09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
downloadable software using artificial intelligence (AI) for enabling computers to perform tasks autonomously; downloadable software using artificial intelligence (AI) for generating other software; downloadable software using artificial intelligence (AI) for autonomously and dynamically selecting and executing tools and workflows in response to user queries, including generating and executing code to extract, process, and analyze data without requiring predefined agent paths or workflows providing temporary use of on-line non-downloadable software and applications using artificial intelligence (AI) for enabling computers to perform tasks autonomously; providing temporary use of on-line non-downloadable software and applications using artificial intelligence (AI) for generating other software; providing temporary use of on-line non-downloadable software and applications using artificial intelligence (AI) for autonomously and dynamically selecting and executing tools and workflows in response to user queries, including generating and executing code to extract, process, and analyze data without requiring predefined agent paths or workflows
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
downloadable software using artificial intelligence (AI) for enabling computers to perform tasks autonomously; downloadable software using artificial intelligence (AI) for generating other software; downloadable software using artificial intelligence (AI) for autonomously and dynamically selecting and executing tools and workflows in response to user queries, including generating and executing code to extract, process, and analyze data without requiring predefined agent paths or workflows providing temporary use of on-line non-downloadable software and applications using artificial intelligence (AI) for enabling computers to perform tasks autonomously; providing temporary use of on-line non-downloadable software and applications using artificial intelligence (AI) for generating other software; providing temporary use of on-line non-downloadable software and applications using artificial intelligence (AI) for autonomously and dynamically selecting and executing tools and workflows in response to user queries, including generating and executing code to extract, process, and analyze data without requiring predefined agent paths or workflows
3.
UTILIZING MACHINE LEARNING TO GENERATE AN APPLICATION PROGRAM
An input specifying a schematic of user interface components of an application program is received. A first group of one or more machine learning models is used to automatically identify the user interface components and associated properties specified in the input. Based on the identified user interface components and the associated properties, a second group of one or more machine learning models is used to automatically generate program code implementing the application program including the user interface components.
42 - Scientific, technological and industrial services, research and design
Goods & Services
providing temporary use of on-line non-downloadable computer software platforms to allow users to create, operate, develop and deploy other automated machine learning software applications and platforms and artificial intelligence software applications and platforms
A training dataset is used to train an unsupervised machine learning trained model. Corresponding gradient values are determined for a plurality of entries included in the training dataset using the trained unsupervised machine learning model. A first subset of the training dataset is selected based on the determined corresponding gradient values and a first threshold value selected from a set of threshold values. A labeled version of the selected first subset is used to train a first supervised machine learning model to detect one or more anomalies.
An indication of a selection of an entry associated with a machine learning model is received. One or more interpretation views associated with one or more machine learning models are dynamically updated based on the selected entry.
G06F 18/23213 - Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
G06F 18/243 - Classification techniques relating to the number of classes
G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
An input dataset is sorted into a first version of data and a second version of data. The first version of data is associated with a first period of time and the second version of data is associated with a second period of time. The second period of time is a shorter period of time than the first period of time. A first set of one or more machine learning models is generated based on the first version of data. A second set of one or more machine learning models is generated based on the second version of data. The first set of one or more machine learning models and the second set of one or more machine learning models are combined to generate an ensemble model. A prediction based on the ensemble model is outputted. The prediction indicates abnormal behavior associated with the input dataset.
A plurality of initial machine learning models are determined based on a plurality of original features. The plurality of initial machine learning models are filtered by selecting a subset of the initial machine learning models as one or more surviving machine learning models. One or more evolved machine learning models are generated. At least one of the evolved machine learning models is based at least in part on one or more new features, which are based at least in part on a transformation of at least one of features of the one or more surviving machine learning models. Corresponding validation scores associated with the one or more evolved machine learning models and corresponding validation scores associated with the one or more surviving machine learning models are compared. At least one of the one or more evolved machine learning models or the one or more surviving machine learning models are selected as one or more new selected surviving machine learning models.
Input data associated with a machine learning model is classified into a plurality of clusters. A plurality of linear surrogate models are generated. One of the plurality of linear surrogate models corresponds to one of the plurality of clusters. A linear surrogate model is configured to output a corresponding prediction based on input data associated with a corresponding cluster. Prediction data associated with the machine learning model and prediction data associated with the plurality of linear surrogate models are outputted.
G06F 18/23213 - Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
G06F 18/243 - Classification techniques relating to the number of classes
G06F 3/01 - Input arrangements or combined input and output arrangements for interaction between user and computer
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
downloadable computer software platform to allow users to search, create, operate, develop and deploy other automated machine learning software applications, software platforms, and artificial intelligence software applications for purposes of data analysis; downloadable computer software platform to allow users to prepare data for analysis, cleanse, aggregate, and manipulate data in order to perform advanced data analysis providing temporary use of on-line non-downloadable computer software platform to allow users to search, create, operate, develop and deploy other automated machine learning software applications, software platforms, and artificial intelligence software applications for purposes of data analysis; providing temporary use of on-line non-downloadable computer software platform to allow users to prepare data for analysis, cleanse, aggregate, and manipulate data in order to perform advanced data analysis
A training dataset is used to train an unsupervised machine learning trained model. Corresponding gradient values are determined for a plurality of entries included in the training dataset using the trained unsupervised machine learning model. A first subset of the training dataset is selected based on the determined corresponding gradient values and a first threshold value selected from a set of threshold values. A labeled version of the selected first subset is used to train a first supervised machine learning model to detect one or more anomalies.
A training dataset is used to train an unsupervised machine learning trained model. Corresponding gradient values are determined for a plurality of entries included in the training dataset using the trained unsupervised machine learning model. A first subset of the training dataset is selected based on the determined corresponding gradient values and a first threshold value selected from a set of threshold values. A labeled version of the selected first subset is used to train a first supervised machine learning model to detect one or more anomalies.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
An indication of a selection of an entry associated with a machine learning model is received. One or more interpretation views associated with one or more machine learning models are dynamically updated based on the selected entry.
G06F 18/23213 - Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
G06F 18/243 - Classification techniques relating to the number of classes
G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
Input data associated with a machine learning model is classified into a plurality of clusters. A plurality of linear surrogate models are generated. One of the plurality of linear surrogate models corresponds to one of the plurality of clusters. A linear surrogate model is configured to output a corresponding prediction based on input data associated with a corresponding cluster. Prediction data associated with the machine learning model and prediction data associated with the plurality of linear surrogate models are outputted.
Input data associated with a machine learning model is classified into a plurality of clusters. A plurality of linear surrogate models are generated. One of the plurality of linear surrogate models corresponds to one of the plurality of clusters. A linear surrogate model is configured to output a corresponding prediction based on input data associated with a corresponding cluster. Prediction data associated with the machine learning model and prediction data associated with the plurality of linear surrogate models are outputted.
A plurality of initial machine learning models are determined based on a plurality of original features. The plurality of initial machine learning models are filtered by selecting a subset of the initial machine learning models as one or more surviving machine learning models. One or more evolved machine learning models are generated. At least one of the evolved machine learning models is based at least in part on one or more new features, which are based at least in part on a transformation of at least one of features of the one or more surviving machine learning models. Corresponding validation scores associated with the one or more evolved machine learning models and corresponding validation scores associated with the one or more surviving machine learning models are compared. At least one of the one or more evolved machine learning models or the one or more surviving machine learning models are selected as one or more new selected surviving machine learning models.
A plurality of initial machine learning models are determined based on a plurality of original features. The plurality of initial machine learning models are filtered by selecting a subset of the initial machine learning models as one or more surviving machine learning models. One or more evolved machine learning models are generated. At least one of the evolved machine learning models is based at least in part on one or more new features, which are based at least in part on a transformation of at least one of features of the one or more surviving machine learning models. Corresponding validation scores associated with the one or more evolved machine learning models and corresponding validation scores associated with the one or more surviving machine learning models are compared. At least one of the one or more evolved machine learning models or the one or more surviving machine learning models are selected as one or more new selected surviving machine learning models.
An input dataset is sorted into a first version of data and a second version of data. The first version of data is associated with a first period of time and the second version of data is associated with a second period of time. The second period of time is a shorter period of time than the first period of time. A first set of one or more machine learning models is generated based on the first version of data. A second set of one or more machine learning models is generated based on the second version of data. The first set of one or more machine learning models and the second set of one or more machine learning models are combined to generate an ensemble model. A prediction based on the ensemble model is outputted. The prediction indicates abnormal behavior associated with the input dataset.
Data associated with one or more data sources is transformed into a format associated with a common ontology using one or more transformers. One or more machine learning models are generated based at least in part on the transformed data. The one or more machine learning models and the one or more transformers are provided to a remote device.
An input dataset is sorted into a first version of data and a second version of data. The first version of data is associated with a first period of time and the second version of data is associated with a second period of time. The second period of time is a shorter period of time than the first period of time. A first set of one or more machine learning models is generated based on the first version of data. A second set of one or more machine learning models is generated based on the second version of data. The first set of one or more machine learning models and the second set of one or more machine learning models are combined to generate an ensemble model. A prediction based on the ensemble model is outputted. The prediction indicates abnormal behavior associated with the input dataset.
G06F 15/18 - in which a program is changed according to experience gained by the computer itself during a complete run; Learning machines (adaptive control systems G05B 13/00;artificial intelligence G06N)
Data associated with one or more data sources is transformed into a format associated with a common ontology using one or more transformers. One or more machine learning models are generated based at least in part on the transformed data. The one or more machine learning models and the one or more transformers are provided to a remote device.
