论文标题
高科技公司的深度技术追踪
Deep Technology Tracing for High-tech Companies
论文作者
论文摘要
技术变革和创新至关重要，尤其是对于高科技公司而言。但是，影响其未来研发（R＆D）趋势的因素既复杂又各种，这是为高科技公司进行技术追踪的一项非常困难的任务。为此，在本文中，我们开发了一种新颖的数据驱动解决方案，即深技术预测（DTF）框架，以自动找到向每个高科技公司定制的最可能的技术方向。特别是，DTF由三个组成部分组成：潜在竞争者认可（PCR），协作技术识别（CTR）和深技术追踪（DTT）神经网络。一方面，PCR和CTR旨在分别捕捉企业之间的竞争关系和技术之间的协作关系。对于另一个人来说，DTT旨在建模公司与技术之间的动态互动与所涉及的上述关系。最后，我们在现实世界的专利数据上评估了DTF框架，实验结果清楚地证明，DTF可以通过利用混合动力因素来精确地帮助展示公司的未来技术重点。
Technological change and innovation are vitally important, especially for high-tech companies. However, factors influencing their future research and development (R&D) trends are both complicated and various, leading it a quite difficult task to make technology tracing for high-tech companies. To this end, in this paper, we develop a novel data-driven solution, i.e., Deep Technology Forecasting (DTF) framework, to automatically find the most possible technology directions customized to each high-tech company. Specially, DTF consists of three components: Potential Competitor Recognition (PCR), Collaborative Technology Recognition (CTR), and Deep Technology Tracing (DTT) neural network. For one thing, PCR and CTR aim to capture competitive relations among enterprises and collaborative relations among technologies, respectively. For another, DTT is designed for modeling dynamic interactions between companies and technologies with the above relations involved. Finally, we evaluate our DTF framework on real-world patent data, and the experimental results clearly prove that DTF can precisely help to prospect future technology emphasis of companies by exploiting hybrid factors.
