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Extract from the Register of European Patents

EP About this file: EP3821361

EP3821361 - METHOD AND SYSTEM FOR GENERATING SYNTHETICALLY ANONYMIZED DATA FOR A GIVEN TASK [Right-click to bookmark this link]
StatusThe application is deemed to be withdrawn
Status updated on  17.05.2024
Database last updated on 05.10.2024
FormerRequest for examination was made
Status updated on  16.04.2021
FormerThe international publication has been made
Status updated on  18.01.2020
Most recent event   Tooltip17.05.2024Application deemed to be withdrawnpublished on 19.06.2024  [2024/25]
Applicant(s)For all designated states
Imagia Cybernetics Inc.
6650, rue St-Urbain
Suite 100
Montréal, Québec H2S 3G9 / CA
[2021/20]
Inventor(s)01 / CHANDELIER, Florent
1245 chemin du Lac Saint-Louis Lery
Québec J6N 1A9 / CA
02 / JESSON, Andrew
802, Avenue Dollard, Apt. 5 Outremont
Québec H2V 337 / CA
03 / HAVAEI, Mohammad
4-2945 chemin Bedford
Montréal, Québec H3S 1G3 / CA
04 / DIJORIO, Lisa
4593 Cartier
Montréal, Québec H2H 1W9 / CA
05 / LOW-KAM, Cecile
4593 Cartier
Montréal, Québec H2H 1W9 / CA
06 / CHAPADOS, Nicolas
7081 Christophe Colomb
Montréal, Québec H2S 2H4 / CA
07 / SOUDAN, Florian
5838, av de Chateaubriand
Montréal, Québec H2S 2N2 / CA
 [2021/20]
Representative(s)Germain Maureau
12, rue Boileau
69006 Lyon / FR
[N/P]
Former [2021/20]Verriest, Philippe, et al
Cabinet Germain & Maureau
12, rue Boileau
BP 6153
69466 Lyon Cedex 06 / FR
Application number, filing date19833256.112.07.2019
[2021/20]
WO2019IB55972
Priority number, dateUS201862697804P13.07.2018         Original published format: US 201862697804 P
[2021/20]
Filing languageEN
Procedural languageEN
PublicationType: A1 Application with search report
No.:WO2020012439
Date:16.01.2020
Language:EN
[2020/03]
Type: A1 Application with search report 
No.:EP3821361
Date:19.05.2021
Language:EN
The application published by WIPO in one of the EPO official languages on 16.01.2020 takes the place of the publication of the European patent application.
[2021/20]
Search report(s)International search report - published on:CA16.01.2020
(Supplementary) European search report - dispatched on:EP22.03.2022
ClassificationIPC:G16H10/60, G06F21/62, G06N3/02
[2022/16]
CPC:
G06F21/6254 (EP,KR,US); F16D65/22 (IL); B60T11/18 (IL);
F16D65/0056 (IL); G06F21/79 (KR); G06N20/00 (US);
G06N3/045 (EP); G06N3/088 (EP); G16H10/60 (EP,KR,US);
F16D2051/003 (IL); F16D2121/02 (IL); F16D2123/00 (IL) (-)
Former IPC [2021/20]G06F21/60, G16H10/60
Designated contracting statesAL,   AT,   BE,   BG,   CH,   CY,   CZ,   DE,   DK,   EE,   ES,   FI,   FR,   GB,   GR,   HR,   HU,   IE,   IS,   IT,   LI,   LT,   LU,   LV,   MC,   MK,   MT,   NL,   NO,   PL,   PT,   RO,   RS,   SE,   SI,   SK,   SM,   TR [2021/20]
TitleGerman:VERFAHREN UND SYSTEM ZUR ERZEUGUNG VON SYNTHETISCH ANONYMISIERTEN DATEN FÜR EINE BESTIMMTE AUFGABE[2021/20]
English:METHOD AND SYSTEM FOR GENERATING SYNTHETICALLY ANONYMIZED DATA FOR A GIVEN TASK[2021/20]
French:PROCÉDÉ ET SYSTÈME DE GÉNÉRATION DE DONNÉES SYNTHÉTIQUEMENT ANONYMISÉES POUR UNE TÂCHE DONNÉE[2021/20]
Entry into regional phase19.01.2021National basic fee paid 
19.01.2021Search fee paid 
19.01.2021Designation fee(s) paid 
19.01.2021Examination fee paid 
Examination procedure19.01.2021Examination requested  [2021/20]
07.10.2022Amendment by applicant (claims and/or description)
01.02.2024Application deemed to be withdrawn, date of legal effect  [2024/25]
20.02.2024Despatch of communication that the application is deemed to be withdrawn, reason: renewal fee not paid in time  [2024/25]
Fees paidRenewal fee
12.05.2021Renewal fee patent year 03
15.07.2022Renewal fee patent year 04
Penalty fee
Additional fee for renewal fee
31.07.202305   M06   Not yet paid
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Responsibility for the accuracy, completeness or quality of the data displayed under the link provided lies entirely with the Unified Patent Court.
Documents cited:Search[A]  - Edward Choi ET AL, "Proceedings of Machine Learning for Healthcare 2017 JMLR W&C Track Volume 68 Generating Multi-label Discrete Patient Records using Generative Adversarial Networks", (20180111), URL: https://arxiv.org/pdf/1703.06490.pdf, (20200305), XP055674157 [A] 1-12 * the whole document *
 [A]  - BRILAND HITAJ ET AL, "Deep Models Under the GAN : Information Leakage from Collaborative Deep Learning", PROCEEDINGS OF THE 2017 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY , CCS '17, New York, New York, USA, (20171030), doi:10.1145/3133956.3134012, ISBN 978-1-4503-4946-8, pages 603 - 618, XP055536296 [A] 1-12 * the whole document *

DOI:   http://dx.doi.org/10.1145/3133956.3134012
International search[A]US2018165475  (VEERAMACHANENI KALYAN KUMAR [US], et al) [A] 1-12*whole document*;
 [A]  - CHOI, E. et al., "Generating Multi-label Discrete Patient Records using Generative Adversarial Networks", arXiv:1703.06490v3 [cs.LG, (20180111), page 20, XP055674157 [A] 1-12 *whole document*
 [A]  - XIE, L. et al., "Differentially Private Generative Adversarial Network", arXiv:1802.06739vl [cs.LG, (20180219), page 9, XP081216601 [A] 1-12 *whole document*
 [A]  - ACS, G. et al., "Differentially Private Mixture of Generative Neural Networks", arXiv:1709.04514vl [cs.LG, (20170913), page 11, XP033279252 [A] 1-12 *whole document*

DOI:   http://dx.doi.org/10.1109/ICDM.2017.81
 [A]  - Jamie Hayes, Melis Luca, Danezis George, De Cristofaro Emiliano, "LOGAN: Evaluating Privacy Leakage of Generative Models Using Generative Adversarial Networks", arXiv, (20170501), pages 1 - 18, XP055768176 [A] 1-12 *whole document*
 [A]  - HITAJ, B. et al., "Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning", Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, CCS '17, Dallas, Texas, USA, (20171030), doi:10.1145/3133956.3134012, pages 603 - 618, XP055536296 [A] 1-12 *whole document*

DOI:   http://dx.doi.org/10.1145/3133956.3134012
by applicant   - CHOI et al., "Proceedings of Machine Learning for Healthcare", 2017 JMLR W&C Track, vol. 68, (20180111), URL: https://arxiv.org/pdf/1703.06490.pdf, XP055674157
    - HITAJ et al., "Proceedings Of The 2017 Acm Sigsac Conference On Computer And Communications Security", CCS '17, (20171030), pages 603 - 618
    - "Towards Safe Deep Learning: Unsupervised Defense Against Generic Adversarial Attacks", OpenReview HyI6s40a
    - "conditional generative adversarial nets", arXiv: 1411.1784
    - "Generative adversarial text to image synthesis", arXiv:1605.05396
    - "RenderGAN: generating realistic labelled data", arXiv: 1611.01331
    - "Privacy-preserving generative deep neural networks support clinical data sharing", bioarxkiv: 159756
    - "Generating differentially private datasets using GANs", OpenReview rJv4XWZA-, ICLR 2018
    - "What uncertainties do we need in Bayesian deep learning for computer vision?", Advances in Neural Information Processing Systems, (20170000), vol. 30, pages 5580 - 5590
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