Abstract: This relates to intelligent automated assistants and, more specifically, to intelligent context sharing and task performance among a collection of devices with intelligent automated assistant capabilities. An example method includes, at a first electronic device participating in a context-sharing group associated with a first location: receiving a user voice input; receiving, from a context collector, an aggregate context of the context-sharing group; providing at least a portion of the aggregate context and data corresponding to the user voice input to a remote device; receiving, from the remote device, a command to perform one or more tasks and a device identifier corresponding to a second electronic device; and transmitting the command to the second electronic device based on the device identifier, wherein the command causes the second electronic device to perform the one or more tasks.

Abstract: This relates to intelligent automated assistants and, more specifically, to intelligent context sharing and task performance among a collection of devices with intelligent automated assistant capabilities. An example method includes, at a first electronic device participating in a context-sharing group associated with a first location: receiving a user voice input; receiving, from a context collector, an aggregate context of the context-sharing group; providing at least a portion of the aggregate context and data corresponding to the user voice input to a remote device; receiving, from the remote device, a command to perform one or more tasks and a device identifier corresponding to a second electronic device; and transmitting the command to the second electronic device based on the device identifier, wherein the command causes the second electronic device to perform the one or more tasks.

Abstract: Techniques are described herein that are capable of performing intent-based scheduling via a digital personal assistant. For instance, an intent of user(s) to perform an action (a.k.a. activity) may be used to schedule time (e.g., on a calendar of at least one of the user(s)) in which the action is to be performed. Examples of performing an action include but are not limited to having a meeting, working on a project, participating in a social event, exercising, and reading.

Abstract: Techniques are described herein that are capable of performing intent-based scheduling via a digital personal assistant. For instance, an intent of user(s) to perform an action (a.k.a. activity) may be used to schedule time (e.g., on a calendar of at least one of the user(s)) in which the action is to be performed. Examples of performing an action include but are not limited to having a meeting, working on a project, participating in a social event, exercising, and reading.

Abstract: This relates to intelligent automated assistants and, more specifically, to intelligent context sharing and task performance among a collection of devices with intelligent automated assistant capabilities. An example method includes, at a first electronic device participating in a context-sharing group associated with a first location: receiving a user voice input; receiving, from a context collector, an aggregate context of the context-sharing group; providing at least a portion of the aggregate context and data corresponding to the user voice input to a remote device; receiving, from the remote device, a command to perform one or more tasks and a device identifier corresponding to a second electronic device; and transmitting the command to the second electronic device based on the device identifier, wherein the command causes the second electronic device to perform the one or more tasks.

Abstract: This relates to intelligent automated assistants and, more specifically, to intelligent context sharing and task performance among a collection of devices with intelligent automated assistant capabilities. An example method includes, at a first electronic device participating in a context-sharing group associated with a first location: receiving a user voice input; receiving, from a context collector, an aggregate context of the context-sharing group; providing at least a portion of the aggregate context and data corresponding to the user voice input to a remote device; receiving, from the remote device, a command to perform one or more tasks and a device identifier corresponding to a second electronic device; and transmitting the command to the second electronic device based on the device identifier, wherein the command causes the second electronic device to perform the one or more tasks.

Abstract: Systems and processes for providing personalized suggestions indicating that a task may be performed using a digital assistant of an electronic device are provided. An example method includes, at an electronic device with a display, receiving context data associated with the electronic device; determining, based on the context data, a task that may be performed by a digital assistant of the electronic device in response to a natural-language expression; determining, based on the context data, whether suggestion criteria associated with the determined task are satisfied; and in accordance with a determination that the suggestion criteria are satisfied, providing a suggestion indicating that the determined task may be performed using the digital assistant of the electronic device.

Abstract: Aspects of the technology described herein provide an efficient user interface that enables users to respond to tasks quickly by providing automated quick task notifications via an audio channel. An audio channel quick task system includes components for recognizing and extracting quick tasks from content (e.g., interpersonal communications, composed content, line of business (LOB) application documents), and for prioritizing and routing the quick tasks to the user via an audio channel at an appropriate and relevant time. The system is enabled to process a user response, determine an action for handling the quick task, and execute the action on behalf of the user (e.g., pass a reply to a requestor, pass an instruction to an application or service, queue the quick task notification, delegate the quick task to another user or bot, forward the quick task to a companion device, or launch an application on a companion device).

Abstract: Apparatus and methods can be implemented to determine correctness and/or consistency of communications in a variety of applications. In various embodiments, a communication in a communication channel can be monitored, a feature from the monitored communication can be extracted for comparison with one or more sample features to determine correctness and/or an identification of a contradiction of the extracted feature from which remedial action can be taken. Additional apparatus, systems, and methods are disclosed.

Abstract: Methods, systems, and computer programs are presented for a smart communications assistant with an audio interface. One method includes an operation for getting messages addressed to a user. The messages are from one or more message sources and each message comprising message data that includes text. The method further includes operations for analyzing the message data to determine a meaning of each message, for generating a score for each message based on the respective message data and the meaning of the message, and for generating a textual summary for the messages based on the message scores and the meaning of the messages. A speech summary is created based on the textual summary and the speech summary is then sent to a speaker associated with the user. The audio interface further allows the user to verbally request actions for the messages.

Abstract: A Personal Virtual Assistant (PVA) system takes existing inputs and processes them to understand the user's personality, intent, etc., through analysis of features such as the user's tone, language use, vocal volume, facial expressions, and context from device use and surroundings. As a result of the processing, the PVA system augments how it responds and interacts with the user. Such augmented responses may include simple things like changing the assistant's tone to complimenting the user's mood, as well as changing her use of language, volume, and the way she shares information, like giving good news when the user might be down, or suppressing bad news when the time is not right for the user to optimally process it.

Abstract: Methods, systems, and computer programs are presented for a smart communications assistant with an audio interface. One method includes an operation for getting messages addressed to a user. The messages are from one or more message sources and each message comprising message data that includes text. The method further includes operations for analyzing the message data to determine a meaning of each message, for generating a score for each message based on the respective message data and the meaning of the message, and for generating a textual summary for the messages based on the message scores and the meaning of the messages. A speech summary is created based on the textual summary and the speech summary is then sent to a speaker associated with the user. The audio interface further allows the user to verbally request actions for the messages.

Abstract: A Personal Virtual Assistant (PVA) system takes existing inputs and processes them to understand the user's personality, intent, etc., through analysis of features such as the user's tone, language use, vocal volume, facial expressions, and context from device use and surroundings. As a result of the processing, the PVA system augments how it responds and interacts with the user. Such augmented responses may include simple things like changing the assistant's tone to complimenting the user's mood, as well as changing her use of language, volume, and the way she shares information, like giving good news when the user might be down, or suppressing bad news when the time is not right for the user to optimally process it.

Abstract: Techniques for enabling an artificial intelligence system to infer grounded intent from user input, and automatically suggest and/or execute actions associated with the predicted intent. In an aspect, core task descriptions are extracted from actionable statements identified as containing grounded intent. A machine classifier receives the core task description, actionable statements, and user input to predict an intent class for the user input. The machine classifier may be trained using unsupervised learning techniques based on weakly labeled clusters of the core task description extracted over a training corpus. The core task description may include verb-object pairs.

Abstract: Aspects of the technology described herein provide an efficient user interface that enables users to respond to tasks quickly by providing automated quick task notifications via an audio channel. An audio channel quick task system includes components for recognizing and extracting quick tasks from content (e.g., interpersonal communications, composed content, line of business (LOB) application documents), and for prioritizing and routing the quick tasks to the user via an audio channel at an appropriate and relevant time. The system is enabled to process a user response, determine an action for handling the quick task, and execute the action on behalf of the user (e.g., pass a reply to a requestor, pass an instruction to an application or service, queue the quick task notification, delegate the quick task to another user or bot, forward the quick task to a companion device, or launch an application on a companion device).

Abstract: A system that analyses content of electronic communications may automatically extract requests or commitments from the electronic communications. In one example process, a processing component may analyze the content to determine one or more meanings of the content; query content of one or more data sources that is related to the electronic communications; and based, at least in part, on (i) the one or more meanings of the content and (ii) the content of the one or more data sources, automatically identify and extract a request or commitment from the content. Multiple actions may follow from initial recognition and extraction, including confirmation and refinement of the description of the request or commitment, and actions that assist one or more of the senders, recipients, or others to track and address the request or commitment, including the creation of additional messages, reminders, appointments, or to-do lists.

Abstract: Apparatus and methods can be implemented to determine correctness and/or consistency of communications in a variety of applications. In various embodiments, a communication in a communication channel can be monitored, a feature from the monitored communication can be extracted for comparison with one or more sample features to determine correctness and/or an identification of a contradiction of the extracted feature from which remedial action can be taken. Additional apparatus, systems, and methods are disclosed.

Abstract: Automatic extraction and application of conditional tasks from content is provided. A conditional task system includes a classifier that is trained and used to identify conditional tasks and to learn appropriate times and methods to engage a user for reminding the user about conditional tasks. The conditional task system includes components for enabling an automated detection of a conditional task, extracting of attributes that characterize a condition associated with the task, using information about the condition to determine how to monitor for satisfaction of the condition, determining when and how to engage the user about the task, and notifying the user at an appropriate time and using an appropriate method when the condition is satisfied.

Abstract: Methods, systems, and computer programs are presented for a smart communications assistant with an audio interface. One method includes an operation for getting messages addressed to a user. The messages are from one or more message sources and each message comprising message data that includes text. The method further includes operations for analyzing the message data to determine a meaning of each message, for generating a score for each message based on the respective message data and the meaning of the message, and for generating a textual summary for the messages based on the message scores and the meaning of the messages. A speech summary is created based on the textual summary and the speech summary is then sent to a speaker associated with the user. The audio interface further allows the user to verbally request actions for the messages.

Abstract: Generating talking points on behalf of a user and providing the talking points to the user is provided. Talking points can help users to quickly and easily start a personalized conversation that enables participants of the conversation to make a connection, thus eliciting better communication and collaboration. Talking points can be generated based on commitments or requests identified in a previous conversation, anomalies or patterns identified in context data, or information items of interest collected from various data sources. One or more top-ranking talking points are surfaced to the user to help the user start a new conversation or to continue a previous conversation.