Abstract: Systems are configured for processing user queries to generate search results that are contextually relevant for the user based on customer values associated with a corresponding customer schema associated with the user and that are indexed in a customer value index. When search queries are received, they are processed to identify customer values associated with the user context and to perform an initial search query based on the initial search query terms. The systems also generate additional altered search queries to perform contemporaneously, based on the initial search terms and restructured/reformatted based on the customer values. Resulting supplement search results are obtained for the additional altered search queries, which are merged with the initial search results. The merged results are then ranked and provided to the user. These systems facilitate obtaining search results that are more relevant than results obtained by conventional systems.

Abstract: Systems are configured for generating and utilizing training data to train learn-to-rank type models in a manner that preserves privacy of client data used for generating the training data. The systems extract features and patterns of the user queries, search results and user interactions with the search results without tracking, storing or transmitting underlying values of the user data to preserve privacy of the user data. Systems are also configured to infer search result quality based on at least the user behavior data, and optionally query intentions, and to generate and label corresponding training data accordingly. This training data is applied to learn-to-rank type models to train the learn-to-rank type model to improve search quality of search results provided by the learn-to-rank type models when new user queries are processed that having features and patterns corresponding to the filtered and labelled training data.

Abstract: Systems and devices to perform content authoring and establish conversation models for automated agents such as chatbots and virtual assistants are disclosed. In an example, operations for content authoring, to produce a conversation model, include: identifying respective intents from conversation segments in an unstructured data source; generating a knowledge graph of the conversation model to organize the identified intents; linking the intents in the knowledge graph to properties of the respective conversations, for properties used to guide a subject conversation with the conversation model; and outputting the conversation model, to be usable with the automated agent to conduct the subject conversation with a human user.

Abstract: Disclosed are pharmaceutical compositions comprising an active vitamin D compound in emulsion pre-concentrate formulations, as well as emulsions and sub-micron droplet emulsions produced therefrom. The compositions comprise a lipophilic phase component, one or more surfactants, and an active vitamin D compound. The compositions may optionally further comprise a hydrophilic phase component.

Abstract: Disclosed are pharmaceutical compositions comprising an active vitamin D compound in emulsion pre-concentrate formulations, as well as emulsions and sub-micron droplet emulsions produced therefrom. The compositions comprise a lipophilic phase component, one or more surfactants, and an active vitamin D compound. The compositions may optionally further comprise a hydrophilic phase component.

Abstract: Disclosed are pharmaceutical compositions comprising an active vitamin D compound in emulsion pre-concentrate formulations, as well as emulsions and sub-micron droplet emulsions produced therefrom. The compositions comprise a lipophilic phase component, one or more surfactants, and an active vitamin D compound. The compositions may optionally further comprise a hydrophilic phase component.