Alerta

TECHNIQUES FOR PROVIDING SYNCHRONOUS AND ASYNCHRONOUS DATA PROCESSING

Resumen de: US2025315437A1

Techniques discussed herein include dynamically providing synchronous and/or asynchronous data processing by a machine-learning model service. The machine-learning model service (“the service”) executes a stream manager application, a web interface, and a machine-learning model via a common container. The stream manager application can obtain input data (e.g., from an input data stream, a partition of an input data stream, etc.) and provide the data to the machine-learning model through the web interface using a local communication channel (e.g., a loopback interface that bypasses local network interface hardware of the computing device on which the model executes). Prediction results from the model may be provided as output data (e.g., to an output data stream, to a partition of an output data stream, etc.).

EVALUATING EXPLAINABLE ARTIFICIAL INTELLIGENCE MODELS AND AN ARCHITECTURE FOR AN ENSEMBLE EXPLAINABLE MODEL SELECTION

Resumen de: US2025315448A1

A system includes one or more processors to store a first explanatory model (e.g., a SHAP model or a LIME model) and a second explanatory model; execute the machine learning model (e.g., a neural network) using a first set of data to generate a first classification data point; generate a first plurality of explanatory evaluation metrics for the first explanatory model by applying the first explanatory model to the first classification data point; and responsive to the first plurality of explanatory evaluation metrics satisfying an explanatory model selection policy, apply the first explanatory model and the second explanatory model to a second classification data point output by the machine learning model based on a second set of transaction data.

PROCESSING SYSTEM HAVING A MACHINE LEARNING ENGINE FOR PROVIDING A SURFACE DIMENSION OUTPUT

Resumen de: US2025315868A1

Systems and apparatuses for generating surface dimension outputs are provided. The system may collect an image from a mobile device. The system may analyze the image to determine whether they comprise one or more standardized reference objects. Based on analysis of the image and the one or more standardized reference objects, the system may determine a surface dimension output. The system may determine one or more settlement outputs and one or more repair outputs for the driver based on the surface dimension output.

MACHINE LEARNING POWERED ANOMALY DETECTION FOR MAINTENANCE WORK ORDERS

Resumen de: US2025315798A1

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.

ML MODEL PIPELINE FOR QUBO-BASED ANNEALING WORKLOADS

Resumen de: US2025315705A1

One example method includes using a first machine learning (ML) model L to select a set of Lagrangian weights λi for each constraint i defined in a given Hamiltonian function, using λi for every constraint i to compile the Hamiltonian function to a matrix, using a second ML model, trained with λi and hardware telemetry, to make a best hardware Ω selection, selecting a set of hyperparameters Ψi for a given QUBO, λi, and Ω, and solving the given QUBO using the best hardware Ω and the set of hyperparameters Ψi.

SYSTEMS AND METHODS FOR EMOTIONALLY ADAPTIVE FINANCIAL CHATBOT

Resumen de: US2025315824A1

Various examples are directed to systems and methods for emotionally adaptive financial chatbots. A method includes receiving authentication information from a user of the computer system, authenticating the user for a transaction based on the received authentication information, and detecting an abnormal aspect of the transaction based on parameters of the transaction. Upon detecting the abnormal aspect, the method includes determining, using machine learning, an emotional state of the user. The method further includes adapting an interaction style with the user based on the determined emotional state of the user, receiving an input from the user after adapting the interaction style, and implementing additional security requirements for the transaction based on the detected abnormal aspect, the input from the user, and the determined emotional state.

METHOD AND SYSTEM FOR IMPROVING MACHINE LEARNING OPERATION BY REDUCING MACHINE LEARNING BIAS

Resumen de: US2025315738A1

A network operation system and method accesses a training dataset for a network operation predictive model including historical network operation records and historical decision records, generates an inferred protected class dataset by executing a protected class demographic model, executes an algorithmic bias model using as input the historical decision records and the inferred protected class dataset to generate one or more fairness metrics, executes, based on the fairness metrics, a bias adjustment model using as input the historical decision records and the inferred protected class dataset to generate an adjusted training dataset, trains the network operation predictive model using as input the adjusted training dataset, receives an electronic request for a network operation, executes the network operation predictive model using as input at least one attribute of the electronic request for the network operation, and executes the network operation based on a prediction of the network operation predictive model.

MACHINE LEARNING NETWORKS, ARCHITECTURES AND TECHNIQUES FOR DETERMINING OR PREDICTING DEMAND METRICS IN ONE OR MORE CHANNELS

Resumen de: US2025315681A1

This disclosure relates to artificial intelligence (AI) and machine learning networks for predicting or determining demand metrics across multiple channels. An analytics platform can receive channel events from multiple channels corresponding to geographic areas, and channel features related to demand conditions in the channels can be extracted from the channel events. During a training phase, the channel features can be accumulated into one or more training datasets for training one or more demand prediction models. The one or more demand prediction models can be trained to predict or determine demand metrics for each of the channels. The demand metrics can indicate or predict demand conditions based on the current conditions in the channels and/or based on future, predicted conditions in the channels. Other embodiments are disclosed herein as well.

ANALYSIS OF STRUCTURED DATA IN CHAINS OF REPEATABLE ACTIONS WITHIN AN ARTIFICIAL INTELLIGENCE-BASED AGENT ENVIRONMENT

Resumen de: US2025315683A1

A framework for machine learning modeling of structured data that includes one or more artificial intelligence-based agents. These artificial intelligence-based agents are configured to create and execute chains of repeatable actions to perform user-driven and user-defined workflows with a given problem set and identified outcomes. Structured data that has been processed is fed by the artificial intelligence-based agents to language models to formulate actions operate as tools for analyzing a problem set that can be chained together to address a given workflow, in one or more prompts for constructing and delivering the identified outcomes. Chains of repeatable actions for saved and utilized for additional workflows having similar problem sets, and executed based on pre-identified triggers.

METHOD AND SYSTEM FOR ENABLING CONTINUOUS MACHINE LEARNING USING DOMAIN-SPECIFIC LEARNING PROCESSES

Resumen de: US2025315692A1

A method and system for continuous machine learning is disclosed. A set of domain-specific learning processes (LPs) from an external repository are obtained. Each LP of the domain-specific LPs is associated with at least one domain-specific knowledge graph representing learned parameters, patterns, and processing capabilities. Operational data from multiple sources is received and pattern representation is generated. One or more relevant LPs from the set of domain-specific LPs are identified by matching the pattern representation with at least one knowledge graph. The identified one or more LPs are executed to generate execution results and are validated through a contradiction resolution upon detecting the existence of contradictions between execution results and existing domain knowledge during the execution. The one or more LPs and their associated domain-specific knowledge graphs, trust relationships between LPs are updated based on validation outcomes and are submitted to the external repository.

Techniques For Using Machine Learning To Test Integrated Circuit Dies

Resumen de: US2025315583A1

A computing system includes a processor circuit configured to receive test data generated from testing integrated circuit dies in a test flow. The computing system includes a machine learning model that uses the test data generated from the test flow to predict bench results that are indicative of which ones of the integrated circuit dies fail to satisfy a manufacturing protocol when the integrated circuit dies are coupled to circuit boards.

PROACTIVE DEFENSE OF UNTRUSTWORTHY MACHINE LEARNING SYSTEM

Resumen de: US2025315674A1

Methods and systems for inducing model shift in a malicious computer's machine learning model is disclosed. A data processor can determine that a malicious computer uses a machine learning model with a boundary function to determine outcomes. The data processor can then generate transition data intended to shift the boundary function and then provide the transition data to the malicious computer. The data processor can repeat generating and providing the transition data, thereby causing the boundary function to shift over time.

PREDICTING ALBUMINURIA USING MACHINE LEARNING

Resumen de: US2025316377A1

An example embodiment may involve obtaining, by a computing system, an observation of demographic values of an individual, vital sign values of the individual, and blood test values of the individual: applying, by the computing system, a machine learning model to the observation, wherein the machine learning model was trained with a training data set, wherein the training data set contained observations of corresponding demographic values, vital sign values, blood test values, and either urine albumin-to-creatinine ratio (UACR) values or urine protein-to-creatinine ratio (UPR) values for a plurality of individuals, and wherein the machine learning model is configured to provide predictions of whether further observations are indicative of undiagnosed albuminuria or proteinuria; and providing, by the computing system, a prediction of whether the individual exhibits undiagnosed albuminuria or proteinuria based on the observation.

SYSTEM AND METHOD FOR GENERATING A PATH LOSS PROPAGATION MODEL THROUGH MACHINE LEARNING

Resumen de: US2025317224A1

The present disclosure provides a system and a method for generating a path loss propagation model through machine learning. The system generates a path loss propagation model for fifth generation (5G) networks for network planning. The path loss model predicts a reference signal received power/signal to noise interference ratio (RSRP/SINR) by leveraging a fourth generation (4G) user data.

System and Method for Performing Fault and Event Analysis in Electrical Substations

Resumen de: US2025315339A1

A system and method performing fault and event analysis in electrical substations comprises receiving a disturbance record triggered by an intelligent electronic device (IED) at an electrical substation, pre-processing the received disturbance record to extract at least one variable time series data of plurality of electrical parameters, generating a causality matrix based on the extracted at least one variable time series data by applying causal analysis, predicting, using a Machine learning (ML) module, a fault type at least based on the causality matrix, retrieving, from a knowledge database, a plurality of probable causes corresponding to the predicted fault type, determining at least one exact cause from the plurality of probable causes based on the causal pattern, and providing the fault type, the plurality of probable causes, and the at least one exact cause to a user.

MACHINE-LEARNING MODEL(S) FOR ESTIMATING RAN FUNCTIONALITY MACHINE LEARNING MODEL IMPACT ON PERFORMANCE MEASUREMENT COUNTERS

Resumen de: WO2025210109A1

This specification relates to the execution of machine-learning models on user devices. According to a first aspect of this specification, there is described apparatus comprising: means for receiving a network configuration derived from a plurality of machine-learning models, each machine-learning model directed towards a respective one or more radio access network functionalities; means for receiving a plurality of predicted performance measurement counters output from a plurality of machine-learning performance measurement models, each machine-learning prediction measurement model corresponding to one of the plurality of machine-learning models; and means for processing, using a common machine-learning performance measurement counter model, the network configuration and the plurality of predicted performance measurement counters to determine a model output comprising, for one or more performance measurement counters, a respective plurality of impact scores. Each impact score is indicative of a predicted impact of a corresponding machine-learning model in the plurality of machine-learning models on the respective performance measurement counter of said impact score for the network configuration.

SIMULATION-DATA BASED PREDICTIVE MODELS FOR GAS SEPARATION PLANTS

Resumen de: WO2025209965A1

The invention concerns a computer-implemented method for predicting performance parameter values of at least one individual gas separation stage, the method comprising: - receiving (162) a set of data points (21), each data point comprising operating parameter values indicative of a configuration or state of a separation stage of a gas separation plant (1800) and comprising performance parameter values obtained by simulating the operation of said separation stage given the operating parameter values of said data point; - using (164) the received set of data points as a training data set in a machine learning process for generating a trained predictive model (1704-1708, 2304); - receiving (166) input parameter values being indicative of one or more operation parameter values for the at least one separation stage; - using (168) the trained model for predicting performance parameter values of the at least one individual separation stage as a function of the input parameter values; and - outputting (170) the predicted performance parameter values for use in the design or control of the single separation stage or of the plant comprising the same.

SYSTEM AND METHOD FOR PERFORMING FAULT AND EVENT ANALYSIS IN ELECTRICAL SUBSTATIONS

Resumen de: EP4629009A1

The present disclosure describes a system and method performing fault and event analysis in electrical substations is disclosed. The method comprises the step of receiving a disturbance record triggered by an intelligent electronic device (IED) at an electrical substation, preprocessing the received disturbance record to extract at least one variable time series data of plurality of electrical parameters, generating a causality matrix based on the extracted at least one variable time series data by applying causal analysis, predicting, using a Machine learning (ML) module, a fault type at least based on the causality matrix, retrieving, from a knowledge database, a plurality of probable causes corresponding to the predicted fault type, determining at least one exact cause from the plurality of probable causes based on the causal pattern, and providing the fault type, the plurality of probable causes, and the at least one exact cause to a user.

CHANNEL RECOMMENDATIONS USING MACHINE LEARNING

Resumen de: CN120283235A

Techniques are discussed herein for generating user profile data, including one or more frequent channels, related users, and/or related topics within a communication platform. In some examples, a machine learning model may receive user interaction data (sent messages, read messages, channel publication, shared documents, frequent keywords used, etc.) associated with a communication platform, and output one or more frequent channels, related users, and/or related topics. The communication platform may then associate the one or more frequent channels, related users, and/or related topics with profile data for the users. In some examples, a communication platform may present different frequent channels, related users, and/or related topics associated with a profile page based on interaction actions associated with a user account viewing the profile page.

PREDICTION DEVICE, PREDICTION METHOD, AND PREDICTION PROGRAM

Resumen de: EP4629144A1

A prediction device that accurately and efficiently predicts drug discovery of desired drugs as well as efficacy and side effects of drugs by integrating chemical substance information of compounds, information acquired at the time of administration to the cells, and biological or clinical information. The prediction device has an acquisition unit that acquires chemical substance information and pharmacological information of the drug; an estimation unit that estimates estimated information of the drug by performing machine learning using the chemical substance information and pharmacological information; and an output unit that predicts and output both efficacy and side effects of the drug on an organism by retraining a model of the machine learning on the basis of the estimated information.

SYSTEMS AND METHODS FOR MACHINE LEARNING FEATURE GENERATION

Resumen de: WO2024119010A1

A method and apparatus for generating an ML model may include: generating an ML feature template comprising a first grouping of first ML feature variables and a second grouping of second ML feature variables; generating ML features by combining a respective one of each of the first ML feature variables with a respective one of each of the second ML feature variables; training a first ML model utilizing the ML features and first training data to generate an ML output; analyzing the ML output to determine a prediction accuracy of the ML features; based on the prediction accuracy of the ML features, selecting a subset of the ML features; training a second ML model based on the subset of the ML features and the first training data; and providing a network transaction to the second ML model to generate a classification of the network transaction.

METHODS, SYSTEMS AND RELATED MACHINE LEARNING TOOLS FOR ACCELERATED PLANT PRODUCTIVITY AND ATMOSPHERIC CARBON SEQUESTRATION

Resumen de: WO2025207133A1

Systems and methods for accelerating plant biomass growth and plant-mediated sequestration of atmospheric carbon, in particular, for selection of microbial drivers thereof from naturally occurring fungal species and/or strains are disclosed. The systems or methods may facilitate identification and propagation of a growth-promoting fungal consortium from a natural fungal microbiome. Sampling kits to collect soil samples are provided. Sample nucleic acid material may be extracted from the soil to generate a fungal microbiome dataset comprising of nucleic acid sequences. A machine learning tool, trained on high productivity ecosystems data, may processes the microbiome dataset to identify the growth-promoting fungal consortium. Propagation may include introducing a soil sample portion into a forest bioreactor to cultivate the growth-promoting fungal consortium, followed by inoculum preparation and application onto plants at a geographic location. Monitoring plant productivity post-inoculation may be achieved using an array of sensors to assess the efficacy of the fungal consortium.

OBSERVABILITY-BASED CONFIGURATION REMEDIATION FOR COMPUTING ENVIRONMENTS

Resumen de: US2025307101A1

Observability-based configuration remediation for use in a computing environment is disclosed. For example, a method includes detecting an incident in a computing environment and obtaining information related to the incident, the information including a dynamic state information set and a static state information set. The method further includes summarizing the information related to the incident as a textual prompt and then inputting the textual prompt into one or more machine learning models such that the one or more machine learning models, in response, generates an output including a resolution to the incident.

SYSTEM AND METHOD FOR PREDICTING INSULIN RESISTANCE OR PANCREATIC BETA-CELL FUNCTION AND COMPUTER READABLE MEDIUM THEREOF

Resumen de: US2025308709A1

A system and a method for predicting insulin resistance and/or pancreatic β-cell function are provided, where a machine learning model is utilized to predict insulin resistance and/or pancreatic a decline of β-cell function of a subject in need thereof based on a feature set extracted from a database. Therefore, clinicians or the subject can be warned to take necessary actions on, and adjust related medical treatment or lifestyle before the subject is diagnosed with diabetes mellitus. In addition, a computer readable medium thereof is also provided.

Selectively Providing Machine Learning Model-Based Services

Nº publicación: US2025307758A1 02/10/2025

Solicitante:

MAPLEBEAR INC [US]
Maplebear Inc

Resumen de: US2025307758A1

An online concierge system provides arrival prediction services for a user placing an order to be retrieved by a shopper. An order may have a predicted arrival time predicted by a model that may err under some conditions. To reduce the likelihood of providing the predicted arrival time (and related services) when the arrival time may be incorrect, the prediction model and related services are throttled (e.g., selectively provided) based on one or more predicted delivery metrics, which may include a time to accept the order by a shopper and a predicted portion of late orders that will be delivered past the respective predicted arrival times. The predicted delivery metrics are compared with thresholds and the result of the comparison used to selectively provide, or not provide, the predicted delivery services.