Abstract: A conversational assistant for conversational engagement platform can contain various modules including a user-model augmentation module, a dialogue management module, and a user-state analysis input/output module. The dialogue management module receives metrics tied to a user from the other modules to understand a current topic and a user's emotions regarding the current topic from the user-state analysis input/output module and then adapts dialogue from the dialogue management module to the user based on dialogue rules factoring in these different metrics. The dialogue rules also factors in both i) a duration of a conversational engagement with the user and ii) an attempt to maintain a positive experience for the user with the conversational engagement. A flexible ontology relationship representation about the user is built and stores learned metrics about the user over time with each conversational engagement, and then in combination with the dialogue rules, drives the conversations with the user.

Abstract: Methods, computing devices, and computer-program products are provided for implementing a virtual personal assistant. In various implementations, a virtual personal assistant can be configured to receive sensory input, including at least two different types of information. The virtual personal assistant can further be configured to determine semantic information from the sensory input, and to identify a context-specific framework. The virtual personal assistant can further be configured to determine a current intent. Determining the current intent can include using the semantic information and the context-specific framework. The virtual personal assistant can further be configured to determine a current input state. Determining the current input state can include using the semantic information and one or more behavioral models. The behavioral models can include one or more interpretations of previously-provided semantic information.

Abstract: Methods, computing devices, and computer-program products are provided for implementing a virtual personal assistant. In various implementations, a virtual personal assistant can be configured to receive sensory input, including at least two different types of information. The virtual personal assistant can further be configured to determine semantic information from the sensory input, and to identify a context-specific framework. The virtual personal assistant can further be configured to determine a current intent. Determining the current intent can include using the semantic information and the context-specific framework. The virtual personal assistant can further be configured to determine a current input state. Determining the current input state can include using the semantic information and one or more behavioral models. The behavioral models can include one or more interpretations of previously-provided semantic information.

Abstract: Methods, computing devices, and computer-program products are provided for implementing a virtual personal assistant. In various implementations, a virtual personal assistant can be configured to receive sensory input, including at least two different types of information. The virtual personal assistant can further be configured to determine semantic information from the sensory input, and to identify a context-specific framework. The virtual personal assistant can further be configured to determine a current intent. Determining the current intent can include using the semantic information and the context-specific framework. The virtual personal assistant can further be configured to determine a current input state. Determining the current input state can include using the semantic information and one or more behavioral models. The behavioral models can include one or more interpretations of previously-provided semantic information.

Abstract: A computer system in the form of a voice command platform includes a voice browser and voice-based applications. The voice browser has global-level grammar elements and the voice applications have application-level grammar and grammar elements. A programming feature is provided by which developers of the voice applications can programmably weigh or weight global-level grammar elements relative to the application-level grammar or grammar elements. As a consequence of the weighting, a speech recognition engine for the voice browser is more likely to accurately recognize voice input from a user. The weighting can be applied on the application as a whole, or at any given state in the application. Also, the weighting can be made to the global level grammar elements as a group, or on an individual basis.