Alerta

A BLACK HOLE PREDICTION AND DETECTION SYSTEM BY COMPUTER-AIDED MACHINE LEARNING IN SOFTWARE BACKBONE INFRASTRUCTURE NETWORKS AND AN OPERATION METHOD THEREOF

Resumen de: US20260359604A1

The invention relates to a black hole prediction and detection system by computer-aided machine learning in software backbone infrastructure networks and an operation method thereof. The black hole prediction and detection system by computer-aided machine learning of the invention predicts and detects outlier network behavior anomalies, thereby generating a warning, detects the black hole anomalies which have not yet been seen without any need to retrain the model, allows the black hole anomalies to be directly and instantly predicted without any need for tagging the data, avoids the problem of learning from imbalanced data in the classification problems caused by data tagging being overcome, without requiring extra time and workload, provides general black hole prediction which may adapt to varying topologies, detects the point, contextual and collective anomalies together for black hole prediction, and analyzes and interprets the instances detected as black holes by computer-aided machine learning, thereby explaining the grounds for detection.

SOFTWARE PROCESS MODIFICATION PLATFORM FOR COMPLIANCE

Resumen de: US20260359013A1

Methods and systems are presented for providing a computer platform that manages the impacts of government regulations on existing software processes of an online service provider. A regulation document is obtained from a government agency. The regulation document is processed, and legal obligations relevant to an online service provider are extracted from the regulation document. An ensemble machine learning model is used to recommend, for each of the legal obligations, software controls that can be implemented within one or more software processes of the online service provider to mitigate a risk of the legal obligations. The ensemble machine learning model may include an attribute-based model and a text-based model. An explainable visual interface is provided to present the recommended software controls and context that indicates to a user how the software controls are determined for the legal obligations.

Systems and methods for determining entity attribute representations

Resumen de: AU2026257634A1

A computer implemented method for determining entity attributes. The method comprises determining one or more entity identifiers, determining an entity server address of the entity based on the one or more entity identifiers, wherein the entity server address points to an entity server; verifying the entity server address transmitting a message for request for information to the entity server address, receiving entity information from the entity server; and providing, to a machine learning model, the received entity information. The machine learning model is trained to generate a numerical representations of entities based on the entity information. ct c t

SYSTEMS AND METHODS FOR AUTOMATING ANOMALY DETECTION

Resumen de: US20260359430A1

Systems and methods are disclosed for automating anomaly detection. One method may include retrieving one or more data metrics from a data server, mapping, by a machine learning model, the one or more data metrics using one or more logic rules, determining, by the machine learning model, one or more patterns based on the mapping, determining, by the machine learning model, one or more anomalies based on the one or more patterns, generating, by the machine learning model, a recommendation based on the one or more anomalies, and automatically notifying one or more users of the one or more anomalies and the recommendation.

SYSTEMS AND METHODS TO GENERATE SUGGESTED RESPONSES TO CUSTOMER INQUIRIES FOR CUSTOMER RELATIONSHIP MANAGEMENT

Resumen de: AU2025253676A1

The present disclosure relates to generating suggested responses to customer requests using machine learning models. In one example, a method includes: receiving, from a customer, a customer request via a communication channel; displaying in a customer support user interface the customer request; processing the customer request with a machine learning model to determine a suggested response to the customer request; and displaying in an agent assistance user interface element in the customer support user interface: the suggested response to the customer request; a first user interface element configured to implement the suggested response; and a second user interface element configured to dismiss or modify the suggested response.

METHOD AND APPARATUS FOR PROCESSING PLUGIN-BASED TASK

Resumen de: US20260359073A1

A method for processing a plugin-based task in an intelligent machine learning algorithm design includes: registering first and second plugins in an execution process of a target task, wherein the execution process of the target task includes generating a task processing result based on historical data related to the target task and a large model, and performing a next round of task processing based on a feedback result of the task processing result; in response to completing execution of a step to which the first plugin subscribes, determining whether to trigger expert participation in decision-making according to a first running state of the target task by invoking the first plugin; and in response to completing execution of a step to which the second plugin subscribes, determining whether to trigger training of the large model according to a second running state of the target task by invoking the second plugin.

PREDICTING SPORT OUTCOMES USING MATCH STATE TRANSITIONS

Resumen de: AU2026257629A1

A system and method using machine learning prediction that estimates future sport outcomes by mathematically representing current and future match states as a Bayesian network parameterized by underlying machine learning models. ct c t

ADAPTIVE AND EFFICIENT UNIVERSAL DRAFTER FOR ON-DEVICE SPECULATIVE DECODING IN AUTOREGRESSIVE GENERATIVE ARTIFICIAL INTELLIGENCE MODELS

Resumen de: US20260359734A1

Certain aspects of the present disclosure provide techniques and apparatus for machine learning. In an example method, an input is received at a first generative machine learning model associated with a first vocabulary and being capable of being associated with a plurality of second generative machine learning models, each being associated with a respective second vocabulary. Based on the input, a first token identifier from the second vocabulary associated with an operative second generative machine learning model is mapped to at least one second token identifier associated with the input from the first vocabulary. The operative second generative machine learning model is one of the plurality of second generative machine learning models. A set of draft tokens is generated based on processing the at least one second token identifier using the first generative machine learning model. A response to the input is generated based on the set of draft tokens.

MACHINE LEARNING-BASED SEMANTIC NORMALIZATION, POPULATION, AND VALIDATION OF MULTI-DOCUMENT DATASETS

Resumen de: US20260359274A1

A computer-implemented system automatically detects, analyzes, and resolves data inconsistencies within electronic application datasets using artificial intelligence and rule-based validation. The system performs semantic normalization and validation of multi-document datasets. The system receives diverse documents containing structured and unstructured fields, extracts text, key–value pairs, and contextual features, and generates a canonical semantic representation that encodes syntax, semantics, and relationships. A machine-learning model determines semantic equivalence among fields across documents and maps source values into destination fields of a target document to pre-populate missing data. After pre-fill, a semantic analyzer detects issues including inconsistencies, omissions, contradictions, or formatting defects, assigns severity levels, and presents correction prompts or automatically proposed revisions via a user interface. The system receives and applies corrections, then re-evaluates the dataset with the machine-learning model to confirm resolution and update the semantic representation, enabling iterative completion and validation of complex submissions.

CENTRALIZED PARAMETER MANAGEMENT FOR AUTOMATIC EXECUTION OF PARAMETERIZED INSTRUCTIONS

Resumen de: US20260359126A1

Systems and methods are disclosed for centralized management and application of distributed parameters through a data generation platform. The platform obtains an aggregate rule set including zone-specific parameter sets and zone classifiers for multiple zones, where the rule set determines allocation metric values associated with transaction values of resources. Upon receiving input data including entity identifiers, resource identifiers, and transaction data, the platform maps the data to zones by comparing zone classifiers, filters the aggregate rule set to generate a zone-specific rule subset, and applies the subset to generate allocation metric values. The platform can process requests synchronously through online message queues and/or asynchronously for bulk processing. Machine learning models can use distribution patterns across multiple resource sets to generate overall allocation metrics and modification suggestions. The platform can provide a graphical user interface for displaying allocation metrics, resource information, and/or supplemental explanations.

METHOD OF DECISION-SUPPORT FOR A VEHICLE CONTROL OR RELATED VEHICLE-CONTROL SIMULATION AND ASSOCIATED SYSTEM

Resumen de: EP4838410A1

0001 The invention concerns a method of decision-support for a vehicle-control or vehicle-control related simulation, the method comprises the following phases: an acquisition phase in which a user device (10) acquires contextual data relative to a neurophysiological state of a user, reception phase in which the user device (10) receives a decision-support machine-learning model and a correction machine-learning model from a server (20), a first application phase in which the user device (10) applies the decision-support machine-learning model on the contextual data to obtain decision-support data, a second application phase in which the user device (10) applies the correction machine-learning model on the decision-support data and contextual data to obtain corrected decision-support data, and a supply phase in which the user device (10) provides the corrected decision-support data to the user.

A METHOD FOR DETERMINING ADDITONAL PREDICTED TEST BEHAVIOUR CHARACTERISTICS OF A VIRTUAL MATERIAL

Resumen de: EP4838534A1

0001 The disclosure relates to a method for determining additional predicted test behavior characteristics of a virtual material, wherein the virtual material is associated with predicted test behavior characteristics with respect to at least one virtual material test, the method performed by a processor, the method comprising: applying the virtual material and the associated predicted test behavior characteristics with respect to at least one virtual material test to a machine-learning model, wherein the machine-learning model is adapted to a plurality of training data sets, and determining the additional predicted test behavior characteristics of the virtual material using the machine-learning model.

METHOD AND SYSTEM FOR RESOLVING HARDWARE ISSUES IN AN INDUSTRIAL PLANT

Resumen de: WO2026231067A1

A system and a computer-implemented method for resolving hardware issues is disclosed. The method processes multi-modal inputs such as textual, auditory, visual, or sensor data, to identify hardware issues. Inputs are structured into contextual triads comprising an object, a connector, and meta information. The plurality of interrelationships among are detected to contextualize the hardware issue. A pretrained large language model analyzes the triads and the plurality of interrelationships to generate resolution triads which include step-by-step actions, contextual metadata, and prioritization based on criticality or urgency. The resolution triads are displayed via a user interface, enabling efficient issue resolution. The method uses a dynamically updated digital folksonomy dataset and machine learning to improve troubleshooting speed and accuracy in the industrial plant (106).

A METHOD FOR GENERATING LIGHTING PLANS FOR A LIGHTING CONFIGURATION AND/OR COMMISSIONING

Resumen de: WO2026229177A1

A method for generating lighting plans based on a generative machine learning model, the lighting plans defining one or more parameters for operation of one or more lighting devices of a lighting system in an environment. The method comprising the steps of receiving an input from a user for generating a lighting plan, the input indicative of a lighting configuration and/or commissioning task, determining, by the generative machine learning model, a plurality of lighting plans based on the user input. The method further comprises determining for each lighting plan from the plurality of lighting plans a light effect associated with each lighting plan in the environment and determining a level of disorder in the plurality of lighting plans, wherein the level of disorder is indicative of a variability in the determined light effects associated with the plurality of lighting plans in the environment. If the level of disorder is above a disorder threshold, the method comprises providing a notification to the user.

MATERIAL HANDLING SYSTEM AND METHOD THEREFOR

Resumen de: WO2026231823A1

A material handling system includes a storage array. An automated package transport system stores and retrieves packages to and from the storage array. An automated palletizer, connected to the automated package transport for providing packages to the palletizer, places packages onto a pallet to form a pallet load. A controller has a pallet load generator having a pallet load planner with a plan model having plan parameters that inform generating a pallet load plan of the pallet load, and the pallet load generator has a machine learning resolver configured so as to heuristically resolve, via machine learning application, parameter variance from the plan parameters resulting in an optimizing solution for the pallet load plan.

MACHINE LEARNING AGENTS TRAINED TO ADDRESS BARRIERS TO CHANGE

Resumen de: WO2026231819A1

Introduced here is an approach for addressing patient barriers to behavioral health change and measuring habit formation in relation to behavioral health changes. Methods and systems discussed herein apply specialized agents to a user's data to determine a barrier hindering the user's progress toward behavioral change, a strategy for addressing the barrier, and a tactic for implementing the strategy. An LLM then carries out the tactic using tailored content or conversations with the user. Methods and systems discussed herein further involve assessment of habit formation relating to behavioral change. Based on a dynamic assessment of a user's habit formation, measures to promote behavioral change can be taken.

ARTIFICIAL INTELLIGENCE- AND QUANTUM ALGORITHM-BASED HYBRID INTELLIGENT SECTORAL OPTIMISATION SYSTEM FOR SMART MATCHING

Resumen de: WO2026229279A1

The invention relates to a smart matching system for use in the building sector, the system comprising a dynamic sectoral database (20), a machine learning multi-criteria matching engine (30), a self-adaptive dynamic scoring module (40) comprising a mechanism for automatically adjusting the prioritisation of the providers and that is configured to adapt continuously according to the evolution of the descriptive records, feedback from the users and other data relating to said providers, as well as a digital user interaction interface (10). The system is capable of receiving a service request in a free-form or structured language, and of returning a prioritised, contextualised and self-adaptive list of providers.

SYSTEMS AND METHODS FOR AN ANALYTIC PLATFORM

Resumen de: US20260350430A1

Systems and methods are disclosed for constructing a data model using an analytic platform. One method may include receiving a data project, the data project including a solution request, determining, based on the solution request, a first data set from one or more data repositories, accessing the one or more data repositories to locate data of the first data set, storing the first data set in one or more data repositories, processing the first data set to provide for processed data, constructing, by a trained machine learning model, a data model based on the processed data and the solution request, determining a solution based on the constructed data model and the solution request, and automatically providing the solution and the constructed data model to one or more applications.

NETWORK FLOW MODEL FOR SECURITY GRAPH BASED THREAT DETECTION

Resumen de: US20260350166A1

In various examples, a security graph is generated based on computing resources within a computing environment, and a machine learning model is used to determine placement information for threat scanners. For example, control plane and/or data plane logs are used to generate the security graph which includes source and sink nodes. Continuing this example, the machine learning model generates predictions based on the security graph, and the predictions are used to determine the optimal placement of threat scanners within the computing environment.

SYSTEMS AND METHODS FOR IDENTIFICATION OF IDLE SERVERS BY IMPLEMENTING MULTIDIMENSIONAL PROCESS AND NETWORK PROXIMITY METRICS

Resumen de: US20260349818A1

0000 A computer implemented method for identifying an idle server is disclosed. The method includes receiving metric data from a server; determining, by a first machine learning system, a multidimensional embedding that defines a signature of the server, the multidimensional embedding being based on the metric data; applying, by a second machine learning system, a clustering algorithm to the multidimensional embedding to determine a first cluster from a set of clusters; determining, based on the first cluster, that the server is an idle server; and deactivating the idle server.

STRUCTURED FEATURE-CHANNEL TOKEN REPRESENTATION FOR MACHINE-LEARNING MODELS

Resumen de: WO2026229937A1

A machine-learning system processes an ordered stream of channelized token representations, each token position including one or more structured feature channels, such as token-content, selector, hierarchy or conditional-vocabulary, reconstruction, state/control, formatting, entity, and audit/visibility channels. Selector information activates, masks, or conditions channel-specific prediction paths, applicability masks, vocabulary or codebook slices, and decoder or reconstruction behavior. Predictable literals, formatting, grammatical envelopes, entity references, and object syntax may be carried in non-content channels attached to carrier tokens, reducing separate substantive token predictions while preserving surface reconstruction. The architecture supports conditional vocabularies, object and encoding-object templates, persistent or successor-predicted state, reasoning-control state, hidden audit channels, entity-register references, future-defined or training-deferred channels, and shared processing of text, image, audio, structured, and tool tokens in a unified multimodal stream.

MACHINE LEARNING MODELS USING FRAGMENTOMICS BASED FEATURES FOR GERMLINE SOMATIC DISCRIMINATION

Resumen de: WO2026231781A1

The present disclosure provides a comprehensive framework for distinguishing germline and somatic variants in cell-free nucleic acid samples by integrating fragmentomics-based features with statistical modeling and machine learning classification. The disclosed approach leverages orthogonal biological signals derived from cfDNA fragmentation patterns to complement conventional allele-based metrics.

MACHINE LEARNING SYSTEMS TO IDENTIFY DATA DRIFT FOR MACHINE-LEARNING MODELS TRAINED TO GENERATE PREDICTIONS AND RECOMMENDATIONS

Resumen de: WO2026231813A1

A method can include: receive a historical data set, extract feature vectors from the historical data set corresponding to the input features for the historical recidivism model, split the feature vectors into a training set and a test set, generate candidate machine-learning models by executing machine-learning algorithms using as input the training set, calculate a relative accuracy of the recidivism model and the candidate machine-learning models using an error rate for the recidivism model and error rates of the candidate machine-learning models, determine a drift based on a difference between a feature importance of the input features for the recidivism model and a feature importance of the input features for the candidate machine¬ learning models, and in response to the drift exceeding a predetermined drift threshold and/or the relative accuracy being below a predetermined accuracy threshold, generate an alert including the drift and the relative accuracy.

MEDICAL MODELING ARCHITECTURE, INTELLIGENCE AND METHODS

Resumen de: AU2025317582A1

System and methods for computer modeling in medicine. A sort of period table of medical models is described for personalized diagnostics, prognostics and therapeutics, including at least 80 major categories of medical models. Generative artificial intelligence and geometric deep learning techniques, and algorithms including 2D and 3D graph machine learning and GenAI algorithms, are described, tailored and applied to diagnostic disease description, prognostic prediction and therapeutic development and management, including generation of novel synthetic drugs. The AI and machine learning techniques and algorithms are applied to understand each individual's genetic, RNA and protein anomalies that represent the source of many unique patient diseases. AI-enabled software agents assist physicians and researchers in building patient medical models. Several personalized medicine applications of individualized medical modeling include cardiovascular

METHOD AND SYSTEM TO DETERMINE NEED FOR HOSPITAL ADMISSION AFTER ELECTIVE SURGICAL PROCEDURES

Nº publicación: US20260351387A1 05/11/2026

Solicitante:

NEW YORK SOC FOR THE RELIEF OF THE RUPTURED AND CRIPPLED MAINTAINING THE HOSPITAL FOR SPECIAL [US]
NEW YORK SOCIETY FOR THE RELIEF OF THE RUPTURED AND CRIPPLED, MAINTAINING THE HOSPITAL FOR SPECIAL

Resumen de: US20260351387A1

A computer-implemented method includes: accessing electronic healthcare records of a group of patients, wherein each patient has received an elective surgical procedure; extracting a data structure encoding a plurality of features of each patient from the group of patients, wherein a subset of the group of patients undergo at least one hospital-based intervention after receiving the elective surgical procedure; determining, using a machine learning algorithm that operates on the data structure, a Shapley value for each of the features, wherein the Shapley value indicates a likelihood for each patient with a corresponding feature to receive at least one hospital-based intervention; identifying a subset of the plurality of features; and based on the identified subset of features, establishing a predictive tool to predict a combined likelihood for a patient to receive a hospital-based intervention.