Although many countries have achieved rapid advancements in science and technology, they still rely on other nations in certain key technological fields. Effective prediction of technological risks is vital for national economic growth. Recently, countries have placed a strong emphasis on technological security. Technological risks arise from factors such as technology itself, its environment and management. Predicting these risks helps nations, enterprises and institutions achieve independent and controllable technology. It also enables effective management and control of potential risks. Risk prediction research encounters challenges such as technological diversity and the complexity of technological risks. To address the above issues, we propose a patent data meta-path-based technological risk prediction method. This method takes into account both technological diversity and the complexity of technological risks when predicting risks. Technological risk prediction involves analyzing potential risks and their degrees of severity in technological development. Compared to the baseline methods using our collected patent data, our method performs better in evaluation metrics, demonstrating its applicability in predicting technological risks.

As competition in intellectual property intensifies, patent activities have become a key indicator to measure international innovation and competitiveness. Patent events refer to events that occur throughout a patent lifecycle, and their frequency directly reflects a country’s innovation and competitive abilities. Therefore, the accurate and efficient extraction of patent events is crucial for countries to track patent development trends and optimize their patent strategies. There is relatively little research on patent event extraction, and its main challenges are: (1) the lack of annotated datasets in the field of patent event extraction restricts the training and evaluation of models. (2) Missing and ambiguous arguments during the extraction process, due to the complexity of the event text. To solve these challenges, we propose an external knowledge network-based patent event extraction method. (1) A patent event dataset is constructed, and common event types and arguments are summarized. (2) An external knowledge network is built based on patent data characteristics, and it helps us better capture event arguments in text. (3) The classification ability of event arguments is enhanced by using adversarial learning and multiple attention mechanisms. Our method is better than the baseline methods in evaluation metrics, making it more suitable for the automatic extraction of patent events.

As competition in intellectual property intensifies, countries around the world are actively engaging in patent activities.The number of patent activities has become an important indicator of international innovation and competitiveness.Patent events refer to the events related to patents, these events can include patent applications, patent grants, patent transfers, patent litigation, and more.The more frequent the patent events, the higher the country's innovation and competitiveness.Accurate and efficient extraction of patent events will help countries understand patent trends and plan their patent activities.Consequently, this has become a popular research topic.The main challenges in patent event extraction include the difficulty in handling unstructured data and the interference of noise with extraction.Additionally, existing event extraction methods have the following limitations:1.Absence of methods that utilize external knowledge related to the data to assist in event extraction, and 2. Lack of awareness in argument, leading to the omission of hidden relationships and features.To solve the above problems, we propose an external knowledge network based patent event extraction method.1.To perform event extraction in the patent domain, we constructed a patent event dataset.2. To enhance argument prompts and connections, we built an external knowledge network incorporating patent data features.3. To represent features, we used multiple attention mechanisms to enhance feature computation.Our method outperforms baseline methods in evaluation metrics, making it more suitable for the automatic extraction of patent events.

Although China's scientific and technological strength and technical level have achieved rapid development, there is still dependence on other countries in some key technological fields.Effectively predicting technological risks is crucial to national economic development.In recent years, China has paid great attention to technological security.Technological risks refer to risks caused by factors such as the technology itself, the technological environment, and the technological management methods.Through technological risk prediction, we can facilitate the achievement of independent and controllable technology for our country, enterprises and institutions.Furthermore, we can effectively manage and control potential risks.Risk prediction research faces some challenges including: technological complexity, content diversity, and technological differences.To respond to the above issues, we propose a patent data meta-path based technological risk prediction method.In the method, technological complexity, content diversity, and technological differences are considered when we predict technological risk.Technological risk prediction is a process to analyze potential risks in technological development as well as their risk degrees.Our method is compared with the baseline method on the patent data we collected.The results show that the technological risk prediction method outperforms the baseline method in the evaluation measurements.Such method can be applied to predict technological risks.