Alerta

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

Resumen de: 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, System, and Computer Program Product for Improving Machine Learning Models

Resumen de: 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.

Schema Transformation For Managing An Application Build

Resumen de: US20260277602A1

Techniques for generating a schema transformation for application data to monitor and manage the application in a runtime environment are disclosed. A system runs an application plugin in a runtime environment to identify data generated by application modules in one or both of an application build process and an application execution process. The application plugin is a software program executed together with the application build process. The application plugin identifies a source schema associated with application data. The application plugin identifies a target schema associated with an analysis program or machine learning model. The application plugin generates a schema transformation to convert application runtime data into a target data set. The system applies the target data set to an analysis program, such as a machine learning model, to generate output analysis data associated with the application.

System, Method, and Computer Program Product for Feature Similarity-Based Monitoring and Validation of Models

Resumen de: US20260278480A1

Systems, methods, and computer program products calculate a historical feature similarity point distribution associated with a first machine learning model, calculate a real-time feature similarity point distribution associated with a second machine learning model, and automatically provide, based on a comparison of the historical feature similarity point distribution to the real-time feature similarity point distribution, an indication of whether the second machine learning model is aligned with the first machine learning model.

COMMUNICATION SYSTEM USING MACHINE LEARNING FOR ENCODING/DECODING MODELS IN DISTRIBUTED UNIT BASE STATIONS

Resumen de: US20260280764A1

A communication system includes: a communication terminal (UE) capable of encoding data by using an encoding model that encodes and outputs data that has been input; and a base station including a central unit (CU) and one or more distributed units (DU), the distributed units decoding data encoded with the encoding model by using a decoding model that, when encoded data is input, decodes and outputs the data, and the central unit or the distributed units perform machine learning by using learning data including data transmitted by the communication terminal without performing encoding to generate the encoding model and the decoding model, and notify the communication terminal of a learning result of the encoding model.

DIGITAL THERAPEUTIC PLATFORM

Resumen de: US20260279579A1

Systems and methods are provided for monitoring health. An exemplary method includes: collecting a first data regarding a patient during an in-office visit; providing a remote monitoring service for remotely monitoring the patient’s health; remotely collecting, using the remote monitoring service, a second data of the patient; providing a probabilistic network for assigning metric-based information to the plurality of data using a plurality of conditional probabilities; processing, using the probabilistic network, the first data and the second data using the probabilistic network; generating, using the processed plurality of data, one or more machine learning models for producing a knowledge base trained to recognize pattern types in the data; generating, using the knowledge base, one or more artificial intelligent features for recommending treatment options based on the data regarding the patient; and providing, using the one or more artificial intelligent features, one or more treatment recommendations for improving the patient’s health.

SYSTEM TO DETECT, ASSESS AND COUNTER DISINFORMATION

Resumen de: US20260278427A1

A computer-readable medium for the identification, measurement, and combatting of the influence of large-scale creation and distribution of disinformation is herein disclosed. An embodiment of this invention is comprised of one or more repositories of data which involve online comments and articles and attributes derived from them, one or more technical targeting systems, a content analysis system, a cost and influence estimation system, a dialog system, a performance management system, a bot design and test system, a security system, a multimedia content generator, one or more machine learning components, a data collection mechanism, separate consumer and human operator applications, and a mechanism for the creation and management of bots across multiple channels.

MACHINE LEARNING BASED MEDICAL DATA CHECKER

Resumen de: US20260279525A1

A method of verifying multi-modal medical data is proposed. The method comprises: accessing multi-modal medical data of a subject, the multi-modal medical data comprising a medical image of a specimen slide, wherein a specimen in the specimen slide was collected from the subject; generating a prediction pertaining to a biological attribute of the medical image based on the medical image; determining a degree of consistency between the biological attribute of the medical image and other modalities of the multi-modal medical data; and outputting, based on the degree of consistency, an indication of whether the multi-modal medical data contain inconsistency.

METHODS AND SYSTEMS FOR THE ESTIMATION OF THE COMPUTATIONAL COST OF SIMULATION

Resumen de: US20260278215A1

Estimating the computational cost of simulation using a machine learning model. An example method includes inputting a feature data set into a machine learning model. The feature data set includes model geometry metadata and simulation metadata. The method further includes predicting, using the machine learning model, a computational cost characteristic for a simulation process.

COMPUTER-BASED SYSTEMS CONFIGURED FOR ENTITY RESOLUTION AND INDEXING OF ENTITY ACTIVITY

Resumen de: US20260278418A1

In order to facilitate the entity resolution and entity activity tracking and indexing, systems and methods include receiving first source records from a first database and second source records from a record database. A candidate set of second source records is determined by a heuristic search in the set of second source records. A candidate pair feature vector associated with each candidate pair of first and second source records is generated. An entity matching machine learning model predicts matching first source records for each candidate second source record based on the respective candidate pair feature vector. An aggregate quantity associated with the matching first source records is aggregated from a quantity associated with each first source record, and a quantity index for each candidate second source record is determined based the aggregate quantities. Each quantity index is displayed to a user.

EARLY WARNING AND COLLISION AVOIDANCE

Resumen de: US20260279201A1

Among other things, equipment is located at an intersection of a transportation network. The equipment includes an input to receive data from a sensor oriented to monitor ground transportation entities at or near the intersection. A wireless communication device sends to a device of one of the ground transportation entities, a warning about a dangerous situation at or near the intersection, there is a processor and a storage for instructions executable by the processor to perform actions including the following. A machine learning model is stored that can predict behavior of ground transportation entities at or near the intersection at a current time. The machine learning model is based on training data about previous motion and related behavior of ground transportation entities at or near the intersection. Current motion data received from the sensor about ground transportation entities at or near the intersection is applied to the machine learning model to predict imminent behaviors of the ground transportation entities. An imminent dangerous situation for one or more of the ground transportation entities at or near the intersection is inferred from the predicted imminent behaviors. The wireless communication device sends the warning about the dangerous situation to the device of one of the ground transportation entities.

SYSTEM, RFID CHIP, SERVER AND METHOD FOR CAPTURING VEHICLE DATA

Resumen de: US20260279526A1

0000 A system includes a plurality of tracking devices, such as RFID tags, affixed to items, such as vehicles, a data collection engine, client devices and backend devices. The backend devices include trained machine learning models, business logic, and attributes of a plurality of events. A plurality of data collection engines and systems send attributes of new events to the backend devices. The backend devices can track the items and predict particular outcomes of new events based upon the attributes of the new events utilizing the trained machine learning models.

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

Resumen de: 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.

APPORTIONING DATA CENTER COMPUTE POWER

Resumen de: US20260278689A1

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.

Smart Micro Fulfillment Centers Powered by Machine Learning Based Clusters

Resumen de: US20260278532A1

A system and method are disclosed for determining inventory to stock at a supply chain site. The method includes determining a set of inventory clusters identifying inventory items that have been purchased together, ranking the set of inventory clusters based on a number of times each cluster of inventory items was purchased together, determining an amount of shelf space needed for each of the set of inventory clusters, ranking any inventory clusters that were purchased an equal number of times based on the amount of shelf space needed to generate a cluster ranking, and determining inventory to stock at the supply chain site based on the cluster ranking. The method further includes generating an inventory stocking plan based on the determined inventory to stock, and implementing the inventory stocking plan at the supply chain site based on the inventory stocking plan using one or more pieces of automated stocking machinery.

MACHINE LEARNING FRAMEWORK TO DETECT AND MONITOR COMPLIANCE MATTERS

Resumen de: WO2026193314A1

System apparatus, article of manufacture, method and/or computer program embodiments are provided for detecting and monitoring compliance matters. An example method may include obtaining a first set of image data corresponding to an environment from one or more image sensors; processing the first set of image data using a first machine learning algorithm to yield annotated image data that identifies at least one object within the environment; processing the annotated image data using a second machine learning algorithm to determine one or more attributes associated with the at least one object within the environment; and generating, based on the one or more attributes, an evidence package corresponding to the at least one object within the environment, the evidence package being configured to facilitate a determination of one or more action items associated with the at least one object.

RADIO ACTIVITY DETECTION IN WIRELESS NETWORKS

Resumen de: 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.

AI DISCOVERY

Resumen de: 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

Resumen de: 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

Resumen de: 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 CLUSTERING AND SEPARATING GRAPH NETWORKS

Resumen de: US20260278467A1

Systems and methods for decomposing an optimization problem to reduce solve time. Input data associated with the optimization problem is read and the problem is represented as a graph comprising nodes and edges. A machine learning technique clusters the nodes into a plurality of clusters, and an association between constraints and edges spanning the clusters is obtained. For each constraint, a distribution of predicted load among the clusters is determined based on demand information generated by a machine learning model trained on the input data. At least one constraint is then split across the plurality of clusters in accordance with the predicted load distribution.

MODEL ECOSYSTEM

Resumen de: 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.

COMPUTER-BASED SYSTEMS CONFIGURED TO ORCHESTRATE A TRANSFER OF A DATA OBJECT AND METHODS OF USE THEREOF

Resumen de: WO2026193487A1

In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving a request for data communication from an external entity to a particular internal enterprise system; identifying the distinct internal system-specific data ontology associated with the particular internal enterprise system; utilizing a machine learning matrix to dynamically compare the request for the data communication to the distinct internal system-specific data ontology; generating a standardized data ontology; verifying the plurality of protocols associated with the standardized data ontology; generating a standardized application programming interface between the external entity and the particular internal enterprise system; and utilizing an adapter to orchestrate a transfer of a data object from the particular internal enterprise system to the external entity.

RESIDUAL INITIALIZATION FOR TRAINING NODE NETWORKS

Resumen de: US20260278384A1

0000 This disclosure describes systems, methods, and computer-readable media for initializing and training differentiable logic gate networks for machine-learning inference. In various embodiments, parameters of learnable logic gate operators are initialized using a residual initialization that biases the initial probability distribution of discrete gate choices toward a feedforward operation, such as a wire or an inverter, thereby preserving information flow and mitigating vanishing gradients in deep networks. During training, the gate parameters are updated so that gates can remain as feedforward connections or transition to other logic operations as needed. The residual initialization can be applied to convolutional logic gate-tree architectures and pooling operations, and trained networks can be discretized and synthesized into efficient logic circuits in hardware or software.

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

Nº publicación: US20260279571A1 17/09/2026

Solicitante:

UNIV PUERTO RICO [US]
University of Puerto Rico

Resumen de: 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.