Innovation is cumulative and interdependent: successful inventions build on prior knowledge within technological fields and may also affect success across related ones. Yet these dimensions are often studied separately in the innovation literature. This paper asks whether patent success across technological categories can be represented within a single dynamic framework that jointly captures within-category reinforcement, cross-category spillovers, and a set of aggregate regularities observed in patent data. To address this question, we propose a model of interacting reinforced Bernoulli processes in which the probability of success in a given category depends on past successes both within that category and across other categories. The framework yields joint predictions for success probabilities, cumulative successes, relative success shares, and cross-category dependence. We implement the model using granted US patent families from GLOBAL PATSTAT (1980-2018), defining category-specific success through a cohort-normalized forward-citation index. The empirical analysis shows that successful innovations continue to accumulate, but less than proportionally to the growth in patent opportunities, while technological categories remain interdependent without becoming homogeneous. Under a mean-field restriction, the model-based inferential exercise yields an estimated interaction intensity of 0.643, pointing to positive but non-maximal interaction across technological categories.

翻译：创新具有累积性和相互依赖性：成功的发明既基于技术领域内的既有知识，也可能影响相关领域的成功。然而，在创新文献中，这些维度通常被分开研究。本文探讨专利在各技术类别中的成功是否能够在一个统一的动态框架中进行表征，该框架同时捕捉类别内强化、跨类别溢出效应以及专利数据中观察到的一组整体规律。为解答此问题，我们提出一个交互强化伯努利过程模型，其中给定类别中的成功概率既取决于该类别内部的过去成功，也取决于其他类别中的过去成功。该框架为成功概率、累积成功、相对成功份额以及跨类别依赖性提供了联合预测。我们利用GLOBAL PATSTAT（1980-2018）中已授权的美国专利族实施该模型，通过队列归一化的前向引用指数定义类别特定成功。实证分析表明，成功创新持续累积，但其增长比例低于专利机会的增长，而技术类别之间保持相互依赖性，但并未趋于同质化。在平均场约束下，基于模型的推断分析得出交互强度估计值为0.643，表明技术类别之间存在正向但非最大化的交互作用。