Machine learning

MACHINE LEARNING PROGRAM, DETERMINATION PROGRAM, MACHINE LEARNING METHOD, DETERMINATION METHOD, MACHINE LEARNING DEVICE, AND DETERMINATION DEVICE

Resumen de: EP4579535A1

A machine learning program for causing a computer to execute a process includes training a machine learning model by machine learning that uses a cost function in which each element of a matrix obtained by relaxing a discrete variable to be optimized to a continuous matrix becomes a discrete optimization problem as a cost function in a search process that performs a search by adopting continuous relaxation into the discrete optimization problem.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

SYSTEMS AND METHODS FOR COMPREHENSIVE AND STANDARDIZED IMMUNE SYSTEM PHENOTYPING AND AUTOMATED CELL CLASSIFICATION

Resumen de: AU2023389234A1

Method and systems for generating an immune profile for a subject are described. In some instances, the methods comprise contacting at least a first aliquot of a sample from the subject with at least a first immunophenotyping panel to fluorescently-label cells contained within the sample; processing the fluorescently-labeled cells using a full spectrum flow cytometer to generate fluorescence intensity data, or data derived therefrom, for fluorescently-labeled cells from the sample; providing at least a subset of the fluorescence intensity data, or data derived therefrom, for the fluorescently-labeled cells as input to an ensemble machine learning model configured to process the data and classify individual cells as belonging to one of a plurality of distinct immune cell sub-populations; and outputting a total cell count or cell frequency for each of the plurality of distinct immune cell sub-populations in the sample as part of an immune profile for the subject.

SYSTEMS AND METHODS FOR ARTIFICIAL INTELLIGENCE ANALYSIS OF SECURITY ACCESS DESCRIPTIONS

Resumen de: US2025193247A1

Some implementations described herein relate to a system for artificial intelligence analysis of security access descriptions. The system identifies a security access description. The system determines metadata information associated with the security access description. The system determines, by processing the security access description using a first set of one or more machine learning models, a descriptive quality label associated with the security access description. The system determines, by processing the security access description using a second set of one or more machine learning models, one or more descriptive components associated with the security access description and one or more descriptive component labels that correspond to the one or more descriptive components. The system provides the metadata information, the descriptive quality label, the one or more descriptive components, and/or the one or more descriptive component labels.

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.

GENERATING ENCODED TEXT BASED ON SPOKEN UTTERANCES USING MACHINE LEARNING SYSTEMS AND METHODS

Resumen de: US2025190686A1

Systems and methods for generating encoded text representations of spoken utterances are disclosed. Audio data is received for a spoken utterance and analyzed to identify a nonverbal characteristic, such as a sentiment, a speaking rate, or a volume. An encoded text representation of the spoken utterance is generated, comprising a text transcription and a visual representation of the nonverbal characteristic. The visual representation comprises a geometric element, such as a graph or shape, or a variation in a text attribute, such as font, font size, or color. Analysis of the audio data and/or generation of the encoded text representation can be performed using machine learning.

PROCESSING DATA FOR PREDICTING PIPE FAILURE

Resumen de: US2025190877A1

A system receives and automatically transforms utility pipe attribute data and pipe break data. The missing and/or incorrect entries in the pipe attributes and/or break 5 data is automatically identified and correct values for these entries are is automatically imputed to generate improved datasets of the pipe attribute data and break data. The improved data can be used to build a model with machine learning. Predictions of future likelihood of failure for pipe sections in a network of pipes can be made based on the model. A national database can be created that is filled with environmental data that has been transformed, optimized, merged, and imputed. The national database can be used for many customers to save computational costs. The national database can be used to build the failure prediction model for utility companies thereby saving computational costs.

RAPID DATA ANNOTATION FOR MACHINE LEARNING

Resumen de: US2025190824A1

Systems and Methods are described herein for rapid data annotation for ML. Aspects comprise a method for inferencing with an ensemble of agents (“EoAs”), comprising receiving data for processing by one or more agents of the EoAs; selecting a plurality of Bright pool agents from the EoAs; performing a first inference operation with each Bright pool agent of plurality of Bright pool agents based on the received data to generate a plurality of intermediate outputs; performing ground-truthing on one or more of an intermediate outputs and a final output to generate one or more labeled outputs; and storing the labeled outputs in a data repository.

MULTI-DIMENSIONAL RISK OPTIMIZATION OF PREDICTIVE MODELS

Resumen de: US2025190883A1

A computer-implemented method comprising: receiving a set of candidate trained machine learning models and a set of evaluation dimensions; generating risk scores for each of the candidate trained machine learning models over each of the evaluation dimensions; determining correlations between the evaluation dimensions based, at least in part, on the generated risk scores; and performing an optimization calculation to identify a subset of the set of candidate trained machine learning models, wherein each of the candidate trained machine learning models in the subset optimizes an overall risk measure over all of the evaluation dimensions, wherein the optimization calculation is based, at least in part, on the determined correlations.

SIGNAL AWARE MODEL LEARNING

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.

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

Nº publicación: US2025190851A1 12/06/2025

Solicitante:

MICROSOFT TECHNOLOGY LICENSING LLC [US]
Microsoft Technology Licensing, LLC

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