Machine learning

REDUCING FALSE POSITIVES USING CUSTOMER FEEDBACK AND MACHINE LEARNING

Absstract of: US2024265405A1

A method of reducing a future amount of electronic fraud alerts includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that generates an electronic fraud alert, transmitting the alert to a mobile device of a customer, and receiving from the mobile device customer feedback indicating that the alert was a false positive or otherwise erroneous. The method also includes inputting the data detailing the financial transaction into a machine learning program trained to (i) determine a reason why the false positive was generated, and (ii) then modify the rules-based engine to account for the reason why the false positive was generated, and to no longer generate electronic fraud alerts based upon (a) fact patterns similar to fact patterns of the financial transaction, or (b) data similar to the data detailing the financial transaction, to facilitate reducing an amount of future false positive fraud alerts.

MACHINE LEARNING POWERED ANOMALY DETECTION FOR MAINTENANCE WORK ORDERS

Absstract of: US2024265352A1

An industrial work order analysis system applies statistical and machine learning analytics to both open and closed work orders to identify problems and abnormalities that could impact manufacturing and maintenance operations. The analysis system applies algorithms to learn normal maintenance behaviors or characteristics for different types of maintenance tasks and to flag abnormal maintenance behaviors that deviate significantly from normal maintenance procedures. Based on this analysis, embodiments of the work order analysis system can identify unnecessarily costly maintenance procedures or practices, as well as predict asset failures and offer enterprise-specific recommendations intended to reduce machine downtime and optimize the maintenance process.

SYSTEMS AND METHODS FOR MACHINE LEARNING MODEL SELECTION FOR TIME SERIES DATA

Absstract of: US2024265273A1

A method for machine learning model selection for time series data is disclosed. Sets of time series data is obtained. The time series data is clustered using a clustering algorithm. A similarity value of the clusters is evaluated and a quantity of clusters is selected. Machine learning models are evaluated using a center of each cluster of time series data. A machine learning model is selected for each cluster. Selection may be updated.

FEDERATED LEARNING WITH SINGLE-ROUND CONVERGENCE

Absstract of: US2024265268A1

A method including classifying a cluster training dataset by, for each datapoint of the cluster training dataset: obtaining a student prediction from each of a plurality of respective student machine-learning models for each a plurality of nodes of a cluster; performing a voting of the student predictions from the plurality of respective student machine-learning models of at least a portion of the plurality of nodes of the cluster to determine a respective classification for the datapoint; and labeling the datapoint of the cluster training dataset with the respective classification. The method also can include training a cluster machine-learning model of the cluster using the cluster training dataset, as classified. Other embodiments are described.

Methods and Systems for Improved Document Processing and Information Retrieval

Absstract of: US2024265041A1

Disclosed are methods, systems, devices, apparatus, media, and other implementations that include a method for document processing (particularly for training of a machine learning question answering platform, and for ingestion of documents). The method includes obtaining a question dataset (e.g., either from public or private repositories of questions) comprising one or more source questions for document processing by a machine learning question-and-answer system that provides answer data in response to question data submitted by a user, modifying a source question from the question dataset to generate one or more augmented questions with equivalent semantic meanings as that of the source question, and processing a document with the one or more augmented questions.

TECHNIQUES TO EMBED A DATA OBJECT INTO A MULTIDIMENSIONAL FRAME

Absstract of: US2024265063A1

Various embodiments are generally directed to techniques for embedding a data object into a multidimensional frame, such as for training an autoencoder to generate latent space representations of the data object based on the multidimensional frame, for instance. Additionally, in one or more embodiments latent space representations of data objects may be classified, such as with a machine learning algorithm. Some embodiments are particularly directed to embedding a data object comprising a plurality of object entries into a three-dimensional (3D) frame.

SYSTEMS AND METHODS FOR LIGHTWEIGHT MACHINE LEARNING MODELS

Absstract of: US2024265299A1

Systems and methods for generating lightweight or surrogate models using explainability vectors. In some aspects, the system receives a first machine learning model trained to determine resource consumption by a user system. The first machine learning takes as input a first set of features. The system processes the first machine learning model to extract an explainability vector. Based on the explainability vector, the system rearranges the first set of features to generate a second set of features. The system processes the values for the first set of features to generate values for the second set of features corresponding to user profiles and trains a second machine learning model which takes as input the second set of features.

FAILURE PREDICTION AND REMEDIATION USING MACHINE LEARNING

Absstract of: US2024265292A1

Techniques for failure prediction and remediation are disclosed. For example, a method comprises training one or more machine learning algorithms with a training dataset corresponding to a plurality of users, wherein the training dataset comprises at least one of product purchase data, product service data and product return data corresponding to the plurality of users. In the method, an input dataset corresponding to at least one user is received. The input dataset comprises at least one of product purchase data, product service data and product return data corresponding to the user. The input dataset is analyzed using the one or more machine learning algorithms. The method further comprises predicting, based at least in part on the analyzing, a likelihood of whether at least one product corresponding to the user will fail to be returned to a product providing entity when a return of the product has been requested.

Machine-Learning-Based Chemical Analysis

Absstract of: US2024265380A1

The present disclosure describes techniques for generating a digital twin to represent the chemical properties, elemental properties, elemental components, parametric components, and/or molecular components for a resource. A sample of a resource may be obtained and analyzed to identify one or more molecular descriptors contained in the resource. Further analysis of the one or more molecular descriptors and/or the resource may identify gaps in the data and/or information about the resource. Using machine-learning models and a chemistry knowledgebase, the gaps in the data and/or information about the resource may be filled. Further, the machine-learning models described herein may be used to generate a digital twin of the resource that represents the resource in a digital form such that the resource may be tracked accurately throughout its lifecycle, including how the resource may change due to environmental conditions, storage conditions, and/or custodial changes.

DETERMINISTIC INFERENCE FOR MACHINE LEARNING MODELS WITH VARIABLE BEHAVIOR

Absstract of: WO2024162970A1

Provided are systems and methods that enable deterministic inference for machine learning models with variable behavior. In particular, the present disclosure relates to a system in which a machine-learned model has a variable processing portion that is configured to variably apply one or more of a plurality of different processing operations when processing an input. According to an aspect of the present disclosure, one or more seed values can be used to deterministically control which of the plurality of different processing operations are applied by the machine-learned model when processing a given set of input data.

ARTIFICIAL INTELLIGENCE (AI) ASSISTED DIGITAL DOCUMENTATION FOR DIGITAL ENGINEERING

Absstract of: WO2024163759A1

A digital documentation system for preparation of engineering documents utilizing one or more artificial intelligence (Al) algorithms is provided. The system includes a user interface for selecting and populating templates with data, and one or more Al algorithms for creating and recommending templates, and preparing documents based on the recommended templates. The system uses natural language processing and semantic analysis algorithms to understand the content of the templates, documents, and associated engineering data, and to generate and recommend relevant templates to the user based on user prompts. The system also uses machine learning and predictive modeling and decision-tree algorithms to assist with the preparation of documents, by generating suggestions for data fields and values based on the user's previous inputs and the overall context of the document and available engineering data, including model data and metadata from digital models accessed in a zero-trust framework.

SMART RING SYSTEM FOR MONITORING UVB EXPOSURE LEVELS AND USING MACHINE LEARNING TECHNIQUE TO PREDICT HIGH RISK DRIVING BEHAVIOR

Absstract of: US2024262368A1

A method for predicting risk exposure includes receiving a particular set of data acquired via a sensor. The method for predicting risk exposure further can include analyzing, via a machine learning (ML) model, the particular set of data. The analyzing can include determining that the particular set of data represents a particular light exposure pattern corresponding to a light exposure pattern correlated with a risk pattern. The ML model can be trained with a first set of data and a second set of data to identify a correlation between the light exposure pattern and the risk pattern. The method for predicting risk exposure also can include predicting a risk exposure for a user based on the particular set of data. The method for predicting risk exposure also can include providing a notice indicating the risk exposure.

PROCESSES FOR MAINTENANCE OF MODULES OF LIGHT SOURCES IN SEMICONDUCTOR PHOTOLITHOGRAPHY

Absstract of: WO2024161222A1

A computer-implemented process for maintaining a light source includes using a computer, for each of M machine-learning models, with each model trained to classify a state of a module of a light source as requiring maintenance or not requiring maintenance, to (1) form pairs of performance parameters of a set of N performance parameters, (2) score each of the pairs to produce pair scores, and (3) sum the pair scores to produce a model score; also to implement the model with the highest score and use the implemented model to repeatedly classify, over time, a state of a specific module of a specific light source as requiring maintenance or not requiring maintenance; and to perform maintenance of the specific module when indicated by the model, wherein M is an integer greater than one and N is an integer greater than two.

DEVICE AND METHOD FOR AUTOMATED, THREE-DIMENSIONAL BUILDING DATA MODELLING

Absstract of: WO2024160612A1

The invention relates to a device for creating a digital model of an existing building, or an existing industrial plant, or an existing infrastructure, according to one embodiment. The device comprises an input unit (110) for providing point cloud data representing the building or the industrial plant or the infrastructure. The device also comprises a processing unit (120) having an artificial intelligence module (125) for creating the model according to the point cloud data, wherein the artificial intelligence module (125) is trained using machine learning.

SMART LISTING CREATION BASED ON MACHINE-LEARNING ANALYSIS

Absstract of: US2024265412A1

Systems and methods are directed to automatic listing generation and inventory management based on machine-learning analysis. The system trains a time series-based machine learning (ML) model that forecasts sales. During inference time, the system determines one or more potential categories for a user based on custom preferences and previous analytic queries of the user. High demand items in the one or more potential categories are then applied to the ML model, which outputs probabilities of predicted sales for the high demand items. The system then determines items having a potential inventory gap by cross-checking current inventory with items having a probability outputted by the ML model that satisfies a probability threshold. For each item that satisfies the probability threshold, competitor sales data, predicted sales data derived from the ML model, and editable fields displaying automatic listing thresholds that trigger the automatic generation of a corresponding listing are presented in a user interface.

Automatically Generating Machine Learning Models for Software Tools That Operate on Source Code

Absstract of: US2024264807A1

A method includes receiving a code insight request requesting a code insight for target source code. The code insight request includes the target source code and a tool type indicator specifying that the software development tool includes one of a code labeling type of software development tool or a code transformation type of software development tool. The method also includes obtaining a machine learning model based on the tool type indicator and generating the code insight using the machine learning model. The code insight includes one of a predicted label for the target source code when the tool type indicator specifies that the software development tool includes the code labeling type of software development tool, or a predicted code transformation for the target source code when the tool type indicator specifies that the software development tool includes the code transformation type of software building tool.

SUGGESTING EXECUTABLE ACTIONS IN RESPONSE TO DETECTING EVENTS

Absstract of: US2024267453A1

Systems and processes for providing, via an electronic device, suggested user actions. The suggested actions are provided in response to detecting an occurrence of a predefined event occurring in the user's day. The occurrence of the anchor is encoded in signals generated by the electronic device. The occurrence of the anchor is detectable via monitoring and analysis of electronic signals. Based on the user's previous interactions with the device, the occurrence of the anchor is indicative of user behavior and/or action taken in response to the anchor. Machine learning (ML) is employed to train an anchor model to associate actions taken in response to anchor occurrences. The trained anchor model is employed to detect anchors and provide suggested actions in response to the detected anchor occurrence. The suggested action is based on a type of anchor occurrence and contextual conditions of the anchor occurrences.

MACHINE LEARNING EXPLANATION PROGRAM, DEVICE, AND METHOD

Absstract of: EP4411600A1

A machine learning explanation apparatus generates rules each including a condition and a conclusion for a case where the condition is satisfied, based on training data used for training of a machine learning model, extracts, in the generated rules, a set of rules {R_i} of a family of subsets that is to be a cover of a certain rule K based on the training data, selects rules R_i for which the difference between confidence (conf) of rule K and confidence (conf) of rule R_i is less than a predetermined threshold, and outputs, for an inference result of the machine learning model, explanatory information including rule K and rule R_i remaining after deleting the selected rules R_i among the set of rules {R_i}.

LINER HANGER OPERATIONS FRAMEWORK

Absstract of: WO2024159060A1

A method may include receiving data from field equipment during performance of a liner hanger job at a wellsite; generating an inference as to an occurrence of an event associated with the performance of the liner hanger job based on at least a portion of the data using one or more machine learning models; and controlling the performance of the liner hanger job based at least in part on the inference.

MONOCULAR DEPTH AND OPTICAL FLOW ESTIMATION USING DIFFUSION MODELS

Nº publicación: WO2024159082A2 02/08/2024

Applicant:

GOOGLE LLC [US]
GOOGLE LLC