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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.

TECHNOLOGY FOR INEXPENSIVE AND QUANTIFIED ASSESSMENT OF INFANT SUCKLING BEHAVIOR

Resumen de: US20260334811A1

Provided herein is a method for evaluating infant suckling behavior, comprising: collecting suckling data from an infant using a vacuum measurement device, transmitting the collected suckling data to a processor, processing the collected suckling data by the processor using a pre-trained Machine Learning (ML) algorithm to identify one or more abnormalities in the infant's suckling behavior, wherein the ML algorithm has been trained on a set of training data comprising a plurality of dimensions associated with the training data, and wherein the identified one or more abnormalities are corresponding to one or more of the plurality of dimensions.

UNCERTAINTY PREDICTION MODELS

Resumen de: US20260340512A1

A method of determining an uncertainty of a machine learning model. The method includes initializing a plurality of models, each with a unique set of model parameters, reading a training dataset, selecting a unique subset of the training dataset for each of the plurality of models, training each of the plurality of models according to its unique set of model parameters and unique subset of the training data, reading a validation dataset, applying each of the plurality of models to the validation dataset to obtain a plurality of output distributions, determining an uncertainty from the plurality of output distributions.

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.

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

Resumen de: US20260341408A1

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.

COMPUTER-BASED SYSTEMS HAVING COMPUTER ENGINES AND DATA STRUCTURES CONFIGURED FOR MACHINE LEARNING DATA INSIGHT PREDICTION AND METHODS OF USE THEREOF

Resumen de: US20260340456A1

Disclosed herein are method, system, and computer product embodiments for generating a textual summary of a data set based on traversing a decision tree according to sequence and rank numbers related to a query. Subsets of the data set may receive a rank number indicating the relevancy of the subset of data to the query. In response to traversing the decision tree, a textual summary representative of the data set and subsets of data may be generated and displayed. The textual summary may also include a course of action recommendation based on the culmination of the data set and relevant data subsets.

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

Resumen de: US20260340394A1

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.

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.

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.

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

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.

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.

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.

SELECTABLE ENCRYPTION FOR 5G OPEN RADIO ACCESS NETWORK

Resumen de: US20260332402A1

0000 Techniques for encrypting data within a communication network include receiving, at a first module, data packets encrypted using mathematical encryption. A machine-learning model trained to detect cybersecurity threats analyzes the data packets to determine the existence of a cybersecurity threat. In response to determining a threat, encryption is switched from mathematical encryption to quantum encryption. The data packets are encrypted using a quantum encryption key to generate quantum-encrypted data packets. The quantum encryption key is transmitted from the first module to a second module over a quantum key distribution (QKD) channel, and the quantum-encrypted data packets are transmitted from the first module to the second module.

COALITION LEARNING FOR TRAINING OF DISTRIBUTED MACHINE LEARNING WORKLOADS

Resumen de: US20260330309A1

A method performed by a network entity in a mobile network is disclosed to determine a configuration for a machine learning workload. The method includes determining that the machine learning workload and one or more other machine learning workloads are members of a same coalition, training the machine learning workload using a first set of trial configurations, sharing a subset of the first set of trial configurations with one or more network entities that are responsible for configuring the one or more other machine learning workloads, obtaining a second set of trial configurations shared by the one or more network entities that are responsible for configuring the one or more other machine learning workloads, and warm starting a configuration optimization process for the machine learning workload using the second set of trial configurations.

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.

SYSTEM AND METHOD FOR DEEP LEARNING ON ATTACK ENERGY VECTORS

Resumen de: US20260332637A1

A method, computer program product, and computer system for identifying potential social engineering activity associated with two or more communications on two or more communication channels of a plurality of communication channels. Identification may be based on a determination that a current score for the two or more communication channels is greater than a threshold score for a user profile.

AI Generated Creative Content Based on Shared Memories

Resumen de: US20260330272A1

There is provided a system including a computing platform having a hardware processor and a system memory storing a software code and a memory data structure storing memory features for an artificial intelligence interactive character (AIIC). The hardware processor is configured to execute the software code to elicit, using the AIIC, a reminiscence from a user, predict, using a trained machine learning model and the reminiscence, at least one user memory feature, identify, using the memory data structure, one or more of the memory features for the AIIC based on a similarity between the predicted at least one user memory feature and the memory features for the AIIC, determine a mood modifier based on emotion information of the identified one or more of the memory features for the AIIC, and generate a creative composition for performance by the AIIC based on the mood modifier.

AN ENSEMBLE APPROACH TO PREDICTIVE DAMAGE ANALYTICS

Resumen de: US20260330330A1

The present disclosure relates to systems and methods that use predictive analytics modeling, such as to identify risk profiles as it applies to potential damage to assets, persons, or other items. In one implementation, for example, an artificial intelligence/machine learning (AI) model or engine is adapted to be used as a mechanism for achieving predictive analytics from as-yet unknown adverse future events against one or a plurality of items. The AI model is adapted to evaluate a plurality of known factors for one or a set of evaluation items. The AI model is adapted to build an AI training set based on aggregate data to narrow down a plurality of clusters from the aggregate data, use AI on individual items in each cluster, compare the output across clusters to find one or more ranked elements; and identify individual items through the characteristics to provide the training set.

AUTOMATIC WELL LOG RECONSTRUCTION

Resumen de: US20260328091A1

0000 Machine learning techniques for reconstructing a target well log are presented. The techniques include: storing a dictionary that includes a statistical distribution similarity quantification for each common feature of each pair of well logs in a well log data set; for at least one cluster of the target well log, ranking the well logs in the well log data set based on the dictionary, where the ranking is according to a statistical distribution similarity to the target well log; selecting a validation set of well logs and a training set of well logs; iteratively producing a reconstruction model, where each step of the iteration includes training using the training set of well logs and validating using the validation set of well logs; reconstructing a feature in the target well log using reconstructed values for the feature output by the reconstruction model; and providing the reconstructed target well log.

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

Nº publicación: US20260330260A1 22/10/2026

Solicitante:

AMAZON TECH INC [US]
Amazon Technologies, Inc.

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.