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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.

DIFFERENTIALLY PRIVATE FEDERATED EXTREME GRADIENT BOOSTING

Resumen de: US20260195639A1

Training a differential privacy-aware (DP-aware) machine learning model includes transmitting epsilon hyperparameters to federated learning (FL) nodes. A differential privacy-aware (DP-aware) machine learning model is generated based on noise-infused surrogate histograms received from the FL nodes, each noise-infused surrogate histogram based on an epsilon hyperparameter and representing a node-specific dataset. The DP-aware machine learning model is transmitted to the FL nodes. A DP-aware aggregate histogram is generated by merging DP-aware gradients and DP-aware Hessians determined by the FL nodes based on each FL node generating predictions by applying the DP-aware machine learning model to a node-specific dataset therein. A decision tree of the DP-aware machine learning model is expanded by dividing data in one or more decision tree nodes. The machine learning model is iteratively trained by successively merging further DP-aware gradients and DP-aware Hessians generated by FL nodes based on updated versions of the DP-aware machine learning model.

WEATHER MODELLING

Resumen de: US20260195659A1

A computer-implemented method of training a first machine learning model to determine a local weather modifier, the method comprising: obtaining a first training data set comprising a plurality of training samples, each training sample comprising an observed local weather modifier in a geographical region and one or more geographic features of the geographical region, wherein each geographic feature has a feature location and a feature property; training the first machine learning model to predict a local weather modifier for another geographical region based on one or more geographic features of the other geographical region.

SYSTEM AND METHOD FOR PREDICTING SHOE TRACTION USING COMPUTER VISION

Resumen de: US20260191300A1

0000 In one embodiment, a method includes accessing an image depicting a portion of a shoe and a background of the shoe, segmenting the portion of the shoe from the background by machine-learning models, extracting features configured for traction prediction by the machine-learning models, and determining a traction performance associated with the shoe based on the extracted feature by the machine-learning models.

Resource Extraction and Processing System

Resumen de: US20260195189A1

0000 Provided are methods, systems, devices, apparatuses, and tangible non-transitory computer readable media for resource extraction and processing. Resource document data comprising resource documents associated with resource allocation instructions for distribution of resources to entities can be received. Resource document fields and resource document field values of the resource documents can be determined. Based on inputting the document data into a machine-learning model, resource data associated with the resource documents can be generated. The machine-learning model can be configured to parse the resource documents and determine relationships associated with the entities, the resources, or provisions of the resource documents. Based on the resource data, key provisions of the provisions of the resource documents can be determined. Furthermore, a resource profile based on the resource data can be generated. The resource profile can comprise indications associated with the key provisions of the resource documents.

AUTOMATED DATA CLASSIFICATION

Resumen de: US20260195416A1

Automatically classifying data fields given a set of their sample values (e.g., schema mapping) is disclosed. These models aim to infer attribute and entity structure defined by a platform and/or automated classification system. These models may rely upon both public machine learning models as well as proprietary model weights owned by the platform and/or automated classification system (and/or associated organizations or enterprises).

SYSTEMS AND METHODS FOR PREDICTING EFFICACY OF CHECKPOINT INHIBITOR THERAPIES

Resumen de: WO2026148247A1

Predicting treatment benefits of immune checkpoint inhibitor drugs (ICIs) without resorting to advanced genomic or immunologic assays is a major unmet clinical need. This disclosure provides a predictive model using machine-learning approaches based on routine laboratory test results in clinical practice.

Federated Learning applications for secure and private Machine Learning in Oil and Gas Industry

Resumen de: US20260195608A1

0000 Techniques for training a global model for use at a host of oilfield application sites based on raw data obtained from the application sites without direct exposure of the data to the global model. The techniques include developing and distributing a global model with a predetermined set of parameter weights. The model is then locally employed at each application site by a local computer which maintains the integrity of the acquired data during performance of the oilfield application. The data is used to update the parameter weights based on real-time circumstances. Thus, the parameter weights may be transmitted to the centralized computer for updating of the global model. Further, the updated global model may continue to direct other applications and the process continued in a beneficial feedback loop manner.

ANOMALY DETECTION USING GRAPH NEURAL NETWORKS

Resumen de: US20260197331A1

0000 Example implementations relate to detecting a terminated entity in a network environment. A network activity dataset including data representative of network activity within a network environment and a plurality of data records is received. Each data record in the plurality of data records includes a set of attributes. A graph that links systems having a first role in the data representative of network activity and a subset of the plurality of data records is generated. Feature information from the set of attributes for one or more data records in the subset of the plurality of data records in the graph is aggregated. A machine learning model is trained based on the aggregated feature information derived from the graph. Using the trained model, a determination representing a likelihood that a respective system having the first role in the data representative of network activity is linked to the terminated entity is generated.

DEEP LEARNING-BASED PATH PLANNING METHOD CONSIDERING HULL STABILITY OF UNMANNED SURFACE VEHICLE IN RESPONSE TO MARITIME ENVIRONMENT AND DEVICE THEREFOR

Resumen de: KR20260108793A

본 발명의 다양한 실시예에 따르면, 무인수상정의 선체 안정성을 고려한 딥러닝 기반 경로계획 장치는 상기 무인수상정의 내부에 위치한 센서 또는 외부로부터 상기 무인수상정의 상태 정보 또는 해상 환경 정보를 입력받고, 무인수상정의 상태 정보 또는 해상 환경 정보를 입력받아 무인수상정 롤(Roll) 정보를 출력하도록 미리 학습된 무인수상정 전복 위험도 예측 모델에 상기 입력받은 무인수상정의 상태 정보 또는 해상 환경 정보를 입력하고, 상기 무인수상정 전복 위험도 예측 모델로부터 출력된 상기 무인수상정 롤 정보를 미리 정의된 휴리스틱(heuristic) 함수에 적용하고, 상기 휴리스틱 함수 값을 기반으로 상기 무인수상정의 이동 경로를 결정할 수 있다.

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.

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.

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.

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 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.

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.

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.

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.

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 VARIABLE OPTIMIZATION METHOD, BIORESOURCE PRODUCTION METHOD, AND BIORESOURCE PRODUCTION SYSTEM

Nº publicación: EP4773049A1 08/07/2026

Solicitante:

CHITOSE LABORATORY CORP [JP]
Chitose Laboratory Corp.

Resumen de: EP4773049A1

0001 Provided are a control variable optimization method capable of determining improved culture conditions using a predictive model based on machine learning, and a bioresource production method and a bioresource production system using the same. 0002 A bioresource production system S according to another aspect of the present invention includes: a cultivation system B for performing bioresource production; and a control variable optimization system A for optimizing control variables obtained from the cultivation system. 0003 The control variable optimization system separates the control variables into initial variables and manipulated variables, creates predictive models adapted to the initial variables and the manipulated variables, respectively, and optimizes the control variables by combining the predictive models.