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FORMULATION GRAPH FOR MACHINE LEARNING OF CHEMICAL PRODUCTS

Resumen de: CN120693653A

A chemical recipe for a chemical product may be represented by a digital recipe diagram for a machine learning model. The digital recipe graph may be input to a graph-based algorithm, such as a graph neural network, to produce feature vectors that are denser descriptions of the chemical product than the digital recipe graph. The feature vectors may be input to a supervised machine learning model to predict one or more attribute values of the chemical product to be produced by the recipe without actually having to pass through the production process. The feature vectors may be input to an unsupervised machine learning model that is trained to compare the chemical products based on the feature vectors of the chemical products. The unsupervised machine learning model may recommend alternative chemical products based on the comparison.

AUTOMATED METHOD FOR VIRTUAL TECHNICAL ASSISTANCE IN THE CORRECTION OF COMPUTER VULNERABILITIES THROUGH COMBINED USAGE OF SOFTWARE AUTOMATION AND ARTIFICIAL INTELLIGENCE TECHNICS

Resumen de: EP4675478A1

The invention relates to an automatic method for technical assistance in the correction of vulnerabilities of a computer system, where the aforementioned method comprises at least the following steps:a) receiving information relative to computer system vulnerabilities by text, voice, file input or through data exchange;b) launching tools based on artificial intelligence algorithms and machine learning to understand the information and requests entered relative to vulnerabilities;c) performing parsing and text mining of the information received and process it through intelligent data matching with information relative to the computer vulnerabilities present in a database or through interaction with external online vulnerability databases;d) in case of unsatisfactory results, starting an extended online search by web scraping using deep learning algorithms and unsupervised machine learning;e) updating an internal vulnerability database with the additional information found; andf) generating a vulnerability report accompanied by relative remediation.

SYSTEMS AND METHODS FOR USING MACHINE LEARNING TO PREDICT CRITICAL CONSTRAINTS

Resumen de: AU2024229742A1

A computer-implemented method and computer program product for predicting a required committed capacity of an electric utility are provided. The method includes the steps of: (a) performing a stochastic optimization of raw data to produce a total committed capacity from conventional thermal units as a target data, wherein the raw data comprises grid operating conditions; (b) combining the total committed capacity from conventional thermal units with raw features and engineered features to generate training data; (c) training a machine learning model for predicting the required committed capacity of the electric utility using the generated training data; (d) predicting the required committed capacity of the electric utility using the trained machine learning model; and (e) running an augmented version of a deterministic dispatch optimization model based on the predicted required committed capacity of the electric utility. The computer program performs the aforementioned steps.

ARTIFICIAL INTELLIGENCE-ASSISTED BUILDING AND EXECUTION OF A FEDERATED DATA LAYER FOR ENTERPRISE ENGINEERING

Resumen de: EP4675520A1

Artificial Intelligence-assisted building/execution of federated data layer for enterprise engineering: A system trains at least one machine-learning model to identify information about industrial assets from training data, then map the information to a federated data model. The system retrieves information about data from an application in an industrial asset. The at least one machine-learning model identifies types of the data, relationships between the data, and patterns of the data, from the information and based on data types, data relationships, and data patterns in the federated data model. The at least one machine-learning model maps the types of the data, the relationships between the data, and the patterns of the data to the federated data model. The system identifies knowledge about the types of the data, the relationships between the data, and/or the patterns of the data in the federated data model, in response to a query about data.

A computer-implemented method for adapting a machine learning model of an advanced driver assistant system to an updated set of tasks

Resumen de: GB2642288A

Method of adapting a multi-task machine learning model of an advanced driver assistance system (ADAS) 16 to an updated set of tasks, comprising: providing a pre-trained machine learning model defined by a pre-trained weight matrix; for each new task generating a task specific adaptive weight matrix from a task specific training dataset, the task specific adaptive weight matrix formed by matrix multiplication of a first low rank (LoRA) matrix and a second LoRA matrix; merging task specific adaptive weight matrices to obtain a merged adaptive weight matrix; and adding the merged adaptive weight matrix to the pre-trained weight matrix. Generating the task specific adaptive weight matrix involves a maximum likelihood estimation followed by a stochastic gradient descent based on a stochastic weight averaging gaussian process. The merged adaptive weight matrix is determined by the averaging the task specific adaptive weight matrix by stacking the first LoRA matrices of each task in a column vector and the second LoRA matrices of each task in a row vector and performing matrix multiplication of the column vector with the row vector. The merged adaptive weight matrix is determined using an evolutionary method and selecting the best performing matrix as the fully-trained weight matrix.

TRAINING A MACHINE LEARNING MODEL

Resumen de: WO2024181895A1

A method performed by a node (100) in a communications network (107). The method comprises: obtaining (202) vulnerability data comprising a plurality of vulnerabilities detected on a host server during a vulnerability scan, matching (204) a first vulnerability in the plurality of vulnerabilities to a first subset of co-occurring vulnerabilities in the plurality of vulnerabilities, the first subset of co-occurring vulnerabilities overlapping in time with the first vulnerability, and training (206) a machine learning model using the first vulnerability as an example input and the first subset of co-occurring vulnerabilities as an example output of the machine learning model.

DEEP-LEARNING BASED PERSONA SPECIFIC INSIGHT GENERATOR

Resumen de: WO2024181975A1

A method for generating persona specific insights. The method may include receiving sensor data associated with a device; extracting features from the received sensor data; processing the features using a machine learning model to generate machine learning metrics; ingesting the machine learning metrics and the features to generate insights data associated with the device; generating personas data using the insights data and the features, and mapping the insights to the personas data; generating custom insights using the insights data, the personas data, and the features, wherein the custom insights are text-based summaries; and disseminating each of the custom insights to respective persona of the personas data to place service orders associated with the device.

METHODS AND SYSTEMS FOR MODELING MULTILAYER NETWORKS

Resumen de: WO2026006705A1

Systems and methods for characterizing a multilayer network, including: obtaining a multilayer network including a plurality of multiplex heterogenous networks and a plurality of bipartite networks connecting the multiplex heterogeneous networks in the plurality of multiplex heterogeneous networks, in which each multiplex heterogeneous network in the plurality of multiplex heterogeneous networks includes a plurality of nodes; applying a trained machine learning algorithm to relate the plurality of multiplex heterogeneous networks to an embedding space, in which the trained machine learning algorithm uses random walk with restart; using the embedding space of the plurality of multiplex heterogeneous networks to extract information corresponding to interactions between the plurality of nodes of each multiplex heterogeneous network in the plurality of multiplex heterogeneous networks; and outputting the embedding space and the extracted information to a user.

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

Resumen de: WO2026003235A1

Method of decision-support for a vehicle or related vehicle simulation, the method being executed by a system comprising a server (20), a client device (10) and a database (30), the method comprising the following phases: a. acquisition phase (100) in which the server (20) receives an input data from a device onboard of the vehicle, b. selection phase (200) in which the server (20) obtain a plurality of machine learning results R1, …, Rn and computes at least one selection score, then at least one selection score being used to select a preferred machine- learning model MLp, c. transmission phase (300) in which the server (20) sends the result Rp as a recommendation to the client device (10), d. supply phase (400) in which the client device (10) provides the recommendation to a user (60), e. return phase (500) in which the client device returns decision data to a database (30).

SYSTEM AND METHOD FOR OBSERVABILITY AND DATA AUDIT USING IMPLICIT DATA DEPENDENCY CAPTURE

Resumen de: WO2026006680A1

The disclosure is directed to systems, methods, and computer-readable media for observability and data audit using implicit data dependency capture. Data dependency information can be intercepted, for example, as a user trains or otherwise interacts with a machine learning (ML) model. Data dependency information can include information regarding files, data sources, inputs, outputs, storage buckets, storage directories, and/or other pertinent information. A log of the data dependency information can be reviewed to determine ML model provenance.

SYSTEMS AND METHODS FOR USING ARTIFICIAL INTELLIGENCE FOR FRAUD DETECTION USING AN ENUMERATION DETECTION SYSTEM

Nº publicación: WO2026006480A1 02/01/2026

Solicitante:

FIDELITY INFORMATION SERVICES LLC [US]
FIDELITY INFORMATION SERVICES, LLC