Recent advances in Pretrained Language Models (PLMs) and Large Language Models (LLMs) have demonstrated transformative capabilities across diverse domains.The field of patent analysis and innovation is not an exception, where natural language processing (NLP) techniques presents opportunities to streamline and enhance important tasks-such as patent classification and patent retrieval-in the patent cycle.This not only accelerates the efficiency of patent researchers and applicants, but also opens new avenues for technological innovation and discovery.Our survey provides a comprehensive summary of recent NLPbased methods-including multimodal onesin patent analysis.We also introduce a novel taxonomy for categorization based on tasks in the patent life cycle, as well as the specifics of the methods.This interdisciplinary survey aims to serve as a comprehensive resource for researchers and practitioners who work at the intersection of NLP, Multimodal AI, and patent analysis, as well as patent offices to build efficient patent systems.

Online hatred has become an increasingly pervasive issue, affecting individuals and communities across various digital platforms. To combat hate speech in such platforms, counter narratives (CNs) are regarded as an effective method. In recent years, there has been growing interest in using generative AI tools to construct CNs. However, most of the generative models produce generic responses to hate speech and can hallucinate, reducing their effectiveness. To address the above limitations, we propose a counter narrative generation method that enhances CNs by providing non-aggressive, fact-based narratives with relevant background knowledge from two distinct sources, including a web search module. Furthermore, we conduct a comprehensive evaluation using multiple metrics, including LLM-based measures for persuasion, factuality, and informativeness, along with human and traditional NLP evaluations. Our method significantly outperforms baselines, achieving an average factuality score of 0.915, compared to 0.741, 0.701, and 0.69 for competitive baselines, and performs well in human evaluations.

In this paper, we introduce IMPACT (Integrated Multimodal Patent Analysis and Creation Dataset for Design Patents), a large-scale multimodal patent dataset with detailed captions for design patent figures. Our dataset includes half a million design patents comprising 3.61 million figures along with captions from patents granted by the United States Patent and Trademark Office (USPTO) over a 16-year period from 2007 to 2022. We incorporate the metadata of each patent application with elaborate captions that are coherent with multiple viewpoints of designs. Even though patents themselves contain a variety of design figures, titles, and descriptions of viewpoints, we find that they lack detailed descriptions that are necessary to perform multimodal tasks such as classification and retrieval. IMPACT closes this gap thereby providing researchers with necessary ingredients to instantiate a variety of multimodal tasks. Our dataset has a huge potential for novel design inspiration and can be used with advanced computer vision models in tandem. We perform preliminary evaluations on the dataset on the popular patent analysis tasks such as classification and retrieval. Our results indicate that integrating images with generated captions significantly improves the performance of different models on the corresponding tasks. Given that design patents offer various benefits for modeling novel tasks, we propose two standard computer vision tasks that have not been investigated in analyzing patents as future directions using IMPACT as a benchmark viz., 3D Image Construction and Visual Question Answering (VQA). To facilitate research in these directions, we make our IMPACT dataset and the code/models used in this work publicly available at https://github.com/AI4Patents/IMPACT.

Alzheimer’s disease is a neurological disease that affects the healthy cells of the brain and results in people having long-term memory loss, thinking problems, disorientation, behavioral inconsistencies and finally death. When the disease gets detected, the pathological load is already high, and there is no coming back from there. This neurodegenerative disease consists of three general stages, which we classified in this research and that includes very mild (early stage), mild (middle stage) and finally, the moderate stage (late-stage). Using transfer learning, we implemented five existing efficient and recent Convolutional Neural Networks (CNN) models such as VGG19, Inception- ResNetv2, ResNet152v2, EfficientNetB5 and EfficientNetB6, and another custom one of our own. Later, we ensembled thrice with multiple combinations of the models to enhance our outcome. This led us to achieve our proposed model, which is a weighted average ensemble of all the six classifiers mentioned above and this novel approach gave us an accuracy of 96%, which was quite satisfactory compared to any other existing models.