include a wearable device capable of collecting visual data, motion data, or a combination thereof. A system may include a processor for analyzing the data to assess physical functioning.

COMPUTATIONAL-BASED PREDICTIONS OF POST-INDUCTION HYPOTENSION

Resumen de: WO2025199425A1

A method for predicting a mean blood pressure (MBP) post induction and associated with post-induction hypotension (PIH) in a patient is presented. The method includes accessing a data set including demographics and clinical data. The demographics and clinical data include a set of preoperative input variables and anesthetic induction input variables. The method includes refining a set of hyperparameters associated with a machine¬ learning model trained to generate a prediction of MBP post induction. The process includes preprocessing the data set by executing encoding processes on each of the set of preoperative input variables and anesthetic induction input variables, calculating cross- validation losses based on the preprocessed set of preoperative input variables and anesthetic induction input variables and a predetermined set of target MBP values, updating the set of hyperparameters based on the cross-validation losses, and outputting the prediction of MBP post induction based on the updated set of hyperparameters.

SYSTEMS AND METHODS FOR USING MACHINE LEARNING TO PREDICT CRITICAL CONSTRAINTS

Resumen de: AU2024229742A1

A computer-implemented method and computer program product for predicting a required committed capacity of an electric utility are provided. The method includes the steps of: (a) performing a stochastic optimization of raw data to produce a total committed capacity from conventional thermal units as a target data, wherein the raw data comprises grid operating conditions; (b) combining the total committed capacity from conventional thermal units with raw features and engineered features to generate training data; (c) training a machine learning model for predicting the required committed capacity of the electric utility using the generated training data; (d) predicting the required committed capacity of the electric utility using the trained machine learning model; and (e) running an augmented version of a deterministic dispatch optimization model based on the predicted required committed capacity of the electric utility. The computer program performs the aforementioned steps.

SYSTEMS AND METHODS TO PROCESS ELECTRONIC IMAGES FOR DETERMINING TREATMENT

Resumen de: US2025299802A1

A computer-implemented method for processing digital pathology images, the method including receiving a plurality of digital pathology images of at least one pathology specimen, the pathology specimen being associated with a patient. The method may further include determining receiving metadata corresponding to the plurality of digital pathology images, the metadata comprising data regarding previous medical treatment of the patient. Next, the method may include providing the medical images and metadata as input to a machine learning system, the machine learning system having been trained by receiving as input historical treatment information and digital images labeled with a predicted treatment regimen. Lastly, the method may include outputting, by the machine learning system, a treatment effectiveness assessment.

Method for Obtaining Domain-Informed ML/AI Model, Method for Analysing and/or Predicting Drive System and/or Drive Apparatus Behavior, Control Apparatus, Drive Application System, and Computer Program Product

Resumen de: US2025299017A1

A method for obtaining a domain-informed machine learning/artificial intelligence, ML/AI, model for drive analytics includes obtaining first data indicative of a set of data points, wherein each data point is associated with a behavior of a drive apparatus and/or drive system. The method further comprises obtaining second data indicative of domain knowledge comprising physics knowledge associated with a behavior of the drive apparatus and/or drive system and/or with an environment of the drive apparatus and/or drive system. The method further comprises training a machine learning/artificial intelligence, ML/AI, model by jointly utilizing the first data and the second data to obtain the domain-informed ML/AI model for drive analytics.

Product Metrics Monitoring and Anomaly Detection Using Machine Learning Models

Resumen de: US2025301187A1

A method may include determining a combination of values of attributes represented by reference data associated with computing devices by training a machine learning model based on an association between (i) respective values of the attributes and (ii) the computing devices entering a device state. The combination may be correlated with entry into the device state. The method may also include selecting a subset of the computing devices that is associated with the combination of values. The method may additionally include determining a first rate at which computing devices of the subset have entered the device state during a first time period and a second rate at which one or more computing devices associated with the combination have entered the device state during a second time period, and generating an indication that the two rates differ.

SECURE AND PRIVATE HYPER-PERSONALIZATION SYSTEM AND METHOD

Resumen de: US2025298920A1

A secured virtual container is enabled to securely store personal data corresponding to a user, where such data is inaccessible to processes running outside the secured virtual container. The secured virtual container may also include an execution environment for a machine learning model where the model is securely stored and inaccessible. Personal data may be feature engineered and provided to the machine learning model for training purposes and/or to generate inference values corresponding to the user data. Inference values may thereafter be relayed by a broker application from the secured virtual container to applications external to the container. Applications may perform hyper-personalization operations based at least in part on received inference values. The broker application may enable external applications to subscribe to notifications regarding availability of inference values. The broker may also provide inference values in response to a query.

GENERATING AND UTILIZING PERFORATIONS TO IMPROVE DECISION MAKING

Resumen de: US2025299070A1

An embodiment for managing machine learning models to generate and utilize perforations within machine learning models to improve their ability to consider and learn from exception decisions. The embodiment may detect an exception decision in a base model. The embodiment may automatically determine a feature associated with the base model in making the exception decision. The embodiment may automatically identify a remaining additional feature in making the exception decision, and generating a perforation corresponding to the remaining additional feature. The embodiment may, in response to detecting a subsequent decision including a shared additional feature to the generated perforation, automatically validate a feature boundary within the generated perforation. The embodiment may automatically outputting a decision recommendation for the subsequent decision using both the base model and the generated perforation.

SYSTEMS AND METHODS FOR RESOURCE ALLOCATION

Resumen de: US2025299088A1

An apparatus for resource allocation, may include at least a processor; and a memory communicatively connected to the at least processor, wherein the memory contains instructions configuring the at least processor to receive a resource datum and a periodic activity pattern datum; identify input metadata as a function of the resource datum and the periodic activity pattern datum; select a resource allocation machine learning model as a function of the input metadata; and generate a resource allocation datum using the resource allocation machine learning model.

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

Resumen de: US2025299231A1

Systems and apparatuses for generating object dimension outputs and predicted object outputs are provided. The system may collect an image from a mobile device. The system may analyze the image to determine whether it contains one or more standardized reference objects. Based on analysis of the image and the one or more standardized reference objects, the system may determine an object dimension output. The system may also determine a predicted object output that includes additional objects predicted to be in a room corresponding to the image. Using object dimension outputs and the predicted object output, the system may determine an estimated repair cost.

SYSTEMS AND METHODS TO PROCESS ELECTRONIC IMAGES FOR DETERMINING TREATMENT

Resumen de: US2025299803A1

A computer-implemented method for processing digital pathology images, the method including receiving a plurality of digital pathology images of at least one pathology specimen, the pathology specimen being associated with a patient. The method may further include determining receiving metadata corresponding to the plurality of digital pathology images, the metadata comprising data regarding previous medical treatment of the patient. Next, the method may include providing the medical images and metadata as input to a machine learning system, the machine learning system having been trained by receiving as input historical treatment information and digital images labeled with a predicted treatment regimen. Lastly, the method may include outputting, by the machine learning system, a treatment effectiveness assessment.

SYSTEM AND METHODS FOR PREDICTING FEATURES OF BIOLOGICAL SEQUENCES

Resumen de: US2025299780A1

Techniques for predicting performance of biological sequences. The technique may include using a statistical model configured to generate output indicating predictions for an attribute of biological sequences, the biological sequences generated using a machine learning model trained on training data. The statistical model is configured to allow for at least some of the predictions to occur outside a distribution of labels in the training data.

HELLINGER DECISION TREES FOR FRAUD DETECTION

Resumen de: AU2024234871A1

A Hellinger decision tree can detect fraudulent transactions in a data set of financial transactions. Applying the Hellinger decision tree uses a Hellinger distance. The Hellinger decision tree can be part of a machine learning algorithm. In an example, the Hellinger decision tree is a positive and unbalanced Hellinger decision tree used with an imbalanced positive and unlabeled data.

METHOD FOR OBTAINING DOMAIN-INFORMED ML/AI MODEL, METHOD FOR ANALYSING AND/OR PREDICTING DRIVE SYSTEM AND/OR DRIVE APPARATUS BEHAVIOR, CONTROL APPARATUS, DRIVE APPLICATION SYSTEM, AND COMPUTER PROGRAM PRODUCT

Resumen de: EP4621643A1

A method for obtaining a domain-informed machine learning/artificial intelligence, ML/AI, model for drive analytics. The method comprises obtaining first data indicative of a set of data points, wherein each data point is associated with a behavior of a drive apparatus and/or drive system. The method further comprises obtaining second data indicative of domain knowledge comprising physics knowledge associated with a behavior of the drive apparatus and/or drive system and/or with an environment of the drive apparatus and/or drive system. The method further comprises training a machine learning/artificial intelligence, ML/AI, model by jointly utilizing the first data and the second data to obtain the domain-informed ML/AI model for drive analytics.

A SELF-ADAPTIVE FAULT CORRELATION SYSTEM BASED ON CAUSALITY MATRICES AND MACHINE LEARNING

Nº publicación: EP4620171A1 24/09/2025

Solicitante:

ALTICE LABS S A [PT]
Altice Labs, S.A

Resumen de: WO2024104614A1

The present invention describes a self-adaptive system capable of extracting correlations between multiple faults from network topologies, with the innovative component being the data pre-processing phase generating causality matrices to provide as an input to ML models. The proposed fault correlation system is responsible for, without any configuration, identi fying the hierarchical relationships between the multiple alarms, allowing for a better understanding of the causality and impact of each mal function, hence assisting the implementation of RCA rules. This allows, not only for a huge dimensionality reduction of alarms needed to be processed by a TO ' s, but also signi ficantly increases the knowledge about the topology, thus reducing downtime and increasing the quality of service of the network and services.