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週次
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課程內容與指定閱讀
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教學活動與課前、課後作業
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學生學習投入時間
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1
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(2/22) Introduction to the course and Patent Specification
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宋老師與許老師共同授課
To introduce the patent specification of U.S. Pat. US 6,637,447
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3 hrs
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2
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(2/29) Introduction to Patent Big Data
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宋老師授課
To introduce the Bibliographic data, text, and file wrapper of U.S. Pat. US 10,375,357
Reading Assignments:
1. WIPO, The Role of Patent Information in Supporting Innovation
2. Ch3. Understanding Patent Data
3. Ch4. Claims, “Legally, Less is More”
Foundation of Statistics I:
台大開放式課程「統計與生活」
單元一：資料哪裡來（一）
單元二：資料哪裡來（二）
單元三：實驗設計資料（一）
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15 hrs
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3
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(3/7) Patent Search and Patent Data Retrieval
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宋老師授課
To introduce how to conduct patent searches and retrieve patent data
Reading Assignments:
1. WIPO, WIPO Guide to using Patent Information
2. WIPO, Patent Information and Development
Foundation of Statistics II:
台大開放式課程「統計與生活」
單元四：實驗設計資料（二）
單元五：資料之圖表展示
單元六：資料之敘述
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15 hrs
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4
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(3/14) Patent Citations Analysis
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宋老師授課
To introduce the patent citations analysis
Reading Assignments:
Ch. 6: Patent Citations Analysis
Foundation of Statistics III:
台大開放式課程「統計與生活」
單元七：兩個變數之關係
單元八：機率
單元九：機率模型
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5
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(3/21) Bibliographic-based Patent Analysis for Strategic Technology Management (1)
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宋老師授課
To introduce the patent analytics of bibliographic data
Reading Assignment:
Ernst, H. (2003). Patent information for strategic technology management. World patent information, 25(3), 233-242.
Foundation of Statistics IV:
台大開放式課程「統計與生活」
單元十：模擬
單元十一：期望值
單元十二：信賴區間
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15 hrs
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6
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(3/28) Bibliographic-based Patent Analysis for Strategic Technology Management (2)
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宋老師授課
To introduce the patent analytics of bibliographic data
Reading Assignments:
Sick, N., Merigó, J. M., Krätzig, O., & List, J. (2021). Forty years of World Patent Information: A bibliometric overview. World Patent Information, 64, 102011.
Foundation of Statistics V:
台大開放式課程「統計與生活」
單元十三：顯著性檢定
單元十四：統計推論的應用
單元十五：交叉列表與卡方檢定
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15 hrs
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7
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(4/4) 清明節
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放假
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8
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(4/11) Semantic-based Patent Analysis for Strategic Technology Management (1)
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宋老師授課
To introduce the patent analytics of textual data
Reading Assignments:
Bonino, D., Ciaramella, A., & Corno, F. (2010). Review of the state-of-the-art in patent information and forthcoming evolutions in intelligent patent informatics. World Patent Information, 32(1), 30-38.
AI for Business:
政大磨課師「商業人工智慧導論」簡士鎰老師
單元一：AI與大數據導論
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20 hrs
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9
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(4/18) Semantic-based Patent Analysis for Strategic Technology Management (2)
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宋老師授課
To introduce the patent analytics of textual data
Reading Assignments:
1. Chen, L., Xu, S., Zhu, L., Zhang, J., Lei, X., & Yang, G. (2020). A deep learning based method for extracting semantic information from patent documents. Scientometrics, 125(1), 289-312.
2. Aristodemou, L., & Tietze, F. (2018). The state-of-the-art on Intellectual Property Analytics (IPA): A literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data. World Patent Information, 55, 37-51.
AI for Business:
政大磨課師「商業人工智慧導論」簡士鎰老師
單元二：資料處理、資料探勘、機器學習、模型結果評估
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20 hrs
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10
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(4/25) Application of Patent Analytics on the Determination and Prediction of Patent Validity
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宋老師授課
Application of Patent Analytics on the Determination and Prediction of Patent Validity
Reading Assignments:
1. Arts, S., Cassiman, B., & Gomez, J. C. (2018). Text matching to measure patent similarity. Strategic Management Journal, 39(1), 62-84.
2. Raghupathi, V., Zhou, Y., & Raghupathi, W. (2018). Legal decision support: Exploring big data analytics approach to modeling pharma patent validity cases. IEEE Access, 6, 41518-41528.
AI for Business:
政大磨課師「商業人工智慧導論」簡士鎰老師
單元三：電腦視覺、自然語言處理
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20 hrs
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11
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(5/2) Application of Patent Analytics on Innovation Management and Strategy
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宋老師授課
Application of Patent Analytics on Innovation Management and Strategy
Reading Assignments:
1. Aristodemou, L., Tietze, F., Athanassopoulou, N., & Minshall, T. (2017). Exploring the future of patent analytics: a technology roadmapping approach.
2. Guderian, C. C., Bican, P. M., Riar, F. J., & Chattopadhyay, S. (2021). Innovation management in crisis: patent analytics as a response to the COVID‐19 pandemic. R&D Management, 51(2), 223-239.
Python for Machine Learning:
政大磨課師「應用機器學習與Python」林怡伶老師
單元一：Python入門
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20 hrs
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12
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(5/9) Application of Patent Analytics on Technology Forecast and Industry Analysis (1)
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許老師授課
Ch. 9: Is Innovation Design-or Technology-Driven? Dyson
Python for Machine Learning:
政大磨課師「應用機器學習與Python」林怡伶老師
單元二：線性迴歸、決策樹
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15 hrs
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13
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(5/16) Application of Patent Analytics on Technology Forecast and Industry Analysis (2)
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許老師授課
Ch. 10: Predict Strategic Pivot Points: Bose
Python for Machine Learning:
政大磨課師「應用機器學習與Python」林怡伶老師
單元三：向量機、模型優化
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15 hrs
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14
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(5/23) Application of Patent Analytics on Technology Forecast and Industry Analysis (3)
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許老師授課
Ch. 11: Who Drives Innovation? Apple
Python for Machine Learning:
政大磨課師「應用機器學習與Python」林怡伶老師
單元四：分群、資料降維、關聯分析
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15 hrs
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15
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(5/30) Application of Patent Analytics on Technology Forecast and Industry Analysis (4)
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許老師授課
Ch. 12: Knowledge Acquisition and Assimilation After M&As: Adobe
Python for Machine Learning:
政大磨課師「應用機器學習與Python」林怡伶老師
單元五：網路爬蟲
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15 hrs
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16
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(6/6) Application of Patent Analytics on Technology Forecast and Industry Analysis (5)
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許老師授課
Ch. 13: Learn to Build Design Innovation Team: Samsung Versus LG
Python for Machine Learning:
政大磨課師「應用機器學習與Python」林怡伶老師
單元六：自然語言處理
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15 hrs
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17
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(6/13) 期末報告課堂討論
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期末報告，每位報告40分鐘，問答與講評10分鐘
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10 hrs
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18
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(6/20) 期末回顧與分享
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期末回顧與分享
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10 hrs
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