Systems and methods may be used to generate and use intent predictions to enhance user experience. The intent predictions may describe the data required to resolve a user request included in a user input (e.g., question, search query, and the like) submitted by a user. The intent predictions may be generated using a machine learning model that comprises a model framework for extracting features and classifying user inputs into intent classes based on the extracted features. The intent predictions may be integrated into an information service to improve business metrics including contact rate, transfer rate, helpful rate, and net total promoter score.

A method, apparatus and product for chat-based application interface for automation. Using a natural language interface, receiving user input. Based on the user input, determining an automation process of a computer program having a user interface (UI), to be executed. The automation process is executed by utilizing the UI to input data thereto or execute functionality thereof. Additionally or alternatively, a conversation to be implemented by a natural language interface may be defined. The conversation is configured to obtain from the user one or more values corresponding to one or more parameters. The conversation is associated with a parameterized automation process depending on the one or more parameters. The parameterized automation process is invoked automatically by a natural language interface and using one or more values provided by the user to the natural language interface for the one or more parameters.

A method, apparatus and product for chat-based application interface for automation. Using a natural language interface, receiving user input. Based on the user input, determining an automation process of a computer program having a user interface (UI), to be executed. The automation process is executed by utilizing the UI to input data thereto or execute functionality thereof. Additionally or alternatively, a conversation to be implemented by a natural language interface may be defined. The conversation is configured to obtain from the user one or more values corresponding to one or more parameters. The conversation is associated with a parameterized automation process depending on the one or more parameters. The parameterized automation process is invoked automatically by a natural language interface and using one or more values provided by the user to the natural language interface for the one or more parameters.

A voice system of a moving machine is a voice system of a moving machine driven by a driver who is exposed to an outside of the moving machine and includes: a noise estimating section which estimates a future noise state based on information related to a noise generation factor; and a voice control section which changes an attribute of voice in accordance with the estimated noise state, the voice being voice to be output to the driver.

Systems, devices, and methods discussed herein provide improved autonomous agent applications that are configured to provide explanations in response to user-submitted questions. Training data comprising a question, and an explanation pair may be accessed. A discourse tree and an explanation chain can be constructed from the explanation. The explanation chain may identify logical relationships between two entities of elementary discourse units identified from the discourse tree. A query may be submitted for the two entities, and a set of search results can be mined to identify text linking the two entities. An additional discourse tree can be generated from the text of a search result. The additional discourse tree can be combined with the original discourse tree to generate a complete discourse tree. A model may be trained using this augmented data (e.g., the complete discourse tree) to improve the quality of explanations provided by the autonomous agent application.

Systems, devices, and methods discussed herein provide improved autonomous agent applications that are configured to provide explanations in response to user-submitted questions. Training data comprising a question, and an explanation pair may be accessed. A discourse tree and an explanation chain can be constructed from the explanation. The explanation chain may identify logical relationships between two entities of elementary discourse units identified from the discourse tree. A query may be submitted for the two entities, and a set of search results can be mined to identify text linking the two entities. An additional discourse tree can be generated from the text of a search result. The additional discourse tree can be combined with the original discourse tree to generate a complete discourse tree. A model may be trained using this augmented data (e.g., the complete discourse tree) to improve the quality of explanations provided by the autonomous agent application.

Implementations are provided for providing responsive audio recordings to user queries that are prerecorded by human beings, rather than generated automatically using speech synthesis processing. In various implementations, a query provided by a user at an input component of a computing device may be used to search a corpus of voice recordings From the searching, a plurality of candidate responsive voice recordings may be identified and ranked based on measures of credibility associated with speakers that created the candidate responsive voice recordings. Based on the ranking, one or more of the plurality of candidate responsive voice recordings may be provided for presentation to the user at an output component of the same computing device or a different computing device.

The invention relates to a system (100) and method (200) for providing contextual information and actions to make a conversation meaningful and engaging. The method (200) comprises the steps of identifying a contact from various data sources (101) and collecting the relevant information from one or more web-based applications and databases, wherein the collected information is mapped to create one or more discussion points by one or more prediction servers (102). Post-conversation suggestions are provided by one or more suggestion servers (103), wherein the discussion points and the post-conversation suggestions are displayed on a user interface device (104) for allowing the user to have meaningful and engaging conversations.

The invention relates to a system (100) and method (200) for providing contextual information and actions to make a conversation meaningful and engaging. The method (200) comprises the steps of identifying a contact from various data sources (101) and collecting the relevant information from one or more web-based applications and databases, wherein the collected information is mapped to create one or more discussion points by one or more prediction servers (102). Post-conversation suggestions are provided by one or more suggestion servers (103), wherein the discussion points and the post-conversation suggestions are displayed on a user interface device (104) for allowing the user to have meaningful and engaging conversations.

A mechanism is provided in a data processing system to implement a cognitive natural language processing (NLP) system with descriptor uniqueness identification to support named entity mention clustering. The mechanism annotates a set of documents from a corpus of documents for entity types and mentions, collects descriptor usages from all documents in the corpus of documents, analyzes the descriptor usages to classify the descriptors as base terms or modifier terms, generates compatibility scores for the descriptors, and performs entity merging of entity clusters based on the compatibility scores.