Machine learning

IDENTIFICATION OF NETWORK EVENTS REPRESENTING A NETWORK SECURITY THREAT

Resumen de: WO2025163013A1

A computer-implemented method is provided for training a machine learning model to identify one or more network events associated with a network and representing a network security threat. The method comprises: a) obtaining a first dataset comprising data representative of a plurality of network events in a first network; b) obtaining a second dataset comprising data representative of a plurality of network events in a second network; c) performing covariate shift analysis on the first dataset and the second dataset to identify and classify a plurality of differences between the first dataset and the second dataset; d) performing domain adaptation on the first dataset, based on a classified difference, to generate a training dataset; e) training a machine learning model using the training dataset to produce a trained threat detection model.

Automated Payments Performance Monitoring, Alerting and Recommendation Framework

Resumen de: US2025252412A1

A method may include determining a combination of values of attributes represented by reference data associated with payment transaction by training a machine learning model based on an association between (i) respective values of the attributes and (ii) the payment transactions having a given result. The combination may be correlated with having the given result. The method may also include selecting a subset of the payment transactions that is associated with the combination of values. The method may additionally include determining a first rate at which payment transactions of the subset have the given result during a first time period and a second rate at which one or more payment transactions associated with the combination have the given result during a second time period, and generating an indication that the two rates differ.

MODEL GENERATION METHOD AND INFERENCE PROGRAM

Resumen de: WO2025164720A1

A model generation device according to one aspect of the present disclosure acquires eyeball-related data measured from a subject who is viewing content, uses the acquired eyeball-related data to perform machine learning of an inference model, and outputs the results of the machine learning. The machine learning includes training the inference model to acquire, from the eyeball-related data, the ability to infer a semantic representation in an information space corresponding to content included in the viewed content. Thus, the present disclosure provides a technique for easily inferring, at low cost, content perceived by an individual while viewing content.

MULTI-PHASE TRAINING OF MACHINE LEARNING MODELS FOR SEARCH RESULTS RANKING

Resumen de: US2025252112A1

A method and system for training a machine-learning algorithm (MLA) to rank digital documents at an online search platform. The method comprises training the MLA in a first phase for determining past user interactions of a given user with past digital documents based on a first set of training objects including the past digital documents generated by the online search platform in response to the given user having submitted thereto respective past queries. The method further comprises training the MLA in a second phase to determine respective likelihood values of the given user interacting with in-use digital documents based on a second set of training objects including only those past digital documents with which the given user has interacted and respective past queries associated therewith. The MLA may include a Transformer-based learning model, such as a BERT model.

IOT DEVICE IDENTIFICATION BY MACHINE LEARNING WITH TIME SERIES BEHAVIORAL AND STATISTICAL FEATURES

Resumen de: US2025254189A1

Identifying Internet of Things (IoT) devices with packet flow behavior including by using machine learning models is disclosed. A set of training data associated with a plurality of IoT devices is received. The set of training data includes, for at least some of the exemplary IoT devices, a set of time series features for applications used by the IoT devices. A model is generated, using at least a portion of the received training data. The model is usable to classify a given device.

METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT FOR IDENTIFYING PROPENSITIES USING MACHINE-LEARNING MODELS

Resumen de: WO2025165604A1

The method may include inputting a first set of data into a first model; for each user in the second group, generating a first similarity score; generating a relevance score for each parameter; determining a subset of parameters based on relevance; inputting the subset of parameters, a second set of data, and a third set of data into a second model; generating a space-partitioning data structure based on the second set of data; for each user in the first group, determining a feature distance between a representation of the user in the first group and a representation of a user in the second group based on the third set of data and the space-partitioning data structure; for each user in the second group, generating a second similarity score; and for each user in the second group, generating an overall similarity score.

Multi-Sourced Machine Learning Model-Based Artificial Intelligence Character Training and Development

Resumen de: US2025252341A1

A system includes a hardware processor configured to execute software code to receive interaction data identifying an action and personality profiles corresponding respectively to multiple participant cohorts in the action, generate, using the interaction data, an interaction graph of behaviors of the participant cohorts in the action, simulate, using a behavior model, participation of each of the participant cohorts in the action to provide a predicted interaction graph, and compare the predicted and generated interaction graphs to identify a similarity score for the predicted interaction graph relative to the generated interaction graph. When the similarity score satisfies a similarity criterion, the software code is executed to train, using the behavior model, an artificial intelligence character for interactions. When the similarity score fails to satisfy the similarity criterion, the software code is executed to modify the behavior model based on one or more differences between the predicted and generated interaction graphs.

SELF-IMPROVING ARTIFICIAL INTELLIGENCE PROGRAMMING

Resumen de: US2025252338A1

Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. In an example method, a current program state comprising a set of program instructions is accessed. A next program instruction is generated using a search operation, comprising generating a probability of the next program instruction based on processing the current program state and the next program instruction using a machine learning model, and generating a value of the next program instruction based on processing the current program state, the next program instruction, and a set of alternative outcomes using the machine learning model. An updated program state is generated based on adding the next program instruction to the set of program instructions.

MACHINE LEARNING SIGNALING AND OPERATIONS FOR WIRELESS LOCAL AREA NETWORKS (WLANS)

Resumen de: CN119949012A

An apparatus for wireless communication by a first wireless local area network (WLAN) device has a memory and one or more processors coupled to the memory. The processor is configured to transmit a first message indicating support of the first WLAN device for machine learning. The processor is also configured to receive a second message from a second WLAN device. The second message indicates support of the second WLAN device for one or more machine learning model types. The processor is configured to activate a machine learning session with the second WLAN device based at least in part on the second message. The processor is further configured to receive machine learning model structure information and machine learning model parameters from the second WLAN device during the machine learning session.

IDENTIFICATION OF NETWORK EVENTS REPRESENTING A NETWORK SECURITY THREAT

Resumen de: EP4597929A1

A computer-implemented method is provided for training a machine learning model to identify one or more network events associated with a network and representing a network security threat. The method comprises: a) obtaining a first dataset comprising data representative of a plurality of network events in a first network; b) obtaining a second dataset comprising data representative of a plurality of network events in a second network; c) performing covariate shift analysis on the first dataset and the second dataset to identify and classify a plurality of differences between the first dataset and the second dataset; d) performing domain adaptation on the first dataset, based on a classified difference, to generate a training dataset; e) training a machine learning model using the training dataset to produce a trained threat detection model. In this way,

Systems and methods for optimizing hyperparameters for machine learning models

Resumen de: GB2637695A

A combined hyperparameter and proxy model tuning method is described. The method involves iterations for hyperparameters search 102. In each search iteration, candidate hyperparameters are considered. An initial (‘seed’) hyperparameter is determined by initialization function 110, and used to train (104) one or more first proxy models on a target dataset 101. From the first proxy model(s), one or more first synthetic datasets are sampled using sampling function 108. A first evaluation model is fitted to each first synthetic dataset, for each candidate hyperparameter, by applying fit function 106 enabling each candidate hyperparameter from hyperparameter generator 112 to be scored. Based on the respective scores assigned to the candidate hyperparameters, a candidate hyperparameter is selected and used (103) to train one or more second proxy models on the target dataset. Hyperparameter search may be random, grid and Bayesian. Scores by scoring function 114 can be F1 scores. Uses include generative causal model with neural network architectures.

SYSTEMS AND METHODS OF DETERMINING DYNAMIC TIMERS USING MACHINE LEARNING

Resumen de: WO2024073382A1

Dynamic timers are determined using machine learning. The timers are used to control the amount of time that new data transaction requests wait before being processed by a data transaction processing system. The timers are adjusted based on changing conditions within the data transaction processing system. The dynamic timers may be determined using machine learning inference based on feature values calculated as a result of the changing conditions.

GENERATING DYNAMIC UTILIZATION MEASURES FOR AIRCRAFT BASED ON ENVIRONMENTAL CONDITIONS

Resumen de: EP4596425A1

The present disclosure provides techniques for dynamic utilization of aircraft based on environmental conditions. A proposed flight plan for an aircraft is received. Environment data representing a set of environmental conditions at a source airport indicated in the proposed flight plan is collected. Weather data representing a set of environmental conditions at a destination airport indicated in the proposed flight plan is collected. Operation data related to the aircraft indicated in the proposed flight plan is received. Aircraft engine degradation of the aircraft is dynamically simulated based on the collected environment data and the received operation data using a trained machine learning (ML) model. The simulated aircraft engine degradation is output.

Machine learning techniques for generating domain-aware sentence embeddings

Resumen de: GB2637669A

Performing predictive inferences on a first natural language document having first sentences 611 and a second natural language document having second sentences 612, wherein, for each sentences from the first and second sentences a sentence embedding is generated using a sentence embedding machine learning model 601, the embedding model being generated by updating parameters of an initial embedding model, preferably pretrained, based on a similarity determination model error measure, for each sentence pair comprising first and second sentences determining, using the similarity determination machine learning model 602 and sentence embedding for each sentence 621, 622, an inferred similarity measure 631, for each similarity measure a predictive output is generated and prediction-based actions are performed based on the output. The predictive output may comprise generating a cross-document relationship graph with nodes and edges representing relationships between sentences. The similarity determination model error measure can be based on a deviation measure and a ground-truth similarity measure for a training sentence pair. The first document data object is preferably a user-provided query and the predictive output a search result.

MULTI-SOURCED MACHINE LEARNING MODEL-BASED ARTIFICIAL INTELLIGENCE CHARACTER TRAINING AND DEVELOPMENT

Resumen de: EP4597360A1

A system includes a hardware processor configured to execute software code to receive interaction data identifying an action and personality profiles corresponding respectively to multiple participant cohorts in the action, generate, using the interaction data, an interaction graph of behaviors of the participant cohorts in the action, simulate, using a behavior model, participation of each of the participant cohorts in the action to provide a predicted interaction graph, and compare the predicted and generated interaction graphs to identify a similarity score for the predicted interaction graph relative to the generated interaction graph. When the similarity score satisfies a similarity criterion, the software code is executed to train, using the behavior model, an artificial intelligence character for interactions. When the similarity score fails to satisfy the similarity criterion, the software code is executed to modify the behavior model based on one or more differences between the predicted and generated interaction graphs.

CLOSED-LOOP OPTIMIZATION OF GENERAL REACTION CONDITIONS FOR HETEROARYL SUZUKI-MIYAURA COUPLING

Resumen de: MX2025004899A

Disclosed are systems and methods for rapidly generating general reaction conditions using a closed-loop workflow leveraging matrix down-selection, machine learning, and robotic experimentation. In certain aspects, provided is a method, comprising: selecting a reaction pair comprising a first molecule and a second molecule; wherein the first molecule is selected from a first matrix and the second molecule is selected from a second matrix; selecting one or more reaction conditions for the reaction pair, the selection based on historic use of the one or more reaction conditions and a structural and functional diversity of the selected reaction pair; automatically performing, by a robotic system, an initial round of reactions between the selected reaction pair under the selected one or more reaction conditions.

CLINICAL DATA ANALYSIS

Resumen de: WO2025157774A1

Computer-implemented methods of providing a clinical predictor tool are described, comprising: obtaining training data comprising, for each of a plurality of patients, values for a plurality of clinical variables comprising a variable indicative of a diagnosis or prognosis and one or more further clinical variables; and training a clinical predictor model to predict the variable indicative of a diagnosis or prognosis using said training data, wherein obtaining the training data comprises obtaining synthetic clinical data comprising values for a plurality of clinical variables for one or more patients by obtaining a directed acyclic graph (DAG) edges corresponding to conditional dependence relationships inferred from real clinical data comprising values for the plurality of clinical variables for a plurality of patients, and obtaining values for each node of the DAG using a machine learning model and multivariate conditional probability table. Computer-implemented methods of obtaining synthetic clinical data are also described.

DOCUMENT STRUCTURE EXTRACTION, DOCUMENT TEMPLATE GENERATION, AND DOCUMENT GENERATION RULES

Resumen de: WO2025159880A1

A method, a system, and a computer program product for generation of document rules. A structural arrangement of one or more portions of each electronic document in a plurality of electronic documents is determined using one or more machine learning models. One or more parameters associated with each electronic document in the plurality of electronic documents are identified. One or more document generation rules are generated based on one or more parameters and the structural arrangement of one or more portions. One or more document generation rules are generated for each type of electronic document in the plurality of electronic documents. One or more document generation rules are stored in a storage location.

SYSTEMS AND METHODS FOR USING ARTIFICIAL INTELLIGENCE FOR RULES-BASED MODELING OF ELECTRONIC TRANSACTIONS

Resumen de: WO2025159851A1

A method for rules-based modeling may include capturing a plurality of historical transaction data of a client account. The method may further include extracting a plurality of item level features from the plurality of historical transaction data. The method may further include providing the plurality of item level features to a predictive machine-learning model. The predictive machine-learning model may be trained to identify patterns within the plurality of item level features and generate a projected balance for the client account based on the identified patterns. The method may further include transmitting the projected balance to a user interface.

SYSTEMS AND METHODS OF OPTIMIZING RESOURCE ALLOCATION USING MACHINE LEARNING AND PREDICTIVE CONTROL

Resumen de: US2025245050A1

A computer system includes a transceiver that receives over a data communications network different types of input data from multiple source nodes and a processing system that defines for each of multiple data categories, a set of groups of data objects for the data category based on the different types of input data. Predictive machine learning model(s) predict a selection score for each group of data objects in the set of groups of data objects for the data category for a predetermined time period. Control machine learning model(s) determine how many data objects are permitted for each group of data objects based on the selection score. Decision-making machine learning model(s) prioritize the permitted data objects based on one or more predetermined priority criteria. Subsequent activities of the computer system are monitored to calculate performance metrics for each group of data objects and for data objects actually selected during the predetermined time period. Predictive machine learning model(s) and decision-making machine learning model(s) are adjusted based on the performance metrics to improve respective performance(s).

GENERATING DYNAMIC UTILIZATION MEASURES FOR AIRCRAFT BASED ON ENVIRONMENTAL CONDITIONS

Resumen de: US2025244759A1

The present disclosure provides techniques for dynamic utilization of aircraft based on environmental conditions. A proposed flight plan for an aircraft is received. Environment data representing a set of environmental conditions at a source airport indicated in the proposed flight plan is collected. Weather data representing a set of environmental conditions at a destination airport indicated in the proposed flight plan is collected. Operation data related to the aircraft indicated in the proposed flight plan is received. Aircraft engine degradation of the aircraft is dynamically simulated based on the collected environment data and the received operation data using a trained machine learning (ML) model. The simulated aircraft engine degradation is output.

On-Device Machine Learning Platform

Resumen de: US2025245533A1

The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a context provider that securely injects context features into collected training examples and/or client-provided input data used to generate predictions/inferences. Thus, the on-device machine learning platform can enable centralized training example collection, model training, and usage of machine-learned models as a service to applications or other clients.

Using Optical Remote Sensors And Machine Learning Models To Predict Agronomic Field Property Data

Resumen de: US2025245532A1

In some embodiments, a computer-implemented method for predicting agronomic field property data for one or more agronomic fields using a trained machine learning model is disclosed. The method comprises receiving, at an agricultural intelligence computer system, agronomic training data; training a machine learning model, at the agricultural intelligence computer system, using the agronomic training data; in response to receiving a request from a client computing device for agronomic field property data for one or more agronomic fields, automatically predicting the agronomic field property data for the one or more agronomic fields using the machine learning model configured to predict agronomic field property data; based on the agronomic field property data, automatically generating a first graphical representation; and causing to display the first graphical representation on the client computing device.

Load Balancing For Mixture Of Experts Machine Learning

Resumen de: US2025245553A1

Aspects of the disclosure are directed to improving load balancing for serving mixture of experts (MoE) machine learning models. Load balancing is improved by providing memory dies increased access to computing dies through a 2.5D configuration and/or an optical configuration. Load balancing is further improved through a synchronization mechanism that determines an optical split of batches of data across the computing die based on a received MoE request to process the batches of data. The 2.5D configuration and/or optical configuration as well as the synchronization mechanism can improve usage of the computing die and reduce the amount of memory dies required to serve the MoE models, resulting in less consumption of power and lower latencies and complexity in alignment associated with remotely accessing memory.

METHODS, SYSTEMS AND COMPUTER-READABLE MEDIA FOR ACTIVELY MONITORING PATENT INFRINGEMENT

Nº publicación: WO2025156057A1 31/07/2025

Solicitante:

PIONEERIP INC [CA]
PIONEERIP INC

Resumen de: WO2025156057A1

Systems and methods for fine-tuning a machine learning model for use in patent analytics and infringement system are disclosed herein. The method involves providing, in a memory, a litigation dataset comprising a plurality of historical patent litigation records, each patent litigation record comprising at least one patent claim, and a litigation outcome corresponding to each of the at least one patent claim; receiving, at a processor in communication with the memory, a product/service content item associated with a candidate patent litigation record in the plurality of historical patent litigation records; updating, at the processor, the candidate patent litigation record based on the product/service content item; and generating, at the processor, a vector database based on the litigation dataset, the vector database used to fine-tune a machine learning model. Systems and methods for identifying products and services relevant to a patent claim and for ranking results are also described.