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MULTI-FACTOR ANTI-PHISHING SYSTEMS AND METHODS

Absstract of: US20260222445A1

The present application describes a multi-factor anti-phishing system and method, featuring real-time local analysis of webpages on user devices. The system comprises a feature extraction system integrated with a web browser to extract a comprehensive set of features from visited webpages. These features include URL and host characteristics, content and structure indicators, resource files and scripts, form and action elements, and embedded media analysis. A machine learning (ML) model, trained on these features, analyzes the extracted data to predict phishing risks. The system uses a cloud secure enclave to manage allow lists and block lists, process encrypted feedback, and retrain the ML model, such that sensitive information remains confidential. The retrained ML model is periodically distributed to user devices to enhance phishing detection capabilities.

SYSTEMS AND METHODS FOR MACHINE LEARNING IMPLEMENTED STATE TRANSITION PLANNING

Absstract of: WO2026156458A1

Systems and methods for the generation of a state transition plan by using a diffusion model to predict state representations. The diffusion model can be constrained using at least one constraint function during training where the at least one constraint function models a start and an end state in the reverse diffusion process. The diffusion model is trained to learn a constrained score function corresponding to a score function comprising a score offset based on the at least one constraint function. At inference, the diffusion model can be provided with observed states of one or more agents to generate predicted states for the one or more agents.

SYSTEMS AND METHODS FOR GENERATING PERSONALIZED CONTENT

Absstract of: US20260220503A1

In some embodiments, apparatuses and methods are provided herein useful to generate personalized content. In some embodiments, a system comprising a processing resource; a machine readable medium storing instructions that, when executed, cause the processing resource to: aggregate, session data during a client session; update, periodically and during the interaction session, one or more inference indicators associated with the client in an inference cache storage, the one or more inference indicators being determined via a trained machine learning model using the session data and historical data in a historical data database; identify a trigger event based on client interactions; retrieve, in response to the trigger event, at least one inference indicator associated with the client from the inference cache storage; and generate a personalized content for display on the client device based on the at least one inference indicator retrieved from the inference cache storage.

ELECTRONIC PLATFORM FOR DETECTING BUGS IN SMART CONTRACT CODE

Absstract of: WO2026161028A1

A computational method and an electronic platform for detecting bugs in smart contract code parses smart contract code to generate heterogeneous contract graph (HCG) where each node represents a semantic or syntactical segment of the smart contract code and each edge represents a control flow. Each node is processed with a tokenizer of a pretrained Large Language Model (LLM) that returns a vector of the embeddings for each of the tokens. A plurality of meta relations from the nodes, the edges or both defining how connected nodes interact are also extracted. These are then processed by a trained machine learning model, trained on training dataset comprising meta relations and vectors of correct smart contract code and buggy smart contract code to detect a presence of one or more bugs in the received smart contract code and for each bug estimate a location of the respective bug in the source code.

BATTERY LIFE ESTIMATION APPARATUS AND METHOD

Absstract of: US20260219323A1

A battery life estimation apparatus includes a charging/discharging unit configured to charge/discharge a battery and a controller configured to calculate a partial accumulative capacity by charging/discharging the battery in a partial voltage period corresponding to a period from a first voltage to a second voltage, and estimate a life corresponding to an entire voltage period of the battery by inputting the first voltage, the second voltage, and the partial accumulative capacity to an estimation model trained based on machine learning.

DETERMINING CONFIDENCE TO START VALUES FOR POWER GENERATION SYSTEMS USING MACHINE LEARNING MODELS

Absstract of: US20260221775A1

0000 Presented herein are systems and methods for applying machine learning (ML) models to determine start confidence values for power generators. The computing system can identify a first plurality of parameters of a first power generator. The plurality of parameters can identify operations of the first power generator. The computing system can apply the first plurality of parameters to a ML model to determine a first confidence value identifying a first likelihood of the first power generator to start upon initiation. The computing system can provide an output based on the first confidence value for the first power generator.

System, Method, and Computer Program Product for Determining Feature Importance

Absstract of: US20260220533A1

Provided is a system that includes a processor to receive a dataset comprising a plurality of feature values of a plurality of features; determine, for each feature of the plurality of features, a plurality of sequence deviation metrics; generate a plurality of sets of features for the plurality of sequence deviation metrics, wherein each set of features comprises a ranked set of features for a sequence deviation metric; train a plurality of machine learning models based on the plurality of sets of features, wherein the plurality of machine learning models comprises a machine learning model for each sequence deviation metric; determine a performance metric of each trained machine learning model for each sequence deviation metric; and select a ranked set of features for a sequence deviation metric that corresponds to a trained machine learning model that has a highest performance metric. Methods and computer program products are also provided.

EXPLAINABLE MACHINE-LEARNING TECHNIQUES FROM MULTIPLE DATA SOURCES

Absstract of: US20260220531A1

0000 In some aspects, a computing system can generate and optimize a hybrid machine learning model for risk assessment based on predictor variables associated with a target entity. The hybrid machine learning model can be trained using training vectors with sets of training predictor variables and training outputs corresponding to the respective sets of training predictor variables. The predictor variables associated with the target entity may include unknown values and the training predictor variables or trainings output may also include unknown values. Additionally, the computing system can generate explanatory data for the target entity to indicate relationships between changes in the risk indicator and changes in the predictor variables associated with the target entity. The risk indicator and the explanatory data can be used in controlling access of the target entity to interactive computing environments.

METHOD FOR IMPROVING THE CLASSIFICATION OF A DIGITAL DOCUMENT USING A PLURALITY OF LEARNING MODELS

Absstract of: US20260220960A1

0000 A method for improving the classification of a document from a plurality of machine learning models, includes receiving a digital document; executing a first learning function generated from a first machine learning model trained from a first training domain processing the first input data sequence and making it possible to classify a document type and generate a prediction of a first automatic action to be performed on the digital document, acquiring a first corrective action from a user relating to the modification of the movement of the first digital document to a second directory; generating a first annotation; modifying the first training domain by adding the first annotation; generating a retraining of the first machine learning model.

SYSTEMS AND METHODS FOR PREDICTION OF ANTIBIOTIC RESISTANCE FROM BACTERIAL GENOMES

Absstract of: US20260220336A1

Systems, methods, and devices disclosed herein provide antibiotic resistance predictions using a pan-antibiotic resistance prediction (PARP) model. The PARP model includes a machine learning system trained with a training data set of genetic information associated with a plurality of bacterial species, and/or antibiotic feature information associated with a plurality of antibiotics. The PARP model is deployed to a cloud-based service for scalability which provides access to the PARP model for clinic devices, hospital devices, and/or laboratory devices. For instance, a web-based portal of the cloud-based service receives a genomic sequence associated with a particular bacterial isolate, uploaded via a remote device. The PARP model outputs a predictive indication of an antibiotic resistance, for the particular bacterial isolate. The predictive indication can include a bar graph (e.g., presented at a graphical user interface) showing, for the particular bacterial isolate, susceptibility/resistance predictions for a plurality of antibiotics.

SYSTEMS AND METHODS FOR TRANSFORMING AND STORING DATA FROM MULTIPLE STUDIES

Absstract of: US20260221293A1

There is disclosed a method and system for combining datasets. Study results may be retrieved. Each study result may include datapoints. Each datapoint may include attributes. Study questions may be extracted from the study results. The questions may be converted into a standardized format. Categories may be assigned to the questions. The questions may be grouped together into groups. A response scale may be determined for each of the groups. The responses may be rescaled using the corresponding response scale. A final dataset may be generated by combining the study results. Features may be selected using the final dataset. A machine learning algorithm may be trained using the features of the final dataset.

SYSTEM AND METHOD FOR DYNAMICALLY PREDICTING FRAUD USING MACHINE LEARNING

Absstract of: US20260220715A1

0000 A computing device configured to communicate with a central server in order to predict likelihood of fraud in current transactions for a target claim. The computing device then extracts from information stored in the central server (relating to the target claim and past transactions for past claims including those marked as fraud), a plurality of distinct sets of features: text-based features derived from the descriptions of communications between the requesting device and the endpoint device, graph-based features derived from information relating to a network of claims and policies connected through shared information, and tabular features derived from the details related to claim information and exposure details. The features are input into a machine learning model for generating a likelihood of fraud in the current transactions and triggering an action based on the likelihood of fraud (e.g. stopping subsequent related transactions to the target claim).

CONSTRAINT BASED INFERENCE AND MACHINE LEARNING SYSTEM

Absstract of: US20260220438A1

0000 A method including receiving a pre-determined constraint on user actions. A constraint vector is generated based on the pre-determined constraint. The constraint vector is input into a machine learning model. A first output is generated from the machine learning model by executing the machine learning model using the constraint vector as a first input to the machine learning model. The constraint vector is converted into a legal action mask. A probability vector is generated by executing a masked softmax operator. The masked softmax operator takes, as a second input, the first output. The masked softmax operator takes, as a third input, the legal action mask. The masked softmax operator generates, as a second output, the probabilities vector. Action outputs are generated by applying a sampling system to the probability vector. The action outputs include a subset of the user actions, and wherein the subset includes only allowed user actions.

MACHINE LEARNING MODEL MANAGER

Absstract of: 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

Absstract of: 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

Absstract of: 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

Absstract of: 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

Absstract of: 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.

AI SUPERINTELLIGENCE APPLICATION STATE MACHINE

Absstract of: 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.

ADAPTIVE LOCATION TECHNOLOGY HANDOFFS WITH ASSET TRACKERS

Absstract of: 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

Absstract of: 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

Absstract of: 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

Absstract of: 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-Based Information Management Engine

Absstract of: 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.

STATUS INFORMATION FOR IDENTIFIERS RELATED TO ARTIFICIAL INTELLIGENCE OR MACHINE LEARNING

Nº publicación: US20260222970A1 30/07/2026

Applicant:

QUALCOMM INCORPORATED [US]
QUALCOMM Incorporated

Absstract of: US20260222970A1

0000 Methods, systems, and devices for wireless communications are described. A wireless device may obtain, from a network entity, status information corresponding to one or more identifiers that are associated with one or more network settings. The one or more network settings may relate to communication of reference signaling for an artificial intelligence or machine learning (AI/ML)-based positioning or sensing procedure or measurement of reference signaling for an AI/ML-based positioning or sensing procedure. The wireless device may perform an operation to control the AI/ML-based positioning or sensing procedure based on the status information corresponding to the one or more identifiers. The wireless device and the network entity may communicate based on the status information corresponding to the one or more identifiers and the AI/ML-based positioning or sensing procedure control operation.