Abstract: Aspects include methods, systems, and computer-program products providing virtual assistant domain functionality. A natural language query including one or more words is received. A collection of natural language modules is accessed. The collection natural language modules are configured to process sets of natural language queries. A natural language module, from the collection of natural language modules, is identified to interpret the natural language query. An interpretation of the natural language query is computed using the identified natural language module. A response to the natural language query is returned using the computed interpretation.

Abstract: Aspects include methods, systems, and computer-program products providing virtual assistant domain functionality. A natural language query including one or more words is received. A collection of natural language modules is accessed. The collection natural language modules are configured to process sets of natural language queries. A natural language module, from the collection of natural language modules, is identified to interpret the natural language query. An interpretation of the natural language query is computed using the identified natural language module. A response to the natural language query is returned using the computed interpretation.

Abstract: A method and system for controlling a GUI on a user's network-connected device, the control being provided by a telephone call between the user and a speech recognition and speech synthesis system. An example of a restaurant ordering system is provided. The user calls a phone number and is guided through a verbal ordering process that includes one or more of: adding an item, deleting an item, changing quantities, changing sizes, and changing details of an item. The user's choices are added to a display so that a current status of the order is visible to the user. The GUI is updated as changes are made to the order. The GUI can also request additional information, upsell items, and show menus. The GUI aids the user in confirming that the order is correct. The system provides the final order to a restaurant for fulfillment.

Abstract: A computer-implemented method is provided to support a food ordering system for food items from a menu of a restaurant using natural language. Expressions made for ordering are used to recommend a food item that a user has a high probability of wanting to include in an order. The recommendation engine is trained using machine learning. Expressions are collected and parsed to identify words that might indicate food items offered by the restaurant. The words are provided to a restaurant owner to identify food items on a menu, with which the words are associated.

Abstract: A system that includes a stand-alone device or a server connected client device are in communication with a server and provide recommendations. The device includes an input component, a storage component, a processor and an output component. The server-connected client device includes an input component that receives the user's request, a communication component that communicates the request to the server and receives the recommendation from the server, and an output component that provides the recommendation to user.

Abstract: Aspects include methods, systems, and computer-program products providing virtual assistant domain functionality. A natural language query including one or more words is received. A collection of natural language modules is accessed. The collection natural language modules are configured to process sets of natural language queries. A natural language module, from the collection of natural language modules, is identified to interpret the natural language query. An interpretation of the natural language query is computed using the identified natural language module. A response to the natural language query is returned using the computed interpretation.

Abstract: A platform provides for developers of applications, such as devices, with natural language interfaces to configure the availability of vertical domain modules in applications. Modules can include grammars for parsing natural language expressions and interfaces to data sources. Third party developers can create modules with pricing models for their usage or access to their data. Device developers can browse or search available modules and test their performance for specific queries. The platform provides for devices users to access the chosen modules as configured by device developers and for charging and payment between users, application developers, and module developers.

Abstract: Systems parse natural language expressions to extract items and values of their attributes and store them in a database. Systems also parse natural language expressions to extract values of attributes of user preferences and store them in a database. Recommendation engines use the databases to make recommendations. Parsing is of speech or text and uses conversation state, discussion context, synonym recognition, and speaker profile. Database pointers represent relative attribute values. Recommendations use machine learning to crowdsource from databases of many user preferences and to overcome the cold start problem. Parsing and recommendations use current or stored values of environmental parameters. Databases store different values of the same user preference attributes for different activities. Systems add unrecognized attributes and legal values when encountered in natural language expressions.

Abstract: Systems parse natural language expressions to extract items and values of their attributes and store them in a database. Systems also parse natural language expressions to extract values of attributes of user preferences and store them in a database. Recommendation engines use the databases to make recommendations. Parsing is of speech or text and uses conversation state, discussion context, synonym recognition, and speaker profile. Database pointers represent relative attribute values. Recommendations use machine learning to crowdsource from databases of many user preferences and to overcome the cold start problem. Parsing and recommendations use current or stored values of environmental parameters. Databases store different values of the same user preference attributes for different activities. Systems add unrecognized attributes and legal values when encountered in natural language expressions.

Abstract: A system, method, and non-transitory computer readable medium provide for a visual display of a user interface for a voice-based virtual assistant system. After displaying a transcription of user speech and performing requested actions, the system allows the user to provide, by speech or manual input, an indication of satisfaction or dissatisfaction. For transcription errors, the user is presented an opportunity to correct the transcription text. The system can present several transcription hypotheses to the user, and allow the user to choose among them, or to edit one of them, as the intended transcription. A back-end server system uses the corrected transcription to train a machine learning model to perform more accurate speech recognition or provide more useful actions for future users. A system can save one or more speech recognition transcription hypotheses and check corrected results against the other transcriptions to further improve models.

Abstract: A speech and natural language-based virtual assistant parses user utterances and analyzes them in the context of recent prior actions to detect sentiment and indicators of satisfaction or dissatisfaction. Indicators are stored in a database in association with the prior command and resulting action. Databases can include timestamps, clarifications made by users, and a knowledge graph of facts. Machine learning, applied to the database, train models to deliver improved results in future user engagements.

Abstract: A platform provides for developers of applications, such as devices, with natural language interfaces to configure the availability of vertical domain modules in applications. Modules can include grammars for parsing natural language expressions and interfaces to data sources. Third party developers can create modules with pricing models for their usage or access to their data. Device developers can browse or search available modules and test their performance for specific queries. The platform provides for devices users to access the chosen modules as configured by device developers and for charging and payment between users, application developers, and module developers.

Abstract: Virtual assistants provide results in response to user commands and analyze user utterances in response to the result. The analysis can interpret words, recognized from the utterance, as being negative indicators that imply user dissatisfaction. Virtual assistants request follow-up information from users. Analysis also interprets words as indicators of clarification and collect information to add to a knowledgebase. Machine learning algorithms use recognized words to train a behavioral model to improve results. Virtual assistants also infer, from replacement of words in successive commands, that earlier commands had word recognition errors and infer, from addition of words, that earlier commands had interpretation errors. Virtual assistants act locally or as devices in communication with servers.

Abstract: The present invention extends to methods, systems, and computer program products for a natural language module store. In general, the invention can be used to manage natural language modules offered through a natural language module store. Natural language module (NLM) developers can post NLMs at a NLM store to make the NLMs available for use by others. Developers can select NLMs for inclusion in natural language interpreters (NLIs) containing (and possibly integrating the functionality of) one or more NLMs. Prior to selecting a NLM, a developer can search or browse NLMs to identify an appropriate NLM. Optionally, a developer can test a NLM in the NLM store prior to inclusion in an NLI. For example, multiple NLMs purporting to provide the same specified natural language functionality can be tested relative to one another prior to selection of one of the NLMs for inclusion in an NLI.

Abstract: The present invention extends to methods, systems, and computer program products for a natural language module store. In general, the invention can be used to manage natural language modules offered through a natural language module store. Natural language module (NLM) developers can post NLMs at a NLM store to make the NLMs available for use by others. Developers can select NLMs for inclusion in natural language interpreters (NLIs) containing (and possibly integrating the functionality of) one or more NLMs. Prior to selecting a NLM, a developer can search or browse NLMs to identify an appropriate NLM. Optionally, a developer can test a NLM in the NLM store prior to inclusion in an NLI. For example, multiple NLMs purporting to provide the same specified natural language functionality can be tested relative to one another prior to selection of one of the NLMs for inclusion in an NLI.

Abstract: Technologies described relate to coordination between audio and tracking of the corresponding text in an audio recognition mode and an audio playback mode. Optionally, audio recognition includes receiving a signal corresponding to an audio track; determining an identity of the audio track and a current audio position within the audio track; displaying on a display a portion of a text that is linked and synchronized to the audio track identified and automatically scrolling the portion of the text displayed in pace with the audio track playing; and displaying a current text indicator that emphasizes current text, wherein the current text indicator is visually synchronized on the display to current audio playing from the audio track. Optionally include redetermining the current audio position in the audio track from the signal received and updating synchronization of the current text indicator with the current audio playing.