IP Library Granted Patent US 12,651,127
Granted Patent B2
US 12,651,127 · App. 18/240,870 · Granted Jun 9, 2026

Dynamic questions and clusters for events

Inventors: Bryan Tamayo (Allen, TX); Unique Samantha Carey (Little Elm, TX); Catherine Lueatrice Lovett (Dallas, TX)
Assignee: Capital One Services, LLC
G06F40/40G06F40/30G06N20/00
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Quick Facts
Patent No.
US 12,651,127
App. No.
18/240,870
Granted
Jun 9, 2026
Kind
B2
Abstract

In some implementations, a conferencing system may receive a set of responses, corresponding to a set of questions, associated with a set of users. The conferencing system may identify, for a first user in the set of users, a subset of matching users in the set of users, based on the set of responses. The conferencing system may output an indication of the subset of matching users. The conferencing system may generate, by applying a machine learning model to the set of responses, at least one suggested cluster for a breakout subset of the set of users. The conferencing system may determine, based on the at least one suggested cluster, at least one possible location. The conferencing system may output an indication of the at least one suggested cluster and the at least one possible location.

Claims (75)

1 . A system for generating dynamic questions and clusters for events, the system comprising:

one or more memories; and

one or more processors, communicatively coupled to the one or more memories, configured to:

generate, by applying a first machine learning model to a dataset associated with a past bundle of events, a set of questions;

output the set of questions for a set of users registering for a current bundle of events;

receive a set of responses, corresponding to the set of questions, associated with the set of users;

identify, for a first user in the set of users, a subset of matching users in the set of users, based on the set of responses;

output an indication of the subset of matching users;

generate, by applying a second machine learning model to the set of responses, at least one suggested cluster for a breakout subset of the set of users;

determine, based on a location of a device associated with a second user in the set of users, attendance of the second user at a particular event in the current bundle of events; and

output, based on the attendance of the second user at the particular event, an indication of the at least one suggested cluster.

2 . The system of claim 1 , wherein the one or more processors, to receive the set of responses, are configured to:

receive the set of responses, from one or more devices associated with the set of users, during a registration procedure associated with the current bundle of events.

3 . The system of claim 1 , wherein the one or more processors are configured to:

receive, from a device associated with an organizer of the current bundle of events, a confirmation of the at least one suggested cluster; and

transmit, to one or more devices associated with the breakout subset, an indication of the at least one suggested cluster.

4 . The system of claim 1 , wherein the one or more processors are configured to:

receive, from one or more devices associated with the set of users, feedback associated with a first event in the current bundle of events,

wherein the at least one suggested cluster is further based on applying the second machine learning model to the feedback.

5 . The system of claim 4 , wherein the one or more processors are configured to:

identify, for a user in the set of users other than the first user, a second event in the current bundle of events, based on the feedback; and

output an indication of the second event.

6 . The system of claim 1 , wherein the one or more processors are configured to:

identify a change in registration associated with an event in the current bundle of events; and

output at least one possible location based on the change in registration.

7 . The system of claim 1 , wherein the one or more processors, to determine the attendance of the second user at the particular event, are configured to:

determine that the device associated with the second user was within a particular distance of a location associated with the particular event,

wherein the particular distance satisfies an attendance threshold.

8 . A method of generating dynamic questions and clusters for events, comprising:

receiving a set of responses, corresponding to a set of questions, associated with a set of users;

identifying, for a first user in the set of users, a subset of matching users in the set of users, based on the set of responses;

outputting an indication of the subset of matching users;

generating, by applying a machine learning model to the set of responses, at least one suggested cluster for a breakout subset of the set of users;

determining, based on the at least one suggested cluster, at least one possible location;

determining, based on a location of a device associated with a second user in the set of users, attendance of the second user at a particular event in a current bundle of events; and

outputting, based on the attendance of the second user at the particular event, an indication of the at least one suggested cluster and the at least one possible location.

9 . The method of claim 8 , further comprising:

determining that a device, associated with the first user, was within a particular distance of the device associated with the second user for an amount of time that satisfies a networking threshold, wherein the particular distance satisfies a closeness threshold; and

updating the subset of matching users based on the amount of time.

10 . The method of claim 8 , wherein determining the attendance of the second user at the particular event comprises:

determining that the device associated with the second user was within a particular distance of a location associated with the particular event,

wherein the particular distance satisfies an attendance threshold.

11 . The method of claim 8 , further comprising:

outputting an invitation to a chat room associated with the at least one suggested cluster.

12 . The method of claim 8 , further comprising:

receiving feedback associated with an event in a current bundle of events,

wherein the at least one suggested cluster is further determined based on the feedback.

13 . The method of claim 8 , further comprising:

determining at least one possible host based on the at least one suggested cluster; and

outputting an indication of the at least one possible host.

14 . A non-transitory computer-readable medium storing a set of instructions for responding to questions and clusters for events, the set of instructions comprising:

one or more instructions that, when executed by one or more processors of a device, cause the device to:

transmit at least one response corresponding to at least one question;

receive an indication of at least one user, suggested for networking, based on the at least one response;

receive an invitation to a chat room associated with a cluster of users based on the at least one response;

transmit an indication of a location associated with the device, wherein the location associated with the device is associated with a first event in a bundle of events;

receive, in response to transmitting the indication of the location associated with the device, a request for feedback associated with the first event;

transmit, in response to the request for feedback, feedback associated with the first event, and

receive an indication of a second event, in the bundle of events, based on the feedback and the at least one user suggested for networking.

15 . The non-transitory computer-readable medium of claim 14 , wherein the one or more instructions, when executed by the one or more processors, cause the device to:

receive an indication of at least one additional user, suggested for networking, based on the location associated with the device, wherein the location is associated with the at least one user.

16 . The non-transitory computer-readable medium of claim 14 , wherein the one or more instructions, when executed by the one or more processors, cause the device to:

transmit an indication to skip a third event, in the bundle of events, based on the feedback.

17 . The non-transitory computer-readable medium of claim 14 , wherein the one or more instructions, when executed by the one or more processors, cause the device to:

transmit a registration request for the bundle of events; and

receive a prompt with the at least one question,

wherein the at least one response is transmitted in response to the prompt.

18 . The non-transitory computer-readable medium of claim 14 , wherein the one or more instructions, when executed by the one or more processors, cause the device to:

transmit a confirmation associated with the second event; and

receive an indication of a location for the second event in response to the confirmation.

19 . The non-transitory computer-readable medium of claim 14 , wherein the one or more instructions, when executed by the one or more processors, cause the device to:

receive an indication of a breakout group for the cluster of users.

20 . The non-transitory computer-readable medium of claim 19 , wherein the one or more instructions, when executed by the one or more processors, cause the device to:

transmit a confirmation associated with the breakout group; and

receive an indication of a location for the breakout group in response to the confirmation.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 1, 2023
From: TAMAYO, BRYAN; CAREY, UNIQUE SAMANTHA; LOVETT, CATHERINE LUEATRICE
To: CAPITAL ONE SERVICES, LLC
Reel/Frame 064780/0463 →
Continuity (1)
Related Publication 20250077787A1 · Mar 6, 2025
References Cited (22)
US 8972414B2 · Posse et al. · 2015 [cited by applicant]
US 9519684B2 · Xu · 2016 [cited by applicant]
US 9654425B2 · Heiferman et al. · 2017 [cited by applicant]
US 10007721B1 · Klein · 2018 [cited by examiner]
US 10529004B2 · Caralis · 2020 [cited by examiner]
US 11323493B1 · Xi · 2022 [cited by examiner]
US 11356980B2 · Tarchala · 2022 [cited by examiner]
US 11587000B2 · Pinard · 2023 [cited by examiner]
US 11930056B1 · Ren · 2024 [cited by examiner]
US 12277609B1 · Shaw · 2025 [cited by examiner]
US 20170109446A1 · Wu et al. · 2017 [cited by applicant]
US 20200228358A1 · Rampton · 2020 [cited by examiner]
US 20230401539A1 · Chandra · 2023 [cited by examiner]
US 20250005329A1 · Roitman · 2025 [cited by examiner]
Amershi, Saleema, James Fogarty, and Daniel S. Weld, “ReGroup: Interactive Machine Learning for On-Demand Group Creation in Social Networks”, May 2012, Proceedings of the SIGCHI Conference on Human Factors in Computing … [cited by examiner]
Li, Ruichang, Honglei Zhu, Liao Fan, and Xuekun Song, “Hybrid Deep Framework for Group Event Recommendation”, Jan. 2020, IEEE Access, vol. 8, pp. 4775-4784. (Year: 2020). [cited by examiner]
De Pessemier, Toon, Jeroen Minnaert, Kris Vanhecke, Simon Dooms, and Luc Martens, “Social Recommendations for Events”, Oct. 2013, 2013 RecSys Workshop in conjunction with the 7th ACM Conference on Recommender Systems. (… [cited by examiner]
Hada, Praddumn Singh “AI based Event Management Web Application”, May 2022, 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON), vol. 1, pp. 562-566. (Year: 2022). [cited by examiner]
Zhang, Jason Shuo, Mike Gartrell, Richard Han, Qin Lv, and Shivakant Mishra, “GEVR: An Event Venue Recommendation System for Groups of Mobile Users”, Mar. 2019, Proceedings of the ACM on Interactive, Mobile, Wearable an… [cited by examiner]
Boutsis, Ioannis, Stavroula Karanikolaou, and Vana Kalogeraki, “Personalized Event Recommendations using Social Networks”, Jun. 2015, Proceedings of the 2015 16th IEEE International Conference on Mobile Data Management,… [cited by examiner]
Asabere, Nana Yaw, Feng Xia, Wei Wang, Joel Rodrigues, Filippo Basso, and Jianhua Ma, “Improving Smart Conference Participation through Socially-Aware Recommendation”, Oct. 2014, IEEE Transactions on Human-Machine Syste… [cited by examiner]
“Cvent Attendee Hub: An attendee engagement engine for all your in-person, virtual, and hybrid events,” retrieved from https://www.cvent.com/en/event-marketing-management/attendee-hub on Jul. 12, 2023, 17 pages. [cited by applicant]