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研究生:呂文惠
研究生(外文):Wen-Hui Lu
論文名稱:對話式人工智慧專利佈局分析與應用發展策略
論文名稱(外文):Patent Analysis and Application Development Strategies for Conversational AI
指導教授:王永心王永心引用關係
指導教授(外文):Yung-Hsin Wang
口試委員:王永心
口試委員(外文):Yung-Hsin Wang
口試日期:2020-07-28
學位類別:博士
校院名稱:大同大學
系所名稱:資訊工程學系(所)
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:132
中文關鍵詞:智慧導購人工智慧對話式人工智慧專利分析技術功效矩陣聊天機器人智慧客服對話式商務
外文關鍵詞:Smart Shopping AssistantConversational CommerceConversational AIArtificial IntelligencePatent AnalysisTechnology-Performance MatrixChatbotSmart Customer Service
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近年來,基於人工智慧強化的自然語言處理技術,對話式人工智慧之研發與產業應用展現令人驚艷的進展,但因技術平台與應用各異,對於研發機構在技術研發與應用導入的選擇上提供了難題。參酌比較國際大廠以及研究機構的研發佈局將可為台灣投入對話式人工智慧技術與應用開發提供重要的指引。
本研究以Derwent Innovation專利資料庫所收錄之專利資料為基礎,檢索並分析全球人工智慧主要大廠專利投入狀況,從而了解大廠對於自然語言處理與對話式人工智慧相關技術的佈局重點。並進一步針對對話式人工智慧技術之全球專利進行檢索分析,並鎖定中國、美國與台灣專利之主要專利權人,分析其專利佈局重點,以作為台灣企業與研究機構投入對話式人工智慧技術與應用研發之參考。
從全球與台灣主要專利權人的技術分類來看,自然語言資料處理、神經網路、機器學習以及語音辨識相關技術是當前各企業投入重點。本研究提出:(1)優先開發文字對話系統、(2)結合現有語音文字轉換次系統,縮短開發時間、(3)鎖定企業應用需求開發多元解決方案、(4)結合台灣硬體產品積極開發海外應用市場等四項建議。
Recently, the R&D and industrial applications of conversational AI, based on the AI-empowered natural language processing technology, has shown impressive process. However, as the technology platforms and applications vary among different organizations and industriesl, it is difficult for research orgranizations to choose from many R&D and application focuses. Through the analysis of R&D deployment of leading interational companies and research institutes, it is possible to find clues and paths for Taiwan in the development of conversational AI technologies and applications.
Based on the patent database of Derwent Innovation, this research searched and analyzed global AI patents and explored the patent portfolios of major assignees such as IBM, Microsoft, and Google. A further analysis was conducted to the global patents of conversational AI technology, with the focus on Chinese, American and Taiwanes assignees. Suggestions were delivered to Taiwan’s industrial players and R&D organizations.
According to the results of this research, natural language data processing, neural network, machine learning, and speech recognition are among the R&D focuses of major players. It is suggested that Taiwanese companies and research institutes should: (1) choose text-based conversational system as the R&D priority, (2) incoporate the speech-recognition function from commercial systems to accelerate R&D, (3) target business application needs to develop diverse enterprise solutions, and (4) collaborate with Taiwan’s hardware product suppliers to explore oversea markets.
誌謝 i
摘要 ii
ABSTRACT iii
目次 iv
圖次 vi
表次 ix
第壹章 緒論 1
第一節 研究背景 1
第二節 研究動機與目標 8
第貳章 文獻回顧 11
第一節 對話式人工智慧之技術與應用發展 11
第二節 專利佈局研究與人工智慧專利趨勢 19
第參章 研究方法 26
第一節 專利佈局分析流程 26
第二節 DERWENT INNOVATION專利資料庫 29
第三節 專利檢索策略之研擬 33
第肆章 全球人工智慧專利解析 36
第一節 專利檢索 36
第二節 管理層面分析 39
第三節 領先企業專利技術佈局分析 45
第四節 領先大學專利技術佈局分析 49
第五節 大廠專利技術佈局視覺化比較分析 53
第六節 小結 64
第伍章 全球對話式人工智慧專利佈局分析 66
第一節 專利檢索 66
第二節 管理層面分析 67
第三節 中國專利主要專利權人佈局分析 73
第四節 美國專利主要專利權人佈局分析 79
第五節 中國美國專利權人佈局比較 89
第六節 台灣專利主要專利權人佈局分析 94
第七節 標竿主要專利權人技術功效佈局 99
第八節 小結 109
第陸章 結論與建議 111
第一節 結論 111
第二節 台灣投入對話式人工智慧發展之策略建議 113
第三節 研究限制 116
第四節 未來研究方向 118
參考文獻 119
中文部份 119
英文部份 122
中文部份
王世仁、王世堯(2003)。智慧財產權剖析–論生物科技專利策略與實務。全華科技圖書股份有限公司:新北市。
王平、張原豐、邵子軒、郭溥村、羅濟群、陳裕禎(2013)。專利分析與技術創新─以互動式遊戲專利佈局為例。資訊管理學報第20卷第1期,39-76
王平、阮揚洲、林孝忠(2020)。專利分析探討我國業者在胎壓偵測系統之技術發展與競爭。資訊管理學報第27卷第1期,1-54。
王美雅、陳保秀、陳欽雨(2011)。兩岸大學專利活動之比較研究。商管科技季刊第12卷第3期,263-290。
朱泓任(2017/10/24)。臺灣AI元年 科技部5年160億打造AI新生態。Newstalk新聞,取自https://newtalk.tw/news/view/2017-10-24/101480
行政院(2019)。台灣AI行動計畫—掌握契機,全面啟動產業AI化。取自:https://www.ey.gov.tw/Page/5A8A0CB5B41DA11E/a8ec407c-6154-4c14-8f1e-d494ec2dbf23
李智揮(2019)。提供AIOT資料生態鏈,建構政府智慧服務。政府機關資訊通報第362期,1-7。
李崇僖(2019)。從專利資訊解析人工智慧創新模式。專利師第39期,34-46頁。取自http://lawdata.com.tw/File/PDF/J441/A00700039_034.pdf
林秀英(2018)。透析全球人工智慧專利與早期投資大戰。Findit平台新興領域動向研究,取自https://findit.org.tw/upload/research/research_20180413020.pdf
林家聖(2006)。專利檢索系統與分析方法之探討與革新(碩士論文)。國立政治大學,台北市。
陳妍錦、呂新科、羅嘉惠、林芃君、簡志維(2013)。專利地圖分析與檢索技術之探討。第九屆知識社群國際研討會論文集,923-933。
張展誌、劉智遠(2017)。以專利布局支持新技術產業化。智慧財產權月刊第224期,6-21。
許有進(2018)。臺灣發展人工智慧之挑戰與機會。國土及公共治理學刊第6卷第四期,28-39。
郭彥鋒、簡大翔、莊宗翰、吳家豪(2019)。我國金融機構專利布局分析與建議。智慧財產權月刊第244期,6-29。
陳省三(2009)。專利地圖。智慧財產權的發展與商業模式應用研究系列課程簡報。取自http://cc.ee.ntu.edu.tw/~giee/announce/943_U0370/0508200901.pdf
陳賜賢(2019)。光達系統美國專利布局分析。資策會產業情報研究所產業研究報告,取自https://mic.iii.org.tw/aisp/Reports.aspx?id=CDOC20190314003
陳昭妤(2017)。論人工智慧創作與發明之法律保護–以著作權與專利權權利主體為中心(碩士論文)。國立政治大學,台北市。
陳綺萱(2019)。深度學習之專利研究(碩士論文)。國立臺灣師範大學,台北市。
陳歆、楊慶泉(2001)。國外專利申請決策。智慧財產權月刊第30期,38-67。
黃孝怡(2018)。策略性專利佈局:從企業專利策略到專利佈局。智慧財產權月刊第236期,5-29。
曾厚強、陳柏琳、宋曜廷(2017)。探究使用基於類神經網路之特徵於文本可讀性分類。中文計算語言學期刊第22卷第2期,31-46。
楊千旻(2012)。高科技產業之專利布局與策略聯盟。專利師第11期,115-136。
葉士緯、黃振榮(2017)。合作專利分類(CPC)實施現況之探討與應用。智慧財產權月刊第217期,5-14。
葉席吟(2019)。主要國家專利發展趨勢觀測與影響力評析。科技政策觀點第8期,61-67。
經濟部智慧財產局(2019)。我國人工智慧相關專利申請概況及申請人常見核駁理由分析。取自https://www.tipo.gov.tw/tw/cp-85-859330-1189b-1.html
經濟部智慧財產局(2020)。中華民國專利資訊檢索系統。取自https://twpat.tipo.gov.tw/
劉佳佳、董旻、方曙(2007)。國外專利分析工具的比較研究。現代圖書情報技術第2期,67-74。

英文部份
Abdel-Hamid, O., Mohamed, A.-R., Jiang, H., Deng, L., Penn, G., & Yu, D. (2015). Convolutional neural networks for speech recognition. IEEE/ACM Transactions on Audio, Speech Language Process 22(10), 1533-1545.
Aditya, A., Parikshit, A., Gaurav, D., & Piyush, D. (2018). Speech Recognition using Recurrent Neural Networks. Proceeding of 2018 IEEE International Conference on Current Trends toward Converging Technologies, 1-4. DOI: 10.1109/ICCTCT.2018. 8551185
Arora, S., Batra, K., & Singh, S. (2013). Dialogue System: A Brief Review. arXiv:1306. 4134
Brayne, S., McKellar, S., & Tzafestas, K. (2018). Artificial Intelligence in the Life Sciences & Patent Analytics. Available at https://www.ip-pragmatics.com/media/1049/ip-pragmatics-artificial-intelligence-white-paper.pdf
Brenier, J. (2017). An Overview of Conversational AI. Available at https://georgianpartners.com/conversational-ai-overview/
CBC News (2016). Google DeepMind computer AlphaGo sweeps human champ in Go matches. Available at https://www.cbc.ca/news/technology/go-google-alphago-lee-sedol-deepmind-1.3488913
Chang, S.-B. (2012). Using patent analysis to establish technological position: Two different strategic approaches. Technological Forecasting & Social Change 79, 3-15.
Chauhan, N. K. & Singh, K. (2018). A Review on Conventional Machine Learning vs Deep Learning. Proceedings of 2018 International Conference on Computing, Power and Communication Technologies (GUCON), 347-352.
Chen, G., Ye, D., Xing, Z., Chen, J., & Cambria, E. (2017). Ensemble Application of Convolutional and Recurrent Neural Networks for Multi-label Text Categorization. Proceedings of 2017 International Joint Conference on Neural Network, 2377-2383.
Chen, L., Tan, B., Long, S., & Yu, K. (2018). Structured Dialogue Policy with Graph Neural Networks. Proceedings of the 27th International Conference on Computational Linguistics, 1257-1268.
Chen, Y.-N., Celikyilmaz, A., & Hakkani-Tür, D. (2018). Deep Learning for Dialogue Systems. Proceedings of the 27th International Conference on Computational Linguistics: Tutorial Abstracts, 25-31. Available at https://www.aclweb.org/ anthology/C18-3006.pdf
Chen, Z., Eavani, H., Chen, W., Liu, Y., & Wang, W.Y. (2019). Few-Shot NLG with Pre-Trained Language Model. Accepted by ACL 2020. arXiv:1904.09521
Cho, K., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, 1724-1734.
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. Proceedings of NIPS 2014 Workshop on Deep Learning. arXiv:1412.3555
Clarivate Analytics (2019). Derwent Innovation. Available at https://clarivate.com/derwent /solutions/derwent-innovation/
Clarivate Analytics (2020). Derwent Innovation. Available at https://clarivate.com/derwent /solutions/derwent-innovation/
Colby, K.M. (1981). Modeling a paranoid mind. Behavior and Brain Sciences 4(4), 515-534.
Collobert, R. & Weston, J. (2008). A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning. Proceedings of the 25th International Conference on Machine Learning, 160-167.
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. (2011). Natural language processing (almost) from scratch. Journal of Machine Learning Research 12, 2493-2537.
Contreras, I. & Vehí, J. (2018). Artificial Intelligence for Diabetes Management and Decision Support: Literature Review. Journal of Medical Internet Research 20(5), 1-37.
Czajkowski, A. (2011). Overview of Free & CommercialPatent Databases. WIPO Research Report. Available at https://www.wipo.int/export/sites/www/tisc/en/ppt/Philippines/ overview_of_DBs.pdf
Dachapally, P.R. & Ramanam, S. (2017). In-depth Question classification using Convolutional Neural Networks. arXiv:1804.00968
Deloitte Digital (2019). Conversational AI The next wave of customer and employee experiences. Available at https://www2.deloitte.com/content/dam/Deloitte/au/ Documents/strategy/au-deloitte-conversational-ai.pdf
Dey, R. & Salem, F.M. (2017). Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks. Proceedings, 2017 IEEE 60th International Midwest Symposium on Circuits and Systems. arXiv:1701.05923
van Doren, D., Koenigstein, S., & Reiss, T. (2013). The development of synthetic biology: a patent analysis. Systems and Synthetic Biology 7, 209–220. DOI: 10.1007/s11693-013-9121-7
Dušek, O., Novikova, J., & Rieser, V. (2020). Evaluating the state-of-the-art of End-to-End Natural Language Generation: The E2E NLG challenge. Computer Speech & Language 59, 123-156.
Gao, J., Galley, M., & Li, L. (2019). Neural Approaches to Conversational AI. Foundations and Trends in Information Retrieval 13(2-3), 127–298. DOI: 10.1561/1500000074
Gatt, A. & Krahmer, E. (2018). Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation. Journal of Artficial Intelligence Research 61, 65-170.
Greenemeier, L. (2017). 20 Years after Deep Blue: How AI Has Advanced Since Conquering Chess. Scientific American. Available at https://www.scientificamerican. com/article/20-years-after-deep-blue-how-ai-has-advanced-since-conquering-chess/
Haenlein, M. & Kaplan, A. (2019). A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review 61(4), 5–14.
Henderson, M., Thomson, B. & Young, S. (2013). Deep Neural Network Approach for the Dialog State Tracking Challenge. Proceedings, SIGDIAL 2013 Conference, 467-471.
Hidemichi, F. & Shunsuke, M. (2018). Trends and priority shifts in artificial intelligence technology invention: A global patent analysis. Economic Analysis and Policy 58, 60-60. DOI: 10.1016/j.eap.2017.12.006
Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A Fast Learning Algorithm for Deep Belief Nets. Neural Computation 18(7): 1527–1554.
Hinton, G. E. (2007). Learning multiple layers of representation. TRENDS in Cognitive Sciences 11(10): 428-434.
Hutson, M. (2017). AI Glossary: Artificial intelligence, in so manywords. Science 357, 19-20.
IBM (n.d.). Learn how to operationalize AI in your business. Available at https://www.ibm.com/watson.
InQuartik (2020). Artificial Intelligence Patents: Patent Technology Trends For AI. Availabe at https://www.inquartik.com/inf-ai-patent-trends/
IPOS (2019). ARTIFICIAL INTELLIGENCE: ITS EVOLVING NATURE AND FUTURE PROSPECTS. Available at https://www.imda.gov.sg/-/media/Imda/Files/Industry-Development/Infrastructure/Technology/Technology-Roadmap/Patent-Analytics-for-Artificial-Intelligence.pdf
IPO, UK (2019). Artificial Intelligence – An overview of AI patenting. Available at https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/817610/Artificial_Intelligence_-_A_worldwide_overview_of_AI_ patents.pdf
IP Australia (2019). Machine Learning Innovation A Patent Analytics Report. Available at https://www.ipaustralia.gov.au/sites/default/files/reports_publications/patent_analytics_report_on_machine_learning_innovation.pdf
Ishwari, K.S.D., Aneeze, A.K.R.R., Sudheesan, S., Karunaratne, H.J.D.A., Nugaliyadde, A., & Mallawarrachchi, Y. (2019). Advances in Natural Language Question Answering: A Review. arXiv:1904.05276
Joint Research Centre [JRC] (2020). AI Watch – Defining Artificial Intelligence. JRC Technical Report JRC118163. Available at https://publications.jrc.ec.europa.eu/ repository/bitstream/JRC118163/jrc118163_ai_watch._defining_artificial_intelligence_1.pdf
JPO (2019). Recent Trends in AI-related Inventions–Report. Available at https://www.jpo. go.jp/e/system/patent/gaiyo/ai/document/ai_shutsugan_chosa/report.pdf
Kawano, Y. & Yanai, K. (2014). Food image recognition with deep convolutional features. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, 589-593.
Khurana, D., Koli, A., Khatter, K., & Singh, S. (2017). Natural Language Processing: State of The Art, Current Trends and Challenges. arXiv:1708.05148
Kim, H.-Y., Lee, J., Yeo, N.Y., Astrid, M., Lee, S.-K., & Kim, Y.-K. (2018). CNN based Sentence Classification with Semantic Features using Word Clustering. Proceedings of 2018 International Conference on Information and Communication Technology Convergence, 484-488. DOI: 10.1109/ICTC.2018.8539546
Kim, H. & Lee, J.-H. (2016). A Recurrent Neural Networks Approach for Estimating the Quality of Machine Translation Output. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 494-498. DOI: 10.18653/v1/N16-1059
Kim, H. & Jeong, Y.-S. (2019). Sentiment Classification Using Convolutional Neural Networks. Applied Science 9, 2347-2360. DOI:10.3390/app9112347
Kim, J. & Lee, S. (2015). Patent databases for innovation studies: A comparative analysis of USPTO, EPO, JPO and KIPO. Technology Forecasting and Social Change 92, 332-345.
Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, 1746–1751.
Lei, T., Shi, Z., Liu, D., Yang, L., & Zhu, F. (2018). A novel CNN-based method for Question Classification in Intelligent Question Answering. Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence, 1–6. DOI: 10.1145/3302425.3302483
Li, K., Xu, H., Want, Y., Povey, D., & Khudanpur, S. (2018). Recurrent Neural Network Language Model Adaptation for Conversational Speech Recognition. Proceedings, Interspeech 2018, 1413-1417.
Li., X., Chen, Y.-N., Li, L., Gao, J., & Cdlikyilmaz, A. (2017). End-to-End Task-Completion Neural Dialogue Systems. Processings of the 8th International Joint Conference on Natural Language 1, 733-743. arXiv:1703.01008
Mahata, S.K., Das, D., & Bandyopadhyay, S. (2018). MTIL2017: Machine Translation Using Recurrent Neural Network on Statistical Machine Translation. Journal of Intelligent Systems 2018, 1-7. DOI: 10.1515/jisys-2018-0016.
Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., & McClosky, D. (2014). The Stanford CoreNLP natural language processing toolkit. Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations, 55–60. DOI: 10.3115/v1/P14-5010
Marietto, M., Aguiar, R., Barbosa, G., Botelho, W., Pimentel, E., Franca, R., & Silva, V. (2013). Artificial Intelligence MArkup Language: A Brief Tutorial. Proceedings of 2013 International Journal of Computer Science and Engineering Survey. DOI: 10.5121/ijcses.2013.4301
MarketsandMarkets (2019). Conversational AI Market worth $15.7 billion by 2024. Available at https://www.marketsandmarkets.com/PressReleases/conversational-ai.asp.
Masche, J. & Le, N. (2017). A Review of Technologies for Conversational Systems. Proceedings of the 5th International Conference on Computer Science, Applied Mathematics and Applications. DOI: 10.1007/978-3-319-61911-8_19
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013a). Efficient Estimation of Word Representations in Vector Space. Proceedings, ICLR 2013. arXiv:1301.3781
Mikolov, T., Karafiát, M., Burget, L., Černocký, J.H., & Khudanpur, S. (2010). Recurrent neural network based language model. Proceedings, Interspeech 2010, 1045-1048
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013b). Distributed Representations ofWords and Phrases and their Compositionality. Proceedings of the 26th International Conference on Neural Information Processing Systems 2, 3111–3119. arXiv: 1310.4546
Mrkšić, N., Ó Séaghdha, D., Wen, T.-H., Thomson, B., & Young, S. (2017). Neural Belief Tracker: Data-Driven Dialogue State Tracking. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics 1, 1777-1788. Available at https://www.aclweb.org/anthology/P17-1163.pdf
Nagajyothi, D & Siddaiah, P. (2018). Speech Recognition Using Convolutional Neural Networks. International Journal of Engineering & Technology 7, 133-137.
Nassif, A.B., Shahin, I., Attili, I., Azzeh, M., & Shaalan, K. (2019). Speech Recognition Using Deep Neural Networks: A Systematic Review. IEEE Access 7, 19143-19165. DOI: 10.1109/ACCESS.2019.2896880
Nowak, J., Taspinar, A., & Scherer, R. (2017). LSTM Recurrent Neural Networks for Short Text and Sentiment Classification. Proceedings of 2017 International Conference on Artificial Intelligence and Soft Computing, 553-562.
OECD (2009). OECD Science, Technology and Industry Scoreboard 2009. Available at https://www.oecd-ilibrary.org/docserver/sti_scoreboard-2009-en.pdf?expires=1593 365265&id=id&accname=guest&checksum=FCFEB75C99EDF6DAEFD863B8BEBCB2F8
Park, J., Boo, Y., Choi, I., Shin, S., & Sung, W. (2018). Fully Neural Network Based Speech Recognition on Mobile and Embedded Devices. Proceedings of the 32nd Conference on Neural Information Processing Systems, 10642–10653. DOI: 10.5555/3327546. 3327722
Pascanu, R., Mikolov, T., & Bengio, Y. (2013). On the difficulty of training recurrent neural networks. Proceedings of Machine Learning Research 28(3), 1310-1318.
Poria, S., Cambria, E., & Gelbukh, A. (2016). Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Systems 108, 42-49.
Radhika, K., Bindu, K.R., & Latha, P. (2018). A Text Classification Model Using Convolution Neural Network and Recurrent Neural Network. International Journal of Pure and Applied Mathematics 19(15), 1549-1554.
Reddy, S., Chen, D., & Manning, C.D. (2019). CoQA: A Conversational Question Answering Challenge. Transactions of the Association for Computational Linguistics, 7, 249-266. DOI: 10.1162/tacl_a_00266
Taghaboni‐Dutta, F., Trappey, A. J. C., Trappey, C. V., & Wu, H. (2009). An exploratory RFID patent analysis. Management Research News 32(12), 1163-1176.
Tractica (2019). Artificial Intelligence Market Forecasts. Tractica Research Report, published 4Q 2019.
Turing, A. M. (1950). Computing Machinery and Intelligence. Mind 49, 433-460.
Uragun, B. & Rajan, R. (2011). Developing an appropriate data normalization method. Proceedings of the 10th International Conference on Machine Learning and Applications, 195-199. DOI: 10.1109/ICMLA.2011.53
Veeriah, V., Zhuang, N., & Qi, G.-J. (2015). Differential Recurrent Neural Networks for Action Recognition. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 4041-4049
Wallace, R.S. (2003). The Elements of AIML Style. Available at https://files.ifi.uzh.ch/cl/ hess/classes/seminare/chatbots/style.pdf
Wang, B., Wang, A., Chen, F., Wang, Y., & Kuo, C.-C. J. (2019). Evaluating Word Embedding Models: Methods and Experimental Results. APSIPA Transactions on Signal and Information Processing 8(19), 1-14. DOI: 10.1017/ATSIP.2019.12
Weisz, G., Budzianowski, P., Su, P.-H., & Gašić, M. (2018). Sample efficient deep reinforcement learning for dialogue systems with large action spaces. arXiv: 1802.03753
Weizenbaum, J. (1966). ELIZA – A Computer Program for the Study of Natural Language Communication between Man and Machine. Communications of the ACM 9(1), 36-45.
Wen, T.-H., Gašić, M., Mrkšić, N., Su, P.-H., Vandyke, D., & Young, S. (2015). Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 1711–1721. DOI: 10.18653/v1/D15-1199
Wen, T.-H., Gašić, M., Mrkšić, N., Rojas-Barahona, L.M., Su, P.-H., Ultes, S., Vandyke, D., & Young, S. (2016). A network-based end-to-end trainable task-oriented dialogue system. Proceedings, EACL 2016. arXiv:1604.04562.
WIPO (2019). WIPO Technology Trends 2019–Artificial Intelligence. Available at https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf
Yao, K., Zweig, G., Hwang, M.-Y., Shi, Y., & Yu, D. (2013). Recurrent Neural Networks for Language Understanding. Proceedings, Interspeech 2013, 2524-2528. DOI: 10.13140/2.1.2755.3285

Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018). Recent Trends in Deep Learning Based Natural Language Processing. IEEE Computational intelligence magazine, August 2018, 55-75.
Yu, H. (2016). From Deep Blue to DeepMind: What AlphaGo Tells Us. Predictive Analytics and Futurism, July 2016, 42-45.
Yu, L., Pan, Y., & Wu Y. (2009). Research on Data Normalization methods in Multi-attribute Evaluation. Proceedings of International Conference on Computational Intelligence and Software Engineering, 1-5.
Zemčik, T. (2019). A Brief History of Chatbots. Proceedings, AICAE 2019, 14-18.
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