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System, Method, and Computer Program Product for Encoding Feature Interactions Based on Tabular Data Using Machine Learning

Resumen de: US2025190756A1

Methods, systems, and computer program products are provided for encoding feature interactions based on tabular data. An exemplary method includes receiving a dataset in a tabular format including a plurality of rows and a plurality of columns. Each column is indexed to generate a position embedding matrix. Each column is grouped based on at least one tree model to generate a domain embedding matrix. An input vector is generated based on the dataset, the position embedding matrix, and the domain embedding matrix. The input vector is inputted into a first multilayer perceptron (MLP) model to generate a first output vector, which is transposed to generate a transposed vector. The transposed vector is inputted into a second MLP model to generate a second output vector, which is inputted into at least one classifier model to generate at least one prediction.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

AGENT-BASED MODELING OF MACHINE-LEARNING TASKS

Resumen de: WO2025116908A1

Described is a system for generating an inference output on candidate data based on reference data by identifying a reference subset of the reference data items with a transformation rule shared in common, accessing candidate data that indicates candidate initial states of candidate data items without indicating any transformed states of the candidate data items, and identifying a candidate subset of the candidate data items based on the identified reference subset of the reference data items. The system then transforms the candidate data items in the candidate subset from their candidate initial states to candidate transformed states based on the transformation rule that defines the goal attained by each one of the reference data items in the reference subset by transforming from its reference initial state to its reference transformed state, and generates an output that indicates the candidate transformed states of the candidate subset of the candidate data items.

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.

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.

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.

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

Nº publicación: AU2023383086A1 05/06/2025

Solicitante:

SNORKEL AI INC
SNORKEL AI, INC

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.