US 11,889,024 B2
Caller verification via carrier metadata
John Cornwell, Atlanta, GA (US); and Terry Nelms, II, Atlanta, GA (US)
Assigned to Pindrop Security, Inc., Atlanta, GA (US)
Filed by Pindrop Security, Inc., Atlanta, GA (US)
Filed on Sep. 20, 2022, as Appl. No. 17/948,991.
Application 17/948,991 is a continuation of application No. 16/992,789, filed on Aug. 13, 2020, granted, now 11,470,194.
Claims priority of provisional application 62/888,978, filed on Aug. 19, 2019.
Prior Publication US 2023/0014180 A1, Jan. 19, 2023
Int. Cl. H04M 3/00 (2006.01); H04M 5/00 (2006.01); H04L 12/66 (2006.01); H04M 3/51 (2006.01); H04M 3/22 (2006.01); H04M 3/42 (2006.01); G06F 18/214 (2023.01)
CPC H04M 3/5175 (2013.01) [G06F 18/214 (2023.01); H04M 3/2218 (2013.01); H04M 3/2281 (2013.01); H04M 3/42059 (2013.01); G06V 2201/10 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for caller verification, the method comprising:
obtaining, by the computer, a call-data pair of call data received for a current call, the call-data pair including a carrier metadata value of a type of carrier metadata value correlated to a derived metadata value of a type of derived metadata value;
obtaining, by the computer, probability data according to the call data of the current call, the probability data comprising a machine-learning architecture trained to determine a risk score for the call data and a probability value indicating a probability of occurrence of the call-data pair;
generating, by the computer, a feature vector for the current call based upon the probability value, the derived metadata value, and the carrier metadata value of the call-data pair;
generating, by the computer, the risk score for the current call by applying the machine-learning architecture on the feature vector of the current call; and
identifying, by the computer, the current call as a fraudulent call in response to the computer determining that the risk score satisfies a fraud threshold score.