Abstract: A processor may obtain historic clickstream data indicating a plurality of interactions with a user interface (UI) by a plurality of users. The processor may select at least one user for real-time monitoring by processing, using a machine learning (ML) model, the historic clickstream data and at least one user feature and predicting, from the processing, that the at least one user will utilize a UI resource. The processor may monitor ongoing clickstream data of the selected at least one user and configure the UI resource according to the ongoing clickstream data.

Abstract: Systems and methods are used to generate contact type predictions that route user customer service requests within a support platform. The contact type predictions are generated using a hybrid model that includes a deep learning component and a business logic component. The deep learning component may generate a multi-channel output based on text features and context features. The multi-channel output is modified based on one or more business rules to generate the contact type predictions.

Abstract: Embodiments disclosed herein provide language-agnostic routing prediction models. The routing prediction models input text queries in any language and generate a routing prediction for the text queries. For a language that may have sparse training text data, the models, which are machine learning models, are trained using a machine translation to a prevalent language (e.g., English) to the language having sparse training text data -with the original text corpus and the translated text corpus being an input to multi-language embedding layers. The trained machine learning model makes routing predictions for text queries for the language having sparse training text data.

Abstract: Systems and methods are used to generate contact type predictions that route user customer service requests within a support platform. The contact type predictions are generated using a hybrid model that includes a deep learning component and a business logic component. The deep learning component may generate a multi-channel output based on text features and context features. The multi-channel output is modified based on one or more business rules to generate the contact type predictions.

Abstract: Systems and methods are used to generate contact type predictions that route user customer service requests within a support platform. The contact type predictions are generated using a hybrid model that includes a deep learning component and a business logic component. The deep learning component may generate a multi-channel output based on text features and context features. The multi-channel output is modified based on one or more business rules to generate the contact type predictions.

Abstract: Systems and methods are used to generate contact type predictions that route user customer service requests within a support platform. The contact type predictions are generated using a hybrid model that includes a deep learning component and a business logic component. The deep learning component may generate a multi-channel output based on text features and context features. The multi-channel output is modified based on one or more business rules to generate the contact type predictions.

Abstract: Aspects of the present disclosure provide techniques for training a machine learning model. Embodiments include providing features of a plurality of content items as inputs to an embedding model and receiving embeddings of the plurality of content items as outputs from the embedding model. Embodiments include receiving a data set comprising features of a plurality of users associated with content items of the plurality of content items that correspond to the plurality of users. Embodiments include generating a training data set for a machine learning model, wherein the training data set comprises the features of the plurality of users associated with respective labels indicating which respective embeddings of the embeddings correspond to each respective user of the plurality of users. Embodiments include training the machine learning model, using the training data set, to output corresponding embeddings of relevant content items for users based on features of the users.

Abstract: Systems and methods are used to generate contact type predictions that route user customer service requests within a support platform. The contact type predictions are generated using a hybrid model that includes a deep learning component and a business logic component. The deep learning component may generate a multi-channel output based on text features and context features. The multi-channel output is modified based on one or more business rules to generate the contact type predictions.

Abstract: A ski comprising a front runner (1), a centre runner (2) and a rear runner (3); the runners (1, 2, 3) connectable together to form a longitudinal running surface, the centre runner further comprising a locking mechanism (4) operable to urge the front and rear runners into engagement with the centre runner. Part (5, 7) of the locking mechanism is concealed within the body of the ski and the remaining part (12, 13) is positioned in-between the ski boot (16) bindings so that when the ski boot is clamped into the bindings it forms a protective canopy over the locking mechanism and the assembly takes on the appearance of a one piece ski When the ski is dismantled it can be carried in a case compatible with automated luggage handling systems and inside automobiles without the need for a roof rack.

Abstract: A ski comprising a front runner (1), a centre runner (2) and a rear runner (3); the runners (1, 2, 3) connectable together to form a longitudinal running surface, the centre runner further comprising a locking mechanism (4) operable to urge the front and rear runners into engagement with the centre runner. Part (5, 7) of the locking mechanism is concealed within the body of the ski and the remaining part (12, 13) is positioned in-between the ski boot (16) bindings so that when the ski boot is clamped into the bindings it forms a protective canopy over the locking mechanism and the assembly takes on the appearance of a one piece ski When the ski is dismantled it can be carried in a case compatible with automated luggage handling systems and inside automobiles without the need for a roof rack.

Abstract: This invention relates to fence, or preassembled fence section, comprising a plurality of upright elements spanning at least two rails, the arrangement being such that the upright elements are positioned within apertures in said rails, and there being inserts interacting between a said rail and an upright member to maintain the relative disposition of same.

Abstract: The present invention relates to a connector for connecting a section of a first item to a section of a second item, the connector characterised in that the connector is capable of concertinaing in on itself on at least one side.

Abstract: An emergency exit monitor system for school buses provides both an audible and visual signal to the driver when one of the emergency exits is opened. The school bus comprises a plurality of emergency exits. One or more sensors detect when an emergency exit is open. A monitor communicates with the sensors and provides a visual and audible signal to the driver when a sensor detects an emergency exit is open. In one embodiment, the monitor additionally identifies which of the emergency exits are open.

Abstract: The present invention relates to a connector for connecting a section of a first item to a section of a second item, the connector characterised in that the connector is capable of concertinaing in on itself on at least one side.