Abstract: Representative embodiments disclose mechanisms to create analytic metrics for bots. In one set of embodiments, the system creates a custom URI that encodes bot specific information in a URI format. An analytic record is created based on the URI and the analytic record can be logged and/or sent directly to an analytic platform designed to provide analytic metrics for websites. In this fashion, website analytic platforms can be used to provide analytic metrics for bots. In another set of embodiments, analytic records are created that are logged and/or sent directly to a bot specific analytic platform. Such a platform may help derive bot specific analytic metrics.

Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for training conversational language models are presented. An embedding library may be generated and maintained. Exemplary target inputs and associated intent types may be received. The target inputs may be encoded into contextual embeddings. The embeddings may be added to the embedding library. When a conversational entity receives a new natural language input, that new input may be encoded into a contextual embedding. The new embedding may be added to the embedding library. A similarity score model may be applied to the new embedding and one or more embeddings for the exemplary target inputs. Similarity scores may be calculated based on the application of the similarity score model. A response may be generated by the conversational entity for an intent type for which a similarity score exceeds a threshold value.

Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for training conversational language models are presented. An embedding library may be generated and maintained. Exemplary target inputs and associated intent types may be received. The target inputs may be encoded into contextual embeddings. The embeddings may be added to the embedding library. When a conversational entity receives a new natural language input, that new input may be encoded into a contextual embedding. The new embedding may be added to the embedding library. A similarity score model may be applied to the new embedding and one or more embeddings for the exemplary target inputs. Similarity scores may be calculated based on the application of the similarity score model. A response may be generated by the conversational entity for an intent type for which a similarity score exceeds a threshold value.

Abstract: Representative embodiments disclose mechanisms to create analytic metrics for bots. In one set of embodiments, the system creates a custom URI that encodes bot specific information in a URI format. An analytic record is created based on the URI and the analytic record can be logged and/or sent directly to an analytic platform designed to provide analytic metrics for websites. In this fashion, website analytic platforms can be used to provide analytic metrics for bots. In another set of embodiments, analytic records are created that are logged and/or sent directly to a bot specific analytic platform. Such a platform may help derive bot specific analytic metrics.

Abstract: Embodiments are provided for creating a personalized search index and supplementing searches with an additional set of keywords. A first set of keywords is received from a user. A first index associated with the user is searched using the first set of keywords to generate a first set of search results. The first index includes content identified by the user. A second set of keywords is defined based on the first set of search results. A second index is searched using the second set of keywords to generate a second set of search results. At least a portion of the first and second sets of search results is provided to the user. Some embodiments are implemented as a cloud computing service.