Alerta

ENHANCING LONG-CONTEXT REASONING CAPABILITIES OF MACHINE LEARNING MODELS

Resumen de: US20260195640A1

Each short reasoning data item can be automatically decomposed into a background and an inquiry on the background. A plurality of materials can be automatically generated based on the background. Each of the plurality of materials can indicate a key information point of the background. A long-context background can be automatically constructed by randomly embedding the plurality of materials into a set of irrelevant materials. A plurality of long reasoning data items can be automatically generated by combining the long-context background with the inquiry corresponding to each short reasoning data item.

METHOD FOR SCHEDULING PEER DEMANDS IN A CONSENSUS PROCESS FOR A BLOCKCHAIN

Resumen de: WO2026146097A1

The collected information is distributed (306) between input data and output data with a view to training (308) a machine learning model in order to obtain predictions identifying which peers are most likely to transmit missing blocks and at what time. The machine learning model thus trained enables each peer to determine a scheduling of demands made by said peer on the other peers in the consensus process for the elaboration of the blockchain, according to the predictions obtained. The consensus process is therefore more efficient.

METHOD AND SYSTEM FOR PREDICTING ABDOMINAL AORTIC ANEURYSM (AAA) GROWTH

Resumen de: US20260196354A1

0000 There are provided methods, systems and non-transitory storage mediums for predicting growth of an abdominal aortic aneurysm (AAA) of a patient having been diagnosed with AAA. Segmented regions of interest (ROI) comprising the aorta and adjacent structures are received by segmenting a set of images. A wall shear stress parameter and intraluminal thickness parameter is determined. A 3D parametric mesh comprising a plurality of concentric 3D mesh layers is generated, where each concentric 3D mesh layer includes a same predetermined number of nodes. The generation includes encoding the segmented ROIs, the wall shear stress parameter and the intraluminal thickness parameter as features at respective node locations in the 3D parametric mesh. A trained growth prediction machine learning model predicts, based at least on a subset of features of the 3D parametric mesh, if the given patient will show AAA growth. The training of the growth prediction model is also disclosed.

SYSTEMS AND METHODS FOR ENHANCED MACHINE LEARNING TECHNIQUES FOR KNOWLEDGE MAP GENERATION AND USER INTERFACE PRESENTATION

Resumen de: US20260195614A1

Systems and methods for extracting information from documents and constructing corresponding knowledge maps with respect to defined knowledge models. Deep-learning based models for Natural Language Processing (NLP) are applied to tokenize words, tag, parse, and lemmatize sentences of input documents. Then an information extractor traverses the dependency tree of NLP object to recursively extract the entities of interest to the knowledge models. Finally, a knowledge map constructor traverses the dependency tree of NLP object to determine the relationships among the extracted entities and construct knowledge maps recursively following the defined knowledge models.

SYSTEM AND METHODS FOR PREDICTIVE MODELING BASED UPON MULTIMODAL GEOTAGGED DATA

Resumen de: US20260195641A1

Systems and methods for utilizing geotagged data for predictive modeling are disclosed. The method may include, such as by one or more processors, transceivers, and/or sensors: (1) receiving a first set of geotagged data from devices associated with a user; (2) processing data received from data sources for supplemental data corresponding to locations in the first set of geotagged data; (3) inputting the first set of geotagged data and the supplemental data into a machine-learning model, wherein the machine-learning model is trained to generate (i) an event prediction corresponding to event occurrences at the locations, and/or (ii) recommendations corresponding to the predicted events; (4) generating a risk profile for the locations based upon a frequency of the event occurrences of the predicted events; and/or (5) presenting a visual and/or audible prediction presentation based upon the event prediction, the risk profile, and/or the recommendations to user via a user device.

A TRAFFIC PREDICTION AND TRANSPORTATION OPTIMIZATION SYSTEM AND A METHOD THEREOF

Resumen de: WO2026147367A1

The invention relates to improving traffic prediction, travel efficiency optimization and road safety in intelligent transportation systems by collecting traffic data from sensor networks and processing it with spatial-temporal data analysis methods and machine learning algorithms.

A SYSTEM FOR PROCESSING, ANALYZING, AND CLASSIFYING GRAPH DATA IN MACHINE LEARNING AND DATA SCIENCE

Resumen de: WO2026147368A1

The invention relates to a system for processing, analyzing, and classifying graph data in the fields of machine learning and data science, and an operation method of said system.

DATA DIGITIZATION VIA CUSTOM INTEGRATED MACHINE LEARNING ENSEMBLES

Resumen de: US20260195338A1

0000 Data digitization via custom integrated machine learning ensembles is provided. For example, a system integrates multiple trained machine learning ensembles to identify, extract, and map data. The system receives a data set from sources. The system identifies ensembles can include machine learning models that can determine an outcome. The system filters a subset of data from the data set. The system identifies a layout for the data set based on a vendor type, data type, and the data set. The system executes a block detection module to identify blocks of the layout. The system executes a header detection module. The system executes a policy detection module to identify the headers as policies. The system transforms, based on the headers, the layout, the blocks, and the policies, the data set into a second file type, and presents the transformed data set for integration into a capital management system.

Network Packet Capture Analysis Using Machine Learning Model

Resumen de: US20260197254A1

0000 Embodiments relate to analyzing network packets in a telecommunication networks using machine learning models. The network packets are correlated and then labeled to indicate successes or failures in a subtask of communication flow. Features are extracted based on the labels and correlated network packets. The extracted features are applied to a machine learning model to predict or infer success or failure of the entire communication flow. The result from the machine learning model may again be applied to subsequent machine learning models to predict root cause of a failure or to predict or infer the type of success. In this way, more accurate diagnosis of network issues in the telecommunication networks may be made in a more expedient manner.

MACHINE-LEARNING SYSTEM FOR CONTRACT INTELLIGENCE AUTOMATION USING CONTRACT DIGITIZATION INTO MACHINE PARSEABLE OBJECTS AND METHOD THEREOF

Resumen de: WO2026145883A1

Proposed is a novel machine learning system (1) for contract intelligence automation, and corresponding method for training the machine learning system for automated text analysis and for applying it. A plurality of contracts (2) with a plurality of clauses (22) is received by the system (1), wherein wordings of equivalent clauses (22) vary across contracts (2); contract text (21) is read into a data processing system (15); clause text chunks (121) are identified, that are equivalent across contracts (2), and assigned a contract term category (122). Considering a respective semantic context (131) of each of the contracts (2), a semantic meaning (132) of each of the clause text chunks (121) is determined and encoded in a clause embedding space (141) and stored in vector database (14); data automation tasks can be performed using entries of the vector database (14), particularly automatic consistency monitoring (197) and outlier detection across contracts (2) and a monitoring of clause (22) nuances across contracts (2) e.g. via a graphical representation (16).

AUTOMAED CREATION OF END-TO-END MACHINE LEARNING PIPELINE

Resumen de: US20260195599A1

0000 An example operation includes one or more of determining a predictive task and one or more constraints of the predictive task based on inputs to a graphical user interface of a software application, executing a machine learning model on input data including the predictive task and the one or more constraints to select a LLM from among a plurality of available LLMs for executing the predictive task, additionally executing the machine learning model on the LLM and the input data to determine a plurality of components of the predictive pipeline including the LLM, and instantiating an instance of the predictive pipeline including the plurality of components and the LLM via the software application.

GENERATING DATA BRIEFS USING GENERATIVE MACHINE LEARNING MODELS

Resumen de: US20260195356A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for using a generative machine learning model to generate a data brief that characterizes the contents of a data store. In one aspect, a method comprises determining a user category of a user; generating a data brief that characterizes content from one or more data stores using a generative machine learning model by determining a prompt for the generative machine learning model based on the user category of the user, processing (i) the prompt and (ii) at least a subset of data stored in the data stores using the generative machine learning model to generate a model output in response the prompt, and including the generated model output in the data brief; and providing, to the user and by way of the user interface, a representation of the data brief generated using the generative machine learning model.

MACHINE LEARNING BASED STORAGE TIERING

Resumen de: US20260195633A1

0000 Mechanisms are provided for dynamically placing data for in-memory distributed query processing based on machine learning computer models. The mechanisms obtain machine learning training data comprising first features extracted from logs and metadata associated with data accesses in response to in-memory distributed query processing. The mechanisms execute machine learning training of a machine learning computer model based on the machine learning training data, to thereby train the machine learning computer model to predict data accesses based on the first features. The mechanisms execute the trained machine learning computer model on second features extracted from runtime data to generate a prediction of one or more datasets that will be accessed. The mechanisms execute a movement of the one or more datasets from high latency storage devices to memory based on the prediction.

Techniques for Self-Guided Hyper Personalization Governance Using Nested Machine Learning Models

Resumen de: US20260195695A1

Systems and methods are described for managing governance operating parameters through the use of machine learning. The method involves: (i) receiving knowledge data, wherein the knowledge data is indicative of one or more operating parameters; (ii) analyzing, using a nested machine learning model comprising a plurality of agent models trained using composite knowledge data, an input associated with a subset of the one or more operating parameters indicated by the composite knowledge data, wherein the composite knowledge data is generated by one or more data processing machine learning models based on the knowledge data; (iii) determining, based on the analyzing, a compliance action, wherein the compliance action is associated with configuring the subset of the one or more operating parameters associated with the input; and (iv) generating, by the one or more processors using the nested machine learning model, a recommendation associated with the compliance action.

COMPUTER-READABLE RECORDING MEDIUM HAVING STORED THEREIN INFORMATION PROCESSING PROGRAM, INFORMATION PROCESSING DEVICE, AND INFORMATION PROCESSING METHOD

Resumen de: US20260196304A1

0000 A non-transitory computer-readable recording medium having stored therein an information processing program causing a computer to perform a process including: in classification processing on input graph structure data using a machine learning model, acquiring a contribution degree in the classification processing for each of a plurality of partial regions included in graph structure data; and determining an evaluation for the machine learning model based on similarity between the contribution degree and designation information for the partial region of the graph structure data.

CONTROL APPARATUS FOR RADIO ACCESS NETWORK AND COMPUTER READABLE STORAGE MEDIUM

Resumen de: US20260197249A1

A control apparatus for a radio access network (RAN), includes: a collection unit configured to control the RAN based on a learning model and to collect first learning data from the RAN; a determination unit configured to determine usefulness of the first learning data in machine learning for the learning model; and a processing unit configured to perform processing to select second learning data from the first learning data based on the usefulness of the first learning data for transmission to another control apparatus that performs the machine learning.

METHOD AND APPARATUS FOR PREDICTING INJURIES

Resumen de: US20260195821A1

0000 The present system provides a method and apparatus for predicting a likelihood of injury of an individual. The system generates a frailty score that represents the likelihood of a person being injured. The frailty score is generated by using Artificial Intelligence (AI) and machine learning using a specialized data set. The frailty score can then trigger actions to reduce the possibility of injury or to determine whether to engage in the injury risking behavior at all.

System, Method, and Computer Program Product for Early Detection of a Merchant Data Breach Through Machine-Learning Analysis

Resumen de: US20260195760A1

Provided are systems, methods, and computer program products for early detection of a merchant data breach through machine-learning analysis. An example system includes a processor configured to receive transaction authorization request data. The processor is also configured to generate a metric based on security-testing transaction activity. The processor is further configured to generate features for training one or more models. The processor is further configured to generate a first dataset based on the features and associated with a plurality of merchants, and a second dataset based on the features and associated with a previously breached merchant. The processor is further configured to train an ensembled model to associate merchants with a likelihood of data breach. The processor is further configured to determine a breached merchant, automatically freeze a transaction, retrain the ensembled model, and determine another breached merchant based on the updated models.

PRECISION TREATMENT WITH MACHINE LEARNING AND DIGITAL TWIN TECHNOLOGY FOR OPTIMAL METABOLIC OUTCOMES

Resumen de: US20260191466A1

A patient health management platform accesses a metabolic profile for a patient and biosignals recorded for the patient during a current time period comprising sensor data and/or lab test data collected for the patient. The platform encodes the biosignals into a vector representation and inputs the vector representation into a patient-specific metabolic model to determine a metabolic state of the patient at a conclusion of the current time period. The patient-specific metabolic model comprises a set of parameter values determined based on labels assigned to the previous metabolic states and a function representing one or more effects of the plurality of biosignals of the personalized metabolic profile. The platform compares the determined metabolic state of the patient to a threshold metabolic state representing a target metabolism. The platform generates a patient-specific treatment recommendation outlining instructions for the patient to improve the determined metabolic state to the functional metabolic state.

SYSTEMS AND METHODS FOR AUTHENTICATING A RESOURCE SYSTEM

Resumen de: US20260197315A1

Systems and methods are disclosed for determining authenticity of a resource system. The method includes receiving a dataset that includes a first subset and a second subset associated with a first resource system; down-sampling the first subset but not the second subset; generating a first feature for a machine learning model based on the down-sampled first subset; generating a second feature for the machine learning model based on the second subset; generating, via input of at least one of the first feature or the second feature into the machine learning model that is trained to output a fraudulent measure, one or more data objects indicative of validating the fraudulent measure; and initiating performance of one or more prediction-based actions in response to the generating.

PROCESSOR PERFORMANCE TUNING METHOD AND ELECTRONIC DEVICE USING THE SAME

Resumen de: US20260195231A1

0000 A processor performance tuning method and an electronic device using the same are provided. The method may include the following steps. A training dataset is created. The training dataset may include a device design parameter, an actual performance test score, and an actual target design parameter of each of a plurality of tested electronic devices. A machine learning model is trained based on the training dataset. A device design parameter of an electronic device to be tested is received. By utilizing the trained machine learning model according to the device design parameter of the electronic device to be tested, a predicted target design parameter of the electronic device to be tested is predicted. An operation is performed according to the predicted target design parameter.

MACHINE LEARNING BASED DISAMBIGUATION IN A KNOWLEDGE AWARE CONVERSATION SYSTEM

Resumen de: US20260195357A1

Aspects of the present disclosure provide techniques for machine learning based disambiguation. Embodiments include receiving a query via a user interface; generating an enriched query by rewording the query based on conversation history data associated with the query. Embodiments include retrieving relevant information from a data store based on using an embedding of the enriched query to perform a semantic search. Embodiments include providing the enriched query and the relevant information to a language processing machine learning model along with a prompt that instructs the language processing machine learning model to generate an answer to the enriched query based on the relevant information and to generate a disambiguation question if one or more conditions are met. Embodiments include receiving an output from the language processing machine learning model in response to the prompt. Embodiments include providing a response to the query via the user interface based on the output.

Forward-Forward Training for Machine Learning

Resumen de: US20260195643A1

0000 Example implementations provide a computer-implemented method for training a machine-learned model, the method comprising: processing, using a layer of the machine-learned model, positive input data in a first forward pass; updating one or more weights of the layer to adjust, in a first direction, a goodness metric of the layer for the first forward pass; processing, using the layer, negative input data in a second forward pass; and updating the one or more weights to adjust, in a second direction, the goodness metric of the layer for the second forward pass.

A SELF-ADAPTIVE FAULT CORRELATION SYSTEM BASED ON CAUSALITY MATRICES AND MACHINE LEARNING

Resumen de: US20260195611A1

The present invention describes a self-adaptive system capable of extracting correlations between multiple faults from net-work topologies, with the innovative component being the data preprocessing phase generating causality matrices to provide as an input to ML models. The proposed fault correlation system is responsible for, without any configuration, identifying the hierarchical relationships be-tween the multiple alarms, allowing for a better understanding of the causality and impact of each malfunction, hence assisting the implementation of RCA rules. This allows, not only for a huge dimensionality reduction of alarms needed to be processed by a TO's, but also significantly increases the knowledge about the topology, thus reducing downtime and increasing the quality of service of the network and services.

PARAMETER TUNING METHOD AND APPARATUS, AND DEVICE

Nº publicación: US20260195119A1 09/07/2026

Solicitante:

HUAWEI TECH CO LTD [CN]
HUAWEI TECHNOLOGIES CO., LTD.

Resumen de: US20260195119A1

0000 This application provides example parameter tuning methods. In one example method, a high-dimensional parameter space is divided based on one or more groups of software configuration parameters that are configured for software and corresponding software performance parameters, to form M high-dimensional parameter subspaces, where the M high-dimensional parameter subspaces satisfy: a similarity between data in any one of the high-dimensional parameter subspaces is greater than a similarity threshold, and a difference between amounts of data included in any two of the high-dimensional parameter subspaces is not greater than an amount threshold. M machine learning models are invoked to learn the M high-dimensional parameter subspaces. A target high-dimensional parameter subspace is selected from the M high-dimensional parameter subspaces, and a to-be-configured software configuration parameter is determined by using a machine learning model corresponding to the target high-dimensional parameter subspace.