Microsoft Technology Licensing, LLC

Justin Brooks Cranshaw

Todd D Newman

Michael Brian Palmer

Pamela Bhattacharya

Mayerber Loureiro De Carvalho Neto

Benjamin Gene Cheung

Charles Yin-che Lee

Lili Cheng

William L Portnoy

Thomas Matthew Laird-McConnell

Qi Lu

Scott Joseph Counts

Charles Yin-Che Lee

Daniel James Driscoll

Steve Ickman

Craig Jensen

James H Lewallen

Curtis N Von Veh

Roberta Rene Moeur

Anthony Lee Carbary

Stevie Nicole Chancellor

Emad Mohamed Hamdy Elwany

Thomas Matthew Laird-Mcconnell

Todd Daniel Newman

A user interface (UI) for visualizing search data provides techniques for grouping and organizing aggregate data that shows the categories of topics included in search queries from a large number of individual users. Raw search queries are categorized into one of a number of topical categories. The search queries are assigned to a geographic location based on geolocations of computing devices generating the search queries. The UI presents a map that shows the number of search queries per topical category for each geographic location displayed in the current UI view. As a result of this UI design, a user can easily understand the interaction between geographic location and frequency of search query topics. Trends in the geographic distribution of searches and in the categories of topics searched are also easily understood from this UI design by changing the time range of the search queries displayed.

User interface for visualizing search data

A software agent, that is used to assist in providing a service, receives communications from a set of users that are attempting to use the software agent. The communications include communications that are interacting with the software agent, and communications that are not interacting with the software agent. The software agent performs natural language processing on all communications to identify such things as user sentiment, user concerns or other items in the content of the messages, and also to identify actions taken by the users in order to obtain a measure of user satisfaction with the software agent. One or more action signals are then generated based upon the identified user satisfaction with the software agent.

Signal analysis in a conversational scheduling assistant computing system

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.

Multi-modal conversational intercom

A set of nodes are organized into a graph to represent a workflow to enable the dynamic and directed management of that workflow in a decentralized system. Each node maintains a value necessary for execution of the workflow, and includes code to populate that value. A workflow agent manages the population of the values according to an identified dependency structure for the nodes relative to the workflow. As changes are made to the workflow, the workflow agent ensures that values and dependencies of the nodes stay up-to-date. Each node retains historic values, which enables the workflow agent to query several states of the workflow throughout time as changes are made thereto. The dynamic management of the nodes improves the responsiveness of the system to changes, thereby improving computational efficiency.

Dynamic directed graph workflows

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Justin Brooks Cranshaw