Machine learning

Construction Knowledge Graph

Absstract of: US20260023889A1

An example computing platform is configured to (i) receive a data asset related to a construction project; (ii) determine, via a first machine-learning algorithm, at least one physical location within the construction project to which the received data asset is related; (iii) associate the received data asset with the determined physical location; (iv) based on the determined physical location, determine, via a second machine-learning algorithm, a respective relationship between the received data asset and one or more other data assets related to the construction project; and (v) add the received data asset to a construction knowledge graph as a node that is connected to one or more other respective nodes that represent the one or more other data assets.

Systems and Methods for Facial Recognition Training Dataset Adaptation with Limited User Feedback in Surveillance Systems

Absstract of: US20260023820A1

Various embodiments provide systems and methods for updating a training dataset so that the generated machine learning model can adapt to both short-term and long-term face variations including, for example, head pose, dressing, lighting conditions, and/or aging.

REDUCING FALSE POSITIVES USING CUSTOMER FEEDBACK AND MACHINE LEARNING

Absstract of: US20260024101A1

A method of reducing a future amount of electronic fraud alerts includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that generates an electronic fraud alert, transmitting the alert to a mobile device of a customer, and receiving from the mobile device customer feedback indicating that the alert was a false positive or otherwise erroneous. The method also includes inputting the data detailing the financial transaction into a machine learning program trained to (i) determine a reason why the false positive was generated, and (ii) then modify the rules-based engine to account for the reason why the false positive was generated, and to no longer generate electronic fraud alerts based upon (a) fact patterns similar to fact patterns of the financial transaction, or (b) data similar to the data detailing the financial transaction, to facilitate reducing an amount of future false positive fraud alerts.

SAFETY NET ENGINE FOR MACHINE LEARNING-BASED NETWORK AUTOMATION

Absstract of: US20260025327A1

In one embodiment, a device obtains data regarding routing decisions made by a machine learning-based predictive routing engine for a network. The device determines, based on the data regarding the routing decisions, a behavior of the machine learning-based predictive routing engine. The device compares the behavior of the machine learning-based predictive routing engine to a behavioral policy for the machine learning-based predictive routing engine. The device adjusts operation of the machine learning-based predictive routing engine, when the behavior of the machine learning-based predictive routing engine violates the behavioral policy.

VEHICLE TRAJECTORY TREE STRUCTURE INCLUDING LEARNED TRAJECTORIES

Absstract of: US20260021828A1

Techniques for generating a tree structure based on multiple machine-learned trajectories are described herein. A planning component (“ML system”) within a vehicle may receive and encode various types of sensor and/or vehicle data. The ML system can provide the encoded data as input to multiple machine-learning models (“ML models”), each of which may be trained to output a unique candidate trajectory for the vehicle follow. In some examples, each ML model may be trained to output a unique type of learned trajectory that causes the vehicle to perform a certain type of action. Using the learned candidate trajectories, the ML system may generate a tree structure that includes some or all of the candidate trajectories. The vehicle may determine a control trajectory based on the generation and traversal of the tree structure using a tree search algorithm, and may follow the control trajectory within the environment.

SYSTEM AND METHOD FOR DYNAMIC MULTI-PARTY VERIFICATION OF GENERATIVE ARITIFICIAL INTELLIGENCE SYSTEMS

Absstract of: US20260025388A1

A model verification system and associated method for employing a multi-party verification technique to verify machine learning models and generative AI systems. The models and associated systems can be deployed in an enterprise and require verification to ensure that cohorts are properly verifying the models and systems and evaluation to ensure that the models and systems operate responsibly and achieve intended outcomes. A dynamic, multi-stakeholder blinded verification process can be employed for the continuous verification and evaluation of machine learning models and the systems that use them. This helps promote unbiased, reproducible verification, evaluation and assessments by preventing potential biases from cohorts form part of the verification process.

DEVICE, SYSTEM AND METHOD FOR FEDERATED LEARNING USING RISK AUDITS

Absstract of: US20260025400A1

A computing device, that is configured to configure a global machine learning model, performs respective electronic risk audits of client devices configured to train respective local machine learning models that correspond to a global machine learning model. Based on respective electronic risk scores of one or more of the client devices, determined via the respective electronic risk audits, the computing device implements one or more parameter privacy adjustment methods on respective parameters received from the client devices prior to using the respective parameters to configure the global machine learning model, wherein respective client devices determined to have higher electronic risk scores have more of the parameter privacy adjustment methods applied than other respective client devices determined to have lower electronic risk scores. The computing device provides, to the client devices, the global machine learning model configured according to the respective parameters as adjusted.

Selection of random access preamble

Absstract of: GB2642672A

Determination and implementation of a random access channel (RACH) preamble selection policy (PSP). An apparatus such as a distributed unit (DU) 420 of a first radio access technology (RAT) determines a RACH PSP based upon first information. The DU receives from another DU of a second RAT, second information at step (6) and updates the RACH PSP at step (7) based upon the first and second information. At step (8) the RACH PSP is transmitted to a user equipment (UE), 410. The UE selects a RACH preamble based upon the selection policy and transmits the preamble to the DU. The RACH PSP may comprise a probability distribution parameter which may include a type of distribution function, e.g. normal, Gaussian or exponential distribution, a parameter associated with a distribution function or allocation information of RACH preambles. The information may comprise: a mode or state of operation of the apparatus, an arrival rate of random access requests for the apparatus, a number of RACH preamble collisions at the apparatus or load information of the apparatus. A trained machine learning model or algorithm may be used to determine the RACH PSP based on the information to reduce potential RACH preamble collisions.

DEVICE, SYSTEM AND METHOD FOR FEDERATED LEARNING USING RISK AUDITS

Absstract of: EP4682769A1

A computing device, that is configured to configure a global machine learning model, performs respective electronic risk audits of client devices configured to train respective local machine learning models that correspond to a global machine learning model. Based on respective electronic risk scores of one or more of the client devices, determined via the respective electronic risk audits, the computing device implements one or more parameter privacy adjustment methods on respective parameters received from the client devices prior to using the respective parameters to configure the global machine learning model, wherein respective client devices determined to have higher electronic risk scores have more of the parameter privacy adjustment methods applied than other respective client devices determined to have lower electronic risk scores. The computing device provides, to the client devices, the global machine learning model configured according to the respective parameters as adjusted.

DETERMINING LABELS OF INHERITANCE DATASETS USING SIMULATED DATA INSTANCES

Absstract of: WO2026015162A1

Disclosed is a method for determining inheritance labels of users based on inheritance datasets of the users. The method includes generating a plurality of reference panels for a plurality of data-inheritance origins, each reference panel corresponding to a data-inheritance origin and comprising reference-panel datasets representative of the data-inheritance origin. The method constructs a plurality of simulated data trees that are built using the reference-panel datasets that are selected from the plurality of reference panels. The method generates a plurality of simulated inheritance datasets representing a plurality of simulated named entities, each representing a descendant named entity in one of the simulated data trees. The method trains a machine learning model to determine inheritance labels of an inheritance dataset.

SYSTEMS AND METHODS FOR GENERATING DYNAMIC TRANSIT ROUTES

Absstract of: US20260016310A1

A computing device comprising: obtaining telematics data generated by an autonomous vehicle; building, using a machine learning algorithm, a transit model based at least in part upon the telematics data; generating, based at least in part upon the transit model, a dynamic transit route; calculating a potential benefit comprising at least one of an amount of fuel cost savings, reduced travel time, insurance savings, or environmental pollution reduction when the dynamic route is used compared to a different route; transmitting a notification comprising the dynamic route and the potential benefit to a display or touchscreen of the autonomous vehicle; receiving, via the display screen or touchscreen, a selection input indicating acceptance or declination of the dynamic route; when the selection input indicates declination, modifying the route; and when the selection input indicates acceptance, instructing the autonomous vehicle to autonomously drive along the dynamic route.

INTELLIGENT AND REAL-TIME TASK GUIDANCE SYSTEM FOR SURGICAL OPERATING ROOMS

Absstract of: WO2026015586A1

Systems and methods are described for determining and assigning tasks for performing medical procedures. The system may be configured to receive a plurality of data streams related to a medical procedure, wherein the plurality of data streams includes one or more of system data, medical environment data, and indications of personnel performing the medical procedure; analyze, using a task generation machine learning model, the plurality of data streams to generate natural language output relating to one or more tasks to be performed in furtherance of the medical procedure, wherein one or more inputs into the task generation machine learning model includes inputting embeddings of the plurality of data streams; analyze, via a task assignment machine learning model, the one or more tasks to assign the tasks to respective personnel; and provide indications to the respective personnel for performing the respective tasks assigned to the respective personnel.

APPLICATION OF ARTIFICIAL INTELLIGENCE MACHINE LEARNING (AIML) MODELS ASSOCIATED WITH SAME FUNCTIONALITY

Absstract of: WO2026015208A1

Disclosed are techniques for wireless communication. In an aspect, a processing device may receive, from a server device, a request for an output based on application of a plurality of artificial intelligence machine learning (AIML) models associated with a same functionality. The processing device may apply the plurality of AIML models to obtain a plurality of respective candidate outputs, the plurality of candidate outputs being associated with the functionality. The processing device may transmit the output to the server device in response to the request, the output indicating at least one of the plurality of candidate outputs.

DETERMINING LABELS OF INHERITANCE DATASETS USING SIMULATED DATA INSTANCES

Absstract of: US20260017284A1

Disclosed is a method for determining inheritance labels of users based on inheritance datasets of the users. The method includes generating a plurality of reference panels for a plurality of data-inheritance origins, each reference panel corresponding to a data-inheritance origin and comprising reference-panel datasets representative of the data-inheritance origin. The method constructs a plurality of simulated data trees that are built using the reference-panel datasets that are selected from the plurality of reference panels. The method generates a plurality of simulated inheritance datasets representing a plurality of simulated named entities, each representing a descendant named entity in one of the simulated data trees. The method trains a machine learning model to determine inheritance labels of an inheritance dataset.

AUTOMATED FEATURE SELECTION FOR SPLIT NEURAL NETWORKS

Absstract of: US20260017517A1

A computer-implemented method and apparatus for feature selection using a distributed machine learning (ML) model in a network comprising a plurality of local computing devices and a central computing device is provided. The method includes training, at each local computing device, the ML model during one or more initial training rounds using a group of input features representing a input features layer of the ML model. The method further includes generating, at each local computing device, based on the one or more initial training rounds, feature group values. The method further includes transmitting, from each local computing device, to the central computing device, the generated feature group values. The method further includes receiving, at each local computing device, from the central computing device, central computing device gradients. The method further includes computing, at each local computing device, local computing device gradients, using the received central computing device gradients. The method further includes generating, at each local computing device, a gradient trajectory for each input feature in the group of input features based on the computed local computing device gradients. The method further includes identifying, at each local computing device, based on the generated gradient trajectory, whether each input feature in the group of input features is non-contributing. The method further includes removing, at each local computing device, from the group

MACHINE LEARNING MODEL CONTINUOUS TRAINING SYSTEM

Absstract of: US20260019655A1

Described is a system for performing a set of machine learning model training operations that include: accessing media content items associated with interaction functions initiated by users of an interaction system, generating training data including labels for the media content items, extracting features from a media content item of the media content items, identifying additional media content items to include in the training data based on the extracted features from the media content item, processing the training data using a machine learning model to generate a media content item output; and updating one or more parameters of the machine learning model based on the media content item output. The system checks whether retraining criteria has been met, and repeats the set of machine learning model training operations to retrain the machine learning model.

COMMUNICATION METHOD AND APPARATUS

Absstract of: US20260019345A1

A communication method and apparatus. A first device sends capability information to a second device, so that the second device can send control information to the first device based on the capability information. The control information is usable to indicate that a first machine learning (ML) model corresponding to a first management function (MnF) on the first device is allowed to be trained. The first device trains the first ML model based on the control information.

Response Prediction for Electronic Communications

Absstract of: US20260017544A1

Systems, methods, and apparatuses are described herein for performing sentiment analysis on electronic communications relating to one or more image-based communications methods, such as emoji. Message data may be received. The message data may correspond to a message that is intended to be sent but has not yet been sent to an application. Using a first machine learning model, one or more subsets of the plurality of emoji may be determined. The one or more subsets of the plurality of emoji may comprise one or more different types and quantities of emoji, and may each correspond to the same or a different sentiment. Using a second machine learning model, one or more emojis may be selected from the one or more subsets. The one or more emojis selected may correspond to responses to the message.

MULTI-TASKING MODEL TRAINING METHOD AND MULTI-TASKING PERFORMING METHOD USING MACHINE LEARNING MODEL TRAINED ON BASIS THEREOF

Absstract of: EP4679330A1

A multi-tasking model training method and a multi-tasking performing method using a machine learning model trained on the basis thereof, according to an embodiment of the present invention, may mutually transfer and learn knowledge data of a latent space for each task through geometric alignment in one integrated latent space in order to process a multi-task for output according to a plurality of domains.

METHOD FOR PROVIDING INTELLIGENT RESPONSE AGENT BASED ON ADVANCED INFERENCE AND ESTIMATION FUNCTION, AND SYSTEM THEREFOR

Absstract of: EP4679287A1

A method and system for providing an intelligent response agent based on a sophisticated reasoning and speculation function according to an embodiment of the present disclosure can generate and provide response data for queries related to specialized documents using a deep-learning neural network that implements a stepwise process for a sophisticated reasoning and speculation function.

Method for improving accuracy of machine learning models

Absstract of: GB2642421A

Method for training a neuro-symbolic machine learning model, comprising: for each image depicting at least two objects of a training dataset: inputting the image into a neural module 102, 200, 202 to obtain bounding boxes and features therein (digit); inputting each bounding box and object feature into a symbolic module (106, Fig.1; rest of Fig.2) to obtain a plurality of possible labels i.e. partial labels 212 and possible relationships 210 as a new partially-labelled training dataset; and training the neuro-symbolic model (neural module and the symbolic module) by calculating a loss from a ground truth label for the image. The symbolic module may use a set of logical rules to constrain the labels and explanations (R1-R5, Fig.7). The trained neuro-symbolic model may generate a scene graph, perform action recognition, perform visual question answering (Fig.4) or control an autonomous or semi-autonomous electronic device. The electronic device may be a moveable robot or a wearable augmented reality device.

SYSTEM AND METHOD FOR LEARNED EMITTER IDENTIFICATION AND TRACKING

Absstract of: WO2026010990A1

A system and method are described for emitter identification and tracking in an electronic warfare (EW) environment. The system includes an antenna array configured to receive signals from radio frequency (RF) emitters during a dwell. Processing circuitry converts the received signals into digital signals. Pulses are detected and characteristics of the pulses determined to form pulse descriptor words (PDWs). The PDWs obtained during the dwell are deinterleaved using unsupervised machine learning to form clusters. The clusters are categorized using one or more supervised machine learning algorithms to determine whether the PDWs correspond to known or unknown emitters and the results tracked as in or out of library emitters. After merging the in or out of library emitters, an emitter report is generated and used to update a library of emitter profiles used by the supervised machine learning algorithms as well as determine countermeasures to generate.

INPUT FOR MACHINE LEARNING PREDICTION MODULE IN WIRELESS COMMUNICATION SYSTEM

Absstract of: WO2026010874A1

A UE (102) receives (306), from a network entity (104), a configuration configuring a first set of RSs associated with an ML prediction module. The UE receives (310) a second set of RSs different from the first set of RSs. The UE transmits (316) a prediction report based on a measurement of the second set of RSs being used in an input to the ML prediction module. A UE (102) receives (806), from a network entity (104), a configuration configuring a plurality of RSs associated with a plurality of ML prediction modules. The UE receives (808) an indication indicating an RS associated with an ML prediction module. The ML prediction module is being executed at the UE. The UE transmits (816) a prediction report output from the ML prediction module based on a measurement of the RS being used as an input to the ML prediction module.

ADAPTABLE, SCALABLE, AND AUTONOMOUS PROTECTION VERIFICATION AND DECISION SUPPORT

Absstract of: WO2026010799A1

A method includes obtaining (302, 802) information associated with assets and/or personnel to be protected and executing (306-322, 804) a set of weighting functions and a set of algorithms for protecting the assets and/or personnel. The weighting functions and algorithms are arranged in multiple levels of a hierarchy. Each level of the hierarchy includes one or more of the weighting functions and one or more of the algorithms. The one or more weighting functions and the one or more algorithms in at least one level of the hierarchy are applied across a timeline. The method also includes applying (330, 818) an artificial intelligence/machine learning (AI/ML) algorithm (608) across the timeline to update results due to one or more changes during one or more operations involving the assets and/or personnel.

TRIGGER-BASED DATA INGESTION FOR MACHINE LEARNING USING EDGE DEVICE

Nº publicación: WO2026010723A1 08/01/2026

Applicant:

EDGEIMPULSE INC [US]
EDGEIMPULSE INC