Machine learning

DISTRIBUTED INFERENCE ENGINE

Resumen de: WO2025254902A1

A distributed inference engine system that includes multiple inference engines is disclosed. A particular inference engine of the multiple inference engines may receive a prompt and its associated data, and divide the data into multiple data portions that are distributed to the multiple inference engines. Operating in parallel, and using a machine-learning model and respective data portions, the multiple inference engines generate an initial token. The multiple inference engines also generate, in parallel and using corresponding portions of the machine-learning model and the initial token, a subsequent token.

POSITIONING AND SENSING MACHINE LEARNING MODEL ASSOCIATION RULES

Resumen de: WO2025254972A1

In some aspects, a network node obtains one or more first quantities corresponding to positioning, sensing, or both machine learning model inputs, wherein the one or more first quantities comprise one or more channel measurements, quality indicators of the one or more channel measurements, timestamps of the one or more channel measurements, or any combination thereof, obtains one or more second quantities corresponding to positioning, sensing, or both machine learning model outputs, wherein the one or more second quantities comprise one or more ground truth labels, quality indicators of the one or more ground truth labels, timestamps of the one or more ground truth labels, or any combination thereof, and determines whether the one or more first quantities are associated with the one or more second quantities based on one or more association rules.

QUANTUM-ASSISTED VERIFICATION OF ELECTRONIC CIRCUIT FUNCTIONALITY

Resumen de: WO2025252418A1

The present subject matter relates to a method for proving a statement descriptive of a functionality of an electronic circuit using a quantum computer and a proof assistant. The method comprises: encoding a current proof state into a vector of real numbers of a fixed length, the current proof state defining a task for proving at least part of the statement, encoding the vector into a quantum state of a quantum system of the quantum computer, using the quantum state as an input quantum state by a quantum machine learning model for providing an output quantum state whose measurement represents a proof step for the defined task, measuring the output quantum state, thereby obtaining the proof step for the defined task, providing the proof step to the proof assistant, in response to providing the proof step, receiving a next proof state from the proof assistant.

DEVICE CAPABILITY DISCOVERY METHOD AND WIRELESS COMMUNICATION DEVICE

Resumen de: US2025379798A1

The disclosure provides a device capability discovery method and a wireless communication device. The wireless communication device transmits a capability message of the wireless communication device to a source device having a pool of machine learning (ML) models. The capability message shows whether the wireless communication device is capable of executing multiple ML models. The wireless communication device downloads if needed, and activates one or more ML models from a subset in the pool of ML models. The subset in the pool of ML models matches the capability message of the wireless communication device.

TASK-SPECIFIC MODIFICATION OF PRE-TRAINED LANGUAGE MODELS

Resumen de: WO2025255369A1

Methods and systems are disclosed for selectively modifying the behavior of a pre-trained language model with respect to a designated task. A task-specific subspace is identified by training low-rank matrices for selected layers of the trained machine learning model, while freezing other parameters. The identified subspace is used to either attenuate or enhance task contributions by adjusting one or more model weight matrices. In some embodiments, overlapping subspaces are discriminated to preserve related task performance. These operations can be performed without access to original training data or full retraining. Some aspects of the disclosed techniques can allow efficient knowledge removal or addition in language models while minimizing adverse effects on unrelated tasks.

AUTOMATIC ACTIVATION AND CONFIGURATION OF ROBOTIC PROCESS AUTOMATION WORKFLOWS USING MACHINE LEARNING

Resumen de: US2025378389A1

Automatic activation and configuration of robotic process automation (RPA) workflows using machine learning (ML) is disclosed. One or more parts of an RPA workflow may be turned on or off based on one or more probabilistic ML models. RPA robots may be configured to modify parameters, determine how much of a certain resource to provide, determine more optimal thresholds, etc. Such RPA workflows implementing ML may thus be hybrids of both deterministic and probabilistic logic, and may learn and improve over time by retraining the ML models, adjusting the confidence thresholds, using local/global confidence thresholds, providing or adjusting modifiers for the local confidence thresholds, implement a supervisor system that monitors ML model performance, etc.

Method for Filtering User Feedback and/or Output of a Machine Learning Model

Resumen de: US2025378309A1

A computer implemented method for filtering user feedback and/or output of a machine learning model, comprising: providing a filter for filtering user feedback and/or output of a machine learning model; receiving user feedback and/or output of the machine learning model; filtering the user feedback and/or the output with the filter and determining a filtering result, wherein the filtering result comprises at least a detected error; providing the filtering result for further processing.

MACHINE LEARNING IN AGRICULTURAL PLANTING, GROWING, AND HARVESTING CONTEXTS

Resumen de: US2025378507A1

A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.

PROOF OF A COMPUTER PROGRAM'S FUNCTIONALITY USING A QUANTUM COMPUTER AND A PROOF ASSISTANT

Resumen de: WO2025252383A1

The present subject matter relates to a method as follows. A current proof state may be encoded into a vector of real numbers of a fixed length. The current proof state defines a task for proving at least part of the statement. The vector may be encoded into a quantum state of a quantum system of the quantum computer. The quantum state may be used as an input quantum state by a quantum machine learning model for providing by the quantum machine learning model an output quantum state, wherein the measurement of the output quantum state represents a proof step for the defined task. The proof step may be provided to the proof assistant. In response to providing the proof step, a next proof state may be received from the proof assistant. It may be determined whether the received proof state indicates that the proof is completed. In response to determining that the proof is not completed, the received proof state may be used as the current proof state for repeating the method.

Intelligent Orchestration Systems for Energy and Power Management of Edge Devices

Resumen de: US2025377647A1

Disclosed herein are AI-based platforms for enabling intelligent orchestration and management of power and energy. In various embodiments, a machine learning system is trained on a set of energy intelligence data and deployed on an edge device, wherein the machine learning system is configured to receive additional training by the edge device to improve energy management. In some embodiments, the energy management includes management of generation of energy by a set of distributed energy generation resources, management of storage of energy by a set of distributed energy storage resources management of delivery of energy by a set of distributed energy delivery resources, management of delivery of energy by a set of distributed energy delivery resources, and/or management of consumption of energy by a set of distributed energy consumption resources.

METHOD FOR FILTERING USER FEEDBACK AND/OR OUTPUT OF A MACHINE LEARNING MODEL

Resumen de: EP4660880A1

Computer implemented method for filtering user feedback and/or output of a machine learning model, comprising: providing a filter for filtering user feedback and/or output of a machine learning model; receiving user feedback and/or output of the machine learning model; filtering the user feedback and/or the output with the filter and determining a filtering result, wherein the filtering result comprises at least a detected error; providing the filtering result for further processing.

MODEL GENERATION DEVICE, MACHINE LEARNING SYSTEM, MODEL GENERATION METHOD, AND PROGRAM

Resumen de: EP4660892A1

A model generation apparatus includes a parameter identification unit that identifies a parameter affecting misclassification of misclassification data incorrectly classified by a trained classification model, among parameters of the classification model for each type of the misclassification, based on the misclassification data incorrectly classified by the trained classification model; a parameter correction unit that generates a correction parameter obtained by correcting the parameter identified by the parameter identification unit for each type of the misclassification; and a parameter integration unit that generates an integrated model including an integrated parameter obtained by integrating the correction parameters for each type of the misclassification.

MACHINE LEARNING ARCHITECTURE FOR DETECTING MALICIOUS FILES USING STREAM OF DATA

Resumen de: CN120660086A

The invention discloses a method, a system and a computer system for classifying streaming data at an edge device. The method includes obtaining streaming data of a file at the edge device, processing a set of chunks associated with the streaming data of the file using a machine learning model, and classifying the file at the edge device prior to processing all content of the file.

Spectrum vacancy and occupancy forecasting

Resumen de: GB2641642A

A spectrum forecasting system, including a spectrum receiver to monitor a radio spectrum and collect live signal data; a spectrum forecasting deep learning model communicatively coupled to the spectrum receiver to receive the live signal data as input data, infer future vacancies, and chart a path through the future vacancies for assignment. A spectrum forecasting method, including receiving live signal data from a radio spectrum; inputting the live signal data into a spectrum forecasting deep learning model; inferring, using the spectrum forecasting deep learning model, future vacancies; and charting, using the spectrum forecasting deep learning model, a path through the future vacancies for assignment.

PREDICTION OF EQUIPMENT-RELATED EVENTS USING MACHINE LEARNING

Resumen de: US2025370446A1

Techniques are disclosed herein for machine-learning (ML)-assisted event prediction for industrial machines. A first set of embeddings can be generated based on labeled first event data, which can be labeled with classifiers determined based on signaling channel information for the first event data. A neural network can be trained, using the classifiers, to generate (i) a similarity score for the first set of embeddings and the second set of embeddings and (ii) a classifier recommendation for the second set of embeddings. The second set of embeddings can be generated based on data collected using condition monitoring sensors for a particular industrial machine. Accordingly, the system can generate alerts, recommendations, and/or notifications based on the automatically classified data encoded in the second set of embeddings. Incremental training techniques are disclosed for further training the neural network to minimize false positives and/or false negatives.

PREDICTION OF EQUIPMENT-RELATED EVENTS USING MACHINE LEARNING

Resumen de: WO2025250329A1

Techniques are disclosed herein for machine-learning (ML)-assisted event prediction for industrial machines (106). A first set of embeddings (1986a) can be generated based on labeled first event data, which can be labeled with classifiers (1989b) determined based on signaling channel (110b) information for the first event data. A neural network (300) can be trained, using the classifiers (1989b), to generate (i) a similarity score for the first set of embeddings and the second set of embeddings and (ii) a classifier (1989b) recommendation for the second set of embeddings. The second set of embeddings can be generated based on data collected using condition monitoring sensors (104a) for a particular industrial machine. Accordingly, the system (200) can generate alerts, recommendations, and/or notifications based on the automatically classified data encoded in the second set of embeddings.

Federated Machine Learning Management

Resumen de: US2025371429A1

Techniques are disclosed in which a computer system receives, from a plurality of user computing devices, a plurality of device-trained models and obfuscated sets of user data stored at the plurality of user computing devices, where the device-trained models are trained at respective ones of the plurality of user computing devices using respective sets of user data prior to obfuscation. In some embodiments, the server computer system determines similarity scores for the plurality of device-trained models, wherein the similarity scores are determined based on a performance of the device-trained models. In some embodiments, the server computer system identifies, based on the similarity scores, at least one of the plurality of device-trained models as a low-performance model. In some embodiments, the server computer system transmits, to the user computing device corresponding to the low-performance model, an updated model.

METHODS AND SYSTEMS FOR IMPROVED AUTOMATED MACHINE LEARNING AND DATA ANALYSIS

Resumen de: US2025371427A1

The disclosed methods and systems automate the process of building machine learning models. A user interface receives a selection of a dataset for a machine learning experiment. An execution plan for the experiment is determined based on the selected dataset. The experiment is executed according to the execution plan to generate a plurality of machine learning models. The performance of the generated models is evaluated based on one or more performance metrics. A model is selected from the generated models based on the evaluation of the performance metrics. The selected model may be stored for future use.

ANALYSIS AND CORRECTION OF SUPPLY CHAIN DESIGN THROUGH MACHINE LEARNING

Resumen de: US2025371491A1

A dynamic supply chain planning system for analysis of historical lead time data that uses machine learning algorithms to forecast future lead times based on historical lead time data, weather data and financial data related to locations and dates within the supply chain.

METHODS AND APPARATUS FOR MACHINE LEARNING BASED MALWARE DETECTION

Resumen de: US2025371152A1

Apparatus and methods describe herein, for example, a process that can include receiving a potentially malicious file, and dividing the potentially malicious file into a set of byte windows. The process can include calculating at least one attribute associated with each byte window from the set of byte windows for the potentially malicious file. In such an instance, the at least one attribute is not dependent on an order of bytes in the potentially malicious file. The process can further include identifying a probability that the potentially malicious file is malicious, based at least in part on the at least one attribute and a trained threat model.

SYSTEMS AND METHODS FOR PROVIDING LOCATION-BASED TRAVEL OBJECTIVE NOTIFICATIONS

Resumen de: US2025369762A1

In some aspects, the techniques described herein relate to a method including: receiving, by a collaboration service, location data of a user, wherein the location data includes a timestamp; verifying, by the collaboration service and based on a digital itinerary associated with the user, a location of the user; processing data from a data profile associated with the user as input data to a machine learning model; receiving, by the collaboration service and as output of the machine learning model, a plurality of predicted travel objective classifications; determining, by the collaboration service, a plurality of travel objectives, wherein each of the plurality of travel objectives is associated with one of the plurality of predicted travel objective classifications; determining, by the collaboration service, a travel objective within a predefined proximity of the location of the user; and displaying the travel objective to the user via a planning interface.

SYSTEMS AND METHODS OF PROPERTY VALUATION

Resumen de: US2025371570A1

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.

OPTIMAL MULTICORE OPTIMIZATION FOR MACHINE LEARNING MODEL GENERATION

Resumen de: US2025371419A1

According to an embodiment, a method is proposed carried out by a computer system for tuning hyperparameters in a machine learning model, the computer system having a processing unit designed to execute a plurality of processes in parallel. The method comprising executing a plurality of independent hyperparameter search methods in different parallel processes of the processing unit, the results of the tests of the combinations of hyperparameters being stored in a memory in the computer system shared among the various processes, and wherein each process assesses whether a combination of hyperparameters searched for has already been tested by another process based on the results of tests stored in memory, and takes into account, in its own test history, the results of tests stored in the memory if the combination of hyperparameters searched for has already been tested.

METHOD FOR ADJUSTING HYPERPARAMETERS OF A MACHINE LEARNING MODEL

Resumen de: EP4657257A1

Selon un aspect, il est proposé un procédé mis en œuvre par un système informatique (SYS) d'hyperparamètres d'un modèle d'apprentissage automatique, le système informatique (SYS) comportant une unité de traitement (UT) configurée pour exécuter plusieurs processus en parallèle, le procédé comprenant une exécution de plusieurs méthodes de recherche indépendante d'hyperparamètres dans différents processus parallèles de l'unité de traitement (UT), les résultats des tests des combinaisons d'hyperparamètres étant stockés dans une mémoire du système informatique partagée entre les différents processus, et dans lequel chaque processus évalue si une combinaison d'hyperparamètres recherchée a déjà été testée par un autre processus à partir des résultats de tests stockés en mémoire, et prend en compte, dans son propre historique de tests, les résultats de tests stockés dans la mémoire si la combinaison d'hyperparamètres recherchée a déjà été testée.

AUTONOMOUS RECOMMENDATION SYSTEMS USING MACHINE LEARNING

Nº publicación: CA3266564A1 29/11/2025

Solicitante:

THE TORONTO DOMINION BANK [CA]
THE TORONTO-DOMINION BANK

Resumen de: CA3266564A1

An AI-driven recommendation system utilizes a machine learning model and a dynamically updated knowledge graph to generate personalized product recommendations. The system constructs a knowledge graph with nodes and edges representing relationships between users, prior product selections, and historical interactions. A supervised learning framework trains the machine learning model using labeled data from the knowledge graph to predict relevant products based on multidimensional constraints. A graphical user interface (GUI) presents dynamically adjusted interactive elements to capture user preferences. User responses are processed using natural language processing (NLP) to refine predictions and generate recommendations. The system continuously updates the knowledge graph with real-time user feedback and external data, retraining the machine learning model to enhance future recommendations. This adaptive approach enables personalized, context-aware recommendations that evolve based on user interactions and external influences.