An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

A method for operating a voice trigger is provided. In some implementations, the method is performed at an electronic device including one or more processors and memory storing instructions for execution by the one or more processors. The method includes receiving a sound input. The sound input may correspond to a spoken word or phrase, or a portion thereof. The method includes determining whether at least a portion of the sound input corresponds to a predetermined type of sound, such as a human voice. The method includes, upon a determination that at least a portion of the sound input corresponds to the predetermined type, determining whether the sound input includes predetermined content, such as a predetermined trigger word or phrase. The method also includes, upon a determination that the sound input includes the predetermined content, initiating a speech-based service, such as a voice-based digital assistant.

Voice trigger for a digital assistant

Systems and processes are disclosed for handling a multi-part voice command for a virtual assistant. Speech input can be received from a user that includes multiple actionable commands within a single utterance. A text string can be generated from the speech input using a speech transcription process. The text string can be parsed into multiple candidate substrings based on domain keywords, imperative verbs, predetermined substring lengths, or the like. For each candidate substring, a probability can be determined indicating whether the candidate substring corresponds to an actionable command. Such probabilities can be determined based on semantic coherence, similarity to user request templates, querying services to determine manageability, or the like. If the probabilities exceed a threshold, the user intent of each substring can be determined, processes associated with the user intents can be executed, and an acknowledgment can be provided to the user.

Multi-command single utterance input method

At a first electronic device with a display and a microphone: sampling audio input using the first microphone; in accordance with the sampling of audio input using the first microphone, sending stop instructions to a second electronic device with a second microphone, the second electronic device external to the first electronic device, wherein the second electronic device is configured to respond to audio input received using the second microphone, and wherein the stop instructions instruct the second electronic device to forgo responding to audio input received using the second microphone, wherein responding to audio input received using the second microphone comprises providing perceptible output.

Competing devices responding to voice triggers

An exemplary method for identifying media may include receiving user input associated with a request for media, where that user input includes unstructured natural language speech including one or more words; identifying at least one context associated with the user input; causing a search for the media based on the at least one context and the user input; determining, based on the at least one context and the user input, at least one media item that satisfies the request; and in accordance with a determination that the at least one media item satisfies the request, obtaining the at least one media item.

Virtual assistant for media playback

A method for automatically presenting digital content to a user of a computer computes task-related metadata from data which may include i) a most recent event record, ii) a most recent specification received from the user of a task being performed by the user, iii) an automatic prediction of the current task being executed by the user, iii) past event records and associated task identifiers stored in a database and/or iv) content in resources associated with a given task. The task-related metadata is communicated to a digital content service provider. Digital content relevant to the user based on the task-related metadata is then selected, sent to the computer, and presented to the user. Rules and filters control the metadata going out and the content coming in, allowing automatic adaptation based on the current task, characteristics of the content, and other factors.

Thomas G. Dletterich

Papers

Methods for delivering task-related digital content based on task-oriented user activity