Alerta

FORECASTING USING DIFFERENTIAL-BASED MACHINE-LEARNING ARCHITECTURE

Resumen de: EP4560561A1

Conventional forecast models assume that the underlying data are stationary. In the case of non-stationary data, the conventional forecast model must be increased in complexity, or otherwise, must be frequently retrained. Embodiments are disclosed of a forecast model that accounts for non-stationary data, without increased complexity and without an increase in the frequency of retraining, by recasting the forecasting problem as a matter of tracking differentials in the data. In particular, variables (e.g., covariates) are input into differential determinations and/or intermediate machine-learning models to determine differences in past and forecasted values of a plurality of features. These differences are then input into a machine-learning model to predict a change in the value of the target, which is aggregated with a past value of the target to produce a forecasted value of the target.

SYSTEMS AND METHODS FOR USING MACHINE LEARNING TECHNIQUES TO PREDICT ITEM GROUP COMPOSITION

Resumen de: WO2024144913A1

Systems and methods for predicting item group composition are disclosed. A system for predicting item group composition may include a memory storing instructions and at least one processor configured to execute instructions to perform operations including: receiving entity identification information and a timestamp associated with a transaction without receiving information distinguishing items associated with the transaction; determining, based on the entity identification information, a localized machine learning model trained to predict categories of items based on transaction information applying to all of the items associated with the transaction; and applying the localized machine learning model to a model input to generate predicted categories of items associated with the transaction, the model input including the received entity identification information and a timestamp but not including information distinguishing items associated with the transaction.

BUILDING SYSTEM WITH DIGITAL TWIN BASED AGENT PROCESSING

Resumen de: US2025164950A1

A system can include one or more memory devices that can store instructions thereon. The instructions can, when executed by one or more processors, cause the one or more processors to receive timeseries data associated with a building, detect that a new building device has been added to the building, determine that a representation of the new building device is absent from a digital twin of the building, and execute a machine learning model to add the representation of the new building device to the digital twin of the building.

SYSTEMS AND METHODS FOR PROCESSING IMAGES TO DETERMINE IMAGE-BASED COMPUTATIONAL BIOMARKERS FROM LIQUID SPECIMENS

Resumen de: US2025166397A1

A method of using machine learning to output task-specific predictions may include receiving a digitized cytology image of a cytology sample and applying a machine learning model to isolate cells of the digitized cytology image. The machine learning model may include identifying a plurality of sub-portions of the digitized cytology image, identifying, for each sub-portion of the plurality of sub-portions, either background or cell, and determining cell sub-images of the digitized cytology image. Each cell sub-image may comprise a cell of the digitized cytology image, based on the identifying either background or cell. The method may further comprise determining a plurality of features based on the cell sub-images, each of the cell sub-images being associated with at least one of the plurality of features, determining an aggregated feature based on the plurality of features, and training a machine learning model to predict a target task based on the aggregated feature.

SYSTEMS AND METHOD FOR CALCULATING LIABILITY OF A DRIVER OF A VEHICLE

Resumen de: US2025166083A1

Aspects of the present disclosure are related to systems, apparatus, and methods of generating or calculating liability and operational costs of a vehicle based on a driver's handling of the vehicle are described herein. Using a combination of vehicle sensors, video input, and on-board artificial intelligence and/or machine learning algorithms, the systems and methods of the present disclosure can identify risky events performed by the driver of a vehicle and generate, calculate, and evaluate driving scores for the driver of the vehicle and send the calculations to one or more entities.

SYSTEMS AND METHODS FOR HUMAN-IN-THE-LOOP TRAINING OF A MACHINE LEARNING MODEL FOR EXTRACTING TARGETS FROM RECORDS

Resumen de: US2025165993A1

Systems and methods for training a machine learning model by extracting targets from records are disclosed. A method includes receiving data packets including data associated with previous interactions of a user, determining at least one party to the previous interactions with whom the user interacted at a level above a threshold, and comparing the at least one party to a plurality of parties identified in a target database to identify at least one potential target, for each potential target, determining a respective target metric by inputting the data packets into a machine learning model, providing an identification of the at least one potential target, with reference to the respective target metrics, to a user device associated with the user, receiving, via the user device, feedback regarding the identification, and training the machine learning model based on the feedback.

SYSTEMS AND METHODS FOR PRE-SELECTING A MACHINE LEARNING MODEL BASED ON DETERMINED DATASET CHARACTERISTICS

Resumen de: US2025165822A1

Pre-selecting a machine learning model based on determined dataset characteristics may be facilitated. In some embodiments, a time-series dataset may be received by a system. Based on the time-series dataset, the system generates a periodogram to determine power frequencies of a set of frequencies of the time-series dataset. The system may then determine an asymptotic p-value based on the periodogram, where the asymptotic p-value indicates a probability value that a seasonal cycle is part of the time-series dataset. In response to a comparison between the asymptotic p-value and a threshold p-value indicating that the asymptotic p-value fails to exceed the threshold p-value, the system determines a seasonality cycle of the time-series dataset. The system then generates, for display, on a user interface, a recommendation for a machine learning model based on (i) the seasonality cycle of the time-series dataset and (ii) the power frequencies.

SYSTEMS AND METHODS FOR PROCESSING IMAGES TO CLASSIFY THE PROCESSED IMAGES FOR DIGITAL PATHOLOGY

Resumen de: US2025165820A1

Systems and methods are disclosed for receiving a target image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning model to the target image to determine at least one characteristic of the target specimen and/or at least one characteristic of the target image, the machine learning model having been generated by processing a plurality of training images to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the at least one characteristic of the target specimen and/or the at least one characteristic of the target image.

REAL TIME FEEDBACK FROM A MACHINE LEARNING SYSTEM

Resumen de: US2025165819A1

A technique for providing real time feedback from a machine learning system is provided that includes a method and system for interactively training machine learning models. In particular, by separating processing and analysis using static and dynamic models that are trained differently, the disclosed technique enables interactive training and prediction of machine learning models to increase the speed of generating new predictions based on real time feedback. In some cases, a dynamic model is applied to the output of a static model to generate an analysis, a correction of the analysis is received, and the correction is used to retrain the dynamic model. An updated analysis is generated based on reapplying the dynamic model to the output of the static model without having to retrain the static model.

METHOD FOR MAKING THE FUNCTION OF A MACHINE LEARNING ALGORITHM EXPLAINABLE

Resumen de: US2025165821A1

A method for making the function of a machine learning algorithm explainable, wherein the machine learning algorithm is designed to assign input data to one of at least two groups. The method includes: providing input data for the machine learning algorithm; for all of the input data provided, assigning the corresponding input data to one of the at least two groups by means of the machine learning algorithm; selecting data from a first group of the at least two groups; ascertaining, from a second group of at least two groups, data that are most similar to the selected data from all the data contained in the second group; comparing the selected data with the ascertained data to make the machine learning algorithm explainable; and providing corresponding comparison results.

SYSTEMS AND METHODS FOR ITERATIVE FEATURE SELECTION FOR MACHINE LEARNING MODELS

Resumen de: US2025165848A1

Systems and methods for selecting machine learning features using iterative batch feature reduction. In some aspects, the system trains a plurality of candidate models based on a plurality of feature groups split from a first set of features. Each candidate model takes as input a feature group of no more than a first threshold number of features. For each candidate model in the plurality of candidate models, the system processes the candidate model to extract an explainability vector. Based on the explainability vector, the system selects a second threshold number of features from the feature group to generate a slim feature group. The system trains a slim candidate model which takes as input the slim feature group. The system generates a second set of features by combining features from a plurality of slim candidate models.

GENERATING AN EMBEDDING SPACE FROM A TRAINING DATA SET FOR A MACHINE LEARNING ALGORITHM

Resumen de: US2025165865A1

A system for and a method of generating an embedding space from a training data set for a machine learning algorithm. The method includes receiving, through an input of a computer system, an input operating data point by one or more layers of a machine learning algorithm implemented on the computer system, generating an embedding space using a training data set, the embedding space including a plurality of inducing data points, each inducing data point corresponding to a cluster of training data points, comparing the input operating data point against the plurality of inducing data points, determining whether a sufficient set of supporting historical data points exists in the plurality of training data points within a fixed distance from the input operating data point, and generating an alert based on the determining to enable a user or an electronic controller to take appropriate action, and outputting the alert.

STORING AND OBTAINING ATTRIBUTE DATA OF ATTRIBUTES OF MACHINE LEARNING MODELS

Resumen de: US2025165816A1

In some implementations, a controller may receive a request for an inference. The controller may determine, based on the received request for the inference, a first inference model of a plurality of inference models, to generate the inference. The controller may obtain, from a memory associated with an inference cache, first attribute data regarding first attributes of the first inference model. A location of the first attribute data, in the memory, may be determined using the inference cache. The attributes may include weights associated with the first inference model, biases associated with the first inference model, and a structure of the first inference model. The controller may utilize the first attribute data to generate the inference based on the request.

INTERACTIVE REAL-TIME VIDEO SEARCH BASED ON KNOWLEDGE GRAPH

Resumen de: US2025165529A1

A method, computer system, and computer program product are provided for real-time video searching based on augmented knowledge graphs that are generated using machine learning models. Multimedia data is obtained comprising an image portion and an audio portion, and a user query with respect to the multimedia data is obtained. A knowledge graph of the multimedia data is generated using one or more machine learning models based on the image portion and the audio portion, wherein the knowledge graph includes a plurality of entities and relationships between entities. An augmented knowledge graph is generated, wherein the augmented knowledge graph augments the knowledge graph with additional entities and additional relationships between the additional entities using additional data that is obtained from a source external to the multimedia data. A response to the user query is provided based on the augmented knowledge graph.

Closed-Loop Intelligence

Resumen de: US2025165438A1

Methods, computer systems, and computer-storage medium are provided for providing closed-loop intelligence. A selection of data is received, at a cloud service, from a database comprising data from a plurality of sources in a Fast Healthcare Interoperability Resources (FHIR) format to build a data model. After a feature vector corresponding to the data model is extracted, a selection of an algorithm for a machine learning model to apply to the data model is received. A portion of the selection of data is utilized for training data and test data and the machine learning model is applied to the training data. Once the model is trained, the trained machine learning model can be saved at the cloud service, where it may be accessed by others.

Closed-Loop Intelligence

Resumen de: US2025165439A1

Methods, computer systems, and computer-storage medium are provided for providing closed-loop intelligence. A selection of data is received, at a cloud service, from a database comprising data from a plurality of sources in a Fast Healthcare Interoperability Resources (FHIR) format to build a data model. After a feature vector corresponding to the data model is extracted, a selection of an algorithm for a machine learning model to apply to the data model is received. A portion of the selection of data is utilized for training data and test data and the machine learning model is applied to the training data. Once the model is trained, the trained machine learning model can be saved at the cloud service, where it may be accessed by others.

SECURITY-RELATED EVENT ANOMALY DETECTION

Resumen de: US2025168179A1

The technology relates to machine responses to anomalies detected using machine learning based anomaly detection. In particular, to receiving evaluations of production events, prepared using activity models constructed on per-tenant and per-user basis using an online streaming machine learner that transforms an unsupervised learning problem into a supervised learning problem by fixing a target label and learning a regressor without a constant or intercept. Further, to responding to detected anomalies in near real-time streams of security-related events of tenants, the anomalies detected by transforming the events in categorized features and requiring a loss function analyzer to correlate, essentially through an origin, the categorized features with a target feature artificially labeled as a constant. An anomaly score received for a production event is determined based on calculated likelihood coefficients of categorized feature-value pairs and a prevalencist probability value of the production event comprising the coded features-value pairs.

DATA ANALYTICS SYSTEM AND METHOD FOR MACHINE LEARNING SOLUTIONS WITH ZERO CODING

Resumen de: WO2025104745A1

A novel system and method for the development of machine learning solutions without the need for any coding expertise The system includes a highly advanced interactive dashboard designed for data visualization. One of its key features is ensuring that user inputs remain error-free and only request relevant options based on the provided data. This approach revolutionizes the accessibility and usability of machine learning, making it more inclusive for a broader user base, regardless of their coding background. The interactive dashboard streamlines the 10 machine learning solution development process and significantly reduces the barriers to entry, making it a promising innovation in the field of data science and artificial intelligence.

INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND RECORDING MEDIUM

Resumen de: WO2025104804A1

In this information processing device, a complementation means complements ontology data by using a graph machine learning model that has learned the relationship between information items included in the ontology data. A natural language processing model generation means generates a natural language processing model on the basis of the complemented ontology data. The information processing device can assist decision making of a user.

REUSE OF DATA FOR TRAINING MACHINE LEARNING MODELS

Resumen de: EP4557178A1

Example embodiments may relate to systems, methods and/or computer programs for reusing data for training machine learning models. In an example, an apparatus comprises means for receiving a request to collect new user data for training a machine learning model associated with an application. The apparatus may also comprise means for identifying existing stored data suitable for training the machine learning model based upon an ontology. The apparatus may also comprise means for providing access to the identified existing stored data in response to identifying that the data is suitable for training the machine learning model.

GENERATING AN EMBEDDING SPACE FROM A TRAINING DATA SET FOR A MACHINE LEARNING ALGORITHM

Resumen de: EP4557165A1

A system for and a method of generating an embedding space from a training data set for a machine learning algorithm. The method includes receiving, through an input of a computer system, an input operating data point by one or more layers of a machine learning algorithm implemented on the computer system, generating an embedding space using a training data set, the embedding space including a plurality of inducing data points, each inducing data point corresponding to a cluster of training data points, comparing the input operating data point against the plurality of inducing data points, determining whether a sufficient set of supporting historical data points exists in the plurality of training data points within a fixed distance from the input operating data point, and generating an alert based on the determining to enable a user or an electronic controller to take appropriate action, and outputting the alert.

Systems and methods for intelligent machine learning-based malware detection

Resumen de: US2025156546A1

Systems and methods for training a machine learning model for malware detection include steps of collecting a training dataset comprising a plurality of malicious files and a plurality of benign files from one or more sources; extracting features from each file in the training dataset, wherein the features include at least one of n-gram features, entropy features, or domain features; labeling each file in the training dataset as malicious or benign based on a predefined criterion; and applying a supervised machine learning technique to learn patterns in the extracted features and generate a trained machine learning model configured to predict whether a file is malicious or benign based on an incremental packet-based analysis.

Query Processing with Machine Learning

Resumen de: US2025156430A1

The description relates to executing an inference query relative to a database management system, such as a relational database management system. In one example a trained machine learning model can be stored within the database management system. An inference query can be received that applies the trained machine learning model on data local to the database management system. Analysis can be performed on the inference query and the trained machine learning model to generate a unified intermediate representation of the inference query and the trained model. Cross optimization can be performed on the unified intermediate representation. Based upon the cross-optimization, a first portion of the unified intermediate representation to be executed by a database engine of the database management system can be determined, and, a second portion of the unified intermediate representation to be executed by a machine learning runtime can be determined.

ARTIFICIAL INTELLIGENCE IN AN AEROSOL DELIVERY DEVICE

Resumen de: US2025151806A1

An aerosol delivery device is provided that includes sensor(s) to produce measurements of properties during use of the device, and processing circuitry to record data for a plurality of uses of the device, for each use of which the data includes the measurements of the properties. The processing circuitry is configured to build a machine learning model to predict a target variable, using a machine learning algorithm, at least one feature selected from the properties, and a training set produced from the measurements of the properties. The processing circuitry is configured to then deploy the machine learning model to predict the target variable, and control at least one functional element of the device based on the target variable, the target variable being a user profile depending on at least one of the properties, and times and durations of respective user puffs on the device.

ENSEMBLE MACHINE LEARNING SYSTEMS AND METHODS

Nº publicación: US2025157670A1 15/05/2025

Solicitante:

CURAI INC [US]
Curai, Inc

Resumen de: US2025157670A1

Techniques for responding to a healthcare inquiry from a user are disclosed. In one particular embodiment, the techniques may be realized as a method for responding to a healthcare inquiry from a user, according to a set of instructions stored on a memory of a computing device and executed by a processor of the computing device, the method comprising the steps of: classifying an intent of the user based on the healthcare inquiry; instantiating a conversational engine based on the intent; eliciting, by the conversational engine, information from the user; and presenting one or more medical recommendations to the user based at least in part on the information.