Machine learning

IDENTIFICATION OF MARKER SEQUENCES FOR RAPID CLASSIFICATION OF MICROBIAL PATHOGENS THROUGH MACHINE LEARNING ANALYSIS OF GENOME ASSEMBLIES

Resumen de: WO2024249933A1

The disclosure provides methods to identify marker sequences for rapid classification of microbial strains through machine learning analysis of genome assemblies.

METHOD AND SYSTEM FOR PREDICTING MALICIOUS ENTITIES

Resumen de: WO2024249450A1

A computer-implemented method, computer device and computer program are provided for training a Machine Learning model based on a plurality of signal records, each signal record comprising an entity identifier, a signal identifier and a timestamp. Signal records related to an entity can be input into the trained Machine Learning model. The trained Machine Learning model can be used to determine if an entity is malicious.

METHOD AND SYSTEM FOR PREDICTING MALICIOUS ENTITIES

Resumen de: US2024406208A1

A computer-implemented method, computer device and computer program are provided for training a Machine Learning model based on a plurality of signal records, each signal record comprising an entity identifier, a signal identifier and a timestamp. Signal records related to an entity can be input into the trained Machine Learning model. The trained Machine Learning model can be used to determine if an entity is malicious.

MACHINE LEARNING MODEL CONTINUOUS TRAINING SYSTEM

Resumen de: US2024406477A1

Described is a system for performing a set of machine learning model training operations that include: accessing media content items associated with interaction functions initiated by users of an interaction system, generating training data including labels for the media content items, extracting features from a media content item of the media content items, identifying additional media content items to include in the training data based on the extracted features from the media content item, processing the training data using a machine learning model to generate a media content item output; and updating one or more parameters of the machine learning model based on the media content item output. The system checks whether retraining criteria has been met, and repeats the set of machine learning model training operations to retrain the machine learning model.

Systems And Methods For Detecting Long Term Seasons

Resumen de: US2024403719A1

Techniques for machine-learning of long-term seasonal patterns are disclosed. In some embodiments, a network service receives a set of time-series data that tracks metric values of at least one computing resource over time. Responsive to receiving the time-series data, the network service detects a subset of metric values that are outliers and associated with a plurality of timestamps. The network service maps the plurality of timestamps to one or more encodings of at least one encoding space that defines a plurality of encodings for different seasonal patterns. Based on the mapped encodings, the network service generates a representation of a seasonal pattern. Based on the representation of the seasonal pattern, the network service may perform one or more operations in association with the at least one computing resource.

MACHINE-LEARNING DRIVEN REAL-TIME DATA ANALYSIS

Resumen de: US2024403967A1

A data processing system for insurance claims analysis and adjudication implements obtaining policy coverage information for each of a plurality of insurance policies and insurance claim information associated with a plurality of insurance claims associated with an insured user, analyzing the insurance claim information using a first machine learning to obtain event-related claim grouping information; analyzing the event-related claim grouping information and the standardized policy information using the second machine learning model to obtain coverage prediction information comprising a prediction, for each event of the one or more events, identifying a respective insurance policy of the plurality of insurance policies likely to cover the one or more claims associated with each event, the second machine learning model being trained using second training data formatted according to the standard schema; and providing, via a network connection, the coverage prediction information to a computing device associated with the insured user.

METHODS AND APPARATUS FOR TIME-SERIES FORECASTING USING DEEP LEARNING MODELS OF A DEEP BELIEF NETWORK WITH QUANTUM COMPUTING

Resumen de: US2024403671A1

An apparatus including a Deep Belief Network is configured to receive, via a processor, input data. The processor is caused to initialize, based on the input data, weights for a learning model of the DBN. The processor is further caused to generate, via the learning model, a representation of the input data. The weights, the input data, and the representation is to be transmitted to a quantum compute device. The processor is caused to receive sampled values from the quantum compute device using an optimization function associated with the quantum compute device. The processor is further caused to update, based on the sampled values, the weights to train the learning model to produce a trained learning model. The trained learning model is configured to generate an updated representation of the input data. The processor is further caused to generate, via a regression layer, output data based on the updated representation.

System, Method, and Computer Program Product for Identifying Weak Points in a Predictive Model

Resumen de: US2024403715A1

Systems, methods, and computer program products that obtain a plurality of features associated with a plurality of samples and a plurality of labels for the plurality of samples; generate a plurality of first predictions for the plurality of samples with a first machine learning model; generate a plurality of second predictions for the plurality of samples with a second machine learning model; generate, based on the plurality of first predictions, the plurality of second predictions, the plurality of labels, and a plurality of groups of samples of the plurality of samples; determine, based on the plurality of groups of samples, a first success rate associated with the first machine learning model and a second success rate associated with the second machine learning model; and identify, based on the first success rate and the second success rate, a weak point in the machine learning first model or the second model.

TIERED ANOMALY DETECTION

Resumen de: US2024403662A1

For each corresponding configuration item type of a plurality of different configuration item types, a corresponding multi-variate machine learning model of a plurality of multi-variate machine learning models is trained to perform anomaly detection for a corresponding configuration item type of the plurality of different configuration item types. In response to detecting, via a univariate machine learning model, an anomaly associated with a specific configuration item type of the plurality of different configuration item types, an execution of a particular multi-variate machine learning model of the plurality of multi-variate machine learning models is initiated for the specific configuration item type. An output of the execution of the particular multi-variate machine learning model is evaluated to determine an anomaly detection result.

MACHINE LEARNING-BASED SYSTEMS AND METHODS FOR PREDICTING LIVER CANCER RECURRENCE IN LIVER TRANSPLANT PATIENTS

Resumen de: US2024404707A1

Described herein are prediction models based on the transcriptomic, exomic, and/or radiological analyses on tissue samples to predict the likelihood of the original cancer (such as Hepatocellular carcinoma (HCC)) recurrence into the liver transplant. An example computer implemented method for predicting the likelihood of liver cancer recurrence 5 into a liver transplant includes receiving gene expression data related to a liver tissue sample for a subject having a liver cancer, inputting the gene expression data into a trained machine learning model, and predicting, using the trained machine learning model, a risk of recurrence of the liver cancer in the subject after liver transplantation.

Machine Learning and Molecular Simulation Based Methods for Enhancing Binding and Activity Prediction

Resumen de: US2024404619A1

Systems and methods for molecular simulation in accordance with embodiments of the invention are illustrated. One embodiment includes a method for predicting a relationship between a ligand and a receptor. The method includes steps for identifying a plurality of conformations of a receptor, computing docking scores for each of the plurality of conformations and a set of one or more ligands, and predicting a relationship between the set of one or more ligands and the plurality of conformations of the receptor.

Integrative System and Method for Performing Medical Diagnosis Using Artificial Intelligence

Resumen de: US2024404659A1

An integrative system (100) for performing medical diagnosis using artificial intelligence is disclosed. The integrative system (100) combines traditional medicine with modem medical science. Specifically, a baseline of a patient is first established. Subsequently, changes in the baseline of the patient are captured. Machine learning techniques are incorporated into the integrative system (100) for accurate and quantifiable determination of the baseline of the patient as well as the changes in the baseline that leads to diseases. Further, health, wellness and risk scores are generated based on this integrative approach. The integrative system (100) is also configured to generate a recommendation message to the patient based on the generated health, wellness, and disease risk score. The knowledge from the baseline and baseline changes is used for subsequent interventions such as food, medicine, meditation, yoga, panchakarma, acupuncture, music, massage, and the like.

GENERATIVE ARTIFICIAL INTELLIGENCE FOR EMBEDDINGS USED AS INPUTS TO MACHINE LEARNING MODELS

Resumen de: WO2024249164A1

In an example embodiment, a generative artificial intelligence (GAI) model is used to generate embeddings, eliminating the need for a separately trained embedding model or layer. These embeddings may then be used as input to another machine learning model. In some example embodiments, these embeddings are generated on interaction data regarding one or more interactions between a user and digital content presented on one or more online platforms.

MACHINE LEARNING MODEL CONTINUOUS TRAINING SYSTEM

Resumen de: WO2024249773A1

Described is a system for performing a set of machine learning model training operations that include: accessing media content items associated with interaction functions initiated by users of an interaction system, generating training data including labels for the media content items, extracting features from a media content item of the media content items, identifying additional media content items to include in the training data based on the extracted features from the media content item, processing the training data using a machine learning model to generate a media content item output; and updating one or more parameters of the machine learning model based on the media content item output. The system checks whether retraining criteria has been met, and repeats the set of machine learning model training operations to retrain the machine learning model.

ENHANCED PREDICTIVE MODELING USING TIME-SERIES TRAINING DATA

Resumen de: US2024403720A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for enhanced predictive modeling. One of the methods includes obtaining a first prediction from a machine learning model trained to predict future events based on time-series data input; generating a set of one or more features of the first prediction; determining that the set of one or more features of the first prediction satisfy one or more thresholds indicating an event included in the first prediction is atypical; comparing the set of one or more features of the first prediction to one or more other features representing historical events that are included in the time-series data input; identifying, using the comparison, a set of historical events from the historical events that are included in the time-series data input; and generating an adjusted first prediction using the identified set of historical events.

COMPUTER IMPLEMENTED METHOD FOR PREDICTING A LEVEL OF QUALITY OF A VACUUM INTO A VACUUM CHAMBER OF A COATING MACHINE, AND CORRESPONDING DEVICE

Resumen de: WO2024240803A1

The invention relates to a computer implemented method for predicting a level of quality of a vacuum into a vacuum chamber (2) of a coating machine (1) suitable for coating optic elements (8). According to the invention, this method comprises steps of: - acquiring at least one technical feature of the coating machine during coating processes of optic elements, and - predicting said level of quality from the at least one measured technical feature by means of a processing unit that registers a machine learning model, said at least one technical feature being an input of said machine learning model and said level of quality being an output of said machine learning model.

MACHINE LEARNING TO ENHANCE SATELLITE TERMINAL PERFORMANCE

Resumen de: US2024396814A1

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for using machine learning to detect and correct satellite terminal performance limitations. In some implementations, a system retrieves data indicating labels for clusters of network performance anomalies. The system generates a set of training data to train a machine learning model, the set of training data being generated by assigning the labels for the clusters to sets of performance indicators used to generate the clusters. The system trains a machine learning model to predict classifications for communication devices based on input of performance indicators for the communication devices. The system determines a classification for the communication device based on output that the trained machine learning model generates.

DATA COMPRESSION TECHNIQUES FOR MACHINE LEARNING MODELS

Resumen de: US2024394338A1

In some aspects, techniques for creating representative and informative training datasets for the training of machine-learning models are provided. For example, a risk assessment system can receive a risk assessment query for a target entity. The risk assessment system can compute an output risk indicator for the target entity by applying a machine learning model to values of informative attributes associated with the target entity. The machine learning model may be trained using training samples selected from a representative and informative (RAI) dataset. The RAI dataset can be created by determining the informative attributes based on attributes used by a set of models and further extracting representative data records from an initial training dataset based on the determined informative attributes. The risk assessment system can transmit a responsive message including the output risk indicator for use in controlling access of the target entity to an interactive computing environment.

GENERALIZED ENTERPRISE CATALOG CLASSIFICATION FOR SHORTHAND ITEMDESCRIPTORS

Resumen de: US2024394778A1

A system and a method are disclosed for classifying shorthand item descriptors in accordance with an enterprise catalog. An enterprise data management system uses one more models to determine items in the enterprise catalog that match a shorthand descriptor of an item. Shorthand item descriptors may originate from various transaction data such as at point-of-sale (POS) machines or online ordering systems. The enterprise data management system uses a first model to determine a normalized representation of the shorthand item descriptor. The enterprise data management system furthers used a second model to classify the normalized representation as one or more items included in the enterprise catalog, where the second model is trained through a supervised machine learning process using data corresponding to an enterprise catalog of one or more particular enterprises.

MULTI-MODEL INFERENCE PIPELINE AND SYSTEM

Resumen de: US2024394569A1

Various examples are directed to providing a multi-model training and inference pipeline and environment using machine learning for a cloud environment.

NEURAL NETWORKS FOR SCALABLE CONTINUAL LEARNING IN DOMAINS WITH SEQUENTIALLY LEARNED TASKS

Resumen de: US2024394540A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scalable continual learning using neural networks. One of the methods includes receiving new training data for a new machine learning task; training an active subnetwork on the new training data to determine trained values of the active network parameters from initial values of the active network parameters while holding current values of the knowledge parameters fixed; and training a knowledge subnetwork on the new training data to determine updated values of the knowledge parameters from the current values of the knowledge parameters by training the knowledge subnetwork to generate knowledge outputs for the new training inputs that match active outputs generated by the trained active subnetwork for the new training inputs.

Inventory Item Prediction and Listing Recommendation

Resumen de: US2024394625A1

An inventory prediction system is described that outputs a predicted inventory item not included in a user's known inventory using a cross-category directional graph that represents item categories as nodes. The inventory prediction system implements a prediction model trained using machine learning to output the predicted inventory item using the graph and at least one item from the user's known inventory. The inventory prediction system is further configured to generate a listing recommendation for the predicted inventory item. To do so, the inventory prediction system implements a logistic regression model trained using machine learning to calculate a probability that the listing recommendation should be generated using attributes of the predicted inventory item and attributes of currently trending items. The listing recommendation is generated to include a description of, and estimated value for, the predicted inventory item, together with an option to generate a sale listing for the predicted inventory item.

SYSTEMS AND METHODS FOR MANAGING MACHINE LEARNING MODELS

Resumen de: US2024394595A1

The subject matter of this disclosure relates to systems and methods for monitoring and managing machine learning models and related data. Histogram structures can be used to aggregate streams of numerical data for storage and metric calculations. Drift in such data can be identified and monitored over time. When significant drift is detected and/or when model accuracy has deteriorated, models can be automatically refreshed with updated training data and/or replaced with one or more other models. A model controller is used to automate model monitoring and management activities across multiple prediction environments where models are deployed and prediction jobs are executed.

UNCERTAINTY-BASED IN-PRODUCT LEARNING FOR X-RAY

Resumen de: US2024395025A1

A system (SYS) and related method for providing training data. The system is configured to receive a classification result for a class from plural pre-defined classes (Cj). The classification result is produced by a trained machine learning model (M) in response to processing an input image. A decision logic (DL) of the system is configured to analyze input data (pi,qt) comprising the received classification result value (pi) and an uncertainty value (qi) associated with the classification result value. The system outputs, per class, an associated indication whether the input image is or is not useful for re-training the model (M) in respect of the said class.

SYSTEM AND METHOD FOR DIGITAL THERAPEUTICS IMPLEMENTING A DIGITAL DEEP LAYER PATIENT PROFILE

Nº publicación: US2024395375A1 28/11/2024

Solicitante:

APRICITY HEALTH INC [US]
Apricity Health, Inc

Resumen de: US2024395375A1

A system, method, and computer-readable medium are disclosed for digital therapeutics directed to patient care specific to a disease for digital therapeutics that implement digital deep layer patient profile. Patient related information is presented by receiving data that includes patient data, lab result data, machine learning calculation data related to the patient, and physician result data. The data is mapped as to intensities, multiple dimensions and time. The mapping is converted to create an unstructured binary data with binary correlations as a digital deep layer patient profile. The digital deep layer patient profile can be processed with machine learning and image processing algorithms.