Alerta

METHOD FOR DETERMINING AN OPTIMAL INVERSION TIME FOR AN "INVERSION RECOVERY" RADIO FREQUENCY PULSE SEQUENCE OF A MAGNETIC RESONANCE FOR ACQUIRING LATE IMAGES AFTER ADMINISTERING A PARAMAGNETIC CONTRAST MEDIUM

Resumen de: US20260338896A1

A method for determining an optimal inversion time for an “Inversion Recovery” radio frequency pulse sequence of a magnetic resonance for acquiring late images after administering a paramagnetic contrast medium by a control and image capture device associated with a magnet structure of a magnetic resonance apparatus and connected to a computer adapted to exchange information and instructions with the control and image capture device is described, the method including a training sequence including optimizing a machine learning model starting from a plurality of calculated optimal inversion time values correlated to significant data of a related examined subject and specific parameters of a carried-out examination as established in the course of a plurality of sample assessments; and a calculation sequence including a first step wherein significant data of a subject to be examined and specific parameters of an examination carried out by the magnetic resonance apparatus are collected.

Method of Designing Holographic Optical System and Holographic Optical System Designing Program

Resumen de: US20260340229A1

A method of designing a holographic optical system using a holographic optical system model includes: a step of receiving an optical condition; a step of generating a holographic optical system model; a step of setting an initial phase distribution to each of the plurality of holographic optical elements; and a step of performing machine learning for the holographic optical system model.

MACHINE LEARNING TO PREDICT THERAPY PROGRESSION

Resumen de: US20260341457A1

Techniques for progress prediction using machine learning are provided. User data describing a user associated with a therapy plan is received, and a set of features is generated by applying one or more preprocessing transformations to the user data. A predicted therapy progress is generated for the user by processing the set of features using a trained machine learning model, where the predicted therapy progress comprises a stage, of a defined set of stages, associated with the therapy plan. Enrollment of the user in the therapy plan is facilitated based on the predicted therapy progress.

DECISION MAKING BASED ON INTERDEPENDENCIES BETWEEN DIFFERENT LEVELS RELATED TO AN ANOMALY IN COMMUNICATION NETWORK

Resumen de: US20260340340A1

A computer-implemented method performed by a computing device includes a global machine learning, ML model in a communication network that includes a plurality of different levels. The method includes identifying interdependencies between the plurality of different levels related to at least one anomaly in at least one of the different levels. The method further includes deciding, per level of the plurality of different levels, an action to take responsive to the identification of the interdependencies between the plurality of different levels related to at least one anomaly in at least one of the different levels. Related methods and apparatus are also provided.

METHOD FOR DETERMINING POLICY FOR SON AND ELECTRONIC DEVICE USING SAME

Resumen de: US20260340408A1

0000 A method for determining a policy for a Self-Organizing Network (SON), includes: receiving performance management (PM) data of a communication network; generating feature data indicating the PM data by using a deep learning-based encoder; determining an outlier score of the feature data by using a deep learning-based outlier detection model; and selecting, based on the outlier score, one of an artificial intelligence-based policy or a non-artificial intelligence-based policy for performing the SON in the communication network.

METHODS AND SYSTEMS OF FACILITATING A HEALTH ASSESSMENT

Resumen de: US20260341513A1

The present disclosure provides a method of facilitating a health assessment. Further, the method may include receiving a medical inquiry. Further, the medical inquiry may be generated by a user. Further, the method may include analyzing the medical inquiry. Further, the method may include generating an assessment query based on the analyzing. Further, the generation of the assessment query may be based on one or more of a template and a first machine learning model. Further, the template includes a standardized assessment query. Further, the method may include transmitting the assessment query. Further, the method may include receiving a response from the healthcare communication infrastructure. Further, the response corresponds. Further, the method may include analyzing the response. Further, the method may include determining a diagnosis data based on the analyzing. Further, the method may include transmitting the diagnosis data.

METHOD AND SYSTEM FOR GENERATING ADAPTIVE POWER DELIVERY NETWORK IN INTEGRATED CIRCUIT LAYOUT DIAGRAM

Resumen de: US20260340258A1

The present disclosure provides a method, which includes the following steps: obtaining a netlist of an integrated circuit (IC) design; performing an automatic placement and routing (APR) process on the netlist to generate a result layout diagram; and during each operation with the APR process, refining, using a machine-learning model, a power delivery network within a layout diagram generated at each operation within the APR process.

MACHINE-LEARNING TECHNIQUES FOR PREDICTING UNOBSERVABLE OUTPUTS

Resumen de: US20260340509A1

In some aspects, a computing system can generate and optimize a machine learning model to estimate an unobservable capacity of a target system or entity. The computing system can access training vectors which include training predictor variables, training performance indicators, and task quantities. A training performance indicator indicating performance outcome corresponding to the predictor variables and a task quantity associated with a task assigned to the target entity that leads to the training performance indicator. The machine learning model can be trained by performing adjustments of parameters of the machine learning model to minimize a loss function defined based on the training vectors. The trained machine learning model can be used to estimate the capacity of the target system or entity for handling tasks and be used in assigning tasks to the target entity according to the determined capacity.

METHODS AND SYSTEMS FOR LEARNING SAFE DRIVING PATHS

Resumen de: US20260336664A1

Systems and methods for navigating an autonomous vehicle through autonomous driving adversity conditions may include: receiving sensor data from one or more vehicle sensors of the autonomous vehicle, the sensor data including image data depicting surroundings of the autonomous vehicle and inertial measurement data indicating positions of the autonomous vehicle; detecting, by a first machine learning model, based on the sensor data, that the autonomous vehicle is encountering an autonomous driving adversity condition, wherein the autonomous driving adversity condition includes a condition that increases a likelihood of failure for the autonomous driving system in determining a safe driving trajectory; predicting, by a second machine learning model, responsive to detecting the autonomous driving adversity condition, a navigation trajectory for the autonomous vehicle to follow through the autonomous driving adversity condition; and controlling the autonomous vehicle according to the predicted navigation trajectory

Tiered Anomaly Detection

Resumen de: US20260340444A1

For each corresponding configuration item type of a plurality of different configuration item types, a corresponding multi-variate machine learning model of a plurality of multi-variate machine learning models is trained to perform anomaly detection for a corresponding configuration item type of the plurality of different configuration item types. In response to detecting, via a univariate machine learning model, an anomaly associated with a specific configuration item type of the plurality of different configuration item types, an execution of a particular multi-variate machine learning model of the plurality of multi-variate machine learning models is initiated for the specific configuration item type. An output of the execution of the particular multi-variate machine learning model is evaluated to determine an anomaly detection result.

GENERATING HEURISTICS USING WEAKLY-SUPERVISED MACHINE LEARNING AND DIGITAL TWIN SIMULATIONS

Resumen de: US20260340476A1

A computer-implemented method for generating and/or adjusting a heuristic function for a machine learning prediction. A machine learning prediction task including a target entity and attribute is received. Semantic relations are explored to generate relevant entities and attributes related to the target entity and attribute. The heuristic function is generated and/or adjusted based on the relevant entities and attributes.

PARALLELIZED RULES-BASED AND MACHINE LEARNING-BASED GROUPING ANALYSIS AND PREDICTION

Resumen de: US20250209091A1

0000 Among other techniques, techniques for parallelized rules-based and machine learning-based grouping are described. An example method includes: identifying at least two different data records of a plurality of different data records, wherein each data record is associated with a respective entity, wherein each data record includes a plurality of respective record fields and corresponding record field values, and wherein at least a first record field value of a first data record is different from a corresponding first record field value of a second data record; determining, based on a plurality of different grouping rules, whether the respective entity associated with the first data record and the respective entity associated with the second data record comprise a same entity; determining, based on one or more machine learning models, whether the respective entity associated with the first data record and the respective entity associated with the second data record comprise the same entity, wherein the grouping rules-based determination and the machine learning model-based determination are determined in parallel; presenting, in response to the grouping rules-based determination indicating the respective entities are the same entity, a first graphical user interface element of a graphical user interface; presenting a second graphical user interface element of the graphical user interface indicating whether the machine learning-based determination indicates that the resp

SYSTEM AND METHOD OF HIERARCHICAL INFERENCING IN MACHINE LEARNING SYSTEMS

Resumen de: WO2025137194A1

A method of hierarchical inferencing which may include: by a camera, capturing an image of a scene; by at least one computing device: based on a first reduced resolution image of the scene, determining a first probability value of a presence of a specified feature in the reduced resolution image; if the first probability value is greater than a first probability threshold: based on the image or a second reduced resolution image of the scene, determining a second probability value of a presence of the specified feature in the image or the second resolution image; and if the second probability value is greater than a second probability threshold which is greater than the first probability threshold, transmitting a notification that the specified feature is present in the image or the second resolution image.

ARTIFICIAL INTELLIGENCE-BASED DYNAMIC ADAPTATION IN QUANTUM KEY DISTRIBUTION WITHIN TELECOMMUNICATION NETWORKS

Resumen de: EP4832410A1

A computer-implemented method for performing quantum key distribution, QKD, in a telecommunication network between a first communication device and a second communication device, the method comprising:(a) measuring or monitoring, by a monitoring entity, quantum link values of a QKD process carried out with a first set of QKD operational parameters;(b) detecting, by an analytics entity, based on the monitored quantum link values, one or more operational conditions selected from: a condition, in particular a degradation, of the quantum link, a threat to the QKD, or a mobility event affecting a QKD communication party;(c) providing, in response to the detected at least one operational condition, at least a subset of the monitored quantum link values to a dynamic-adaptation-module, a threat-detection-module, a rerouting-module, or a hierarchical-control-module one or all of the modules are implemented within an adaptive-QKD node;(d) determining, by one or more of the modules, a second set of QKD operational parameters based on a one or more machine learning models trained on quantum link data and at least a subset of the quantum link values as input;(e) applying, by an operational module configured to interface with QKD hardware, the second set of QKD operational parameters to adjust the QKD process,wherein the second set of QKD operational parameters enables QKD to be maintained or improved.

SYSTEMS, APPARATUSES, METHODS, AND COMPUTER PROGRAM PRODUCTS FOR ROOT CAUSE IDENTIFICATION AND GUIDANCE FOR ABNORMAL PROCESS CONDITIONS IN PROCESS PLANTS

Resumen de: EP4831760A1

0001 Embodiments of the present disclosure provide techniques for identifying root causes and generating guidance for abnormal conditions in process plants. Plant data associated with a process plant may be obtained. A knowledge graph may be generated based on the plant data. The knowledge graph may include a plurality of operator action nodes corresponding to a plurality of operator actions associated with an abnormal condition. An operator action data may be generated for the abnormal condition based on the knowledge graph. The operator action data may comprise the plurality of operator actions. A set of corrective action may be generated using one or more machine learning models and based on the operator action data. The set of corrective actions may include a set of unique operator actions from the operator action data. The set of corrective actions may be provided for display on a user device.

ARTIFICIALLY INTELLIGENT SYSTEM AND METHOD FOR PROGNOSTIC EVALUATION OF AUTOIMMUNE DISORDERS

Resumen de: US20260331289A1

An artificially intelligent system and method for prognostic evaluation of autoimmune disorders. Certain objects and advantages of the present disclosure include methods and systems configured to aggregate and analyze clinical conversational data, patient biometric data, medical records data, blood biomarker test data, and patient biometric data to tune metrics that are increasingly specific to a patient in the creation of a dynamic patient phenotype to enhance clinical understanding of one or more factors that are specific to the patient's health status. One or more Al framework and engine may facilitate effective integration of communication-related insights into diagnostic and prognostic digital health resources. Exemplary systems, methods, and apparatuses according to the principles herein may comprise machine learning and deep learning techniques for the development of one or more quantitative metrics derived from clinical conversational data.

PAVEMENT MANAGEMENT SYSTEM USING SATELLITE DATA AND MACHINE LEARNING

Resumen de: US20260330619A1

0000 Various examples are provided related to pavement management for maintenance and rehabilitation of roadways. In one example, a method includes obtaining satellite imagery including a portion of a roadway; determining a reflective value corresponding to the portion of the roadway; determining a pavement condition of the portion of the roadway based at least in part upon the reflective value; and identifying a pavement management strategy based at least in part upon the pavement condition. The pavement condition can be determined based upon pavement surface and subsurface conditions of the portion of the roadway. The pavement condition can be determined by a machine learning model trained using a combination of measured, synthesized and/or feature generated training data.

ARTIFICIAL INTELLIGENCE SYSTEM PROVIDING INTERACTIVE MODEL INTERPRETATION AND ENHANCEMENT TOOLS

Resumen de: US20260330260A1

An interactive interpretation session with respect to a first version of a machine learning model is initiated. In the session, indications of factors contributing to a prediction decision are provided, as well indications of candidate model enhancement actions. In response to received input, an enhancement action is implemented to obtain a second version of the model. The second version of the model is stored.

MACHINE LEARNING BASED PROACTIVE PREDICTION AND INTERVENTION IN INFORMATION TECHNOLOGY INFRASTRUCTURE

Resumen de: US20260330252A1

0000 An apparatus maintains data structures characterizing interactions between users of information technology infrastructure, obtains information characterizing an additional interaction, and applies the obtained information to a first machine learning model, wherein the first machine learning model is trained utilizing at least portions of the data structures to predict a particular one of a plurality of possible outcomes for the additional interaction. Responsive to the prediction of the particular outcome, the apparatus triggers a second machine learning model, different than the first machine learning model, to generate one or more communications for delivery to one or more of the devices, the one or more communications providing at least a portion of at least one interventive interaction targeted at achieving an actual outcome that is different than the predicted outcome, and causes the one or more communications to be transmitted to the one or more devices over at least one network.

FEDERATED LEARNING SYSTEM FOR PROCESSING AND PROTECTING LOCAL DATA IN A MEDICAL CENTER

Resumen de: WO2026222035A1

This document describes a local computing system for a medical center, the system comprising one or more storage devices configured to store local training data corresponding to a plurality of medical procedures within the medical center, the local training data comprising a plurality of local training datasets. The storage devices are also configured to store a local machine learning model. Additionally, the system includes processing circuitry having access to the one or more storage devices and configured to receive a global machine learning model from a global computing system, wherein the global machine learning model is trained using global training data corresponding to a plurality of medical procedures outside of the medical center and re-train, using the local training data, the global machine learning model to generate the local machine learning model.

METHOD AND DEVICE FOR GENERATING SYNTHETIC ANOMALY IMAGES OF A PRODUCT IN ORDER TO ENRICH A TRAINING DATASET

Resumen de: US20260330813A1

A method for generating synthetic anomaly images of a product to enrich a training dataset for anomaly detection and/or anomaly classification of the product. The method includes: providing a user input that describes a product and at least one associated item of anomaly information; extracting a subgraph from a predefined knowledge graph, wherein the knowledge graph includes relationships between products, their properties and/or possible anomalies, and the subgraph refines and/or constrains the information of the user input in a domain-specific manner; generating a plurality of specific text prompts based on the information of the extracted subgraph; using the generated text prompts to control a previously trained text-to-image generation model to generate synthetic anomaly images of the product; and providing the generated anomaly images to enrich a training dataset for training a machine learning model for anomaly detection and/or anomaly classification.

MACHINE LEARNING ENABLED CONFORMATION-AWARE PROTEIN STRUCTURE PREDICTION

Resumen de: US20260331213A1

0000 A method for conformation aware protein structure prediction may include receiving an amino acid residue sequence of an input protein molecule, such as at least a portion of an antibody, a nanobody, or a T-cell receptor (TCR). A representation of the amino acid residue sequence of the input protein molecule may be determined to include a conformation token specifying a conformation state of the input protein molecule. The conformation state of the input protein molecule may be an unbound state or a bound state. A protein structure computation model may be applied to determine, based on the representation of the amino acid residue sequence of the input protein molecule, a conformation of the input protein molecule in the conformation state specified by the conformation token. Related systems and computer program products are also provided.

AUTOMATED DECISION MAKING FOR MULTIPLE DATASETS WITH MACHINE LEARNING

Resumen de: US20260329916A1

Systems and methods for automated decision making for multiple datasets with machine learning. A decision making entity can be prompted with an interface to generate user queries for entity documents from multiple entities. A query answer can be generated for the entity documents with an iterative question answer controller. The query answer can be converted into a dimensional matrix. Semantic data regarding the query answer can be visualized from the dimensional matrix with a visualization component of the iterative question answer controller to perform downstream tasks to resolve issues with the multiple entities.

PROVIDER PERFORMANCE SCORING USING SUPERVISED AND UNSUPERVISED LEARNING

Resumen de: AU2026242266A1

Abstract A system and a method are disclosed for a tool that generates a provider score corresponding to a predicted performance of a provider based on data of claims involving the provider. For a given claim, the tool provides the data as input into a supervised machine learning model and receives as output from the supervised machine learning model a predicted performance of the claim. The tool also inputs the data of the claim into an unsupervised machine learning model that is selected based on a stage of claim processing that the claim belongs to and receives as output from the unsupervised machine learning model an identification of a cluster of candidate claims to which the claim belongs. The tool combines the outputs of the supervised machine learning model and the unsupervised machine learning model to generate the provider score. Abstract ct b s t r a c t c t

ENHANCED VALIDITY MODELING USING MACHINE-LEARNING TECHNIQUES

Nº publicación: US20260330262A1 22/10/2026

Solicitante:

LIVE NATION ENTERTAINMENT INC [US]
Live Nation Entertainment, Inc.

Resumen de: US20260330262A1

0000 The present disclosure generally relates to a primary load management system configured to execute machine learning and artificial intelligence techniques to generate predictions of access-right requests that are or are likely to be invalid before the access-right requests are processed for assignment to users or user devices. The present disclosure relates to systems and methods that collect a data set representing characteristics of user devices as the user devices interact with various systems of the primary load management system and train a machine-learning model to predict invalid access-right requests using the collected data set. The collected data set may include a log line that represents each user device, and each log line may be labeled based on an invalidity evaluation. New access-right requests can be processed using the trained machine-learning model to determine whether or not to assign access rights in response to the access-right request.