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: 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.

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: 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: 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: 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: 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.

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: Computer-implemented methods, systems, and computer-readable storage mediums are provided for use with a clinical decision support system for identifying and providing information regarding associations between patient attributes and one or more Adverse Events (AEs). In one example, a process includes processing database information comprising AEs and one or more patient attributes for associations between AEs and patient attributes and identifying at least one association between one or more AEs and one or more patient attributes. The association(s) may be discovered through an association rule discovery process to determine one or more association rules, where each association rule satisfies a confidence, support, and/or other threshold. The exemplary process further provides information or alerts to a user based on the identified or discovered association(s).

Abstract: A method of determining cluster attractors for a plurality of documents comprising at least one term. The method comprises calculating, in respect of each term, a probability distribution indicative of the frequency of occurrence of the, or each, other term that co-occurs with said term in at least one of said documents. Then, the entropy of the respective probability distribution is calculated. Finally, at least one of said probability distributions is selected as a cluster attractor depending on the respective entropy value. The method facilitates very small clusters to be formed enabling more focused retrieval during a document search.