Abstract: A system and method automatically associates an image and passage from a text. In the system and method, a user can choose or supply an image and the system and/or method will choose a limited selection of relevant word passages for the image from a relatively large volume of potential passages. The system and method utilize a computer system wherein a concept generator and a passage generator processes the content of the image so as to assign words to describe the content, then weight the descriptive words (tags) and assign passages based on the tags and weighting. The passages can be filtered so as to remove inappropriate passages.

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: Disclosed are machine learning-based technologies that analyze an audio input and provide speaker state predictions in response to the audio input. The speaker state predictions can be selected and customized for each of a variety of different applications.

Abstract: A system and method automatically associates an image and passage from a text. In the system and method, a user can choose or supply an image and the system and/or method will choose a limited selection of relevant word passages for the image from a relatively large volume of potential passages. The system and method utilize a computer system wherein a concept generator and a passage generator processes the content of the image so as to assign words to describe the content, then weight the descriptive words (tags) and assign passages based on the tags and weighting. The passages can be filtered so as to remove inappropriate passages.

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: Disclosed are machine learning-based technologies that analyze an audio input and provide speaker state predictions in response to the audio input. The speaker state predictions can be selected and customized for each of a variety of different applications.

Abstract: A system or method consistent with an embodiment of the present invention is useful in analyzing large volumes of different types of data, such as textual data, numeric data, categorical data, or sequential string data, for use in identifying relationships among the data types or different operations that have been performed on the data. A system or method consistent with the present invention determines and displays the relative content and context of related information and is operative to aid in identifying relationships among disparate data types. Various data types, such as numerical data, protein and DNA sequence data, categorical information, and textual information, such as annotations associated with the numerical data or research papers may be correlated for visual analysis. A variety of user-selectable views may be correlated for user interaction to identify relationships that exist among the different types of data or various operations performed on the data.

Abstract: A system or method consistent with an embodiment of the present invention is useful in analyzing large volumes of different types of data, such as textual data, numeric data, categorical data, or sequential string data, for use in identifying relationships among the data types or different operations that have been performed on the data. A system or method consistent with the present invention determines and displays the relative content and context of related information and is operative to aid in identifying relationships among disparate data types. Various data types, such as numerical data, protein and DNA sequence data, categorical information, and textual information, such as annotations associated with the numerical data or research papers may be correlated for visual analysis. A variety of user-selectable views may be correlated for user interaction to identify relationships that exist among the different types of data or various operations performed on the data.