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FACILITATING TIME ZONE PREDICTION BASED ON ELECTRONIC COMMUNICATION DATA

Resumen de: US2024323156A1

Methods and systems are provided for facilitating time zone prediction using electronic communication data. Electronic message data associated with a message recipient of electronic communications is obtained. The electronic message data includes message delivery data associated with an electronic message and message response data associated with a response, by the message recipient, to a received electronic message. Using a machine learning model and based on the message delivery data and the message response data, a time-zone score is determined for a time zone. Such a time-zone score can indicate a probability the time zone corresponds with the message recipient. Based on the time-zone score, the time zone is identified as corresponding with the message recipient.

Task Completion Path Generator for Instructional Content

Resumen de: US2024321129A1

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.

ENHANCED DEVICE CLASSIFICATION INCLUDING CROWDSOURCED CLASSIFICATIONS FOR INCREASED ACCURACY

Resumen de: US2024323093A1

Systems, methods, and related technologies for classifying a device on a network are described. A method includes capturing device information corresponding to a device on a network. The method inputs unstructured crowdsourced data on the network into a machine learning model to produce structured crowdsourced data. The method classifies the device based on evaluating the device information with the structured crowdsourced data.

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.

SMART RING SYSTEM FOR MEASURING DRIVER IMPAIRMENT LEVELS AND USING MACHINE LEARNING TECHNIQUES TO PREDICT HIGH RISK DRIVING BEHAVIOR

Resumen de: US2024308530A1

A method for predicting risk exposure can include receiving a set of data collected via a smart ring. The method also can include analyzing, via a trained machine learning (ML) model, the set of data collected via the smart ring to determine whether the set of data collected via the smart ring (a) represents an impairment pattern or (b) correlates to a high-risk pattern, wherein at least one of the impairment pattern or the high-risk pattern correlates to a risk exposure. The method for predicting risk exposure further can include generating a notification to alert a user of the risk exposure. Other embodiments are disclosed.

EFFICIENT HARDWARE ACCELERATOR CONFIGURATION EXPLORATION

Resumen de: US2024311267A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a surrogate neural network configured to determine a predicted performance measure of a hardware accelerator having a target hardware configuration on a target application. The trained instance of the surrogate neural network can be used. in addition to or in place of hardware simulation, during a search process for determining hardware configurations for application-specific hardware accelerators. i.e., hardware accelerators on which one or more neural networks can be deployed to perform one or more target machine learning tasks.

CONTENT RECOMMENDATION BASED UPON CONTINUITY AND GROUPING INFORMATION OF ATTRIBUTES

Resumen de: US2024311662A1

One or more computing devices, systems, and/or methods for content recommendation based upon continuity and grouping information of attributes are provided herein. User interaction data specifying whether users interacted with content items, user attributes of the users, and content attributes of the content items is obtained. A data structure is populated with the user interaction data. The data structure is modified by inserting a set of sub-fields into the data structure for a user attribute. A sub-field is populated with a value representing an option of the user attribute. The set of sub-fields are an encoding of continuity information and grouping information representing options for the user attribute. The data structure is processed using machine learning functionality to generate a model. The model is utilized to generate a prediction as to whether a user will interact with a content item.

ARTIFICIAL-INTELLIGENCE ARCHITECTURE FOR DETECTING DOCUMENT MANIPULATION

Resumen de: US2024311661A1

The present disclosure generally relates to techniques for constructing an artificial-intelligence (AI) architecture. The present disclosure relates to techniques for executing the AI architecture to detect whether or not characters in a digital document have been manipulated. The AI architecture can be configured to classify each character in a digital document as manipulated or not manipulated by constructing a graph for each character, generating features for each node of the graph, and inputting a vector representation of the graph into a trained machine-learning model to generate the character classification.

Machine Learning Model Understanding As-A-Service

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.

AUTOMATED PARALLELIZED PROCESSING OF DECISION-TREE GUIDELINES USING ELECTRONIC RECORD

Resumen de: US2024311699A1

A machine learning model for traversing a decision tree, the machine learning model trained from a structured data set including a first set of key-value pairs and subject-specific criteria using the key-value pairs. The first set of key-value pairs is transformed into a second set of key-value pairs, which are projected to a subject-specific point within a multi-dimensional space. The decision tree includes decision and leaf nodes. Each leaf node is connected to a root node via a leaf-node-specific trajectory. Each decision node corresponds to a criterion using a value in the second set of key-value pairs. For each leaf node, a leaf-node-specific point within the multi-dimensional space is determined using the leaf-node-specific trajectory, and a similarity score is determined using the leaf-node-specific and subject-specific points. A subset of the leaf nodes is identified using the scores. State or protocol information for each leaf node in the subset is retrieved.

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.

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.

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.

INVERSE REINFORCEMENT LEARNING FOR ADAPTIVE CRUISE CONTROL

Resumen de: US2024308514A1

An example operation includes one or more of obtaining sensor data captured by one or more sensors of a vehicle when the vehicle is traveling along a road behind a lead vehicle, determining a size of the lead vehicle, predicting, via execution of a machine learning model, a recommended gap distance of the vehicle between the vehicle and the lead vehicle based on the obtained sensor data and the determined size, and notifying the vehicle of the recommended gap distance.

INDUSTRIAL VIRTUAL ASSISTANT PLATFORM WITH ROBOTIC PROCESS AUTOMATION FOR KNOWLEDGE AND INSIGHTS MANAGEMENT

Resumen de: AU2023215061A1

An Industrial Virtual Assistant (IVA) platform with Robotic Process Automation that operates like a Digital Knowledge Companion and allows operational staff at industrial facilities to have natural language conversations with the IVA to obtain information about, and to control operations of, industrial facilities, and which automates certain processes based in part on those natural language conversations. In an embodiment, the platform uses a Robotic Process Automater (RPA) to ingest information from documentation, human inputs, and operational data from the facility, organize that information into a knowledge graph containing comprehensive facility information, and apply machine learning algorithms to the knowledge graph to provide natural language responses to human queries and to automate certain processes of the facility.

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

SYSTEMS, METHODS, AND APPARATUSES FOR AUTO-SCALING VOLATILE MEMORY ALLOCATION IN AN ELECTRONIC NETWORK

Resumen de: US2024311287A1

Systems, computer program products, and methods are described herein for auto-scaling volatile memory allocation in an electronic network. The present invention is configured to access metadata of at least one volatile memory component, wherein the metadata is associated with at least one application; determine a current volatile memory allocation for the metadata; determine a current metadata format of the metadata; apply the metadata to a volatile memory allocation machine learning model; generate, based on the application of the metadata to the volatile memory allocation machine learning model, a new volatile memory allocation for the metadata; and apply the new volatile memory allocation to the metadata of the at least one volatile memory component, wherein the application of the new volatile memory allocation to the metadata comprises at least one of an upscaling, a downscaling, or a constant.

PROVIDING A SECURE AND COLLABORATIVE FEEDBACK MECHANISM FOR MACHINE LEARNING MODELS

Resumen de: US2024311682A1

A device may receive, from a user device, a machine learning model, training data, and user input for the machine learning model, and may process the training data and the user input, with the machine learning model, to generate a prediction and an explanation of the prediction. The device may provide the prediction and the explanation to the user device and may receive, from the user device, prediction feedback for the prediction and explanation feedback for the explanation. The device may determine whether an agreement is achieved between the prediction feedback and the explanation feedback based on a threshold and may update the machine learning model based on the agreement being achieved. The device may cryptographically protect the updated machine learning model to generate an updated and cryptographically protected machine learning model and may perform actions based on the updated and cryptographically protected machine learning model.

MACHINE LEARNING TECHNOLOGIES FOR PREDICTING ORDER FULFILLMENT

Resumen de: US2024220926A1

Systems and methods for training and using deep learning artificial neural networks are provided. According to certain aspects, a deep learning artificial neural network is initially trained using a training dataset, and is used to analyze shipping data and order data associated with a shipping agreement and output a probability that the shipping agreement will be successfully fulfilled. The deep learning artificial neural network is updated with information indicating the order data associated with the shipping agreement and the probability that the shipping agreement will be successfully fulfilled.

Training machine learning models to predict characteristics of adverse events using intermittent data

Resumen de: AU2024213145A1

TRAINING MACHINE LEARNING MODELS TO PREDICT CHARACTERISTICS OF ADVERSE EVENTS USING INTERMITTENT DATA Methods, systems, and apparatus for providing a ML model for inference, the ML model having been trained using a first set of training data to provide predictions associated with an adverse event, after training of the ML model, receiving data from one or more data sources, the data representative of characteristics relevant to predictions associated with the adverse event, providing a second set of training data, determining, by a trigger module, a trigger decision based on a set of signals at least partially determined from the second set of training data, the trigger decision indicating whether the ML model is to be one of updated and retrained based on the second set of training data, and selectively executing one of updating and retraining of the ML model using at least a portion of the second set of training data in response to the trigger decision.

AUTOMATED ACCOUNT MAINTENANCE AND FRAUD MITIGATION TOOL

Resumen de: AU2024213170A1

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support automated account maintenance and fraud mitigation for secure accounts such as vendor master accounts or client master accounts. To illustrate, a system receives a request from a user to update an account. The system extracts request data from the request, for example using natural language processing and optical character recognition. The system performs validation operation(s) (e.g., entry validation, location validation, domain validation, etc.), in some implementations using one or more machine learning models. Upon successful validation, if the user is an authorized contact for the account, the system authenticates the request (e.g., via request of an authorization code) and updates the account. If the user is not an authorized contact, the system transmits authentication requests to the authorized contact(s) and, based on receipt of responses from the authorized contact(s), the system updates the account.

Identifying recurring events using automated semi-supervised classifiers

Resumen de: AU2024201262A1

Systems and methods for training machine learning models are disclosed. An example method includes receiving historical event timing data including event data for a first portion including events from a first time period, and a second portion comprising events from a second time period not including the first time period, predicting, based on the first portion of the historical event timing data, a first plurality of predicted events, the first plurality of predicted events corresponding to the second time period, determining a first subset of predicted events to be accurate predictions based at least in part on comparing the first plurality of predicted events to the historical events occurring within the second time period, generating training data based at least in part on the first subset of the first plurality of predicted events, and training the machine learning model based at least in part on the training data. (~1 ~ I2 cn Cln Qn -

SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR MANAGING AUTOMATED HEALTHCARE DATA APPLICATIONS USING ARTIFICIAL INTELLIGENCE

Resumen de: AU2023236265A1

Provided is a system for managing automated healthcare data applications using artificial intelligence (Al) that includes at least one processor programmed or configured to receive healthcare data from a data source, determine a classification of the healthcare data using a machine learning model, wherein the machine learning model is configured to provide a predicted classification of an automated healthcare data analysis application of a plurality of automated healthcare data analysis applications based on an input, and provide the healthcare data to an automated healthcare data analysis application based on the classification of the healthcare data. Methods and computer program products are also disclosed.

METHOD OF DIAGNOSING AND/OR MONITORING A LUBRICANT DISPENSER

Nº publicación: US2024301996A1 12/09/2024

Solicitante:

GRAF PAUL [DE]
HAUPT THOMAS [DE]
LENHART MATTHIAS [DE]
GRAF Paul,
HAUPT Thomas,
LENHART Matthias

Resumen de: US2024301996A1

An electromechanically operated lubricant dispenser having a container filled with lubricant and an electromechanical drive detachably connected to the container for conveying lubricant from the container to an outlet is diagnosed by first providing measurement data with the drive or one or several sensors integrated in the drive and/or in the container for one or more detected variables. In addition at least one condition of the lubricant dispenser is determined from the measurement data and finally the measurement data or data generated therefrom is processed as input data by an algorithm trained with methods of machine learning that classifies a condition of the lubricant dispenser on the basis of the input data.