Replication Data for: Linguistic Metrics for Patent Disclosure: Evidence from University Versus Corporate Patents

Encouraging disclosure is important for the patent system, yet the technical information in patent applications is often inadequate. We use algorithms from computational linguistics to quantify the effectiveness of disclosure in patent applications. Relying on the expectation that universities have more ability and incentive to disclose their inventions than corporations, we analyze 64 linguistic features of patent applications, and show that university patents are more readable by 0.4 SD of a synthetic measure of readability. Results are robust to controlling for non-disclosure-related invention heterogeneity. Testing the usefulness of linguistic metrics with disclosure and readability evaluations by an engineering student ``expert'' panel and by examining USPTO 112 (a)---lack of disclosure---rejection, we find modest support for our approach. The ability to quantify disclosure opens new research paths and potentially facilitates improvement of disclosure.鼓励技术披露对于专利制度而言至关重要，然而专利申请中的技术信息往往不够充分。我们采用计算语言学（computational linguistics）算法，对专利申请的披露有效性开展量化分析。基于“高校相较于企业具备更强的发明披露能力与动机”这一预设，我们对专利申请的64项语言特征展开系统分析，结果显示：相较于企业专利，高校专利在可读性合成指标上的得分高出0.4个标准差。上述结果在控制与披露无关的发明异质性后依然稳健。我们通过工程学学生专家小组开展的披露与可读性评估，以及审查美国专利商标局（USPTO）第112条(a)款中"披露缺失"驳回情形的方式，对语言指标的实用性进行验证，结果为我们的研究方法提供了一定程度的支撑。量化披露程度的能力为相关研究开辟了全新路径，也有望推动专利披露质量的提升。