IP Library › Granted Patent US 12,250,344
Granted Patent B2
US 12,250,344 · App. 18/423,858 · Granted Mar 11, 2025

Caller verification via carrier metadata

Inventors: John Cornwell (Atlanta, GA); Terry Nelms, II (Atlanta, GA)
Assignee: Pindrop Security, Inc.
H04M3/5175G06F18/214H04M3/2218H04M3/2281H04M3/42059G06V2201/10
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Quick Facts
Patent No.
US 12,250,344
App. No.
18/423,858
Granted
Mar 11, 2025
Kind
B2
Abstract

Embodiments described herein provide for passive caller verification and/or passive fraud risk assessments for calls to customer call centers. Systems and methods may be used in real time as a call is coming into a call center. An analytics server of an analytics service looks at the purported Caller ID of the call, as well as the unaltered carrier metadata, which the analytics server then uses to generate or retrieve one or more probability scores using one or more lookup tables and/or a machine-learning model. A probability score indicates the likelihood that information derived using the Caller ID information has occurred or should occur given the carrier metadata received with the inbound call. The one or more probability scores be used to generate a risk score for the current call that indicates the probability of the call being valid (e.g., originated from a verified caller or calling device, non-fraudulent).

Claims (31)

1. A computer-implemented method comprising:

obtaining, by a computer, call data for a current call originated at a calling device, the call data including one or more carrier metadata values of one or more types of carrier metadata values and one or more derived metadata values of one or more types of derived metadata values;

generating, by the computer, a first score for the current call based upon comparing a portion of the call data for the current call against prior call data of one or more prior calls stored in one or more databases;

obtaining, by the computer, a call-data pair using the call data of the current call, the call-data pair includes a carrier metadata value of a type of carrier metadata value correlated to a derived metadata value of a type of derived metadata value;

generating, by the computer, a second score for the current call based upon probability data indicating a probability of occurrence of the call-data pair; and

generating, by the computer, a third score for the current call based upon the first score generated using the prior call data of the one or more prior calls and the second score generated using the probability data.

2. The method according to claim 1 , wherein the prior call data of the one or more prior calls includes a stored fraud likelihood score for the carrier metadata value in the call data, and wherein the computer generates the first score according to the stored fraud likelihood score for the carrier metadata value.

3. The method according to claim 1 , wherein the prior call data of the one or more prior calls includes prior carrier metadata value in the prior call data, and wherein the computer generates the first score based upon comparing the prior carrier metadata value against the carrier metadata value of the call data of the current call.

4. The method according to claim 1 , wherein generating the third score includes adjusting, by the computer, the first score in accordance with the second score to generate the third score.

5. The method according to claim 1 , further comprising verifying, by the computer, the calling device in response to determining that the third score satisfies a verification threshold that the carrier metadata for the current call is associated with a verified calling device.

6. The method according to claim 1 , further comprising detecting, by the computer, the current call as fraudulent in response to determining that the third score satisfies a fraud risk threshold.

7. The method according to claim 1 , wherein the probability data used for the second score includes a probability table for the type of carrier metadata value correlated to the type of derived metadata value of the call-data pair, the probability table indicating the probability of occurrence of the carrier metadata value and the derived metadata value of the call-data pair.

8. The method according to claim 1 , wherein the probability data used for the second score includes a machine-learning architecture trained to generate the second score indicating the probability of occurrence of the call-data pair.

9. The method according to claim 1 , wherein the computer obtains a plurality of call-data pairs using the call data of the current call, wherein the computer generates a plurality of second scores corresponding to the plurality of call-data pairs, and wherein the computer generates the third score using the first score and the plurality of second scores.

10. The method according to claim 1 , wherein at least one carrier metadata value indicates at least one of: Jurisdiction Information Parameter (JIP), Originating Line Information (OLI), P-Asserted-Identity, an originating switch, an originating trunk, or Caller ID.

11. The method according to claim 1 , wherein at least one derived metadata value indicates at least one of: a carrier, a geographic location, or a line type.

12. A system comprising:

a server comprising a processor configured to:

obtain call data for a current call originated at a calling device, the call data including one or more carrier metadata values of one or more types of carrier metadata values and one or more derived metadata values of one or more types of derived metadata values;

generate a first score for the current call based upon comparing a portion of the call data for the current call against prior call data of one or more prior calls stored in one or more databases;

obtain a call-data pair using the call data of the current call, the call-data pair includes a carrier metadata value of a type of carrier metadata value correlated to a derived metadata value of a type of derived metadata value;

generate a second score for the current call based upon probability data indicating a probability of occurrence of the call-data pair; and

generate a third score for the current call based upon the first score generated using the prior call data of the one or more prior calls and the second score generated using the probability data.

13. The system according to claim 12 , wherein the prior call data of the one or more prior calls includes a stored fraud likelihood score for the carrier metadata value in the call data, and wherein the computer generates the first score according to the stored fraud likelihood score for the carrier metadata value.

14. The system according to claim 12 , wherein the prior call data of the one or more prior calls includes prior carrier metadata value in the prior call data, and wherein the computer generates the first score based upon comparing the prior carrier metadata value against the carrier metadata value of the call data of the current call.

15. The system according to claim 12 , wherein, when generating the third score, the computer is configured to adjust the first score in accordance with the second score to generate the third score.

16. The system according to claim 12 , wherein the computer is configured to verify the calling device in response to the computer determining that the third score satisfies a verification threshold that the carrier metadata for the current call is associated with a verified calling device.

17. The method according to claim 12 , wherein the computer is configured to detect the current call as fraudulent in response to determining that the third score satisfies a fraud risk threshold.

18. The system according to claim 12 , wherein the probability data used for the second score includes a probability table for the type of carrier metadata value correlated to the type of derived metadata value of the call-data pair, the probability table indicating the probability of occurrence of the carrier metadata value and the derived metadata value of the call-data pair.

19. The system according to claim 12 , wherein the probability data used for the second score includes a machine-learning architecture trained to generate the second score indicating the probability of occurrence of the call-data pair.

20. The system according to claim 12 , wherein the computer obtains a plurality of call-data pairs using the call data of the current call, wherein the computer generates a plurality of second scores corresponding to the plurality of call-data pairs, and wherein the computer generates the third score using the first score and the plurality of second scores.

Assignments (2)
SECURITY INTEREST Recorded Jun 26, 2024
From: PINDROP SECURITY, INC.
To: HERCULES CAPITAL, INC., AS AGENT
Reel/Frame 067867/0860 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 4, 2024
From: CORNWELL, JOHN; NELMS, TERRY, II
To: PINDROP SECURITY, INC.
Reel/Frame 067619/0725 →
Continuity (4)
Continuation 17948991 · Sep 20, 2022
Continuation 16992789 · Aug 13, 2020
Provisional Application 62888978 · Aug 19, 2019
Related Publication 20240171680A1 · May 23, 2024
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