The present disclosure relates to systems, methods, and non-transitory computer-readable media for generating pairs of natural language queries and corresponding query-language representations. For example, the disclosed systems can generate a contextual representation of a prior-generated dialogue sequence to compare with logical-form rules. In some implementations, the logical-form rules comprise trigger conditions and corresponding logical-form actions for constructing a logical-form representation of a subsequent dialogue sequence. Based on the comparison to logical-form rules indicating satisfaction of one or more trigger conditions, the disclosed systems can perform logical-form actions to generate a logical-form representation of a subsequent dialogue sequence. In turn, the disclosed systems can apply a natural-language-to-query-language (NL2QL) template to the logical-form representation to generate a natural language query and a corresponding query-language representation for the subsequent dialogue sequence.

A method, system and computer readable medium for performing a cognitive search operation comprising: receiving training data, the training data comprising information based upon user interaction with cognitive attributes; performing a machine learning operation on the training data; generating a cognitive profile based upon the information generated by performing the machine learning operation; and, performing a cognitive search operation on a corpus of content based upon the cognitive profile, the cognitive search operation returning cognitive results specific to the cognitive profile of the user.

Validating actions in a digital assistant-based application is provided. The system identifies an application with a conversational interface. The system selects an action from an action repository and generates, via a natural language processor, a trigger phrase for input into the application. The system executes the application to process the trigger phrase to identify an action of the application. The system identifies a parameter used by the application to execute the action, and generates, based on the parameter and via execution of the conversational interface of the application, a first query responsive to the trigger phrase. The system generates a first response to the first query for input into the application. The system determines, based on execution of the application to process the first response, a state of the application. The system evaluates the state to determine an error code and provide a notification based on the error code.

Embodiments disclosed herein generally relate to a system and method for initiating an interactive chat via HTTP request. A web server of an organization computing system receives the HTTP request from a web client executing on a remote client. The HTTP request is triggered by a selection of a dialogue request embedded in an electronic mail message. The web server transmits an API call to a back-end computing system of the organization computing system based on information included in the HTTP request. The back-end computing system parses the API call to identify a user identifier corresponding to a user of the remote client device and a request identifier corresponding to the selected dialogue request embedded in the electronic mail message. The back-end computing system initiates the interactive chat via a text-based communication channel. The back-end computing system generates and transmits an electronic message comprising a response to the dialogue request.

A method and apparatus for formulating a query by a digital assistant is provided herein. During operation a digital assistant will receive a query from a user. The query will have a type of device mentioned within the query. In response, the digital assistant will listen for any nearby device to announce itself. The query will then be modified by the digital assistant to include a device identification heard in the announcement. Results from the modified query will be provided to the user.

The present disclosure discloses a method for acquiring a structured question-answering (QA) model, a QA method and corresponding apparatuses, and relates to knowledge graph and deep learning technologies in the field of artificial intelligence technologies. A specific implementation solution involves: acquiring training samples corresponding to N structured QA database types, the training samples including question samples, information of the structured QA database types and query instruction samples used by the question samples to query structured QA databases of the types, N being an integer greater than 1; and training a text generation model by using the training samples to obtain the structured QA model, wherein the question samples and the information of the structured QA database types are taken as input to the text generation model, and the query instruction samples are taken as target output of the text generation model.

Provided is an information search system by which high-speed search is possible commonly used across a plurality of districts, the system including: a database (12) that stores a plurality of pieces of information that are text-searchable; a query sentence acceptance unit (26) that accepts a query sentence; an inputted search keyword extractor (44) that extracts an inputted search keyword from the query sentence; a retrieval executor (40) that executes retrieval processing from the database using the inputted search keyword; a local management apparatus (100) that stores district material in a local database (104); and an information management apparatus (110) that executes character extraction processing on the material stored in the local database and converts a file format of the material according to a size thereof, stores the material in a temporary memory as stored material, and outputs the stored material to the database.

In one embodiment, a method includes receiving at a head-mounted device a speech input from a user and a visual input captured by cameras of the head-mounted device, wherein the visual input comprises subjects and attributes associated with the subjects, and wherein the speech input comprises a co-reference to one or more of the subjects, resolving entities corresponding to the subjects associated with the co-reference based on the attributes and the co-reference, and presenting a communication content responsive to the speech input and the visual input at the head-mounted device, wherein the communication content comprises information associated with executing results of tasks corresponding to the resolved entities.

The disclosed subject matter relates to a system and method for providing an automated assistant that retrieves information from a knowledge base in response to a user's natural language question. A user's natural language question voice is transformed into an intermediate representation. From the intermediate representation, a cypher query is generated which may be used to query the database. The query results are provided in response to the user. The transformation into the intermediate representation is database independent while the cypher query is dependent upon the database queried.

A method for changing an itinerary based on a user change request is disclosed. The method may commence with receiving an itinerary request associated with one or more passengers. The method may continue with receiving a user itinerary change request associated with the itinerary network. The method may continue with generating an itinerary object associated with the user itinerary change request. The method may continue with modifying the itinerary network based on the itinerary object. The method may continue with processing the itinerary network using a topology of the itinerary network to create a plurality of tuples, the plurality of tuples including at least flight tuples and hotel tuples. The method may continue with performing a content search for the plurality of tuples for each of the one or more passengers. The method may continue with generating feasible itinerary solutions based on results of the content searches.