Alerta

Schema Transformation For Managing An Application Build

Absstract of: 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

Absstract of: 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

Absstract of: 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

Absstract of: 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

Absstract of: 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

Absstract of: 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

Absstract of: 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

Absstract of: 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

Absstract of: 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

Absstract of: 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.

A METHOD FOR GENERATING TEST CASES FOR TESTING A SOFTWARE PROGRAM

Absstract of: EP4807565A1

0001 The invention relates to a method (100) for generating test cases for testing a software program, comprising: - Training (101) a first machine learning model (1) for a next-token prediction based on a training data set, the training data set comprising test cases for testing the software program in a code format, - Providing (102) a labelled training data set, the labelled training data set comprising test cases that are each associated with at least one characteristic of the software program, - Performing (103) a respective fine-tuning of the trained first machine learning model (1) for the next-token prediction based on the labelled training data set for each characteristic of the software program to provide a specialized machine learning model (2) for each respective characteristic, - Generating (104) the test cases for testing the software program for at least one of the characteristics of the software program by utilizing the first machine learning model (1) and/or a respective specialized machine learning model (2) that was fine-tuned for said at least one of the characteristics.

Mitigating cyber-threats in a network

Absstract of: GB2704692A

A method for mitigating cyber-threats in a target network comprising a plurality of nodes. A decentralised multi-agent model with a plurality of local models is deployed to the target network S210. Each of the plurality of local models monitors a local region of the target network to obtain local network information S220 and predicts threat mitigation actions based on the local network information S230. The decentralised multi-agent model may be a machine learning model, trained using reinforcement learning. The local models may be trained using one or more training networks, using a trainer system which iteratively evaluates a performance of the multi-agent model and adjusts the local models. Figure 2

Machine learning with PII protection and explainability

Absstract of: GB2704709A

Processing input data 300, comprising receiving an encrypted model parameter update, wherein at least model parameters relating to personally identifiable information (PII) are encrypted using a homomorphic encryption algorithm 302. The encrypted model parameter update is decrypted using the homomorphic encryption algorithm 304. The decrypted model parameter update is applied to model parameters of a machine learning model 306. The machine learning model receives input data comprising personally identifiable information 308. A machine learning model is used to process the input data to produce output data 310. Finally, an explainability algorithm is applied to the output data 312. The receiving of the encrypted model parameter update may comprise the receiving of an encrypted model parameter update from a server device that aggregate the encrypted model parameter update from local encrypted model parameter updates received from a plurality of user devices. Fig. 3

FLEXIBLE MACHINE LEARNING MODEL COMPRESSION

Absstract of: WO2025101721A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for compresses a machine learning model having a plurality of parameters. In one aspect, one of the methods includes obtaining trained values of a set of parameters for at least a portion of a machine learning model; identifying one or more dense ranges for the trained values; determining a least number of bits required to represent each trained value within the one or more dense ranges; identifying a second format having a range that is smaller than a range of the first format; and generating a compressed version of the at least a portion of the machine learning model.

Control parameter optimization

Absstract of: GB2704660A

A computer system comprises a parameter optimization module 102 for optimizing input parameters of a process having a binary output, the module comprising: probabilistic machine learning model 1021 trained to model a relationship between a first and a second controllable input parameter of the process and the binary output, the relationship defining an optimized pair of input parameter values 104; and batched binary Bayesian Optimizer 1022 configured to generate, from the trained probabilistic machine-learning model, a list 105 comprising training pairs of input parameter values. The parameter-optimization-module 102 is configured to output the optimized pair of input parameter values 104 and the list 105 of training pairs of input parameter values, and to receive binary output values of the process performed using the training pairs of input parameter values. The computer system is configured to periodically retrain to generate a new optimized pair 104 of input values and a new list 105 of training pairs of input parameters. Also claimed is assigning received requests to a first or second group to perform the process using the optimized or training input parameters, respectively. Utilizing batched binary Bayesian optimization creates a much smaller training data set by selecting training parameters with the greatest impact when model 1021 is retrained. The computer system operates using optimized input parameters 104 that produce desired results, yet still producing new trai

MACHINE LEARNING BASED SEMICONDUCTOR MEASUREMENT MODELS TRAINED USING HISTORICAL DATA

Absstract of: US20250238690A1

Methods and systems for using historical measurement data to train a present state, machine learning (ML) based measurement model are described herein. This approach takes advantage of the correlation between structural characteristics of measured samples fabricated in accordance with different design revisions, process revisions, or both. In one aspect, a present state, ML based measurement model is trained using training data associated with measurements of a plurality of instances of a current version of a semiconductor structure in a present state of a semiconductor process flow and training data associated with measurements of a plurality of instances of a historical version of the semiconductor structure in the present state of the semiconductor process flow. In some examples, training data also includes prior state measurement data. Historical training data, prior state training data, or both, may be derived from actual reference measurements, in-line, production measurements, or both.

A FISH SPECIES DISTRIBUTION AND ABUNDANCE PREDICTION METHOD BASED ON ENHANCED ENSEMBLE MACHINE LEARNING

Nº publicación: NL4002365A 14/09/2026

Applicant:

SOUTH CHINA SEA FISHERIES RESEARCH INST CHINESE ACADEMY OF FISHERY SCIENCES [CN]
SANYA TROPICAL FISHERIES RES INSTITUTE\u200C [CN]
SOUTH CHINA SEA FISHERIES RESEARCH INSTITUTE, CHINESE ACADEMY OF FISHERY SCIENCES
SANYA TROPICAL FISHERIES RESEARCH INSTITUTE\u200C

Absstract of: NL4002365A

0001 The present invention discloses a fish species distribution and abundance prediction method based on enhanced ensemble machine learning, relating to the technical field of fish ecological prediction. This method integrates the characterization capability of enhanced ensemble learning for complex environmental responses and the representation capability of hierarchical models for community ecological processes, and can effectively resolve nonlinear fixed effects, spatial dependence, and species interactions, thereby improving the accuracy and ecological interpretability of multi-species prediction.