Alerta

PREDICTING NANOBODY STABILITY AND GENERATING STABILITY-MODULATED SEQUENCES

Resumen de: WO2025259965A1

In some embodiments, a computer-implemented method of generating one or more artificial amino acid sequences representing artificial proteins predicted to have a different level of stability than a protein represented by an input amino acid sequence is provided. A computing system receives the input amino acid sequence and uses a tuned base machine learning model to generate an encoding of the input amino acid sequence. The tuned base machine learning model was fine-tuned to encode characteristics of proteins having the different level of stability. The computing system generates the one or more artificial amino acid sequences by decoding the encoding of the input amino acid sequence.

PARAMETER ADJUSTMENT VISUALIZATION DEVICE, PARAMETER OPTIMIZATION DEVICE, PARAMETER ADJUSTMENT VISUALIZATION PROGRAM, AND PARAMETER ADJUSTMENT VISUALIZATION METHOD

Resumen de: WO2025258091A1

The present invention comprises: a parameter selection unit (101) that selects a parameter; a partial dependency calculation unit (102) that calculates a partial dependency value of an evaluation index value relating to the parameter selected by the parameter selection unit (101), on the basis of a parameter adjustment data set including a plurality of sets of parameter values and evaluation index values, and a trained machine learning model that can output information indicating a predicted value of the evaluation index value; an uncertainty calculation unit (103) that calculates, on the basis of the trained machine learning model and the parameter adjustment data set, an uncertainty value of the evaluation index value relating to the parameter selected by the parameter selection unit (101); and an uncertainty-attached partial dependency plot output unit (104) that outputs information indicating the partial dependency value calculated by the partial dependency calculation unit (102) and the uncertainty value calculated by the uncertainty calculation unit 103.

COMPUTER SYSTEM, METHOD, AND DEVICE FOR ACTIVE LEARNING

Resumen de: US2025384668A1

Systems, and methods, and devices for active learning are provided. The system includes a data storage device, dataset analysis tool, synthetic data generation module, anomaly module, image search engine module, explainable AI module, automated training platform, and optionally, a federated learning module. The system may be configured to operate on a general purpose or purpose-built computer, and may further include a processor, memory, and network interface. The system, through interaction of its constituent components, analyzes a provided dataset and generates synthetic data to augment data within the provided dataset. This provided data and generated data is used to train a machine learning model. The system may be operated iteratively to continuously improve the machine learning model trained by the system by applying explainable artificial intelligence techniques with little to no human intervention.

MACHINE LEARNING MODEL REGISTRY

Resumen de: US2025384356A1

Systems and methods to utilize a machine learning model registry are described. The system deploys a first version of a machine learning model and a first version of an access module to server machines. Each of the server machines utilizes the model and the access module to provide a prediction service. The system retrains the machine learning model to generate a second version. The system performs an acceptance test of the second version of the machine learning model to identify it as deployable. The system promotes the second version of the machine learning model by identifying the first version of the access module as being interoperable with the second version of the machine learning model and by automatically deploying the first version of the access module and the second version of the machine learning model to the plurality of server machines to provide the prediction service.

LANGUAGE MODEL AND ONTOLOGY ASSISTED MACHINE LEARNING SERVICE

Resumen de: US2025384290A1

Computer-implemented systems and methods including language models for explaining and resolving code errors. A computer-implemented method may include: receiving one or more user inputs identifying a data set and providing a first user request to perform a first task based on at least a portion of the data set, wherein the data set is defined by an ontology; using a large language model (“LLM”) to identify a first machine learning (“ML”) model type from a plurality of ML model types; using the LLM to identify a first portion of the data set to be used to perform the first task; using the LLM to generate a first ML model training configuration; and executing the first ML model training configuration to train a first custom ML model, of the first ML model type, to perform the first task.

ENSEMBLE MACHINE-LEARNING MODELS TO DETECT RESPIRATORY SYNDROMES

Resumen de: US2025385007A1

Provided is a process including: obtaining, with one or more processors, a set of data comprising a plurality of patient records, selecting a subset of the plurality of parameters for inputs into a machine learning system, generating a classifier using the machine learning system based on the training data and the subset of the plurality of parameters for inputs; receiving, with one or more processors, patient record of a first user; performing an analysis, with one or more processors, to identify acoustic measures from a voice sample of the first user.

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.

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.

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.

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.

SYSTEM AND METHOD FOR TEACHING MACHINE LEARNING MODELS TO RECOGNIZE CONCEPTS IN MULTIMEDIA DOCUMENTS THROUGH NATURAL LANGUAGE INTERACTION AND MIXED-INITIATIVE LEARNING

Resumen de: AU2024274930A1

A method and system for training machine learning models using natural language interactions as well as techniques utilizing machine learning models trained using natural language interactions. A method includes applying a language model to text of a set of natural language interactions in order to output a set of domain-specific language (DSL) data, wherein the set of natural language interactions is between a user and at least one other entity, wherein the set of natural language interactions indicates at least one user-defined concept; querying a knowledge base based on the set of DSL data in order to obtain at least one DSL query result; integrating the at least one DSL query result with a structured representation of the natural language interactions in order to create at least one contextualized DSL query result; and training the language model using the at least one contextualized DSL query result.

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.

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.

UNIVERSAL EMBEDDING BASED ENTITY RETRIEVAL MODEL

Resumen de: US2025378307A1

Aspects of the disclosure include methods for leveraging a universal embedding based entity retrieval deep learning model for candidate recommendations. A method can include receiving a request for a candidate pair having a first entity and a second entity and generating a filtered candidate pool including a first number of candidates. The filtered candidate pool can include a subset of an initial candidate pool having a second number of candidates larger than the first number of candidates. A learned distance function is selected from a plurality of distance functions. At least one distance function was predetermined prior to receiving the request and at least one distance function is generated in response to receiving the request. A distance measure is determined for each candidate in the filtered candidate pool using the learned distance function and a response is returned including top K candidates according to the determined distance measures.

SYSTEMS AND METHODS FOR MANAGING OIL AND GAS PRODUCTION

Resumen de: US2025378508A1

Implementations claimed and described herein provide systems and methods for managing natural resource production. The systems and methods use a machine learning model to generate categorizations associated with communication data. The machine learning model is built from historical data.

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.

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.

SYSTEMS AND METHODS FOR RETRIEVING INFORMATION FROM KNOWLEDGE GRAPHS BASED ON QUERY CONTEXT

Resumen de: US2025378355A1

Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for retrieving a subgraph that is used to generate one or more answer outputs responsive to an input query by: (i) generating one or more context embeddings that are associated with an input query, (ii) identifying one or more candidate node paths and one or more node relations based on a knowledge graph, (iii) identifying, using a predictive machine learning model, one or more context-relationship rankings based on the one or more candidate node paths, the one or more node relations, and the one or more context embeddings, and (iv) generating one or more subgraph data objects based on the one or more context-relationship rankings.

INTELLIGENT AND AUTOMATED SYSTEM FOR SOLVING COMPUTATIONAL PROBLEMS USING QUANTUM COMPUTATION

Resumen de: US2025378358A1

One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to an intelligent and automated system to solve quantum computing related problems. The computer-implemented system can comprise a memory that can store computer-executable components. The computer-implemented system can further comprise a processor that can execute the computer-executable components stored in the memory, wherein the computer-executable components can comprise a recommendation component that can employ a machine learning model to generate, based on an input, a recommendation comprising a combination of entities comprising, one or more quantum circuits, one or more algorithms, one or more quantum hardware units, one or more error mitigation or error correction techniques, and one or more quantum procedures, to solve a defined problem comprised in the input.

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.

ADVERSARIAL TRAINING OF MODEL FOR QUESTION ANSWERING

Resumen de: WO2025251220A1

Embodiments of the present disclosure provide a solution for adversarial model training. A method comprises: generating a prompt input using an adversarial machine learning model; providing the prompt input to a target machine learning model, to generate a response to the prompt input; determining a first reward score for the response with respect to the prompt input; and fine-tuning the target machine learning model according to a first optimization objective, the first optimization objective being configured to increase or maximize the first reward score for the target machine learning model.

FLUID PUMPING OPERATIONS FRAMEWORK

Resumen de: WO2025255511A1

A method can include receiving data from field equipment during performance of a fluid pumping job that includes multiple stages at a wellsite; generating an inference as to an occurrence of one of the multiple stages associated with the performance of the fluid pumping job based on at least a portion of the data using a machine learning model; and assessing the performance of the fluid pumping job based at least in part on the inference.

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.

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

Nº publicación: EP4659128A1 10/12/2025

Solicitante:

PALO ALTO NETWORKS INC [US]
Palo Alto Networks, Inc

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.