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AUTOMATED PERFORMANCE MONITORING AND RETRAINING OF MACHINE LEARNING MODELS

Resumen de: US20260228603A1

0000 Disclosed are systems and methods that automatically monitor the performance of machine learning models operating in a production system and that improve them through automated retraining to meet configured performance standards.

SCALABLE MACHINE LEARNING PLATFORM WITH REAL-TIME MODEL SERVING

Resumen de: WO2026165123A1

A system and method for machine learning platform management includes a unified architecture for developing and deploying machine learning models at scale. The platform integrates feature generation, model training, and inference services through a centralized interface. The system processes source data through a feature platform to generate training datasets and real- time features. A multi-stage training pipeline enables automated model experimentation through configurable workflows combining core frameworks and user modeling code. The platform implements specialized inference services optimized for high-throughput ranking and recommendation use cases, with distributed feature stores and local caching for efficient feature serving. A comprehensive monitoring system tracks model performance, feature distributions, and prediction quality through automated anomaly detection. The platform enables rapid experimentation while maintaining production reliability through automated deployment orchestration, optimized inference engines, and continuous feedback loops for model improvement.

MULTI-TASK MACHINE LEARNING MODELS FOR DIGITAL PATHOLOGY

Resumen de: WO2026165288A2

Techniques for using multi-task machine learning models for digital pathology are described herein. In an example, a system accesses an image of a biological sample of a patient having a medical condition. The system can process one or more embeddings using two or more models to obtain a primary continuous variable associated with an outcome for the patient and one or more auxiliary properties of the biological sample.

DIGITAL TWIN FOR TOUCHLESS MONITORING

Resumen de: WO2026163047A1

Traditionally, synthetic training data is unavailable or minimally available for training one or more machine-learning (ML) models for non-contact monitoring of a specific patient. To address this issue, a digital twin of the specific patient is generated within a virtual environment representative of a physical environment. In aspects, both the digital twin and the virtual environment can be altered in multiple ways to generate synthetic training data, which can be used to train and/or update one or more ML models for the specific patient. Based on depth data captured by a depth camera, the one or more ML models may recognize a posture, presence, and/or movement of the specific patient in the physical environment.

Systems and methods for interaction stacking detection

Resumen de: AU2026200494A1

A method can include receiving a request associated with an interaction involving a target entity. The method can include receiving identity data about the target entity and historical data about historical interactions associated with the target entity. The method can include providing the identity data to a first machine-learning model to generate first output. The method can include applying the historical data to rules to generate rule outcomes. The method can include providing the rule outcomes to a second machine-learning model to generate second output. The method can include generating a recommendation including a response to the request. The method can include providing a responsive message including a command executable to automatically control the response to the request. an a n E- -L R E C E I V E A R E Q U E S T F R O M A C O M P U T I N G D E V I C E I N V O L V I N G A N I N T E R A C T I O N A N D A T A R G E T E N T I T Y P R O V I D E A F I R S T S U B S E T O F D A T A A S S O C I A T E D W I T H T H E R E Q U E S T T O A A P P L Y A S E C O N D S U B S E T O F T H E D A T A T O A S E T O F R U L E S T O G E N E R A T E A S E T O F R U L E O U T C O M E S P R O V I D E T H E S E T O F R U L E O U T C O M E S T O A S E C O N D M A C H I N E - L E A R N I N G M O D E L T O G E N E R A T E A B I N A R Y I N D I C A T I O N G E N E R A T E A R E C O M M E N D A T I O N B A S E D O N T H E P R O B A B I L I T Y S C O R E A N D T H E B I N A R Y I N D I C A T I O

METHODS TO SUPPORT AIML FALLBACK OPERATION

Resumen de: US20260230845A1

0000 A method may be implemented by a wireless transmit/receive unit (WTRU), including receiving information indicating first applicability conditions and a first configuration for a first artificial intelligence or machine learning (AIML) model category and second applicability conditions and a second configuration for a second AIML model category. It may be determined whether the first AIML model category is applicable to a cell based on the first applicability conditions and first configuration, and whether the second AIML model category is applicable based on the second applicability conditions and second configuration. A report may be sent indicating applicability of the first AIML model category and second AIML category to the cell. It may be determined that the first AIML model category is no longer applicable based on the first configuration. An indication that indicates that the first AIML model category is no longer applicable to the cell may be sent.

METHODS AND APPARATUSES FOR EXPLAINABLE AI-BASED NETWORK-CONTROLLED SYSTEM OPTIMIZATION

Resumen de: US20260230843A1

0000 A wireless transmit/receive unit (WTRU) may be configured to determine one or more explainability artificial intelligence (XAI) capabilities of the WTRU. The WTRU may receive XAI configuration information from a network that indicates one or more of an explainability mode, an explainability domain type, a configuration of XAI outcome storage, one or more triggers associated with an XAI report, or an XAI report configuration. The WTRU may determine one or more XAI parameters associated with an XAI model. The one or more XAI parameters may indicate features that have a greatest impact on predictions made by an artificial intelligence (AI) or machine learning (ML) inference model. The WTRU may determine to trigger an XAI report based on one or more outcomes of the XAI model. The WTRU may send the XAI report to the network.

TRAINING A MACHINE LEARNING SYSTEM FOR TRANSACTION DATA PROCESSING

Resumen de: EP4787244A2

A method of training a supervised machine learning system to detect anomalies within transaction data is described. The method includes obtaining a training set of data samples; assigning a label indicating an absence of an anomaly to unlabelled data samples in the training set; partitioning the data of the data samples in the training set into two feature sets, a first feature set representing observable features and a second feature set representing context features; generating synthetic data samples by combining features from the two feature sets that respectively relate to two different uniquely identifiable entities; assigning a label indicating a presence of an anomaly to the synthetic data samples; augmenting the training set with the synthetic data samples; and training a supervised machine learning system with the augmented training set and the assigned labels.

ENSEMBLE MACHINE LEARNING FOR TIME SERIES DATA WITH GAPS

Resumen de: WO2025071598A1

Ensemble machine learning can be used to make predictions based on time series data with gaps. Multiple models are trained on different (overlapping) sets or portions of the available time series data, and the predictions from the different models are aggregated to generate predictions. The models can include one model trained on all of the time series data and a second model trained using just the data points that immediately follow the gaps. Models in an ensemble can also include models that use all features of the data points and models that use only a subset of features of the data points.

METHOD AND SYSTEM FOR INTERPRETING COMMON INTERACTION UTILITY AMONGST MULTIPLE BLACKBOX ARTIFICIAL INTELLIGENCE MODELS

Resumen de: EP4787251A1

0001 The present application relates to the technical field of machine learning and discloses a method and system for interpreting common interaction effect among multiple black-box artificial intelligence models. The method and system can automatically analyze common interactions modeled among multiple different artificial intelligence models, and can also analyze common interactions modeled by the same model when the input is subjected to different perturbations. The implementation of the method and system comprises the following steps: providing an input sample; using multiple black-box models to perform prediction on the same data, or using a single black-box model to perform prediction on different data, to obtain multiple sets of prediction results; based on the multiple sets of prediction results, modeling the interactions among input units of the sample, computing the interaction strengths of combinations formed among input units, and expressing the output of each set of models as "AND interaction effect" and "OR interaction effect" among input unit combinations; and learning common "AND interaction effect" and "OR interaction effect" shared among different artificial intelligence models.

DETERMINATION OF THE ESTIMATED LENS POSITION OF AN INTRAOCULAR LENS USING THREE-DIMENSIONAL ANTERIOR SEGMENT GEOMETRY AND MACHINE LEARNING

Resumen de: WO2025106772A1

A method for determining the relationship between measured variables of the anterior segment in a patient's eye pre-operatively and the post-operative position, and optionally tilt, of the implanted intraocular lens (IOL) is described, based on quantitative optical coherence tomography imaging of the eye of a patient and a machine learning architecture to determine the best regression model. Formulas are further described obtained using such method to determine the Estimated Lens Position (ELP), and optionally tilt, from measured variables in a patient pre-operatively to incorporate in IOL power calculation formulas or ray-tracing based IOL power selection.

SYSTEMS AND METHODS FOR SMART SETTING CONFIGURATION

Resumen de: US20260219896A1

Systems and methods are disclosed for smart setting configurations. Example methods may include ingesting a plurality of items of data, wherein each item of data is retrieved from a data source within a plurality of data sources associated with a user account on a social media platform, for each item of data, providing the item of data as input to a distinct machine learning algorithm in a plurality of contextual machine learning algorithms, and providing output of the plurality of contextual machine learning algorithms to an additional machine learning algorithm as input. Some methods may include formulating a setting configuration for the user account on the social media platform based at least in part on output of the additional machine learning algorithm, and generating a recommendation comprising the setting configuration to a user that operates the user account.

AI SUPERINTELLIGENCE APPLICATION STATE MACHINE

Resumen de: US20260220714A1

0000 The present invention provides a method and apparatus for a thinking system using an ASI distributed Application State Machine that connects to real, accurate, dedicated absolute data, providing privacy, security, and eliminating hallucinated AI. Specifically, one embodiment of the present invention discloses an ASI Command, Control, Communications and Computer Intelligence (C4I) system for independent, intelligent AI Prompting and ASI Browsing. The ASI Application State Machine comprises: ASI Application data structure network; ASI Enterprise Interface State Machine, said data structures interfacing with a back-end channel, a Finite State Machine embedded in each data structure; and Application Service Information Base providing a uniform interface for ASI Application data structure identities. Additional embodiments disclose ASI Browser displaying ASI Applications; ASI Agents; ASI Application Network Nodes; ASI Operating System; ASI Switch; ASI Database of real, accurate, dedicated absolute data, for Machine Learning to train AI Models, for performing real-time, bi-directional transactions, connecting to billing.

ESTIMATING LOCAL FEATURE IMPORTANCE

Resumen de: US20260220540A1

Here is generation of a local explanation of a machine learning (ML) inference, and this generation is accelerated by accurate estimation of local feature value importances from a multioutput regression by an attribution metamodel. Into a feature vector, a computer stores values for respective features and stores an inference, from the feature values, by an ML model. A multioutput regression by an attribution metamodel includes inferentially generating, from the feature vector, local value importances respectively for the feature values. Based on the local value importances, a local explanation of the inference is generated and displayed. For accelerated training with early stopping, an adaptively selected training corpus involves incrementally adding additional datapoints to the training corpus until the attribution metamodel accurately learns.

MACHINE LEARNING MODEL MANAGER

Resumen de: US20260220522A1

Techniques are described for a system configured to obtain, from a customer system, a set of features that define a customer use case for a software service, wherein the software service is offered by the customer system, and wherein operation of the software service is monitored by the operations management system; select, based on the set of features, a machine learning model from a plurality of machine learning models; configure, based on selecting the machine learning model, an instance of the machine learning model to perform the customer use case; detect event data associated with the software service; determine, by at least applying the instance of the machine learning model to the event data, a disruption to the software service; and output an indication of the disruption.

DETERMINING RISK SCORES USING OBJECTIVE-BASED MACHINE LEARNING

Resumen de: US20260220641A1

0000 Examples may be related to anomaly detection using machine learning. An example may involve receiving a risk assessment request regarding a transaction; generating feature data based on the risk assessment request; and determining, using a machine learning model, a risk score based on the feature data. The machine learning model may be trained based on an objective function characterizing a plurality of objectives. Recommendation data regarding the transaction may be generated based on the risk score, and transmitted to a computing device.

DYNAMIC DATA INGESTION FOR DEPLOYING MACHINE LEARNING MODELS

Resumen de: US20260220541A1

0000 Methods and systems are presented for providing a graph-based framework for evaluating feature candidates for a machine learning model. A production graph is generated to represent relationships among various assets of an organization. The production graph is used by various machine learning models for obtaining input data to perform the corresponding tasks. When it is determined that data corresponding to a feature candidate selected for the machine learning model is missing from the graph, instead of modifying the production graph, a new graph schema that defines one or more additional vertex types or one or more additional edge types is generated. New graph data is also generated based on the new graph schema. In response to a query corresponding to the feature candidate, a merged graph is generated by incorporating the new graph data into the production graph. A query result is obtained based on traversing the merged graph.

SYSTEMS AND METHODS FOR DEEP-AND-WIDE MACHINE LEARNING

Resumen de: WO2026161862A1

Systems and methods for deep-and-wide learning (DWL) in accordance with embodiments of the invention are illustrated. One embodiment includes a system for training a machine learning model, including a processor, and a memory, the memory containing a DWL application that configures the processor to obtain training data includes a plurality of data samples, extract high-dimensional features from the training data, wherein the high-dimensional features capture intra-sample characteristics of individual data samples, extract low-dimensional features from the training data using dimensionality reduction wherein the low-dimensional features capture inter-sample relationships among the plurality of data samples, integrate the high-dimensional features and the low-dimensional features to form a combined feature vector, and train a machine learning model using the combined feature vector.

EFFICIENT ON-DEVICE MACHINE LEARNING MODEL UPDATES

Resumen de: US20260220526A1

Examples relate to systems and methods for providing a machine learning model on a mobile device. The systems and methods store a base machine learning (ML) model on a user system, the base ML model trained to perform a first task in an individual domain. The systems and methods receive input that selects a second task in the individual domain and access parameter update information associated with the second task. The systems and methods update the base ML model based on the parameter update information associated with the second task and generate an output corresponding to the second task by processing an input by the updated base ML model.

ADAPTIVE LOCATION TECHNOLOGY HANDOFFS WITH ASSET TRACKERS

Resumen de: US20260223065A1

Systems and methods are disclosed for determining asset tracker location using multimodal fingerprints and adaptive machine learning techniques. A process obtains data comprising sequences of events, location sources, time values, and sensor measurements to generate multimodal fingerprints. These fingerprints are fused to define “liquid” boundaries with improved certainty. Machine learning models are trained and updated using deep learning architectures that classify and predict asset tracker location based on multimodal fingerprints, motion, and environmental data. Adaptive handoff processes dynamically switch among GPS, Wi-Fi, UWB, dead-reckoning, or other location technologies depending on detected motion changes and operational context, thereby conserving power and optimizing accuracy. An integrated system includes processors, force sensors, radios, inertial sensors, and edge-based AI/ML modules that continuously update location models locally and in conjunction with server-based systems for improved geospatial tracking performance across diverse environments.

System and method for performing a self-supervised training, validation, and execution of an evaluation model for evaluating underperforming machine-learning models

Resumen de: US20260220529A1

0000 A system includes a memory configured to store a set of model parameters associated with a machine-learning model. The machine-learning model is identified as having generated one or more prediction outputs that is deviating from an expected prediction output. The system further includes a processor operably coupled to the memory and configured to, while the machine-learning model is actively generating the one or more prediction outputs, access the set of model parameters, train a first machine-learning model to generate one or more model performance metrics associated with the machine-learning model based on the set of model parameters, in response to training the first machine-learning model, execute the first machine-learning model to generate the one or more model performance metrics, and identify the machine-learning model as underperforming based on whether the one or more model performance metrics satisfies a performance metric threshold.

TELECOMMUNICATION SYSTEM FAILURES PREDICTION THROUGH MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

Resumen de: US20260222276A1

0000 In an embodiment, a method may be implemented in a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the computer system interconnected with a telecommunications system, the method comprising: receiving, at the computer system, data relating to operation of the telecommunication system, obtaining, at the computer system, at least one machine learning model trained to detect and predict faults in the operation of the telecommunication system, selecting, at the computer system, computing infrastructure upon which to execute the at least one machine learning model, wherein the selected computing infrastructure comprises a mesh of interconnected micro-applications, executing, at the computer system, the at least one machine learning model using the selected computing infrastructure to detect and predict faults in the operation of the telecommunication system, and automatically correcting at least some of the detected faults.

HETEROGENEOUS MODEL TREES FOR MACHINE LEARNING WITH DISCONTINUOUS DATASETS

Resumen de: US20260220489A1

A method includes obtaining at least one dataset containing one or more discontinuities, where the one or more discontinuities split data of the at least one dataset into multiple partitions. The method also includes generating feature crosses associated with the at least one dataset. The method further includes generating a decision tree structure based on at least some of the feature crosses, where (i) the decision tree structure includes multiple leaf nodes and (ii) each leaf node corresponds to a different one of the multiple partitions. In addition, the method includes, for each leaf node of the decision tree structure, generating a machine learning model that models data of the corresponding partition.

MACHINE LEARNING MODEL EXPLANATION GENERATION

Resumen de: US20260220423A1

In some examples, a system obtains an approximation function for a machine learning (ML) model, the approximation function providing an approximate representation of a latent representation of latent features in a latent space produced by the ML model. The system presents a visualization of the latent features using the approximation function, and the system generates a refined latent space based on user input in the visualization. The system generates an explanation regarding which latent features of the latent space are primary contributors to a decision of the ML model.

Machine Learning-Based Information Management Engine

Nº publicación: US20260220493A1 30/07/2026

Solicitante:

ORACLE INT CORP [US]
Oracle International Corporation

Resumen de: US20260220493A1

A system that trains a machine learning model to determine capture levels for target computations is disclosed. The system utilizes training data encompassing attribute sets and information levels for various computation types. For a form field value computation, the system determines associated attributes. The trained model processes these attributes to establish an appropriate information storage level. Based on this level, the system selects a relevant subset of information related to the computation or its result. The system then stores this selected subset in association with the computed value. The system uses feedback to retrain the model to enhance its performance.