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MACHINE LEARNING MODEL CONTINUOUS TRAINING SYSTEM

Resumen de: EP1000000A1

The invention relates to an apparatus (1) for manufacturing green bricks from clay for the brick manufacturing industry, comprising a circulating conveyor (3) carrying mould containers combined to mould container parts (4), a reservoir (5) for clay arranged above the mould containers, means for carrying clay out of the reservoir (5) into the mould containers, means (9) for pressing and trimming clay in the mould containers, means (11) for supplying and placing take-off plates for the green bricks (13) and means for discharging green bricks released from the mould containers, characterized in that the apparatus further comprises means (22) for moving the mould container parts (4) filled with green bricks such that a protruding edge is formed on at least one side of the green bricks.

COALITION LEARNING FOR TRAINING OF DISTRIBUTED MACHINE LEARNING WORKLOADS

Resumen de: EP1000000A1

The invention relates to an apparatus (1) for manufacturing green bricks from clay for the brick manufacturing industry, comprising a circulating conveyor (3) carrying mould containers combined to mould container parts (4), a reservoir (5) for clay arranged above the mould containers, means for carrying clay out of the reservoir (5) into the mould containers, means (9) for pressing and trimming clay in the mould containers, means (11) for supplying and placing take-off plates for the green bricks (13) and means for discharging green bricks released from the mould containers, characterized in that the apparatus further comprises means (22) for moving the mould container parts (4) filled with green bricks such that a protruding edge is formed on at least one side of the green bricks.

METHOD AND APPARATUS FOR TRAINING MODEL FOR ARTIFICIAL INTELLIGENCE/MACHINE LEARNING-BASED COMMUNICATION

Resumen de: EP4723506A1

Provided are a method and apparatus for training a model for artificial intelligence and/or machine learning (AI/ML)-based communication. A terminal receives, from a base station, data for AI/ML model training, and performs AI/ML model training based on the received data. After performing the AI/ML model training, the terminal transmits, to the base station, a first message indicating termination of collection of the data.

METHODS AND SYSTEMS OF PREDICTING TOTAL LOSS EVENTS

Resumen de: US20260091748A1

A mobile device detects a crash event using one or more sensors of a mobile device. The mobile device records a first set of data from the one or more sensors of the mobile device. The mobile device generates a first feature vector including the first set of data and available values for one or more additional data types. The mobile device executes a first machine-learning model selected from a plurality of machine-learning models based on the one or more additional data types for which there are available values to generate a first confidence of a total loss event.

IDENTIFYING NOISE IN VERBAL FEEDBACK USING ARTIFICIAL TEXT FROM NON-TEXTUAL PARAMETERS AND TRANSFER LEARNING

Resumen de: US20260094061A1

Methods and systems are provided for classifying free-text content using machine learning. Free-text content (e.g., customer feedback) and parameter values organized according to a schema are received. A free-text corpus is generated, and an artificial-text corpus is generated by applying rules to the parameter values. The artificial-text corpus is generated by converting the parameter values into a finite set of words based on the rules and concatenating the words of the finite set of words into a fixed sequence wordlist. Feature vectors (e.g., sentence embeddings) based on the free-text corpus and the artificial-text corpus are combined and forwarded to a machine learning model for classification. The machine learning model may be trained with a bias towards a specified metric (e.g., precision, recall, F1 score). The model may be trained using transfer learning with training data from a different category of free-text content (e.g., a different category of customer feedback).

ENHANCED SOCIAL MEDIA PLATFORMS BASED ON MACHINE LEARNING BASED AGGREGATION OF TRENDING EVENTS

Resumen de: AU2024354389A1

An example method for automatic generation of content based on an aggregation of trending events is provided. The method includes determining, by a computing device, a topic of interest. The method also includes determining additional information related to the topic of interest. The determining of the additional information includes, generating a prompt based on the topic of interest, submitting the prompt to an information search and retrieval system, and retrieving the additional information as an output of the information search and retrieval system. The method also includes generating, by a generative artificial intelligence model, a piece of annotated content associated with the topic of interest. The piece of annotated content comprises media content annotated with at least one selectable graphical object that links to the additional information. The method also includes providing, by the computing device, the piece of annotated content.

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

Resumen de: AU2024407921A1

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.

Multi-Stage Federated Learning in Wireless Networks

Resumen de: US20260094032A1

first group of AI agents to train and report, per each AI agent of the first group, a respective first partial AI or machine learning (ML) (AI/ML) model to the AI manager, receive the first partial model from each AI agent of the first group, generate a first version of a global model from the first partial models, if the first version of the global model is determined to be trustworthy, select a second group of AI agents to train and report, per each AI agent of the second group, a respective second partial AI/ML model to the AI manager, receive the second partial models and aggregate the second partial models and the first version of the global model into a second version of the global model.

METHODS OF PREDICTING PROPERTIES OF A CHEMICAL SYSTEM USING SURROGATE MODELS

Resumen de: US20260094677A1

Methods of predicting physicochemical properties of a chemical system using a family of surrogate or reduced order models, trained on first principle simulation results. The models are created using machine learning techniques. The chemical system can be a complex multicomponent and multiphase system such as produced water.

Granular Taxonomy for Customer Support Augmented with AI

Resumen de: US20260094166A1

A computer-implemented method for augmenting customer support is disclosed in which a granular taxonomy is formed to classify tickets based on customer issue topic. A dashboard and user interface of performance metrics may be generated for the topics in the taxonomy.Recommendations may also be generated to aid servicing customer support issues for topics in the taxonomy. This may include generating information to aid in determining topics for generating automated responses or generating recommended answers for particular topics. In some implementations, an archive of historic tickets is used to generate training data for a machine learning model to classify tickets.

PRIVACY PRESERVING IDENTITY RESOLUTION USING DEEP LEARNING

Resumen de: WO2026072270A1

A system and method for privacy-preserving identity resolution using deep learning enables accurate matching of personally identifiable information (PH) while maintaining data security. The system employs a deep learning model trained with transformer architecture and contrastive learning on third-party identity graph data. Custom tokenizers process data by leveraging hierarchical structures and domain-specific characteristics. The trained model generates vector embeddings that enable fuzzy matching, accounting for variations in spellings, typographical errors, and data inconsistencies. A vector database stores embeddings for nearest neighbor searches to identify potential identity matches. The system enables identity resolution without requiring Pll data movement from first-party environments. The invention facilitates building accurate first-party identity graphs and enables secure collaboration between parties without exposing underlying Pll data.

CENTRALIZED PLATFORM FOR ENHANCED AUTOMATED MACHINE LEARNING USING DISPARATE DATASETS

Resumen de: US20260094009A1

Systems and techniques are disclosed for a centralized platform for enhanced automated machine learning using disparate datasets. An example method includes receiving user specification of one or more data sources to be integrated with the system, the data sources storing datasets to be utilized to train one or more machine learning models by the system, and the datasets reflecting user interaction data. A dataset is imported from the data source, and machine learning models are automatically trained based a particular machine learning model recipe of a plurality of machine learning model recipes. A first trained machine learning model is implemented, with the system being configured to respond to queries based on the implemented machine learning model, and with the responses including personalized recommendations.

HIERARCHICAL MONITORING OF ARTIFICIAL INTELLIGENCE OR MACHINE LEARNING MODELS FOR AIR INTERFACE

Resumen de: WO2026072207A1

Certain aspects of the present disclosure provide techniques for performing wireless communication. In some aspects, the techniques include obtaining a first metric associated with a model that is associated with wireless communication; and obtaining, in response to a trigger condition associated with the first metric being satisfied, a second metric associated with the model, the second metric providing a different measure of the model than the first metric.

PREDICTIVE MACHINE LEARNING MODELS BASED ON DATA COLLECTED FROM A VARIETY OF SOURCES

Resumen de: WO2026072162A1

The computer-based methods and systems presented in this disclosure provide prediction occurrence of an event for an individual on a user device of the individual. The system receives, from a plurality of remote devices, pieces of input data about the individual. The system pre-processes the pieces of input data to make them ready to be processed by respective input modules of a machine learning model running on the system. Each input module is associated with a respective marker and processes the pre-processed data for that marker. Outputs of the input modules are further processed by the model. The model provides an output indicating respective probabilities that particular events happen. The system can generate one or more alerts based on the output of the model, and can send the alerts to contacts of the individual.

SYSTEMS AND METHODS FOR GENERATING AND DEPLOYING MACHINE LEARNING APPLICATIONS

Resumen de: US20260094427A1

A method comprising receiving data associated with a business, the data comprising first values for first attributes; processing the data, in accordance with a common data attribute schema that indicates second attributes, to generate second values for at least some of the second attributes including a group of attributes, the second values including a group of attribute values for the group of attributes; identifying, using the common data attribute schema and from among pre-existing software codes, software code implementing an ML data processing pipeline configured to generate a group of feature values; processing the group of attribute values with the software code to obtain the group of feature values; and either providing the group of feature values as inputs to a machine learning (ML) model for generating corresponding ML model outputs, or using the group of feature values to train the ML model.

A METHOD FOR DETERMINING A HARDWARE ARCHITECTURE FOR A MACHINE LEARNING MODEL

Resumen de: EP4718234A1

The invention relates to a method (100) for determining an hardware architecture (3) for a machine learning model (50), comprising:- Providing (101) an initial hardware architecture (1), the initial hardware architecture (1) describing hardware components (2) and computing characteristics of said hardware components (2),- Providing (102) the machine learning model (50),- Converting (103) the machine learning model (50) to an intermediate representation, the intermediate representation depicting a topology and/or a temporal structure of the machine learning model (50) as a graph structure,- Analysing (104) the intermediate representation to determine a memory footprint of the machine learning model (50),- Determining (105) the hardware architecture (3) for the machine learning model (50) based on the initial hardware architecture (1) and a result of the analysing (104).Furthermore, the invention relates to a computer program, an apparatus, and a storage medium for this purpose.

AUTOMATED MACHINE LEARNING BASED WORKFLOW FOR TIMESERIES FORECASTING

Resumen de: EP4718195A1

In an embodiment, workflow for timeseries forecasting may be performed based on automated machine learning. Sensor data for measurement parameter is received from plurality of sensors installed in built environment and the received sensor data is stored in table of relational database. Cut-off record associated with previous training checkpoint is determined of the forecasting model for the measurement parameter. Records including new records are determined for which respective timestamps occur after the measurement timestamp of cut-off record. Size of the determined records are compared with threshold size and training dataset is prepared. The forecasting model is trained on the training dataset based on the comparison.

DEEP NEURAL NETWORKS (DNN) INFERENCE USING PRACTICAL EARLY EXIT NETWORKS

Resumen de: US20260086912A1

The present disclosure relates to methods and systems for providing inferences using machine learning systems. The methods and systems receive a load forecast for processing requests by a machine learning model and split the machine learning model into a plurality machine learning model portions based on the load forecast. The methods and systems determine a batch size for the requests for the machine learning model portions. The methods and systems use one or more available resources to execute the plurality of machine learning model portions to process the requests and generate inferences for the requests.

MACHINE LEARNING TECHNIQUES FOR GENERATING CONTROLLER LOGIC

Resumen de: US20260086524A1

Embodiments of the present disclosure relate to generating controller logic. Indication of a controller logic generation request associated with an asset identifier may be received. A prompt template set associated with a controller logic generation workflow may be identified based on the asset identifier. The prompt template of the prompt template set may comprise one or more instruction sets. The prompt template set may be input into a large language model comprising one or more transformer neural networks and configured to generate a controller logic configuration file for the asset identifier based on the prompt template set and intent classification associated with each prompt template. The controller logic configuration file may be received from the large language model. Performance of one or more prediction-based actions may be initiated based on the controller logic configuration file.

IMAGE CLASSIFICATION USING MACHINE LEARNING INCLUDING WEAKLY-LABELED DATA

Resumen de: US20260087618A1

A system may receive a plurality of digital histology images, wherein each of the plurality of digital histology images is labeled with a respective image-level classification. A system may extract a plurality of tiles from each of the plurality of digital histology images. A system may create a first dataset comprising the plurality of tiles and respective image-level classifications. A system may train a first machine learning model using the first dataset. A system may create a second dataset by sampling the first dataset based on respective classifications and respective uncertainty measures for each of the plurality of tiles output by the trained first machine learning model. A system may train a second machine learning model using the second dataset, wherein the trained second machine learning model is configured to classify one or more tiles of a digital histology image.

AUTOMATED MACHINE LEARNING FAULT MODELING WITH GROUPING

Resumen de: US20260086257A1

Methods, computing systems, and computer-readable media for a machine learning method of modeling fault-related properties of a geological region are presented. The techniques include: obtaining seismic geological data for a geological region; obtaining from a user identifications of a plurality of faults in the geological region; automatically generating values for descriptors of respective faults of the plurality of faults; automatically partitioning faults of the plurality of faults into a plurality of groups according to the values for the descriptors; obtaining a mapping of respective groups of the plurality of groups to modeling parameter values; applying the mapping to a fault in the geological region outside of the plurality of faults to obtain a modeling parameter value for the fault outside of the plurality of faults; and modeling a fault-related property of the geological region based on the modeling parameter value for the fault outside of the plurality of faults.

METHOD FOR SINGLE CELL ANTIMICROBIAL SUSCEPTIBILITY TESTING IN A SUB-DOUBLING TIME

Resumen de: US20260085340A1

Methods and systems for antibacterial susceptibility testing of a bacterium are provided. The method includes exposing a bacterium to an antimicrobial agent. A series of images of the bacterium is captured over time after exposure The series of images are captured during an imaging period. For each image of the series of images, the method includes extracting a value of each feature in a set of morphological features of the bacterium. The set of morphological features includes one or more of area, aspect ratio, length, circularity, perimeter, angularity, curvature, ferret, pole, roundness, sinuosity, width, trajectory, morphology, orientation, solidity, and z-score. A rate of change is calculated for each feature of the set of morphological features during the imaging period. An inhibition status of the bacterium is determined using a machine-learning classifier applied to input data.

MODEL-BASED TASK PROCESSING

Resumen de: US20260087382A1

Embodiments of the disclosure provide a solution for model-based task processing. A method includes: obtaining a base parameter set of a pre-trained base machine learning model, and a first parameter set and a second parameter set of a trained low-rank machine learning model for a first task; applying a Hadamard operator on the base parameter set and the first parameter set, to obtain an intermediate parameter set; aggregating the second parameter set and the intermediate parameter set, to obtain an update parameter set; fine-tuning the base parameter set with the update parameter metric, to obtain a fine-tuned parameter set for a target machine learning model corresponding to the first task; and applying the target machine learning model to perform a model inference for the first task with the fine-tuned parameter set.

Decision Tree Algorithms in Machine Learning To Learn and To Predict Innovations

Resumen de: US20260087375A1

0000 An expert system for innovation discovery in the field of artificial intelligence (AI) applies decision tree algorithms structured around a rules-based reasoning methodology. The system trains on innovation datasets comprising both data and non-data types, including target variables representing key attributes of innovations and proximal variables that approximate them. Through a machine learning architecture, decision nodes are configured to evaluate these variables and generate predictive models. The architecture enables continual learning via reinforcement mechanisms and communication ports that facilitate data flow from external tools, cloud storage, and software. Differentiated nodes are assigned weights, roles, and activation logic to refine decision-making and improve model accuracy. The expert system integrates human-defined heuristic rules with AI capabilities to support early-stage ideation, concept development, and innovation pattern recognition. This system can operate as an autonomous AI agent, a core reasoning engine, or as part of a digital business model in platform ecosystems to enhance innovation discovery, reduce hallucinations, and support transparent, verifiable AI outcomes.

SYSTEMS AND METHODS FOR COLLISION DETECTION

Nº publicación: US20260087858A1 26/03/2026

Solicitante:

MOTIVE TECH INC [US]
MOTIVE TECHNOLOGIES, INC

Resumen de: US20260087858A1

A method for detecting vehicle collisions using multi-stage data analysis is described. Telematics data from a vehicle-installed computing device is received and processed through a heuristic filter to identify potential collisions. A feature vector is generated from the filtered data and input into a trained predictive model, which classifies the vector as representing a collision or not. The method then retrieves associated dashcam footage and uses it, along with the predictive model's output, to confirm the occurrence of a collision. Upon confirmation, a notification is transmitted to a remote computing device. This approach combines telematics data analysis, machine learning prediction, and video verification to achieve accurate collision detection and notification.