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70
results.
Updated on
09/05/2025 [07:13:00]
Results 50 to 70 of 70
Absstract of: WO2025073138A1
An AI-based system that uses data sets of existing banner ads, advertising materials, and design and marketing parameters, based on image algorithms and technology, to generate, evaluate, and predict performance of banner ads and present them to specific customers or specific types of customers. The graph-based model is used to generate original banner ads, which are then evaluated by a machine learning model, which assigns them scores. The highest-scoring banner ads are then presented to customers. A genetic algorithm in combination with iterative evaluation and generation are used to diversify design and choose the highest ranked banner ads.
Absstract of: WO2025076257A1
The present system provides a method and apparatus for predicting a likelihood of injury of an individual. The system generates a frailty score that represents the likelihood of a person being injured. The frailty score is generated by using Artificial Intelligence (Al) and machine learning using a specialized data set. The frailty score can then trigger actions to reduce the possibility of injury or to determine whether to engage in the injury risking behavior at all.
Absstract of: WO2025076103A1
Methods and apparatus for artificial intelligence control of process control systems are described. An example non-transitory machine readable storage medium comprising instructions to cause programmable circuitry to at least: collect a measurement of an operation of a process; utilize machine learning based on a state of the process and a goal function that references one or more measurement(s); and modify operation of a controller based on the machine learning.
Absstract of: US2025118057A1
An unlabelled or partially labelled target dataset is modelled with a machine learning model for classification (or regression). The target dataset is processed by the machine learning model; a subgroup of the target dataset is prepared for presentation to a user for labelling or label verification; label verification or user re-labelling or user labelling of the subgroup is received; and the updated target dataset is re-processed by the machine learning model. User labelling or label verification combined with modelling an unclassified or partially classified target dataset with a machine learning model aims to provide efficient labelling of an unlabelled component of the target dataset.
Absstract of: US2025114710A1
A game modification engine modifies configuration settings affecting game play and the user experience in computer games after initial publication of the game, based on device level and game play data associated with a user or cohort of users and on machine-learned relationships between input data and a use metric for the game. The modification is selected to improve performance of the game as measured by the use metric. The modification may be tailored for a user cohort. The game modification engine may define the cohort automatically based on correlations discovered in the input data relative to a defined use metric.
Absstract of: US2025117632A1
A system, method, and computer-program product includes obtaining a decisioning dataset comprising a plurality of favorable decisioning records and at least one unfavorable decisioning record; detecting, via a machine learning algorithm, a favorable decisioning record of the plurality of favorable decisioning records that has a vector value closest to a vector value of the unfavorable decisioning record; executing a counterfactual assessment between the favorable decisioning record and the unfavorable decisioning record; generating an explainability artifact based on one or more bias intensity metrics to explain a bias in a machine learning-based decisioning model; and in response to generating the explainability artifact, displaying the explainability artifact in a user interface.
Absstract of: US2025117662A1
The invention relates to a computer-implemented method for determining similarity relations between various tables by means of machine-learning computing modules.
Absstract of: US2025117685A1
An iterative machine learning interatomic potential (MLIP) training method. The training method includes training a first multiplicity of first MLIP models in a first iteration of a training loop. The training method further includes training a second multiplicity of second MLIP models in a second iteration of the training loop in parallel with the first training step. The training method also includes combining the first MLIP models and the second MLIP models to create an iteratively trained MLIP configured to predict one or more values of a material. The MLIP may be a Gaussian Process (GP) based MLIP (e.g., FLARE). The MLIP may be a graph neural network (GNN) based MLIP (e.g., NequIP or Allegro).
Absstract of: WO2025074193A1
A method of detecting sample anomalies within a laboratory information management system includes obtaining a first result for a sample, processing the first result via a univariate machine learning model, processing a plurality of results for the sample via a multivariate machine learning model in response to the univariate machine learning model generating a normal output for the first result, and flagging, within the laboratory information management system, the sample for rejection processing in response to the multivariate machine learning model generating an abnormal output for the plurality of samples. The first result represents a first type of result, the univariate machine learning model is trained using unsupervised machine learning, the plurality of results includes the first result, each of the plurality of results represents a different type of result for the sample, and the multivariate machine learning model trained using unsupervised machine learning.
Absstract of: US2025117710A1
Provided is a method of deploying a multimodal large model, an electronic device and a storage medium, relating to field of artificial intelligence technology, and in particular, to fields of deep learning and model deployment. The method includes: splitting a first multimodal large model into a visual part and a linguistic part; determining a first static graph model corresponding to the visual part and a second static graph model corresponding to the linguistic part; and deploying the first multimodal large model based on the first static graph model and the second static graph model.
Absstract of: US2025117797A1
A method includes receiving, by a processor of a transaction processing entity, a transaction attempt. The method includes receiving a risk score from a risk strategy decision model, the risk score being determined from a machine learning model. The method includes in response to receiving the risk score, determining, whether the risk score exceeds a threshold indicating the transaction attempt is potentially fraudulent. In response to determining the risk score exceeds the threshold: the method includes determining, whether to approve or decline the transaction attempt; and determining a reason for approving or declining the transaction attempt based on one or more variables contributing to the risk score. The method includes outputting an indication to approve or decline the transaction attempt in response to determining whether to approve or decline the transaction attempt and the reason for approving or declining the transaction attempt.
Absstract of: US2025117664A1
A system, method, and computer-program product includes obtaining a decisioning dataset comprising a plurality of favorable decisioning records and at least one unfavorable decisioning record; detecting, via a machine learning algorithm, a favorable decisioning record of the plurality of favorable decisioning records that has a vector value closest to a vector value of the unfavorable decisioning record; executing a counterfactual assessment between the favorable decisioning record and the unfavorable decisioning record; generating an explainability artifact based on one or more bias intensity metrics to explain a bias in a machine learning-based decisioning model; and in response to generating the explainability artifact, displaying the explainability artifact in a user interface.
Absstract of: US2025117575A1
The present invention is related to data processing methods and systems thereof. According to an embodiment, the present invention provides a method of processing documents using a machine learning model. The process begins by accessing data files and extracting information from them, which is subsequently stored. This document information, along with the machine learning model trained on various document formats, is used to classify the data files and generate tabular data. From this tabular data, data objects are created and included in an output data file. The information from the output file is then used to update the data of the machine learning model, optimizing it for improved future document processing. There are other embodiments as well.
Absstract of: US2025117537A1
A method for interactive explanations in industrial artificial intelligence systems includes providing a machine learning model and a set of test data, a set of training data and a set of historical data simulating a piping and process equipment; predicting a result for the piping and process equipment based on the machine learning model using the set of test data and the set of training data, wherein the set of historical data is used by the machine learning model to predict at least one parameter of the piping and process equipment; and presenting the predicted at least one parameter on a piping and instrumentation diagram of the piping and process equipment.
Absstract of: US2025116678A1
A method of detecting sample anomalies within a laboratory information management system includes obtaining a first result for a sample, processing the first result via a univariate machine learning model, processing a plurality of results for the sample via a multivariate machine learning model in response to the univariate machine learning model generating a normal output for the first result, and flagging, within the laboratory information management system, the sample for rejection processing in response to the multivariate machine learning model generating an abnormal output for the plurality of samples. The first result represents a first type of result, the univariate machine learning model is trained using unsupervised machine learning, the plurality of results includes the first result, each of the plurality of results represents a different type of result for the sample, and the multivariate machine learning model trained using unsupervised machine learning.
Absstract of: US2025119448A1
To analyze cybersecurity threats, an analysis module of a processor may receive log data from at least one network node. The analysis module may identify at least one statistical outlier within the log data. The analysis module may determine that the at least one statistical outlier represents a cybersecurity threat by applying at least one machine learning algorithm to the at least one statistical outlier.
Absstract of: US2025119451A1
Embodiments disclosed include methods and apparatus for visualization of data and models (e.g., machine learning models) used to monitor and/or detect malware to ensure data integrity and/or to prevent or detect potential attacks. Embodiments disclosed include receiving information associated with artifacts scored by one or more sources of classification (e.g., models, databases, repositories). The method includes receiving inputs indicating threshold values or criteria associated with a classification of maliciousness of an artifact and for selecting sample artifacts. The method further includes classifying and selecting the artifacts, based on the criteria, to define a sample set, and based on the sample set, generating a ground truth indication of classification of maliciousness for each sample artifact in the sample set. The method further includes using the ground truth indications to evaluate and display, via an interface, a representation of a performance of sources of classification and/or quality of data.
Absstract of: US2025119360A1
A method for improving communication network performance comprises identifying a favorability status of individual predictions and/or decisions of a plurality of decisions of a machine-learning algorithm acting on the communication network. The favorability statuses are stored with corresponding values of network parameters used as features in the algorithm. A counterfactual algorithm is generated, e.g., by generating a tree-based classification algorithm, based on the stored favorability statuses and network parameter values, to derive rules for producing a favorable status based on one or more of the network parameters. A proposed recourse action comprising a change in at least one of the network parameters is identified, based on the rules, and a decision network, such as a Bayesian inference network, is generated for determining a confidence level estimating a reliability of achieving a favorable status by changing the network parameter(s). Whether to implement the proposed recourse action is determined, based on the confidence level.
Absstract of: WO2023232876A1
A method of building a computer implemented data Classifier for Classifying data from a certain context is provided, whereby the Classifier is based on a model obtained by transfer learning combining Probabilistic Graphical Models (PGM) and arbitrary, context independent machine learned models enabled by special modelling patterns, where Variables representing outputs of machine learned models are added to the PGM.
Nº publicación: EP4533340A1 09/04/2025
Applicant:
VISA INT SERVICE ASS [US]
Visa International Service Association
Absstract of: CN119278456A
Methods, systems, and computer program products are provided for encoding feature interactions based on tabular data. An example method includes receiving a data set in a table format including a plurality of rows and a plurality of columns. Each column is indexed to generate a position embedding matrix. Each column is grouped based on at least one tree model to generate a domain embedding matrix. An input vector is generated based on the data set, the position embedding matrix, and the domain embedding matrix. The input vector is input into a first multilayer perceptron (MLP) model to generate a first output vector, and the first output vector is transposed to generate a transposed vector. The transposed vector is input into a second MLP model to generate a second output vector, and the second output vector is input into at least one classifier model to generate at least one prediction.
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