IP Library › Granted Patent US 12,701,189
Granted Patent B2
US 12,701,189 · App. 18/646,213 · Granted Aug 4, 2026

Identifying a voicemail message as spam based on a similarity to a spam message

Inventors: Melinda Min Xiao-Devins (San Jose, CA); Haifeng Geng (San Jose, CA); Mengxiao Qian (Santa Clara, CA); Fengcai Sun (Hangzhou, CN); Hui Wen (Hangzhou City, CN)
Assignee: Zoom Communications, Inc.
H04M3/53366H04M1/663H04M3/436H04M3/5335
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Quick Facts
Patent No.
US 12,701,189
App. No.
18/646,213
Granted
Aug 4, 2026
Kind
B2
Abstract

Voicemail spam detection is performed based on content of voicemail messages. The content of an incoming voicemail message is compared to a spam template that includes a representation of a spam voicemail. Spam templates may be generated based on spam indications provided by users for voicemail messages they have received. User indications for sufficiently similar voicemail messages may be aggregated by maintaining a vote count for a spam template that reflects how many times a user has indicated a matching voicemail message is spam. A spam template may also include an occurrence count that reflects how many times voicemail messages matching a spam template have been detected in a telephony system. An incoming voicemail message may be compared to spam templates and, responsive to a match of content and/or a corresponding vote count or occurrence count meeting a condition, the voicemail message may be identified as spam.

Claims (35)

1 . A method, comprising: determining a cosine similarity between one or more word vectors representing a voicemail message and one or more word vectors of a spam message; and responsive to the cosine similarity between the voicemail message and the spam message exceeding a threshold, identifying the voicemail message as spam, wherein the spam message is an audio recording, and wherein the cosine similarity is determined based on a distance metric between the audio recording of the spam message and an audio recording of the voicemail message.

2 . The method of claim 1 , wherein the spam message includes a vote count that reflects a number of times that one or more users have indicated that a second voicemail message matching the spam message is spam.

3 . The method of claim 2 , comprising:

comparing the vote count of the spam message to a spam threshold; and

invoking a spam mitigation measure on the voicemail message when the vote count exceeds the spam threshold.

4 . The method of claim 1 , wherein the spam message further comprises an occurrence count reflecting a number of received voicemail messages that have been matched to the spam message, and further comprising:

comparing the occurrence count of the spam message to a second threshold, wherein the voicemail message is identified as spam responsive to the occurrence count exceeding the second threshold.

5 . The method of claim 1 , further comprising:

receiving an indication from a user that a second voicemail message is spam;

comparing the second voicemail message to the spam message; and

responsive to a match between the second voicemail message and the spam message, incrementing a vote count of the spam message.

6 . The method of claim 1 , further comprising:

blocking the voicemail message from entering a voicemail inbox of a user.

7 . The method of claim 1 , further comprising:

deleting the voicemail message.

8 . The method of claim 1 , further comprising:

displaying a message in a user interface indicating that the voicemail message is spam.

9 . A system, comprising: a processor, and a memory, wherein the memory stores instructions executable by the processor to: determine a cosine similarity between one or more word vectors representing a voicemail message and one or more word vectors of a spam message; and responsive to the cosine similarity between the voicemail message and the spam message exceeding a threshold, identify the voicemail message as spam, wherein the spam message is an audio recording, and wherein the cosine similarity is determined based on a distance metric between the audio recording of the spam message and an audio recording of the voicemail message.

10 . The system of claim 9 , wherein the spam message includes a vote count that reflects a number of times that one or more users have indicated that a second voicemail message matching the spam message is spam.

11 . The system of claim 10 , wherein the memory stores instructions executable by the processor to:

compare the vote count of the spam message to a spam threshold; and

invoke a spam mitigation measure on the voicemail message when the vote count exceeds the spam threshold.

12 . The system of claim 9 , wherein the spam message further comprises an occurrence count reflecting a number of received voicemail messages that have been matched to the spam message, and wherein the memory stores instructions executable by the processor to:

compare the occurrence count of the spam message to a second threshold, wherein the voicemail message is identified as spam responsive to the occurrence count exceeding the second threshold.

13 . The system of claim 9 , wherein the memory stores instructions executable by the processor to:

receive an indication from a user that a second voicemail message is spam;

compare the second voicemail message to the spam message; and

responsive to a match between the second voicemail message and the spam message, increment a vote count of the spam message.

14 . A non-transitory computer-readable storage medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising: determining a cosine similarity between one or more word vectors representing a voicemail message and one or more word vectors of a spam message; and responsive to the cosine similarity between the voicemail message and the spam message exceeding a threshold, identifying the voicemail message as spam, wherein the spam message is an audio recording, and wherein the cosine similarity is determined based on a distance metric between the audio recording of the spam message and an audio recording of the voicemail message.

15 . The non-transitory computer-readable storage medium of claim 14 , wherein the spam message includes a vote count that reflects a number of times that one or more users have indicated that a second voicemail message matching the spam message is spam.

16 . The non-transitory computer-readable storage medium of claim 15 , wherein the operations further comprise:

comparing the vote count of the spam message to a spam threshold; and

invoking a spam mitigation measure on the voicemail message when the vote count exceeds the spam threshold.

17 . The non-transitory computer-readable storage medium of claim 14 , wherein the spam message further comprises an occurrence count reflecting a number of received voicemail messages that have been matched to the spam message, and wherein the operations further comprise:

comparing the occurrence count of the spam message to a second threshold, wherein the voicemail message is identified as spam responsive to the occurrence count exceeding the second threshold.

Assignments (2)
CHANGE OF NAME Recorded Jan 7, 2025
From: ZOOM VIDEO COMMUNICATIONS, INC.
To: ZOOM COMMUNICATIONS, INC.
Reel/Frame 069839/0593 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 20, 2024
From: XIAO-DEVINS, MELINDA MIN; GENG, HAIFENG; QIAN, MENGXIAO; SUN, FENGCAI; WEN, HUI
To: ZOOM VIDEO COMMUNICATIONS, INC.
Reel/Frame 067467/0393 →
Priority Claims (1)
CN 202110127306.3 · Jan 29, 2021 · national
Continuity (2)
Continuation 17191668 · Mar 3, 2021
Related Publication 20240275886A1 · Aug 15, 2024
References Cited (47)
US 7222157B1 · Sutton, Jr. et al. · 2007 [cited by applicant]
US 7475118B2 · Leiba et al. · 2009 [cited by applicant]
US 7680890B1 · Lin · 2010 [cited by applicant]
US 7860928B1 · Anderson · 2010 [cited by applicant]
US 8051134B1 · Begeja et al. · 2011 [cited by applicant]
US 9519682B1 · Pujara et al. · 2016 [cited by applicant]
US 10606850B2 · Crudele et al. · 2020 [cited by applicant]
US 10699246B2 · Wieneke et al. · 2020 [cited by applicant]
US 10979464B1 · Lang et al. · 2021 [cited by applicant]
US 11651032B2 · Jayaraman · 2023 [cited by examiner]
US 20020116463A1 · Hart · 2002 [cited by applicant]
US 20040128355A1 · Chao et al. · 2004 [cited by applicant]
US 20040177110A1 · Rounthwaite et al. · 2004 [cited by applicant]
US 20040215793A1 · Ryan et al. · 2004 [cited by applicant]
US 20040267893A1 · Lin · 2004 [cited by applicant]
US 20050060638A1 · Mathew et al. · 2005 [cited by applicant]
US 20050080856A1 · Kirsch · 2005 [cited by applicant]
US 20050080857A1 · Kirsch et al. · 2005 [cited by applicant]
US 20050097435A1 · Prakash et al. · 2005 [cited by applicant]
US 20050160148A1 · Yu · 2005 [cited by applicant]
US 20050165895A1 · Rajan et al. · 2005 [cited by applicant]
US 20050262209A1 · Yu · 2005 [cited by applicant]
US 20060026242A1 · Kuhlmann et al. · 2006 [cited by applicant]
US 20070071200A1 · Brouwer · 2007 [cited by applicant]
US 20070076853A1 · Kurapati et al. · 2007 [cited by applicant]
US 20070133757A1 · Girouard et al. · 2007 [cited by applicant]
US 20070150773A1 · Srivastava · 2007 [cited by applicant]
US 20080123823A1 · Pirzada · 2008 [cited by examiner]
US 20080144783A1 · Kumar et al. · 2008 [cited by applicant]
US 20080155036A1 · Pirzada · 2008 [cited by examiner]
US 20080201651A1 · Hong et al. · 2008 [cited by applicant]
US 20090147930A1 · Rice · 2009 [cited by applicant]
US 20100153107A1 · Kawai · 2010 [cited by applicant]
US 20120167208A1 · Buford et al. · 2012 [cited by applicant]
US 20120324019A1 · Ordogh · 2012 [cited by applicant]
US 20130136245A1 · Reyes et al. · 2013 [cited by applicant]
US 20150350399A1 · Feller · 2015 [cited by applicant]
US 20170005962A1 · Lewin-Eytan · 2017 [cited by examiner]
US 20180131799A1 · Kashimba et al. · 2018 [cited by applicant]
US 20180159808A1 · Pal et al. · 2018 [cited by applicant]
https://www.youmail.com/home/feature/call-blocker, Call Blocker | YouMail, Call Blocker, Block Spammers, telemarketers and unwanted calls forever, 5 pages, 2021. [cited by applicant]
How to Prevent Spam Callers From Leaving Voicemail, Josh Kirschner, 12 pages, Jul. 15, 2019. [cited by applicant]
GloVe: Global Vectors for Word Representation, Jeffrey Pennington, Richard Socher, Christopher D. Manning, Computer Science Department, Stanford University, Stanford, CA 94305, 12 pages. Jan. 2014. [cited by applicant]
Mature, Inc., Stop Robocalls and Spam Calls with Mutare's Innovative Voice Spam Filter Solution for Business, 3 pages, Sep. 17, 2020. [cited by applicant]
https://blog.zoom.us/ucaas-platform-whats-new-for-meetings-phone-webinar-zoomtopia-2020/, UCaaS Platform: What's New for Meetings, Phone & Webinar, 7 pages, Oct. 14, 2020. [cited by applicant]
A Voice spam Filter to Clean Subscribers' Mailbox, Seyed Amir Iranmanesh, Hemant Sengar, and Haining Wang, Department of Computer Science, College of William and Mary, Williamsburg, VA 23187, and Technology Development … [cited by applicant]
https://support.google.com/voice/thread/78428232?hl=en, What does the Spam Filter Setting do as far as Calls?—Google Voice Community, 2 pages, Oct. 22, 2020. [cited by applicant]