Method and system for facilitating convergence

Patent metadata

Field	Value
Publication number	US-2023186247-A1
Application number	US-202217707641-A
Country	US
Kind code	A1
Filing date	Mar 29, 2022
Priority date	Dec 14, 2021
Publication date	Jun 15, 2023
Grant date	—

Abstract

A system and method for facilitating convergence includes receiving a request to schedule a meeting at a meeting time, retrieve at least one of user data, contextual data, facility data and map data, the user data including a list of a plurality of meeting participants for the meeting, providing at least one of the list of the plurality of meeting participants, the meeting time, the user data, and the facility data to a trained machine-learning (ML) model for predicting a location at which two or more of the plurality of meeting participants will be located within a given time period prior to the meeting time, receiving the predicted location as an output from the trained ML model, and identifying a meeting location from among one or more meeting venues based on the predicted location of the two or more of the plurality of meeting participants, and providing the meeting location for display in a first selectable user interface element.

First claim

What is claimed is:

1 . A data processing system comprising:
a processor; and a memory in communication with the processor, the memory comprising executable instructions that, when executed by the processor, cause the data processing system to perform functions of:
receiving a request to schedule a meeting at a meeting time;
retrieving at least one of user data, contextual data, facility data and map data, the user data including a list of a plurality of meeting participants for the meeting;
providing at least one of the list of the plurality of meeting participants, the meeting time, the user data, and the facility data to a trained machine-learning (ML) model for predicting a location at which two or more of the plurality of meeting participants will be located within a given time period prior to the meeting time;
receiving the predicted location as an output from the trained ML model; and
identifying a meeting location from among one or more meeting venues based on the predicted location of the two or more of the plurality of meeting participants; and
providing the meeting location for display in a first selectable user interface element.

2 . The data processing system of claim 1 , wherein at least one of the user data or contextual data includes at least one of a user preference for one or more of the plurality of meeting participant and an accessibility requirement for one or more of the plurality of meeting attendees, and the meeting location is identified based on the predicted location and at least one of the user preference or accessibility requirement.

3 . The data processing system of claim 2 , wherein the user preference includes at least one of preferred mode of transportation, dietary restrictions, venue preferences, parking preferences, and carbon emission preferences.

4 . The data processing system of claim 1 , wherein the meeting location is identified based on geographic proximity of the predicted location of the two or more of the plurality of meeting participants to venue locations of the one or more meeting venues.

5 . The data processing system of claim 1 , wherein the meeting location is identified based on travel time from the predicted location of the two or more of the plurality of meeting participants to venue locations of the one or more meeting venues.

6 . The data processing system of claim 1 , wherein the meeting location is provided for display on a map.

7 . The data processing system of claim 1 , wherein the memory comprises executable instructions that, when executed by processor, further cause the data processing system to perform functions of providing a second selectable user interface element for automatically making a reservation at the identified meeting location for the meeting.

8 . The data processing system of claim 1 , wherein the meeting location is identified based on travel time from the meeting location to a location to which one or more of the plurality of meeting participants will travel to after the meeting.

9 . A method for identifying a meeting location for a meeting comprising:
receiving a request to schedule the meeting at the meeting time; retrieving at least one of user data, contextual data, facility data and map data, the user data including a list of a plurality of meeting participants for the meeting; providing at least one of the list of the plurality of meeting participants, the meeting time, the user data, and the facility data to a trained machine-learning (ML) model for predicting a location at which two or more of the plurality of meeting participants will be located within a given time period prior to the meeting time; receiving the predicted location as an output from the trained ML model; and identifying a meeting location from among one or more meeting venues based on the predicted location of the two or more of the plurality of meeting participants; and providing the meeting location for display in a first selectable user interface element.

10 . The method of claim 9 , wherein the meeting location is identified based on geographic proximity of the predicted location of the two or more of the plurality of meeting participants to venue locations of the one or more meeting venues.

11 . The method of claim 9 , wherein the meeting location is identified based on travel time from the predicted location of the two or more of the plurality of meeting participants to venue locations of the one or more meeting venues.

12 . The method of claim 9 , wherein the meeting location is provided for display on a map.

13 . The method of claim 9 , further comprising providing a second selectable user interface element for automatically making a reservation at the identified meeting location for the meeting.

14 . The method of claim 9 , wherein the meeting location is identified based on travel time from the meeting location to a location to which one or more of the plurality of meeting participants will travel to after the meeting.

15 . A non-transitory computer readable medium on which are stored instructions that, when executed, cause a programmable device to perform functions of:
receiving a request to schedule a meeting at a meeting time; retrieving at least one of user data, contextual data, facility data and map data, the user data including a list of a plurality of meeting participants for the meeting; providing at least one of the list of the plurality of meeting participants, the meeting time, the user data, and the facility data to a trained machine-learning (ML) model for predicting a location at which two or more of the plurality of meeting participants will be located within a given time period prior to the meeting time; receiving the predicted location as an output from the trained ML model; and identifying a meeting location from among one or more meeting venues based on the predicted location of the two or more of the plurality of meeting participants; and providing the meeting location for display in a first selectable user interface element.

16 . The data processing system of claim 15 , wherein the meeting location is identified based on geographic proximity of the predicted location of the two or more of the plurality of meeting participants to venue locations of the one or more meeting venues.

17 . The non-transitory computer readable medium of claim 15 , wherein the meeting location is identified based on travel time from the predicted location of the two or more of the plurality of meeting participants to venue locations of the one or more meeting venues.

18 . The non-transitory computer readable medium of claim 15 , wherein the memory comprises executable instructions that, when executed by processor, further cause the data processing system to perform functions of providing a second selectable user interface element for automatically making a reservation at the identified meeting location for the meeting.

19 . The non-transitory computer readable medium of claim 15 , wherein the meeting location is provided for display on a map.

20 . The non-transitory computer readable medium of claim 15 , wherein the meeting location is identified based on travel time from the meeting location to a location to which one or more of the plurality of meeting participants will travel to after the meeting.

Assignees

• Microsoft Technology Licensing Llc

Inventors

• Kenyon Heidi
• Chong Choo Yei
• Slater Megan Elizabeth
• Dollar Christopher Michael
• Copfer Wende Ellen
• Kannepalli Venkata Sreekanth
• Erfani Shabnam
• Huang Amy Lieh-An
• Shah Neha Parikh
• Doran Edward Michael

Classifications

• G06N5/04

  Inference or reasoning models · CPC title

• G06Q10/0631 Primary

  Resource planning, allocation, distributing or scheduling for enterprises or organisations · CPC title

• G06N20/00

  Machine learning · CPC title

• G06Q10/1095 Primary

  Physics · mapped topic

• G06Q10/1093

  Calendar-based scheduling for persons or groups · CPC title