特許・実用新案 検索・分析

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    関連キーワード

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    出願年

    • 1977 1977 1
    • 1978 1978 1
    • 1980 1980 1
    • 1982 1982 2
    • 1983 1983 1
    • 1984 1984 2
    • 1985 1985 1
    • 1986 1986 11
    • 1987 1987 21
    • 1988 1988 69
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    • 1992 1992 687
    • 1993 1993 765
    • 1994 1994 811
    • 1995 1995 883
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    • 2004 2004 2147
    • 2005 2005 2279
    • 2006 2006 2722
    • 2007 2007 3050
    • 2008 2008 2761
    • 2009 2009 2325
    • 2010 2010 2400
    • 2011 2011 2737
    • 2012 2012 3051
    • 2013 2013 3163
    • 2014 2014 3398
    • 2015 2015 3378
    • 2016 2016 2300
    • 2017 2017 80
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    公開年

    • 1993 1993 348
    • 1994 1994 442
    • 1995 1995 384
    • 1996 1996 281
    • 1997 1997 213
    • 1998 1998 171
    • 1999 1999 163
    • 2000 2000 140
    • 2001 2001 341
    • 2002 2002 1210
    • 2003 2003 1586
    • 2004 2004 1798
    • 2005 2005 1996
    • 2006 2006 2056
    • 2007 2007 2301
    • 2008 2008 2955
    • 2009 2009 2747
    • 2010 2010 2664
    • 2011 2011 2379
    • 2012 2012 2511
    • 2013 2013 2755
    • 2014 2014 2896
    • 2015 2015 3264
    • 2016 2016 3923
    • 2017 2017 1845
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    公告/登録公報発行年

    • 1978 1978 1
    • 1981 1981 1
    • 1982 1982 1
    • 1983 1983 1
    • 1985 1985 1
    • 1987 1987 2
    • 1988 1988 9
    • 1989 1989 24
    • 1990 1990 68
    • 1991 1991 102
    • 1992 1992 186
    • 1993 1993 213
    • 1994 1994 286
    • 1995 1995 346
    • 1996 1996 426
    • 1997 1997 525
    • 1998 1998 719
    • 1999 1999 641
    • 2000 2000 653
    • 2001 2001 706
    • 2002 2002 673
    • 2003 2003 719
    • 2004 2004 723
    • 2005 2005 768
    • 2006 2006 1107
    • 2007 2007 994
    • 2008 2008 1151
    • 2009 2009 1292
    • 2010 2010 1873
    • 2011 2011 1972
    • 2012 2012 2176
    • 2013 2013 2322
    • 2014 2014 2432
    • 2015 2015 2236
    • 2016 2016 2472
    • 2017 2017 1112
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    公開種別 (Kind Code)

    • A1 A1 36842
    • B2 B2 20466
    • A A 8448
    • B1 B1 4511
    • A9 A9 61
    • E1 E1 57
    • A2 A2 16
    • E E 11
    • H H 3
    • U U 3
    • その他 2
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    出願国

    • US US 45345
    • JP JP 4655
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    IPCサブクラス

    • G06F 電気的デジタルデータ処理(計... G06F 17281
    • G06K データの認識;データの表示;... G06K 5607
    • G06N 特定の計算モデルに基づくコン... G06N 5418
    • A61B 診断;手術;個人識別(生物学... A61B 4336
    • G06Q 管理目的,商用目的,金融目的... G06Q 3891
    • G01N 材料の化学的または物理的性質... G01N 3826
    • H04L デジタル情報の伝送,例.電信... H04L 2368
    • H04N 画像通信,例.テレビジョン[... H04N 2343
    • G06T イメージデータ処理または発生... G06T 2296
    • G10L 音声の分析または合成;音声認... G10L 2074
    • その他 37502
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    テーマコード

    • 5B078 学習型計算機 5B078 1848
    • 5L096 イメージ分析 5L096 677
    • 5B057 画像処理 5B057 520
    • 5B053 アナログ計算機 5B053 510
    • 5H004 フィードバック制御一般 5H004 346
    • 5D015 音声認識 5D015 273
    • 5B049 特定用途計算機 5B049 251
    • 5L049 管理・経営・業務システム,電... 5L049 236
    • 2G045 生物学的材料の調査,分析 2G045 202
    • 4B024 突然変異または遺伝子工学 4B024 197
    • その他 8435
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    USPCクラス

    • 382 画像解析 (Image analysis) 382 4186
    • 706 データ処理:人工知能 (Data proce... 706 3349
    • 705 データ処理:金融、ビジネスの... 705 3190
    • 600 手術 (Surgery) 600 2698
    • 702 データ処理:測定、較正、また... 702 2616
    • 435 化学:分子生物学および微生物... 435 2614
    • 707 データ処理:データベースおよ... 707 2309
    • 700 データ処理:一般的なコントロ... 700 1613
    • 704 データ処理:音声信号処理、言... 704 1501
    • 709 電気コンピュータおよびデジタ... 709 1285
    • その他 28732
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    出願人 (JP:最新)

    • 日本電信電話株式会社 日本電信電話株式会社 209
    • 株式会社東芝 株式会社東芝 180
    • キヤノン株式会社 キヤノン株式会社 168
    • 株式会社日立製作所 株式会社日立製作所 149
    • 日本電気株式会社 日本電気株式会社 120
    • 富士通株式会社 富士通株式会社 117
    • 株式会社リコー 株式会社リコー 114
    • ソニー株式会社 ソニー株式会社 103
    • 三菱電機株式会社 三菱電機株式会社 81
    • 株式会社デンソー 株式会社デンソー 68
    • その他 3800
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    特許事務所 (JP:最新)

    • 特許業務法人スズエ国際特許事... 特許業務法人スズエ国際特許事務所 179
    • 伊東国際特許事務所 伊東国際特許事務所 158
    • 特許業務法人志賀国際特許事務... 特許業務法人志賀国際特許事務所 100
    • 大塚国際特許事務所 大塚国際特許事務所 85
    • 三好内外国特許事務所 三好内外国特許事務所 75
    • 山本秀策特許事務所 山本秀策特許事務所 73
    • 中村誠特許事務所 中村誠特許事務所 71
    • 鈴榮特許綜合事務所 鈴榮特許綜合事務所 68
    • ゾンデルホフ&アインゼル法律... ゾンデルホフ&アインゼル法律特許事務所 65
    • 山本特許法律事務所 山本特許法律事務所 64
    • その他 4476
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    代理人 (JP:最新)

    • 鈴江 武彦 鈴江 武彦 174
    • 伊東 忠彦 伊東 忠彦 152
    • 村松 貞男 村松 貞男 94
    • 志賀 正武 志賀 正武 90
    • 大塚 康徳 大塚 康徳 86
    • 山本 秀策 山本 秀策 78
    • 大塚 康弘 大塚 康弘 77
    • 木村 秀二 木村 秀二 76
    • 高柳 司郎 高柳 司郎 76
    • 橋本 良郎 橋本 良郎 74
    • その他 10184
  • 絞り込み

    出願人/譲受人 (US:付与)

    • MICROSOFT MICROSOFT 1001
    • INTERNATIONAL BUSINESS MA... INTERNATIONAL BUSINESS MACHINES 644
    • GOOGLE GOOGLE 337
    • CANON CANON 273
    • GENERAL ELECTRIC GENERAL ELECTRIC 260
    • QUALCOMM QUALCOMM 249
    • AT&T MOBILITY II AT&T MOBILITY II 226
    • SIEMENS AKTIENGESELLSCHAF... SIEMENS AKTIENGESELLSCHAFT 218
    • ROCKWELL AUTOMATION TECHN... ROCKWELL AUTOMATION TECHNOLOGIES 211
    • SONY SONY 191
    • その他 22831
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    代理人/事務所 (US:付与)

    • FISH & RICHARDSON P C FISH & RICHARDSON P C 398
    • KNOBBE MARTENS OLSON & BE... KNOBBE MARTENS OLSON & BEAR 393
    • TUROCY & WATSON TUROCY & WATSON 355
    • AMIN TUROCY & WATSON AMIN TUROCY & WATSON 339
    • LEE & HAYES LEE & HAYES 280
    • MICKY MINHAS MICKY MINHAS 256
    • BRIAN ROFFE BRIAN ROFFE 227
    • OBLON SPIVAK MCCLELLAND M... OBLON SPIVAK MCCLELLAND MAIER & NEUSTADT P C 210
    • MORRISON & FOERSTER MORRISON & FOERSTER 207
    • FOLEY & LARDNER FOLEY & LARDNER 195
    • その他 26053
  • 絞り込み

    審査記録 最終処分コード (JP)

    • A01 特許 A01 1663
    • A09 未審査請求によるみなし取下 A09 1563
    • A99 未処分 A99 1293
    • A04 取下 A04 98
    • A05 放棄 A05 28
    • A45 出願却下処分(登録) A45 9
    • A11 国内優先権に基づくみなし取下 A11 1
  • 絞り込み

    審査記録 査定種別コード (JP)

    • 0 査定無し 0 1971
    • 1 登録査定 1 1610
    • 2 拒絶査定 2 1074
  • Method and system for approximating deep neural networks for anatomical object detection
    A method and system for approximating a deep neural network for anatomical object detection is discloses. A deep neural network is trained to detect an anatomical object in medical images. An approximation of the trained deep neural network is calculated that reduces the computational complexity of the trained deep neural network. The anatomical object is detected in an input medical image of a patient using the approximation of the trained deep neural network.
    • 特許
    • 出願番号 : US201514706108
    • 出願日 : 2015-05-07
    • 公開番号 : US2016328643
    • 公開日 : 2016-11-10
    • 公告/登録公報番号 : US9633306
    • 公告/登録公報発行日 : 2017-04-25
    • 出願人 : SIEMENS HEALTHCARE
    • 発明者 : DAVID LIUNATHAN LAY...
  • GENERATING LARGER NEURAL NETWORKS
    Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a larger neural network from a smaller neural network. In one aspect, a method includes obtaining data specifying an original neural network; generating a larger neural network from the original neural network, wherein the larger neural network has a larger neural network structure including the plurality of original neural network units and a plurality of additional neural network units not in the original neural network structure; initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same outputs from the same inputs as the original neural network; and training the larger neural network to determine trained values of the parameters of the original neural network units and the additional neural network units from the initialized values.
    • 特許出願公開
    • 出願番号 : US201615349901
    • 出願日 : 2016-11-11
    • 公開番号 : US2017140272
    • 公開日 : 2017-05-18
    • 存続期間満了日 (JP) : 1970-01-01
    • 出願人 : GOOGLE
    • 発明者 : IAN GOODFELLOWTIANQI CHEN...
  • System for determining the occupancy state of a seat in a vehicle and controlling a component based thereon
    System for determining the occupancy of a seat in a vehicle using pattern recognition technologies and techniques that apply to any combination of transducers that provide information about seat occupancy, for example, weight sensors, capacitive sensors, inductive sensors, ultrasonic, optical, electromagnetic, motion, infrared and radar sensors. A processor is coupled to the transducers for receiving data therefrom and processes the data to obtain an output indicative of the seat's current occupancy state. A combination neural network is resident in the processor and is created from data sets, each representing a different occupancy state of the seat and being formed from data from the transducers while the seat is in that occupancy state. The combination neural network produces the output indicative of the current occupancy state of the seat upon inputting a data set representing the current occupancy state of the seat and being formed from data from the transducers.
    • 特許
    • 出願番号 : US20010853118
    • 出願日 : 2001-05-10
    • 公開番号 : US2002059022
    • 公開日 : 2002-05-16
    • 公告/登録公報番号 : US6445988
    • 公告/登録公報発行日 : 2002-09-03
    • 出願人 : AUTOMOTIVE TECHNOLOGIES INTERNATIONAL
    • 代理人/特許事務所 : BRIAN ROFFE
    • 発明者 : DAVID S BREEDJEFFREY L MORIN...
    • スコア : 8417.031
    • 被引用件数 (JP・US) : 61
    • 引用件数 (国内) : 46
    • 引用件数 (国外) : 4
    • 関連特許
  • Method for selecting medical and biochemical diagnostic tests using neural network-related applications
    Methods are provided for developing medical diagnostic tests using decision-support systems, such as neural networks. Patient data or information, typically patient history or clinical data, are analyzed by the decision-support systems to identify important or relevant variables and decision-support systems are trained on the patient data. Patient data are augmented by biochemical test data, or results, where available, to refine performance. The resulting decision-support systems are employed to evaluate specific observation values and test results, to guide the development of biochemical or other diagnostic tests, too assess a course of treatment, to identify new diagnostic tests and disease markers, to identify useful therapies, and to provide the decision-support functionality for the test. Methods for identification of important input variables for a medical diagnostic tests for use in training the decision-support systems to guide the development of the tests, for improving the sensitivity and specificity of such tests, and for selecting diagnostic tests that improve overall diagnosis of, or potential for, a disease state and that permit the effectiveness of a selected therapeutic protocol to be assessed are provided. The methods for identification can be applied in any field in which statistics are used to determine outcomes. A method for evaluating the effectiveness of any given diagnostic test is also provided.
    • 特許
    • 出願番号 : US19970912133
    • 出願日 : 1997-08-14
    • 公開番号 : US2001023419
    • 公開日 : 2001-09-20
    • 公告/登録公報番号 : US6678669
    • 公告/登録公報発行日 : 2004-01-13
    • 出願人 : ADEZA BIOMEDICAL
    • 代理人/特許事務所 : HELLER EHRMAN WHITE & MCAULIFFESTEPHANIE L SEIDMAN
    • 発明者 : DUANE DESIENOJEROME LAPOINTE
    • スコア : 8084.211
    • 被引用件数 (JP・US) : 128
    • 引用件数 (国内) : 41
    • 引用件数 (国外) : 12
    • 関連特許
  • Method of setting optimum-partitioned classified neural network and method and apparatus for automatic labeling using optimum-partitioned classified neural network
    A method of automatic labeling using an optimum-partitioned classified neural network includes searching for neural networks having minimum errors with respect to a number of L phoneme combinations from a number of K neural network combinations generated at an initial stage or updated, updating weights during learning of the K neural networks by K phoneme combination groups searched with the same neural networks, and composing an optimum-partitioned classified neural network combination using the K neural networks of which a total error sum has converged; and tuning a phoneme boundary of a first label file by using the phoneme combination group classification result and the optimum-partitioned classified neural network combination, and generating a final label file reflecting the tuning result.
    • 特許
    • 出願番号 : US20040788301
    • 出願日 : 2004-03-01
    • 公開番号 : US2004172238
    • 公開日 : 2004-09-02
    • 公告/登録公報番号 : US7444282
    • 公告/登録公報発行日 : 2008-10-28
    • 出願人 : SAMSUNG ELECTRONICS
    • 代理人/特許事務所 : STAAS & HALSEY
    • 発明者 : JAE WON LEEJEONG SU KIM...
    • スコア : 7024.389
    • 被引用件数 (JP・US) : 18
    • 引用件数 (国内) : 13
    • 関連特許
  • Device for the autonomous bootstrapping of useful information
    A discovery system employing a neural network, training within this system, that is stimulated to generate novel output patterns through various forms of perturbation applied to it, a critic neural network likewise capable of training in situ within this system, that learns to associate such novel patterns with their utility or value while triggering reinforcement learning of the more useful or valuable of these patterns within the former net. The device is capable of bootstrapping itself to progressively higher levels of adaptive or creative competence, starting from no learning whatsoever, through cumulative cycles of experimentation and learning. Optional feedback mechanisms between the latter and former self-learning artificial neural networks are used to accelerate the convergence of this system toward useful concepts or plans of action.
    • 特許
    • 出願番号 : US20060429803
    • 出願日 : 2006-05-08
    • 公開番号 : US2007011119
    • 公開日 : 2007-01-11
    • 公告/登録公報番号 : US7454388
    • 公告/登録公報発行日 : 2008-11-18
    • 代理人/特許事務所 : H FREDERICK RUSCHEHUSCH BLACKWELL SANDERS
    • 発明者 : STEPHEN L THALER
    • スコア : 6746.391
    • 被引用件数 (JP・US) : 8
    • 引用件数 (国内) : 11
    • 引用件数 (国外) : 1
    • 関連特許
  • Engine control system using a cascaded neural network
    A method, system and machine-readable storage medium for monitoring an engine using a cascaded neural network that includes a plurality of neural networks is disclosed. In operation, the method, system and machine-readable storage medium store data corresponding to the cascaded neural network. Signals generated by a plurality of engine sensors are then inputted into the cascaded neural network. Next, a second neural network is updated at a first rate, with an output of a first neural network, wherein the output is based on the inputted signals. In response, the second neural network outputs at a second rate, at least one engine control signal, wherein the second rate is faster than the first rate.
    • 特許
    • 出願番号 : US20020145131
    • 出願日 : 2002-05-15
    • 公開番号 : US2003217021
    • 公開日 : 2003-11-20
    • 公告/登録公報番号 : US7035834
    • 公告/登録公報発行日 : 2006-04-25
    • 出願人 : CATERPILLAR
    • 代理人/特許事務所 : FINNEGAN HENDERSON FARABOW GARRETT & DUNNER
    • 発明者 : EVAN EARL JACOBSON
    • スコア : 5534.753
    • 被引用件数 (JP・US) : 41
    • 引用件数 (国内) : 26
    • 関連特許
  • Feedback elimination method and apparatus
    A method and apparatus for detecting a singing frequency in a signal processing system using two neural-networks is disclosed. The first one (a hit neural network) monitors the maximum spectral peak FFT bin as it changes with time. The second one (change neural network) monitors the monotonic increasing behavior. The inputs to the neural-networks are the maximum spectral magnitude bin and its rate of change in time. The output is an indication whether howling is likely to occur and the corresponding singing frequency. Once the singing frequency is identified, it can be suppressed using any one of many available techniques such as notch filters. Several improvements of the base method or apparatus are also disclosed, where additional neural networks are used to detect more than one singing frequency.
    • 特許
    • 出願番号 : US20050095045
    • 出願日 : 2005-03-31
    • 公開番号 : US2006227978
    • 公開日 : 2006-10-12
    • 公告/登録公報番号 : US7742608
    • 公告/登録公報発行日 : 2010-06-22
    • 出願人 : POLYCOM
    • 代理人/特許事務所 : WONG CABELLO LUTSCH RUTHERFORD & BRUCCULERI
    • 発明者 : JAMES STEVEN JOINERKWAN KIN TRUONG
    • スコア : 5495.398
    • 被引用件数 (JP・US) : 4
    • 引用件数 (国内) : 5
    • 関連特許
  • AUGMENTING NEURAL NETWORKS TO GENERATE ADDITIONAL OUTPUTS
    Methods, systems, and apparatus, including computer programs encoded on computer storage media, for augmenting neural networks to generate additional outputs. One of the systems includes a neural network and a sequence processing subsystem, wherein the sequence processing subsystem is configured to perform operations comprising, for each of the system inputs in a sequence of system inputs: receiving the system input; generating an initial neural network input from the system input; causing the neural network to process the initial neural network input to generate an initial neural network output for the system input; and determining, from a first portion of the initial neural network output for the system input, whether or not to cause the neural network to generate one or more additional neural network outputs for the system input.
    • 特許出願公開
    • 出願番号 : US201514977201
    • 出願日 : 2015-12-21
    • 公開番号 : US2016189027
    • 公開日 : 2016-06-30
    • 発明者 : ALEXANDER BENJAMIN GRAVESIVO DANIHELKA...
  • Method, system, and program for converting application program code to executable code using neural networks based on characteristics of the inputs
    Provided is a compiler to map application program code to object code capable of being executed on an operating system platform. A first neural network module is trained to generate characteristic output based on input information describing attributes of the application program. A second neural network module is trained to receive as input the application program code and the characteristic output and, in response, generate object code. The first and second neural network modules are used to convert the application program code to object code.
    • 特許
    • 出願番号 : US20000737337
    • 出願日 : 2000-12-15
    • 公開番号 : US2004122785
    • 公開日 : 2004-06-24
    • 公告/登録公報番号 : US6826550
    • 公告/登録公報発行日 : 2004-11-30
    • 出願人 : INTERNATIONAL BUSINESS MACHINES
    • 代理人/特許事務所 : DAVID W VICTORKONRAD RAYNES & VICTOR
    • 発明者 : CHUNG TIEN NGUYENMICHAEL WAYNE BROWN
    • スコア : 5108.469
    • 被引用件数 (JP・US) : 10
    • 引用件数 (国内) : 11
    • 引用件数 (国外) : 1
    • 関連特許