Alerta

HYDRAULIC FRACTURING FRAMEWORK

Resumen de: WO2025122496A1

A method can include receiving input for a group of wells in a subsurface region, where the group of wells defines a hydraulically fractured production unit; predicting production data for the group of wells using a machine learning model; and outputting the predicted production data.

ARC FAULT DETECTION USING MACHINE LEARNING

Resumen de: US2025192537A1

In aspects of the present disclosure, a circuit interrupter includes a housing, a conductive path, a switch which selectively interrupts the conductive path, sensor(s), memory, and a controller within the housing. The sensor(s) measure electrical characteristic(s) of the conductive path. The memory stores an arc detection program that implements a machine learning model and includes a field-updatable program portion and a non-field-updatable program portion, where the field-updatable program portion includes program parameters used by the non-field-updatable program portion to decide between presence or absence of an arc fault. The controller executes the arc detection program to compute input data for the machine learning model based on the sensor measurements, decide between presence of an arc event or absence of an arc event based on the input data, and cause the switch to interrupt the conductive path when the decision indicates presence of an arc event.

A CAUSALITY MATRIX CREATION PROCESS FOR SEVERELY UNBALANCED AND EVOLVING ENVIRONMENTS

Resumen de: WO2025120515A1

The present invention describes a new causality matrix creation process for analyzing correlations between events in any severely unbalanced deterministic environment, thus allowing the creation of a robust dataset for machine learning models. The proposed innovative process seamlessly integrates multiple functionalities to enhance its resilience in addressing the inherent challenges of real-world environments, ensuring optimal extraction of correlations while mitigating the risk of false cor-relations. Furthermore, specific optimizations are detailed to streamline the process, not only diminishing its complexity and execution time but also minimizing hardware requirements. These enhancements render the solution scalable to accommodate diverse sizes of real environments, a critical attribute in the context of big data.

MACHINE LEARNING MODEL CORRECTION

Resumen de: US2024046349A1

A method, in some implementations, may include obtaining output from a machine learning (ML) model responsive to input data, obtaining initial training data representing training data used to train the ML model, generating, based on the output from the ML model and the initial training data, correction training data that represents a desired alteration to the output from the ML model responsive to one or more particular subgroups in the input data, generating, based on the correction training data, a correction ML model configured to receive, as input, the input data and to output correction values which, when combined with the output from the ML model, perform the desired alteration, and generating corrected output as a combination of the output from the ML model and the output correction values from the correction ML model, and providing, for display, the corrected output.

Systems and methods of property valuation

Resumen de: GB2636300A

The disclosure features a method which includes inputting or receiving information on one or more features of a plurality of residential properties and prices of the residential properties including a marketed price, a listing price, and a closing price, providing the information to a Machine Learning Algorithm to determine the relationship between the one or more features and the prices of the residential properties to create a Machine Learned Model, inputting or receiving information on one or more features of a new residential property into the Machine Learned Model, and predicting a base price of the new residential property from the Machine Learned Model based on the one or more features of the new residential property. The disclosure also features one or more non- transitory, computer-readable storage media storing instructions capable of performing the method and a computer or computer system capable of performing the method.

UTILIZING MACHINE LEARNING AND A SMART TRANSACTION CARD TO AUTOMATICALLY IDENTIFY OPTIMAL PRICES AND REBATES FOR ITEMS DURING IN-PERSON SHOPPING

Resumen de: US2025182156A1

A device may receive, from a client device of a customer, item data identifying a price of an item and customer data identifying the customer, where the item data may be received by a transaction card from a price tag of the item. The device may receive price data identifying prices associated with multiple items and other data identifying locations, availabilities, and terms of the multiple items, and may process the item data, the price data, and the other data, with a machine learning model, to identify an optimal price for the item. The device may provide, to the client device, data identifying the optimal price and data identifying a merchant associated with the optimal price, and may receive transaction data identifying the item, the optimal price, and the merchant when the customer purchases the item. The device may perform actions based on the transaction data.

METHODS, SYSTEMS, AND DEVICES TO VALIDATE IP ADDRESSES

Resumen de: US2025184345A1

Aspects of the subject disclosure may include, for example, obtaining a first group of Internet Protocol (IP) addresses from a group of network devices, and determining a second group of IP addresses from the first group of IP addresses includes possible malicious IP addresses utilizing a machine learning application. Further embodiments can include obtaining a first group of attributes of malicious IP addresses from a first repository, and determining a third group of IP addresses from the second group of IP addresses includes possible malicious IP addresses based on the first group of attributes. Additional embodiments can include receiving user-generated input indicating a fourth group of IP addresses from the third group of IP addresses includes possible malicious IP addresses, and transmitting a notification to a group of communication devices indicating that the fourth group of IP address includes possible malicious IP addresses. Other embodiments are disclosed.

SYSTEMS AND METHODS FOR PROGRAMMATIC LABELING OF TRAINING DATA FOR MACHINE LEARNING MODELS VIA CLUSTERING AND LANGUAGE MODEL PROMPTING

Resumen de: AU2023383086A1

Embodiments introduce an approach to semi-automatically generate labels for data based on implementation of a clustering or language model prompting technique and can be used to implement a form of programmatic labeling to accelerate the development of classifiers and other forms of models. The disclosed methodology is particularly helpful in generating labels or annotations for unstructured data. In some embodiments, the disclosed approach may be used with data in the form of text, images, or other form of unstructured data.

CLOSED-LOOP OPTIMIZATION OF GENERAL REACTION CONDITIONS FOR HETEROARYL SUZUKI-MIYAURA COUPLING

Resumen de: AU2023366930A1

Disclosed are systems and methods for rapidly generating general reaction conditions using a closed-loop workflow leveraging matrix down-selection, machine learning, and robotic experimentation. In certain aspects, provided is a method, comprising: selecting a reaction pair comprising a first molecule and a second molecule; wherein the first molecule is selected from a first matrix and the second molecule is selected from a second matrix; selecting one or more reaction conditions for the reaction pair, the selection based on historic use of the one or more reaction conditions and a structural and functional diversity of the selected reaction pair; automatically performing, by a robotic system, an initial round of reactions between the selected reaction pair under the selected one or more reaction conditions.

NEURAL PROCESSING UNIT AND WI-FI SCHEDULING

Resumen de: WO2025117989A1

Technology disclosed herein may include an access point including a processing device. The processing device may generate, at an access point, a machine learning model previously trained using training traffic data; identify, at the access point, traffic data; provide, at the access point, the traffic data to the machine learning model; predict, at the access point, a traffic pattern using the machine learning model; and determine, at the access point, a scheduling characteristic based on the traffic pattern.

SUPERVISOR MACHINE LEARNING MODELS FOR TRANSFORMING STATES

Resumen de: WO2025116904A1

Described is a system for transforming an arrangement of data items by accessing reference data, inferring an arrangement transformation rule that controlled a first transformation of the reference arrangement of reference data items from the reference initial state to the reference transformed state, accessing candidate data, and causing a supervisor machine learning model to generate a full output that indicates a candidate transformed state of the candidate arrangement based on the arrangement transformation rule that controlled the first transformation, the supervisor machine learning model causing a supervisee machine learning model to generate a partial output based on the item transformation rule the controlled the second transformation, the partial output indicating a candidate data item in the candidate transformed state, the supervisor machine learning model generating the full output based on the partial output generated by the supervisee machine learning model.

MACHINE LEARNING MODEL ARRANGEMENT IDENTIFICATION

Resumen de: WO2025116907A1

Described is a system for arrangement identification by accessing reference data that indicates reference initial states of reference data items and indicates reference transformed states of the reference data items, identifying a reference arrangement that exhibits a fixed set of proportional relationships, inferring a transformation rule, accessing candidate data, identifying a candidate arrangement that exhibits the fixed set of proportional relationships, and generating an output that indicates the candidate arrangement of data items in a candidate transformed state determined based on the transformation rule.

PIPELINED EXECUTION OF GENERATIVE ARTIFICIAL INTELLIGENCE MODELS

Resumen de: WO2025111787A1

Techniques and apparatus for efficiently generating a response to an input query using a generative artificial intelligence model in a pipelined execution environment. An example method generally includes loading a first portion of a machine learning model, wherein the first portion of the machine learning model is associated with a first inference; loading a second portion of the machine learning model, wherein the second portion of the machine learning model is associated with a second inference; and while loading the second portion of the machine learning model, generating the first inference based on an input data set and the first portion of the machine learning model.

CONCURRENT OPTIMIZATION OF MACHINE LEARNING MODEL PERFORMANCE

Resumen de: US2025181978A1

Certain aspects of the present disclosure provide techniques for concurrently performing inferences using a machine learning model and optimizing parameters used in executing the machine learning model. An example method generally includes receiving a request to perform inferences on a data set using the machine learning model and performance metric targets for performance of the inferences. At least a first inference is performed on the data set using the machine learning model to meet a latency specified for generation of the first inference from receipt of the request. While performing the at least the first inference, operational parameters resulting in inference performance approaching the performance metric targets are identified based on the machine learning model and operational properties of the computing device. The identified operational parameters are applied to performance of subsequent inferences using the machine learning model.

METHOD AND SYSTEM FOR PREDICTING ABDOMINAL AORTIC ANEURYSM (AAA) GROWTH

Resumen de: AU2023380279A1

There are provided methods, systems and non-transitory storage mediums for predicting growth of an abdominal aortic aneurysm (AAA) of a patient having been diagnosed with AAA. Segmented regions of interest (ROI) comprising the aorta and adjacent structures are received by segmenting a set of images. A wall shear stress parameter and intraluminal thickness parameter is determined. A 3D parametric mesh comprising a plurality of concentric 3D mesh layers is generated, where each concentric 3D mesh layer includes a same predetermined number of nodes. The generation includes encoding the segmented ROIs, the wall shear stress parameter and the intraluminal thickness parameter as features at respective node locations in the 3D parametric mesh. A trained growth prediction machine learning model predicts, based at least on a subset of features of the 3D parametric mesh, if the given patient will show AAA growth. The training of the growth prediction model is also disclosed.

MACHINE LEARNING BASED OCCUPANCY FORECASTING

Resumen de: WO2025117106A1

Embodiments determine a final occupancy prediction for a check-in date for a plurality of hotel rooms. Embodiments receive historical reservation data including a plurality of booking curves for the hotel rooms corresponding to a plurality of reservation windows, the historical reservation data including a plurality of features. Based on the historical reservation data, embodiments generate a first occupancy prediction for the check-in date using a first model and generate a second occupancy prediction for the check-in date using a second model. Embodiments determine a best performing model from at least the first model and the second model uses a corresponding occupancy prediction corresponding to the best performing model as the final occupancy prediction for the check-in date.

Method And System For Key Predictors And Machine Learning For Configuring Cell Performance

Resumen de: US2025183392A1

A method of managing battery performance may include obtaining, via a measurement device, measurements of one or more parameters relating to one or more cells; generating or updating, based on the measurements, a machine learning model; and generating, using the machine learning model, cell performance prediction data for use in managing at least one cell. Each cell includes a cathode, a separator, and a silicon-dominant anode. The measurements of the one or more parameters correspond to a plurality of different types of data. The measurements include one or more of: measurements of cells or cell components before formation or cycling, measurements from formation cycles for one or more cells, measurements from a number of cycles after formation for one or more cells, and measurements of characteristics of cell components prior to cell assembly.

MODULAR MACHINE LEARNING SYSTEMS AND METHODS

Resumen de: US2025181676A1

A computer system is provided that is designed to handle multi-label classification. The computer system includes multiple processing instances that are arranged in a hierarchal manner and execute differently trained classification models. The classification task of one processing instance and the executed model therein may rely on the results of classification performed by another processing instance. Each of the models may be associated with a different threshold value that is used to binarize the probability output from the classification model.

DYNAMIC FILTER RECOMMENDATIONS

Resumen de: US2025181587A1

A user preference hierarchy is determined from user response to images. Images may be tagged using machine learning models trained to determine values for images. Products are clustered according to product vectors. Images of products within a cluster are clustered according to composition and groups of images are selected from image clusters for soliciting feedback regarding user preference for products of a cluster. Feedback is used to train a user preference model to estimate affinity for a product vector. A user may provide feedback regarding a price point and products are weighted according to a distribution about the price point. The distribution may be asymmetrical according to direction of movement of the price point. Filters may be dynamically defined and presented to a user based on popularity and frequency of occurrence of attribute-value pairs of search results and based on feedback regarding the search results.

FEATURE DIMENSIONALITY REDUCTION FOR MACHINE LEARNING MODELS

Resumen de: US2025181991A1

Provided is a method, system, and computer program product for performing automated feature dimensionality reduction without accuracy loss. A processor may determine a first training value associated with a first dataset of a machine learning model. The processor may rank features of the first dataset in relation to the first training value. The processor may compare the ranked features of the first dataset to a predetermined threshold. The processor may generate a second dataset from the first dataset by removing a third dataset, the third dataset having a set of features that did not meet the predetermined threshold. The processor may determine a second training value associated with the second dataset. The processor may compare the first training value to the second training value. In response to the second training value being lower than the first training value, the processor may analyze the third dataset with a dimensionality reduction algorithm.

METROLOGY AND PROCESS CONTROL FOR SEMICONDUCTOR MANUFACTURING

Resumen de: US2025181941A1

A semiconductor metrology system including a spectrum acquisition tool for collecting, using a first measurement protocol, baseline scatterometric spectra on first semiconductor wafer targets, and for various sources of spectral variability, variability sets of scatterometric spectra on second semiconductor wafer targets, the variability sets embodying the spectral variability, a reference metrology tool for collecting, using a second measurement protocol, parameter values of the first semiconductor wafer targets, and a training unit for training, using the collected spectra and values, a prediction model using machine learning and minimizing an associated loss function incorporating spectral variability terms, the prediction model for predicting values for production semiconductor wafer targets based on their spectra.

TELECOMMUNICATION SYSTEM FAILURES PREDICTION THROUGH MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

Resumen de: US2025184212A1

In an embodiment, a method may be implemented in a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the computer system interconnected with a telecommunications system, the method comprising: receiving, at the computer system, data relating to operation of the telecommunication system, obtaining, at the computer system, at least one machine learning model trained to detect and predict faults in the operation of the telecommunication system, selecting, at the computer system, computing infrastructure upon which to execute the at least one machine learning model, wherein the selected computing infrastructure comprises a mesh of interconnected micro-applications;, executing, at the computer system, the at least one machine learning model using the selected computing infrastructure to detect and predict faults in the operation of the telecommunication system, and automatically correcting at least some of the detected faults.

METHOD, SYSTEM AND COMPUTER PROGRAM PRODUCT FOR DATA MINING WITH ARTIFICIAL INTELLIGENCE (AI) BASED SMART AGENTS

Resumen de: WO2025117883A1

The invention relates to an AI-driven system for data analytics, processing, mining, and user interaction, utilizing large language models (LLMs) and machine learning (ML) techniques. The system enables personalized, real-time access to company data, guided by AI Agents. These Agents handle tasks such as data extraction, transformation, and loading, with a multi-stage processing pipeline that includes raw data ingestion, curation, and modeling. Specialized Agents like Fixing and Modeling Agents ensure data quality, analysis, and visualization. The system also integrates with BI dashboards for generating insights and predictive analytics. Users interact via natural language queries (NLQs) to receive context-aware, AI-generated answers, including various types of plots, graphs and charts, thus improving decision-making and data management efficiency.

INTER-NODAL SHARING OF GENERATIVE MODELS

Resumen de: WO2025116903A1

A machine facilitates inter-nodal sharing of a generative model by accessing a machine learning model that includes a plurality of nodes. Each node is configured to produce future outputs from future inputs based on a generative model of that node. Each node is also configured to update its generative model based on past feedback received by that node in response to past outputs of that node. The machine associates a first node with a second node based on a comparison of first updates of a first generative model of the first node to second updates of a second generative model of the second node. The machine determines a replacement generative model to replace the second generative model of the second node. The machine replaces the second generative model of the second node with the determined replacement generative model, thus configuring the second node to use the replacement generative model.

MACHINE LEARNING MODEL MULTI-DIMENSIONAL BEHAVIOR IDENTIFICATION

Nº publicación: WO2025116905A1 05/06/2025

Solicitante:

STEM AI INC [US]
STEM AI, INC

Resumen de: WO2025116905A1

Described is a system that generates an inference output on candidate data based on reference data by accessing reference data that indicates reference initial states of reference data items and reference transformed states of the reference data items, inferring a first transformation rule indicative of a first transformation of a first subset of the first reference data items from the first reference initial state to the first reference transformed state, inferring a second transformation rule, generating reference data items indicating a generated second reference transformed state from the second reference initial state, verifying the first transformation rule and the second transformation rule, accessing candidate data that indicates a candidate initial state of candidate data items without indicating any transformed states of the candidate data items, and generating candidate data items indicating a candidate transformed state.