Abstract: A method for natural language interaction includes recording speech provided by a human user. The recorded speech is translated into a machine-readable natural language input relating to an interaction topic. An interaction timer is maintained that tracks a length of time since a last machine-readable natural language input referring to the interaction topic was translated. Based on a current value of the interaction timer being greater than an interaction engagement threshold, a message relating to the interaction topic is delivered with a first natural language phrasing that includes an interaction topic reminder. Based on the current value of the interaction timer being less than the interaction engagement threshold, the message relating to the interaction topic is delivered with a second natural language phrasing that lacks the interaction topic reminder.

Abstract: A method for use with a computing device is provided. The method may include executing one or more programs of an intelligent digital assistant system at a processor and presenting a user interface to a user. At the processor, the method may include receiving natural language user input from the user, parsing the user input at an intent handler to determine an intent template with slots, populating the slots in the intent template with information from user input, and performing resolution on the intent template to partially resolve unresolved information. If a slot with missing slot information exists in the partially resolved intent template, a loop may be executed at the processor to fill the slots. The method may include, at the processor, determining that all required information is available and resolved and generating a rule based upon the intent template with all required information being available and resolved.

Abstract: To address the issues of handling conversations with multiple users, an intelligent digital assistant system is provided. The system may include at least one microphone configured to receive an audio input, a speaker configured to emit an audio output, and a processor. The processor may be configured engage in a conversation with a first user, and, concurrent with the first user being engaged in the conversation with the system, recognize speech of one or more additional users in the audio input. The processor may process the recognized speech of the one or more additional users to determine a context for each additional user, and execute a conversation disentanglement module to select and perform one or more predetermined conversation disentanglement actions according to the context of the recognized speech of each additional user.

Abstract: Intelligent assistant systems, methods and computing devices are disclosed for training a machine learning-based parser to derive user intents. A method comprises analyzing with a feeder parser a surface form of a user input. A user intent underlying the surface form is derived by the feeder parser. The surface form and the user intent are provided to a machine learning-based parser and used to enhance a training set of the machine learning-based parser.

Abstract: A method for use with a computing device is provided. The method may include executing one or more programs of an intelligent digital assistant system at a processor and presenting a user interface to a user. At the processor, the method may include receiving natural language user input from the user, parsing the user input at an intent handler to determine an intent template with slots, populating the slots in the intent template with information from user input, and performing resolution on the intent template to partially resolve unresolved information. If a slot with missing slot information exists in the partially resolved intent template, a loop may be executed at the processor to fill the slots. The method may include, at the processor, determining that all required information is available and resolved and generating a rule based upon the intent template with all required information being available and resolved.

Abstract: A method for natural language interaction includes recording speech provided by a human user. The recorded speech is translated into a machine-readable natural language input relating to an interaction topic. An interaction timer is maintained that tracks a length of time since a last machine-readable natural language input referring to the interaction topic was translated. Based on a current value of the interaction timer being greater than an interaction engagement threshold, a message relating to the interaction topic is delivered with a first natural language phrasing that includes an interaction topic reminder. Based on the current value of the interaction timer being less than the interaction engagement threshold, the message relating to the interaction topic is delivered with a second natural language phrasing that lacks the interaction topic reminder.

Abstract: A method for use with a computing device is provided. The method may include executing one or more programs of an intelligent digital assistant system at a processor and presenting a user interface to a user. At the processor, the method may include receiving natural language user input from the user, parsing the user input at an intent handler to determine an intent template with slots, populating the slots in the intent template with information from user input, and performing resolution on the intent template to partially resolve unresolved information. If a slot with missing slot information exists in the partially resolved intent template, a loop may be executed at the processor to fill the slots. The method may include, at the processor, determining that all required information is available and resolved and generating a rule based upon the intent template with all required information being available and resolved.

Abstract: A method for natural language interaction includes recording speech provided by a human user. The recorded speech is translated into a machine-readable natural language input relating to an interaction topic. An interaction timer is maintained that tracks a length of time since a last machine-readable natural language input referring to the interaction topic was translated. Based on a current value of the interaction timer being greater than an interaction engagement threshold, a message relating to the interaction topic is delivered with a first natural language phrasing that includes an interaction topic reminder. Based on the current value of the interaction timer being less than the interaction engagement threshold, the message relating to the interaction topic is delivered with a second natural language phrasing that lacks the interaction topic reminder.

Abstract: A method to execute computer-actionable directives conveyed in human speech comprises: receiving audio data recording speech from one or more speakers; converting the audio data into a linguistic representation of the recorded speech; detecting a target corresponding to the linguistic representation; committing to the data structure language data associated with the detected target and based on the linguistic representation; parsing the data structure to identify one or more of the computer-actionable directives; and submitting the one or more of the computer-actionable directives to the computer for processing.

Abstract: To address the issues of handling conversations with multiple users, an intelligent digital assistant system is provided. The system may include at least one microphone configured to receive an audio input, a speaker configured to emit an audio output, and a processor. The processor may be configured engage in a conversation with a first user, and, concurrent with the first user being engaged in the conversation with the system, recognize speech of one or more additional users in the audio input. The processor may process the recognized speech of the one or more additional users to determine a context for each additional user, and execute a conversation disentanglement module to select and perform one or more predetermined conversation disentanglement actions according to the context of the recognized speech of each additional user.

Abstract: A method for use with a computing device is provided. The method may include executing one or more programs of an intelligent digital assistant system at a processor and presenting a user interface to a user. At the processor, the method may include receiving natural language user input from the user, parsing the user input at an intent handler to determine an intent template with slots, populating the slots in the intent template with information from user input, and performing resolution on the intent template to partially resolve unresolved information. If a slot with missing slot information exists in the partially resolved intent template, a loop may be executed at the processor to fill the slots. The method may include, at the processor, determining that all required information is available and resolved and generating a rule based upon the intent template with all required information being available and resolved.

Abstract: A method for natural language interaction includes recording speech provided by a human user. The recorded speech is translated into a machine-readable natural language input relating to an interaction topic. An interaction timer is maintained that tracks a length of time since a last machine-readable natural language input referring to the interaction topic was translated. Based on a current value of the interaction timer being greater than an interaction engagement threshold, a message relating to the interaction topic is delivered with a first natural language phrasing that includes an interaction topic reminder. Based on the current value of the interaction timer being less than the interaction engagement threshold, the message relating to the interaction topic is delivered with a second natural language phrasing that lacks the interaction topic reminder.

Abstract: Intelligent assistant systems, methods and computing devices are disclosed for training a machine learning-based parser to derive user intents. A method comprises analyzing with a feeder parser a surface form of a user input. A user intent underlying the surface form is derived by the feeder parser. The surface form and the user intent are provided to a machine learning-based parser and used to enhance a training set of the machine learning-based parser.