Microsoft Technology Licensing, LLC

Warren David Johnson, Iii

Pamela Bhattacharya

Charles Yin-Che Lee

Jessica Lundin

Mayerber Loureiro De Carvalho Neto

Tachen Chester Ni

Chala Fekadu Fufa

Suri Raman

Ben Cheung

Juliana Pena Ocampo

In non-limiting examples of the present disclosure, systems, methods and devices for assigning conference rooms are presented. A meeting request may be received by an electronic meeting service. Meeting fit scores may be calculated for the meeting request and one or more conference rooms. The meeting fit scores may be based on location, capacity, and/or audio-visual capabilities. The meeting request may be assigned to a conference room with a highest meeting fit score. A meeting request may be re-assigned to a different conference room based on a conference room becoming available that has a higher meeting fit score. A meeting request may be re-assigned to a different conference room based on characteristics of the meeting request being modified (e.g., fewer invitees, more invitees, different location specified), and thus, the meeting fit scores for conference rooms changing based on those modifications.

Electronic meeting analysis for automatic conference room assignment

In non-limiting examples of the present disclosure, systems, methods and devices for prioritizing calendar events with artificial intelligence are presented. A request to schedule a new calendar event for a specified period may be received. A conflicting calendar event during the specified time period may be identified. An event priority score for the new calendar event may be compared with an event priority score for the conflicting calendar event, wherein the event priority scores are generated via application of a statistical machine learning model to a plurality of factors associated with the new calendar event and the conflicting calendar event. A selectable option to replace the conflicting calendar event with the new calendar event may be presented if the event priority score for the new calendar event is higher than the event priority score for the conflicting calendar event.

Artificial intelligence for calendar event conflict resolution

In non-limiting examples of the present disclosure, systems, methods and devices for routing meeting requests by a digital assistant service are presented. A request to schedule a meeting between an invitee and a principal may be received by a digital assistant service, wherein the request is sent by an agent of the principal. The digital assistant service may determine that the agent is a delegate of the principal with scheduling authority. The digital assistant service may further determine that follow-up information for the meeting is required, and the digital assistant service may route an electronic message requesting the follow-up information directly the agent-delegate. Other aspects describe mechanisms for routing meeting requests from third parties directly to delegates, rather than sending those communications directly to principals.

Routing of meeting requests and follow-up queries by digital assistants

In non-limiting examples of the present disclosure, systems, methods and devices for matching user tone to digital assistant response types and tones while assisting with meeting scheduling are presented. An electronic message may be received by a digital assistant service. The digital assistant service may detect an intent to schedule a meeting and identify an urgency level associated with the message. The digital assistant may respond to the scheduling user with a message having a tone corresponding to the identified urgency level. The digital assistant may also perform a follow-up action for scheduling the meeting in a manner consistent with the urgency level of the scheduling user. For example, the digital assistant may attempt to schedule the meeting in a higher priority manner if there is a high urgency associated with the message, and a lower priority manner if there is a low urgency associated with the message.

Urgency and emotion state matching for automated scheduling via artificial intelligence

A computer-implemented technique is described herein for rescheduling events within calendars to achieve a target objective with respect to target occurrences of a particular kind in the calendars. For instance, the target occurrences may correspond to instances of free time, and the target objective is to increase the amount of minimum-sized free-time blocks within the calendars, e.g., by favoring the introduction of large free-time blocks of minimum size (e.g., two-hour blocks), and disfavoring the presence of smaller-sized free-time blocks scattered among calendar events. In other words, the technique defragments the calendars with respect to free time expressed in those calendars. At the same time, the technique honors (when possible) participant-related constraints associated with calendar events. Further, the technique penalizes modification of an event as a function of a temporal distance over which the event is moved; this penalization has the effect of reducing disruption to the calendars.

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Sammamish, WA, United States of America

Warren David Johnson, Iii