This work describes a methodology to carry out the analysis of technological sectors, using information contained in patent documents. The use of this information by professors and students of chemistry courses and related areas is still little used. The consequence of this is the repetition of efforts to obtain some results of research and development. The use of patent big data and the construction of scenarios, using several variables, is even rarer in Brazil and still little explored in the world. This work presents patentometry in a theoretical context and its practical application, taking as an example the area of rare earth in Brazil. Analyses will be presented in this work, such as the development of technologies over time and study of the maturity of the sector in question; potential markets; the most significant technology developers in the field; technological sectors related to rare earth in Brazil; attractiveness of the Brazilian market; the evolution of priority sectors; technological specialization; patent quality indices and innovation indices; indexes of technologies internationalization and cooperation. It is expected that with the Patentometry, some of the barriers of insertion in the market of new technologies will be better known and minimized.

BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. Each US patent application is filed under a Cooperative Patent Classification (CPC) code. There are nine such classification categories:

• A (Human Necessities),
• B (Performing Operations; Transporting),
• C (Chemistry; Metallurgy),
• D (Textiles; Paper),
• E (Fixed Constructions),
• F (Mechanical Engineering; Lightning; Heating; Weapons; Blasting),
• G (Physics),
• H (Electricity), and
• Y (General tagging of new or cross-sectional technology)

There are two features:

• description: detailed description of patent.
• summary: Patent abstract.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('big_patent', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

According to Cognitive Market Research, the global Patent Analytics Services Market size will be USD 1393.8 million in 2025. It will expand at a compound annual growth rate (CAGR) of 15.00% from 2025 to 2033.

North America held the major market share for more than 40% of the global revenue with a market size of USD 515.71 million in 2025 and will grow at a compound annual growth rate (CAGR) of 13.7% from 2025 to 2033.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 404.29 million.
APAC held a market share of around 23% of the global revenue with a market size of USD 334.51 million in 2025 and will grow at a compound annual growth rate (CAGR) of 17.8% from 2025 to 2033.
South America has a market share of more than 5% of the global revenue with a market size of USD 52.96 million in 2025 and will grow at a compound annual growth rate (CAGR) of 15.4% from 2025 to 2033.
Middle East had a market share of around 2% of the global revenue and was estimated at a market size of USD 55.75 million in 2025 and will grow at a compound annual growth rate (CAGR) of 15.5% from 2025 to 2033.
Africa had a market share of around 1% of the global revenue and was estimated at a market size of USD 30.66 million in 2025 and will grow at a compound annual growth rate (CAGR) of 14.7% from 2025 to 2033.
Patent Valuation Services category is the fastest growing segment of the Patent Analytics Services industry

Market Dynamics of Patent Analytics Services Market

Key Drivers for Patent Analytics Services Market

Rising number of patent filings in different industries to Boost Market Growth: Businesses prioritize patent filings in order to establish distinctive brands and produce cutting-edge goods. The patent analytics services market has grown as a result of the rising number of patent filings in different industries each year. For instance, the International WIPO's Patent Cooperation Treaty (PCT) Organizations Report 2023 estimates that 278,100 patent applications were submitted under the PCT in 2022, a 0.3% increase over the year before. There is a huge need to implement cutting-edge platforms and solutions to handle such as large patent filings as a result of the rise in international patent filings and worldwide R&D expenditures. Patent analytics services are in high demand due to the rise in patent filings in the healthcare and pharmaceutical industries. In addition, the rise in government funding and worldwide healthcare spending are to responsible for the growth in patent filings.

Integration of AI and big data to Boost Market Growth: Another major factor propelling the patent analytics services market is the combination of AI and big data, which improves the capacity to swiftly and accurately handle and analyze enormous volumes of patent data. AI systems can automatically categorize and decipher intricate patent paperwork, spot patterns, and forecast future events, enabling businesses to maintain their innovative edge. Additionally, big data makes it possible to combine information from many sources, giving an all-encompassing picture of the patent market. When combined, they enhance decision-making by providing more thorough insights, decreasing manual labor, and raising the precision of competition analysis and patent searches. The need for sophisticated patent analytics services is increased by this integration, which enables companies to handle the complexity of intellectual property more effectively.

Key Restraints for Patent Analytics Services Market

High cost of these services Will Limit Market Growth: The market for patent analytics services is severely constrained by higher prices, especially given the advanced technology and knowledge needed for efficient analysis. Advanced tools, which frequently make use of AI and big data, require a significant cost outlay for deployment, license, and upkeep. Additionally, smaller businesses or start-ups may find these expenses excessive, which would limit their capacity to fully utilize patent analytics. The total cost is further increased by the requirement for skilled workers to operate and interpret these instruments. Furthermore, the high cost may discourage companies from implementing new technologies or enhancing their patent analytics skills, which could hinder market expansion and restrict the general availability of these vital tools.

Key Trends for Patent A...

From 2015 to 2020, there were around ******* patents filed in India. Out of which, around ** thousand patents were in the field of emerging technologies. Emerging technologies include internet of things, big data, cloud, edge & real-time processing, cyber security, and artificial intelligence.

In this paper, we extend some usual techniques of classification resulting from a large-scale data-mining and network approach. This new technology, which in particular is designed to be suitable to big data, is used to construct an open consolidated database from raw data on 4 million patents taken from the US patent office from 1976 onward. To build the pattern network, not only do we look at each patent title, but we also examine their full abstract and extract the relevant keywords accordingly. We refer to this classification as semantic approach in contrast with the more common technological approach which consists in taking the topology when considering US Patent office technological classes. Moreover, we document that both approaches have highly different topological measures and strong statistical evidence that they feature a different model. This suggests that our method is a useful tool to extract endogenous information.

Robotics Patent Analytics Platforms Market Outlook

According to our latest research, the global Robotics Patent Analytics Platforms market size reached USD 1.45 billion in 2024, demonstrating robust momentum driven by increasing demand for IP-driven innovation and the rise of automation technologies. The market is poised to expand at a CAGR of 14.8% from 2025 to 2033, with the total market value forecasted to reach USD 4.34 billion by 2033. The surge in patent filings in the robotics sector, coupled with the need for advanced analytics and competitive intelligence, continues to fuel market growth as organizations seek to protect their intellectual property and gain a strategic edge in an innovation-driven landscape.

The primary growth factor for the Robotics Patent Analytics Platforms market is the exponential increase in patent filings and intellectual property (IP) activities within the robotics industry. As robotics technologies evolve rapidly, organizations across sectors are investing heavily in research and development, resulting in a swelling volume of patent applications. This surge necessitates sophisticated analytics platforms capable of efficiently searching, analyzing, and managing patent data. The need to identify white spaces, avoid infringement risks, and benchmark technological advancements against competitors has made robotics patent analytics platforms indispensable for corporates, research institutes, and law firms. Furthermore, the integration of artificial intelligence and machine learning in these platforms enhances their ability to deliver actionable insights, further propelling market adoption.

Another significant driver is the growing emphasis on competitive intelligence and strategic portfolio management. As robotics becomes a cornerstone of Industry 4.0, organizations are leveraging patent analytics to monitor competitor activities, identify emerging trends, and make informed decisions regarding mergers, acquisitions, and collaborations. Patent analytics platforms provide a comprehensive view of the IP landscape, allowing end-users to assess the strength and weaknesses of their portfolios and those of their rivals. This intelligence is critical for shaping R&D investments, formulating go-to-market strategies, and maintaining a leadership position in a fiercely competitive market. The platforms’ capabilities in legal and compliance management also help organizations navigate complex regulatory environments, reducing the risk of costly litigation.

Technological advancements in cloud computing and big data analytics are further catalyzing the adoption of robotics patent analytics platforms. Cloud-based deployment models offer scalability, flexibility, and cost-effectiveness, making advanced analytics accessible to a broader range of users, including small and medium enterprises. The integration of natural language processing, semantic search, and visualization tools enhances user experience and enables deeper insights from vast patent databases. As a result, organizations can quickly identify innovation hotspots, assess technology trajectories, and streamline their IP management processes. These technological enablers, combined with the growing awareness of the strategic value of patents, are expected to sustain the market’s upward trajectory over the forecast period.

From a regional perspective, North America leads the Robotics Patent Analytics Platforms market, driven by a high concentration of robotics innovators, robust IP frameworks, and strong investment in research and development. Europe follows closely, benefiting from supportive government policies and a dynamic industrial automation ecosystem. The Asia Pacific region is witnessing the fastest growth, fueled by rapid industrialization, increasing patent activity, and expanding technology hubs in countries such as China, Japan, and South Korea. Latin America and the Middle East & Africa are gradually catching up, as awareness of IP management and analytics grows among local enterprises and government agencies. The global spread of robotics innovation ensures a broad and sustained demand for advanced patent analytics solutions across all major regions.

Graph and download economic data for U.S. Granted Utility Patents Originating in Big Horn County, WY (PATENTCOUNTY56003) from 2000 to 2015 about Big Horn County, WY; patent granted; WY; patents; intellectual property; origination; and USA.

Fork this notebook to get started on accessing data in the BigQuery dataset by writing SQL queries using the BQhelper module.

Context

The Office Action Research Dataset for Patents contains detailed information derived from the Office actions issued by patent examiners to applicants during the patent examination process. The “Office action” is a written notification to the applicant of the examiner’s decision on patentability and generally discloses the grounds for a rejection, the claims affected, and the pertinent prior art.

Content

This initial release consists of three files derived from 4.4 million Office actions mailed during the 2008 to mid-2017 period from USPTO examiners to the applicants of 2.2 million unique patent applications.

Acknowledgements

A working paper describing this dataset is available and can be cited as Lu, Qiang and Myers, Amanda F. and Beliveau, Scott, USPTO Patent Prosecution Research Data: Unlocking Office Action Traits (November 20, 2017). USPTO Economic Working Paper No. 2017-10. Available at SSRN: https://ssrn.com/abstract=3024621 (link is external).

This effort is made possible by the USPTO Digital Services & Big Data portfolio and collaboration with the USPTO Office of the Chief Economist (OCE). The OCE provides these data files for public use and encourages users to identify fixes and improvements. Please provide all feedback to: EconomicsData@uspto.gov.

Data Origin: https://bigquery.cloud.google.com/dataset/patents-public-data:uspto_oce_office_actions

Banner photo by Trent Erwin on Unsplash

U.S. Granted Utility Patents Originating in Big Stone County, MN was 1.00000 Number in January of 2015, according to the United States Federal Reserve. Historically, U.S. Granted Utility Patents Originating in Big Stone County, MN reached a record high of 3.00000 in January of 2000 and a record low of 0.00000 in January of 2001. Trading Economics provides the current actual value, an historical data chart and related indicators for U.S. Granted Utility Patents Originating in Big Stone County, MN - last updated from the United States Federal Reserve on December of 2025.

U.S. Granted Utility Patents Originating in Big Horn County, WY was 9.00000 Number in January of 2015, according to the United States Federal Reserve. Historically, U.S. Granted Utility Patents Originating in Big Horn County, WY reached a record high of 9.00000 in January of 2015 and a record low of 0.00000 in January of 2001. Trading Economics provides the current actual value, an historical data chart and related indicators for U.S. Granted Utility Patents Originating in Big Horn County, WY - last updated from the United States Federal Reserve on October of 2025.

Graph and download economic data for New Patent Assignments in Big Horn County, MT (USPTOISSUED030003) from Sep 1980 to Nov 2022 about Big Horn County, MT; patent assignment; patents; MT; and USA.

With the rapid advancement of the Fourth Industrial Revolution, international competition in technology and industry is intensifying. However, in the era of big data and large-scale science, making accurate judgments about the key areas of technology and innovative trends has become exceptionally difficult. This paper constructs a patent indicator evaluation system based on the dimensions of key and generic patent citation, integrates graph neural network modeling to predict key common technologies, and confirms the effectiveness of the method using the field of genetic engineering as an example. According to the LDA topic model, the main technical R&D directions in genetic engineering are genetic analysis and detection technologies, the application of microorganisms in industrial production, virology research involving vaccine development and immune responses, high-throughput sequencing and analysis technologies in genomics, targeted drug design and molecular therapeutic strategies..., These datasets were obtained by the Incopat patent database for cited patents (2013-2022) in the field of genetic engineering. Details for the datasets are provided in the README file. This directory contains the selection of the patent datasets. 1) Table of key generic indicators for nodes (partial 1).csv This file consists of 10 indicators of patents: technical coverage, patent families, patent family citation, patent cooperation, enterprise-enterprise cooperation, industry-university-research cooperation, claims, citation frequency, layout countries, and layout countries. 2) Table of key generic indicators for nodes (partial 2).csv This file consists of 10 indicators of patents: technical convergence, cited countries, inventors, citations, homologous countries/areas, degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, and PageRank. 3) patent.content The content file contains descriptions of the patents in the following format:

This README file was generated on 2023-11-25 by Mingli Ding.

GENERAL INFORMATION

1. Author Information Investigators Contact Information Name: Mingli Ding; Wangke Yu; Shuhua Wang Institution: Jingdezhen Ceramic University Address: Jingdezhen, Jiangxi, China Email: mlding1@163.com
2. Date of data collection:2013-2022

DATA & FILE OVERVIEW

1. File List:

A) Table of key generic indicators for nodes (partial 1).csv

B) Table of key generic indicators for nodes (partial 2).csv

C) patent.content

D) patent.cites

E) Graph neural network modeling highest accuracy for different dimensions.csv

F) Prediction effects of key generic technologies.csv

DATA-SPECIFIC INFORMATION FOR: Table of key generic indicators for nodes (partial 1).csv

1. Number of variables: 10
2. Number of cases/rows: 72489
3. Variable List:

• technical coverage: number of n...,

The DISCERN dataset was developed to support academic research on corporate innovation by linking data on U.S. publicly listed firms from Standard & Poor’s Compustat database to their patents and scientific publications. A key feature of DISCERN is its comprehensive coverage of firms’ subsidiaries and their ownership changes over time, which is crucial for accurately mapping corporate innovation. Patents and publications may be assigned to various legal entities within a firm’s organizational structure. Subsidiaries may change ownership in M&A events. By accounting for these ownership linkages over time, DISCERN enables researchers to construct more precise measures of firms’ knowledge production and examine the factors influencing their R&D investment decisions.

Version 2.0 incorporates several key improvements over the previous version of DISCERN. First, we shift to using the PatentsView database as the main source of patent data and OpenAlex as the main source of scientific publication data. PatentsView is publicly available and continuously maintained directly by the United States Patents & Trademarks Office (USPTO). OpenAlex is currently the only open data source of scientific publication metadata. Using freely available data sources allows us to share both the patent and the publication datasets openly. This enhances data access, which was previously limited due to the use of propriety data. Second, the updated dataset now covers the period from 1980 to 2021, providing an additional six years of data. Third, we transition to using Securities and Exchange Commission (SEC) filings as the primary source of subsidiary data, allowing us to trace ownership linkages further back to the mid-1990s and ensuring a higher degree of reliability compared to the Orbis data used in the original version, which was less reliable and had comprehensive coverage only from 2008. Finally, by transitioning to PatentsView and additional data sourced from the USPTO, we expand the scope of the dataset to include pre-grant patent applications and patent re-assignment information. This addition allows users to study patent applications regardless of grant status and to observe ownership transitions beyond those related to mergers and acquisitions.

A special thanks and appreciation go to Sanskriti Purohit and Ron Rabi for their diligent work and dedication to this effort.

The dataset is freely available under the O-UDA-1.0 License, permitting unrestricted use for research and commercial purposes. We request that users provide proper citations when utilizing the dataset. The license also allows for the creation of derivative datasets based on DISCERN, with the condition that creators ask their downstream users to cite the original authors appropriately.

If you use the data, please add these citations:

1. Arora, A., Belenzon, S., Cioaca, L., Sheer, L, Shin, H.M. & Shvadron, D. (2024). DISCERN 2.0: Duke Innovation & SCientific Enterprises Research Network [Dataset]. In Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.3594642

2. Arora, A., Belenzon, S., Cioaca, L., Sheer, L, & Shvadron, D. (2024). Back to the Future: Are Big Firms Regaining their Scientific and Technological Dominance? Evidence from DISCERN 2.0 (available soon)

Graph and download economic data for U.S. Granted Utility Patents Originating in Big Stone County, MN (PATENTCOUNTY27011) from 2000 to 2015 about Big Stone County, MN; patent granted; patents; intellectual property; origination; MN; and USA.

Patent Landscaping AI Market Outlook

According to our latest research, the global Patent Landscaping AI market size in 2024 stands at USD 1.82 billion, driven by the rapid adoption of artificial intelligence in intellectual property analytics, with a robust CAGR of 19.7% projected through 2033. By 2033, the market is forecasted to reach a substantial USD 8.98 billion, reflecting the increasing necessity for advanced patent analytics across industries. The primary growth factor for this market is the escalating demand for AI-driven patent analysis tools that streamline innovation processes, ensure competitive intelligence, and support strategic decision-making in R&D-intensive sectors.

The growth trajectory of the patent landscaping AI market is heavily influenced by the surging volume of global patent filings and the complexity of intellectual property landscapes. As organizations strive to maintain a competitive edge, the ability to efficiently analyze vast patent datasets for trends, prior art, and infringement risks becomes paramount. AI-powered patent landscaping solutions offer unparalleled speed and accuracy, automating the extraction of actionable insights from millions of patent documents. This capability not only enhances R&D productivity but also reduces the time and cost associated with manual patent analysis, driving widespread adoption across industries such as pharmaceuticals, electronics, and automotive.

Another significant driver is the increasing integration of patent landscaping AI platforms with enterprise systems, enabling seamless workflow automation and strategic IP portfolio management. The convergence of AI, big data analytics, and cloud computing has revolutionized how organizations conduct patent landscaping, facilitating real-time monitoring of patent trends, competitor activities, and emerging technologies. Furthermore, advancements in natural language processing (NLP) and machine learning algorithms have improved the contextual understanding of patent documents, making AI tools indispensable for legal teams, innovation managers, and research institutes. These technological advancements are expected to fuel sustained market growth throughout the forecast period.

The patent landscaping AI market is also experiencing growth due to the heightened emphasis on intellectual property protection in emerging economies. Governments and regulatory bodies are increasingly recognizing the strategic value of patents in fostering innovation and economic growth. As a result, there is a growing demand for sophisticated patent analytics solutions among corporates, law firms, and government agencies. Additionally, the need for efficient due diligence in mergers and acquisitions, freedom-to-operate analyses, and patent risk assessments is propelling the adoption of AI-driven patent landscaping tools. This trend is particularly pronounced in sectors such as biotechnology, chemicals, and IT, where patent portfolios are critical assets.

Regionally, North America remains at the forefront of the patent landscaping AI market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The presence of leading technology providers, a mature intellectual property ecosystem, and substantial R&D investments underpin the strong market position of North America. Meanwhile, Asia Pacific is witnessing the fastest growth, fueled by the rapid digital transformation of industries and the increasing focus on innovation-driven economic policies. Europe also demonstrates significant market potential, supported by robust patent regulations and a thriving start-up ecosystem. Latin America and the Middle East & Africa are emerging as promising markets, albeit at a nascent stage, with growing awareness of the benefits of AI in patent analytics.

Component Analysis

The patent landscaping AI market is segmented by component into software and services, each playing a crucial role in enabling organizations to harness the full potential of AI-driven patent analytics. T

Intellectual Property Analytics AI Market Outlook

According to our latest research, the Intellectual Property Analytics AI market size reached USD 1.48 billion in 2024, driven by increasing demand for advanced analytics in managing and protecting intellectual property assets. The market is set to expand at a robust CAGR of 18.7% from 2025 to 2033, with the forecasted market size projected to attain USD 7.94 billion by 2033. This remarkable growth is primarily attributed to the rapid adoption of artificial intelligence technologies by enterprises and legal entities aiming to streamline IP management, gain actionable insights, and enhance strategic decision-making. As per our latest research, the marketÂ’s expansion is underpinned by the convergence of big data, AI-driven analytics, and the increasing complexity of global IP landscapes.

One of the primary growth factors for the Intellectual Property Analytics AI market is the exponential increase in patent filings and intellectual property registrations worldwide. As organizations continue to innovate and expand their portfolios, the need for sophisticated tools to analyze vast volumes of IP data has become paramount. AI-powered analytics solutions are capable of processing and interpreting complex patent, trademark, and copyright data at unprecedented speeds, providing organizations with clear competitive intelligence and risk assessment. This capability is particularly valuable for multinational corporations and research institutions that operate across diverse jurisdictions, where manual analysis would be both time-consuming and prone to error. The surge in global R&D activities and cross-border collaborations further amplifies the demand for advanced IP analytics, cementing the role of AI as a transformative force in this sector.

Another significant driver of market growth is the increasing pressure on organizations to protect their intellectual property assets from infringement, counterfeiting, and unauthorized use. With the proliferation of digital content and the globalization of markets, IP theft and litigation have become major concerns for businesses. AI-driven IP analytics platforms enable proactive monitoring of potential infringements, identification of emerging threats, and timely enforcement actions. These platforms utilize machine learning, natural language processing, and predictive analytics to detect patterns of infringement, track unauthorized usage, and forecast litigation risks. As a result, organizations are increasingly investing in AI-based IP management solutions to safeguard their innovations, maintain competitive advantage, and ensure compliance with evolving regulatory frameworks.

The marketÂ’s growth is also propelled by the evolving role of intellectual property in shaping corporate strategies and valuation. In the knowledge economy, intangible assets such as patents, trademarks, and copyrights constitute a significant portion of enterprise value. AI-powered IP analytics enables organizations to maximize the value of their IP portfolios by identifying monetization opportunities, optimizing licensing strategies, and supporting M&A due diligence. Furthermore, these tools facilitate benchmarking against industry peers, uncovering whitespace for innovation, and aligning IP strategy with business objectives. The integration of AI into IP analytics not only enhances operational efficiency but also empowers decision-makers with actionable insights, driving sustainable growth and long-term value creation.

AI-Assisted Patent Analytics is revolutionizing the way organizations handle their intellectual property. By leveraging AI technologies, these analytics provide deeper insights into patent landscapes, enabling companies to make informed decisions about their IP strategies. The integration of AI allows for the rapid processing of vast datasets, identifying trends and potential areas for innovation. This capability is particularly beneficial for companies looking to stay ahead in competitive markets, as it offers a clearer understanding of the patent ecosystem and helps in pinpointing opportunities for growth and development.

From a regional perspective, North America currently dominates the Intellectual Property Analytics AI market, accounting for the largest share in 2024, attributed to the presence of leading technology provider

Three evident and meaningful characteristics of disruptive technology are the zeroing effect that causes sustaining technology useless for its remarkable and unprecedented progress, reshaping the landscape of technology and economy, and leading the future mainstream of technology system, all of which have profound impacts and positive influences. The identification of disruptive technology is a universally difficult task. Therefore, the paper aims to enhance the technical relevance of potential disruptive technology identification results and improve the granularity and effectiveness of potential disruptive technology identification topics. According to the life cycle theory, dividing the time stage, then constructing and analyzing the dynamic of technology networks to identify potential disruptive technology. Thereby, using the LDA topic model further to clarify the topic content of potential disruptive technologies. This paper takes the large civil UAVs as an example to prove the feas..., Through the analysis of the technology life cycle, the division of the patents, the construction of the technology network, the identification of nodes leaping, the clustering of technical topics, we aim to identify potential disruptive technology. Â

Procedures:

Knowledge flow: being familiar with the technical background knowledge in the field of large civil UAVs, and accomplishing the technical decomposition. Invention patents: analyzing the technology life cycle by the loget lab to separate the invention patents into four parts. According to each part, constructing the IPC technical network and identifying the leapfrogging and diffusible nodes. Technical topics: making use of the LDA model to cluster and explain the broad and various content of the inventions.

Â

Testing: Dividing the inventions of the embryonic stage into two groups and examining them by means of the Mann-Whitney test. Finally, the result shows the huge differences in the patent value, sustaining influence, and c..., , This README file was generated on 2023-11-25 by Mingli Ding.

GENERAL INFORMATION

1. Title of Dataset: technical network in the field of large civilian UAVs

2. Author Information

Investigators Contact Information Name: Mingli Ding; Wangke Yu; Ran Li; Zhenzhen Wang; Jianing Li Institution: Jingdezhen Ceramic University Address: Jingdezhen, Jiangxi, China Email:

1. Date of data collection:2005-2018

DATA & FILE OVERVIEW

1. File List:

A)patent (2005-2008).csv

B)patents (2009-2012).csv

C)patents (2013-2015).csv

D)patents (2016-2018).csv

E)technical network (2005-2008).csv

F)technical network (2009-2012).csv

G)technical networks (2013-2015).csv

H)technical network (2016-2018).csv

DATA-SPECIFIC INFORMATION FOR: patent (2005-2008).csv

1. Number of variables: 2

2. Number of cases/rows: 234

3. Variable List:

   • source: the source of the edges in the technical network
   • target: the target of the edges in the technical network

4. Specialized fo...

The Office Action Research Dataset for Patents contains detailed information derived from the Office actions issued by patent examiners to applicants during the patent examination process. The “Office action” is a written notification to the applicant of the examiner’s decision on patentability and generally discloses the grounds for a rejection, the claims affected, and the pertinent prior art. This initial release consists of three files derived from 4.4 million Office actions mailed during the 2008 to mid-2017 period from USPTO examiners to the applicants of 2.2 million unique patent applications. A working paper describing this dataset is available and can be cited as Lu, Qiang and Myers, Amanda F. and Beliveau, Scott, USPTO Patent Prosecution Research Data: Unlocking Office Action Traits (November 20, 2017). USPTO Economic Working Paper No. 2017-10. Available at SSRN: https://ssrn.com/abstract=3024621. This effort is made possible by the USPTO Digital Services & Big Data portfolio and collaboration with the USPTO Office of the Chief Economist (OCE). The OCE provides these data files for public use and encourages users to identify fixes and improvements. Please provide all feedback to: EconomicsData@uspto.gov.

Multiple recent studies have focused on unraveling the content of the medicinal chemist’s toolbox. Here, we present an investigation of chemical reactions and molecules retrieved from U.S. patents over the past 40 years (1976–2015). We used a sophisticated text-mining pipeline to extract 1.15 million unique whole reaction schemes, including reaction roles and yields, from pharmaceutical patents. The reactions were assigned to well-known reaction types such as Wittig olefination or Buchwald–Hartwig amination using an expert system. Analyzing the evolution of reaction types over time, we observe the previously reported bias toward reaction classes like amide bond formations or Suzuki couplings. Our study also shows a steady increase in the number of different reaction types used in pharmaceutical patents but a trend toward lower median yield for some of the reaction classes. Finally, we found that today’s typical product molecule is larger, more hydrophobic, and more rigid than 40 years ago.