Alerta

MACHINE LEARNING POTENTIAL FOR FUNCTIONAL ORGANIC MOLECULAR CRYSTALS

Resumen de: US20260301886A1

This disclosure is directed to machine learning-based methods for modelling energies, forces, and stress tensors of organic molecular crystals. Methods for training a machine learning (ML) potential for analysis of crystal surface energies and crystal morphologies, and workflows using such ML potentials for predicting a surface energy and a crystal morphology and for modelling crystal structure relaxation of an organic molecular crystal are disclosed.

MACHINE LEARNING MODEL PARAMETER TRANSFER

Resumen de: US20260304149A1

0000 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; obtain a set of data samples for inputting to the ML model; determine a maximum change to the set of model parameters that is permitted without performance of the ML model degrading beyond a tolerance threshold value; quantize, based on the determined maximum change, the set of model parameters to generate quantized model parameters; and transmit the quantized model parameters to a further node.

AUTOMATED GENERATION OF CYBERSECURITY RISKS AND THREATS-RELATED DATASETS FOR MACHINE LEARNING

Resumen de: WO2026202853A1

The system and method envisaged by the present disclosure collect information related to cybersecurity-related vulnerabilities corresponding to a data management environment, and generate datasets for training predetermined machine learning models to identify the possibility of occurrence of corresponding cybersecurity attacks on the data management environment. An artificial intelligence (AI) model stores information indicative of cybersecurity-related vulnerabilities embodied in the data management environment. The AI model identifies cybersecurity-related factors that influence each of the cybersecurity-related vulnerabilities, applies a pre-defined set of statistics to the cybersecurity-related factors, and determines the severity of the cybersecurity-related factors based on the set of statistics. Subsequently, the AI model generates training vectors whose elements include an indication of whether the data management environment is vulnerable to cybersecurity attacks, considering the cybersecurity-related factors and their severity. A training dataset comprising the training vectors is also created to train the machine learning models.

INTEGRITY INFORMATION COMMUNICATIONS FOR ARTIFICIAL INTELLIGENCE OR MACHINE LEARNING POSITIONING PROCEDURES

Resumen de: WO2026206640A1

Some wireless communications systems may perform one or more positioning procedures based on one or more artificial intelligence or machine learning (AI/ML) models. Some positioning applications may be sensitive to position estimate errors. The term "integrity" or variations thereof may refer to a value or measure of confidence (e.g., trust) or probability relative to an accuracy of a position. Some examples of the techniques described herein may provide approaches for determining integrity information for positioning procedures that are based on one or more AI/ML models. For instance, some AI/ML models may not be deterministic or may respond differently when input data includes different ranges of errors. Accordingly, integrity information may be determined differently for AI/ML models than for linear positioning models. In some approaches, a dataset with established error bounds may be utilized to determine integrity information corresponding to a positioning procedure.

BOND PULL TEST SYSTEM IN WIRE BONDING OF SEMICONDUCTOR PACKAGE USING MACHINE LEARNING AND TEST METHOD THEREOF

Resumen de: US20260298786A1

0000 A method of testing the tensile strength of a bonding wire included in a semiconductor package is disclosed. The method of testing a tensile strength of a bonding wire included in a disclosed semiconductor package includes: collecting data related to the tensile strength of the bonding wire; consolidating the collected data into a unified database; generating a machine learning model using the unified database, the model configured to determine a tensile strength value of the bonding wire; receiving a tensile strength parameter associated with the bonding wire; predicting a tensile strength value corresponding to the tensile strength parameter using the machine learning model; and determining whether the bonding wire is abnormal based on the predicted strength value.

SYSTEMS AND METHODS FOR ITERATIVE MACHINE LEARNING MODEL OPTIMIZATION THROUGH ADAPTIVE PARAMETER ADJUSTMENT

Resumen de: US20260300831A1

Described herein are systems and methods that related to techniques for iterative machine learning model optimization through adaptive parameter adjustment. In an example, a system can be configured to obtain input data that includes at least one user input specifying a type of endpoint. Based on the input data, the system can identify features from a set of features to use in evaluating multiple models, with each feature corresponding to diagnostic criteria used to represent aspects of treatment profiles maintained in a database. The system can train and evaluate performance of each of the models and generate a user interface to display performance metrics for the models. In some examples, this user interface can be provided to a device that generated the input data to show the performance metrics for the models.

MACHINE LEARNING MODEL, MODEL TRAINING METHOD, DATA COLLECTION SYSTEM, AND ON-VEHICLE DEVICE

Resumen de: WO2026204891A1

A machine learning model (50) causes a computer to function as a first input unit (S10, S120), an inference result generation unit (S20, S130), a first output unit (S30, S140), a second input unit (S120), a weak point identification unit (S150 to S200), a collection condition generation unit (S220), and a second output unit (S230). The first input unit receives input of first input data. The inference result generation unit performs inference on the basis of the first input data and generates an inference result. The first output unit outputs the inference result. The second input unit receives input of second input data including a plurality of pieces of first input data and a plurality of pieces of ground truth data. The weak point identification unit identifies a weak point in the inference. The collection condition generation unit generates a collection condition corresponding to the weak point. The second output unit outputs collection condition data indicating the collection condition.

MACHINE LEARNED TRANSLATION BETWEEN DIFFERENT POWER GRID MODELS

Resumen de: WO2026207124A1

Methods, systems, and apparatus, including medium-encoded computer program products, for using machine learning techniques to translate between different grid representations accurately and efficiently. Aspects include providing, as input to a machine learning model a first power grid model representation of a first type, wherein the machine learning model is trained to translate an input power grid model representations of an input type into a output power grid model representation of output type that is different from the input type, and the machine learning model is capable of translating a plurality of different input types into a plurality of different output types; and generating, by the machine learning model, a second power grid model representation of a second type that is different from the first type.

AGENT FOR HOSTING AND FACILITATING VIRTUAL MEETING

Resumen de: US20260300928A1

A method includes initializing an artificial intelligence (AI) agent configured to host and facilitate meetings; and integrating the AI agent with one or more external tools. Responsive to a request to host and facilitate a meeting, the AI agent generates meeting details including a meeting topic, participants, and a start time. The AI agent using a generative machine learning model generates a meeting plan based on the request. The AI agent executes the meeting plan by interacting with meeting participants using natural language processing, accessing relevant information from the one or more external tools, and updating meeting-related data using the one or more external tools. The AI agent using the generative machine learning model generates a meeting summary based on the executed meeting plan.

NEXT DEVICE PREDICTION SYSTEM AND METHOD

Resumen de: US20260303644A1

0000 The techniques described herein relate to a method including: collecting, by a processor, network data from a household network, the network data including one or more of device inventory data, application usage data, and network usage patterns; generating, by the processor, a feature vector based on the network data; inputting, by the processor, the feature vector into a machine learning model trained to predict future device additions to the household network; receiving, by the processor, a prediction from the machine learning model, the prediction identifying a device type predicted to be added to the household network; storing, by the processor, the prediction in a database; monitoring, by the processor, the household network to detect actual device additions; comparing, by the processor, the actual device additions with the prediction to determine a prediction accuracy value; and updating, by the processor, the machine learning model based on the prediction accuracy value.

MACHINE LEARNING BASED ENTITY EXTRACTION

Resumen de: US20260300628A1

0000 Embodiments disclosed herein relate to systems for training and implementing models to extract entities from legal descriptions for further property-related determinations. Specifically, the entity-based search system comprises a plurality of entity services, each configured to access a model for extraction of a particular entity. In a training phase, the entity-based search system may generate a set of training data to specifically train each model for extraction of a particular entity. To curate a training data set, the entity-based search system may implement distance-based calculations to determine a diverse set of training descriptions. The training set may be labeled with ground truth data corresponding to the embedded entities within the description. In an inference phase, the entity-based search system may access a legal description and prompts for input into the model. Each model may be configured to extract entities from the legal description.

ANALYTICAL FRAMEWORK WITH CONTEXT-LOCALIZED SUMMARIES

Resumen de: US20260300408A1

Systems and methods for performing analytics using context localized summaries include generating, on a data set with a machine learning system, at least one context localization (CL) summary; and creating, with the machine learning system, secondary analytics with the CL summary and at least a portion of the data set. Other embodiments may be described and/or claimed.

SYSTEM AND METHOD FOR INTERFACING DIGITAL PLATFORM WITH EXISTING ECOSYSTEM TO ENABLE PEER-TO-PEER INTERACTIONS

Resumen de: US20260295436A1

A method includes receiving, via an encrypted connection and based on a determination that an application programming interface (API) integration is not available for gameplay content, a video stream of the gameplay content captured from a client device. The method also includes processing the video stream by an encoder to extract key frames from the video stream. The method also includes applying, using at least one machine learning model and the extracted key frames, machine learning classifiers trained on game-specific visual patterns to determine a gameplay result associated with the video stream. The method also includes generating, using the at least one machine learning model, a confidence score for the determined gameplay result. The method also includes transmitting, to the client device based on a determination that the confidence score is above a threshold, an outcome of a peer-to-peer challenge corresponding to the determined gameplay result.

APPARATUS, SYSTEMS, AND METHODS FOR STORE-LEVEL ANALYSIS USING ARTIFICIAL INTELLIGENCE

Resumen de: US20260300833A1

Apparatus, systems, and methods for store-level analysis using artificial intelligence are disclosed. An example apparatus includes at least one programmable circuit to generate a feature vector based on an alert report, the feature vector including features corresponding to alert types; execute a supervised machine learning model using the feature vector to generate a supervised output for an alert in the alert report, the supervised output representing a likelihood of the alert warranting an action; execute an unsupervised machine learning model using the feature vector to generate an unsupervised output; classify the alert based on at least one of the supervised output or the unsupervised output; and cause an electronic device to execute the action based on the classification of the alert, the action associated with a status of the alert or a request for additional store-level data.

SIGNALING DETAILS FOR APPLICABILITY REPORTING

Resumen de: WO2026207378A1

Certain aspects of the present disclosure provide techniques for reporting applicability reporting for artificial intelligence (Al) and/or machine learning (ML) inference scenarios. An example method, performed at a user equipment (UE), generally includes obtaining first signaling configuring the UE with a machine learning (ML) model associated with a use case, outputting a reference report with first information indicating a first set of one or more functionalities that are available to be applied by the UE for inference related to the use case, and outputting a subsequent report with updated information that indicates one or more changes relative to the first set.

SUPPORTING DOMAIN DRIFT IN EDGE DEVICE NETWORKS VIA COMMUNITY DETECTION

Resumen de: US20260300811A1

0000 Techniques for identifying and correcting domain drift in an edge node are disclosed. A service identifies edge nodes of a network. The service determines that a first edge node has experienced domain drift. As a result, the first machine learning (ML) model of the first edge node is consuming data from a new domain. The edge nodes are clustered into at least a first and second community based on domain attributes. The first community is associated with a first domain, and the second community is associated with a second domain. The service determines that the new domain of the first ML model corresponds to the first domain. The service either deploys a common ML model used by edge nodes in the first community to the first edge node or, alternatively, retrains the first ML model using domain data of the first community.

EFFICIENT DRAFT LANGUAGE MODEL FOR SPECULATIVE DECODING IN AUTOREGRESSIVE GENERATIVE ARTIFICIAL INTELLIGENCE MODELS

Resumen de: WO2026199095A1

Certain aspects of the present disclosure provide techniques and apparatus for machine learning. In an example method, an input is received at a first generative machine learning (ML) model associated with a first vocabulary. Draft token (s) are generated based on the input using the first model. Generating the draft token (s) includes, for each draft token, (i) generating a first token identifier for the draft token based on processing the input using the first model, (ii) mapping the first token identifier to a second token identifier within a second vocabulary for a second generative ML model associated with the first model, where the first vocabulary is a proper subset of the second vocabulary, and (iii) associating the draft token with the second token identifier. A response to the input is generated, using the first model, based on receiving an indication that the draft token(s) have been verified by the second model.

Automated LLM-Based Estimation of Intent of Users Accessing Files and Organizational Resources

Resumen de: US20260300739A1

0000 A computerized method includes: (a) receiving a user-provided query directed to an Artificial Intelligence (AI) based assistant tool, that is configured to answer queries based on information from organizational databases of an organization; (b) feeding the user-provided query into a Machine Learning (ML) classification model, to classify the user-provided query as one of: (b1) a possibly-malicious query, or (b2) a non-malicious query; (c1) if the ML classification model classified the user-provided query as non-malicious, then: processing the user-provided query by the AI-based assistant tool; (c2) conversely, if the ML classification model classified the user-provided query as possibly-malicious, then: feeding the user-provided query to a Large Language Model (LLM) based Intent Detector that performs LLM analysis of the user-provided query and of organizational context, that provides LLM-generated output indicating whether the user-provided query is indeed malicious and violates organizational policies.

DETERMINING SAMPLING FREQUENCY OF INPUT/OUTPUT (I/O) REQUESTS FOR RANSOMWARE DETECTION IN COMPUTATIONAL STORAGE SYSTEMS

Resumen de: US20260300488A1

0000 In some embodiments, a method to determine a sampling frequency of input/output (I/O) requests for ransomware detection in a computational storage device (CSD). The method can comprise receiving, from a computational storage device (CSD), a plurality of features and a sampling rate, the plurality of features extracted from a plurality of I/O requests sampled by the CSD using the sampling rate. The method can further comprise providing the sample rate and the plurality of sampled I/O operations to a plurality of machine learning (ML) models. Each ML model can be trained on a training dataset comprising a plurality of training sampled operations, and a training sampling rate. The method can further comprise receiving, from each of the plurality of ML models, a compatibility metric associated with the training sampling rate of that ML model.

Sentiment And Alignment Scoring As A Proxy For Explicit User Feedback In Conversational Artificial Intelligence Systems

Resumen de: US20260300774A1

Artificial intelligence chatbot performance is improved through automated conversation quality analysis and training example generation. Chatbot conversations are processed via dual pathways, a sentiment analysis model measuring user satisfaction and an alignment analysis model evaluating query-answer semantic correspondence. These analyses produce alignment and sentiment scores that are combined into a sentiment and alignment deviation composite score called a “SADI score.” The SADI score quantifies response accuracy and satisfaction changes across conversation segments, enabling identification of problematic patterns. When SADI scores indicate significant deviations from performance thresholds, the new training examples are generated from the problematic conversations. The generated examples focus on segments showing sentiment or alignment degradation. A target machine learning model receives parameter updates using these examples, creating a feedback loop where user interactions drive model refinement. The systematic approach enables chatbot adaptation based on real interactions while addressing manual analysis limitations and implicit feedback incorporation.

APPARATUS AND METHOD FOR VALIDATION, TRAINING, TESTING AND MONITORING FOR ARTIFICIAL INTELLIGENCE/MACHINE LEARNING MODELS AND FUNCTIONALITIES

Resumen de: WO2026202381A2

An apparatus for validating at least one AI/ML model or functionality according to an embodiment is provided. The apparatus comprises a validation module (120) configured to conduct an evaluation by evaluating a performance and/or an applicability of the at least one AI/ML model or functionality depending on classes and/or attributes and/or performance criteria. The validation module (120) is configured to generate a validation output indicating whether the at least one AI/ML model or functionality meet one or more predefined criteria depending on the evaluation.

ENABLING SEMANTIC QUERIES IN RELATIONAL DATABASES CONTAINING FREE-FORM TEXT

Resumen de: US20260300325A1

Systems, methods, and computer program products for training a machine learning model are disclosed. A method may comprise accessing a relational database comprising a first table, the first table comprising an array of entries, wherein each entry is associated with a record; providing the plurality of entries as input to an encoder model; reading a plurality of embeddings generated by the encoder model based on the input thereto such that an embedding characterizing each entry is read; identifying an array of labels based on the plurality of embeddings, wherein the array of labels comprises a label for each entry; generating a training relational database based on the relational database and the identified labels, the training relational database comprising a second table, the second table comprising the array of entries and the array of labels; and training a first machine learning model based on at least the training relational database.

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

Nº publicación: US20260300634A1 01/10/2026

Solicitante:

SOCIALTRENDLY INC D/B/A BLACKBIRD AI [US]
SocialTrendly, Inc. d/b/a Blackbird.AI

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.