Patent Analytics Software Market Outlook

According to our latest research, the global patent analytics software market size reached USD 1.72 billion in 2024, reflecting robust growth in response to the rising demand for intellectual property management and innovation tracking across industries. The market is projected to expand at a CAGR of 13.8% from 2025 to 2033, ultimately reaching a forecasted value of USD 5.14 billion by 2033. This growth is primarily driven by the increasing complexities of patent landscapes, the surge in patent filings globally, and the need for advanced analytics to inform strategic business decisions.

One of the most significant growth factors propelling the patent analytics software market is the exponential increase in patent filings and intellectual property (IP) assets worldwide. As companies across sectors strive to innovate and protect their inventions, the volume and complexity of patent data have surged, making manual analysis impractical. Patent analytics software emerges as a crucial tool, enabling organizations to efficiently mine, visualize, and interpret vast datasets, thus supporting informed decision-making in R&D investments, competitive intelligence, and risk management. The integration of artificial intelligence (AI) and machine learning (ML) technologies into these platforms further enhances their capability to deliver actionable insights, automate repetitive tasks, and identify hidden patterns within patent databases, thereby driving adoption among corporates, research institutions, and legal entities.

Another key driver is the growing emphasis on strategic IP management as a core business function. Corporations are increasingly recognizing the value of their patent portfolios not only as protective measures but also as strategic assets for mergers, acquisitions, and licensing opportunities. Patent analytics software facilitates comprehensive portfolio management, enabling enterprises to assess the strength, relevance, and geographical coverage of their IP assets. This, in turn, helps companies identify white spaces, avoid infringement risks, and optimize their innovation strategies. Furthermore, the globalization of R&D activities and the expansion of multinational operations necessitate tools that can analyze patents across multiple jurisdictions, languages, and technology domains, further fueling market growth.

As the patent analytics software market continues to evolve, Robotics Patent Analytics Platforms are emerging as a specialized area of interest. These platforms are designed to address the unique challenges and opportunities presented by the robotics industry, which is characterized by rapid technological advancements and a high volume of patent filings. By leveraging advanced analytics tools, these platforms enable organizations to gain insights into the competitive landscape, identify emerging trends, and optimize their intellectual property strategies. The integration of robotics-specific data sets, machine learning algorithms, and visualization tools allows users to efficiently navigate complex patent landscapes, assess the strength and relevance of their IP assets, and make informed decisions about R&D investments and strategic partnerships. As the robotics industry continues to expand, the demand for tailored analytics solutions is expected to grow, creating new opportunities for vendors and end-users alike.

The patent analytics software market is also benefiting from the digital transformation of legal and research sectors. Legal firms, research institutes, and government agencies are increasingly leveraging these solutions to streamline patent prosecution, monitor competitor activities, and support policy-making processes. The adoption of cloud-based deployment models is making advanced analytics accessible to a broader range of users, including small and medium enterprises (SMEs) that previously lacked the resources for such sophisticated tools. Additionally, the integration of patent analytics with other enterprise systems, such as innovation management and business intelligence platforms, is enhancing the value proposition for end-users and driving further market penetration.

From a regional perspective, North America continues to dominate the patent analytics software market, owing to its advanced technological infrastructure, hi

(i) Statistics collected by European Patent Office and published in statistics and trends centre data visualisation service. The annual Patent Index reports on the European patent applications and European patents granted in one calendar year. In collating the data to be included, a cut-off date at around five weeks after the end of the reported year is taken. This means that each Patent Index is a “snapshot” of the situation as it was best understood on the cut-off date. Any slight differences between the Patent Index and the statistics and trends centre data visualisation service can be attributed to subsequent reassignments of technology field, applicant or country to a handful of applications after the cut-off date. (ii) European patent applications and European patents granted in one calendar year (iii) EPO Member STates 38 (iv) Patent filings at the EPO

(i) Statistics collected by European Patent Office and published in statistics and trends centre data visualisation service. The annual Patent Index reports on the European patent applications and European patents granted in one calendar year. In collating the data to be included, a cut-off date at around five weeks after the end of the reported year is taken. This means that each Patent Index is a “snapshot” of the situation as it was best understood on the cut-off date. Any slight differences between the Patent Index and the statistics and trends centre data visualisation service can be attributed to subsequent reassignments of technology field, applicant or country to a handful of applications after the cut-off date. (ii) European patent applications and European patents granted in one calendar year (iii) EPO Member STates 38 (iv) Patent filings at the EPO

Based on the above, this study selected A-share listed manufacturing enterprises from 2014 to 2023 as the research sample. Data element data are extracted from the annual reports of companies published on the official websites of Shenzhen Stock Exchange and Shanghai Stock Exchange. In order to ensure the validity and robustness of the constructed indicators, the measurement of disruptive innovation draws on the patent data of China National Intellectual Property Administration (CNIPA), covering the period from 2000 to 2023. The specific measurement method is described in detail in Section 3.2. Additional company-level data mainly come from the China Stock Market and Accounting Research (CSMAR) database and Wind Information Co., Ltd.

Patent Landscaping AI Market Outlook

According to our latest research, the global Patent Landscaping AI market size in 2024 is valued at USD 1.32 billion, with a robust growth trajectory driven by technological advancements and the increasing complexity of intellectual property management. The market is forecasted to reach USD 5.86 billion by 2033, exhibiting a remarkable CAGR of 17.9% during the forecast period of 2025 to 2033. This growth is primarily attributed to the escalating demand for efficient, data-driven patent analysis and competitive intelligence solutions across multiple industries, as organizations strive to safeguard innovation and streamline R&D investments in an increasingly competitive global environment.

The primary growth factor fueling the Patent Landscaping AI market is the exponential rise in patent filings and intellectual property assets globally. As companies and research institutions continue to invest in innovation, the volume and complexity of patent data have surged, making traditional manual analysis methods inefficient and error-prone. The integration of AI-driven solutions enables organizations to automate the extraction, classification, and visualization of patent data, thereby enhancing the accuracy and speed of patent landscaping. This, in turn, empowers stakeholders to identify technological trends, detect white spaces, and make informed strategic decisions. The need for real-time insights, coupled with the pressure to mitigate infringement risks and optimize R&D investments, is compelling companies across sectors such as pharmaceuticals, electronics, and automotive to adopt Patent Landscaping AI solutions.

Another significant driver is the growing emphasis on competitive intelligence and innovation management. In industries characterized by rapid technological evolution, such as information technology and biotechnology, organizations are leveraging Patent Landscaping AI to gain a comprehensive understanding of the intellectual property landscape. AI-powered tools facilitate the identification of emerging technologies, potential collaborators, and competitors’ strategic moves. Furthermore, these platforms enable the mapping of patent portfolios against market trends, regulatory changes, and scientific advancements, which is crucial for maintaining a competitive edge. The ability to extract actionable insights from vast, unstructured patent datasets is transforming how corporates, law firms, and research institutes approach IP strategy, contributing to the sustained expansion of the market.

The proliferation of cloud computing and advancements in natural language processing (NLP) and machine learning algorithms are further accelerating market growth. Cloud-based Patent Landscaping AI solutions offer scalability, flexibility, and cost-effectiveness, making them accessible to organizations of all sizes, including small and medium enterprises. Enhanced NLP capabilities allow for the analysis of multilingual patent documents and extraction of nuanced technical information, broadening the applicability of these solutions across global markets. Additionally, regulatory initiatives promoting innovation and IP protection, particularly in emerging economies, are fostering greater adoption of AI-driven patent analytics. These factors, combined with strategic collaborations between technology providers and industry stakeholders, are expected to sustain the momentum of the Patent Landscaping AI market throughout the forecast period.

From a regional perspective, North America currently leads the Patent Landscaping AI market, driven by a high concentration of technology companies, robust R&D investments, and a mature intellectual property framework. However, the Asia Pacific region is poised for the fastest growth, fueled by increasing patent activity in countries like China, Japan, and India, as well as supportive government policies aimed at fostering innovation. Europe also represents a significant market, benefiting from strong regulatory standards and a vibrant ecosystem of research institutes and technology-driven enterprises. Latin America and the Middle East & Africa, while still emerging, are witnessing gradual adoption as awareness of the strategic value of patent analytics grows among corporates and government agencies.

Component Analysis

The Patent Landscaping AI market is segmented by component into software and services, each playing a vital role in delivering comprehensive solutions to en

Robotics Patent Analytics Platforms Market Outlook

According to our latest research, the global Robotics Patent Analytics Platforms market size in 2024 stands at USD 1.34 billion, with a robust year-on-year growth trajectory. The market is exhibiting a strong compound annual growth rate (CAGR) of 15.1% from 2025 to 2033. By the end of 2033, the market is projected to reach approximately USD 4.33 billion, driven by the increasing complexity of robotics intellectual property (IP) landscapes and the growing necessity for advanced analytics tools in patent management. The surge in global robotics innovation, particularly in automation, artificial intelligence, and manufacturing sectors, is a primary growth factor propelling the demand for sophisticated patent analytics platforms.

The growth of the Robotics Patent Analytics Platforms market is significantly influenced by the exponential rise in patent filings in the robotics domain. As robotics technologies advance rapidly, organizations are racing to secure intellectual property rights to maintain a competitive edge. This dynamic has created a pressing demand for analytics platforms capable of efficiently managing, analyzing, and extracting actionable insights from vast and complex patent databases. The integration of artificial intelligence and machine learning into these platforms has further enhanced their ability to provide predictive analytics, trend forecasting, and competitive intelligence, which are crucial for strategic decision-making in R&D and innovation management.

Another key growth driver is the increasing adoption of robotics across diverse industries such as healthcare, automotive, logistics, and consumer electronics. As these industries invest heavily in robotics innovation, they encounter a surge in patent activity, necessitating advanced analytics solutions to navigate the patent landscape effectively. The rise of Industry 4.0 and the proliferation of connected devices have also contributed to the complexity and volume of patent data, making traditional manual analysis methods obsolete. Robotics patent analytics platforms, equipped with advanced algorithms and visualization tools, enable organizations to streamline patent searches, assess portfolio strengths, and identify white spaces for innovation, thereby accelerating the pace of technological advancement.

Furthermore, global regulatory frameworks and the increasing emphasis on IP protection are fostering the adoption of robotics patent analytics platforms. Governments and regulatory bodies are tightening patent examination processes, making it critical for organizations to conduct thorough prior art searches and legal status tracking. The ability of these platforms to provide real-time legal status updates, monitor competitor activities, and support portfolio management is becoming indispensable for enterprises, research institutions, and law firms alike. The rising awareness about the strategic value of IP assets is prompting organizations to invest in robust analytics platforms to safeguard their innovations and mitigate litigation risks.

Regionally, North America dominates the Robotics Patent Analytics Platforms market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States, in particular, is home to a vibrant robotics ecosystem and a mature IP infrastructure, driving substantial demand for advanced analytics solutions. Europe is witnessing significant growth, fueled by strong government support for innovation and a high concentration of robotics research centers. Asia Pacific, led by countries like China, Japan, and South Korea, is emerging as a lucrative market due to rapid industrialization, increasing R&D investments, and a surge in patent filings. Latin America and the Middle East & Africa are gradually catching up, supported by growing awareness of IP management and the adoption of robotics technologies in emerging economies.

Component Analysis

The Component segment of the Robotics Patent Analytics Platforms market is bifurcated into Software and Services. Software solutions form the backbone of this market, providing the computational power and advanced algorithms required to process and analyze large volumes of patent data. These platforms incorporate features such as natural language processing, semantic search, and machine lear

BackgroundPatent foramen ovale (PFO) is among the most common congenital heart defects. Over the last two decades, the number of research publications on PFO has increased. This study aims to identify and describe the current state, hotspots, and emerging trends in PFO research over the previous 20 years using bibliometric analysis and visual mapping.MethodsThe Web of Science Core Collection was searched for all publications on PFO research, which were then included in the study. CtieSpace, VOSviewer, and Excel software were used to visualize general information, publication output, countries/regions, authors, journals, influential papers, and keyword trends in this field.ResultsThis comprehensive analysis included 14,495 publications from 6,190 institutions across 115 countries. The United States dominated with the highest number of publications (2,407) and international collaborations. Mas JL made significant contributions to the PFO field, while Meier B emerged as a leading author, publishing 81 articles during the past 20 years. There were strong international collaborations among countries, institutions, and authors. Stroke, Circulation, and the New England Journal of Medicine were the most cited journals, with 13,124, 10,136, and 9,867 citations, respectively.ConclusionsThis bibliometric study revealed that recent research frontiers primarily focused on the diagnosis and clinical management of patients with PFO. Future studies are expected to delve deeper into the biological mechanisms by which PFO contributes to stroke, the efficacy and limitations of PFO closure techniques, and the exploration of genetic variations associated with PFO and their roles in disease susceptibility.

As important carriers of innovation activities, patents, sci-tech achievements and papers play an increasingly prominent role in national political and economic development under the background of a new round of technological revolution and industrial transformation. However, in a distributed and heterogeneous environment, the integration and systematic description of patents, sci-tech achievements and papers data are still insufficient, which limits the in-depth analysis and utilization of related data resources. The dataset of knowledge graph construction for patents, sci-tech achievements and papers is an important means to promote innovation network research, and is of great significance for strengthening the development, utilization, and knowledge mining of innovation data. This work collected data on patents, sci-tech achievements and papers from China's authoritative websites spanning the three major industries—agriculture, industry, and services—during the period 2022-2025. After processes of cleaning, organizing, and normalization, a patents-sci-tech achievements-papers knowledge graph dataset was formed, containing 10 entity types and 8 types of entity relationships. To ensure quality and accuracy of data, the entire process involved strict preprocessing, semantic extraction and verification, with the ontology model introduced as the schema layer of the knowledge graph. The dataset establishes direct correlations among patents, sci-tech achievements and papers through inventors/contributors/authors, and utilizes the Neo4j graph database for storage and visualization. The open dataset constructed in this study can serve as important foundational data for building knowledge graphs in the field of innovation, providing structured data support for innovation activity analysis, scientific research collaboration network analysis and knowledge discovery.The dataset consists of two parts. The first part includes three Excel tables: 1,794 patent records with 10 fields, 181 paper records with 7 fields, and 1,156 scientific and technological achievement records with 11 fields. The second part is a knowledge graph dataset in CSV format that can be imported into Neo4j, comprising 10 entity files and 8 relationship files.

A new and versatile visualization tool, based on a descriptor accounting for ligand–receptor interactions (LiRIf), is introduced for guiding medicinal chemists in analyzing the R-groups from a congeneric series. Analysis is performed in a reference-independent scenario where the whole biologically relevant chemical space (BRCS) is represented. Using a real project-based data set, we show the impact of this tool on four key navigation strategies for the drug discovery process. First, this navigator analyzes competitors’ patents, including a comparison of patents coverage and the identification of the most frequent fragments. Second, the tool analyzes the structure–activity relationship (SAR) leading to the representation of reference-independent activity landscapes that enable the identification not only of critical ligand–receptor interactions (LRI) and substructural features but also of activity cliffs. Third, this navigator enables comparison of libraries, thus selecting commercially available molecules that complement unexplored spaces or areas of interest. Finally, this tool also enables the design of new analogues, which is based on reaction types and the exploration purpose (focused or diverse), selecting the most appropriate reagents.

Context

The Patents View API can easily query all the granted patents in US. But the result table is not very comprehensive and have repeat records for different values for a particular column. I wrote a script to comprehend it and pulled only data rich columns on which various analysis can be done.

Content

This dataset consists of all granted patents in US in the first quarter of 2019 (Jan - Mar). I wanted to analyze this to see which industries are leading in innovations, sectors and technologies used in these patents and see if we could draw some patterns.

This dataset is a publicly available dataset and you can check all available columns here - http://www.patentsview.org/api/patent.html.

Inspiration

I am still building my analyzing and visualization dashboard. Open to any questions that you may want to see answered from this dataset.

Note, this is the first version of our upload, when we were still learning about Zenodo. Please click on the second version link (version links are in a box on the lower right of this page), and you will see a) a preview of our own visualisation and b) a slight cosmetic improvement of that visualisation. But the data sets on both versions are identical.

Datasets and presentations concerning the strength of the EU and "the rest of the world" relative to China and the USA, for the purpose of illustrating and counteracting / better informing narratives concerning a "new AI cold war". The materials authored by us may be freely used under the terms of the MIT License, which appears in its entirety in both the dataset and the presentation. The other materials are only curated by us, taken from Twitter as examples of misinformation pertaining to this concern.

Authors: The original analysis was conducted primarily by Malikova in collaboration with Bryson. An associated publication is anticipated where Bryson is the lead author.

Contributors: independently followed Malikova's procedures to check her work. Inconsistencies were triple checked and resolved.