Machine learning

SYSTEMS AND METHODS FOR PERSONALIZED GUIDANCE

Resumen de: US20260030516A1

Described are systems and method for personalized search results, including a memory storing instructions, a trained machine learning model, and a processor operatively connected to the memory and configured to execute the instructions to perform operations, including receiving the sequence of search queries from a user device associated with a user, predicting the likely next search query from the user by inputting the received sequence of search queries into the trained machine learning model, generating predicted search results by applying the likely next search query, generating the personalized search results by appending the predicted search results to search results from a most recent query of the sequence of queries from the user, and causing the user device to display the personalized search results.

MACHINE LEARNING TECHNIQUES FOR GENERATING VISUALIZATION RECOMMENDATIONS

Resumen de: US20260030528A1

A visualization recommendation system generates recommendation scores for multiple visualizations that combine data attributes of a dataset with visualization configurations. The visualization recommendation system maps meta-features of the dataset to a meta-feature space and configuration attributes of the visualization configurations to a configuration space. The visualization recommendation system generates meta-feature vectors that describe the mapped meta-features, and generates configuration attribute sets that describe the attributes of the visualization configurations. The visualization recommendation system applies multiple scoring models to the meta-feature vectors and configuration attribute sets, including a wide scoring model and a deep scoring model. In some cases, the visualization recommendation system trains the multiple scoring models using the meta-feature vectors and configuration attribute sets.

ENRICHMENT AND RECONCILIATION OF FINANCIAL TRANSACTION DATA

Resumen de: WO2026022822A1

A system is configured to provide data pertaining to a record, performing the following method: (a) obtain, from a first source(s), a first record indicative of an actual financial transaction, paid via the first source; (b) perform enrichment on the first record, thereby determining: a counterparty and/or a financial classification category associated with the first record. The enrichment utilizes machine learning model(s) trained to identify correspondence, of first records indicative of actual financial transactions associated with a business entity, to second data. The second data are obtained from second source(s), distinct from the first source(s); (c) derive an enriched first record, based on the enrichment; and (d) provide the enriched first record.

Information Processing Method, Program, and Information Processing Device

Resumen de: US20260030319A1

Provided is an information processing method, etc. that assists a user in interpreting behavior of a generated machine learning model. In the information processing method, a computer executes processing of recording a plurality of sets of an explanatory data vector xn input to an existing machine learning model (21) and an objective data vector yn output from the machine learning model (21) in association with each other, calculating an interpretation matrix A_dagger which is a vector product of an explanatory matrix X in which a plurality of sets of the explanatory data vector xn is arranged and a generalized inverse matrix of an objective matrix Y in which the objective data vector yn is arranged in an order corresponding to the explanatory data vector X, and outputting a chart (41, 42, and 43) related to the interpretation matrix A_dagger.

COMPUTER SYSTEM AND METHOD FOR CLASSIFYING ASSETS IN AUTOMATED AND INDUSTRIAL CONTROL SYSTEMS

Resumen de: US20260029780A1

Classifying one or more assets in an automated and industrial control system (AIC) according to a classification standard. In a computer monitoring tool, a classification query is received for an asset managed by the AIC. Responsive to this classification query, the computer monitor tool retrieves a listing of candidate ontology classes for the queried asset utilizing information received from a semantic data model of known assets. The computer monitor tool then captures, preferably from a database coupled to the AIC, certain classification attribute variables associated with the queried asset. Additionally, the computer monitor tool receives user information describing certain building information associated with the queried asset. The computer monitor tool then generates a computer query configured for requesting results from a machine learning (ML) algorithm indicative of one or more classification standards for the queried asset.

CAPABILITIES AND SAFE PLUGINS

Resumen de: US20260030039A1

Disclosed are methods for managing execution of plugins of a machine-learning based system. A plugin configuration defines inputs required by the plugin and capabilities provided by the plugin. Capabilities describe the plugin’s functionality, such as how the plugin affects the response, what type of content the plugin generates, etc. In some configurations, when responding to a prompt, a collection of relevant plugins is identified. Configurations of these plugins may be analyzed to optimize execution, including determining optimal execution order or enabling parallel execution. Plugin configurations may also be analyzed to improve security by conditionally preventing one plugin from accessing the output of another. Plugin configurations may also be used to inform a client what plugins will run and what results they may yield. This enables the client to optimize and streamline how the response is displayed.

MACHINE LEARNING TECHNIQUES FOR DISCOVERING KEYS IN RELATIONAL DATASETS

Resumen de: AU2024318556A1

Techniques for discovering primary, unique, and/or foreign keys for relational datasets are described. The techniques include profiling the relational datasets to obtain respective data profiles; identifying one or more primary key candidates for a first relational dataset using a first data profile of the first relational dataset and a first trained machine learning model; identifying one or more foreign key proposals for a second relational dataset using the one or more primary key candidates by performing a subset analysis of the second relational dataset with respect to the first relational dataset; identifying one or more foreign key candidates for the second relational dataset using the first data profile, a second data profile of the second relational dataset, and a second trained machine learning model different from the first trained machine learning model; and outputting the at primary key candidate(s) and the foreign key candidate(s).

CAPABILITIES AND SAFE PLUGINS

Resumen de: WO2026024342A1

Disclosed are methods for managing execution of plugins of a machine-learning based system. A plugin configuration defines inputs required by the plugin and capabilities provided by the plugin. Capabilities describe the plugin's functionality, such as how the plugin affects the response, what type of content the plugin generates, etc. In some configurations, when responding to a prompt, a collection of relevant plugins is identified. Configurations of these plugins may be analyzed to optimize execution, including determining optimal execution order or enabling parallel execution. Plugin configurations may also be analyzed to improve security by conditionally preventing one plugin from accessing the output of another. Plugin configurations may also be used to inform a client what plugins will run and what results they may yield. This enables the client to optimize and streamline how the response is displayed.

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

Resumen de: 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.

SAFETY NET ENGINE FOR MACHINE LEARNING-BASED NETWORK AUTOMATION

Resumen de: 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.

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

Resumen de: 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

Resumen de: 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.

Construction Knowledge Graph

Resumen de: 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.

REDUCING FALSE POSITIVES USING CUSTOMER FEEDBACK AND MACHINE LEARNING

Resumen de: 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.

VEHICLE TRAJECTORY TREE STRUCTURE INCLUDING LEARNED TRAJECTORIES

Resumen de: 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.

PREDICTION SELECTION FOR ITEM IDENTIFIERS USING EFFICIENT SELECTION ALGORITHM

Resumen de: WO2026019632A1

A smart system, such as a smart shopping cart system, uses an efficient selection algorithm to select an item identifier prediction for an item. The smart cart system uses a set of machine-learning models to generate identifier predictions based on images. To select an item identifier, the smart system applies an efficient selection algorithm to the predictions from the machine-learning models. An efficient selection algorithm is an algorithm that requires minimal computational resources to perform. For example, the efficient selection algorithm may be a simple majority algorithm that selects the identifier prediction generated by a majority of the models or a weighted voting algorithm where each model's vote is weighted by some metric. The smart system applies the efficient selection algorithm to select an item identifier prediction from the ones generated by the models. The smart system may display content related to the item associated with the item identifier prediction.

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

Resumen de: WO2026019909A1

A model verification system and associated method for employing a multi-party verification technique to verify machine learning models and generative Al 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.

METHOD FOR GENERATING A SET OF MANUFACTURING DATA MAKING IT POSSIBLE TO MANUFACTURE A COSMETIC COMPOSITION AND ASSOCIATED DEVICES

Resumen de: WO2026017666A1

The present invention relates to a method for generating a set of manufacturing data of a cosmetic composition capable of complying with at least one acceptability criterion, said method comprising a step of: - obtaining an set to be completed and optionally at least one constraint parameter to be complied with by the chemical composition, and - applying a technique to the set to be completed and optionally the at least one stress parameter to obtain a completed set, the technique including using at least one machine learning model, the at least one machine learning model being capable of completing a set to be completed.

DEVICE, SYSTEM AND METHOD FOR FEDERATED LEARNING USING RISK AUDITS

Resumen de: 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.

Selection of random access preamble

Resumen de: 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.

AUTOMATED FEATURE SELECTION FOR SPLIT NEURAL NETWORKS

Resumen de: 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

DETERMINING LABELS OF INHERITANCE DATASETS USING SIMULATED DATA INSTANCES

Resumen de: 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.

SYSTEMS AND METHODS FOR GENERATING DYNAMIC TRANSIT ROUTES

Resumen de: 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.

DETERMINING LABELS OF INHERITANCE DATASETS USING SIMULATED DATA INSTANCES

Resumen de: 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.

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

Nº publicación: WO2026015586A1 15/01/2026

Solicitante:

INTUITIVE SURGICAL OPERATIONS INC [US]
INTUITIVE SURGICAL OPERATIONS, INC

Resumen de: 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.