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61
resultados
Última actualización
04/02/2026 [07:20:00]
Resultados 50 a 61 de 61
Resumen de: US20260010807A1
A method for predicting risk exposure can include receiving data from a sensor. The method for predicting risk exposure also can include analyzing the data via a machine learning (ML) model. The analyzing can include determining that the data represents a light exposure pattern correlated with a risk pattern. The ML model can be trained with training data indicative of the light exposure pattern and indicative of the risk pattern to identify a correlation between the light exposure pattern and the risk pattern. The method for predicting risk exposure further can include predicting a risk exposure for a user based on the analyzing the data. The method for predicting risk exposure further can include providing a notice indicating the risk exposure, as predicted. Other embodiments are disclosed herein.
Resumen de: US20260010624A1
The technology relates to cybersecurity attacks and cloud-based security, and specifically to detecting malicious embeddings in document destined for a networked system. Such embeddings can be delivered in the form of malicious macros and/or malicious OLE objects stored within document files. The technology intercepts a document that is compatible with an MS Office file format, finds embedded code, engineers at least five features that characterize the embedded code. The technology inputs the engineered features to a trained machine learning model and determines, as a threat level, a likelihood of malicious embedding from at least the engineered features of the embedded code. Based on the threat level, the technology can block the document with a malicious threat level, accept the document with a non-malicious threat level, and or isolate the document with a suspicious threat level.
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.
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.
Resumen de: EP4674678A1
According to one embodiment of the present invention, there is provided a battery life evaluation system including: a data collection unit configured to obtain information about route information, charge/discharge patterns, driving patterns, and cell states from an electric vehicle; and a life evaluation unit configured to predict the life of a battery installed in the electric vehicle by performing machine learning based on the obtained information.
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.
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.
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.
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.
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.
Nº publicación: EP4675519A1 07/01/2026
Solicitante:
M T RICCI S R L [IT]
M.T. Ricci S.r.l
Resumen de: EP4675519A1
The method for assessing the seismic risk for existing buildings introduces an integrated and codified flow of work steps through a mixed qualitative-quantitative assessment with the combination of the expeditious method with the scientific method, performed on a strategic subset of the population, an operational and targeted use of artificial intelligence with the use of supervised machine learning models, validated on an empirical basis, with APS (accuracy, precision, sensitivity) metrics calculated on a verification sample, with a significant reduction in time with the application of the validated model to a remaining subset S' (67-75% of the building heritage) without the need for a complete engineering assessment, offering a modular structure which may be adapted to different territorial and typological contexts, with the selection of the most relevant variables (P1/P'1, P4/P'4, P6/P'6, P7/P'7) performed through the ANOVA statistical analysis and the Chi-Square analysis, also thanks to a systemic approach with the definition of a clear, replicable operational flow, scalable and provided with objective validation criteria.
BOPI
Sede Electrónica
