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DEVICE TELEMETRY PROCESSING SYSTEM WITH PROBABILISTIC CLASSIFICATION AND PREDICTIVE HEALTH MODELING

Resumen de: US20260310574A1

0000 Systems and methods for managing mobile device lifecycle data by collecting disparate data from external downstream sources and combining it with deep device-level data captured directly from individual devices including telemetry, hardware diagnostics, and physical access data. The combined data trains multiple machine learning models applied to individual and aggregate devices throughout the device lifecycle. A time-series analysis model processes device telemetry data to generate a cumulative stress score representing aggregated device wear, and a survival analysis model generates an estimated remaining life prediction, both stored in a device passport data structure linked to a device identifier. A probabilistic classifier with uncertainty scoring routes devices through bypass or standard processing pathways based on fault prediction confidence. A cross-modal reasoning engine employing multimodal large language models correlates diagnostic logs with device photographs to generate unified device condition assessments.

REAL-TIME TELEMETRY SHARING BETWEEN QUANTUM MANAGEMENT INSTANCES

Resumen de: US20260311020A1

0000 One example method is directed to quantum job orchestration, and includes obtaining, by an orchestration instance, telemetry from respective control planes associated with one or more QPUs (quantum processing units), providing the telemetry as input to an ML (machine learning) model of the orchestration instance, using the ML model to perform an inferencing process whose output is a profile that comprises information usable to make an orchestration decision for a job, based on the profile, orchestrating the job to a particular one of the QPUs, and broadcasting the profile to one or more other orchestration instances.

Centralized Multi-Source Incident Data Clearinghouse Architecture for Incident Intelligence and Cross-System Event Correlation

Resumen de: US20260310681A1

A centralized multi-source incident data clearinghouse receives heterogeneous incident data from public safety and non-emergency service systems, normalizes and correlates the data into unified incident profiles independent of any individual source, distributes enriched structured outputs via a shared integration layer that abstracts direct point-to-point integrations, reduces duplicated computation and latency, and incorporates operational outcome feedback into adaptive machine-learning refinement without performing call routing.

IDENTIFYING OPTIMAL WEIGHTS TO IMPROVE PREDICTION ACCURACY IN MACHINE LEARNING TECHNIQUES

Resumen de: US20260311066A1

0000 A computer-implemented method, system and computer program product for improving prediction accuracy in machine learning techniques. A teacher model is constructed, where the teacher model generates a weight for each data case. The current student model is then trained using training data and the weights generated by the teacher model. After training the current student model, the current student model generates state features, which are used by the teacher model to generate new weights. A candidate student model is then trained using training data and these new weights. A reward is generated by comparing the current student model with the candidate student model using training and testing data, which is used to update the teacher model if a stopping rule has not been satisfied. Upon a stopping rule being satisfied, the weights generated by the teacher model are deemed to be the “optimal” weights which are returned to the user.

PROCESS AND SYSTEM FOR GENERATION OF COKE DRUM OUTAGE PROJECTIONS

Resumen de: US20260310246A1

0000 An apparatus includes at least one processing device configured to obtain sensor data from sensors associated with one or more coke drums in a refinery. The at least one processing device is also configured to generate an outage projection for an end of a feed cycle of at least one of the coke drums by processing, utilizing a machine learning model, information derived from the sensor data characterizing (i) an amount of feedstock which has been fed to the at least one coke drum during the feed cycle, (ii) a time at which a level indicator for the at least one coke drum first exceeds a designated threshold value, and (iii) a density of the feedstock fed to the at least one coke drum during the feed cycle. The at least one processing device is further configured to provide, at a user interface, the generated outage projection.

COMPLETE HUMAN-IN-THE-LOOP AGENTIC AI SYSTEM FOR CHURN PREDICTION AND IN-TIME RETENTION PLANNING AND EXECUTION

Resumen de: US20260310944A1

A computer-implemented method for customer retention uses an agentic artificial intelligence pipeline comprising an orchestration layer that coordinates a plurality of specialized AI agents via a schema-validated inter-agent communication protocol. Customer data may be collected and processed through the AI agents. A custom churn modeling agent may automatically select and train a machine learning algorithm and generate local and global explanations for each churn prediction. Survival analysis techniques, including Kaplan-Meier estimators, may estimate when each at-risk customer is likely to churn. A Bayesian Network may be constructed to identify probable churn causes and map them to corresponding retention treatments. A Large Language Model agent conditioned on the identified causes and treatments may generate personalized retention communications. Retention recommendations may be presented to a human operator for approval prior to implementation. Observed retention outcomes may be used to update Bayesian Network conditional probability distributions to improve future recommendations.

DQN-BASED TASK PLANNING

Resumen de: US20260310507A1

0000 One example method includes receiving a mission in natural language format, which specifies an action to be executed by an agent on behalf of a user. A first embedding operation is performed on the mission to create a first vector representation. From a pool of tasks, a first task is selected for the agent to execute the mission. A second embedding operation generates a second vector representation of this task, while a third embedding operation produces a third vector representation of the task's execution order. These vector representations are concatenated to form a single vector. A machine learning model then utilizes this concatenated vector to generate a task plan. This plan details the first task and its execution order, along with additional tasks and their respective execution orders, guiding the agent in mission completion.

ERRONEOUS ANSWER RISK DETERMINATION DEVICE, ERRONEOUS ANSWER RISK DETERMINATION METHOD, AND PROGRAM

Resumen de: WO2026210775A1

Provided is an erroneous answer risk determination device capable of accurately determining an erroneous answer risk of a machine learning model. The erroneous answer risk determination device includes an erroneous answer risk determination unit that determines an erroneous answer risk on the basis of a result of comparing a first set and a second set, the first set being a set of errors in an estimation function, which is an estimation result of an unknown function, allowed on the basis of correctness of an output domain with respect to M input domains in a first composite function obtained by composing the estimation function and a first known function, and the second set being a set of errors in the estimation function allowed on the basis of correctness of an output domain with respect to M input domains in a second composite function obtained by composing the estimation function and a second known function different from the first known function.

TECHNIQUES TO EMBED A DATA OBJECT INTO A MULTIDIMENSIONAL FRAME

Resumen de: US20260310752A1

0000 Various embodiments are generally directed to techniques for embedding a data object into a multidimensional frame, such as for training an autoencoder to generate latent space representations of the data object based on the multidimensional frame, for instance. Additionally, in one or more embodiments latent space representations of data objects may be classified, such as with a machine learning algorithm. Some embodiments are particularly directed to embedding a data object comprising a plurality of object entries into a three-dimensional (3D) frame.

ON-DEVICE MACHINE LEARNING PLATFORM

Resumen de: US20260311067A1

0000 The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a context provider that securely injects context features into collected training examples and/or client-provided input data used to generate predictions/inferences. Thus, the on-device machine learning platform can enable centralized training example collection, model training, and usage of machine-learned models as a service to applications or other clients.

SYSTEMS AND METHODS FOR FEDERATED LEARNING ENABLED 5G/6G MULTI-TASK SEMANTIC COMMUNICATIONS FRAMEWORK

Resumen de: US20260312879A1

Example implementations encompass systems and methods for federated learning-assisted multi-task semantic communications tailored for 5G/6G wireless systems. A Centralized Server (CS) hosts a knowledge base with interconnected Machine Learning (ML)-based semantic codec models, extracting and synthesizing semantic information across various tasks using single or multi-modal observations. The CS connects with Decentralized Servers (DSs) and Edge Servers (ESs), which handle task-related data transmission and reception. This setup enables training of global models for semantic communications through federated learning. ML models in DS knowledge bases allow local model updates, leveraging ES-assisted transmissions via a Local Model Aggregator (LMA). The CS aggregates model weights from the DSs using a Global Model Aggregator (GMA). Semantic transmission requests can be initiated from ESs and forwarded to the CS, which responds by providing semantic encoding and decoding models to ensure efficient communication of semantic information between involved parties.

METHOD, MEDIUM, AND SYSTEM FOR VIRTUAL AGENTS TO HELP CUSTOMERS AND BUSINESSES

Resumen de: US20260311229A1

A system and method are provided for dynamically selecting and invoking application programming interfaces (APIs), tools, functions, and workflows based on natural-language inputs and contextual user information. A virtual agent server maintains a semantic registry comprising identifiers, parameter definitions, and semantic annotations associated with a plurality of APIs and executable actions. A natural-language input is processed to determine a desired action and contextual information. Runtime semantic matching is performed between the natural-language input and semantic annotations to dynamically select one or more executable actions without use of a precompiled intent-specific routing definition. Parameter values are derived using dialog histories, contextual inference, runtime context information, and machine learning models. The system transmits a invocation request configured to cause a client application or external execution system to invoke the selected API, tool, function, or workflow, or alternatively executes the selected executable action on a server-side execution system using the derived parameter values.

METHOD FOR CONSTRUCTING MACHINE LEARNING MODEL FOR RECOMMENDING AIRWAY OPENING POSTURE ANGLE

Resumen de: US20260311061A1

0000 Disclosed is a method for constructing a machine learning model for recommending an airway opening posture angle. The method includes collecting independent variable data affecting a glottis exposure effect when opening a patient airway, conducting an airway opening experiment, taking a posture angle corresponding to an optimal glottis exposure image as an optimal posture angle, performing single-factor analysis on the independent variables, and screening out statistically significant independent variable data as candidate prediction variables. The method further includes preselecting machine learning models, training and verifying the models, evaluating using AUC values, and selecting a model with a highest AUC value as a prediction model for recommending the optimal airway opening posture angle. The obtained prediction model accurately recommends the optimal airway opening posture angle to expose a glottis of the patient for medical personnel, reducing or avoiding a risk of glottis exposure difficulty in bronchoscopy diagnosis and treatment.

MACHINE LEARNING TECHNIQUES FOR DISCOVERING KEYS IN RELATIONAL DATASETS

Resumen de: US20260310634A1

0000 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).

Computer Implemented Method for Predicting a Level of Quality of a Vacuum into a Vacuum Chamber of a Coating Machine, and Corresponding Device

Resumen de: US20260308751A1

The invention relates to a computer implemented method for predicting a level of quality of a vacuum into a vacuum chamber (2) of a coating machine (1) suitable for coating optic elements (8). According to the invention, this method comprises steps of: —acquiring at least one technical feature of the coating machine during coating processes of optic elements, and —predicting said level of quality from the at least one measured technical feature by means of a processing unit that registers a machine learning model, said at least one technical feature being an input of said machine learning model and said level of quality being an output of said machine learning model.

REMOTE VALIDATION OF AUTOMATED TELLER MACHINES (ATMs) USING MACHINE LEARNING

Resumen de: US20260311570A1

0000 Aspects of the disclosure relate to a remote validation platform for ATMs. The platform may train a reconfiguration model to output sets of configuration instructions. The platform may receive a first information stream from a first ATM. The platform may receive one or more additional information streams from sources associated with the first ATM. The platform may generate, using the reconfiguration model, a first set of configuration instructions based on the first information stream and the one or more additional information streams. The platform may cause reconfiguration of the first ATM based on the first set of configuration instructions. The platform may refine the reconfiguration model based on the first set of configuration instructions.

SYSTEMS AND METHODS FOR IMPROVED MACHINE LEARNING MODELS

Resumen de: US20260311183A1

In some embodiments, the techniques described herein relate to a method including: receiving, from an application in operable communication with a user interface, an indication of a proposed beneficiary of a transaction that has not been executed, the transaction being associated with a financial institution, conducting, by the application, pre-processing on an identifier of the proposed beneficiary and the financial institution, selecting multiple features associated with the proposed beneficiary, the multiple features being identified by adding one feature of the multiple features and determining the higher performance of a SHAPley analysis in a validation, conducting a Bayesian hyperparameter optimization on a grid space including a depth, determining at hyperparameter set based on a focal loss function, training a machine learning model based on a hyperparameter to identify fraud based on the beneficiary account identifier, beneficiary bank identifier, and the beneficiary bank country, and blocking the transaction based on an identification of fraud.

- GENERATING AND GLOBALLY TUNING APPLICATION-SPECIFIC MACHINE LEARNING ACCELERATORS

Resumen de: WO2022235251A1

Methods, systems, and apparatus, including computer-readable media, are described for globally tuning and generating ML hardware accelerators. A design system selects an architecture representing a baseline processor configuration. An ML cost model of the system generates performance data about the architecture at least by modeling how the architecture executes computations of a neural network that includes multiple layers. Based on the performance data, the architecture is dynamically tuned to satisfy a performance objective when the architecture implements the neural network and executes machine-learning computations for a target application. In response to dynamically tuning the architecture, the system generates a configuration of an ML accelerator that specifies customized hardware configurations for implementing each of the multiple layers of the neural network.

MACHINE LEARNING-BASED TEXT RECOGNITION SYSTEM WITH FINE-TUNING MODEL

Resumen de: EP4818981A2

A non-transitory processor-readable medium stores instructions to be executed by a processor. The instructions cause the processor to receive a first trained machine learning model that generates a transcription based on a document. The instructions cause the processor to execute the first trained machine learning model and a second trained machine learning model to generate a refined transcription based on the transcription. The instructions cause the processor to execute a quality assurance program to generate a transcription score based on the document and the transcription. The instructions cause the processor to execute the quality assurance program to generate a refined transcription score based on the refined transcription and at least one of the document or the transcription. The at least one refined transcription score indicates an automation performance better than an automation performance for the at least one transcription score.

Machine learning prediction of ion trap filling functions

Resumen de: GB2705175A

A method for facilitating machine learning prediction of ion trap filling functions is disclosed. The method comprises causing a mass analyser to perform a scan using an injection or accumulation time. The injection time is correlated to a requested or desired ion population size by an ion trap filling function, which may be non-linear. The coefficients of the ion trap filling function are predicted by a machine learning model based on current operational parameters of the mass analyser, e.g. the scan range or width of the mass analyser, pass range or width of an ion trap of the mass analyser, or the number of ion guide injections. The injection time may be scaled using a pre-scan ion flux estimate or an ion species distribution. The machine learning model may be trained in a supervised fashion and updated via backpropagation based on an error between the actual ion population size and the requested ion population size. The machine learning model may be a random forest regressor or a deep learning neural network. A system and a computer program are also claimed. Figure 15

INFORMATION PROCESSING PROGRAM, INFORMATION PROCESSING METHOD, AND INFORMATION PROCESSING DEVICE

Resumen de: EP4819147A1

An information processing program causes a computer to execute a process including: determining a weight of an input feature in a regression model, the regression model predicting an amino-acid sequence of a virus after mutation using an amino-acid sequence of the virus as the input feature, the determining being based on a first feature related to a three-dimensional (3D) structure of a protein of the virus and a second feature related to a contribution to prediction of a machine learning model, the contribution being obtained based on the first feature.

MACHINE-LEARNING-BASED PREDICTION AND DYNAMIC CORE ALLOCATION ARCHITECTURE

Resumen de: EP4818902A1

0001 Techniques and apparatuses are described for implementing a machine-learning (ML) based prediction and dynamic core allocation architecture. In example aspects, a plurality of parameters related to one or more operating conditions (e.g., temperature of a core) of one or more processors are generated. The plurality of parameters are used as an input for an ML-based model, which outputs a prediction of future parameters. The prediction is used as input for one or more process modules. The one or more process modules parse the prediction to provide a decision output, which is used to modify one or more facets of the one or more processors (a frequency, a voltage, core assignments for tasks, etc.). In this way, a system-on-chip can dynamically manage multiple operations concems for the one or more processors, such as balancing performance and temperature concerns to realize a tailored user experience.

COMPUTER-IMPLEMENTED METHOD AND SYSTEM FOR STRUCTURING AND VALUATING DATA ASSOCIATED WITH NON-FUNGIBLE ASSETS

Resumen de: EP4818985A1

Disclosed is a computer-implemented method for an automated valuation of non-fungible asset is performed by computing node(s) (202), communicably coupled to user device(s) (208) and data repository(s) (204), wherein the computing nodes, the data repository and the user device are connected via a computing network, comprises: receiving input data comprises at least one parameter that is a unique identifier of the non-fungible asset; capturing published data, wherein the published data comprises a second set of parameters; preprocessing the captured data for normalization of the published data to generate an output data that is structured; executing a pre-trained valuation model (212) to identify a proxy for parameters that are missing from the captured data; and applying a predictive machine learning model by executing a predictive valuation model (214) to generate valuation result for the non-fungible asset with a pre-set accuracy and display the valuation result at a screen.

FAULT PREDICTION METHOD AND APPARATUS, AND RELATED DEVICE

Resumen de: EP4818906A1

0001 A fault prediction method and apparatus, and a related device are provided, relating to the field of computer technologies. Multiple pieces of error data are obtained, where each piece of error data indicates that a CE occurs in a storage medium. The multiple pieces of error data are classified. Fault prediction is performed, by using a first machine learning model, on first-type error data obtained through classification, and fault prediction is performed, by using a second machine learning model, on second-type error data obtained through classification, to obtain corresponding prediction results indicating whether a UCE occurs in the storage medium. A precision of the first machine learning model in predicting a UCE is greater than a first threshold, and a recall of predicting of the second machine learning model in predicting a UCE is greater than a second threshold. In this way, fault prediction is performed on different types of error data by using the machine learning model with a high prediction precision and the machine learning model with a high recall, so that both a precision and a recall of fault prediction can be considered, thereby effectively improving an overall prediction effect for the multiple pieces of error data.

ANALYZING VIEWER BEHAVIOR IN REAL TIME

Nº publicación: EP4819625A2 07/10/2026

Solicitante:

ADEIA MEDIA HOLDINGS INC [US]
Adeia Media Holdings Inc.

Resumen de: EP4819625A2

Systems, methods and articles of manufacture for are provided for analyzing user behavior in real time by ingesting telemetry data related to a streaming media application; feeding the telemetry data to a machine learning model (MLM) that produces a User Experience (UX) command based on the telemetry data and prior telemetry data received from the content streaming application; selecting content items to provide to the client device based on the telemetry data; determining, based on the telemetry data, whether the client device has sufficient free resources to receive the UX command and the content items in a current time window while providing a predefined level of service; when client device has sufficient free resources to receive the UX command and the content items, encapsulating the UX command with the content items in a content stream; and transmitting the content stream to the client device.