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AEROSOL DELIVERY DEVICE COMPRISING ARTIFICIAL INTELLIGENCE

Resumen de: MX2026010422A

An aerosol delivery device is provided that includes sensor(s) to produce measurements of properties during use of the device, and processing circuitry to record data for a plurality of uses of the device, for each use of which the data includes the measurements of the properties. The processing circuitry is configured to build a machine learning model to predict a target variable, using a machine learning algorithm, at least one feature selected from the properties, and a training set produced from the measurements of the properties. The processing circuitry is configured to then deploy the machine learning model to predict the target variable, and control at least one functional element of the device based thereon. The device may also include a digital camera to capture an image of a face of an attempted user to enable facial recognition to alter a locked state of the device.

HYBRID ARTIFICIAL INTELLIGENCE CLASSIFIER

Resumen de: MX2026010538A

System, methods, apparatuses, and computer program products are disclosed for generating and using a hybrid artificial intelligence classifier for classifying input into one or more nodes of a taxonomy. Training data is received for at least a first portion of the taxonomy and used to train a supervised machine learning (ML) model to classify input into the first portion of the taxonomy having training data. A large language model (LLM) taxonomy is determined for at least a second portion of the taxonomy. The hybrid AI classifier classifies input based on a first classification obtained by providing the input to the supervised ML, and a second classification obtained by providing at least the input and the LLM taxonomy to a pre-trained LLM.

APPARATUS AND METHOD FOR INSPECTING BATTERY

Resumen de: MX2026010476A

A method for inspecting a battery, according to an embodiment of the present invention, relates to a method for inspecting the quality of a battery in a manufacturing process, the method comprising the steps of: acquiring an image capturing at least a portion of the exterior of the battery to preprocess the image; detecting one or more defect candidate regions in the preprocessed image by using one or more detection algorithms; extracting position information of the defect candidate regions and shape feature information of the defect candidate regions; and inputting, into a pre-trained machine learning model, information related to the detection algorithms, the position information of the defect candidate regions, and the shape feature information of the defect candidate regions, to determine whether corresponding defect candidate shapes are defective.

METHOD AND SYSTEM FOR GENERATING EXPLAINABLE TASK-BASED OPTIMIZATION

Resumen de: US20260301007A1

0000 The present disclosure relates to a method for explainable task-based optimization. The method includes receiving a target decision variable that is of interest to the user and an auxiliary dataset associated with a decision-making problem. Further, the method includes generating a prediction of the target decision variable using a forecasting machine learning (ML) model. Furthermore, the method includes determining an optimal decision variable for the decision-making problem with uncertain parameters and variables. Furthermore, the method includes executing the optimal decision variable and determining an actual cost outcome. Furthermore, the method includes updating the forecasting ML model using a rolling window technique based on the actual cost outcomes. Furthermore, the method includes evaluating the actual cost outcome of an executed decision based on comparing with the optimal performance and updating optimization parameters based on the actual cost outcome to refine the forecasting ML model.

AUTOMATED DATASET LABELING

Resumen de: US20260300716A1

0000 A method includes generating a first set of coarse-grained labels, a first set of fine-grained labels, a second set of coarse-grained labels, and a second set of fine-grained labels. The first set of coarse-grained labels are generated by a first machine learning (ML) model and based on textual content and contextual information of each data point. The first set of fine-grained labels are generated by a second ML model and based on textual content and contextual information of each labeled data point. The second set of coarse-grained labels and the second set of fine-grained labels are generated by a third ML model and based on textual content and contextual information of each data point. The method also includes validating the subset of the input dataset based on comparisons of the first set of coarse-grained labels with the second set of coarse-grained labels and comparisons of the first set of fine-grained labels with the second set of fine-grained labels.

MULTIMODAL, MULTILINGUAL, AND SCALABLE CROSS-PLATFORM SYSTEM FOR AUTOMATED NARRATIVE EXTRACTION USING LLMS/VLMS

Resumen de: US20260300634A1

0000 Techniques to extract narratives from unstructured data using generative machine-learning models are provided. A computer-implemented method can include accessing a plurality of unstructured data items from one or more digital communication platforms. The computer-implemented method can also include applying a narrative-discovery machine-learning model to the plurality of unstructured data items to extract a set of narratives. In some instances, a narrative of the set of narratives includes semantic content. The computer-implemented method can also include generating a set of narrative mappings. A narrative mapping can include a corresponding narrative of the set of narratives and one or more assigned unstructured data items. In some instances, the assigned unstructured data items include content that substantially matches the semantic content of the corresponding narrative. The computer-implemented method can also include generating a narrative output that includes the set of narrative mappings.

NETWORK OPTIMIZATION BASED ON QUANTUM POSITIONING OF USER DEVICES

Resumen de: US20260301097A1

0000 A method performed by a processing system including at least one processor includes collecting, from a quantum sensor in a communications network, position data for a public safety device, tracking a movement pattern and a usage pattern of the public safety device by providing the position data as input to a quantum machine learning model that generates the movement pattern and the usage pattern as an output, determining, based on an analysis of the movement pattern and the usage pattern, that an emergency service should be contacted, and placing a call to the emergency service.

METHOD AND SYSTEM FOR PREPROCESSING OPTIMIZATION OF STREAMING VIDEO DATA USING MACHINE LEARNING

Resumen de: US20260301385A1

0000 A method of improving a main output of a main AI model includes processing first video data using a machine learning model includes continuously analyzing incoming video data via a first processing pipeline and concurrently via a second processing pipeline. The first processing pipeline can include preprocessing incoming video data according to first preprocessing parameters that format the incoming video data to create the first video data and processing the first video data by the main AI model to determine the main output that is indicative of a first inference dependent upon the first video data. The second processing pipeline can include identifying first test preprocessing parameters that are predictive to produce a first test output that satisfies a baseline criterion and preprocessing the incoming video data according to the first test preprocessing parameters that format the incoming video data to create first test video data.

USING MACHINE-LEARNING MODELS TO GENERATE ADAPTIVE USER INTERFACES FOR ATTRIBUTE SELECTION AND ITEM REPLACEMENT SELECTION

Resumen de: US20260299971A1

An online system presented herein generates adaptive and personalized user interfaces for attribute selection and item replacement selection. The online system fulfills an order by picking items at a source and delivering the items to a user. When an item is not found at the source, a picker attempts to find a suitable replacement item. To find acceptable replacements, the online system allows the user, when placing an order, to specify attributes of an item that are important for that order. To provide an improved user interface that helps the user select attributes for a given item, the online system selects a subset of the item's attributes that are likely important and relevant to potential replacements. If the item is not found, the user's attribute selection is communicated to the picker, as well as used to determine and rank replacements for displaying at a user interface of a picker's device.

FAST QUANTIZATION FOR NEURAL NETWORKS

Resumen de: US20260300806A1

0000 A processing unit compares a first graph that represents a machine learning (ML) model and a second graph that represents a previously quantized ML model generated by quantizing a previous version of the ML model. Based on the comparison, a processing unit selectively reuses a first subset of quantization parameters from the previously quantized ML model and re-quantizes a second portion of the version of the ML model to form a second subset of the quantization parameters. The first subset is formed by quantizing a first portion of the previous version of the ML model. The processing unit combines the first subset and the second subset to form an updated quantized ML model. In some cases, the version of the ML model has a floating-point representation, and the previously quantized ML model is generated by quantizing a previous version of the ML model to form a fixed point representation.

ARTIFICIAL INTELLIGENCE FOR COGNITIVE INTERFACES

Resumen de: US20260300689A1

In an example embodiment, a machine learning framework is provided that enhances reliability of analysis of non-invasive cognitive input, correlates the cognitive input with additional non-cognitive input, and identifies context-based possible selections from software processes. A graphical user interface may be provided that depicts the possible selections to the user in a manner that makes it clear to the user which thoughts lead to which selections. This graphical user interface may be iteratively updated with each selection by the user, with each selection leading to one or more possible selections displayed in the graphical user interface being replaced with different possible selections. The result is a seamless framework that aids in enhancing cognitive interfaces.

FEDERATED LEARNING WITH DISTINCT COMMON COMPONENTS AND SPARSE COMPONENTS

Resumen de: WO2026206327A1

A federated learning system and method for training neural networks addresses the challenge of efficiently merging locally trained models while maintaining data privacy. The system includes a server and multiple clients, where the server dispatches machine learning models to clients for local training. The server decomposes updated model weights into common and sparse components using robust principal component analysis (RPCA), applying distinct scaling factors to enhance model accuracy and convergence speed. This approach can be particularly advantageous in scenarios where data privacy is of significant importance, such as in smart devices and medical applications. The method iteratively refines the global model by leveraging client-specific variations and shared patterns, ensuring that client-specific data is not transferred to a central server.

SELF-LEARNING SYSTEMS AND METHODS USING MOVING EUCLIDEAN DISTANCE MACHINE-LEARNING MODELS FOR ANOMALY AND TREND DETECTION IN TIMESERIES DATA

Resumen de: US20260300807A1

0000 Presented are self-learning systems for anomaly detection in timeseries datasets utilizing Euclidean geometry modeling and methods of operating such systems. A method of analyzing datasets to detect anomalies includes a system controller receiving a timeseries dataset containing a sequence of data values recorded within a select time period, and determining a window length defining a number of the sequential data values assigned to each data window. The controller assigns a respective subsequence of the data values to each data window in a series of data windows, and generates a distance value matrix with matrix cells that contain Euclidean distances between respective pairs of the data windows. The controller generates a shortest distance (SD) matrix containing SD values that each defines a shortest distance between each data window and every other data window. The SD array is analyzed to determine if any SD value is irregular and thereby indicates an anomaly.

COMMUNICATIONS FOR DATA COLLECTION CONFIGURATIONS

Resumen de: WO2026206724A1

Some examples of the techniques described herein may relate to data collection for training one or more artificial intelligence or machine learning (AI/ML) models to predict one or more measurements or measurement events. For example, signaling may be utilized for data collection for AI/ML model training. A trained AI/ML model may be utilized for beam management, channel state information (CSI) measurement prediction, CSI feedback prediction for a two-sided model, positioning enhancements, radio resource management (RRM) measurement prediction, or measurement event prediction, among other examples. Some approaches to RRM measurement prediction may include cell-level measurement prediction or beam-level measurement prediction. A user equipment (UE) may be configured by a source gNB with a set of cells to measure, a set of beams on each cell to measure, a set of cells for measurement prediction, or a set of beams on each cell for measurement prediction.

SYSTEMS AND METHODS FOR AUTOMATED, "SELF-DRIVING" LABORATORY ANALYSIS OF BIOLOGICAL AND CHEMICAL SPECIMENS

Resumen de: WO2026207543A1

A self-driving laboratory system and associated methods for autonomous scientific experimentation are disclosed. The system integrates automated laboratory instrumentation, multi-modal sensors, and artificial intelligence models to design, execute, and iteratively optimize biological or chemical experiments. A computing platform receives research objectives, generates experimental protocols, and controls laboratory instruments to perform experimental procedures. Experimental data are collected from imaging systems, analytical instruments, and environmental sensors and are integrated using machine learning and sensor fusion techniques to estimate experimental state. Optimization algorithms, including probabilistic and multi-objective models, analyze the data to generate modified experimental protocols that improve experimental outcomes. The system may dynamically adjust environmental parameters and measurement strategies during experimentation and may share learning across distributed laboratory sites. Digital twin models may simulate laboratory workflows to guide experimental planning. The disclosed platform enables efficient exploration of complex experimental parameter spaces while improving reproducibility, resource utilization, and discovery in automated laboratory environments.

MACHINE LEARNING MODEL PARAMETER TRANSFER

Resumen de: US20260300068A1

Various aspects of the present disclosure relate to a node for wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and operable to cause the node to: obtain a machine learning (ML) model comprising of a set of model parameters; for each model parameter in the set of model parameters, determine a sensitivity of the model parameter, and provide error protection to the model parameter based on the sensitivity of the model parameter, the sensitivity of the model parameter indicating a degree of toleration for a value of the model parameter to vary without performance of the ML model degrading beyond a tolerance threshold value; and transmit the error protected model parameters to a further node.

INTERACTIVE MULTIMEDIA AND METHODS FOR DESIGNING INTERACTIVE MULTIMEDIA BASED ON DECISION TREES GENERATED BY MACHINE LEARNING

Resumen de: WO2026206375A1

An interactive multimedia with multiple levels and a method for designing the interactive multimedia are provided. The interactive multimedia includes: a first level program, providing at least one first level interactive activity to determine one of multiple lines for the user to proceed from a first level to a next level; and a second level program, providing at least one second level interactive activity to determine one of multiple lines for the user to proceed from the second level to a next level. The levels and the lines of the interactive multimedia are designed at least partially based on a decision tree generated by a machine learning technique with a source data including predictor variables indicating attributes of respondents and a target variable indicating related outcomes of the respondents.

MACHINE LEARNING FRAMEWORK TO DETECT AND MONITOR COMPLIANCE MATTERS

Resumen de: US20260301451A1

0000 System apparatus, article of manufacture, method and/or computer program embodiments are provided for detecting and monitoring compliance matters. An example method may include obtaining a first set of image data corresponding to an environment from one or more image sensors; processing the first set of image data using a first machine learning algorithm to yield annotated image data that identifies at least one object within the environment; processing the annotated image data using a second machine learning algorithm to determine one or more attributes associated with the at least one object within the environment; and generating, based on the one or more attributes, an evidence package corresponding to the at least one object within the environment, the evidence package being configured to facilitate a determination of one or more action items associated with the at least one object.

MACHINE LEARNING PLATFORM AND FEATURE GENERATION FOR EFFICIENT DATA PROCESSING

Resumen de: US20260300771A1

0000 A system enables agile model development to speed up innovation by data scientists. Model training and deployment are coordinated and standardized to reduce redundancy. Data is obtained for feature generation and reformatted and de-sensitized for storage. The features are stored in locations available to all models and training modules of a system so data does not need to be adjusted for new models. To generate a machine learning model, the system establishes a cohort for evaluation by the model. A model template and features for use by the model are identified. The selected template and features are used for experimentation and evaluation. Model training artifacts, such as model weights are subsequently recorded in a model store and the model scripts and settings can then be registered in a centralized database where it can be accessed for execution.

UTILIZING MACHINE LEARNING TO EVALUATE CHAT BOT INTERACTIONS

Resumen de: US20260303554A1

The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that utilize machine learning to evaluate an electronic communication interaction between an artificial intelligence communication bot and a user. For example, the disclosed systems can utilize a configurable machine learning model that enables easy addition, deletion, or editing of evaluation prompts that guide the machine learning model in evaluating the electronic communication interaction from the AI communications bot. In particular, the disclosed systems can utilize the evaluation prompts with an electronic communication transcript from the AI communications bot to generate communication analysis objects that represent statistics and/or data on the behavior and/or operation of the AI communications bot. In addition, the disclosed systems can utilize the evaluation prompts with an electronic communication transcript from the AI communications bot to trigger digital actions determined from the electronic communication interaction.

MACHINE LEARNING MODEL AUTOMATED BUG IDENTIFICATION

Resumen de: US20260300137A1

0000 Aspects of the present disclosure relate to automated bug identification using machine learning models. Embodiments include instructing a machine learning model, via a prompt, to generate an output according to instructions in the prompt. Embodiments include providing the machine learning model, via the prompt, one or more transcripts, wherein each transcript is associated with a set of user communication data. Embodiments include instructing the machine learning model, via the prompt, to parse the one or more transcripts and create a classification for each given transcript of the one or more transcripts based on one or more attributes of the given transcript. Embodiments include instructing the machine learning model, via the prompt, to provide the classification for each given transcript as the output in a structured object format. Embodiments include receiving the output from the machine learning model in response to the prompt and performing an action based on the output.

MACHINE LEARNING MODEL INFERENCE SIMULATION

Resumen de: US20260300818A1

0000 Example embodiments are directed to a system that facilitates machine learning model inference simulation. A model inference simulation request is received. A model specification is identified based on the model inference simulation request. A job workflow is generated based on the model specification and the model inference simulation request. A model inference simulation result is generated using a machine learning model associated with the model specification. The model inference simulation result is then caused to be displayed on a user interface.

DISTRIBUTED GENERATION OF INFERENCES USING HOSTED MACHINE LEARNING MODELS

Resumen de: US20260300781A1

0000 Systems, methods, and computer program products for distributed generation of inferences using hosted machine learning models are disclosed. A method may comprise fetching, from a remote server, a first checklist comprising one or more pre-processing steps and a second checklist comprising one or more post-processing steps; performing the one or more pre-processing steps at a client on an input dataset to generate a pre-processed input dataset; transmitting, from the client to the remote server, the pre-processed input dataset to be processed by the machine learning model; receiving, at the client from the remote server, an output dataset; performing the one or more post-processing steps at the client on the output dataset to generate a post-processed output dataset; and presenting the post-processed output dataset via a client computing platform at the client..

IDENTIFYING SEPARATION-RELATED PROBLEMS IN PETS

Resumen de: AU2025374609A1

The present disclosure relates to systems, methods, and program applications for identifying separation-related problems in a pet. The methods, for example, can include identifying the presence or absence of multiple behavioral signs exhibited by a pet where each of the multiple behavioral signs are given a sign score based on binary annotations representing either the presence or the absence of each of the behavioral signs, and grouping subsets of the multiple behavioral signs into one of multiple principal component behavioral groupings using the binary annotations to generate principal component scores for each of the multiple principal component behavioral groupings. Methods can also include using one or more machine-learning algorithms under the control of at least one processor for accessing and correlating the principal component scores for each of the multiple principal component behavioral groupings with a population cluster associated with a type of separation-related problem.

DYNAMIC CONTEXT ADJUSTMENT DURING A MACHINE LEARNING MODEL INFERENCE PROCESS

Nº publicación: US20260300816A1 01/10/2026

Solicitante:

AMAZON TECH INC [US]
Amazon Technologies, Inc.

Resumen de: US20260300816A1

Systems and methods are provided to dynamically adjust contextual data of a machine learning model during an inference process of the machine learning model. A system can identify one or more tokens generated by the machine learning model. The machine learning model may, during an inference process, generate the one or more tokens based on first contextual data. The system can determine that the one or more tokens satisfy one or more first parameters. The system can obtain second contextual data associated with the one or more tokens based on determining that the one or more tokens satisfy the one or more first parameters and can provide the second contextual data to the machine learning model. The machine learning model may generate an output based on the second contextual data and at least a portion of the first contextual data.