隨著科技產業技術的快速發展，許多公司與研究機構紛紛利用專利文件與學術論文作為技術預測的樣本，希望能夠從中獲得新技術的資訊，以利開發，取得市場先機。世界上約有90%~95%的研發成果，可以在專利說明書中找到；因此，有效運用專利資訊可縮短研發時間、節省研究經費。又，各國政府於大專院校或研究機構投入大量的研發經費，其所發表的學術論文更是基礎研究最主要的知識載體。本研究透過專利及學術論文成長曲線的分析與比較，試圖歸納兩者間之於各技術領域之行為脈絡與先驅性，協助從事技術預測的企業或研究人員清楚掌握其使用樣本之特性，而更進一步能夠有效率、更有方向性的進行技術預測。
本研究以醫學、藥學、營建土木、化學、生物科技、能源、電子電機…等數項技術作為研究標的，分別搜集美國專利資料庫之專利文件以及Web of Science資料庫之學術論文。然後計算各技術領域之成長曲線，比較專利文件與學術論文間各項參數之差異，再利用統計檢定，歸納出各領域之一般性原則與特性，以利未來技術預測樣本使用之準確性。
研究結果顯示，技術成長經常有兩階段發展的情況，並非一次上升，而是階段性的突破。利用專利文件與學術論文作技術成長中點（Midpoint）的預測，時間性上是專利文件早於學術論文。比較專利文件與學術論文之技術成長曲線可發現，技術起始區段係學術論文較早開始發展的；技術之早期成長區段，以專利之成長速率較為劇烈；技術之成長後期與飽和區段，已成熟的技術專利之成長曲線斜率會趨於減緩，學術論文雖亦可見其成長速率減緩，但相較之下則仍略微上升。

As the technologies and industries develop rapidly, many companies and institutions try to forecast technology in order to be more competitive. Through analyzing patents and academic publications data, it is possible to get some very useful information. It is said that extracting from the patent specification, there will be 90% to 95% knowledge of the research comes out, which means all the research results are almost visible. So, if use the data of patent efficiently, then it is definitely could cut down the time and reduce the expense of the researches. Besides, every country invests a lot into universities and the research institutions for strengthening the power of science and technology. Due to this reason, there are more and more papers formed as academic publications. All the detail records in the academic publications could be also very practical for the further development. As above mentioned, patents and academic publications are both important to represent the trend of technology flow. Both of them have their own advantages and disadvantages for being sample for technology forecasting. This paper aims to compare and analyze the difference between patents and academic publications from different fields of technologies by S-curve. Via the result, researchers could easily catch the characteristic of each sample of technology forecasting. Then, the more successfully and much precise forecast could be performed.

This paper used several fields of technology as the research data; those are Medicine, Pharmacy, Chemistry, Construction, Energy, Computer science, Electronics and the Bioengineering. Each of them has two kinds of data: patents and academic publications. Patents extracted from United State Patent and Trademark Office, and the academic publications extracted from Web of Science database. Through calculated the growth curves of each technologies, compared the parameters between patent and academic publication and the statistic test, we can get the generalize rules and the characteristics of each fields to help researchers when they are using them to do technology forecast.

The result indicates that the techniques grow as two sections frequently. It is because of the techniques breakthrough step by step which is so called “discontinuous innovation”. The midpoint between patents and academic publications, statistically, is lead by patent one year on average. Separate the S-curve into three parts, we discovered that in start section, the techniques always start from academic publications; in the earlier-growth section, the growth rate of the patents is much speedy.; in the late- growth-and-saturation section, the patent is always stagnated. On the contrary, the academic publication is comparatively going up with steady speed.

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