Abstract: Machine-learning-aided automatic taxonomy for web data. In an embodiment, a training dataset of annotated features is used to train a model to predict a class in a taxonomy of web-based activities. The features may be derived from a uniform resource locator (URL) of an online resource and associated metadata. During operation, the features may be extracted from the URL and metadata of each activity record in web data. The trained model may be applied to the extracted features for each activity record to predict a class within the taxonomy. The predicted taxonomic class may be stored in association with the URL that was extracted from the activity record to produce a taxonomized URL.

Abstract: Machine-learning-aided automatic taxonomy for web data. In an embodiment, a training dataset of annotated features is used to train a model to predict a class in a taxonomy of web-based activities. The features may be derived from a uniform resource locator (URL) of an online resource and associated metadata. During operation, the features may be extracted from the URL and metadata of each activity record in web data. The trained model may be applied to the extracted features for each activity record to predict a class within the taxonomy. The predicted taxonomic class may be stored in association with the URL that was extracted from the activity record to produce a taxonomized URL.

Abstract: Machine-learning-aided automatic taxonomy for web data. In an embodiment, a training dataset of annotated features is used to train a model to predict a class in a taxonomy of web-based activities. The features may be derived from a uniform resource locator (URL) of an online resource and associated metadata. During operation, the features may be extracted from the URL and metadata of each activity record in web data. The trained model may be applied to the extracted features for each activity record to predict a class within the taxonomy. The predicted taxonomic class may be stored in association with the URL that was extracted from the activity record to produce a taxonomized URL.

Abstract: The diversity of job titles prevents the extraction of information from job titles to be automated and scalable. Accordingly, disclosed embodiments utilize a machine-learning model to classify job titles by one or more characteristics, such as job level and/or job function. The characteristic(s) may be extracted from the job titles to be used as an input to a persona model that predicts a persona score, indicating the relative importance of a person to a sales opportunity.

Abstract: Machine-learning-aided automatic taxonomy for web data. In an embodiment, a training dataset of annotated features is used to train a model to predict a class in a taxonomy of web-based activities. The features may be derived from a uniform resource locator (URL) of an online resource and associated metadata. During operation, the features may be extracted from the URL and metadata of each activity record in web data. The trained model may be applied to the extracted features for each activity record to predict a class within the taxonomy. The predicted taxonomic class may be stored in association with the URL that was extracted from the activity record to produce a taxonomized URL.

Abstract: Machine-learning-aided automatic taxonomy for marketing automation and customer relationship management. In an embodiment, a plurality of machine-learning models are trained to classify activity records into action, channel, and type classes, using a training dataset of annotated features. During operation, relevant features may be extracted from each activity record, and each model may be applied to a respective set of those features to classify the activity record into an action class, channel class, and type class. These classes may then be stored in association with the activity record as a taxonomized activity record.

Abstract: Disclosed herein is a bottle flip support including a support base with a top surface and a bottom surface. The bottom surface is configured to be put on a support ground. At least one cavity is disposed on the top surface of the support base to support a flipped bottle. The cavity includes a shoulder cavity configured to hold a shoulder portion of the flipped bottle and a lip cavity configured to accommodate a lip portion of the flipped bottle.

Abstract: Disclosed herein is a camera device including a main body, a hang strap and a swivel-preventing device. The main body includes a first magnetic mutually-attracting component. The hang strap includes a support section to be hung on a hanger. The hang strap has two opposite ends respectively connected the main body. The swivel-preventing device includes a second magnetic mutually-attracting component and an attachment strap. The attachment strap has an end connected to the second magnetic mutually-attracting component and an opposite end connected to the support section, wherein the first and second magnetic mutually-attracting components are configured to be mutually attracted.