blank Quick help
blank Maintenance news

Scheduled maintenance

Regular maintenance outages:
between 05.00 and 05.15 hrs CET (Monday to Sunday).

Other outages
Availability
Register Forum

2022.02.11

More...
blank News flashes

News Flashes

New version of the European Patent Register – SPC proceedings information in the Unitary Patent Register.

2024-07-24

More...
blank Related links

Release notes
European Patent Register Assistant
European Patent Register Resource Book
European Patent Register – pocket guide
FAQ - European Patent Register
FAQ - Register Alert
Federated Register (Flyer)
Global Dossier (Flyer)
Register events
Deep link coverage
Federated Register content
The Patent Information Tour
Fair use charter

Extract from the Register of European Patents

EP About this file: EP3059699

EP3059699 - NEURAL NETWORK TRAINING METHOD AND RECOGNITION APPARATUS [Right-click to bookmark this link]
Former [2016/34]NEURAL NETWORK TRAINING METHOD AND APPARATUS, AND RECOGNITION METHOD AND APPARATUS
[2019/43]
StatusNo opposition filed within time limit
Status updated on  01.01.2021
Database last updated on 24.08.2024
FormerThe patent has been granted
Status updated on  24.01.2020
FormerGrant of patent is intended
Status updated on  09.10.2019
FormerExamination is in progress
Status updated on  08.12.2017
FormerRequest for examination was made
Status updated on  30.06.2017
Most recent event   Tooltip08.07.2022Lapse of the patent in a contracting state
New state(s): MK		published on 10.08.2022  [2022/32]
Applicant(s)For all designated states
Samsung Electronics Co., Ltd.
129, Samsung-ro
Yeongtong-gu
Suwon-si
Gyeonggi-do 16677 / KR
[2020/09]
Former [2016/34]For all designated states
Samsung Electronics Co., Ltd.
129, Samsung-ro
Yeongtong-gu
Suwon-si
Gyeonggi-do 16677 / KR
Inventor(s)01 / YOO, Sang Hyun
c/o Samsung Advanced Institute of Technology, 97
Samsung 2-ro, Giheung-gu, Yongin-si
Gyeonggi-do 17113 / KR
02 / MOON, Taesup
c/o Samsung Advanced Institute of Technology, 97
Samsung 2-ro, Giheung-gu, Yongin-si
Gyeonggi-do 17113 / KR
 [2016/34]
Representative(s)Grünecker Patent- und Rechtsanwälte PartG mbB
Leopoldstraße 4
80802 München / DE
[2016/34]
Application number, filing date15191549.327.10.2015
[2016/34]
Priority number, dateKR2015002507723.02.2015         Original published format: KR 20150025077
[2016/34]
Filing languageEN
Procedural languageEN
PublicationType: A2 Application without search report 
No.:EP3059699
Date:24.08.2016
Language:EN
[2016/34]
Type: A3 Search report 
No.:EP3059699
Date:28.12.2016
Language:EN
[2016/52]
Type: B1 Patent specification 
No.:EP3059699
Date:26.02.2020
Language:EN
[2020/09]
Search report(s)(Supplementary) European search report - dispatched on:EP30.11.2016
ClassificationIPC:G06N3/04, G06N3/08, // G10L25/30, G10L15/16
[2017/51]
CPC:
G06N3/082 (EP,US); G06N3/08 (KR,US); G06F18/00 (KR);
G06N20/00 (US); G06N3/044 (EP,US); G06N3/063 (US);
G10L15/00 (KR); G10L15/16 (EP,US); G10L25/30 (EP,US) (-)
Former IPC [2016/52]G06N3/04, G06N3/08, // G10L25/30, G06F21/32, G06K9/00, G06T7/00, G10L15/16
Former IPC [2016/34]G06N3/04, G06N3/08
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 [2017/31]
Former [2016/34]AL,  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 
TitleGerman:TRAININGSVERFAHREN FÜR NEURONALES NETZWERK UND ERKENNUNGSVORRICHTUNG[2019/43]
English:NEURAL NETWORK TRAINING METHOD AND RECOGNITION APPARATUS[2019/43]
French:PROCÉDÉ DE FORMATION DE RÉSEAU NEURONAL ET APPAREIL DE RECONNAISSANCE[2019/43]
Former [2016/34]TRAININGSVERFAHREN UND -VORRICHTUNG FÜR NEURONALES NETZWERK UND ERKENNUNGSVERFAHREN UND -VORRICHTUNG
Former [2016/34]NEURAL NETWORK TRAINING METHOD AND APPARATUS, AND RECOGNITION METHOD AND APPARATUS
Former [2016/34]PROCÉDÉ DE FORMATION DE RÉSEAU NEURONAL ET APPAREIL ET PROCÉDÉ ET APPAREIL DE RECONNAISSANCE
Examination procedure27.10.2015Date on which the examining division has become responsible
23.06.2017Amendment by applicant (claims and/or description)
23.06.2017Examination requested  [2017/31]
08.12.2017Despatch of a communication from the examining division (Time limit: M04)
17.04.2018Reply to a communication from the examining division
04.02.2019Despatch of a communication from the examining division (Time limit: M04)
04.06.2019Reply to a communication from the examining division
10.10.2019Communication of intention to grant the patent
21.01.2020Fee for grant paid
21.01.2020Fee for publishing/printing paid
21.01.2020Receipt of the translation of the claim(s)
Opposition(s)27.11.2020No opposition filed within time limit [2021/05]
Fees paidRenewal fee
27.10.2017Renewal fee patent year 03
26.10.2018Renewal fee patent year 04
24.10.2019Renewal fee patent year 05
Opt-out from the exclusive  Tooltip
competence of the Unified
Patent Court		 See the Register of the Unified Patent Court for opt-out data
Responsibility for the accuracy, completeness or quality of the data displayed under the link provided lies entirely with the Unified Patent Court.
Lapses during opposition  TooltipHU27.10.2015
AL26.02.2020
AT26.02.2020
CY26.02.2020
CZ26.02.2020
DK26.02.2020
EE26.02.2020
ES26.02.2020
FI26.02.2020
HR26.02.2020
IT26.02.2020
LT26.02.2020
LV26.02.2020
MC26.02.2020
MK26.02.2020
MT26.02.2020
NL26.02.2020
PL26.02.2020
RO26.02.2020
RS26.02.2020
SE26.02.2020
SI26.02.2020
SK26.02.2020
SM26.02.2020
TR26.02.2020
BG26.05.2020
NO26.05.2020
GR27.05.2020
IS26.06.2020
PT19.07.2020
IE27.10.2020
LU27.10.2020
BE31.10.2020
CH31.10.2020
LI31.10.2020
[2022/31]
Former [2022/30]HU27.10.2015
AL26.02.2020
AT26.02.2020
CY26.02.2020
CZ26.02.2020
DK26.02.2020
EE26.02.2020
ES26.02.2020
FI26.02.2020
HR26.02.2020
IT26.02.2020
LT26.02.2020
LV26.02.2020
MC26.02.2020
MT26.02.2020
NL26.02.2020
PL26.02.2020
RO26.02.2020
RS26.02.2020
SE26.02.2020
SI26.02.2020
SK26.02.2020
SM26.02.2020
TR26.02.2020
BG26.05.2020
NO26.05.2020
GR27.05.2020
IS26.06.2020
PT19.07.2020
IE27.10.2020
LU27.10.2020
BE31.10.2020
CH31.10.2020
LI31.10.2020
Former [2022/27]HU27.10.2015
AT26.02.2020
CY26.02.2020
CZ26.02.2020
DK26.02.2020
EE26.02.2020
ES26.02.2020
FI26.02.2020
HR26.02.2020
IT26.02.2020
LT26.02.2020
LV26.02.2020
MC26.02.2020
MT26.02.2020
NL26.02.2020
PL26.02.2020
RO26.02.2020
RS26.02.2020
SE26.02.2020
SI26.02.2020
SK26.02.2020
SM26.02.2020
TR26.02.2020
BG26.05.2020
NO26.05.2020
GR27.05.2020
IS26.06.2020
PT19.07.2020
IE27.10.2020
LU27.10.2020
BE31.10.2020
CH31.10.2020
LI31.10.2020
Former [2021/46]AT26.02.2020
CZ26.02.2020
DK26.02.2020
EE26.02.2020
ES26.02.2020
FI26.02.2020
HR26.02.2020
IT26.02.2020
LT26.02.2020
LV26.02.2020
MC26.02.2020
NL26.02.2020
PL26.02.2020
RO26.02.2020
RS26.02.2020
SE26.02.2020
SI26.02.2020
SK26.02.2020
SM26.02.2020
BG26.05.2020
NO26.05.2020
GR27.05.2020
IS26.06.2020
PT19.07.2020
IE27.10.2020
LU27.10.2020
BE31.10.2020
CH31.10.2020
LI31.10.2020
Former [2021/37]AT26.02.2020
CZ26.02.2020
DK26.02.2020
EE26.02.2020
ES26.02.2020
FI26.02.2020
HR26.02.2020
IT26.02.2020
LT26.02.2020
LV26.02.2020
MC26.02.2020
NL26.02.2020
PL26.02.2020
RO26.02.2020
RS26.02.2020
SE26.02.2020
SI26.02.2020
SK26.02.2020
SM26.02.2020
BG26.05.2020
NO26.05.2020
GR27.05.2020
IS26.06.2020
PT19.07.2020
LU27.10.2020
BE31.10.2020
CH31.10.2020
LI31.10.2020
Former [2021/36]AT26.02.2020
CZ26.02.2020
DK26.02.2020
EE26.02.2020
ES26.02.2020
FI26.02.2020
HR26.02.2020
IT26.02.2020
LT26.02.2020
LV26.02.2020
MC26.02.2020
NL26.02.2020
PL26.02.2020
RO26.02.2020
RS26.02.2020
SE26.02.2020
SI26.02.2020
SK26.02.2020
SM26.02.2020
BG26.05.2020
NO26.05.2020
GR27.05.2020
IS26.06.2020
PT19.07.2020
LU27.10.2020
BE31.10.2020
Former [2021/31]AT26.02.2020
CZ26.02.2020
DK26.02.2020
EE26.02.2020
ES26.02.2020
FI26.02.2020
HR26.02.2020
IT26.02.2020
LT26.02.2020
LV26.02.2020
MC26.02.2020
NL26.02.2020
PL26.02.2020
RO26.02.2020
RS26.02.2020
SE26.02.2020
SI26.02.2020
SK26.02.2020
SM26.02.2020
BG26.05.2020
NO26.05.2020
GR27.05.2020
IS26.06.2020
PT19.07.2020
LU27.10.2020
Former [2021/10]AT26.02.2020
CZ26.02.2020
DK26.02.2020
EE26.02.2020
ES26.02.2020
FI26.02.2020
HR26.02.2020
IT26.02.2020
LT26.02.2020
LV26.02.2020
NL26.02.2020
PL26.02.2020
RO26.02.2020
RS26.02.2020
SE26.02.2020
SI26.02.2020
SK26.02.2020
SM26.02.2020
BG26.05.2020
NO26.05.2020
GR27.05.2020
IS26.06.2020
PT19.07.2020
Former [2020/50]CZ26.02.2020
DK26.02.2020
EE26.02.2020
ES26.02.2020
FI26.02.2020
HR26.02.2020
LT26.02.2020
LV26.02.2020
NL26.02.2020
RO26.02.2020
RS26.02.2020
SE26.02.2020
SK26.02.2020
SM26.02.2020
BG26.05.2020
NO26.05.2020
GR27.05.2020
IS26.06.2020
PT19.07.2020
Former [2020/49]CZ26.02.2020
DK26.02.2020
ES26.02.2020
FI26.02.2020
HR26.02.2020
LT26.02.2020
LV26.02.2020
NL26.02.2020
RO26.02.2020
RS26.02.2020
SE26.02.2020
SK26.02.2020
SM26.02.2020
BG26.05.2020
NO26.05.2020
GR27.05.2020
IS26.06.2020
PT19.07.2020
Former [2020/48]DK26.02.2020
ES26.02.2020
FI26.02.2020
HR26.02.2020
LT26.02.2020
LV26.02.2020
NL26.02.2020
RO26.02.2020
RS26.02.2020
SE26.02.2020
SM26.02.2020
BG26.05.2020
NO26.05.2020
GR27.05.2020
IS26.06.2020
PT19.07.2020
Former [2020/47]DK26.02.2020
ES26.02.2020
FI26.02.2020
HR26.02.2020
LT26.02.2020
LV26.02.2020
NL26.02.2020
RS26.02.2020
SE26.02.2020
BG26.05.2020
NO26.05.2020
GR27.05.2020
IS26.06.2020
Former [2020/45]FI26.02.2020
HR26.02.2020
LV26.02.2020
NL26.02.2020
RS26.02.2020
SE26.02.2020
BG26.05.2020
NO26.05.2020
GR27.05.2020
IS26.06.2020
Former [2020/40]FI26.02.2020
HR26.02.2020
LV26.02.2020
RS26.02.2020
SE26.02.2020
BG26.05.2020
NO26.05.2020
GR27.05.2020
IS26.06.2020
Former [2020/39]FI26.02.2020
HR26.02.2020
LV26.02.2020
RS26.02.2020
SE26.02.2020
BG26.05.2020
NO26.05.2020
GR27.05.2020
Former [2020/38]FI26.02.2020
HR26.02.2020
LV26.02.2020
RS26.02.2020
SE26.02.2020
NO26.05.2020
GR27.05.2020
Former [2020/37]FI26.02.2020
HR26.02.2020
LV26.02.2020
RS26.02.2020
SE26.02.2020
NO26.05.2020
Former [2020/35]FI26.02.2020
NO26.05.2020
Documents cited:Search[XA]  - JAN KOUTNÍK ET AL, "A Clockwork RNN", PROCEEDINGS OF THE 31ST INTERNATIONAL CONFERENCE ON MACHINE LEARNING, (20140214), vol. 32, pages 1863 - 1871, XP055289373 [X] 1,2,6-11,13,14 * page 1863, paragraph 1. - page 1864; figure 1 * * page 1865, paragraph 3. - page 1866 * [A] 3-5,12
 [XA]  - PHAM VU ET AL, "Dropout Improves Recurrent Neural Networks for Handwriting Recognition", 2014 14TH INTERNATIONAL CONFERENCE ON FRONTIERS IN HANDWRITING RECOGNITION, IEEE, doi:10.1109/ICFHR.2014.55, ISSN 2167-6445, ISBN 978-1-4799-4335-7, (20140901), pages 285 - 290, (20141209), XP032703488 [X] 1-3,6-14 * page 285, paragraph I - page 287, paragraph IV. A.; figure 2 * * page 289, paragraph IV.D. * [A] 4,5

DOI:   http://dx.doi.org/10.1109/ICFHR.2014.55
 [A]  - Wojciech Zaremba ET AL, "Recurrent Neural Network Regularization", (20140908), URL: https://arxiv.org/pdf/1409.2329v5.pdf, (20161121), XP055321161 [A] 1-14 * page 1, paragraph 1 - page 4, paragraph 3.2; figures 1,2,3 *
 [A]  - Geoffrey E Hinton ET AL, "Improving neural networks by preventing co-adaptation of feature detectors", arXiv:1207.0580v1 [cs.NE], (20120703), URL: http://arxiv.org/abs/1207.0580v1, (20140408), XP055112910 [A] 1-14 * page 1, line 1 - page 2, line 15 * * page 5, line 19 - page 6, line 8 *
The EPO accepts no responsibility for the accuracy of data originating from other authorities; in particular, it does not guarantee that it is complete, up to date or fit for specific purposes.