簡易檢索 / 詳目顯示

研究生: 李怡蓁
Yi-Chen Li
論文名稱: 競爭公司在光達系統之專利強度研究
A Study of Patent Strength for Competitors on Lidar System
指導教授: 劉國讚
Kuo-Tsan Liu
口試委員: 管中徽
Chung-Huei Kuan
蔡鴻文
Hung-Wen Tsai
學位類別: 碩士
Master
系所名稱: 應用科技學院 - 專利研究所
Graduate Institute of Patent
論文出版年: 2020
畢業學年度: 108
語文別: 中文
論文頁數: 131
中文關鍵詞: 光達光學雷達雷射雷達激光雷達專利家族專利技術分析與布局專利地圖專利強度指標
外文關鍵詞: patent strength, patent indicators, LiDAR system
相關次數: 點閱:337下載:14
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 本研究將以自動輔助駕駛之「眼」─光達為主題,進行全球五大專利局光達
    專利趨勢分析,並以世界科技龍頭之美國USPTO 光達專利之主要競爭公司為主,
    分析其專利技術分布;以及剖析主要競爭公司之全球專利家族,並根據全球專利
    家族為基礎,先是透過參考過去已知的專利指標計算九項指標,最終提出六項新
    穎之專利強度,供光達技術競爭者一客觀評斷專利強度之依據。
    六項新穎專利強度指標有:總強度(二種計算方式)、發明獲准率、發明權利範圍、
    技術廣度、技術深度、地域覆蓋廣度,並以五張三維圖視覺化呈現競爭公司於上
    述六項專利強度指標下的專利實力。
    本研究分析結果如下:
    1. 專利強度-技術廣度中,可觀察僅少數競爭公司於分析結果圖中45 度
    線上,象徵過往文獻經常採計之國際專利分類號數有其盲點存在,加
    上本研究自定義更貼近研發角度之技術分類計數,將提供可參考度更
    高之技術廣度分析結果。
    2. 專利強度-技術深度中,有鑑於過往文獻大多僅觀察被引用數作為深
    度之強度象徵,而本研究加以引用數指標的計算,認為競爭公司過往
    專利技術參考得多應在技術深度中獲得肯認,其創造與研發值得重視。
    3. 各家競爭公司選擇專利布局之國家經濟規模相差甚大,故透過專利強
    度-地域覆蓋廣度,除利用平均家族成員數觀察家族規模,更加上已
    獲准專利之總GDP 值,可據此推估出該競爭公司於市場上實施專利
    權時之強度。
    4. 光達技術專利之競爭公司主要所屬國別來源為美國。
    5. 光達專利技術之於四大企業類型分布情況為:車廠主要集中於[
    INTPUT]收集之數據型態及[PROCESS]數據處理;汽車零件商則
    因皆有其主攻零部件,專利技術分佈則較為分散;光達製造商中,其
    專利技術類別分佈多集中於[OUTPUT]LiDAR 統配置;至於其它類
    別中,因GOOGLE+WAYMO 以及UBER 近年於無人駕駛計程車處
    激烈競爭關係,各項子技術類別多能見其專利佈局身影。
    6. 專利強度指標分析中,光達技術專利發明數(家族數)前三名依序為:
    GOOGLE+WAYMO、LUMINAR、GM;總強度(以加總合計)前三名則
    依序為:GOOGLE+WAYMO、LUMINAR、OUSTER。


    This study analyzes the patent trends of five major patent offices in the world on the theme of the eyes of autonomous vehicles – LiDAR, and conducts both quality and quantity technical analysis of major competitors.
    LiDAR patent pools are gathered from official patent database, and manual screen to high related documents. Patent strengths of LiDAR competitors by global patent families are evaluated. First, nine patent strength indicators are calculated, then this study provide six new patent strengths, including : total strength, share of granted patents, claim broadness, technological scope, technological depth, and geographical coverage. Five 3D diagrams based on indicators visualize competitors in different aspects.
    The research results of this study are shown as follows:
    1. In view of technological scope, there are a few competitors on the 45° line in the diagram. It means that there are some blind spots of only calculating numbers of IPC classes in the references. This study provides customized classifications as X axis, it will make the patent strength more convincing to the R&D.
    2. There are many references just use numbers of cited in the past, the higher cited numbers mean higher inventive steps. However, the higher citations mean the invention based on broader prior arts. So this study also calculates the numbers of citaions.
    3. Geographical coverage of GDP can visualize market coverage of one patent family, to avoid the disadvantages of using family members.
    4. The main original competitors are form the United States.
    5. Different types of companies focus on different technical fields:
    (1) Vehicle manufacturers focus on data collection and data processing.
    (2) Automotive parts manufacturers have their own items, so their distribution dispersed relatively.
    (3) LiDAR manufacturers mostly concentrated on data output or LiDAR system’s configuration.
    (4) Due to the fierce competition of self-driving taxi between WAYMO and UBER in recent years, they have patent portfolios on each LiDAR sub-technology.
    6. The top three of the number of patented inventions are GOOGLE plus WAYMO, LUMINAR, and GM. The top three of total patent strength are GOOGLE plus WAYMO, LUMINAR, and OUSTER.

    摘要 I Abstract II 誌謝 III 目錄 IV 圖目錄 VI 表目錄 VIII 第一章 緒論 1 第一節 前言 1 第二節 研究背景 2 第一項 光達發展歷史 2 第二項 光達技術發展 6 第三節 研究目的 8 第四節 文獻探討 8 第一項 光達技術於車輛應用文獻 8 第二項 專利指標文獻 10 第三項 專利強度文獻 14 第四項 外國相關專利強度文獻 15 第五項 專利分析 /專利佈局文獻 16 第六項 光達專利分析文獻 17 第七項 與過往文獻之差異 18 第五節 研究方法與流程 19 第一項 研究方法 19 第二 項 研究流程 20 第二章 光達專利申請全球趨勢分析 23 第一節 前言 23 第二節 檢索策略與範圍界定 23 第一項 檢索策略 23 第二項 關鍵詞與分類號選定 24 第三項 檢索資料庫 25 第四項 五大專利局檢索式 26 第三節 全球五大專利局專利申請趨勢 29 第四節 全球五大專利局主要申請人分析 32 第三章 光達競爭公司美國專利技術分析 35 第一節 前言 35 第二節 美國光達專利池檢索界定 35 第三節 光達競爭公司美國專利技術分布圖 39 V 第四節 第四節 光達競爭公司美國專利技術分布圖之十大技術解析光達競爭公司美國專利技術分布圖之十大技術解析................................................ 40 第五節 第五節 光達競爭公司美國專利技術分布圖布局趨勢光達競爭公司美國專利技術分布圖布局趨勢........................................................................ 63 第六節 第六節 光達競爭公司美國專利技光達競爭公司美國專利技術型態分布分析術型態分布分析................................................................................ 69 第四章 第四章 光達競爭公司全球專利家族強度指標光達競爭公司全球專利家族強度指標................................................................................................................ 73 第一節 第一節 前言前言................................................................................................................................................................................................................ 73 第二節 第二節 光達競爭公司專利家族強度指標定義敘明光達競爭公司專利家族強度指標定義敘明................................................................................ 76 第三節 第三節 光達競爭公司總強度光達競爭公司總強度........................................................................................................................................................ 78 第四節 第四節 光達競爭公司發明獲准率光達競爭公司發明獲准率........................................................................................................................................ 83 第五節 第五節 光達競爭公司發明權利範圍光達競爭公司發明權利範圍................................................................................................................................ 85 第六節 第六節 光達競爭公司技術廣度光達競爭公司技術廣度................................................................................................................................................ 87 第七節 第七節 光達競爭公司技術深度光達競爭公司技術深度................................................................................................................................................ 91 第八節 第八節 光達競爭公司地域覆蓋廣度光達競爭公司地域覆蓋廣度................................................................................................................................ 97 第五章 第五章 研究結論與未來研究展望研究結論與未來研究展望.................................................................................................................................................... 101 第一節 第一節 前言前言............................................................................................................................................................................................................ 101 第二節 第二節 研究結論研究結論............................................................................................................................................................................................ 102 第一項 第一項 光達技術全球專利趨勢分析結果光達技術全球專利趨勢分析結果............................................................................................ 102 第二項 第二項 光達競爭公司美國專利技術分析光達競爭公司美國專利技術分析............................................................................................ 103 第三項 第三項 光達競爭公司全球專利家族強度指標光達競爭公司全球專利家族強度指標............................................................................ 105 第三節 第三節 未來未來研究展望研究展望............................................................................................................................................................................ 114 第一項 第一項 技術與市場變化的腳步技術與市場變化的腳步............................................................................................................................ 114 第二項 第二項 檢索策略與檢索工具的精確擬定檢索策略與檢索工具的精確擬定............................................................................................ 114 第三項 第三項 光達技術專利近況之差異光達技術專利近況之差異.................................................................................................................... 115 第四項 第四項 多面向專利分析的廣度多面向專利分析的廣度............................................................................................................................ 115 參考文獻 參考文獻........................................................................................................................................................................................................................................ 117 中文參考文獻 中文參考文獻........................................................................................................................................................................................................ 117 英文參考文獻 英文參考文獻........................................................................................................................................................................................................ 119

    期刊論文
    [1] Brandon Schoettle, SENSOR FUSION: A COMPARISON OF SENSING CAPABILITIES OF HUMAN DRIVERS AND HIGHLY AUTOMATED VEHICLES, The University of Michigan Sustainable Worldwide Transportation, Report No. SWT-2017-12, August 2017: p.8-13
    [2] Goyer, G. G.; R. Watson, The Laser and its Application to Meteorology, Bulletin of the American Meteorological Society, September 1963, 44 (9): p.564–575 [568]
    [3] Holger Ernst, Patent applications and subsequent changes of performance: evidence from time-series cross-section analyses on the firm level, Research Policy Volume 30, Issue1, January 2001: p.143-157
    [4] Holger Ernst, Patent information for strategic technology management, World Patent Information, Volume 25, Issue 3, September 2003: p.233-242
    [5] Michele Grimaldi, Livio Cricelli, Martina Di Giovanni, Francesco Rogo, The patent portfolio value analysis: A new framework to leverage patent information for strategic technology planning, Technological Forecasting & Social Change, Volume 94, May 2015: p.286-302
    [6] Nikolaos Papageorgiadis, Adam R. Cross, Constantinos Alexiou, International patent systems strength 1998–2011, Journal of World Business 49 (2014): p.586–597
    [7] Xi Yang, Xin Liu, Jun Song, A Study on Technology Competition of Graphene Biomedical Technology Based on Patent Analysis, Applied Sciences 2019, 9, 2613
    專業書籍
    [1] Paul McManamon, Field Guide to Lidar, SPIE Field Guides Volume FG36
    專業報導
    [1] ALEX DAVIES, GM Buys a Lidar Startup That Could Deliver Its Self-Driving Future, at https://www.wired.com/story/gm-cruise-strobe-lidar/ (last visited 02/19/2020)
    [2] Alison Griswold, Alphabet is coming for Uber, at https://qz.com/1486469/waymo-googl-is-coming-for-uber-with-a-driverless-taxi-service/ (last visited 03/07/2020)
    [3] Christoph Hammerschmidt, Continental buys LIDAR business from ASC, at https://www.eenewseurope.com/news/continental-buys-lidar-business-asc (last visited 02/08/2020)
    120
    [4] Christopher Schrecke, Continental and Knorr-Bremse announce a partnership for highly automated driving in commercial vehicles, at https://www.continental.com/en/press/press-releases/commercial-vehicle-aftermarket/2018-09-19-partnership-145812 (last visited 02/08/2020)
    [5] Dara Khosrowshahi (CEO of Uber), Uber and Waymo Reach Settlement, at https://www.uber.com/newsroom/uber-waymo-settlement/ (last visited 02/12/2020)
    [6] Dave Lee, LG Electronics and AEye Announce Strategic Partnership to Address Sensing and Perception Needs of ADAS Market, at https://www.bbc.com/news/technology-50484172 (last visited 02/11/2020)
    [7] Green Car Congress, Quanergy and Chery partner for autonomous vehicles; Chery Lion Smart Partner Program, at https://www.greencarcongress.com/2019/07/20190719-quanergy.html (last visited 02/06/2020)
    [8] INNOVATION / PRODUCTS & TECHNOLOGY, Valeo’s LiDAR, driving the autonomous vehicles, at https://www.valeo.com/en/valeos-lidar-driving-the-autonomous-vehicles/ (last visited 06/02/2020)
    [9] Johana Bhuiyan, Toyota is trusting a startup for a crucial part of its newest self-driving cars, at https://www.vox.com/2017/9/27/16373012/toyota-self-driving-lidar-luminar (last visited 02/07/2020)
    [10] Kirsten Korosec, Waymo to start selling standlone LiDAR sensors, at https://techcrunch.com/2019/03/06/waymo-to-start-selling-standalone-lidar-sensors/ (last visited 02/06/2020)
    [11] Liane Yvkoff, Using Ouster’s Lidar, Nvidia Targets 2022 For Commercial Launch Of Self-Driving Vehicles, at https://www.forbes.com/sites/lianeyvkoff/2019/09/18/using-ousters-lidar-nvidia-targets-2022-for-commercial-launch-of-self-driving-vehicles/#5368e4a96a52 (last visited 02/10/2020)
    [12] Matt Burns, “Anyone relying on lidar is doomed,” Elon Musk says, at https://techcrunch.com/2019/04/22/anyone-relying-on-lidar-is-doomed-elon-musk-says/ (last visited 02/11/2020)
    [13] Megan Rose Dickey, BMW is working with LiDAR company Innoviz to make self-driving cars, at https://techcrunch.com/2018/04/26/bmw-is-working-with-lidar-company-innoviz-to-make-self-driving-cars/ (last visited 02/06/2020)
    [14] PAUL SAWERS, Volvo and Luminar demo advanced lidar tech that gives autonomous cars detailed view of pedestrian movements, at https://venturebeat.com/2018/11/27/volvo-and-luminar-demo-advanced-lidar-tech-that-gives-autonomous-cars-detailed-view-of-pedestrian-movements/ (last visited 02/09/2020)
    121
    [15] Ronan Glon, Bosch’s sharp-sighted lidar rounds out its suite of self-driving technology, at https://www.digitaltrends.com/cars/bosch-announces-first-lidar-for-self-driving-cars-ahead-of-ces-2020/ (last visited 03/07/2020)
    [16] Sam Abuelsamid, BMW Selects Innoviz Solid-State Lidar For 2021 Automated Driving Program, at https://www.forbes.com/sites/samabuelsamid/2018/04/26/bmw-selects-innoviz-solid-state-lidar-for-2021-automated-driving-program/#bd24c4f381c6 (last visited 02/10/2020)
    [17] Sam Abuelsamid, Bosch To Debut New Lidar Sensor At CES 2020, at https://www.forbes.com/sites/samabuelsamid/2020/01/02/bosch-to-debut-new-lidar-sensor-at-ces-2020/#24aa87cf6414 (last visited 02/08/2020)
    [18] Samuel Gibbs, Google's self-driving car: How does it work and when can we drive one?, at https://www.theguardian.com/technology/2014/may/28/google-self-driving-car-how-does-it-work#maincontent (last visited 01/08/2020)
    [19] SEAN HIGGINS, A new lidar from Google sister company Waymo, at https://www.spar3d.com/news/lidar/a-new-lidar-from-google-sister-company-waymo/ (last visited 02/11/2020)
    [20] Toshiba Electronic Devices & Storage Corporation, Toshiba New Algorithm Greatly Improves Angular LiDAR, at https://toshiba.semicon-storage.com/tw/company/news/news-topics/2019/04/automotive-20190422-1.html (last visited 02/06/2020)
    [21] TOSHIBA Global, Toshiba’s New Circuit Technology Realizes Long-Range and High Resolution LiDAR(Light Detection and Ranging) for Reliable Self-Driving Vehicles, at http://www.toshiba.co.jp/about/press/2018_03/pr0501.htm (last visited 02/07/2020)

    QR CODE