Alerta

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.

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.

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.

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.

PREDICTION OF PHARMACOKINETIC PROPERTIES OF CHEMICAL COMPOUNDS

Resumen de: US20260321363A1

0000 Prediction of pharmacokinetic properties of chemical compounds A computer-implemented method of generating a machine-learning model configured to predict the value of one or more pharmacokinetic parameters of a pharmaceutical compound having a chemical structure comprises: receiving training data comprising a plurality of records, each record comprising: data encoding the chemical structure of a pharmaceutical compound; and the values of one or more pharmacokinetic parameters obtained from measurements taken from a human or animal subject after administration of the compound to the human or animal subject; and training the machine-learning model using the training data. Computer-implemented methods of utilizing the generated machine-learning model, particularly in a drug design context, are also provided.

METHOD FOR DETECTING A CHARGING ANOMALY IN CHARGING CURVES OF VEHICLES OF A VEHICLE FLEET, COMPUTER-READABLE MEDIUM, AND SYSTEM

Resumen de: WO2026214886A1

The invention relates to a method for detecting a charging anomaly in charging curves of vehicles of a vehicle fleet, the method comprising: determining feature vectors for a subset of the charging curves of the vehicles of the vehicle fleet; receiving labels for the determined feature vectors of the subset of charging curves, the labels representing a charging anomaly in the associated charging curve of the subset of charging curves; training one or more machine-learning methods for detecting charging anomalies using the determined feature vectors of the subset of charging curves and/or the received labels for the determined feature vectors of the subset of charging curves; receiving further charging curves of the vehicles of the vehicle fleet; and detecting a charging anomaly in the further charging curves using the one or more trained machine-learning methods.

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.

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.

DATA PROCESSING METHOD, SYSTEM AND APPARATUS, AND DEVICE AND MEDIUM

Resumen de: US20260320129A1

0000 Embodiments of the present disclosure provide a data processing method, system, and apparatus, a device, and a medium. The method includes: determining, in response to a data processing request for a target attribute, a target operator of a machine learning model built into a database; determining, based on a timing identifier corresponding to the target attribute, a target time series corresponding to the timing identifier, and a storage node that stores the target time series; and pushing down the target operator to the storage node, so that the storage node executes the data processing request for target timing data in the target time series by using the target operator.

DEVICE ATTRIBUTE DETERMINATION BASED ON PROTOCOL STRING CONVENTIONS

Resumen de: US20260322819A1

0000 A system and method for determining device attributes based on protocol string conventions. A method includes applying at least one machine learning model to an application data set extracted based on at least one first pair of strings, each first pair of strings including a protocol string and a key string indicated in respective fields of communications session data corresponding to a device, wherein each machine learning model is trained based on a training data set including second pairs of strings device attribute labels, wherein each device attribute label corresponds to one of the second pairs of strings, wherein each of the at least one machine learning model is configured to output a predicted device attribute for the device based on the first pair of strings; and determining, based on the output of the at least one machine learning model, at least one device attribute of the device.

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

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.

MACHINE LEARNING FRAMEWORK FOR AUTOMATED REQUEST PROCESSING TASKS

Resumen de: US20260320039A1

Various embodiments of the present disclosure provide machine learning framework for automated request processing tasks that improves the functionality of a computer in various aspects. The techniques comprise generating, by a first machine learning model, a target feature based on a text unit within a message; determining a structured query set based on an entity code associated with the target feature; generating, using a second machine learning model, a prompt for a structured query of the structured query set based on a query record set that comprises a collection of data records associated with an individual; generating, by the second machine learning model based on the prompt, a predicted response to the structured query; and generating, by a third machine learning model based on the predicted response and the structured query set, a predicted output for the message that comprises a message response and an explainable text segment.

SMART RING SYSTEM FOR MONITORING UVB EXPOSURE LEVELS AND USING MACHINE LEARNING TECHNIQUE TO PREDICT HIGH RISK DRIVING BEHAVIOR

Resumen de: US20260316765A1

A method for managing risk can include receiving, from a sensor, data indicative of a light exposure pattern. The method also can include analyzing, via a machine learning (ML) model, the data to predict a level of risk exposure for a user. The ML model can be trained with training data correlating light exposure patterns with risk patterns. The method further can include comparing the level of risk exposure, as predicted, to a threshold. The method additionally can include, in response to the level of risk exposure, as predicted, exceeding the threshold, determining a remediation action to reduce the level of risk exposure for the user. and communicating the remediation action to the user via an output device. Other embodiments are disclosed herein.

SIGNAL DATA SIGNATURE CLASSIFIERS TRAINED WITH SIGNAL DATA SIGNATURE LIBRARIES AND A MACHINE LEARNING DERIVED STRATEGIC BLUEPRINT

Resumen de: US20260320484A1

Systems and methods of the present disclosure enable signal data signature detection using a memory unit and processor, where the memory using stores a computer program or computer programs created by the physical interface on a temporary basis. The computer program, when executed, cause the processor to perform steps to receive a signal data signature recording from at least one data source, receive a dataset of labeled signal data signature recordings including signal data signature recording labels, identify, using at least one machine learning model, boundaries within the dataset of labeled signal data signature recordings, classify the signal data signature recording to produce an output label using a compendium of signal data signature classifiers based on the boundaries within the dataset of labeled signal data signature recordings, determine an output type of the signal data signature recording, and display the output label on a display media.

SUGGESTED QUESTION GENERATION USING MACHINE-LEARNING

Resumen de: US20260320478A1

Suggested question generation using machine learning is described. In one or more examples, training data is synthesized that is configured to train a question machine-learning model to generate a suggested question based on digital content. Synthesis of the training data includes extracting a plurality of portions from one or more items of digital content, generating a plurality of overviews, respectively, based on the plurality of portions, generating one or more profiles based on the plurality of overviews and the plurality of portions, forming one or more prompts to cause a language model to extract text from the item of digital content that has a likelihood of being of interest based on the one or more profiles, and forming at least one prompt to cause the language model to generate a set of training questions as part of the training data based on a respective said profile and the text.

MACHINE-LEARNING A CLASSIFICATION FUNCTION CONFIGURED FOR DETECTION OF MECHANICAL INTERFACES OF A CAD MODEL

Resumen de: US20260320263A1

The disclosure notably relates to a computer-implemented method for machine-learning a classification function configured for detection of mechanical interfaces of a CAD model representing a single mechanical part. The method comprises obtaining a training dataset comprising graphs, each graph representing a CAD model representing a single mechanical part. Each graph comprises one or more nodes representing a canonical surface of the respective CAD model. Each node comprises a label from the list: primary face, secondary face, tertiary face, maintain face, or no interface. Each graph further comprises one or more edges representing a connection between the canonical surfaces. The method further comprises training the classification function based on the dataset to classify, with a label of the label list, each node of a graph representing a CAD model representing a single mechanical part. This provides an improved solution for detecting mechanical interfaces.

DYNAMIC CLUSTERING AND PARALLELIZED MACHINE LEARNING FOR DECENTRALIZED SYSTEMS

Resumen de: US20260320410A1

An intelligent agent node may operate within a decentralized machine learning environment. The intelligent agent node includes a processor and an input/output interface coupled with the processor to communicate via a peer-to-peer network with other intelligent agent nodes. The intelligent agent node self-organizes with at least one of the other intelligent agent nodes into a cluster of intelligent agent nodes based, in part, on determining a similarity metric for each member of the cluster. The intelligent agent node also trains a local deep neural network based on local machine-generated data and exchange deep neural network parameters with other members of the cluster as part of intra-cluster parallelized training of an aggregated deep neural network, which in combination with other aggregated deep neural networks generated by members of other clusters forms a global deep neural network.

MACHINE-LEARNING A CLASSIFICATION FUNCTION CONFIGURED FOR DETECTION OF MECHANICAL INTERFACES OF A CAD MODEL REPRESENTING A MECHANICAL ASSEMBLY OF PARTS

Resumen de: US20260320262A1

0000 A computer-implemented method for machine-learning a classification function configured for detection of mechanical interfaces of a CAD model representing a mechanical assembly of parts. The method includes obtaining a training dataset comprising graphs representing a local contact between parts of a CAD model. The graph includes one or more nodes representing canonical surfaces of the CAD model. Each node comprises a label within the label list: primary face, secondary face, tertiary face, maintain face, and no interface. The graph further includes one or more internal edges and one or more external edges. The method further includes training the classification function based on the training dataset to classify, with a label of the label list, each node of a graph representing a local contact between parts of a CAD model representing a mechanical assembly of parts. This provides an improved solution for detecting mechanical interfaces.

GENERATING A WIKI FOR AN INTERACTIVE KNOWLEDGE BASE UTILIZED BY AN AUTONOMOUS SOFTWARE AGENT

Resumen de: US20260320422A1

The present disclosure relates to systems, methods, and non-transitory computer-readable media that generates a wiki for a code repository available to an autonomous software agent system. In particular, the disclosed systems access a code repository via the autonomous software agent system. Moreover, the disclosed systems generate a plurality of rich representations of the code repository using one or more machine learning models. Additionally, the disclosed systems generate a wiki from the plurality of rich representations, where the wiki includes a visualization of the plurality of rich representations. Moreover, the disclosed systems provide for display in a graphical user interface of a computing device, the wiki along with a prompt feature to search the wiki.

IDENTIFYING AND PROCESSING MARKETING LEADS THAT IMPACT A SELLER'S ENTERPRISE VALUATION

Nº publicación: US20260320638A1 15/10/2026

Solicitante:

HSIP CORP NEVADA TRUST [US]
HSIP Corporate Nevada Trust

Resumen de: US20260320638A1

0000 Systems, methods and computer readable media for automated marketing decisions based on company valuation results derived from ensemble machine learning models include collecting potential customer data for a set of potential customers from a lead source and transmitting the potential customer data to an optimization system. An ensemble machine learning model that establishes a transaction value for each of the set of potential customers based on an enterprise valuation of the seller. A marketing action is then taken. The marketing action may include one or more of budgeting for a transaction with the lead source, targeting communications to members of the set of potential customers, accepting or rejecting members of the set of potential customers, or offloading members of the set of potential customers.