Abstract: Conversational event modeling for determining how to process an input in a dialog stack of a computer-executed conversational system. Receipt of an input at an active dialog in a dialog stack may result in generation of an event corresponding to the input. The event may be propagated through the dialog stack. Dialogs in the dialog stack may generate a confidence value in relation to processing the input, and selection of the dialog in the dialog stack for use in processing the input may be based at least in part on the confidence values. In turn, the conversational system may facilitate improved handling of ambiguous or unrelated inputs at dialogs by propagating the event associated with such an input through the dialog stack. The event creation and processing functions may be provided as parameters for dialogs in a modular dialog creation system.

Abstract: The techniques discussed herein allow an agent that provides dialog-based services to converse with a user via any mode. A central service programmed to be able to communicate via any mode collects information from agents necessary to act on the agents' behalf. In order to ensure that an agent has a maximal presence across modes, the central service provides auxiliary services such as language translation, fidelity upgrading and/or downgrading, modal constraint conformation, etc. Moreover, the central service provides session tracking via user profiles stored at the intercom so that if a user changes a mode by which the user is conversing with an agent, the central service can keep the context of the conversation across the new mode. Furthermore, agents can communicate with other agents via the central service, allowing other agents to provide services to agents or to add their services to an ongoing dialog with a user.

Abstract: The techniques discussed herein allow an agent that provides dialog-based services to converse with a user via any mode. A central service programmed to be able to communicate via any mode collects information from agents necessary to act on the agents' behalf. In order to ensure that an agent has a maximal presence across modes, the central service provides auxiliary services such as language translation, fidelity upgrading and/or downgrading, modal constraint conformation, etc. Moreover, the central service provides session tracking via user profiles stored at the intercom so that if a user changes a mode by which the user is conversing with an agent, the central service can keep the context of the conversation across the new mode. Furthermore, agents can communicate with other agents via the central service, allowing other agents to provide services to agents or to add their services to an ongoing dialog with a user.

Abstract: Systems, methods, and computer-readable media are presented that, in operation, generate a checklist from content in response to a checklist request from a user. More particularly, in response to the checklist request, a content type of the content identified by the checklist request is determined. The content is then analyzed, according to a determined content type, to identify a set of enumerative elements, the set of enumerative elements comprising at least two enumerative elements. Thereafter, for each enumerative element of the set of enumerative elements, a checklist item is generated, the checklist item corresponding to the enumerative element and including subject matter of the content corresponding to the enumerative element. Further, the generated checklist item is added as an item of the generated checklist. After iterating each enumerative element of the set of enumerative elements, the generated checklist is returned in response to the checklist request.

Abstract: Systems, methods, and computer-readable media are presented that, in operation, generate a checklist from content in response to a checklist request from a user. More particularly, in response to the checklist request, a content type of the content identified by the checklist request is determined. The content is then analyzed, according to a determined content type, to identify a set of enumerative elements, the set of enumerative elements comprising at least two enumerative elements. Thereafter, for each enumerative element of the set of enumerative elements, a checklist item is generated, the checklist item corresponding to the enumerative element and including subject matter of the content corresponding to the enumerative element. Further, the generated checklist item is added as an item of the generated checklist. After iterating each enumerative element of the set of enumerative elements, the generated checklist is returned in response to the checklist request.

Abstract: The techniques described herein are directed to cross-channel communication. The techniques can employ a central method, system, or mode (“Cross-Mode Manager” or “CMM” herein), which is enabled to send and/or receive messages over a multiplicity of modes. Users can enable the CMM through a variety of methods described herein on modes which the user uses to communicates. Once enabled on at least one channel which the user uses to communicate, the presence of the CMM across a multiplicity of modes allows a user to create and/or join a cross-channel communication session maintained by the CMM and to communicate between the user's communication mode and any of the multiplicity of modes for which the CMM has a presence. In other words, the techniques permit users to communicate across disparate channels from within a channel of their choosing.

Abstract: A topic feed is generated for publication or distribution to a set of followers or recipients who have elected to receive the topic feed from the user or based on the subject matter content of a topic input in the topic feed. The topic feed is distributed and includes the topic input. Experts in subject matter can be identified based on the user's topic input and/or text in the topic feed that the followers interact with.

Abstract: Systems, methods and computer-readable media are presented for processing a checklist from a checklist template. In contrast to typical checklist processing, the disclosed embodiments include a checklist instance, generated from a checklist template, which includes structured data storage, unstructured data storage, a checklist, and an execution state. Upon an indication to pause execution of the checklist, the checklist instance is stored in a data store such that, upon resumption of execution, the values of the structured, unstructured, and execution state are restored. Upon detecting that an executed checklist item corresponds to markup content, an analysis of the markup content is made to identify entry fields within the content that correspond to structured data fields of the checklist instance. Entry fields with a corresponding field in the checklist can be prepopulated with the values of the checklist data fields.

Abstract: The techniques described herein are directed to cross-channel communication. The techniques can employ a central method, system, or mode (“Cross-Mode Manager” or “CMM” herein), which is enabled to send and/or receive messages over a multiplicity of modes. Users can enable the CMM through a variety of methods described herein on modes which the user uses to communicates. Once enabled on at least one channel which the user uses to communicate, the presence of the CMM across a multiplicity of modes allows a user to create and/or join a cross-channel communication session maintained by the CMM and to communicate between the user's communication mode and any of the multiplicity of modes for which the CMM has a presence. In other words, the techniques permit users to communicate across disparate channels from within a channel of their choosing.

Abstract: The techniques discussed herein allow an agent that provides dialog-based services to converse with a user via any mode. A central service programmed to be able to communicate via any mode collects information from agents necessary to act on the agents' behalf. In order to ensure that an agent has a maximal presence across modes, the central service provides auxiliary services such as language translation, fidelity upgrading and/or downgrading, modal constraint conformation, etc. Moreover, the central service provides session tracking via user profiles stored at the intercom so that if a user changes a mode by which the user is conversing with an agent, the central service can keep the context of the conversation across the new mode. Furthermore, agents can communicate with other agents via the central service, allowing other agents to provide services to agents or to add their services to an ongoing dialog with a user.

Abstract: Systems, methods, and computer-readable media are presented that, in operation, generate a checklist from content in response to a checklist request from a user. More particularly, in response to the checklist request, a content type of the content identified by the checklist request is determined. The content is then analyzed, according to a determined content type, to identify a set of enumerative elements, the set of enumerative elements comprising at least two enumerative elements. Thereafter, for each enumerative element of the set of enumerative elements, a checklist item is generated, the checklist item corresponding to the enumerative element and including subject matter of the content corresponding to the enumerative element. Further, the generated checklist item is added as an item of the generated checklist. After iterating each enumerative element of the set of enumerative elements, the generated checklist is returned in response to the checklist request.

Abstract: A topic feed is generated for publication or distribution to a set of followers or recipients who have elected to receive the topic feed from the user or based on the subject matter content of a topic input in the topic feed. The topic feed is distributed and includes the topic input. Experts in subject matter can be identified based on the user's topic input and/or text in the topic feed that the followers interact with.

Abstract: A topic feed is generated for publication or distribution to a set of followers or recipients who have elected to receive the topic feed from the user or based on the subject matter content of a topic input in the topic feed. The topic feed is distributed and includes the topic input. Experts in subject matter can be identified based on the user's topic input and/or text in the topic feed that the followers interact with.

Abstract: When a user enters an information retrieval query, a topic feed generator automatically generates a topic feed for publication or distribution to a set of followers or recipients who have elected to receive the topic feed from the user or based on the subject matter content of the query. A feed distributor component automatically distributes the topic feed, which includes the query, to the identified recipients. A search component executes the search and returns results, corresponding to the query, to the user.

Abstract: A topic feed is generated for publication or distribution to a set of followers or recipients who have elected to receive the topic feed from the user or based on the subject matter content of a topic input in the topic feed. The topic feed is distributed and includes the topic input. Experts in subject matter can be identified based on the user's topic input and/or text in the topic feed that the followers interact with.

Abstract: When a user enters an information retrieval query, a topic feed is generated for publication or distribution to a set of followers or recipients who have elected to receive the topic feed from the user or based on the subject matter content of the query. The topic feed is distributed and includes the query. Experts in subject matter can be identified based on the user's queries and other text in the topic feed that the followers interact with.

Abstract: When a user enters an information retrieval query, topic feed is generated for publication or distribution to a set of followers or recipients who have elected to receive the topic feed from the user or based on the subject matter content of the query. The topic feed is distributed and includes the query. The user's interests are tracked based on text in the user's queries and other text in the topic feed that the user interacts with.

Abstract: When a user enters an information retrieval query, a topic feed is generated for publication or distribution to a set of followers or recipients who have elected to receive the topic feed from the user or based on the subject matter content of the query. The topic feed is distributed and includes the query. Experts in subject matter can be identified based on the user's queries and other text in the topic feed that the followers interact with.

Abstract: When a user enters an information retrieval query, a topic feed generator automatically generates a topic feed for publication or distribution to a set of followers or recipients who have elected to receive the topic feed from the user or based on the subject matter content of the query. A feed distributor component automatically distributes the topic feed, which includes the query, to the identified recipients. A search component executes the search and returns results, corresponding to the query, to the user.

Abstract: When a user enters an information retrieval query, topic feed is generated for publication or distribution to a set of followers or recipients who have elected to receive the topic feed from the user or based on the subject matter content of the query. The topic feed is distributed and includes the query. The user's interests are tracked based on text in the user's queries and other text in the topic feed that the user interacts with.