With the increasing demand for inventive products, finding out inventive design solutions hidden in different industrial engineering domains has always been a challenge for engineers. In addition, patent documents are full of the latest inventive knowledge inside. In this paper, we rely on the assumption that an engineering problem may have an inventive practical solution in another scientific domain as long as they are similarly described. Therefore, we focus on applying machine learning techniques, more particularly neural networks to determine the similarity between patent problems. Technically, a trained bidirectional LSTM neural network, called Manhattan LSTM is integrated into our approach named SAM-IDM to predict the similarity between sentences. We experimentally show that Manhattan LSTM outperforms other baseline approaches in a labelled sample dataset of SNLI corpus. We then experiment our approach on a real-world U.S. patent dataset and we demonstrate that it presents promising results in terms of sentence similarity matching and inventiveness. An inventive design case is detailed to illustrate its performance and practicality.

随着对发明产品的需求不断增加，寻找隐藏在不同工业工程领域中的发明设计解决方案一直是工程师面临的挑战。此外，专利文件中充斥着最新的发明知识。在本文中，我们假设一个工程问题可能会在另一个科学领域中具有创造性的实际解决方案，只要对它们进行类似的描述即可。因此，我们专注于应用机器学习技术，尤其是神经网络来确定专利问题之间的相似性。从技术上讲，称为曼哈顿LSTM的经过训练的双向LSTM神经网络已集成到我们的名为SAM-IDM的方法中，以预测句子之间的相似性。我们通过实验证明，在标记为SNLI语料的样本数据集中，曼哈顿LSTM优于其他基准方法。然后，我们在现实世界中的美国专利数据集上对我们的方法进行了实验，并证明了该方法在句子相似度匹配和创造性方面提供了可喜的结果。详细说明了本发明的设计案例以说明其性能和实用性。