Alerta

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.

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.

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

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.

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.

AI Superintelligence Application State Machine

Resumen de: US20260320693A1

0000 The present invention provides a method and apparatus for a thinking system using an ASI distributed Application State Machine that connects to real, accurate, dedicated absolute data, providing privacy, security, and eliminating hallucinated AI. Specifically, one embodiment of the present invention discloses an ASI Command, Control, Communications and Computer Intelligence (C4I) system for independent, intelligent AI Prompting and ASI Browsing. The ASI Application State Machine comprises: ASI Application data structure network; ASI Enterprise Interface State Machine, said data structures interfacing with a back-end channel, a Finite State Machine embedded in each data structure; and Application Service Information Base providing a uniform interface for ASI Application data structure identities. Additional embodiments disclose ASI Browser displaying ASI Applications; ASI Agents; ASI Application Network Nodes; ASI Operating System; ASI Switch; ASI Database of real, accurate, dedicated absolute data, for Machine Learning to train AI Models, for performing real-time, bi-directional transactions, connecting to billing.

METHOD AND SYSTEM FOR GENERATING A RESPONSE STRUCTURE USING A CHATBOT USER INTERFACE

Resumen de: US20260320082A1

A system for generating a response structure using a chatbot user interface, wherein the system includes a display device, a memory, and at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive a query using a chatbot interface operating on the display device, generate an action protocol as a function of the query, including: generating a set of requested data constraints using a constraint machine-learning model, and mapping the set of requested data constraints to a range of database entries, generate a response structure as a function of the action protocol, wherein generating the response includes: retrieving requested data from a database, inputting the requested data and the query into a second large language model, and generating the response structure using the second large language model, and configure the display device to display the response structure using the chatbot interface.

HEURISTIC OPERATIONAL NETWORK FOR DEEP LEARNING SEARCH AND MACHINE LEARNING MODEL SEARCH, DESIGN AND DEVELOPMENT

Resumen de: US20260320496A1

0000 Computer-automated tools to assist machine learning model development may be suitable for users without domain expertise or specialized expertise in particular model types. An initial grid search via gradient boosting is applied to a source data set and target to generate a reduced search space including prioritized set of features ranked by importance value. A genetic algorithm searches the supervised learning classification problem space with various model choices and feature engineering. AI model training techniques may include machine learning test models, test hyperparameters, and network architectures. Feature engineering generates new features, and model development promotes features with fitness scores satisfying a defined threshold. Feature engineering may employ feature combinations, scaling, normalization, and feature convolutions. ML model development may apply a genetic operator including one or more of crossover, mutation, and selection. Iterative test ML model development generates pre-trained models that can yield code for deployment of production models.

AUTOMATED SENSING AND CONTROL SYSTEM WITH DATA ANALYTICS AND ARTIFICIAL INTELLIGENCE

Resumen de: AU2025234748A1

The present disclosure relates to training a machine learning model based on a dataset comprising sales volume, product distribution logistics records, and product manufacturing data. The present disclosure further relate to extracting from the model a prediction of at least one item selected from a group consisting of future sales volume of an existing product, consumer interest for new products, and failure rates for product distribution equipment.

PREDICTION DATA CALIBRATION FOR CONTENT DISTRIBUTION

Resumen de: US20260320429A1

0000 Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for distributing digital components to client devices. The system receives a calibration request for calibrating a machine learning model, generates, according to the request, a calibration dataset. The system performs one or more calibration processes to generate one or more calibration thresholds using the calibration dataset, and applies the one or more calibration thresholds to the prediction scores generated by the machine learning model to generate calibrated prediction data including one or more predicted classifications. The system distributes the digital components to the client devices using calibrated prediction data.

GENERATING DIGITAL COMPONENTS USING CUSTOMIZED MACHINE LEARNING MODELS

Resumen de: US20260320472A1

0000 One example method includes obtaining, by an artificial intelligence (AI) system, first model parameters of a first machine learning model; generating a second machine learning model based on the first model parameters, the second machine learning model being at a given hierarchical layer of a hierarchical model structure; refining the second machine learning model to obtain an updated second machine learning model comprising second model parameters, wherein the first model parameters and the second model parameters have a different value for at least one model parameter; and generating, by the AI system, a third machine learning model based on the second model parameters, wherein (i) the third machine learning model is at a lower hierarchical layer of the hierarchical model structure beneath the given hierarchical layer of the second machine learning model, and (ii) the second machine learning model and the third machine learning model have a first common attribute.

GENERATING DIGITAL COMPONENTS USING CUSTOMIZED MACHINE LEARNING MODELS

Resumen de: US20260320471A1

One example method includes obtaining, by an artificial intelligence (AI) system, first model parameters associated with a first machine learning model at a first hierarchical layer; generating, by the AI system, a second machine learning model including the first model parameters, the second machine learning model at a second hierarchical layer beneath the first hierarchical layer; and refining, by the AI system, the second machine learning model to obtain an updated second machine learning model including second model parameters, where the first model parameters and the second model parameters have different value for at least one model parameter, and the second model parameters are unshareable with the first machine learning model.

METHOD AND SYSTEM FOR MAIZE MAPPING USING MULTI-SOURCE SATELLITE IMAGERY AND MACHINE LEARNING

Resumen de: WO2026216662A1

The present invention relates to a method and system for maize mapping using multi-source satellite imagery and machine learning. The method for maize mapping, according to an embodiment of the present invention, is performed by the system for maize mapping and comprises: a first step of acquiring LiDAR data and optical data captured by a satellite; a second step of training a first random forest classifier by using data of a first area including a height and optical characteristics of maize; a third step of inputting LiDAR data and optical data captured by a satellite of a second area into the trained first random forest classifier to infer maize cultivation areas; a fourth step of filtering outliers in the inferred maize cultivation areas by using phenological growth cycle information of maize; a fifth step of inferring a maize cropping intensity in the filtered maize cultivation areas; and a sixth step of outputting maize location points including point data by excluding paddy field areas from the maize cultivation areas in which the maize cropping intensity is inferred.

EVENT REPORTING FOR ARTIFICIAL INTELLIGENCE OR MACHINE LEARNING MODEL MONITORING

Resumen de: US20260323351A1

0000 Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive configuration information indicating a plurality of events that are associated with artificial intelligence or machine learning (AI/ML) model monitoring, each event of the plurality of events associated with a respective criterion of a plurality of criteria. The UE may identify, according to one or more criteria of the plurality of criteria, that a set of events, of the plurality of events, corresponding to the one or more criteria have occurred. The UE may transmit a report indicating that the set of events have occurred. Numerous other aspects are described.

Multi-task real-time inference scheduling system machine tool and method thereof

Resumen de: US20260319474A1

The present invention relates to a multi-task real-time inference scheduling system and real-time inference scheduling method of a machine tool, wherein a central control unit is connected to each of one or more individual control units through a network, receives a use context of each machine tool through each individual control unit, generates a multi-task learning model through a neural network, infers multiple tasks required to be performed by the individual control unit of each machine tool through machine learning by using real-time use contexts collected during operation of the machine tool by a use scenario, and schedules the multiple tasks of the machine tool through machine learning.

ATRIAL FLUTTER CLASSIFICATION SYSTEM

Resumen de: US20260314983A1

0000 A system for generating a machine learning (ML) model to identify an atrial flutter (AFL) type of a cardiac arrhythmia is provided. For each of a plurality of cardiograms, the system generates training data by identifying one or more portions of that cardiogram that relate to the AFL type to which that cardiogram is mapped. For each of a plurality of the portions, the system generates a feature vector that includes the portion and the additional features and a label that is based on the AFL type. The system trains the ML model using the training data to learn weights for the ML model. The ML model inputs a cardiogram and additional features and outputs an AFL type.

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.

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.

MACHINE LEARNING FOR BINDING AFFINITY PREDICTION USING A SET OF SUPPORT LIGANDS

Resumen de: WO2026217425A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting binding affinities of ligands. In one aspect, a method comprises: processing a model input characterizing a test ligand and a target molecule using a global binding affinity computational model to predict a global binding affinity score for the test ligand; selecting a set of support ligands for the test ligand from a database of example ligands; processing a model input that comprises data characterizing the test ligand and each of the support ligands using a local binding affinity machine learning model to generate a predicted error in the global binding affinity score for the test ligand; generating a final binding affinity score for the test ligand by combining the global binding affinity score for the test ligand and predicted error for the test ligand; and outputting the final binding affinity score for the test ligand.

MACHINE UNLEARNING

Resumen de: WO2026217055A1

A computer-implemented method (1000) for machine unlearning is presented The method comprises obtaining (1002) a request (2) indicating one or more X. X represents one or more input samples to be unlearned. The method further comprises for each X: obtaining (1004) U based on X, obtaining (1006) L based on X, updating (1008) F based on W'. W' is obtained so as to increase U, based on W, U, L, LR. F represents a machine learning model trained with at least X. W represents one or more model parameters of F. U represents one or more quantified uncertainty values for X. L represent one or more quantified loss values for X. LR represents a learning rate of F. The method (1000) further comprises initiating (1010) a deploying of F.

AI/ML POSITIONING OR SENSING RELAXATION

Resumen de: WO2026217286A1

Aspects presented herein may enable a user equipment (UE) to apply a set of relaxations for artificial intelligence (AI) or machine learning (ML) (AI/ML) related operations, thereby improving the resource managements for the AI/ML related operations. For example, in one aspect of the present disclosure, a UE receives an indication to perform at least one AI/ML operation. The UE obtains a set of relaxation criteria related to the at least one AI/ML operation, where the set of relaxation criteria includes at least one of: (1) a low mobility criterion, (2) a not-at-cell-edge criterion, (3) a stationary criterion, or (4) an AI/ML reduced capability device criterion. The UE performs at least one relaxation operation for the at least one AI/ML operation if the UE satisfies at least one relaxation criterion or all relaxation criteria in the set of relaxation criteria.

MODULAR ML WORKFLOWS FOR ACTIONABLE INSIGHTS FROM NATURAL LANGUAGE

Resumen de: WO2026217842A1

A natural language input from a user specifying a challenge or objective is received. The input is processed using a retrieval-augmented generation (RAG) engine and context-augmented guidance (CAG) engine to identify one or more relevant machine learning modules from a predefined library. A modular execution pipeline that includes the identified modules is dynamically configured by resolving interdependencies using dependency graph analysis that models modules as nodes and inter-module data dependencies as edges. Goal-aware cleansing of user-provided data is applied. The goal-aware cleansing is based on data requirements of at least one of the identified modules. The modular pipeline is executed on the cleansed data to generate actionable insights. The actionable insights are then transmitted to a client device.

DIGITAL TWIN FOR SEED PRODUCTION AND LOGISTICS

Resumen de: WO2026217316A1

Systems, methods, and apparatuses for a system for seed production and logistics are described. Seed production and logistics system data comprising seed variety demand, crop status, environmental conditions, seed conditioning and inventory status associated with a seed production and logistics process may be received, and a data representing a digital twin of the end to end seed production process may be aggregated and harmonized. A machine learning model may process the current state of the digital twin data to generate future state predictions and to recommend seed productions and logistics decisions that will result in improved seed quality and production efficiency.

SYSTEM AND METHODS FOR POLYMER ANALYSIS

Resumen de: WO2026215599A1

A computer-implemented method of processing a measurement signal comprising measurements of a sample by a sensor unit comprising a nanopore includes analysing the measurement signal using a machine learning model to determine that a portion of the measurement signal is associated with a first state of the nanopore, the first state being one of a plurality of states of the nanopore. The method includes determining whether to further analyse the portion of the measurement signal in dependence on the determination that the portion of the measurement signal is associated with the first state of the nanopore.

PREDICTION OF PHARMACOKINETIC PROPERTIES OF CHEMICAL COMPOUNDS

Nº publicación: US20260321363A1 15/10/2026

Solicitante:

HOFFMANN LA ROCHE INC [US]
Hoffmann-La Roche Inc.

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.