Machine learning

HYBRID MACHINE LEARNING METHODS OF TRAINING AND USING MODELS TO PREDICT FORMULATION PROPERTIES

Resumen de: AU2023407504A1

Methods include training a machine learning module to predict one or more target product properties for a prospective chemical formulation, including (a) constructing or updating a training data set from one or more variable parameters; (b) performing feature selection on the training data set; (c) building one or more machine learning models using one or more model architectures; (d) validating the one or more machine learning models; (e) selecting at least one of the one or more machine learning models and generating prediction intervals; (g) interpreting the one or more machine learning models; and (h) determining if the one or more target product properties calculated are acceptable and deploying one or more trained machine learning models, or optimizing the one or more machine learning models by repeating steps (b) to (g). Methods also include application of trained machine learning modules to predict formulation properties from prospective data.

DETECTING COMPATIBILITY MISMATCH BY GENERATIVE ARTIFICIAL INTELLIGENCE

Resumen de: EP4617989A1

Detecting compatibility mismatch by generative artificial intelligence is described. Compatibility data is obtained (e.g., by accessing a database). The compatibility data is associated with a compatibility between items (e.g., items and categories of vehicles or an item and another item) and includes a list of recommended compatibilities between the items and a user reported compatibility for at least one item. A machine learning model is generated for detecting a compatibility mismatch between a first item and a second item and/or between an item and a category of vehicle. At least a portion of the compatibility data is provided as input to generative artificial intelligence to generate the machine learning model. An update to the list of recommended compatibilities is determined based on the detected compatibility mismatch.

DIGITAL LIFESTYLE INTERVENTION SYSTEM USING MACHINE LEARNING AND REMOTE MONITORING DEVICES

Resumen de: WO2024102668A1

Systems and methods for a digital lifestyle intervention system using machine learning and remote monitoring devices is described herein. The disclosed systems can include a processor configured to train, based on historical user data, a personal machine learning model to generate an output indicative of a blood pressure prediction and lifestyle feature impact on blood pressure prediction. The trained personal machine learning model can be further configured to receive user data including blood pressure data for the user and generate output including lifestyle feature impact and a blood pressure prediction by applying the trained personal machine learning model to the received user data. At least one lifestyle recommendation can be generated based on the output of the trained personal machine learning model and/or output from a population model applied to the received user data. The at least one lifestyle recommendation can be provided to a user via a user interface.

SEARCH REQUEST PROCESSING

Resumen de: EP4617903A1

Method, systems and computer programs for handling search requests at a search platform are provided. The search platform determines, using a cache with a number of incomplete search results, one or more of the incomplete search results with first data fields that correspond to the least one search parameter. For each determined incomplete search result, the search platform generates at least one second data field using a machine learning model. The at least one second data field corresponds to at least one search parameter and the at least one first data field of each determined incomplete search result. The search platform assembles a number of completed search results on the basis of the determined incomplete search results and the generated at least one second data field and returns at least one of the completed search results.

MACHINE LEARNING TRAINING DURATION CONTROL

Resumen de: US2024152798A1

Some embodiments select a machine learning model training duration based at least in part on a fractal dimension calculated for a training data dataset. Model training durations are based on one or more characteristics of the data, such as a fractal dimension, a data distribution, or a spike count. Default long training durations are sometimes replaced by shorter durations without any loss of model accuracy. For instance, the time-to-detect for a model-based intrusion detection system is shortened by days in some circumstances. Model training is performed per a profile which specifies particular resources or particular entities, or both. Realistic test data is generated on demand. Test data generation allows the trained model to be exercised for demonstrations, or for scheduled confirmations of effective monitoring by a model-based security tool, without thereby altering the model's training.

SOFTWARE ASSESSMENT TOOL FOR MIGRATING COMPUTING APPLICATIONS USING MACHINE LEARNING

Resumen de: US2024152869A1

A computing system includes a processor; and a memory having stored thereon instructions that, when executed by the one or more processors, cause the system to: receive content migration project parameters, resource migration project parameters and one or more services parameters of a user; scan a tenant computing environment; process the parameters by applying a multiplier display the costs, profits and pricing information. A method includes receiving content migration project parameters, resource migration projecting parameters and one or more services parameters of a user; scanning a tenant computing environment; processing the parameters by applying a multiplier displaying the costs, profits and pricing information. A non-transitory computer readable medium includes program instructions that when executed, cause a computer to: receive content migration project parameters, resource migration project parameters and one or more services parameters of a user; scan a tenant computing environment; process the parameters by applying a multiplier display the costs, profits and pricing information.

SYSTEM AND METHOD FOR RECOGNIZING USER GESTURES OR OTHER MOVEMENT OR STATES

Resumen de: WO2025186543A1

A system comprising: at least one sensor configured to obtain sensor data representing at least one of muscle, nerve and/or brain activity of a user; a processing resource configured to: obtain unlabelled sample data from said sensor data for the user for a plurality of samples; apply a classifier to the sample data and/or data derived from said sample data to obtain a pseudo-label for each sample, wherein the pseudo-label represents similarity of the sample to a selected one of a plurality of gestures, movements or states or to each of the plurality of gestures, movements or states; wherein the system further comprises storage for storing sample data for a set of samples together with the obtained pseudo-label, wherein the processing resource is further configured to: update the sample data stored in the storage based on the obtained pseudo-labels of the samples; calibrate a trained or partially trained gesture recognition model or other machine learning derived model for the user using at least the sample data of the updated sample data stored in the storage.

SYSTEMS AND METHODS FOR REAL-TIME BIDDING

Resumen de: WO2025189094A1

A real-time bidding method includes receiving user input data, generating a first machine learning model that generates a predicted expected performance based on the user input data, and adjusting at least one bid on at least one of at least one keyword and at least one product associated with at least one marketplace, based on the predicted expected performance.

STREAMING ENGINE FOR MACHINE LEARNING ARCHITECTURE

Resumen de: US2025284499A1

A programmable hardware system for machine learning (ML) includes a core and a streaming engine. The core receives a plurality of commands and a plurality of data from a host to be analyzed and inferred via machine learning. The core transmits a first subset of commands of the plurality of commands that is performance-critical operations and associated data thereof of the plurality of data for efficient processing thereof. The first subset of commands and the associated data are passed through via a function call. The streaming engine is coupled to the core and receives the first subset of commands and the associated data from the core. The streaming engine streams a second subset of commands of the first subset of commands and its associated data to an inference engine by executing a single instruction.

WIRELESS DEVICE POWER OPTIMIZATION UTILIZING ARTIFICIAL INTELLIGENCE AND/OR MACHINE LEARNING

Resumen de: AU2025220808A1

A method of reducing a power consumption of wireless communication circuitry of an edge device, the method comprising: learning, by an edge device, a delivery traffic indication map (DTIM) interval of a wireless access point communicatively coupled to the edge device via the wireless communication circuitry of the edge device using machine learning based on a machine learning model that includes an input associated with the DTIM interval; learning, by the edge device, at least one of (i) a number of Broadcasting/Multicasting Traffic messages received from the wireless access point that can be ignored without loss of a communication between the edge device and the wireless access point using machine learning, or (ii) a number of Address Resolution Protocol (ARP) packets received from the wireless access point that can be ignored without loss of a communication between the edge device and the wireless access point using machine learning; and adjusting, by the edge device, a wake-up interval of the wireless communication circuitry of the edge device based on the DTIM interval to reduce the power consumption of the wireless communication circuitry of the edge device. A method of reducing a power consumption of wireless communication circuitry of an edge device, the method comprising: learning, by an edge device, a delivery traffic indication map (DTIM) interval of a wireless access point communicatively coupled to the edge device via the wireless communication circuitry of the ed

SYSTEM AND METHOD FOR CONSTRUCTING DIGITAL DOCUMENTS

Resumen de: US2025284730A1

In some aspects described herein, a computer-based system that is capable of constructing digital documents is provided. In some implementations, a machine learning system is provided that learns certain terms within a document. The terms may be, for example, part of a document that forms a legally-binding contract between two entities. In one implementation of the machine learning system, the machine learning system interoperates within a user interface to show predictions of certain terms within the document to the user. Further, the machine learning system may capture user answers relating to certain terms and provide feedback into the system that learns during operation of the system, improving user interactions, accuracy and reducing the number of user interactions.

MACHINE LEARNED FEATURE RECOMMENDATION ENGINE IN A DIGITAL TRANSACTION MANAGEMENT PLATFORM

Resumen de: US2025284774A1

An online document system provides a recommendation for one or more features within the online document system to an entity. The online document system accesses a set of feature training data to train a machine learning model. The set of feature training data may describe characteristics of entities associated with the online document system and historical activity associated with the entities' usage of the online document system's features. The machine learning model may be configured to identify a feature to recommend to an entity based on the entity's characteristics and history of using other features within the online document system. For example, data representing the entity's user accounts and use of an electronic signature feature is used by the machine learning model to identify a document authentication feature to recommend to the entity. The online document system may then provide the identified feature in a recommendation to the entity.

Automatic Generation of Floor Layouts

Resumen de: US2025285375A1

Aspects described herein relate to the automatic generation of floorplan layouts based on a floorplan image. Image data comprising an image of an area may be accessed. Based on the image data, a floorplan of the area may be determined. A determination of whether constraints associated with occupancy are met may be made. Based on the constraints being met, and based on the floorplan, a light zones map may be generated. Based on the light zones map, spatial zones corresponding to the light zones may be determined. Based on the spatial zones, candidate floorplan layouts may be generated. Based on application of metaheuristic algorithms or machine-learning models to the candidate floorplan layouts, a subset of candidate floorplan layouts may be selected from the plurality of candidate floorplan layouts. Furthermore, floorplan layout data comprising a subset of floorplan layouts for use by a design application may be generated.

Computer-based systems, computing components and computing objects configured to implement dynamic outlier bias reduction in machine learning models

Resumen de: GB2639108A

Receiving training data for a user activity, well drilling; receiving bias criteria; determining a set of model parameters for a machine learning model including: (1) applying the machine learning model to the training data; (2) generating model prediction errors; (3) generating a data selection vector to identify non-outlier target variables based on the model prediction errors; (4) utilizing the data selection vector to generate a non outlier data set; (5) determining updated model parameters based on the non-outlier data set; and (6) repeating steps (1)-(5) until a censoring performance termination criterion is satisfied; training classifier model parameters for an outlier classifier machine learning model; applying the outlier classifier machine learning model to activity-related data to determine non-outlier activity-related data; and applying the machine learning model to the non-outlier activity related data to predict future activity-related attributes for the user activity.

EXTENSIBLE MACHINE LEARNING POWERED BEHAVIORAL FRAMEWORK FOR RISK COVERAGE

Resumen de: US2025272617A1

Some aspects of the present disclosure relate to systems, methods and computer readable media for outputting alerts based on potential violations of predetermined standards of behavior. In one example implementation, a computer implemented method includes: training a natural language-based machine learning model to detect at least one risk of a violation condition in an electronic communication between persons, wherein the violation condition is a potential violation of a first predetermined standard of behavior; receiving a lexicon, wherein the lexicon comprises topic data; receiving connection data representing a relationship between the trained machine learning model and the lexicon; detecting, using the trained machine learning model, the lexicon, and the connection data, a potential violation of a second predetermined standard of behavior; and outputting for display an alert indicating the potential violation of the second predetermined standard of behavior.

Iterative machine learning interatomic potential (MLIP) training methods

Resumen de: GB2639070A

An iterative machine learning interatomic potential (MLIP) training method which includes training a first multiplicity of first MLIP models in a first iteration of a training loop; training a second multiplicity of second MLIP models in a second iteration of the training loop in parallel with the first training step; then combining the first MLIP models and the second MLIP models to create an iteratively trained MLIP configured to predict one or more values of a material. The values may be total energy, atomic forces, atomic stresses, atomic charges, and/or polarization. The MLIP may be a Gaussian Process (GP) based MLIP (e.g. FLARE). The MLIP may be a graph neural network (GNN) based MLIP (e.g. NequIP or Allegro). A third MLIP model may be used when predicted confidence or predicted uncertainty pass a threshold. The MLIP models may use different sets of hyperparameters. The first and second MLIP models may use different starting atomic structures or different chemical compositions. Iteration can involve selection of the model with the lowest error rate. Combination can be to account for atomic environment overlap or atomic changes in energies. Training may be terminated when a model is not near a Pareto front.

USER INTERFACE FOR MATERIALS AND MATERIAL PROPERTIES PREDICTION USING A MACHINE LEARNING MODEL

Resumen de: AU2023409235A1

Machine learning can be used to predict formulations for an output formulation. The machine learning can be implemented by a machine learning model, which employs a forward model and an inverse model. A user interface can be used to gather raw materials selections and output formulation property selections. The selections can be used to generate formulations that comply with selections using the ML model.

SYSTEMS AND METHODS FOR OPTIMIZING HYPERPARAMETERS OF MACHINE LEARNING MODELS USING REDUCTION ITERATION TECHNIQUES

Resumen de: US2025278672A1

The most fundamental task in ML models is to automate the setting of hyperparameters to optimize performance. Traditionally, in machine learning (ML) models hyperparameter optimization problem has been solved using brute-force techniques such as grid search, and the like. This strategy exponentially increases computation costs and memory overhead. Considering the complexity and variety of the ML models there still remains practical difficulties of selecting right combinations of hyperparameters to maximize performance of the ML models. Embodiments of the present disclosure provide systems and methods for hyperparameters optimization in machine learning models and to effectively reduce the hyperparameter search dimensions and identify the important hyperparameter dimensions that are high variable to identify the best hyperparameter thereby saving the computing energy of machine learning process and eliminate categorical dimensions by using a combination of reduction-iteration techniques.

AUTOMATED MULTIVARIATE SYSTEM PERFORMANCE ANALYSIS

Resumen de: WO2025183999A1

The embodiments described herein generally relate to automated performance analysis of a system. Embodiments include receiving parameter values for a plurality of parameters captured during a time period. Embodiments include providing inputs based on the data set to a supervised machine learning model configured to determine significant parameters with respect to a target variable. Embodiments include receiving, from the supervised machine learning model in response to the inputs, an indication of two or more significant parameters from the plurality of parameters with respect to the target variable. Embodiments include generating a multivariate cluster for the target variable based on the two or more significant parameters and determining an anomalous state of the system with respect to the target variable based on the multivariate cluster for the target variable and data captured after the time period.

Artificial Intelligence (AI) Assisted Digital Documentation for Digital Engineering

Resumen de: US2025278526A1

A digital documentation system for preparation of engineering documents utilizing one or more artificial intelligence (AI) algorithms is provided. The system includes a user interface for selecting and populating templates with data, and one or more AI algorithms for creating and recommending templates, and preparing documents based on the recommended templates. The system uses natural language processing and semantic analysis algorithms to understand the content of the templates, documents, and associated engineering data, and to generate and recommend relevant templates to the user based on user prompts. The system also uses machine learning and predictive modeling and decision-tree algorithms to assist with the preparation of documents, by generating suggestions for data fields and values based on the user's previous inputs and the overall context of the document and available engineering data, including model data and metadata from digital models accessed in a zero-trust framework.

SYSTEMS AND METHODS FOR AN ARTIFICIAL INTELLIGENCE/MACHINE LEARNING MEDICAL CLAIMS PLATFORM

Resumen de: US2025278457A1

Embodiments of various systems, methods, and devices are disclosed for generating artificial intelligence or machine learning models for predicting denials of medical claims, predicting approvals of resubmitted medical claims, as well as automatic workflow clustering processes for automatically assigning medical claims to workflow queues using predictive segmentation and smart resource allocation.

AUTOMATED METADATA ASSET CREATION USING MACHINE LEARNING MODELS

Resumen de: US2025278435A1

Systems and methods are described that employ machine learning models to optimize database management. Machine learning models may be utilized to decide whether a new database record needs to be created (e.g., to avoid duplicates) and to decide what record to create. For example, candidate database records potentially matching a received database record may be identified in a local database, and a respective probability of each candidate database record matching the received record is output by a match machine learning model. A list of statistical scores is generated based on the respective probabilities and is input to an in-database machine learning model to calculate the probability that the received database record already exists in the local database.

RADIO FREQUENCY SYSTEM INCLUDING RECOMMENDATION TRAINING AGENT FOR MACHINE LEARNING ALGORITHM AND RELATED METHODS

Resumen de: US2025280373A1

A radio frequency (RF) system may include at least one RF sensor in an RF environment and at least one RF actuator. The RF system may also include at least one processor that includes a machine learning agent configured to use a machine learning algorithm to generate an RF model to operate the at least one RF actuator based upon the at least one RF sensor. The processor may also include a recommendation training agent configured to generate performance data from the machine learning agent, and change the RF environment based upon the performance data so that the machine learning agent updates the machine learning algorithm.

MACHINE LEARNING BASED DEPOLARIZATION IDENTIFICATION AND ARRHYTHMIA LOCALIZATION VISUALIZATION

Resumen de: US2025279208A1

Techniques that include applying machine learning models to episode data, including a cardiac electrogram, stored by a medical device are disclosed. In some examples, based on the application of one or more machine learning models to the episode data, processing circuitry derives, for each of a plurality of arrhythmia type classifications, class activation data indicating varying likelihoods of the classification over a period of time associated with the episode. The processing circuitry may display a graph of the varying likelihoods of the arrhythmia type classifications over the period of time. In some examples, processing circuitry may use arrhythmia type likelihoods and depolarization likelihoods to identify depolarizations, e.g., QRS complexes, during the episode.

CELL CHARACTERISATION

Nº publicación: WO2025180826A1 04/09/2025

Solicitante:

QUEEN MARY UNIV OF LONDON [GB]
QUEEN MARY UNIVERSITY OF LONDON

Resumen de: WO2025180826A1

A method of determining one or more intrinsic mechanical properties and/or size of one or more subcellular components of a cell, the method comprising: determining cell boundary profile data associated with a cell subject to steady or transient deformation; inputting the cell boundary profile data into a machine learning model; and using the machine learning model to determine one or more intrinsic mechanical properties and/or size of one or more subcellular components of the cell based on the cell boundary profile data.