International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 12 | Dec 2025
p-ISSN: 2395-0072
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Cloud-Based SaaS Platform for AI-Driven Patent Analysis and Innovation Insights Dhanashree Amrut Pagar1, Dr. Sivaram Ponnusamy2, Dr. Mohd.Muqeem3 1Dept. of computer Science and Engineering, Sandip University Nashik, Maharashtra, India
2,3Professor Dept. of computer Science and Engineering, Sandip University Nashik, Maharashtra, India
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Abstract - The design, development, and deployment of
their analysis time collecting and normalizing data by hand instead of doing substantive research [1]. This fragmentation leads to major problems in the research process and lowers the quality of patent analysis results.
an AI-powered Patent Analysis Software-as-a-Service (SaaS) platform are thoroughly reviewed in this paper. By combining multi-source data aggregation, artificial intelligence algorithms, and sophisticated visualization techniques, the platform fills important gaps in current patent analysis systems. The platform exhibits notable enhancements in patent search efficiency, analytical depth, and user accessibility thanks to a methodical architecture that consists of a React-based frontend, Node.js backend, and MongoDB database. When compared to traditional tools, comparative analysis shows a 75% reduction in comprehensive analysis time, 90% user satisfaction, and 40% faster patent discovery. By offering a scalable, reasonably priced solution that democratizes advanced patent analytics for researchers, small and medium-sized businesses, and individual inventors, the research advances the field of intellectual property management.
The technological landscape of current patent tools reveals substantial limitations in analytical sophistication. Most available platforms rely on basic keyword search functionalities and manual classification systems, with only approximately 15% incorporating advanced artificial intelligence features [17]. This technological gap results in superficial analytical capabilities, inadequate trend identification mechanisms, and limited predictive analytics for emerging technologies. Furthermore, complex user interfaces and steep learning curves characterize many commercial solutions, reducing overall productivity and adoption rates among potential users [6]. Another significant issue is financial constraints; most academic researchers and 78% of SMEs cannot afford the commercial patent analysis tools, which typically cost over $10,000 per user per year [3]. Well-funded companies maintain their competitive advantages through superior patent intelligence, while smaller organizations find it difficult to carry out thorough intellectual property research. This economic divide results in an innovation gap.
Key Words: Patent Analysis, SaaS Platform, Artificial Intelligence, Multi-source Data Integration, Intellectual Property Management, Patent Search Efficiency, Data Visualization, Cloud Architecture.
1.INTRODUCTION The rapid growth of intellectual property around the world has led to an unprecedented amount of patent data. The World Intellectual Property Organization (WIPO) says that more than 3.4 million patent applications are filed each year around the world [1]. This huge growth gives researchers, innovators, and businesses a lot of chances and a lot of problems to deal with as they try to figure out the complicated world of intellectual property. Patents are important signs of technological progress and useful tools for competitive strategy. However, current patent analysis systems have serious flaws that make it hard to manage intellectual property effectively for different types of users.
Modern web technologies, cloud computing, and artificial intelligence come together to offer revolutionary possibilities for tackling these issues. Although AI and machine learning technologies allow automated classification, semantic search, and predictive analytics at previously unheard-of levels of accuracy, Software-as Service (SaaS) models can offer complex analytical capabilities through affordable subscription-based pricing. An opportunity to transform patent analysis procedures and democratize access to cutting-edge intellectual property analytics is presented by the combination of these technologies into a single platform.
Three big problems with traditional patent analysis systems are that they don't work well with data from different sources, they don't have enough analytical tools, and they cost too much. Patent information is still spread out across many government and business databases, such as the USPTO, EPO, JPO, and other national patent offices. This means that researchers have to spend 40–60% of
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The creation of an AI-powered Patent Analysis SaaS Platform that combines various data sources, applies cutting-edge AI algorithms, and provides a user-friendly interface via contemporary web technologies is how this research paper tackles these pressing issues. Significant gains in user accessibility, analytical depth, and patent
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