Patent examiners and inventors face significant pressure to verify the originality and non-obviousness of inventions, and the intricate nature of patent data intensifies the challenges of patent retrieval. Therefore, there is a pressing need to devise cutting-edge retrieval strategies that can reliably achieve the desired recall. This study introduces FullRecall, a novel patent retrieval approach that effectively manages the complexity of patent data while maintaining the reliability of relevance matching and maximising recall. It leverages IPC-guided knowledge to generate informative phrases, which are processed to extract key information in the form of noun phrases characterising the query patent under observation. From these, the top k keyphrases are selected to construct a query for retrieving a focused subset of the dataset. This initial retrieval step achieves complete recall, successfully capturing all relevant documents. To further refine the results, a ranking scheme is applied to the retrieved subset, reducing its size while maintaining 100% recall. This multi-phase process demonstrates an effective strategy for balancing precision and recall in patent retrieval tasks. Comprehensive experiments were conducted, and the results were compared with baseline studies, namely HRR2 [1] and ReQ-ReC [2]. The proposed approach yielded superior results, achieving 100% recall in all five test cases. However, HRR2[1] recall values across the five test cases were 10%, 25%, 33.3%, 0%, and 14.29%, while ReQ-ReC [2] showed 50% for the first test case, 25% for the second test case, and 0% for the third, fourth, and fifth test cases. The 100% recall ensures that no relevant prior art is overlooked, thereby strengthening the patent pre-filing and examination processes, hence reducing potential legal risks.

Patent similarity analysis plays a crucial role in evaluating the risk of patent infringement. Nonetheless, this analysis is predominantly conducted manually by legal experts, often resulting in a time-consuming process. Recent advances in natural language processing technology offer a promising avenue for automating this process. However, methods for measuring similarity between patents still rely on experts manually classifying patents. Due to the recent development of artificial intelligence technology, a lot of research is being conducted focusing on the semantic similarity of patents using natural language processing technology. However, it is difficult to accurately analyze patent data, which are legal documents representing complex technologies, using existing natural language processing technologies. To address these limitations, we propose a hybrid methodology that takes into account bibliographic similarity, measures the similarity between patents by considering the semantic similarity of patents, the technical similarity between patents, and the bibliographic information of patents. Using natural language processing techniques, we measure semantic similarity based on patent text and calculate technical similarity through the degree of coexistence of International patent classification (IPC) codes. The similarity of bibliographic information of a patent is calculated using the special characteristics of the patent: citation information, inventor information, and assignee information. We propose a model that assigns reasonable weights to each similarity method considered. With the help of experts, we performed manual similarity evaluations on 420 pairs and evaluated the performance of our model based on this data. We have empirically shown that our method outperforms recent natural language processing techniques.

Patent classification aims to assign multiple International Patent Classification (IPC) codes to a given patent. Recent methods for automatically classifying patents mainly focus on analyzing the text descriptions of patents. However, apart from the texts, each patent is also associated with some assignees, and the knowledge of their applied patents is often valuable for classification. Furthermore, the hierarchical taxonomy formulated by the IPC system provides important contextual information and enables models to leverage the correlations between IPC codes for more accurate classification. However, existing methods fail to incorporate the above aspects. In this paper, we propose an integrated framework that comprehensively considers the information on patents for patent classification. To be specific, we first present an IPC codes correlations learning module to derive their semantic representations via adaptively passing and aggregating messages within the same level and across different levels along the hierarchical taxonomy. Moreover, we design a historical application patterns learning component to incorporate the corresponding assignee's previous patents by a dual channel aggregation mechanism. Finally, we combine the contextual information of patent texts that contains the semantics of IPC codes, and assignees' sequential preferences to make predictions. Experiments on real-world datasets demonstrate the superiority of our approach over the existing methods. Besides, we present the model's ability to capture the temporal patterns of assignees and the semantic dependencies among IPC codes.

Abstract-- This paper presents a study of the interconnectivity and interdependence of various Artificial intelligence (AI) technologies through the use of centrality measures, clustering coefficients, and degree of fusion measures. By analyzing the technologies through different time windows and quantifying their importance, we have revealed important insights into the crucial components shaping the AI landscape and the maturity level of the domain. The results of this study have significant implications for future development and advancements in artificial intelligence and provide a clear understanding of key technology areas of fusion. Furthermore, this paper contributes to AI public policy research by offering a data-driven perspective on the current state and future direction of the field. However, it is important to acknowledge the limitations of this research and call for further studies to build on these results. With these findings, we hope to inform and guide future research in the field of AI, contributing to its continued growth and success. AI has the potential to revolutionize a wide range of industries from healthcare and finance to transportation and agriculture [1] last but not least environmental hard and societal changes [2]. With the ability to analyze vast amounts of data and automate tasks that were once exclusively performed by humans, AI is reshaping the way we live and work. Given the potential of AI, it is essential to study and understand its applications, fusion of technologies, changes over the years in the domain as well as societal impacts. This understanding is crucial for policymakers, as they must develop effective policies that keep pace with the rapid advancement of AI technology. Moreover, the study of AI is also relevant for individuals, businesses, and organizations, as they must be prepared to adapt to the changes brought about by AI. The study of AI is crucial in today's era to unlock the full potential of this groundbreaking technology and to address the challenges and opportunities it presents.

Patent classification is an essential task in patent information management and patent knowledge mining. It is very important to classify patents related to artificial intelligence, which is the biggest topic these days. However, artificial intelligence-related patents are very difficult to classify because it is a mixture of complex technologies and legal terms. Moreover, due to the unsatisfactory performance of current algorithms, it is still mostly done manually, wasting a lot of time and money. Therefore, we present a method for classifying artificial intelligence-related patents published by the USPTO using natural language processing technique and deep learning methodology. We use deformed BERT and sentence attention overcome the limitations of BERT. Our experiment result is highest performance compared to other deep learning methods.

"Enormous levels of investment are pouring into this technology. The achievements we have seen so far will surely pale against what the coming decades will bring." Artificial intelligence is getting a lot of attention right now. It was one of the key technological themes at the 2016 World Economic Forum and in June of last year, Andrew Ng, chief scientist at the Chinese web services company Baidu called "AI the new Electricity." As recently as Dec 5, 2016, Google and Elon Musk opened their AI platforms to the public, Uber launched Uber AI Lab and Apple announced that for the first time it will publish their AI research.