Alerta

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.

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

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.

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.

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

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.

ENHANCED VALIDITY MODELING USING MACHINE-LEARNING TECHNIQUES

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.

DETECTING UNAUTHORIZED ONLINE APPLICATIONS USING MACHINE LEARNING

Resumen de: US20260330480A1

A method of detecting whether electronic fraud alerts are false positives includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that determines whether to generate an electronic fraud alert for the financial transaction based upon the data, and, when an electronic fraud alert is generated, inputting the data into a machine learning program trained to identify one or more facts indicated by the data. The method may also include determining whether the identified facts can be verified by customer data and, in response to determining that the facts can be verified, retrieving or receiving first customer data. The method may further include verifying that the electronic fraud alert is not a false positive based upon analysis of the first customer data, and transmitting the verified electronic fraud alert to a mobile device of the customer to alert the customer to fraudulent activity.

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.

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.

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.

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.

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.

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.

SUGGESTING ACTIONS BASED ON MACHINE LEARNING

Resumen de: EP4826622A2

This document describes techniques for suggesting actions based on machine learning. These techniques determine a task that a user desires to perform, and presents a user interface through which to perform the task. To determine this task, the techniques can analyze content displayed on the user device or analyze contexts of the user and user device. With this determined task, the techniques determine an action that may assist the user in performing the task. This action is further determined to be performable through analysis of functionalities of an application, which may or may not be executing or installed on the user device. With some subset of the application's functionalities determined, the techniques presents the subset of functionalities via the user interface. By so doing, the techniques enable a user to complete a task more easily, quickly, or using fewer computing resources.

INDEX FOR RISK OF NON-ADHERENCE IN GEOGRAPHIC REGION WITH PATIENT-LEVEL PROJECTION

Resumen de: US20260320425A1

Methods and systems to train and use an ensemble of artificial intelligence/machine learning (AI/ML) models to extract information from social determinants of health (SDoH), including training each of multiple dimensionality reduction models to reduce dimensionality of socio-demographic variables associated with a respective one of multiple SDoH categories, training a predictive model to predict a patient behavior for a geographic region (e.g., risk of non-adherence to treatment regimens) based on dimensionally reduced SDoH (alone or in combination with selected socio-demographic variables and/or other data), training a patient classification model to classify patients based on prescription transactions, and/or training a regional similarity model to determine a measure of similarity between geographic regions based on SDoH and/or dimensionally reduced SDoH. Also disclosed are techniques to visually represent outputs of the models on a user-interactive display.

Computerized analysis of a trained machine learning model

Resumen de: US20260320431A1

0000 There are provided systems and method comprising, by a processor and memory circuitry, obtaining, for a machine learning model, a set of data points, informative of a set of input vectors and of a set of predicted vectors, wherein each input vector of the set of input vectors has been used to train the machine learning model, using the set of data points to generate a database informative of the machine learning model, wherein the database is informative of terminal nodes, each given terminal being associated with: a plurality of data points of the set, one or more coefficients defining a function fitting a relationship between a plurality of input vectors of the given terminal node and a plurality of predicted vectors of the given terminal node, with a quality meeting an accuracy criterion, wherein the database is usable to generate data informative of the machine learning model.

System, Methods, And Processes For Model Performance Aggregation

Resumen de: US20260320485A1

Methods, systems, and computer-readable media are disclosed herein for a concurrent comparative tool for aggregating computationally generated performance reports a machine-learning data model pipeline for technological performance and/or predictive accuracy. The tool may acquire multiple performance measure reports that that quantitatively asses the performance of a model pipeline based on a configuration file that facilitates validation of the technological performance and predictive accuracy of the model. Additionally, when the conditions defined by the configuration file are met for of the performance measure reports the tool may broadcast a notification to a predetermined system.

Systems and Methods of Determining Vehicle Reparability

Nº publicación: US20260320567A1 15/10/2026

Solicitante:

STATE FARM MUTUAL AUTOMOBILE INSURANCE CO [US]
STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

Resumen de: US20260320567A1

0000 Systems and methods for determining a reparability of a vehicle are provided. In some embodiments, vehicle data is obtained, and a list of variables is generated from the obtained data. A machine learning algorithm may then be trained to generate a reparability metric by: (i) generating correlation metrics between the variables and costs to repair the vehicle, (ii) removing variables with correlation metrics below a threshold, and (iii) training the machine learning algorithm based upon unremoved variables.