Alerta

MODEL ECOSYSTEM

Absstract of: US20260278425A1

A system of machine learning (“ML”) models for making actionable predictions regarding low-incidence events, including a generative ML model that produces synthetic minority-class records to form an augmented training data set, a predictive ML model that has been trained on the augmented training data set, a certainty ML model that produces a certainty estimate, and an explanatory model that produces an explanation. A method for producing actionable predictions of a low-incidence event by applying ML models to imbalanced class data by producing a prediction by a predictive ML model that has been trained on a data set comprising synthetic minority-class data records produced by a generative ML model, and producing a certainty estimate and an explanation. At least one of the certainty estimate or explanation determines an effective or appropriate response to the prediction. The low-incidence event may comprise risk of opioid use disorder.

SYSTEM

Absstract of: US20260278341A1

A system includes a processor that is configured to collect data from a plurality of information sources on the Internet, perform natural language processing on the collected data to analyze the collected data and extract important keywords and phrases from the collected data, evaluate a reliability of information included in the collected data by using a deep learning algorithm, provide evaluated information to a user, evaluate the reliability of the information by taking into account at least a source of the information, consistency of content of the information, a past reliability evaluation of the information or of the source, and an update frequency of the information, provide, to the user, information that is determined to have high reliability without modification, and display a warning for information that is determined to have low reliability and provide a detailed explanation regarding the reliability of the information.

"AN IMPROVED SYSTEM FOR EXTUBATION TYPE PREDICTION"

Absstract of: WO2026190804A1

The present invention relates to an improved system for extubation type predictions in patients undergoing mechanical ventilation for various respiratory or neurological health conditions The system comprises a 5 data acquisition and pre-processing unit (100), a model training and evaluation unit (200), a model deployment and prediction unit (300), and a clinical decision support unit (400). Furthermore, the system continuously collects, processes, and analyzes patient data to minimize medical risks associated with premature or delayed extubation, aiming 10 for improved patient outcomes in critical care settings. Moreover, the system uses advanced machine learning techniques to improve the accuracy and dependability of extubation predictions in real-time, helping medical professionals make well-informed decisions, which leads to better patient safety, fewer complications, and efficient resource 15 utilization.

MODELED CONVERSIONS FOR A CONNECTION NETWORK SYSTEM

Absstract of: US20260278339A1

Modeled conversions for connection networking are described. A method comprises generating a first input vector comprising a set of sequence features for a group of entities using a transformer layer of a machine learning model, generating a second input vector comprising a set of identity profile features for the group of entities using a generative artificial intelligence layer, receiving a third input vector comprising a set of conversion features, concatenating the first input vector, the second input vector, and the third input vector into a concatenated vector, normalizing the concatenated vector to form a normalized vector, projecting the normalized vector to form a projected vector, and generating a first metric based on the projected vector using a multi-gate mixture-of-experts (MMOE) layer of the ML model, the first metric comprising a value indicating whether there is a deterministic entity level conversion event for a group level conversion event.

Systems and Methods for Inferring Biological, Environmental, Ecological, and Therapeutic States from Optical Interaction Dynamics and Multivariable Longitudinal Monitoring

Absstract of: US20260279003A1

Systems and methods are provided for inferring biological, environmental, ecological, therapeutic, industrial, agricultural, or physiologic states from optical interaction signatures and temporal optical interaction dynamics obtained through multivariable longitudinal monitoring. One or more imaging devices or sensors acquire optical interaction data associated with biological matrices, environmental samples, atmospheric systems, industrial processes, or other monitored environments. One or more processors extract quantitative optical interaction features—including Global Edge Coherence (GEC), Localized Edge Dynamics (LEDyn), speckle evolution, aggregation kinetics, and related metrics—and integrate these features with environmental variables, geospatial data, biologic measurements, therapeutic history, and longitudinal multimodal datasets using machine-learning and probabilistic inference engines. In certain embodiments, the system receives and processes structured Digital Twin Manifests (DTMs). The resulting probabilistic inferences support closed-loop adaptive monitoring, real-time therapeutic-response assessment, distributed smartphone-based environmental intelligence, environmental digital biomarkers, and proactive recommendations across laboratory, clinical, ecological, manufacturing, and extraterrestrial applications.

RESOURCE PREDICTION FOR QUANTUM ADVANTAGE ON THE USAGE OF GATED QUANTUM COMPUTERS

Absstract of: US20260278444A1

0000 One example method includes selecting a set of classical computing algorithms, solving, using the classical computing algorithms, respective problem instances of a problem to obtain a set of executions, where respective ML (machine learning) models were trained using the executions of the problem instances to predict a respective amount of classical computing resources needed to execute a respective one of the problem instances by each of the classical computing algorithms, in a loop, using an ensemble ML model to predict the classical resources needed to solve the problem, and when the predicted classical resources exceed defined classical resource limits, ending the loop, constructing a quantum circuit that comprises, as among a respective set of gates and qubits of quantum algorithms in a group of quantum algorithms, a smallest set of gates and qubits, and determining minimum requirements of quantum hardware needed to run the quantum circuit with a specified fidelity.

ADAPTIVE ALIGNMENT TRANSFER FOR MACHINE LEARNING MODELS

Absstract of: US20260277951A1

0000 At least one processor may generate at least one modification for at least one weight of an unaligned target large language model (LLM). The generating may include obtaining at least one delta defining a difference between at least one layer of an unaligned source LLM and the at least one layer of an aligned source LLM and performing at least one mutation operation on the at least one delta. The at least one processor may inject the at least one modification into at least one target layer of the unaligned target LLM, thereby generating an aligned target LLM.

MATERIAL STRUCTURE AND SYNTHESIS PATHWAY GENERATION USING GENERATIVE MODELS

Absstract of: WO2026193491A1

A method for designing a synthesis pathway for a target material is provided herein. The method includes receiving input data related to a target material. The method further includes analyzing the input data using a machine learning model to generate one or more candidate synthesis pathways. The method further includes selecting a synthesis pathway from the candidate synthesis pathways. The method further includes outputting the selected synthesis pathway design.

SYSTEM AND METHODS FOR ONE-SHOT FEDERATED UNLEARNING

Absstract of: WO2026188482A1

A method for training a machine learning model in a federated learning system is disclosed. The federated learning system includes a plurality of client devices and a central server that initializes a global model and aggregates locally trained updates from the client devices to update the global model. The global model and a forgetting threshold are provided to a forget client. The forget client calculates a Fisher information matrix diagonal, and generates a network mask using the Fisher information matrix diagonal and the forgetting threshold. The network mask is an indicator of specialized parameters of the global model for the local training data of the forget client. The forget client transmits the network mask to the central server or all other clients of the federated learning system. The central server or the other clients randomly initialize or set the specialized parameters of the global model.

SYSTEMS AND METHODS FOR AUTOMATED EXIT VERIFICATION

Absstract of: US20260278577A1

0000 Systems and methods provided herein may facilitate store exit verification. Methods may include capturing, in real-time and as a customer approaches an exit, a set of images of one or more items. Methods may include constructing, via a computer vision engine, a preliminary item catalog based on the one or more items detected near the exit. Methods may include constructing, via a machine learning engine and based on the preliminary item catalog, a refined item catalog by cross-referencing attributes of the one or more items against at least one of synthetic data, historical purchase patterns, contextual product groupings, and known inventory information. Methods may include detecting, via a verification engine and in real-time, discrepancies between the refined item catalog and a corresponding transaction log obtained from a transaction database, wherein the customer is allowed to pass through the exit based on the discrepancies.

TECHNOLOGIES FOR MACHINE LEARNING CLOUD BASED CLINICAL OUTCOME PREDICTION

Absstract of: US20260279577A1

0000 Technologies for clinical outcome predictive analysis include a computing device that extracts provider features from an assessment index that are indicative of relational characteristics of a care provider. The computing device determines a clinical outcome performance metric associated with the care provider. The computing device links the provider features and the performance metric in training data and trains a prediction model to predict an outcome score based on provider feature. A computing device may predict a predicted outcome score based on extracted provider features using the trained prediction model and normalize the predicted outcome score to a predicted outcome index. The computing device may monitor ongoing therapeutic alliance status data and predict a risk of alliance failure using a trained prediction model. Other embodiments are described and claimed.

ARTIFICIAL INTELLIGENCE-BASED NETWORK PERFORMANCE PREDICTION TRAINING

Absstract of: US20260281753A1

0000 Certain aspects of the present disclosure provide techniques for training an artificial intelligence-based wireless communication network simulator. An example method includes obtaining first training data associated with a first network simulator, wherein the first training data includes first parameters associated with simulation of a wireless network operating, in a scenario, according to first configurations, wherein the wireless network includes a network node in communication with a user equipment (UE); obtaining second training data associated with a second network simulator, wherein the second training data includes second parameters associated with simulation of the wireless network operating, in the scenario, according to second configurations; generating third training data based at least in part on the first training data and the second training data; and training a machine learning model, associated with prediction of performance metrics of the wireless network, based at least in part on the third training data.

REAL-TIME MEDICAL CODE VALIDATION USING CONTRASTIVE LEARNING AND RETRIEVAL-AUGMENTED GENERATION

Absstract of: US20260279529A1

0000 A system validates medical claim coding information during a coding workflow and before claim submission. The system receives diagnosis and procedure codes for a healthcare encounter and obtains embeddings produced by a contrastive machine-learning model trained on labeled valid and invalid code combinations. An embedding-based compatibility measure is determined using a similarity computation, and multi-code weighting generates an aggregated compatibility determination for encounters involving multiple codes. The aggregated determination is compared to a threshold to assign a preliminary classification of valid, invalid, or needs review. Policy or guidance passages are retrieved and used to confirm, revise, or override the preliminary classification, producing a final classification. A policy-cited explanation is generated from the retrieved passages. The system outputs the final classification and explanation to a user interface, enabling correction of potential coding errors before claim submission.

MODIFIED MECHANICAL SPECIFIC ENERGY SYSTEM AND METHODS USING A TERNARY GRAPH-BASED APPROACH

Absstract of: US20260278331A1

0000 The Modified Mechanical Specific Energy (MSE 3.0) system introduces an advanced, AI-driven drilling optimization approach. Unlike traditional models, MSE 3.0 integrates a ternary graph-based visualization to dynamically analyze cuttings morphology, pipe vibration, and MSE in real time. The system utilizes machine learning algorithms for predictive analytics, second-order optimization for early inefficiency detection, and a cost-effective surface-based measurement approach. By eliminating reliance on downhole sensors, MSE 3.0 reduces operational costs while enhancing drilling efficiency, making it applicable to hydrocarbon, geothermal, and critical mineral extraction operations. A key innovation of MSE 3.0 is the integration of real-time Cuttings Morphology Analysis, providing a direct, continuous assessment of downhole conditions and formation response. This enables precise, data-driven optimization of drilling patterns, significantly improving predictive accuracy and wellbore stability over conventional single-variable MSE models.

SYSTEM AND METHOD OF USING A KNOWLEDGE REPRESENTATION FOR FEATURES IN A MACHINE LEARNING CLASSIFIER

Absstract of: US20260278409A1

Systems and methods are provided for classifying at least one unlabeled content item with a machine-learning classifier. A knowledge representation synthesized based on an object of interest is used as a source of features for which training data is evaluated. The machine learning classifier is trained based on features based on the attributes and the training data.

RADIO ACTIVITY DETECTION IN WIRELESS NETWORKS

Absstract of: US20260281760A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for radio activity detection, e.g., in wireless networks. One of the methods includes detecting one or more signals based on one or more outputs generated by one or more trained machine learning models processing information corresponding to a complex baseband representation of a received RF data.

MODEL INSTRUCTION-QUALITY EVALUATION METHOD AND APPARATUS, COMPUTER DEVICE, AND STORAGE MEDIUM

Absstract of: US20260278402A1

A model instruction—quality evaluation method includes obtaining a model instruction and a plurality of sample sets, and for each sample set of the plurality of sample sets, fusing the model instruction with each quality evaluation sample in the sample set to obtain a model input set corresponding to the sample set, triggering, based on the model input set corresponding to the sample set, a machine learning model corresponding to the model instruction to perform a specified task specified by the model instruction, to obtain a task result set, and determining, based on accuracy of each task execution result in the task result set, a quality evaluation score of the model instruction. The method further includes determining a comprehensive quality evaluation score of the model instruction based on the quality evaluation scores obtained by evaluation based on the plurality of sample sets.

AI DISCOVERY

Absstract of: AU2025219950A1

This disclosure relates generally to detecting artificial intelligence (AI) implementation in a software application comprising one or more application packages (APs). One or more processors extract one or more AP strings from the software application, which each represent an AP; and create a prompt for a machine learning model, trained to generate output text, comprising the one or more AP strings, the prompt representing instructions to provide a classification and provide functionality information of each of the one or more APs, the classification being AI relevant or non-AI relevant and the functionality information describing a functionality of the respective AP. The one or more processors then evaluate the machine learning model on the prompt to generate output text corresponding to the classification and the functionality information of each of the one or more APs; and generate a report of the AI implementation based on the output text.

MACHINE LEARNING SYSTEM, SERVER DEVICE, CLIENT DEVICE, MACHINE LEARNING METHOD, AND COMPUTER READABLE MEDIUM

Absstract of: US20260278420A1

A machine learning system (500) includes a client device (200) that has its own unique learning data and a server device (100) that has training data. Each of the client device (200) and the server device (100) has an AI model. Further, the machine learning system (500) proceeds with learning while keeping the learning data of the client device (200) and the training data of the server device (100) private to each other. The server device (100) includes a fraudulent client detection unit (2530) to estimate a fraudulent client device that performs an attack to disrupts the learning of the AI model in the server device (100).

MODEL MANAGEMENT METHOD AND APPARATUS

Absstract of: US20260278419A1

A model management method and an apparatus, and relates to the field of communication technologies, to reduce a data security risk of an AI model in the wireless field. The method includes: obtaining a first communication data set used for updating a first machine learning model, and sending a second communication data set to a model training function entity. The first processing policy includes a first loss threshold, and the first machine learning model is used for managing a wireless communication service. The second communication data set is obtained by performing a processing operation on suspicious data in the first communication data set according to the first processing policy, and the suspicious data is wireless communication data whose loss value is greater than the first loss threshold.

SYSTEMS AND METHODS FOR PREDICTING DRY MATTER INTAKE IN ANIMALS USING MACHINE LEARNING

Absstract of: US20260271888A1

A computing system may receive data comprising values of a set of variables corresponding to a plurality of animals, the set of the variables including a water intake and a dry matter intake. A computing system can train a machine learning model on the data to provide values of dry matter intake based on the values of the set of the variables. A computing system can after training, provide the machine learning model with input values of at least a subset of the variables associated with at least one animal to generate a value of dry matter intake for the at least one animal.

METHOD FOR DETECTING PRESENCE OF AN ELECTRIC ARC AND ASSOCIATED DEVICE

Absstract of: US20260276691A1

A computer-implemented method for detecting the presence of an electric arc on a power line using a machine learning algorithm, carried out using a time-correlated measurement of the arc voltage Varc, of the current I and of the source voltage Vsource and also a knowledge model for linking the arc voltage Varc and the current I to constants depending on the knowledge model, implemented after a learning phase that involves adjusting a learning model that takes the current I and the source voltage Vsource at input and provides the voltage Varc and the constants of the knowledge model at output; the method including continuously detecting the presence of an arc using a decision function depending on a cost function used in the learning phase.

Obstacle Detection Method and Device for Assisting Vehicle in Driving

Absstract of: US20260276813A1

An obstacle detection method for assisting in vehicle driving includes (i) obtaining ultrasonic echo data captured during vehicle movement, (ii) obtaining information associated with echo intersections based on the ultrasonic echo data, (iii) providing at least part of the ultrasonic echo data and the information associated with the echo intersections as feature data to a machine learning model to obtain detection information for an obstacle, wherein the machine learning model employs at least one of a classification algorithm or a regression algorithm, and (iv) assisting in vehicle driving based on the detection information for the obstacle.

Method, System, and Computer Program Product for Improving Machine Learning Models

Absstract of: US20260278484A1

Methods, systems, and computer program products are provided for improving machine learning models which include receiving a data set including data records; inputting the data set to a pre-trained first machine learning model to generate first embeddings; inputting the first embeddings to a second machine learning model to generate second embeddings in a user-specific embedding space; inputting the plurality of second embeddings to a third machine learning model to extract feature data associated with a feature; inputting an output from a machine learning system and the feature data to a fourth machine learning model to generate a relevance score for each entity; determining a subset of entities based on the relevance score; communicating a feedback request to a user; receiving feedback data from the user; and training at least one of the models based on the feedback data.

IMPEDANCE-BASED CLASSIFICATION OF BIOLOGICAL CELLS OR OTHER PARTICLES VIA MACHINE LEARNING METHODS

Nº publicación: US20260279571A1 17/09/2026

Applicant:

UNIV PUERTO RICO [US]
University of Puerto Rico

Absstract of: US20260279571A1

The disclosure includes a platform for classifying particles (e.g., biological cells) based on impedance measurements of individual particles using a biodevice. A machine learning model can be used to classify cells with high accuracy (e.g., greater than 95%). This can be accomplished without the need for sample preparation or cellular tagging. The biodevice can immobilize individual cells (e.g., mechanically immobilize with a micro-pore). An external AC waveform can be applied, for example, in the 1 Hz to 1 MHz range. The machine learning model may be generated to provide classifications based on, for example, measurements of impedance magnitude and phase angle at discrete frequencies.