Machine learning

FEATURING ENGINEERING BASED ON SEMANTIC TYPES

Resumen de: US2024320252A1

A computer-based system may engineer features based on semantic types. The computer-based system may implement deep learning algorithms and derive a domain-specific feature engineering strategy from semantic type predictions and data profiling. The computer-based system may utilize embedded domain (e.g., financial industry, etc.) knowledge to generate curated features from raw data (e.g., transactional datasets, relational datasets, etc.).

INFORMATION PROCESSING METHOD AND INFORMATION PROCESSING SYSTEM

Resumen de: US2024320552A1

An evaluation result of training data to be used to train a machine learning model is presented.An information processing method that performs processing related to the training data to be used to train the machine learning model includes a determination step of determining a characteristic of each piece of the training data on the basis of an inference result of the machine learning model for the training data, and a presentation step of presenting the evaluation result of the training data based on the determined characteristic. In the determination step, a physical characteristic such as mass, a size, or acting force including attractive force and repulsive force of an object corresponding to the training data is determined on the basis of an expected value for each label output by the machine learning model for the training data.

DEVICE, METHOD, AND SYSTEM FOR WEIGHTED KNOWLEDGE TRANSFER

Resumen de: US2024320507A1

Aspects relate to a privacy preserving public machine learning model that achieves high performance while maintaining data privacy. Further aspects relate to a weighted knowledge transfer device including a feature determination unit to generate a public knowledge transfer dataset and a private knowledge transfer dataset; a data selection unit to generate, based on a similarity calculation of the public knowledge transfer dataset and the private knowledge transfer dataset, a public training dataset and a similarity weight vector; a machine learning model management unit to generate, by processing the public training dataset with a set of machine learning models trained based on the private knowledge transfer dataset, a public label vector that indicates labels for the set of public features; and a knowledge transfer unit to generate a public machine learning model based on the weight vector, the public training dataset, and the public label vector.

LOCATION INTELLIGENCE MANAGEMENT SYSTEM AND METHOD

Resumen de: US2024320515A1

A computer-implemented method of providing location intelligence for a geospatial location, comprising: receiving, from a plurality of location intelligence sources, a plurality of location intelligence data about the geospatial location, wherein the plurality of location intelligence data are not natively interoperable with one another; operating a machine learning (ML) algorithm to reconcile plurality of location intelligence data into a location intelligence digest for the geospatial location; and presenting the location intelligence digest to a human user in a human perceptible form via a human interface device (HID).

Computerized System and Method of Open Account Processing

Resumen de: US2024320526A1

A computerized system and method for health care facilities to reduce manual handling of at least some open account issues. The system provides healthcare facilities with the ability to resolve current open patient account issues by utilizing the data patterns from a facility's historical patient account transaction activity, to create a machine learning model that can predict resolutions to the open accounts. These patterns are then applied to a facility's current transaction data providing next step resolution to each patient account.

Machine Learning Model Deployment in Inference System

Resumen de: US2024320543A1

Deploying machine learning models is provided. A new machine learning model is received for a given problem that corresponds to a service running in a container. A cluster of machine learning models of a plurality of clusters of machine learning models corresponding to the given problem is selected. A cluster performance score is determined for the cluster based on combining a model performance score of each machine learning model in the cluster in accordance with a corresponding weight of each machine learning model. It is determined whether the cluster performance score of the cluster is greater than a minimum cluster performance score threshold. The new machine learning model is added to the cluster to increase predictive accuracy for the given problem while the service is running without interruption in response to determining that the cluster performance score of the cluster is greater than the minimum cluster performance score threshold.

SYSTEM AND METHOD FOR AUTOMATED PRODUCTION AND DEPLOYMENT OF PACKAGED AI SOLUTIONS

Resumen de: US2024320036A1

A data processing method and system for automated construction, resource provisioning, data processing, feature generation, architecture selection, pipeline configuration, hyperparameter optimization, evaluation, execution, production, and deployment of machine learning models in an artificial intelligence solution development lifecycle. In accordance with various embodiments, a graphical user interface of an end user application is configured to provide a pre-configured template comprises an automated ML framework for data import, data preparation, data transformation, feature generation, algorithms selection, hyperparameters tuning, models training, evaluation, interpretation, and deployment to an end user. A configurable workflow is configured 10 to enable a user to assemble one or more transmissible AI build/products containing one or more pipelines and/or ML models for executing one or more AI solutions. Embodiments of the present disclosure may enable full serialization and versioning of all entities relating to an AI build/product for deployment within an enterprise architecture.

HEIDI: ML ON HYPERVISOR DYNAMIC ANALYSIS DATA FOR MALWARE CLASSIFICATION

Resumen de: US2024320338A1

The present application discloses a method, system, and computer system for detecting malicious files. The method includes (a) receiving a sample for malware analysis, (b) applying a machine learning model to obtain a classification for the sample based at least in part on (i) memory artifact data associated with the sample, and (ii) at least one of dynamic execution log data for the sample and static file structures associated with the sample, and (c) determining whether the sample is malicious based at least in part on the classification.

TOOL FOR ANALYZING SPEECH USING AI TECHNIQUES TO DIAGNOSE DEMENTIA

Resumen de: US2024315640A1

Provided herein are methods of training a machine-learning model to detect a neurological condition. The methods include providing a neurological condition dataset comprising neurological condition text embeddings, the neurological condition text embeddings including large language model (LLM) text embeddings from subjects having the neurological condition; providing a control dataset comprising control text embeddings, the control text embeddings including LLM text embeddings from healthy subjects; and training the machine-learning model using the neurological condition dataset and the control dataset, forming a trained machine-learning model configured to detect a neurological condition. Also provided herein are methods of detecting a neurological condition using the trained machine-learning model.

System and Method for Using Artificial Intelligence to Detect Lithium Plating

Resumen de: US2024317108A1

In one aspect, computer-implemented method may include, while a battery pack is charging, receiving, from sensors, measurements associated with the battery pack. The battery pack includes cells. The method may include separating the measurements into separate profiles for the cells, wherein the separate profiles include data pertaining to current, voltage, temperature, or some combination thereof. The method may include identifying, using the separate profiles, features, generating a training dataset by reducing the features based on a mean-comparison technique, a minority scaling technique, or both, and generating a trained machine learning model using the training dataset including the reduced features as labeled input and true lithium plating occurrence statuses as labeled output. The method may include predicting, using the trained machine learning model, an occurrence of lithium plating by inputting subsequently received data into the trained machine learning model.

Method of diagnosing and/or monitoring a lubricant dispenser

Resumen de: AU2024201556A1

Abstract The invention relates to a method of diagnosing and/or monitoring the condition of an electromechanically operated lubricant dispenser, wherein the lubricant dispenser has a container filled with lubricant and an electromechanical drive removably mounted on the container for conveying lubricant from the container to an outlet, the drive or one or more sensors integrated in the drive and/or in the container provide measurement data for one or more detected variables, and at least one condition of the lubricant dispenser is determined on the basis of the measurement data. In the method, the measurement data or data generated therefrom are processed as input data by a classification algorithm trained using machine learning methods that classifies a condition of the lubricant dispenser on the basis of the input data. For this purpose, the measurement data can, for example, be made available at a predetermined sampling rate as time series with a large number of measured values for one or more detected variables and these time series or data generated from them can be processed by the algorithm as input data (E). CY) CY) CN

A SYSTEM AND METHOD FOR POTENTIAL BIAS IDENTIFICATION

Resumen de: WO2024194871A1

The described invention is directed to systems and methods capable of identifying Machine Learning (ML) models that are potentially biased. The system obtains: (a) a list of potentially problematic labels, and (b) at least one code segment, including a plurality of code lines containing one or more commands associated with generating at least one machine learning model from a given data structure. The system extracts the actual labels of the given data structure and compares them to the list of potentially problematic labels. Upon a match between at least one of the extracted actual labels and at least one of the potentially problematic labels, the system performs an action associated with the knowledge that the ML model is potentially biased.

METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT FOR PROVIDING A TYPE AWARE TRANSFORMER FOR SEQUENTIAL DATASETS

Resumen de: WO2024197299A1

Provided are methods that include receiving interaction data associated with a plurality of interactions, the interaction data including interaction records that include a plurality of fields including a static field and a dynamic field, generating a static interaction embedding representation based on static field data associated with the static field and a first transformer model, generating a plurality of dynamic interaction embedding representations based on dynamic field data associated with the dynamic field of a sequence of interaction records and a second transformer model, generating a first intermediate input and a plurality of second intermediate inputs, generating a static sequence embedding representation and dynamic sequence embedding representations based on a third transformer model, and generating at least one prediction based on inputting the static sequence embedding representation and the plurality of dynamic sequence embedding representations to a machine learning model. Systems and computer program products are also disclosed.

TASK COMPLETION PATH GENERATOR FOR INSTRUCTIONAL CONTENT

Resumen de: WO2024196611A1

A computer implemented method includes accessing instructional content that describes a task for completion by a user. Actions described in the instructional content are derived from the instructional content. Telemetry containing logged actions taken by users is accessed and used to identify actions taken that are associated with the task. A machine learning model is used to identify a task completion path endpoint for the instructional content based on the derived actions and actions taken associated with the task.

MACHINE LEARNING MODEL DEPLOYMENT, MANAGEMENT AND MONITORING AT SCALE

Resumen de: CA3239204A1

A system can include a machine learning model training framework that generates trained machine learning models; a metadata configurer that generates metadata for trained machine learning model implementation; and a deployment manager that deploys trained machine learning models, metadata or trained machine learning models and metadata to remote devices according to one or more implementation strategies.

APPARATUS AND METHODS TO PROVIDE INFORMATION SECURITY FOR A MACHINE LEANRING (ML) MODEL BY GENERATING AN EXPLANATION FOR THE ML MODEL WITHOUT ACCESSING THE ML MODEL

Resumen de: US2024311685A1

In an embodiment, a method includes receiving, via a processor of a first compute device, a representation of a set of inputs and a set of outputs that were generated by inputting the set of inputs into a machine learning (ML) model by a set of compute devices not including the first compute device to generate the set of outputs. The method further includes receiving, via the processor, a request for a machine learning (ML) explanation associated with the ML model and at least one explicand. The method further includes generating, via the processor and without using the ML model, a representation of the ML explanation based on the at least one explicand, the set of inputs, and the set of outputs.

TOPOLOGY EXPLORER FOR MESSAGE-ORIENTED MIDDLEWARE USING MACHINE LEARNING TECHNIQUES

Resumen de: US2024311655A1

Methods, apparatus, and processor-readable storage media for implementing topology explorers for message-oriented middleware using machine learning techniques are provided herein. An example computer-implemented method includes obtaining data pertaining to at least one messaging topology associated with at least one message-oriented middleware; predicting one or more anomalies associated with the at least one messaging topology by processing at least a portion of the obtained data using a first set of one or more machine learning techniques; recommending one or more alternate messaging topologies associated with the at least one message-oriented middleware by processing at least a portion of the one or more predicted anomalies and at least a portion of the obtained data using a second set of one or more machine learning techniques; and performing one or more automated actions based on the one or more predicted anomalies and/or the one or more recommended alternate messaging topologies.

Method And System For Implementing Machine Learning Classifications

Resumen de: US2024311386A1

Disclosed is a system, method, and computer program product for implementing a log analytics method and system that can configure, collect, and analyze log records in an efficient manner. Machine learning-based classification can be performed to classify logs. This approach is used to group logs automatically using a machine learning infrastructure.

SYSTEMS AND METHODS FOR GENERATING GRADIENT-BOOSTED MODELS WITH IMPROVED FAIRNESS

Resumen de: US2024311909A1

Systems and methods for generating tree-based models with improved fairness are disclosed. The disclosed process generates a first tree-based machine learning model, which is preferably trained to predict if a financial loan will be repaid. The process also determines an accuracy of the first tree-based machine learning mode. In addition, the process determines a fairness of the first tree-based machine learning model. The fairness is preferably associated with at least one of gender, race, ethnicity, age, marital status, military status, sexual orientation, and disability status. The process then generates a second different tree-based machine learning model, which is preferably trained based on the accuracy of the first tree-based machine learning model and the fairness of the first tree-based machine learning model. The process then combines the first tree-based machine learning model and the second tree-based machine learning model to produce a gradient-boosted machine learning model.

METHOD AND SYSTEM FOR AUTOMATICALLY FORMULATING AN OPTIMIZATION PROBLEM USING MACHINE LEARNING

Resumen de: US2024311548A1

The present disclosure provides a computer implemented method and system for generating an algebraic modelling language (AML) formulation of natural language text description of an optimization problem. The computer implemented method includes generating, based on the natural language text description, a text markup language intermediate representation (IR) of the optimization problem, the text markup language IR including an IR objective declaration that defines an objective for the optimization problem and a first IR constraint declaration that indicates a first constraint for the optimization problem. The computer implemented also includes generating, based on the text markup language IR, the AML formulation of the optimization problem, the AML formulation including an AML objective declaration that defines the objective for the optimization problem and a first AML constraint declaration that indicates the first constraint for the optimization problem. The computer implemented method and system of the present disclosure improves the accuracy in generating an AML formation of an optimization problem than is possible with known solutions, thereby improving the operation of a computer system that applies the computer implemented method.

SYSTEMS AND METHODS FOR OPTIMIZING AGGREGATE VALUES FOR A REQUEST FOR QUOTATION

Resumen de: US2024311895A1

A method for optimizing aggregate values for a request for quotation is provided. The method includes: receiving data associated with a request for quotation for one or more items, the request for quotation including a respective quantity associated with each of the one or more items; assigning, using a first trained machine learning model, one or more aggregate values to the request for quotation; assigning, using a second trained machine learning model, a respective win probability for each of the one or more aggregate values; transmitting the one or more aggregate values and the one or more win probabilities associated with each of the one or more aggregate values to an electronic database; and transmitting the one or more aggregate values and the one or more win probabilities associated with each of the one or more aggregate values to a graphical user interface.

MACHINE-LEARNING BASED DATA ENTRY DUPLICATION DETECTION AND MITIGATION AND METHODS THEREOF

Resumen de: US2024311354A1

Systems and methods of the present disclosure enable a processor to automatically detect duplicate data entries by receiving data entries associated with a user, where each data entry includes a value, a time, an entity identifier, and a location. Pairs of similar data entries are determined by matching the entity identifier and the location pairs data entries. Candidate duplicate data entries are determined based on a proximity in time between data entries of the similar data entries. For each candidate duplicate data entry, a feature vector is generated including the entity identifier, location, value and time, and each feature vector is submitted to a duplicate classification model to automatically determine duplicate data entries from the candidate duplicate data entries, the duplicate classification model being trained according to a historical dispute entries.

A Machine Learning System and Process for Categorising Job Candidates

Resumen de: AU2024201408A1

Abstract Abstract This invention relates to machine learning systems and processes, such as machine learning systems and processes for categorising employment candidates by ranked likelihood of moving from a current role, and particularly for machine learning systems operable to update machine learning output data or training data in response to email actions. 201 '203 ="*:204 206 207 20 Text e -m--, --.- I**** Figure 3 Source 1 99% ---- 306Modeling30 Faue Insights Source n Select and (lean and merge transform Figure 4

UNCERTAINTY QUANTIFICATION FOR MACHINE LEARNING CLASSIFICATION MODELLING

Resumen de: US2024311665A1

Certain aspects of the disclosure provide a method, comprising: processing input data with an ensemble of nonlinear machine learning models; generating a sparse high-dimensional embedding based on one or more leaf nodes of each nonlinear machine learning model in the ensemble of nonlinear machine learning models; projecting the high-dimensional embedding into a lower-dimensional embedding, wherein the lower-dimensional embedding is less sparse than the high-dimensional embedding; processing the lower-dimensional embedding with a linear machine learning model to generate a binary class prediction; determining a confidence for the binary class prediction; and outputting: the binary class prediction if the confidence is greater than or equal to a threshold; or a flipped binary class prediction if the confidence is lower than the threshold.

Machine Learning Model Understanding As-A-Service

Nº publicación: US2024311700A1 19/09/2024

Solicitante:

AT&T INTELLECTUAL PROPERTY I L P [US]
AT&T Intellectual Property I, L.P

Resumen de: US2024311700A1

Concepts and technologies disclosed herein are directed to machine learning model understanding as-a-service. According to one aspect of the concepts and technologies disclosed herein, a model understanding as-a-service system can receive, from a user system, a service request that includes a machine learning model created for a user associated with the user system. The model understanding as-a-service system can conduct an analysis of the machine learning model in accordance with the service request. The model understanding as-a-service system can compile, for the user, results of the analysis of the machine learning model in accordance with the service request. The model understanding as-a-service system can create a service response that includes the results of the analysis. The model understanding as-a-service system can provide the service response to the user system.