Alerta

REAL-TIME CONTROL OF OFF-LINING OF LOCOMOTIVES FOR ENERGY MANAGEMENT

Resumen de: AU2023395626A1

A method of controlling one or more locomotives in a train (102) includes using a machine learning engine and a virtual system modeling engine (324) to model and classify sections of track (106) along which the train (102) is traveling according to the tractive power needs for the train traversing each section of track (106) as a function of an effective weight profile for the train in the section and an effective friction profile for the train in the section of track. The method includes using the results of the effective weight profile, the effective friction profile, and an effective power availability profile to train the virtual system modeling engine (324) using the machine learning engine to model designated areas of the track where the total tractive effort force or dynamic braking force applied by all of the locomotives in the train is less than a tractive effort force or dynamic braking force, respectively, that can be provided by a subset of the available locomotives in the train (102).

COMPUTER IMPLEMENTED METHOD FOR DEFECT RECOGNITION IN AN IMAGING DATASET OF A WAFER, CORRESPONDING COMPUTER READABLE-MEDIUM, COMPUTER PROGRAM PRODUCT AND SYSTEMS MAKING USE OF SUCH METHODS

Resumen de: US2025209603A1

A computer implemented method for defect recognition in an imaging dataset of a wafer in a charged particle beam system comprising an embedded system, the method comprising: i) obtaining an imaging dataset of a wafer; ii) obtaining model data for a model architecture of a machine learning model for defect recognition in the imaging dataset of the wafer, the model architecture being implemented in the embedded system; iii) transferring the model data to a programmable memory of the embedded system; and iv) applying the machine learning model to an imaging dataset of a wafer to recognize defects, comprising executing the embedded system implemented model architecture with the transferred model data.

SYSTEMS AND METHODS FOR FEDERATED LEARNING

Resumen de: US2025209383A1

In one embodiment, one or more computing systems may generate a knowledge graph representing relationships between a global model and a number of local models for updating the global model. Each local model may have access to a local dataset for machine-learning training. The systems may access, from the knowledge graph, a sharing policy associated with the local dataset of a first local model. The systems may determine, based on the knowledge graph and one or more pre-determined criteria, that the sharing policy permits the local dataset of the first local model to be shared with a second local model. The system may cause the second local model to be trained using at least the local dataset of the first local model and the local dataset of the second local model. The system may update the global model using the trained second local model.

SYSTEM AND METHOD OF HIERARCHICAL INFERENCING IN MACHINE LEARNING SYSTEMS

Resumen de: WO2025137194A1

A method of hierarchical inferencing which may include: by a camera, capturing an image of a scene; by at least one computing device: based on a first reduced resolution image of the scene, determining a first probability value of a presence of a specified feature in the reduced resolution image; if the first probability value is greater than a first probability threshold: based on the image or a second reduced resolution image of the scene, determining a second probability value of a presence of the specified feature in the image or the second resolution image; and if the second probability value is greater than a second probability threshold which is greater than the first probability threshold, transmitting a notification that the specified feature is present in the image or the second resolution image.

SYSTEMS AND METHODS FOR FACILITATING CONVERSATION BASED ON TEXT DATA AND SPEECH DATA

Resumen de: WO2025133181A1

A method includes receiving a user input and generating a set of user input tokens based on the user input. The method also includes generating a set of enhanced input tokens by providing the set of user input tokens as input to a first machine learning model. A state is determined based on a previous state and at least one of the set of user input tokens or the set of enhanced input tokens. Predetermined data is retrieved from a database based on the state and at least one of the set of user input tokens or the set of enhanced input tokens. The method also includes generating a set of response tokens by providing the set of user input tokens and the predetermined data as input to a second machine learning model. Based on the set of response tokens, a response is sent to a user device.

METHOD AND APPARATUS FOR PROCESSING MODEL GENERATION RESULT, ELECTRONIC DEVICE AND STORAGE MEDIUM

Resumen de: EP4575884A1

The present disclosure provides a method and apparatus for processing a model generation result, an electronic device and a storage medium, and relates to the field of artificial intelligence technologies, such as machine learning technologies, natural language processing technologies. An implementation includes: disassembling a text generation result of a generative large model to obtain a plurality of result logic units; wherein each result logic unit includes a segment in the text generation result; each segment is capable of independently identifying one premise or conclusion in a logical inference relationship of the text generation result; and the text generation result is a response result generated by the generative large model based on text input information; generating a logical inference graph capable of characterizing a logical inference relationship among the plurality of result logic units based on the plurality of result logic units; and determining whether logical inference of generation of the text generation result by the generative large model is correct or not based on the logical inference graph. With the technology according to the present disclosure, whether logical inference of the generative large model is correct or not can be efficiently and accurately determined.

Knowledge edit in a text-to-image model

Resumen de: GB2636608A

Identifying locations within an text to image machine generative AI model that support the model’s ability to generate a specific visual attribute in the image, editing the generative artificial intelligence model to cause removal of the trained ability, and generating a subsequent digital image with the model in which the visual attribute is removed. Visual attribute may be object, style, colour, viewpoint, action, concept, etc. A generative text-to-speech model is corrupted by adding noise, preferably by applying Gaussian noise to a symmetric encoder-decoder machine learning model or to a text encoder machine learning model, then parts of the model are restored, the partially restored model is used, and the result is compared with the original model. Differences in the image, i.e. visual attribute changes, are detected in the comparison and based on the attributes that are the same, the functionality of the restored parts of the model is associated with the attributes. Parts if the model that are associated with attributes can be selectively edited out of the model to tailor the image generation of the model by removal of visual attributes.

DYNAMIC GENERATION ON ENTERPRISE ARCHITECTURES USING CAPACITY-BASED PROVISIONS

Resumen de: EP4576707A2

The present disclosure is directed to systems and methods for generating an enterprise architecture for an enterprise network. As one example, a method may include: receiving historical information from a plurality of enterprise networks, the historical information comprising information about an enterprise architecture of each of the enterprise networks; analyzing the historical information from the plurality of enterprise networks to generate a network health score for each of the plurality of enterprise networks; training a machine learning model using a plurality of machine learning algorithms based on the historical information and the network health score of each the plurality of enterprise networks; and generating, using the machine learning model, an enterprise architecture for a first enterprise network, the first enterprise network being a new enterprise network or an existing enterprise network from among the plurality of enterprise networks.

AUTOMATIC DETERMINATION OF STIMULATION SETTINGS AND METRICS

Resumen de: WO2025128472A1

A system and method for automated neurostimulation programming uses machine learning to determine target stimulation or discard suboptimal parameters based on actual or anticipated clinical effects. The system includes processors to generate a database of previously acquired lead and/or brain images, tested stimulation settings and associated clinical effects. A region of interest surrounding at least one stimulation lead is identified. Stimulation settings linked to specific clinical effects, including beneficial or detrimental effects, are determined for the region of interest. A trained machine learning model identifies desirable, undesirable, target, or other stimulation parameter values for a new patient by classifying or regressing raw imaging data, from the region of interest. The model is trained to recommend settings that produce desired clinical effects. An output provides the target stimulation parameter values predicted to maximize therapeutic benefit and minimize side effects. This enables automated programming of neurostimulation devices through data-driven machine learning based on clinical outcomes.

DETECTING SYNTHETIC USER ACCOUNTS USING SYNTHETIC PATTERNS LEARNED VIA MACHINE LEARNING

Resumen de: US2025200193A1

The present disclosure relates to systems, methods, and non-transitory computer-readable media that detect synthetic user accounts of a digital system via machine learning. For instance, the disclosed systems can utilize a machine learning model to analyze account features that are related to a user account and generate an indication that the user account is synthetic based on the analysis. The disclosed systems can further disable (e.g., suspend or close) the user account based on determining that the user account is synthetic. In some cases, the machine learning model provides a precision score that indicates a likelihood that the user account is synthetic, and the disclosed systems disable the user account if the precision score satisfies a threshold. In some implementations, the disclosed systems generate the machine learning model using synthetic user accounts detected via one or more rules and other user accounts that are associated with those synthetic user accounts.

DIGITAL TWIN GENERATION AND LOGGING FOR A VEHICLE

Resumen de: US2025201030A1

The present disclosure generates a digital twin of the interior of a vehicle to initiate and track maintenance issues. In one aspect, the digital twin is formed using multiple captured images of the interior of the vehicle where multiple components in those images are identified using a machine learning (ML) model. The components identified by the ML model are then mapped to a model (e.g., a 3D model) of the components that lists their location in the vehicle and an identifier (e.g., a part number or serial number). In this manner, the digital twin can identify, using the identifiers, the various components in the images captured by a camera.

BATCH SELECTION POLICIES FOR TRAINING MACHINE LEARNING MODELS USING ACTIVE LEARNING

Resumen de: EP4571591A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model. In one aspect, a method comprises: generating a set of candidate batches of model inputs; generating, for each candidate batch of model inputs, a respective score for the candidate batch of model inputs that characterizes: (i) an uncertainty of the machine learning model in generating predicted labels for the model inputs in the candidate batch of model inputs, and (ii) a diversity of the model inputs in the candidate batch of model inputs; and selecting the current batch of model inputs from the set of candidate batches of model inputs based on the scores; and training the machine learning model on at least the current batch of model inputs.

INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM

Resumen de: EP4571601A1

A new and improved technology capable of further improving inference accuracy while securing security of a machine learning model by federated learning is proposed.Provided is an information processing apparatus including: a learning unit that learns an inference model by federated learning; and an acquisition unit that acquires, from a plurality of terminals, privacy-protected data obtained by executing privacy protection processing on local data obtained by each of the plurality of terminals, in which the learning unit is configured to: perform learning of the inference model on the basis of the privacy-protected data; and distribute information regarding the inference model including a hyperparameter of the inference model on the basis of a result of the learning to the plurality of terminals, the acquisition unit is configured to acquire, from the plurality of terminals, update information of the inference model obtained by performing the learning of the inference model using the local data as learning data, and the learning unit is configured to update the inference model using the update information.

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.

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.

AUTOMATED DESIGN OF CAMERA PLACEMENT AND ARRANGEMENT FOR IDENTIFYING INVENTORY ITEMS AND SHOPPER DATA

Resumen de: US2025193347A1

Techniques for managing coverage constraints are provided for determining an improved camera coverage plan including a number, a placement, and a pose of cameras that are arranged to track subjects in a three-dimensional real space. The method includes receiving an initial camera coverage plan including a three-dimensional map of a real space, an initial number and initial pose of a plurality of cameras and a camera model including characteristics of the cameras. The method can iteratively apply a machine learning process to an objective function of number and poses of cameras, and subject to a set of constraints, obtain an improved camera coverage plan. The improved camera coverage plan is provided to an installer to arrange cameras to track subjects in the three-dimensional real space.

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.

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.

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.

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.

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.

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

SIGNAL AWARE MODEL LEARNING

Nº publicación: US2025190848A1 12/06/2025

Solicitante:

IBM [US]
INTERNATIONAL BUSINESS MACHINES CORPORATION

Resumen de: US2025190848A1

A method, computer system, and a computer program product are provided. A machine learning model is trained by inputting a code sequence. During the training, a minimal sub-sequence is extracted from the input code sequence. The minimal sub-sequence preserves a prediction that the machine learning model made for the input code sequence. The minimal sub-sequence constitutes a valid program A true class label for the minimal sub-sequence is obtained. The machine learning model is optimized with the true class label and by using the extracted minimal sub-sequence as a proxy for the input code sequence.