Alerta

AUTOMATED VALUATION MODEL (AVM) AND TECHNIQUES FOR REDUCING LEAKAGE IN MACHINE LEARNING DATASETS

Resumen de: US20260289411A1

0000 System, apparatus and method may control receiving time-indexed data comprising values of a plurality of features; generating derived features based on temporal changes of at least one of the plurality of features, the derived features including at least a first derivative of a first value; temporally aligning the plurality of features and the derived features to a reference date such that only data available prior to the reference date is used for training a machine learning model; training a machine learning model on the temporally aligned features and the derived features to predict a forward-looking value; and outputting the forward-looking value.

ENSEMBLE MACHINE LEARNING SYSTEM FOR DYNAMIC RESOURCE ALLOCATION USING CROSS-SOURCE DATA AGGREGATION

Resumen de: US20260289661A1

A computer-implemented method for evaluating resource allocation requests using an ensemble machine learning model is disclosed. A resource request is received, comprising multiple data inputs from different data sources, where each data source provides a specific type of user feature. A first set of machine learning models, each trained on a single data source, generates corresponding first risk scores. A second decision tree-based machine learning model processes the data inputs, leveraging feature dependencies and learned missing feature patterns to generate a second risk score, enabling robust predictions even when certain user features are incomplete or missing. The first and second risk scores are ensembled using an ensemble technique to produce an overall risk score. The ensembled risk score is then transmitted to a resource hosting device, which approves, denies, or adjusts the requested resource based on the assessed risk.

METHODS, SYSTEMS AND COMPUTER READABLE MEDIA FOR TRAINING MACHINE LEARNING MODELS

Resumen de: WO2026197916A1

A method for training a machine learning model, comprising determining a plurality of input data objects from a first unlabelled set of data, and providing the plurality of input data objects to at least one reference model configured to generate a first output for each of the plurality of input data objects. A labelled set of training data is created using the generated first outputs, and a target model is trained using the labelled set of training data. The trained target model is applied to a second unlabelled set of data. A plurality of training data objects is determined from the second unlabelled set of data. The plurality of training data objects is provided as an input to the at least one reference model to generate a second output for each of the plurality of training data objects, and the labelled set of training data is updated.

AUTONOMOUS GENERATIVE INTELLIGENCE ENVIRONMENT METHOD ACROSS OPERATING SYSTEM, DEVICES AND CONNECTED NETWORKS.

Resumen de: WO2026196276A2

A computer-implemented system and method for operating a local cross-application autonomous task routing framework tangibly embodied as a low-level software operational layer designated as 49AGI app via playstore and ios app store. The system architecture intercepts multi-modal input data directly from a plurality of distinct local applications, bypasses visual screen-pixel processing, and manages active task states natively via local processor networks. The 49AGI architecture executes background operations across applications lacking native machine learning models, providing local multi-modal reminder generation, notification scheduling, and native document compilation.

VALIDATION OF RESPONSES GENERATED BY LANGUAGE MODELS

Resumen de: US20260289392A1

Validation of responses generated by a language model is provided and includes identifying, via machine learning, first entity action pairs from a first response of a generative language machine learning model. Each of the first entity action pairs includes a respective entity and a respective action associated with the respective entity. A first validation score is determined based on whether the first entity action pairs are present within a first knowledge base and a second set of validation scores is determined based on whether the first entity action pairs are present within a second knowledge base. A first final validation score is generated based on the first validation score and the second set of validation scores. A first recommendation from the first response is presented based on the first final validation score and operations are controlled based on the presented first recommendation.

CROSS SPLINE NETWORK

Resumen de: US20260289281A1

A computer system for machine learning-based prediction is disclosed. The system applies a neural network-based spline transformation to input variables to generate transformed features. The transformed features are processed using a cross network comprising one or more cross layers, each configured to model feature interactions by combining prior layer outputs with learned weight parameters and applying a “cross” (multiplication) transformation with the input features. An output prediction is generated based on the transformed features processed by the cross network. The disclosed approach enables improved modeling of feature interactions while leveraging neural network-based transformations, facilitating enhanced interpretability and computational efficiency in machine learning applications.

MACHINE LEARNING ENABLED CONFORMATION-AWARE PROTEIN STRUCTURE PREDICTION

Resumen de: US20260290493A1

0000 A method for conformation aware protein structure prediction may include receiving an amino acid residue sequence of an input protein molecule, such as at least a portion of an antibody, a nanobody, or a T-cell receptor (TCR). A representation of the amino acid residue sequence of the input protein molecule may be determined to include a conformation token specifying a conformation state of the input protein molecule. The conformation state of the input protein molecule may be an unbound state or a bound state. A protein structure computation model may be applied to determine, based on the representation of the amino acid residue sequence of the input protein molecule, a conformation of the input protein molecule in the conformation state specified by the conformation token. Related systems and computer program products are also provided.

GENERATING TIMELINES USING MACHINE LEARNING MODELS

Resumen de: WO2026198917A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a timeline. One of the methods includes obtaining a plurality of documents; generating, for each document, a corresponding annotated document; generating, for each corresponding annotated document, an embedded annotated document; generating, for each document, a corresponding timeline for the document; and generating a combined timeline over the plurality of documents by merging each of the corresponding timelines based on at least the embedded annotated document for one or more of the documents.

COMPUTER-IMPLEMENTED SYSTEM AND METHOD FOR ADAPTIVE BEHAVIORAL MODELING AND CONTENT OPTIMIZATION USING ARTIFICIAL INTELLIGENCE TECHNIQUE

Resumen de: US20260289358A1

A method and system for simulating responses from virtual personas, the method comprising: using a computer processor, generating a plurality of target audience descriptions; converting the plurality of target audience descriptions into numerical embeddings; converting a pre-existing personality survey into numerical embeddings; determining, with a machine learning model, compatibility of the plurality of target audience descriptions with respondents of the pre-existing personality survey; forming an array of likelihood scores based on the compatibility, wherein columns of the array correspond to the target audience descriptions and rows correspond to the pre-existing personality survey; selecting a subset of the rows of the array with likelihood scores above a user-selected threshold; generating, from the subset, a set of virtual personas combining the plurality of target audience descriptions and the pre-existing personality survey respondents; and simulating responses using the set of virtual personas.

HYBRID QUANTUM CALCULATING SYSTEM USING ARTIFICIAL INTELLIGENCE AND OPERATION METHOD THEREOF

Resumen de: US20260289381A1

A hybrid quantum calculating system according to the present disclosure comprises a correction information acquisition device acquiring correction information for correcting an error of at least one of a first qubit and a second qubit based on first environment information and second environment information, and a qubit error correction device correcting values of a first qubit register and a second qubit register based on the correction information. Also, the hybrid quantum calculating system acquires module selection information by applying environment information to a dynamic selection machine learning model, and selects at least one of a first encryption module, a second encryption module, and a third encryption module based on the module selection information.

LANGUAGE-BASED CROSS-MODALITY TRANSFER SYSTEM FOR GENERATING VIRTUAL DATA TO ENHANCE ACTIVITY RECOGNITION

Resumen de: US20260289407A1

0000 An exemplary system and method are disclosed for generating training data for artificial intelligence (AI) or machine learning (ML) models to detect or recognize activities of a subject. In one implementation, an example method includes generating, via a motion synthesis model and as a first trained AI model, a set of motion sequence data objects representing motion sequences corresponding with the plurality of activity descriptions, where the generation of the set of motion sequence data objects includes applying an iterative data-variability evaluation operation.

READER LIFE SPAN PREDICTION MODEL

Resumen de: WO2026195627A1

A system for predicting lifespan of a physical access control (PAC) reader device is described. The system collects data associated with a PAC reader. The system processes the data using one or more machine learning (ML) models to generate a prediction of a remaining lifespan of the PAC reader and one or more configuration changes for extending the remaining lifespan of the PAC reader. The system generates an alert comprising the prediction and the one or more configuration changes.

INTEGRATED TELEMETRY PIPELINE

Resumen de: US20260289286A1

0000 Embodiments described herein are directed to a multi-layer telemetry pipeline utilizing Machine Learning (ML) and/or Artificial Intelligence (AI) to perform reduction-based telemetry collection for anomaly detection and/or prediction tasks. Throughout the pipeline the amount of data is reduced at each level. As telemetry pipelines usually collect many data items into a central collector, through layers of aggregation, ML/AI can be integrated into each layer, i.e., the pipeline nodes/layers themselves to execute layers of the algorithm. In other words, the “low-level”, e.g. feature extraction layers of the pipeline can be executed within a local aggregator. This reduces the data to be sent, making it easier but without losing data to be ingested by the algorithm. The “higher level,” result-calculating parts of the algorithm can be executed within a collector.

SYSTEM AND METHOD FOR CALCULATING USER SATISFACTION INDICES BASED ON MULTIMODAL DATA

Resumen de: WO2026198071A1

A non-invasive, privacy-conscious system and method for calculating User Satisfaction Index (USI) in real-time using multi-sensory data fusion. The system is primarily based on radar sensors, ensuring privacy, while optical and acoustic sensors are optional and can be integrated as needed. It employs machine learning algorithms to analyze physiological (heart rate, respiration, micro-movements), emotional response, and behavioral (gestures, vocal intonation, facial expressions) markers. The User Satisfaction Index prediction and recommendation algorithms estimate users' satisfaction across various industries, including retail, healthcare, corporate environments, smart cities, transportation, and other fields.

METHODS, SYSTEMS, INTERFACES AND COMPUTER READABLE MEDIA FOR TRAINING MACHINE LEARNING MODELS

Resumen de: WO2026197915A1

A method for extracting data from a data object comprising receiving, at an extraction application programming interface (API), an extraction request comprising at least one data object or at least one data object reference and an extraction schema comprising one or more target data elements. The method includes determining for each target data element, whether a target model exists for extracting the target data element. Responsive to determining that a target model exists, the target model is applied to the data object to extract one or more values associated with the target data element from the data object. Responsive to determining that a target model does not exist, a general extractor model is applied to the data object to extract one or more values associated with the target data element from the data object. The method further includes generating and outputting an extraction payload.

HANDLING DATABASE OBJECT NAMING IN NL2SQL WITH LARGE LANGUAGE MODELS

Resumen de: US20260289393A1

Techniques are disclosed herein for generating augmented training examples to fine-tune a machine learning model for converting natural language to logical form. In one aspect, a method is disclosed that includes: accessing a set of training examples, wherein each training example includes: (i) an original prompt having a database schema and (ii) a programming language completion corresponding to the original prompt, generating a set of augmented training examples based on the set of training examples, generating a fine-tuning dataset comprising the training examples and the augmented training examples, and fine-tuning a model with the fine-tuning dataset. Generating the set of augmented training examples can include performing an iterative process including: selecting a subset of database object names from the database schema and/or programming language completions and performing transformations to each database object name of the selected subset of database object names within the original prompt and programming language completion.

SYSTEM AND METHOD FOR SMART ELECTRICAL LOAD MANAGEMENT

Resumen de: WO2026198742A1

A system and method of managing an electrical power grid. The method comprises: obtaining measurement data comprising a respective time series for each of a plurality of electrical loads connected to an electrical power grid, each time series comprising electrical power consumption measurements for the corresponding electrical load at each of a plurality of times; processing each of the time series using a machine learning model to determine a respective classification output for each of the electrical loads; processing the measurement data to predict one or more states of the electrical power grid at one or more corresponding times; and adjusting electrical power distribution to one or more of the electrical loads based on the classification outputs for the electrical loads and the predicted one or more states of the electrical power grid.

MODEL DEPLOYMENT SYSTEM FOR GENERATING OPTIMIZED MODELS FOR A TARGET ENVIRONMENT

Resumen de: US20260288437A1

A model deployment system (MDS) is disclosed that includes capabilities for generating an optimized model that is tailored to specific target deployment hardware used to deploy the model in a target environment. The MDS is capable of executing a model inference accelerator (MIA) layer to perform a set of optimizations on a machine learning (ML) model. The MIA layer executes the set of optimizations based on model metadata associated with the ML model to generate an optimized ML model. The execution performed by the MIA layer includes generating an intermediate representation (IR) format for the IR model that identifies model-specific optimizations to be applied to the ML model and target hardware for deploying the ML model. The execution by the MIA layer further includes compiling the IR format of the ML model based on model-specific optimizations and target hardware-specific model optimizations to generate an optimized ML model.

GENERATING SPATIALLY DISTRIBUTED RESPONSE TO QUERY BY INTELLIGENT SELECTION OF TIME-SERIES REMOTE SENSING DATA

Resumen de: US20260288888A1

This disclosure relates generally to a method and system for generating spatially distributed response to query by intelligent selection of time-series remote sensing data. The current available methods for processing and analyzing geospatial data have limitations such as manual satellite data selection, limited natural language query support and the like. The method disclosed receives multi-contextual user query, intelligently selects the relevant time-series satellite data and climate information from repository using intents and entities generated utilizing pre-defined knowledge graph. The method intelligently selects time-series satellite data by creating and utilizing relationship strength-based node ranking graph. This time-series satellite data is combined with the climate data selected from climate repository to generate the spatially distributed response for the multi-contextual user query using machine learning model and large language model. The method enables stakeholders to access actionable insights without needing expertise in satellite data processing and using it for informed agricultural decision-making.

METHOD FOR ESTABLISHING ROBOT COMMUNITY, ROBOT COMMUNITY SYSTEM AND OPERATING METHOD

Resumen de: EP4811249A1

0001 A method for establishing a robot community, a robot community system and an operating method. For establishing the robot community, each of intelligent robots (101) is assigned with a specific attribute, and accordingly a training data is prepared for training the intelligent robot (101) through a machine-learning algorithm. The robot communication system then deploys multiple intelligent robots (101) into a social media and generates community contents associated with each of the intelligent robots (101) according to the attribute and basic data of each of the intelligent robots (101) by a generative intelligent model. Therefore, the robot community system allows users to interact with the intelligent robots (101) and establish social relationships between the users and the intelligent robots (101) in the social media.

PREDICTION METHOD AND PREDICTION SYSTEM

Resumen de: EP4811366A1

To design a material required to satisfy a plurality of properties efficiently. A method performed by a prediction system according to an embodiment of the present invention includes acquiring a composition and a manufacturing condition of a material of interest; and predicting a plurality of properties of the material of interest by inputting the acquired composition and manufacturing condition of the material of interest into a machine learning model for predicting properties of a material based on its composition and manufacturing condition. The machine learning model is based on a mixture of continuous probability distributions.

QUANTUM COMPUTING FOR ACCELERATING MACHINE LEARNING MODEL TRAINING

Resumen de: WO2026075653A2

A system and method to accelerate machine learning training comprises receiving, by a non-quantum computer, a set of data for training a machine learning model; dividing the set of data into a plurality of data batches, each data batch determined according to predetermined requirements of a quantum computer; calculating, by the non-quantum computer, a Jacobian matrix for each of the plurality of data batches, the Jacobian matrix and a loss vector being part of a linear matrix equation for updating weights in the model; arranging, by the non-quantum computer, input data of a polynomial function formatted for a quantum computer; solving, by the quantum computer, a vector of weight updates; and updating the weights in the model.

METHODS AND SYSTEMS FOR OPTIMAL CARE SETTING FOR PATIENTS USING MACHINE LEARNING

Resumen de: WO2025106998A1

A method includes: receiving patient information including information about a current hospitalization of a patient; receiving hospital-unit-volume information corresponding to a rolling window of time, indicating a patient number admitted to, transferred into, or discharged from a hospital unit housing the patient; converting the patient information and the hospital-unit- volume information into a machine-interpretable format for at least one binary output machine learning algorithm; executing the at least one algorithm to generate binary models that predict the likelihood of discharging the patient within each of multiple timeframes; combining the binary models into a single combined model, including optimizing weightings of discharge predictions for each binary model; determining, using the single combined model, at least one weighted discharge readiness quantization; transforming the at least one weighted discharge readiness quantization into at least one discharge prediction; and controlling a user interface to provide the at least one discharge prediction to a user.

SYSTEM AND METHOD FOR MONITORING THE OPERATING CONDITION OF ROTARY ELECTRICAL MACHINES AND AUTOMATIC DETECTION OF MECHANICAL AND ELECTRICAL FAULTS

Resumen de: EP4811625A1

The present invention pertains to methods for operation, maintenance, and monitoring of rotating electrical machines, and relates to a system and a method for monitoring the operational condition and detecting mechanical, electrical, load, and process faults in rotating electrical machines. The proposed system and method, together, provide a more effective way to detect faults early on, with greater installation convenience and scalability than prior art methods, and are more efficient in avoiding production losses, improving operational performance and preventing damage to equipment and risks to operators. The developed method continuously collects electrical current and voltage signals that power the machine, utilizing a data acquisition module and current and voltage transformers. The collected data undergoes stages of compression, encryption, application of Fast Fourier Transform (FFT), subsampling, feature extraction, anomaly detection using statistical and machine learning techniques, and fault classification using machine learning techniques. The proposed system consists of one or more data acquisition modules, one or more gateway devices, a processing center on a cloud computing platform, and an operator interface. In an industrial application, the method can be continually improved with the collection of new data and with validation information from an operator in the face of an identified fault.

MACHINE LEARNING BASED METHOD FOR CONSIDERING CONSTRAINT VIOLATIONS WHEN SELECTING OPTIMAL ACTIONS TO PERFORM IN A COMMUNICATIONS NETWORK

Nº publicación: EP4809677A1 23/09/2026

Solicitante:

ERICSSON TELEFON AB L M [SE]
Telefonaktiebolaget LM Ericsson (publ)

Resumen de: WO2025105999A1

Embodiments described herein relate to methods and apparatuses for initiating control of one or more parameters in a communications network. A computer-implemented method comprises obtaining a first state of the communications network; for each action in a set of actions: predicting a next state of the communications network utilizing a first ML model based on the first state and the action; predicting, utilizing a second ML model, whether taking the action whilst the communications network is in the first state will violate one or more constraints; determining a cost value associated with the action by evaluating a cost function, wherein the cost function comprises: a first component representing one or more performance metrics of the communications network in the next state; and a second component dependent on whether taking the action in the first state will violate the one or more constraints; selecting a selected action from the set of actions, wherein the selected action is associated with a best cost value; and initiating performance of the selected action in the communications network.