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] | Status | No opposition filed within time limit Status updated on 01.01.2021 Database last updated on 24.08.2024 | |
Former | The patent has been granted Status updated on 24.01.2020 | ||
Former | Grant of patent is intended Status updated on 09.10.2019 | ||
Former | Examination is in progress Status updated on 08.12.2017 | ||
Former | Request for examination was made Status updated on 30.06.2017 | Most recent event Tooltip | 08.07.2022 | Lapse 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 date | 15191549.3 | 27.10.2015 | [2016/34] | Priority number, date | KR20150025077 | 23.02.2015 Original published format: KR 20150025077 | [2016/34] | Filing language | EN | Procedural language | EN | Publication | Type: | 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: | EP | 30.11.2016 | Classification | IPC: | 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);
|
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 states | 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 [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 | Title | German: | 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 procedure | 27.10.2015 | Date on which the examining division has become responsible | 23.06.2017 | Amendment by applicant (claims and/or description) | 23.06.2017 | Examination requested [2017/31] | 08.12.2017 | Despatch of a communication from the examining division (Time limit: M04) | 17.04.2018 | Reply to a communication from the examining division | 04.02.2019 | Despatch of a communication from the examining division (Time limit: M04) | 04.06.2019 | Reply to a communication from the examining division | 10.10.2019 | Communication of intention to grant the patent | 21.01.2020 | Fee for grant paid | 21.01.2020 | Fee for publishing/printing paid | 21.01.2020 | Receipt of the translation of the claim(s) | Opposition(s) | 27.11.2020 | No opposition filed within time limit [2021/05] | Fees paid | Renewal fee | 27.10.2017 | Renewal fee patent year 03 | 26.10.2018 | Renewal fee patent year 04 | 24.10.2019 | Renewal fee patent year 05 |
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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. | Lapses during opposition Tooltip | HU | 27.10.2015 | AL | 26.02.2020 | AT | 26.02.2020 | CY | 26.02.2020 | CZ | 26.02.2020 | DK | 26.02.2020 | EE | 26.02.2020 | ES | 26.02.2020 | FI | 26.02.2020 | HR | 26.02.2020 | IT | 26.02.2020 | LT | 26.02.2020 | LV | 26.02.2020 | MC | 26.02.2020 | MK | 26.02.2020 | MT | 26.02.2020 | NL | 26.02.2020 | PL | 26.02.2020 | RO | 26.02.2020 | RS | 26.02.2020 | SE | 26.02.2020 | SI | 26.02.2020 | SK | 26.02.2020 | SM | 26.02.2020 | TR | 26.02.2020 | BG | 26.05.2020 | NO | 26.05.2020 | GR | 27.05.2020 | IS | 26.06.2020 | PT | 19.07.2020 | IE | 27.10.2020 | LU | 27.10.2020 | BE | 31.10.2020 | CH | 31.10.2020 | LI | 31.10.2020 | [2022/31] |
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BE | 31.10.2020 | ||
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IS | 26.06.2020 | ||
PT | 19.07.2020 | ||
IE | 27.10.2020 | ||
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BE | 31.10.2020 | ||
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PT | 19.07.2020 | ||
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PT | 19.07.2020 | ||
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BE | 31.10.2020 | ||
CH | 31.10.2020 | ||
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IS | 26.06.2020 | ||
PT | 19.07.2020 | ||
LU | 27.10.2020 | ||
BE | 31.10.2020 | ||
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PT | 19.07.2020 | ||
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PT | 19.07.2020 | ||
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BG | 26.05.2020 | ||
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IS | 26.06.2020 | ||
PT | 19.07.2020 | ||
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Former [2020/35] | FI | 26.02.2020 | |
NO | 26.05.2020 | 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 * |