Examples are disclosed herein that relate to entity tracking. One examples provides a computing device comprising a logic processor and a storage device holding instructions executable by the logic processor to receive image data of an environment including a person, process the image data using a face detection algorithm to produce a first face detection output at a first frequency, determine an identity of the person based on the first face detection output, and process the image data using another algorithm that uses less computational resources of the computing device than the face detection algorithm. The instructions are further executable to track the person within the environment based on the tracking output, and perform one or more of updating the other algorithm using a second face detection output, and updating the face detection algorithm using the tracking output.

Computing devices and methods for associating a semantic identifier with an object are disclosed. In one example, a three-dimensional model of an environment comprising the object is generated. Image data of the environment is sent to a user computing device for display by the user computing device. User input comprising position data of the object and the semantic identifier is received. The position data is mapped to a three-dimensional location in the three-dimensional model at which the object is located. Based at least on mapping the position data to the three-dimensional location of the object, the semantic identifier is associated with the object.

Intelligent assistant systems, methods and computing devices are disclosed for resolving alias identifiers. A method comprises receiving and parsing data comprising a current user input that includes an alias identifier. The data and/or other sensor data are analyzed to identify the user. Based at least on identifying the user and recognizing the alias identifier, usage pattern data comprising at least one previous user input that includes the alias identifier and corresponding context information is accessed. The usage pattern data is used to resolve the alias identifier to mean the alias identifier in an alias record of a known entity. Based at least on resolving the alias identifier, an output device is controlled to one or more of generate a message and perform an action with respect to the known entity.

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.

A computationally-efficient method for a smart assistant computer to track a human includes receiving data from one or more sensors configured to monitor a physical environment. The data is computer-analyzed to recognize presence of a human in the physical environment, and upon confirming an identity of the human, a first level of computational resources of the smart assistant computer is dedicated to track the human. Upon failing to confirm the identity of the human while a known user is present, a second level of computational resources of the smart assistant computer, greater than the first level, is dedicated to determine the identity of the human. Upon failing to confirm the identity of the human while the known user is absent, a third level of computational resources of the smart assistant computer, is dedicated to determine the identity of the human.

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.

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.

Intelligent assistant systems, methods and computing devices are disclosed for identifying a speaker change. A method comprises receiving audio input comprising a speech fragment. A first voice model is trained with a first sub-fragment from the speech fragment. A second voice model is trained with a second sub-fragment from the speech fragment. The first sub-fragment is analyzed with the second voice model to yield a first confidence value. The second sub-fragment is analyzed with the first voice model to yield a second confidence value. Based at least on the first and second confidence values, the method determines if a speaker of the first sub-fragment is the speaker of the second sub-fragment.

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.

An intelligent assistant records speech spoken by a first user and determines a self-selection score for the first user. The intelligent assistant sends the self-selection score to another intelligent assistant, and receives a remote-selection score for the first user from the other intelligent assistant. The intelligent assistant compares the self-selection score to the remote-selection score. If the self-selection score is greater than the remote-selection score, the intelligent assistant responds to the first user and blocks subsequent responses to all other users until a disengagement metric of the first user exceeds a blocking threshold. If the self-selection score is less than the remote-selection score, the intelligent assistant does not respond to the first user.