Abstract: The present invention provides assistance to a user that is on-boarding an action for a virtual assistant. Methods and systems provide, based on an initial command group, recommendations for additional commands, actions, and applications that are relevant to commands included in the initial command group. The initial command group includes commands that include a sequence of natural language words or tokens. Additional commands are determined. The additional commands are relevant to the initial commands and provided to the user as recommended commands to include in the command group. The additional commands are determined by employing the initial commands as search queries to search preprocessed document clusters. Each document included in the document clusters relates to a potential command to include in the command group. In response to receiving a selection of a recommended command, the command group is updated to include the selected recommended command.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed to providing talk back automation for applications installed on a mobile device. To do so actions (e.g., talk back features) can be created, via the digital assistant, by recording a series of events that are typically provided by a user of the mobile device when manually invoking the desired action. At a desired state, the user may select an object that represents the output of the application. The recording embodies the action and can be associated with a series of verbal commands that the user would typically announce to the digital assistant when an invocation of the action is desired. In response, the object is verbally communicated to the user via the digital assistant, a different digital assistant, or even another device. Alternatively, the object may be communicated to the same application or another application as input.

Abstract: Embodiments described herein are generally directed towards systems and methods relating to a crowd-sourced digital assistant system and related methods. In particular, embodiments facilitate the intuitive creation, maintenance, and distribution of action datasets that include computing events or tasks that can be reproduced when an associated command, stored in an action dataset, is determined received by a digital assistant device. The digital assistant device described herein can generate new action datasets, on-board new action datasets to a remote server, and receive new action datasets or updates to existing action datasets from the remote server. The digital assistant server described herein can receive action datasets, maintain action datasets, and distribute action datasets to one or more digital assistant devices.

Abstract: Disclosed are techniques for generating a language model that is applicable to the interpretation of commands for invoking application-based actions via a digital assistant device. In various embodiments, command templates that are each mapped to one of a plurality of action datasets are obtained to generate synthetic documents of a language model document corpus. Each synthetic document can be modified to include a tag that corresponds to an associated command template from which the document's generation was based. The language model can include a plurality of document clusters that are generated based on the modified synthetic documents, among other things.

Abstract: Disclosed are techniques for the automated discovery and extraction of discourse phrases, or in other words terms that representative of a topic or concept communicated via a plurality of electronic documents, to facilitate the generation of a language model that is applicable to interpreting commands for invoking application-based actions via a digital assistant device. An electronic document corpus can be generated having a plurality of electronic documents that are clustered based on entropy, among other things. The clusters can be associated with a corresponding plurality of cluster attractors that are generally representative of a context of the documents included therein. The clusters can be grouped based on determined relationships, such that the groups can be further analyzed and terms or phrases representative of a discourse associated with the grouped documents can be determined.

Abstract: Disclosed are techniques for automatically extracting discovered topics and/or from determined discourse clusters for the generation of a language model that is applicable to interpreting commands received from a digital assistant device. An electronic document corpus can be generated having a plurality of documents that are clustered based on entropy, among other things. The clusters can be associated with a corresponding plurality of cluster attractors that are generally representative of a context of the documents included therein. The documents within the cluster for each of the document clusters can be analyzed, so that clusters determined representative of a hierarchical discourse community can be determined and logically merged. The merged clusters can be analyzed, such that topics and/or sub-topics can be determined and extracted therefrom, for indexing and storage, among other things.

Abstract: Embodiments described herein are generally directed towards systems and methods relating to a crowd-sourced digital assistant system and related methods. In particular, embodiments facilitate techniques to crowdsource the training of a language model of the crowd-sourced digital assistant system. The digital assistant device can generate new action datasets based on manual inputs detected by the digital assistant device. The manual inputs can be recorded as a set of instructions, which can be interpreted by another digital assistant device to reproduce the detected manual inputs based on a command received by the other digital assistant device. The digital assistant server can receive action datasets, maintain action datasets, and distribute action datasets to one or more digital assistant devices. In various embodiments, the digital assistant device or server can also determine whether received action datasets are related.

Abstract: Embodiments described herein are generally directed towards systems and methods relating to a crowd-sourced digital assistant system and related methods. In particular, embodiments describe techniques for effectively searching, modifying, identifying parameter values, and determining features for selecting action datasets for distribution to digital assistant devices based on commands received therefrom. Action datasets include computing events or tasks that can be reproduced when a command is received by a digital assistant device and communicated to the server device. The digital assistant server described herein can receive action datasets, maintain action datasets, receive commands from digital assistant devices, and effectively select most relevant action datasets for distribution to the digital assistant devices based on the received commands.

Abstract: Embodiments described herein are generally directed towards systems and methods relating to a crowd-sourced digital assistant system and techniques for disambiguating commands based on personalized usage of a digital assistant device, among other things. In various embodiments, the digital assistant device can use personal data, collected device usage data, and other types of collected contextual information, to disambiguate received commands for the proper selection and execution of operations on the digital assistant device. The digital assistant can process and interpret ambiguous commands and even unique user dialects without requiring extensive training to recognize and act on the received commands, even if the particular phraseology of the command has not previously been encountered by the digital assistant.

Abstract: Embodiments described herein are generally directed towards systems and methods relating to a crowd-sourced digital assistant system and related methods. In particular, embodiments describe techniques for effectively searching, modifying, and selecting action datasets for distribution to digital assistant devices based on commands received therefrom. Action datasets include computing events or tasks that can be reproduced when a command is received by a digital assistant device and communicated to the server device. The digital assistant server described herein can receive action datasets, maintain action datasets, receive commands from digital assistant devices, and effectively select most relevant action datasets for distribution to the digital assistant devices based on the received commands.

Abstract: Various embodiments, methods and systems for implementing a digital assistant connectivity system are provided. In operation, a request to receive a unique identifier is communicated from a digital assistant device. The unique identifier is utilized to pair the digital assistant device with a smart assistant device. The unique identifier is received from and generated by a digital assistant server to correspond with the digital assistant device and a corresponding digital assistant device application instance. An instruction to perform an action on the digital assistant device is received at the digital assistant device application. The instruction is communicated based on an established command-driven session between the digital assistant device application and the smart assistant device. The command-driven session is associated with the unique identifier that paired the digital assistant device and the smart assistant device.

Abstract: Systems, methods, and non-transitory computer-readable storage media are provided for crowdsourcing actions and commands of a digital assistant application, irrespective of the languages spoken by users of the digital assistant application. Techniques described herein enable the on-boarding of actions datasets, which include defined commands and actions that result therefrom, from client devices to a remote server device. More specifically, the described techniques facilitate the proper on-boarding, distribution, and retrieval of action datasets regardless of the command language employed by users of the digital assistant application to invoke a properly-determined corresponding action.