專利說明書是一種具有法律效用的文件，主要記載著各項技術發展的訊息和施行方法。同時，透過這些專利文件的分析和利用，不但可以促進產業進步，更可以藉此了解目前該科技產業發展的水平及關鍵技術，即時掌握最新的市場資訊。但是，由於專利文件的特殊用法和撰寫風格，使得在目前的專利文件分析工作極為困難，加上目前沒有足夠的自動化工具來幫助分析，所以大部份的分析工作仍依賴著人工處理，耗時耗力；然而，隨著科技產業的進步，專利文件的產量也以極驚人的速度在成長著如，果仍然以人工的方式逐一分析，勢必造成其產業及研究的停滯或人力、物力上的浪費，故唯有透過自動化分析輔助工具的協助，才能解決目前專利文件在分析上的窘境。
在本論文中，主要提出了一個方法，利用自動化的方式從專利文件中學習出擷取申請專利範圍的結構描述，並利用圖形化的方式呈現給使用者，以方便及加速使用者在專利文件上的分析工作，減少時間及人力上的浪費。本研究主要可分為兩部份，元件擷取及Triple關係擷取；在元件擷取方面，主要利用統計的方式，統計多字詞的頻率及詞性規則來決定何謂專利元件；而在Triple關係擷取方面，我們提供了一個網頁加註的方式來加速收集訓練資料，同時，將此訓練資料轉成相對應的正規表示式規則，並將這些規則做適當的歸納延伸，以用來擷取申請專利範圍的結構。

A patent specification is a document with legal protection. It records the information of technologies and its execution method. With the effective analysis and usage of patent documents, it not only can stimulate the development of industries and progress of science, but also can catch on the technical level and the key technologies of the industry and the science, and has the newest market information in hand. However, due to the specific terms and the specific writing formats of patent document, it is hard to understand for human beings within limited time. And another problem is that, the size of the patent documents is growing rapidly. If we still rely on human to conduct the analysis of the patent documents, it will be very costly in time and human power.
In this thesis, we provide an approach to automatically extract the claim structure from patent documents and show the results in visualization to help human beings to understand much more easily and effectively without wasting time and energy. There are two major goals in our research. One is the element extraction and the other is the triple extraction. In element extraction section, we use statistical method to count the frequency of NGram word and use the combination of part-of-speech to extract the element. And In triple extraction section, we develop a wrapper environment for user to wrap the training data and automatically generate its corresponding patterns with minimal user efforts, and provide an induction algorithm that is an adaptation of string edit distance to induce our training results into abstracted extraction patterns, and use the result patterns to extract the claim structure.

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