Machine learning

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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 -

SYSTEMS AND METHODS FOR OPTIMIZED TRANSACTION PROCESSING

Resumen de: WO2024186824A1

In some aspects, the techniques described herein relate to a method including: providing a plurality of inputs to a payload engine; providing a machine learning engine, wherein the machine learning includes a machine learning model, and wherein the machine learning model is configured to generate output based on the plurality of inputs; providing a rules engine, wherein the rules engine includes a logic tree based on the plurality of inputs; receiving, at the payload engine, a transaction and associated transaction details; generating, by the machine learning engine and based on the transaction, the associated transaction details, and the plurality of inputs, a first transaction processing parameter; generating, by the rules engine, and based on the transaction, the associated transaction details, and the plurality of inputs, a second transaction processing parameter; and combining the transaction, the first transaction processing parameter and the second transaction processing parameter into a transaction payload formula.

WELL COMPLETION SELECTION AND DESIGN USING DATA INSIGHTS

Resumen de: US2024303384A1

Methods and systems are provided for automating well completion selection and design using machine learning and natural language processing. The present disclosure describes a method for designing a well completion, comprising: i) collecting and storing a historical dataset comprising unstructured data related to prior well completions: ii) identifying a plurality of unstructured schematic documents related to prior well completions that are a part of the historical dataset of i): iii) processing each given unstructured schematic document of the plurality of unstructured schematic documents of ii) to generate structured data corresponding to text of the given unstructured schematic document: iv) associating the structured data corresponding to text of the respective unstructured schematic documents of iii) with different well contexts as part of a database: and v) presenting a graphical user interface to a user for designing a well completion, wherein the graphical user interface presents structured data stored in the database of iv) for insight in designing the well completion.

AUTOMATED SYSTEMS FOR MACHINE LEARNING MODEL DEVELOPMENT, ANALYSIS, AND REFINEMENT

Resumen de: US2024303553A1

This application describes systems and methods for generating machine learning models (MLMs). An exemplary method includes obtaining a sample and user input data characterizing a product or service. A subset of the data is selected from the sample based on sampling the sample according to the user input data. An MLM is trained by applying the data subset as training input to the MLM, thereby providing a trained MLM to emulate a customer selection process unique to the product or service. A user interface (UI) configured to receive other user input data and cause the trained MLM to execute on the other user input data, thereby testing the trained MLM, is presented. A summary of results from the execution of the trained MLM is generated and presented in the UI. The summary of results indicates a contribution to the trained MLM of each of a plurality of features.

GRAPH DATABASE TECHNIQUES FOR MACHINE LEARNING

Resumen de: US2024303544A1

A process is provided for using a graph database (e.g., SPOKE) to generate training vectors (SPOKEsigs) and train a machine learning model to classify biological entities. A cohort's input data records (EHRs) are compared to graph database nodes to identify overlapping concepts. Entry nodes (SEPs) associated with these overlapping concepts are used to generate propagated entry vectors (PSEVs) that encode the importance of each database node for a particular cohort, which helps train the model with only relevant information. Further, the propagated entry vectors for a given entity with a known classification can be aggregated to create training vectors. The training vectors are used as inputs to train a machine learning model. Biological entities with an unknown classification can be classified with a trained machine learning model. Entity signature vectors are generated for entities without a classification and input into the trained machine learning model to obtain a classification.

System and Method for Preventing Attacks on a Machine Learning Model Based on an Internal Sate of the Model

Resumen de: US2024303328A1

Disclosed implementations include a method of detecting attacks on Machine Learning (ML) models by applying the concept of anomaly detection based on the internal state of the model being protected. Instead of looking at the input or output data directly, disclosed implementation look at the internal state of the hidden layers of a neural network of the model after processing of data. By examining how different layers within a neural network model are behaving an inference can be made as to whether the data that produced the observed state is anomalous (and thus possibly part of an attack on the model).

Camera Platform Incorporating Schedule and Stature

Resumen de: US2024305751A1

Camera platform techniques are described. In an implementation, a plurality of digital images and data describing times, at which, the plurality of digital images are captured is received by a computing device. Objects of clothing are recognized from the digital images by the computing device using object recognition as part of machine learning. A user schedule is also received by the computing device that describes user appointments and times, at which, the appointments are scheduled. A user profile is generated by the computing device by training a model using machine learning based on the recognized objects of clothing, times at which corresponding digital images are captured, and the user schedule. From the user profile, a recommendation is generated by processing a subsequent user schedule using the model as part of machine learning by the computing device.

INTELLIGENT EDGE COMPUTING PLATFORM WITH MACHINE LEARNING CAPABILITY

Resumen de: US2024305689A1

An edge computing platform with machine learning capability is provided between a local network with a plurality of sensors and a remote network. A machine learning model is created and trained in the remote network using aggregated sensor data and deployed to the edge platform. Before being deployed, the model is edge-converted (“edge-ified”) to run optimally with the constrained resources of the edge device and with the same or better level of accuracy. The “edge-ified” model is adapted to operate on continuous streams of sensor data in real-time and produce inferences. The inferences can be used to determine actions to take in the local network without communication to the remote network. A closed-loop arrangement between the edge platform and remote network provides for periodically evaluating and iteratively updating the edge-based model.

METHODS AND APPARATUSES FOR GENERATING ONE OR MORE ANSWERS RELATING TO FUNCTIONING OF A MACHINE LEARNING MODEL

Resumen de: US2024303539A1

Embodiments described herein relate to methods and apparatuses for generating one or more answers relating to a machine learning, ML, model. A method in a first node comprises obtaining one or more queries relating to a first output of the ML model, wherein the first output of the machine learning, ML, model is intended to fulfil one or more requirements in an environment; for each of the one or more queries performing a reinforcement learning process. The reinforcement learning process comprises: generating a first set of answers to the query based on the one or more requirements; obtaining a first set of rewards associated with the query, wherein each reward in the first set of rewards is associated with a respective answer in the first set of answers, wherein each reward in the first set of rewards is determined based on one or more metrics; and iteratively generating updated sets of answers associated with the query based on a set of rewards associated with a set of answers from a preceding iteration until a terminal set of answers is reached, in which a second set of answers is generated based on the first set of rewards. Responsive to at least one reward from the reinforcement learning process associated with each query meeting a first predetermined criterion, the method then further comprises initiating implementation of the first output of the ML model in the environment.

SYSTEMS AND METHODS FOR OPTIMIZED TRANSACTION PROCESSING

Resumen de: US2024303512A1

In some aspects, the techniques described herein relate to a method including: providing a plurality of inputs to a payload engine; providing a machine learning engine, wherein the machine learning includes a machine learning model, and wherein the machine learning model is configured to generate output based on the plurality of inputs; providing a rules engine, wherein the rules engine includes a logic tree based on the plurality of inputs; receiving, at the payload engine, a transaction and associated transaction details; generating, by the machine learning engine and based on the transaction, the associated transaction details, and the plurality of inputs, a first transaction processing parameter; generating, by the rules engine, and based on the transaction, the associated transaction details, and the plurality of inputs, a second transaction processing parameter; and combining the transaction, the first transaction processing parameter and the second transaction processing parameter into a transaction payload formula.

DOCUMENT-BASED FRAUD DETECTION

Resumen de: US2024303664A1

In a computer-implemented method of facilitating detection of document-related fraud, fraudulent document detection rules may be generated or updated by training a machine learning program using image data corresponding to physical documents, and fraud determinations corresponding to the documents. The documents and fraudulent document detection rules may correspond to a first type of document. Image data corresponding to an image of one of the physical documents may be received, where the physical document corresponds to the first type of document. By applying the fraudulent document detection rules to the image data, it may be determined that the physical document is, or may be, fraudulent. An indication of whether the physical document is, or may be, fraudulent may be displayed to one or more people via one or more respective computing device user interfaces.

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.