Embodiments may determine user intent in conversations with dialogue systems so as to improve the quality of such conversations and to reduce the number of failed conversations. For example, a method may comprise receiving, at a dialogue system, a first text utterance from a user, generating a plurality of second text utterances at the dialogue system in response to the received text utterance, generating a third text utterance based on each generated second text utterance using a trained deep neural network model, generating a score indicating a quality of each conversation, wherein each conversation includes the first text utterance, one of the second text utterances, and the third text utterance based on the one of the second text utterances, and outputting to the user the second text utterance included in the conversation having the highest quality score.

The embodiments herein disclose methods and systems for passive wakeup of a user interaction device and configuring a dynamic wakeup time for a user interaction device, a method includes detecting an occurrence of at least one first non-voice event associated with at least one device present in an Internet of Things (IoT) environment. The method includes detecting an occurrence of at least one successive event associated with the at least one device. The method includes estimating a contextual probability of initiating at least one interaction by a user with the user interaction device on detecting the occurrence of at least one of the at least one first event and the at least one successive event. On determining the estimated contextual probability is above a pre-defined threshold value, the method includes configuring the dynamic wakeup time to switch the user interaction device to a passive wakeup state.

A system is provided for a self-learning policy engine that can be used by various spoken language understanding (SLU) processing components. The system also provides for sharing contextual information from processing performed by an upstream SLU component to a downstream SLU component to facilitate decision making by the downstream SLU component. The system also provides for a SLU component to select from a variety of actions to take. A SLU component may implement an instance of the self-learning policy that is specifically configured for the particular SLU component.

Method, system and product for automatic execution of operations sequences. An operations sequence, which includes a first operation and a second operation, is obtained. The operations sequence comprises at least one user interaction that includes clicking on a clickable element. The operations sequence or portion thereof is automatically executed by mimicking user interactions with the GUI, as indicated in the operations sequence, including by automatically clicking on the clickable element.

An apparatus including a user input receiver; a user voice input receiver; a display; and a processor. The processor is configured to: (a) based on a user input being received through the user input receiver, perform a function corresponding to voice input state for receiving a user voice input; (b) receive a user voice input through the user voice input receiver; (c) identify whether or not a text corresponding to the received user voice input is related to a pre-registered voice command or a prohibited expression; and (d) based on the text being related to the pre-registered voice command or the prohibited expression, control the display to display an indicator that the text is related to the pre-registered voice command or the prohibited expression. A method and non-transitory computer-readable medium are also provided.

An artificial intelligence device for providing a voice recognition service includes a microphone configured to receive a voice command, a memory configured to store an error analysis model for inferring an error cause of voice recognition, an output unit, and a processor configured to determine whether voice recognition of the voice command has failed based on the voice command and voice recognition surrounding information, acquire the error cause from the voice recognition surrounding information using the error analysis model, and output the acquired error cause through the output unit.

Methods and systems for performing contact resolution are described herein. When initiating a communications session using a voice activated electronic device, a contact name may be resolved to determine an appropriate contact with which the communications session may be directed to. Contacts from an individual's contact list may be queried to determine a listing of probable contacts associated with the contact name, and contact identifiers associated with the contact may be determined. Using one or more rules for disambiguating between similar contact names, a single contact may be identified, and a communications session with that contact may be initiated.

A display apparatus includes an input unit configured to receive a user command; an output unit configured to output a registration suitability determination result for the user command; and a processor configured to generate phonetic symbols for the user command, analyze the generated phonetic symbols to determine registration suitability for the user command, and control the output unit to output the registration suitability determination result for the user command. Therefore, the display apparatus may register a user command which is resistant to misrecognition and guarantees high recognition rate among user commands defined by a user.

The present disclosure provides systems, methods, and computer-readable storage devices for enabling user management and control of responses of a virtual assistant for use in responding to questions related to multi-dwelling units without requiring reprogramming of the virtual assistant. To illustrate, a question to be answered by a virtual assistant may be received, and one or more responses to the question may be retrieved from a response database for the virtual assistant. A user interface (UI) may be provided that indicates the one or more responses. A user-selected response may be received via the UI, the user-selected response including a selected response from the one or more responses or a user-created response. An entry in the response database may be updated based on the user-selected response and a priority associated with the entry may be set, such as by increasing the priority based on user selection of the response.

Technologies are disclosed for interacting with a virtual assistant to request updates associated with one or more events and/or perform actions. According to some examples, a user may use their voice to interact with a virtual assistant to receive updates relating to events occurring during a certain period of time. For example, a user may request an update associated with one or more events occurring that day. The system may access data sources (e.g., calendar services, email services, etc.) to obtain data associated with the events, tag the events according to one or more conditions indicated by the data, and/or rank the events according to the tags. In addition, to resolve conditions associated with the events, the virtual assistant may also include options in the update to perform certain actions and/or to provide response data. The virtual assistant may generate the update and audibly provide the update to the user.