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SYSTEMS AND METHODS FOR OPTIMIZING HYPERPARAMETERS FOR MACHINE LEARNING MODELS

Resumen de: US2025190851A1

A combined hyperparameter and proxy model tuning method is described. The method involves multiple search iterations. In each search iteration, candidate hyperparameters are considered. An initial (‘seed’) hyperparameter is determined, and used to train one or more first proxy models on a target dataset. From the first proxy model(s), one or more first synthetic datasets are sampled. A first evaluation model is fitted to each first synthetic dataset, for each candidate hyperparameter, enabling each candidate hyperparameter to be scored. Based on the respective scores assigned to the candidate hyperparameters, a candidate hyperparameter is selected and used to train one or more second proxy models on the target dataset

REDUCING FALSE POSITIVES USING CUSTOMER FEEDBACK AND MACHINE LEARNING

Resumen de: US2025191001A1

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-BASED SELECTION OF GEO-LOCATIONS FOR WIRELESS NETWORK INFRASTRUCTURE

Resumen de: US2025191005A1

A method includes estimating using one or more machine learning models, for each wireless subscriber of a plurality of wireless subscribers, a score that is indicative of an expected profitability associated with the corresponding wireless subscriber. The method also includes identifying a geolocation, wherein within a pre-defined radius from the geo-location, there is at least a threshold data usage level by a subset of the plurality of wireless subscribers. The method also includes determining an aggregate profitability metric associated with the geolocation based upon the estimated scores corresponding to wireless subscribers included in the subset of the plurality of wireless subscribers. The method also includes determining that the aggregate profitability metric associated with the geolocation satisfies a threshold condition, and responsive to determining that the aggregate profitability metric associated with the geolocation satisfies the threshold condition, selecting the identified geolocation as a candidate location for wireless network infrastructure.

Energy Consumption Control Method, System and Computer Program Product

Resumen de: US2025190820A1

The present invention relates to an energy consumption control method. The method includes the steps of performing following steps by a processor and a firmware; continuously detecting and collecting a performance data of the processor, wherein the performance data includes a first performance parameter, a second performance parameter, and a third performance parameter; executing a dual-model machine learning model to predict the first performance parameter based on the performance data; and implementing a fuzzy feedback control mechanism to adjust the first performance parameter based on the detected second and third performance parameters.

COMPUTER-READABLE RECORDING MEDIUM, MACHINE LEARNING METHOD, INFERENCE METHOD, AND INFORMATION PROCESSING APPARATUS

Resumen de: US2025190823A1

A computer is caused to execute a process including: acquiring a training data set including a plurality of pieces of training data in which a plurality of A-mode ultrasonic signals obtained for each of different positions of an object are associated with evaluation results for the object, and for each of the plurality of pieces of training data, weighting a plurality of pieces of feature data acquired based on the plurality of A-mode ultrasonic signals, by using a weighting model that weights feature data, acquiring inference results of evaluation for the object by inputting the plurality of pieces of weighted feature data to a classification model that outputs inference results of evaluation in response to input of the plurality of pieces of feature data, and training the weighting model and the classification model based on the inference results and the evaluation results in the training data.

MACHINE LEARNING ROBUSTNESS THROUGH SENSIBLE DECISION BOUNDARIES

Resumen de: US2025190796A1

Computer systems and computer-implemented methods modify a machine learning network, such as a deep neural network, to introduce judgment to the network. A “combining” node is added to the network, to thereby generate a modified network, where activation of the combining node is based, at least in part, on output from a subject node of the network. The computer system then trains the modified network by, for each training data item in a set of training data, performing forward and back propagation computations through the modified network, where the backward propagation computation through the modified network comprises computing estimated partial derivatives of an error function of an objective for the network, except that the combining node selectively blocks back-propagation of estimated partial derivatives to the subject node, even though activation of the combining node is based on the activation of the subject node.

SYSTEM AND METHOD FOR FUZZY LOGIC BASED MODEL RISK MANAGEMENT

Resumen de: US2025190827A1

Embodiments herein generally relate to a system and method for model risk management (MRM) of an artificial intelligence (AI) or machine learning (ML) model. In at least one example, the system comprises: an AI validation system (AIVS) comprising validation processing subsystems, which comprise a fuzzy logic controller (FLC) to implement a fuzzy logic MRM program associated with the AI/ML model. Validation devices are communicatively coupled to the validation processing subsystems and FLC. The FLC receives metadata related to risk management inputs and outputs for the fuzzy logic MRM program. The metadata is received, and a rule base is created. The MRM program receives the inputs from the validation devices, pre-processes the inputs, and fuzzifies the pre-processed inputs. Rules in the rule base are executed using the fuzzified inputs to calculate rule consequent values, which are aggregated. An output fuzzy state is assigned, and actions are performed based on the assigning.

APPARATUS AND A METHOD FOR GENERATING A DIAGNOSTIC REPORT

Resumen de: US2025191713A1

An apparatus for generating a diagnostic report is disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a user profile from a user. The memory instructs the processor to generate a first set of inquiries as a function of the user profile using an inquiry machine learning model. The memory instructs the processor to receive a first set of inquiry responses from the user as a function of the first set of inquiries. The memory instructs the processor to generate a diagnostic report as a function of the first set of inquiries and the first set of inquiry responses. The memory instructs the processor to display the diagnostic report using a display device.

SYSTEM AND METHOD FOR CONTACTLESS PREDICTIONS OF VITAL SIGNS, HEALTH RISKS, CARDIOVASCULAR DISEASE RISK AND HYDRATION FROM RAW VIDEOS

Resumen de: US2025185924A1

A system and method for contactless predictions of one of vital signs, health risk for a disease or condition, blood biomarker values, and hydration status, the method executed on one or more processors, the method including: receiving a raw video capturing a human subject; determining one of vital signs, health risk for a disease or condition, blood biomarker values, and hydration status using a trained machine learning model, the machine learning model taking the raw video as input, the machine learning model trained using a plurality of training videos where ground truth values for the vital signs, the health risk for a disease or condition, the blood biomarker values, or the hydration status were known during the capturing of the training video; and outputting the predicted vital signs, health risk for a disease or condition, blood biomarker values, or hydration status.

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.

COMPUTER-BASED SYSTEMS AND METHODS FOR MACHINE LEARNING BASED EXCEPTION PREDICTIONS

Resumen de: WO2025120589A1

A failure prediction method including a predicting flow and a model training flow, the predicting flow including receiving a natural language input from a client computer, translating the input into a task by a LLM, selecting a ML model dedicated to the task, receiving first data, converting the first data to second data of a predetermined format, immediately applying, the ML model on the second data for predicting an output and providing a corresponding explanation, storing the second data and the output into historical data in a storage layer, translating the output and the explanation into a prediction in the natural language by the LLM, and transmitting the prediction to the client computer and iterating the predicting flow for a predetermined number of time; and the model training flow including retrieving the historical data from the storage layer, and training the ML model on the historical data.

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.

METHODS AND SYSTEMS OF FACILITATING A HEALTH ASSESSMENT

Resumen de: WO2025120557A1

The present disclosure provides a method of facilitating a health assessment. Further, the method may include receiving a medical inquiry. Further, the medical inquiry may be generated by a user. Further, the method may include analyzing the medical inquiry. Further, the method may include generating an assessment query based on the analyzing. Further, the generation of the assessment query may be based on one or more of a template and a first machine learning model. Further, the template includes a standardized assessment query. Further, the method may include transmitting the assessment query. Further, the method may include receiving a response from the healthcare communication infrastructure. Further, the response corresponds. Further, the method may include analyzing the response. Further, the method may include determining a diagnosis data based on the analyzing. Further, the method may include transmitting the diagnosis data.

SYSTEMS AND METHODS FOR AI-ASSISTED ECHOCARDIOGRAPHY

Resumen de: WO2025118021A1

A method for processing a sparsely populated data source, method for generating a training set for training a model to predict mitral regurgitation from echocardiograph data, and method of predicting heart failure from echocardiograph data including the steps of: retrieving echocardiograph measurement data from a plurality of patient records comprising echocardiography reports; analysing the echocardiograph data to determine unpopulated data fields; populating the unpopulated data fields with imputed echocardiograph data determined by a machine learning model; calculating a probability output from a trained model; analysing echocardiograph measurement data of individual patient records from the echocardiograph data to determine a prediction of the presence of a disease state in the patient on the basis of the calculated probability output; and associating the presence of the disease state to a prediction of heart failure in the patient.

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.

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.

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.

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.

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.

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.

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.

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.