Microsoft Technology Licensing, LLC

Steven Wayne Ickman

Thomas Matthew Laird-McConnell

William L Portnoy

Lili Cheng

Christopher Clayton McConnell

David Raskino

Christopher Lee Mullins

John Anthony Taylor

Gregory Scott Melander

Shahin Shayandeh

Scott Jeff Gellock

David Dawson

Carlos Castro

Matthew Graham Dyor

Todd D Newman

Paul Andrew Steckler

Richard Ignacio Zaragoza

Gabriel Gilabert

Raymond Edward Ozzie

Matthew Bret MacLaurin

William Ramsey

Cezary Antoni Marcjan

Michael A Belshe

Hai Liu

Lars Hasso Liden

Neel Ishwar Murarka

Dane Alexander Glasgow

Tom Laird-McConnell

Rajeev V Karunakaran

Christopher Michael Whitten

Patrick Kinsel

Jonas Barklund

Janice Von Itter

Richard Daniel Marshall

Christopher C Mcconnell

Nathanael P Roberton

Thomas Matthew Laird-Mcconnell

Thomas M Laird-Mcconnell

William D Ramsey

Making an information retrieval process public, so that it can be followed by others, allows capturing of an interest graph that allows people to learn more about shared interests with other people. This also allows items of interest to a trusted resource (such as an expert) to be identified. These items can then be brought to the attention of other users that share the same interest as the expert. In addition, by keeping track of what particular content a user has already seen, the system can bring items of interest to the user's attention, where the user has not yet seen those items.

Identifying unseen content of interest

In non-limiting examples of the present disclosure, systems, methods and devices for enabling conversational bot communications are presented. A marketplace comprising an identity of a plurality of skills may be maintained, each of the plurality of skills associated with a manifest and a conversational bot. A request for execution of a specific skill may be received. A plurality of conversational bots may be identified, each of the plurality of conversational bots being associated with a manifest for executing the specific skill. The plurality of conversational bots may be ranked. The specific skill may be caused to be executed by the highest ranked conversational bot. In some examples, a plurality of highest ranked conversational bots may be surfaced and a bot that is selected may execute the specific skill.

Marketplace for conversational bot skills

A computing system for real-time analyzing meeting conversations using artificial intelligence (AI) is configured to establish a meeting communication channel that facilitates a meeting among multiple client systems. Each of the multiple client systems corresponds to a meeting participant. In response to receiving communication from the multiple client systems, the computing system analyzes currently received communication via an AI engine while the meeting remains active. The currently received communication is communication received within a rolling time window ending at a current time. Based on the analysis, the computing system identifies an agenda item that has a highest correlation with the currently received communication and a sentiment of at least one participant. The computing system then determines a level of interest of the agenda item based on the sentiment of the participant and causes at least one client system to display a notification or an alert or make suggestions.

Conversational AI for intelligent meeting service

The present disclosure relates to processing operations configured to generate and present a chat bot diagnostic visualization that provides real-time visual insights into operation of a chat bot for debugging assistance. A chat bot diagnostic tool is programmed to launch a graphical user interface that displays a visual representation of diagnostic data for debugging a chat bot in real-time. To enable conversational specific data to be reviewed, the present disclosure is configured to implement a middleware adapter within in a run-time stack of a chat bot that is configured to trace data traffic between a chat bot and a channel of an application/service. This enables the chat bot diagnostic tool to observe data traffic and filter the data traffic to identify relevant data for debugging a chat bot. A debugging data visualization of a chat bot interaction is emulated through a graphical user interface of the chat bot diagnostic tool.

Chat bot diagnostic visualization

A method for utilizing shared memory spaces for data lifecycle management in a conversational AI system includes receiving one or more storage requests directing conversational data to multiple nodes of a conversational data object and storing the conversational data that is directed to each different one of the multiple nodes within a different one of multiple discrete memory spaces. Each of the multiple discrete memory spaces is governed by a scope policy defining a lifetime for data stored within the memory space. The method further provides for erasing data of each node of the multiple different nodes of the conversational data object according to the lifetime defined by the scope policy governing the memory space storing the node.

Shared memory space management for conversational artificial intelligence system

A computing system for real-time analyzing meeting conversations using artificial intelligence (AI) is configured to establish a meeting communication channel that facilitates a meeting among multiple client systems. Each of the multiple client systems corresponds to a meeting participant. In response to receiving communication from the multiple client systems, the computing system analyzes currently received communication via an AI engine while the meeting remains active. The currently received communication is communication received within a rolling time window ending at a current time. Based on the analysis, the computing system identifies an agenda item that has a highest correlation with the currently received communication or a task that is assigned to at least one of the participants, and causes, in substantially real time, at least one of the plurality of the client systems to display the agenda item or add the task to a task list.

In non-limiting examples of the present disclosure, systems, methods and devices for enabling conversational bot communications are presented. A natural language input may be received by a first conversational bot. A skill needed to fulfill a request in the natural language input may be identified. A determination may be made that the skill is not executable by the first conversational bot. A determination may be made that the skill is executable by a second conversational bot. A request for execution of the skill may be sent from the first conversational bot to the second conversational bot. A request for information needed for the second conversational bot to execute the skill may be received by the first conversational bot from the second conversational bot. The information may be sent from the first conversational bot to the second conversational bot. The skill may be executed.

Conversational manifests for enabling complex bot communications

A computer-executed conversational system utilizing adaptive dialogs to provide dynamic modification of a conversation structure. The adaptive dialogs facilitate an adaptive and dynamic way to model conversations. A conversational rule may be associated with a trigger received at an adaptive dialog. The conversational rule may populate a plan with steps to provide the conversational structure of the conversational system. Modifying triggers may be received that allow conversational rules to amend the plan including adding steps, removing steps, or modifying steps from the plan. The adaptive dialogs and/or components thereof may be provided as constructible elements in a platform for building a bot such as a developer Software Development Kit (SDK) or the like. In turn, sophisticated conversational modeling may be conveniently facilitated to provide features such as a dialog dispatcher, the ability to handle interruptions, and to build a pluggable, extensible conversational system.

Conversational AI using adaptive dialogs

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.

Conversational event modeling

Methods, systems, and computer program products for dispatching an incoming event are described. A scoring stack is accessed, the scoring stack comprising an identity of one or more tasks, each task corresponding to one or more scorable functions. A scorable tree is generated based on the one or more scorable functions corresponding to the one or more tasks of the scoring stack and the incoming event is processed using the scorable tree to generate one or more scores. One or more actions identified in the scorable tree are performed based on the one or more scores.

Dynamic event processing

Described is a technology by which an interactive timeline user interface shows communication data aggregated from various sources (e.g., a telephone service, a website, an application and/or the like) in time order. To reduce the number of items in the timeline, certain items may be combined into a compressed item, e.g., items from the same source and/or of the same type may be rolled up into a rolled up item. The user may also interact to reply to a data item. For example, an editor may be provided upon interaction with a data item in the timeline, and the type of reply that is sent may automatically match the type of data item, (e.g., a text message reply to a text message).

Aggregated, interactive communication timeline

Many scenarios involve the delivery of messages received from various message sources to recipients, based on subscriptions established between various recipients and message sources. The recipients may be distributively assigned to one or more message servers, either operating independently or as part of an interoperating message server set. Respective message servers may generate a subscription filter indicating whether at least one assigned recipient subscribes to respective message sources, and may utilize the subscription filter to determine, upon receiving a message from a message source, whether to discard the message or store the message for delivery. Interoperating message servers may also exchange subscription filters, and upon receiving a message from any message source, a message server may utilize the subscription filters of the other message servers to determine which other message servers are assigned at least one subscribing recipient, and may retransmit the message only to such message servers.

Delivering messages from message sources to subscribing recipients

Various embodiments enable an application to obtain information associated with a link to content without navigating to the link. The application can be configured to identify a link, receive input to attain information associated with the link, and send a request to a service for the information. In one or more embodiments, a service can be configured to receive a request for information associated with a link, ascertain one or more sources from which to gather the information, determine appropriate mechanisms by which to obtain the information, and acquire the information. Alternately or additionally, the service can further assemble the information into a consumable format, and return the consumable format to a requesting application or device.

Link expansion service

Growing community of inventors

Snoqualmie, WA, United States of America

Steven Wayne Ickman