Resumen de: US2025217826A1
A vehicle data system receives a lead submission through a website supported by the vehicle data system and determines, utilizing a machine learning model, a user value for a lead associated with the lead submission. The user value represents a probability of the lead purchasing a vehicle from a dealer through the website. The vehicle data system determines a user lifetime value for the lead based at least on the user value for the lead. Subsequently, the vehicle data system obtains clickstream identifiers from a search engine and assigns a corresponding user lifetime value to each clickstream identifier. The vehicle data system aggregates the clickstream identifiers and corresponding user lifetime values in a single file and communicates the single file to a search server for consumption. The user lifetime values are utilized by the search engine in search engine marketing processes.
Resumen de: US2025217435A1
A non-transitory computer-readable recording medium storing a machine learning program for causing a computer to execute a process including training a machine learning model by machine learning that uses a cost function in which each element of a matrix obtained by relaxing a discrete variable to be optimized to a continuous matrix becomes a discrete optimization problem as a cost function in a search process that performs a search by adopting continuous relaxation into the discrete optimization problem.
Resumen de: US2025218551A1
A system and method are provided for generative atomistic design of materials. The disclosure herein includes a machine learning system for generating a new material, using multiple predictive machine learning models for atomic level properties to create training data, and/or then using the same predictive machine learning models to refine the output of a generative machine learning system. In use, one or more datasets are received at at least one computing device corresponding to a desired material. Additionally, using at least two machine learning models associated with the at least one computing device, a new dataset is created for the desired material. Further, the at least two machine learning models are trained, using at least semi-supervised learning, based on the one or more datasets, to model properties of the desired material. Still yet, using the at least one computing device, a prediction is outputted comprising the desired material.
Resumen de: WO2025141520A1
In an embodiment, a computer-implemented method for decoding neural activity is provided. In the method, at least one machine learning model is trained using a training data set of EEG data and concurrently collected environmental data collected from data collection participants. Once the at least one machine learning model is trained, EEG data measured from sensors attached to or near a user's head is received. Environmental data describing stimulus the user is exposed to concurrently with the measurement of the EEG data is also received. The EEG data and the environmental data is input into the at least one machine learning model to determine an inference related to the neural activity. Based on the inference, an operation of a computer program is altered.
Resumen de: EP4579535A1
A machine learning program for causing a computer to execute a process includes training a machine learning model by machine learning that uses a cost function in which each element of a matrix obtained by relaxing a discrete variable to be optimized to a continuous matrix becomes a discrete optimization problem as a cost function in a search process that performs a search by adopting continuous relaxation into the discrete optimization problem.
Nº publicación: IL320886A 01/07/2025
Solicitante:
DOW GLOBAL TECH LLC [US]
AGUIRRE VARGAS FABIO [US]
MUKHOPADHYAY SUKRIT [US]
CLARACQ JEROME [NL]
RIJKSEN BART [NL]
GINZBURG VALERIY V [US]
COOKSON PAUL [GB]
SCHMIDT ALIX [US]
IYER SHACHIT SHANKARAN [US]
DOW GLOBAL TECHNOLOGIES LLC,
AGUIRRE VARGAS Fabio,
MUKHOPADHYAY Sukrit,
CLARACQ Jerome,
RIJKSEN Bart,
GINZBURG Valeriy V,
COOKSON Paul,
SCHMIDT Alix,
IYER Shachit Shankaran
Resumen de: US2024203537A1
Methods include training a machine learning module to predict one or more target product properties for a prospective chemical formulation, including (a) constructing or updating a training data set from one or more variable parameters; (b) performing feature selection on the training data set; (c) building one or more machine learning models using one or more model architectures; (d) validating the one or more machine learning models; (e) selecting at least one of the one or more machine learning models and generating prediction intervals; (g) interpreting the one or more machine learning models; and (h) determining if the one or more target product properties calculated are acceptable and deploying one or more trained machine learning models, or optimizing the one or more machine learning models by repeating steps (b) to (g). Methods also include application of trained machine learning modules to predict formulation properties from prospective data.