IP Library › Granted Patent US 12,748,890
Granted Patent B1
US 12,748,890 · App. 18/955,202 · Granted Sep 29, 2026

Anonymized transfer of personally identifiable information

Inventors: Sean Michael Wayne Craig (San Antonio, TX); Roberto Virgillio Jolliffe (San Antonio, TX)
Assignee: United Services Automobile Association (USAA)
G06F21/6254G06F21/602G06K19/06037G06Q40/08
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Quick Facts
Patent No.
US 12,748,890
App. No.
18/955,202
Granted
Sep 29, 2026
Kind
B1
Abstract

Some implementations of the technology relate to anonymizing personally identifiable information (PII) by creating a quick response (QR) code linked to a website that can verify that driver's license information and insurance information of a driver is available and valid, without showing the PII itself. Some implementations can allow another driver in an automobile accident to scan the QR code, enter her own insurance information, and can push the driver's PII to her insurance company. Some implementations can allow the other driver to download an encrypted file with the PII that can be shared with his insurance company. The QR code can be used in other contexts outside of automobile accidents as well, such as when a driver wishes to test drive a car, rent a car, buy a new car, etc., and needs to provide proof of a valid driver's license and insurance coverage.

Claims (66)

1 . A method for anonymizing personally identifiable information associated with a first user for verification, the method comprising:

accessing the personally identifiable information associated with the first user, the personally identifiable information including driver's license information of the first user and/or insurance information associated with a vehicle of the first user;

generating an anonymized identifier referencing the personally identifiable information associated with the first user;

transmitting the anonymized identifier to at least one of a mobile device of the first user, a mobile device of a second user, the vehicle of the first user, a vehicle of the second user, or any combination thereof;

receiving a request, including a version of the anonymized identifier, to verify that the anonymized identifier references the personally identifiable information associated with the first user, the request being generated by the device that received the anonymized identifier;

confirming that the anonymized identifier references the personally identifiable information; and

causing transmission, to a device associated with the second user, of a verification that the anonymized identifier references the personally identifiable information associated with the first user.

2 . The method of claim 1 , further comprising:

receiving personally identifiable information, associated with the second user, including insurance information associated with the vehicle of the second user; and

transmitting, based on the insurance information associated with the vehicle of the second user, the personally identifiable information associated with the first user, to a computing system of an insurance company associated with the second user.

3 . The method of claim 1 , further comprising:

receiving additional data associated with an automobile accident between the vehicle of the first user and the vehicle of the second user, the additional data including at least one of an image of the first user, an image of the second user, an image of the vehicle of the first user, an image of the vehicle of the second user, an image of a license plate of the vehicle of the first user, an image of a license plate of the vehicle of the second user, onboard sensor data from the vehicle of the first user, onboard sensor data from data from the vehicle of the second user, sensor data from the mobile device of the first user, sensor data from the mobile device of the second user, or any combination thereof; and

appending the additional data associated with the automobile accident to the personally identifiable information associated with the first user prior to transmitting the personally identifiable information associated with the first user to a computing system of an insurance company associated with the second user.

4 . The method of claim 1 , further comprising:

encrypting the personally identifiable information associated with the first user;

wherein the transmitting the anonymized identifier includes transmitting the encrypted personally identifiable information associated with the first user to the mobile device of the second user; and

wherein the mobile device of the second user transmits the encrypted personally identifiable information associated with the first user, to a computing system of an insurance company associated with the second user.

5 . The method of claim 1 , wherein the personally identifiable information associated with the first user includes a redacted version of the personally identifiable information of the first user.

6 . The method of claim 1 , wherein the anonymized identifier is a quick response (QR) code.

7 . The method of claim 1 ,

wherein the anonymized identifier is automatically displayed on the mobile device of the first user upon detection of an automobile accident; and

wherein the detection of the automobile accident is performed on the mobile device of the first user by applying a machine learning model trained to receive motion data and provide a corresponding prediction of whether an accident has occurred.

8 . The method of claim 1 ,

wherein the anonymized identifier code is transmitted to the vehicle of the first user; and

wherein the anonymized identifier code is automatically transmitted to the vehicle of the second user upon detection of an automobile accident.

9 . A non-transitory computer-readable storage medium storing instructions, for anonymizing personally identifiable information associated with a first user for verification, the instructions, when executed by a computing system, cause the computing system to:

access the personally identifiable information associated with the first user, the personally identifiable information including driver's license information of the first user and/or insurance information associated with a vehicle of the first user;

generate an anonymized identifier referencing the personally identifiable information associated with the first user;

transmit the anonymized identifier to at least one of a mobile device of the first user, a mobile device of a second user, the vehicle of the first user, a vehicle of the second user, or any combination thereof;

receive a request, including a version of the anonymized identifier, to verify that the anonymized identifier references the personally identifiable information associated with the first user, the request being generated by the device that received the anonymized identifier;

confirm that the anonymized identifier references the personally identifiable information; and

cause transmission, to a device associated with the second user, of a verification that the anonymized identifier references the personally identifiable information associated with the first user.

10 . The non-transitory computer-readable storage medium of claim 9 , wherein the instructions, when executed by the computing system, further cause the computing system to:

receive personally identifiable information, associated with the second user, including insurance information associated with the vehicle of the second user; and

transmit, based on the insurance information associated with the vehicle of the second user, the personally identifiable information associated with the first user, to a computing system of an insurance company associated with the second user.

11 . The non-transitory computer-readable storage medium of claim 9 , wherein the instructions, when executed by the computing system, further cause the computing system to:

receive additional data associated with an automobile accident between the vehicle of the first user and the vehicle of the second user, the additional data including at least one of an image of the first user, an image of the second user, an image of the vehicle of the first user, an image of the vehicle of the second user, an image of a license plate of the vehicle of the first user, an image of a license plate of the vehicle of the second user, onboard sensor data from the vehicle of the first user, onboard sensor data from data from the vehicle of the second user, sensor data from the mobile device of the first user, sensor data from the mobile device of the second user, or any combination thereof; and

append the additional data associated with the automobile accident to the personally identifiable information associated with the first user prior to transmitting the personally identifiable information associated with the first user to a computing system of an insurance company associated with the second user.

12 . The non-transitory computer-readable storage medium of claim 9 , wherein the instructions, when executed by the computing system, further cause the computing system to:

encrypt the personally identifiable information associated with the first user;

wherein the transmitting the anonymized identifier includes transmitting the encrypted personally identifiable information associated with the first user to the mobile device of the second user; and

wherein the mobile device of the second user transmits the encrypted personally identifiable information associated with the first user, to a computing system of an insurance company associated with the second user.

13 . The non-transitory computer-readable storage medium of claim 9 , wherein the personally identifiable information associated with the first user includes a redacted version of the personally identifiable information of the first user.

14 . The non-transitory computer-readable storage medium of claim 9 , wherein the anonymized identifier is a quick response (QR) code.

15 . The non-transitory computer-readable storage medium of claim 9 ,

wherein the anonymized identifier is automatically displayed on the mobile device of the first user upon detection of an automobile accident; and

wherein the detection of the automobile accident is performed on the mobile device of the first user by applying a machine learning model trained to receive motion data and provide a corresponding prediction of whether an accident has occurred.

16 . The non-transitory computer-readable storage medium of claim 9 ,

wherein the anonymized identifier code is transmitted to the vehicle of the first user; and

wherein the anonymized identifier code is automatically transmitted to the vehicle of the second user upon detection of an automobile accident.

17 . A computing system, for anonymizing personally identifiable information associated with a first user for verification, the computing system comprising:

one or more processors; and

one or more memories storing instructions that, when executed by the one or more processors, cause the computing system to:

access the personally identifiable information associated with the first user, the personally identifiable information including driver's license information of the first user and/or insurance information associated with a vehicle of the first user;

generate an anonymized identifier referencing the personally identifiable information associated with the first user;

transmit the anonymized identifier to at least one of a mobile device of the first user, a mobile device of a second user, the vehicle of the first user, a vehicle of the second user, or any combination thereof;

receive a request, including a version of the anonymized identifier, to verify that the anonymized identifier references the personally identifiable information associated with the first user, the request being generated by the device that received the anonymized identifier;

confirm that the anonymized identifier references the personally identifiable information; and

transmit, to a device associated with the second user, a verification that the anonymized identifier references of the personally identifiable information associated with the first user.

18 . The computing system of claim 17 , wherein the instructions, when executed by the one or more processors, further cause the computing system to:

receive personally identifiable information, associated with the second user, including insurance information associated with the vehicle of the second user; and

transmit, based on the insurance information associated with the vehicle of the second user, the personally identifiable information associated with the first user, to a computing system of an insurance company associated with the second user.

19 . The computing system of claim 17 , wherein the personally identifiable information associated with the first user includes a redacted version of the personally identifiable information of the first user.

20 . The computing system of claim 17 ,

wherein the anonymized identifier is automatically displayed on the mobile device of the first user upon detection of an automobile accident; and

wherein the detection of the automobile accident is performed on the mobile device of the first user by applying a machine learning model trained to receive motion data and provide a corresponding prediction of whether an accident has occurred.

Continuity (1)
Continuation 18145498 · Dec 22, 2022
References Cited (114)
US 6853849B1 · Tognazzini · 2005 [cited by applicant]
US 8160764B2 · Choi et al. · 2012 [cited by applicant]
US 8731977B1 · Hardin et al. · 2014 [cited by applicant]
US 9196159B1 · Kerr · 2015 [cited by applicant]
US 9773281B1 · Hanson · 2017 [cited by applicant]
US 9786154B1 · Potter et al. · 2017 [cited by applicant]
US 9842496B1 · Hayward · 2017 [cited by examiner]
US 9886841B1 · Nave et al. · 2018 [cited by applicant]
US 10043372B1 · Hollenstain et al. · 2018 [cited by applicant]
US 10086782B1 · Konrardy et al. · 2018 [cited by applicant]
US 10106156B1 · Nave et al. · 2018 [cited by applicant]
US 10165429B1 · Young et al. · 2018 [cited by applicant]
US 10360742B1 · Bellas et al. · 2019 [cited by applicant]
US 10580306B1 · Harris et al. · 2020 [cited by applicant]
US 10586122B1 · Gingrich et al. · 2020 [cited by applicant]
US 10660806B1 · Nelson-Herron et al. · 2020 [cited by applicant]
US 10692149B1 · Loo et al. · 2020 [cited by applicant]
US 10769456B2 · Sathyanarayana et al. · 2020 [cited by applicant]
US 10789650B1 · Nave et al. · 2020 [cited by applicant]
US 10803527B1 · Zankat et al. · 2020 [cited by applicant]
US 10853882B1 · Leise et al. · 2020 [cited by applicant]
US 10867495B1 · Venetianer et al. · 2020 [cited by applicant]
US 11107161B1 · Hall · 2021 [cited by examiner]
US 11328505B2 · Taccari et al. · 2022 [cited by applicant]
US 11379886B1 · Fields et al. · 2022 [cited by applicant]
US 11503153B1 · Hansen et al. · 2022 [cited by applicant]
US 11605248B2 · Oswald · 2023 [cited by applicant]
US 11620862B1 · Serrao et al. · 2023 [cited by applicant]
US 11669590B2 · Hyland et al. · 2023 [cited by applicant]
US 11679763B2 · Nagasawa · 2023 [cited by applicant]
US 11692838B1 · Gibson et al. · 2023 [cited by applicant]
US 11735050B2 · Garg et al. · 2023 [cited by applicant]
US 11781883B1 · Dabell · 2023 [cited by applicant]
US 11936796B1 · Allen · 2024 [cited by examiner]
US 20020084918A1 · Roach · 2002 [cited by applicant]
US 20090172131A1 · Sullivan · 2009 [cited by applicant]
US 20110117878A1 · Barash et al. · 2011 [cited by applicant]
US 20110279263A1 · Rodkey et al. · 2011 [cited by applicant]
US 20130130639A1 · Oesterling et al. · 2013 [cited by applicant]
US 20140379523A1 · Park · 2014 [cited by applicant]
US 20150084757A1 · Annibale et al. · 2015 [cited by applicant]
US 20150145695A1 · Hyde et al. · 2015 [cited by applicant]
US 20150356306A1 · Carter · 2015 [cited by examiner]
US 20160009279A1 · Jimaa et al. · 2016 [cited by applicant]
US 20160169688A1 · Kweon et al. · 2016 [cited by applicant]
US 20170053461A1 · Pal et al. · 2017 [cited by applicant]
US 20170072850A1 · Curtis et al. · 2017 [cited by applicant]
US 20170213462A1 · Prokhorov · 2017 [cited by applicant]
US 20170248949A1 · Moran et al. · 2017 [cited by applicant]
US 20170248950A1 · Moran et al. · 2017 [cited by applicant]
US 20180061253A1 · Hyun · 2018 [cited by applicant]
US 20180286248A1 · Choi et al. · 2018 [cited by applicant]
US 20180293864A1 · Wedig et al. · 2018 [cited by applicant]
US 20180297593A1 · Pitale et al. · 2018 [cited by applicant]
US 20180300964A1 · Lakshamanan et al. · 2018 [cited by applicant]
US 20180308342A1 · Hodge · 2018 [cited by applicant]
US 20180308344A1 · Ravindranath et al. · 2018 [cited by applicant]
US 20180309592A1 · Stolfus · 2018 [cited by applicant]
US 20180364722A1 · Schlesinger et al. · 2018 [cited by applicant]
US 20190072968A1 · Will, IV et al. · 2019 [cited by applicant]
US 20190095877A1 · Li · 2019 [cited by applicant]
US 20190149661A1 · Klaban · 2019 [cited by applicant]
US 20190174289A1 · Martin et al. · 2019 [cited by applicant]
US 20190202448A1 · Pal et al. · 2019 [cited by applicant]
US 20190253861A1 · Horelik et al. · 2019 [cited by applicant]
US 20190327597A1 · Katz et al. · 2019 [cited by applicant]
US 20190385457A1 · Kim et al. · 2019 [cited by applicant]
US 20200043097A1 · Aznaurashvili · 2020 [cited by examiner]
US 20200059776A1 · Martin et al. · 2020 [cited by applicant]
US 20200105120A1 · Werner et al. · 2020 [cited by applicant]
US 20200274962A1 · Martin et al. · 2020 [cited by applicant]
US 20200312046A1 · Righi et al. · 2020 [cited by applicant]
US 20200372727A1 · Sudhir et al. · 2020 [cited by applicant]
US 20210023946A1 · Johnson et al. · 2021 [cited by applicant]
US 20210027409A1 · Nair et al. · 2021 [cited by applicant]
US 20210030593A1 · Kellogg · 2021 [cited by applicant]
US 20210061209A1 · Park et al. · 2021 [cited by applicant]
US 20210149394A1 · Li · 2021 [cited by applicant]
US 20210217120A1 · Del Forn et al. · 2021 [cited by applicant]
US 20210219257A1 · Anand et al. · 2021 [cited by applicant]
US 20210225155A1 · Potter et al. · 2021 [cited by applicant]
US 20210256257A1 · Taccari et al. · 2021 [cited by applicant]
US 20210266732A1 · Zhou et al. · 2021 [cited by applicant]
US 20210287462A1 · Taylor et al. · 2021 [cited by applicant]
US 20210304593A1 · Matus et al. · 2021 [cited by applicant]
US 20220044024A1 · Sambo et al. · 2022 [cited by applicant]
US 20220058701A1 · Fuchs · 2022 [cited by applicant]
US 20220063609A1 · Nagasawa · 2022 [cited by applicant]
US 20220095975A1 · Aluf et al. · 2022 [cited by applicant]
US 20220169175A1 · Choi · 2022 [cited by applicant]
US 20220321343A1 · Bahrami et al. · 2022 [cited by applicant]
US 20220383256A1 · Roh et al. · 2022 [cited by applicant]
US 20230001871A1 · Neubauer et al. · 2023 [cited by applicant]
US 20230122572A1 · Choi · 2023 [cited by applicant]
US 20230166743A1 · Heck et al. · 2023 [cited by applicant]
US 20230169845A1 · Turner et al. · 2023 [cited by applicant]
US 20230211780A1 · Tanaka et al. · 2023 [cited by applicant]
US 20230242099A1 · Pishehvari et al. · 2023 [cited by applicant]
US 20230282350A1 · Devore et al. · 2023 [cited by applicant]
US 20230298468A1 · Jha et al. · 2023 [cited by applicant]
US 20240089701A1 · Motoyama et al. · 2024 [cited by applicant]
US 20240144750A1 · Bohman · 2024 [cited by examiner]
US 20240169296A1 · Watfa et al. · 2024 [cited by applicant]
DE 102015209853A1 · 2016 [cited by applicant]
JP 2010182287A · 2010 [cited by applicant]
JP 2015504616A · 2015 [cited by applicant]
JP 6940612B2 · 2021 [cited by applicant]
JP 7470486B2 · 2024 [cited by applicant]
WO 2019028349A1 · 2019 [cited by applicant]
WO 2022201639A1 · 2022 [cited by applicant]
Chong et al., “Traffic accident data mining using machine learning paradigms.” Fourth International Conference on Intelligent Systems Design and Applications (ISDA'04), Hungary. 2004. (Year: 2004). [cited by applicant]
Kumeda et al. “Classification of road traffic accident data using machine learning algorithms.” 2019 IEEE 11th international conference on communication software and networks (ICCSN). IEEE, 2019. (Year: 2019). [cited by applicant]
Santo et al. “Machine learning approaches to traffic accident analysis and hotspot prediction.” Computers 10.12 (2021): 157. (Year: 2021). [cited by applicant]
Wang, Junhua, et al. “Modeling when and where a secondary accident occurs.” Accident Analysis & Prevention 130 (2019): 160- 166. (Year: 2019). [cited by applicant]