Abstract: Support for a product may be provided using an interactive chat interface. The interface may provide recommendations for actions the user may take to address the issue. A support assistant tree may be used in providing information to the user via the user interface. The user interface may include options selectable by the user to traverse the support assistant tree. For example, a server may initially record the state of the user as being at the root node of the support assistant tree. Each child node of the root node may be associated with an answer to a question or an option. When the user selects the associated answer or option, the server provides information related to the corresponding node and updates the recorded state of the user to be the corresponding node. The process may be repeated until a leaf node is reached.

Abstract: Methods and systems are disclosed for bridging a natural language processing (NLP) engine to a database engine, enabling natural language queries to be handled by the database engine. The NLP engine produces a query structure, containing an intent and one or more query parameters, for a natural language query. Rules are successively applied to the query structure. When a rule matches, a corresponding action is performed to identify a sub-intent, transform query parameters, or generate a filter string. The sub-intent is mapped to a query destination, which can be an intrinsic or extrinsic service offered by the data engine. A query string is constructed, including the query destination and any filter strings, and transmitted to the database engine to answer the original query. The disclosed technologies are flexible, easily reconfigurable, and future-proof. Variations and use cases are disclosed.

Abstract: Methods and systems are disclosed for bridging a natural language processing (NLP) engine to a database engine, enabling natural language queries to be handled by the database engine. The NLP engine produces a query structure, containing an intent and one or more query parameters, for a natural language query. Rules are successively applied to the query structure. When a rule matches, a corresponding action is performed to identify a sub-intent, transform query parameters, or generate a filter string. The sub-intent is mapped to a query destination, which can be an intrinsic or extrinsic service offered by the data engine. A query string is constructed, including the query destination and any filter strings, and transmitted to the database engine to answer the original query. The disclosed technologies are flexible, easily reconfigurable, and future-proof. Variations and use cases are disclosed.