IP Library Granted Patent US 12,299,701
Granted Patent B2
US 12,299,701 · App. 17/489,102 · Granted May 13, 2025

Utilizing lifetime values of users to select content for presentation in a messaging system

Inventors: Jean Luo (Seattle, WA); Zhehao Zhou (Santa Monica, CA)
Assignee: Snap Inc.
G06Q30/0201G06Q30/0205G06Q30/0277G06T19/006
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Quick Facts
Patent No.
US 12,299,701
App. No.
17/489,102
Granted
May 13, 2025
Kind
B2
Abstract

The subject technology determines monthly active users (MAU) and penetration for global users and users in a specific country. The subject technology predicts monetization values for the users in the specific country. The subject technology determines a lifetime value of the users in the specific country based at least in part on the monetization values. The subject technology selects at least one augmented reality (AR) content generator based at least in part on the determined lifetime value of the users in the specific country. The subject technology causes, at a client device, display of the at least one AR content generator.

Claims (61)

1. A method, comprising:

determining, by one or more hardware processors provided by a messaging server system, monthly active users (MAU) and penetration for global users and users in a specific country;

predicting, by the one or more hardware processors of the messaging server system, monetization values for the users in the specific country;

determining, by a first hardware processor of the messaging server system, a lifetime value of the users in the specific country based at least in part on the monetization values;

selecting, by a second hardware processor accessing a shared memory of the messaging server system, at least one augmented reality (AR) content generator based at least in part on the determined lifetime value of the users in the specific country, the users in the specific country comprising a set of users based on user interactions of augmented reality content generators received from multiple clients devices over a network, the network comprising the Internet, the selecting, using the second hardware processor accessing the shared memory, the at least one AR content generator being further based on determining that a percentage of monetized users on a daily basis is lower than a percentage of the users of the at least one AR content generator;

performing, by the one or more hardware processors of the messaging server system, a validation process of a model to determine the lifetime value of the users in the specific country, the validation process comprising:

determining a frequency of repeat transactions based on a number of users and a number of calibration period transactions;

determining a number of purchases in a holdout period and a number of predicted purchases based on an average of purchases in the holdout period and a number of purchases in a calibration period;

determining, based on the number of purchases in the holdout period and the number of predicted purchases, whether a cumulative error rate is below a predetermined percentage; and

indicating that the model is accurate based at least in part on determining that the cumulative error rate is below the predetermined percentage;

causing, at a client device over the Internet, display of the at least one AR content generator on a display of the client device; and

displaying, at the display of the client device, the at least one AR content generator.

2. The method of claim 1 , further comprising:

determining that users of the at least one AR content generator has a higher penetration in MAU.

3. The method of claim 1 further comprising:

determining that lifetime values of users of the at least one AR content generator are lower than prior lifetime values of the users before using the at least one AR content generator.

4. The method of claim 3 , further comprising:

determining that cumulative revenue for users is higher after using the at least one AR content generator.

5. The method of claim 4 , further comprising:

determining a set of new users that have not accessed the at least one AR content generator.

6. The method of claim 5 , wherein the set of new users have accessed other AR content generators different than the at least one AR content generator.

7. The method of claim 5 , further comprising:

providing, for display, the at least one AR content generator at a particular client device associated with at least one new user from the set of new users.

8. The method of claim 5 , wherein the set of new users are selected to improve monetization based on revenue from online advertisements and sponsored creative tools.

9. A system comprising:

one or more hardware processors; and

a memory including instructions that, when executed by the one or more hardware processors, cause the one or more hardware processors to perform operations comprising:

determining, by a messaging server system, monthly active users (MAU) and penetration for global users and users in a specific country;

predicting, by the messaging server system, monetization values for the users in the specific country, the users in the specific country comprising a set of users based on user interactions of augmented reality content generators received from multiple clients devices over a network, the network comprising the Internet;

determining, by a first hardware processor the messaging server system, a lifetime value of the users in the specific country based at least in part on the monetization values;

selecting, by a second hardware processor accessing a shared memory of the messaging server system, at least one augmented reality (AR) content generator based at least in part on the determined lifetime value of the users in the specific country, the selecting, using the second hardware processor accessing the shared memory, the at least one AR content generator being further based on determining that a percentage of monetized users on a daily basis is lower than a percentage of the users of the at least one AR content generator;

performing, by the messaging server system, a validation process of a model to determine the lifetime value of the users in the specific country, the validation process comprising:

determining a frequency of repeat transactions based on a number of users and a number of calibration period transactions;

determining a number of purchases in a holdout period and a number of predicted purchases based on an average of purchases in the holdout period and a number of purchases in a calibration period;

determining, based on the number of purchases in the holdout period and the number of predicted purchases, whether a cumulative error rate is below a predetermined percentage; and

indicating that the model is accurate based at least in part on determining that the cumulative error rate is below the predetermined percentage;

causing, at a client device over the Internet, display of the at least one AR content generator on a display of the client device; and

displaying, at the display of the client device, the at least one AR content generator.

10. The system of claim 9 , wherein the operations further comprise:

determining that users of the at least one AR content generator has a higher penetration in MAU.

11. The system of claim 9 wherein the operations further comprise:

determining that lifetime values of users of the at least one AR content generator are lower than prior lifetime values of the users before using the at least one AR content generator.

12. The system of claim 11 , wherein the operations further comprise:

determining that cumulative revenue for users is higher after using the at least one AR content generator.

13. The system of claim 12 , wherein the operations further comprise:

determining a set of new users that have not accessed the at least one AR content generator.

14. The system of claim 13 , wherein the set of new users have accessed other AR content generators different than the at least one AR content generator.

15. The system of claim 13 , wherein the operations further comprise:

providing, for display, the at least one AR content generator at a particular client device associated with at least one new user from the set of new users.

16. A non-transitory computer-readable medium comprising instructions, which when executed by a computing device, cause the computing device to perform operations comprising:

determining, by a messaging server system, monthly active users (MAU) and penetration for global users and users in a specific country;

predicting, by the messaging server system, monetization values for the users in the specific country;

determining, by a first hardware processor of the messaging server system, a lifetime value of the users in the specific country based at least in part on the monetization values;

selecting, by a second hardware processor accessing a shared memory of the messaging server system, at least one augmented reality (AR) content generator based at least in part on the determined lifetime value of the users in the specific country, the users in the specific country comprising a set of users based on user interactions of augmented reality content generators received from multiple clients devices over a network, the network comprising the Internet, the selecting, using the second hardware processor accessing the shared memory, the at least one AR content generator being further based on determining that a percentage of monetized users on a daily basis is lower than a percentage of the users of the at least one AR content generator;

performing, by one or more hardware processors of the messaging server system, a validation process of a model to determine the lifetime value of the users in the specific country, the validation process comprising:

determining a frequency of repeat transactions based on a number of users and a number of calibration period transactions;

determining a number of purchases in a holdout period and a number of predicted purchases based on an average of purchases in the holdout period and a number of purchases in a calibration period;

determining, based on the number of purchases in the holdout period and the number of predicted purchases, whether a cumulative error rate is below a predetermined percentage; and

indicating that the model is accurate based at least in part on determining that the cumulative error rate is below the predetermined percentage;

causing, at a client device over the Internet, display of the at least one AR content generator on a display of the client device; and

displaying, at the display of the client device, the at least one AR content generator.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 27, 2025
From: LUO, JEAN; ZHOU, ZHEHAO
To: SNAP INC.
Reel/Frame 070012/0523 →
Continuity (2)
Provisional Application 63085946 · Sep 30, 2020
Related Publication 20220101349A1 · Mar 31, 2022
References Cited (63)
US 10075508B2 · Sivalingam et al. · 2018 [cited by applicant]
US 10417650B1 · Gong · 2019 [cited by examiner]
US 11403652B1 · Growitz et al. · 2022 [cited by applicant]
US 20070050256A1 · Walker · 2007 [cited by examiner]
US 20110258049A1 · Ramer · 2011 [cited by examiner]
US 20140278967A1 · Pal et al. · 2014 [cited by applicant]
US 20140289007A1 · Bhattacharya et al. · 2014 [cited by applicant]
US 20150046252A1 · Hart · 2015 [cited by examiner]
US 20150170147A1 · Geckle et al. · 2015 [cited by applicant]
US 20150243258A1 · Howe · 2015 [cited by examiner]
US 20160086215A1 · Wang · 2016 [cited by examiner]
US 20160292722A1 · Myers et al. · 2016 [cited by applicant]
US 20170372337A1 · Han et al. · 2017 [cited by applicant]
US 20180013844A1 · Foged et al. · 2018 [cited by applicant]
US 20180040161A1 · Tierney · 2018 [cited by examiner]
US 20180068350A1 · Grosso · 2018 [cited by applicant]
US 20180143891A1 · Polisetty et al. · 2018 [cited by applicant]
US 20180211326A1 · Bayley et al. · 2018 [cited by applicant]
US 20190108686A1 · Spivack · 2019 [cited by examiner]
US 20190180319A1 · Jaatinen · 2019 [cited by examiner]
US 20190279236A1 · Fadli · 2019 [cited by applicant]
US 20190295056A1 · Wright · 2019 [cited by applicant]
US 20190303807A1 · Gueye · 2019 [cited by examiner]
US 20190347675A1 · Yang et al. · 2019 [cited by applicant]
US 20200066046A1 · Stahl · 2020 [cited by examiner]
US 20200074738A1 · Hare et al. · 2020 [cited by applicant]
US 20210241321A1 · Downing · 2021 [cited by examiner]
US 20220101355A1 · Luo et al. · 2022 [cited by applicant]
CN 116710945A · 2023 [cited by applicant]
CN 116964613A · 2023 [cited by applicant]
KR 101687012B1 · 2016 [cited by applicant]
KR 20190080244 · 2019 [cited by applicant]
WO 2022072497 · 2022 [cited by applicant]
WO 2022072505 · 2022 [cited by applicant]
Personalized mobile marketing strategies. Tong Siliang; Luo Xueming; Xu, Bo. Journal of the Academy of Marketing Science 48.1:64-78. New York: Springer Nature B.V. (Jan. 2020). [cited by examiner]
Social Media, Quo Vadis? Prospective Development and Implications. Studen, Laura; Tiberius, Victor. Future Internet12.9: 146.MDPI AG. (2020). [cited by examiner]
IZEA Brings Augmented Reality Product Placement to Influencer Marketing. Business Wire [New York] Sep. 21, 2017. [cited by examiner]
U.S. Appl. No. 17/489,332, filed Sep. 29, 2021, Determining Lifetime Values of Users in a Messaging System. [cited by applicant]
“International Application Serial No. PCT/US2021/052665, International Search Report mailed Jan. 21, 2022”, 5 pgs. [cited by applicant]
“International Application Serial No. PCT/US2021/052665, Written Opinion mailed Jan. 21, 2022”, 4 pgs. [cited by applicant]
“International Application Serial No. PCT/US2021/052655, International Search Report mailed Jan. 18, 2022”, 5 pgs. [cited by applicant]
“International Application Serial No. PCT/US2021/052655, Written Opinion mailed Jan. 18, 2022”, 4 pgs. [cited by applicant]
“U.S. Appl. No. 17/489,332, Final Office Action mailed May 8, 2023”, 35 pgs. [cited by applicant]
“U.S. Appl. No. 17/489,332, Non Final Office Action mailed Aug. 30, 2023”, 35 pgs. [cited by applicant]
“U.S. Appl. No. 17/489,332, Non Final Office Action mailed Nov. 9, 2022”, 24 pgs. [cited by applicant]
“U.S. Appl. No. 17/489,332, Response filed Feb. 9, 2023 to Non Final Office Action mailed Nov. 9, 2022”, 13 pgs. [cited by applicant]
“U.S. Appl. No. 17/489,332, Response filed Aug. 8, 2023 to Final Office Action mailed May 8, 2023”, 17 pgs. [cited by applicant]
“Chinese Application Serial No. 202180066868.X, Notification to Make Rectification mailed May 29, 2023”, w/o English translation, 1 pg. [cited by applicant]
“Chinese Application Serial No. 202180066949.X, Notification to Make Rectification mailed May 31, 2023”, W/O English Translation, 1 page. [cited by applicant]
“International Application Serial No. PCT/US2021/052655, International Preliminary Report on Patentability mailed Apr. 13, 2023”, 6 pgs. [cited by applicant]
“International Application Serial No. PCT/US2021/052665, International Preliminary Report on Patentability mailed Apr. 13, 2023”, 6 pgs. [cited by applicant]
Fader, Peter S, et al., “Implementing the BG/NBD model for customer base analysis in Excel”, [Online] Retrieved from the internet: <http://www.brucehardie.com/notes/004/bgnbd_spreadsheet_note>, (2005), 8 pgs. [cited by applicant]
Fader, Peter S, et al., “The Gamma-Gamma model of monetary value”, (Feb. 2, 2013), 9 pgs. [cited by applicant]
Pei, Pei Chen, et al., “Customer Lifetime Value in Video Games Using Deep Learning and Parametric Models”, IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, (2018), 7 pgs. [cited by applicant]
“U.S. Appl. No. 17/489,332, Final Office Action mailed Apr. 10, 2024”, 39 pgs. [cited by applicant]
“European Application Serial No. 21876401.7, Extended European Search Report mailed May 22, 2024”, 6 pgs. [cited by applicant]
“European Application Serial No. 21876395.1, Extended European Search Report mailed Mar. 19, 2024”, 7 pgs. [cited by applicant]
“Korean Application Serial No. 10-2023-7014611, Notice of Preliminary Rejection mailed May 24, 2024”, w/ English translation, 13 pgs. [cited by applicant]
“Korean Application Serial No. 10-2023-7014611, Response filed Jul. 24, 2024 to Notice of Preliminary Rejection mailed May 24, 2024”, W/English Claims, 20 pgs. [cited by applicant]
“Korean Application Serial No. 10-2023-7014675, Notice of Preliminary Rejection mailed Jun. 10, 2024”, w/ English Translation, 12 pgs. [cited by applicant]
“U.S. Appl. No. 17/489,332, Response filed Jan. 2, 2024 to Non Final Office Action mailed Aug. 30, 2023”, 17 pgs. [cited by applicant]
“European Application Serial No. 21876401.7, Response to Communication Pursuant to Rules 161 and 162 filed Oct. 12, 2023”, 11 pgs. [cited by applicant]
“European Application Serial No. 21876395.1, Response to Communication Pursuant to Rules 161 and 162 filed Oct. 12, 2023”, 9 pgs. [cited by applicant]