USPTO Patent Research Articles

Integration of Indian inventor networks in global value chains in pharmaceuticals industry

ABSTRACT The network approach of value chain analysis examines the role and centrality of various actors and their interrelationships with other actors, besides establishing the role of external linkages in this process. This paper tries to combine the effect of research networks on the geographic dispersion of innovative activity in the Indian pharmaceutical industry. We use activity-wise data from USPTO's patents to establish the level of integration of Indian innovative firms in the pharmaceutical global value chain. Using Tobit analysis, we examine the nature of the collaborative pattern of firms and establish whether this pattern of the inventor network level translates to integration into global networks in terms of geographic dispersion. We analyse the geographic dispersion of inventor networks as a function of the characteristics of the innovative actors that coordinate their inventive work. We show the level of integration of Indian innovative firms in the pharmaceuticals global value chain and assess the structure of these research networks. This helped contextualize firms' roles by contrasting them with other institutional types. The level of integration of Indian firms is low in terms of the geographic dispersal of innovation networks, while the private universities and public research centres are more geographically dispersed.

The Significance of Provisional Patent Applications in Protecting Early-Stage Inventions: A Legal and Empirical Analysis

This article critically examines the role of Provisional Patent Applications (PPAs) as strategic tools within the intellectual property framework, particularly highlighting their significance in safeguarding early-stage inventions. By allowing inventors to secure a priority date while still developing their inventions, PPAs provide a time frame when additional funding can be sought and further development can be pursued without losing the claim to the original invention date. This is especially important in industries like information and communication technology and the life sciences, where development timelines are lengthy and investment-intensive. PPAs serve not only as a cost-effective initial step in patent filing but also as a protective mechanism against premature disclosure, safeguarding the novelty of the invention. PPAs do not undergo formal examination and do not result in a granted patent unless followed by a standard patent application. However, they establish a legal placeholder that can be crucial for securing further patent rights. This study explores the legal framework of PPAs across various jurisdictions. The analysis also delves into the specific benefits and strategic considerations associated with drafting and filing PPAs, including their role in facilitating additional research and refinement of the invention. In addition, it presents novel empirical evidence on the growth of PPAs in the United States and their use as priorities in regular USPTO and EPO patent applications. Despite their advantages, PPAs come with limitations, such as the limited time available to convert them into standard patent applications (typically one year) to benefit from their priority date and the precise requirements for subsequent standard patent applications, which this article addresses. Through a detailed exploration of both the theoretical framework and practical applications of PPAs, the article contributes to a deeper understanding of their role in broader patent strategies, advocating for their careful and informed use in the innovation process.

Detecting Upcoming Patent Keywords by Predicting Keyword Trends Using Patent Keyword Network

In this ever-changing technological landscape, the ability to quickly predict technological trends becomes crucial for any company or institute engaged in informed decision-making and strategic planning. Data for predicting technological trends can come from various sources such as patent data, which is easily accessible to the public due to the nature of patents. This research is aimed at patent analysis, focusing on combining the keyword- based method, social network analysis (SNA) method, and neural network prediction to propose a feasible keyword trend prediction method based on patent analysis by targeting upcoming keyword trends. More specifically, we utilize Long Short-Term Memory (LSTM) to predict changes in keyword frequency using keyword centralities as input. To assess the effectiveness of the proposed method, we constructed the input dataset using the USPTO patent database in the Information and Communication Technology (ICT) field. We then experimented to compare the proposed method with the benchmark method. Furthermore, to counteract the unbalanced nature of patent data, the SMOGN method is introduced. The results demonstrate its potential for application in broader contexts.

Are outliers more disruptive? Technological niche, disruptive innovation, and recombinant capability

Whether disruptive technologies are more likely to emerge at the margin or the core of industry has been a long‐debated issue. We theorize the relationship between technological niches and the generation of disruptive innovations. Introducing the main path analysis, we develop a novel indicator to depict firms' technological niche in a focal industry's technological landscape and link it to their performance of disruptive innovation. Further, we investigate its boundary condition by taking firms' recombinant capabilities into consideration. A sample of 29,655 USPTO patents of the electric communication industry (H04W/L) for the period 1995–2019 is used to test our hypotheses. Our research reveals an inverted U‐shaped relationship between a firm's technological niche and its disruptive innovation performance. Besides, firms' recombinant capability could moderate the relationship by steepening the curvilinear. This paper helps reconcile the long debated question of ‘Are Outliers More Disruptive’ by providing a contingency perspective and identifying a non‐monotonic relationship.

Knowledge Flows Within Chinese Administrative Provinces: The Role of Regional Research Structures

This paper explores the determinants of knowledge flows, proxied by patent citations, within 31 Chinese administrative provinces. Relying on patent data from the US Patent and Trademark Office during the period 1995-2019, we are able to identify 27118 patent citation pairs (focusing only on USPTO patents with first inventors residing in China). In order to explain the number of citations from a Province to another, in addition to geographic and technological proximity, we use factor analysis in order to capture the effect of regional research structure. In particular, we measure the private versus public research intensity of each Chinese administrative province. Our econometric results show that, as expected, geographical and technological distance between Provinces are negatively correlated with patent citations. In addition, we find that Chinese administrative provinces that show bigger intensity with regard to public and to private research both receive more patent citations and cite more other patents in other Provinces. This effect is particularly significant when the two regions are specialized in private research, which indicates a strong effect of cognitive proximity. Finally, and at odds with prior research, we find that controlling for social proximity between Provinces does not impact the effect of geographical and technological proximity. These results, if confirmed by further studies, might have significant policy implications.

Technological impact of funded research: A case study of nonpatent references

Abstract Research funding is essential to expand knowledge, foster innovation, and address the complex challenges that shape our future. The scientific literature has extensively addressed the relationship between research funding and academic impact. More recently, several studies have analyzed the technological impact of funded research as measured through citations in patents, known as nonpatent references (NPRs). But there remains much to know about NPRs and the multiplication of case studies is necessary to characterize them. Here we analyze a sample of 7,065 publications funded by the French Foundation for Medical Research (FRM) and the citations of these publications in patents. This study shows the high scientific and technological impacts of FRM funding. Indeed, the publications funded by FRM that are cited in patents are 3.5 times more frequently cited by other publications than the global average (for funded publications in the entire database, this ratio is 2.6). Furthermore, our results also indicate that USPTO patents citing these publications exhibit high-quality indicators. Moreover, five of these patents have led to approved drug products by the United States Food and Drug Administration (FDA). This study provides further evidence of the positive influence that research funding can have on both scientific and technological advancements.

Digitizing the USPTO patent backfile

Abstract The digitization of the US Patent and Trademark Office’s (USPTO) backfile of six million patents undertaken between 1951 and 2001 was a five-decade struggle, featuring several media transitions from print and microfilm to CD-ROMs and, finally, the Web. This mass digitization project is on a similar scale to Google Books and the Internet Archive, but it is rarely discussed within critical digitization scholarship or for its significance as a tool for knowledge production. In this article, I focus on the USPTO’s patent document’s digital and physical material form and how the current paradigm of access and storage of the digital backfile emerged. Through this case study, I build upon Ian Milligan’s distinction between the ‘text’ and ‘platform’ layers of a digitization project to demonstrate how historical decisions regarding format and metadata continue to influence how users retrieve and interpret documents, such as patents, online.

The material basis of modern technologies. A case study on rare metals

Scientific progress in many technologies exploits new materials. The unique properties of a wide range of Rare Metals (RMs) make them key inputs to achieve the functionality of emerging technologies. The speed of technological progress can therefore be influenced by the availability of necessary RM materials. This paper discusses these relations and provides a first exploratory empirical analysis of the link between critical raw materials and frontier technological innovation. By text mining 5,146,615 USPTO patents during the period 1976–2015, we explore the dependence of new inventions of 13 key RMs, finding that the latter play an increasingly important role as the material basis of modern technologies: in the four decades observed, more than 1/10 patents rely on at least one RM. This dependence increases significantly over time and is particularly high for emerging technologies such as semiconductors, nanotechnology, and green energy. Further, we adopt a panel of 5644 technology subgroup-RM pairs to explore the impact of variations in RM supply. The results show that, controlling for science & technology push and demand-pull factors, innovation in RM-based technologies is positively associated with its supply conditions, contributing to the understanding of the shifts of critical materials' use in frontier technologies.

Gefitinib: An Updated Review of its Role in the Cancer Management, its Nanotechnological Interventions, Recent Patents and Clinical Trials.

Gefitinib, a tyrosine kinase inhibitor, is effectively used in the targeted treatment of malignant conditions. It suppresses the signal transduction cascades leading to cell proliferation in the tumors and is now currently approved in several countries globally as secondline and third-line treatment for non-small cell lung cancer (NSCLC). This review is aimed to summarize the journey of gefitinib as an established anticancer drug for the management of various cancers. Moreover, this review will focus on the mechanism of action, established anticancer activities, combination therapy, nanoformulations, as well as recent clinical trials and patents on gefitinib. The data for this review was collected from scientific databases such as PubMed, Science Direct, Google Scholar, etc. Recent patents on gefitinib granted in the last two years were collected from databases Patentscope, USPTO, Espacenet, InPASS and Google Patents. Data for the recent clinical trials were obtained from the U.S. National Library of Medicine database. Recent pre-clinical and clinical studies during the period 2015-2021 demonstrating the efficacy of gefitinib were selected and summarized. Total 31 patents were granted in the year 2020-2021 concerning gefitinib. The efficacy of gefitinib against lung cancer, as well as other cancer types, including breast, prostate, colon, cervix etc., was reviewed. Gefitinib showed significant advantages in being more effective, safer and more stable, and the associated biopharmaceutical problems are addressed by the application of nanotechnology. The combination therapy using gefitinib and various anticancer molecules of natural and synthetic origin has shown an improved anticancer profile.

Uncovering the Biological Applications of Cinnamic Acid Derivatives: A Patent Review

Background: Cinnamic acid, derived from Cinnamomum cassia, is a natural compound known for its wide-ranging therapeutic properties and minimal toxicity. Extensive research has demonstrated the diverse biological activities displayed by cinnamic acid derivatives, encompassing their potential as agents against cancer, diabetes, microbial infections, tuberculosis, malaria, and more. Objective: This review aims to provide an overview of the latest applications detailing the biological activity of cinnamic acid derivatives, as documented in patents. Methods: The published patent data underwent a prior screening and selection process based on their relevance and primary focus: the biological activities of cinnamic acid derivatives as potential drugs. Espacenet, USPTO, and Google Patents were used for this selection. Results: Cinnamic acid derivatives demonstrate a range of activities, including anticancer, antibacterial, anti-inflammatory, analgesic, anticholinesterase, and other properties. These biological activities were investigated across different derivatives, emphasizing their pharmacological potential when compared to reference compounds. Conclusions: Despite several patents have explored the biological properties of cinnamic acid derivatives, there has been a lack of a comprehensive review dedicated to this subject. Accordingly, this review aims to facilitate the discovery of new and diverse potential drugs with various therapeutic profiles.

Technological search strategy and breakthrough innovation: An integrated approach based on main-path analysis

The significance of breakthrough innovations is not only reflected in their technological novelty but also in their impacts on the evolution of industrial technology paths. In the context of accelerating digitalization and industrial upgrading, it is particularly important to investigate how can firms manage their technological search strategies to generate breakthrough outputs. We develop a main-path-based approach to complement the existing method of breakthrough innovations in terms of their impact on industrial technology evolution. Besides, based on a sample of 29,620 USPTO patents of the electric communication industry, for the period 1976–2021, we further link firms' technological search strategies of local and distant modes to their breakthrough innovation and find that the two search modes have respective and joint effects on firms' breakthrough performance concerning both technological novelty and industrial impact. This paper not only provides a more integrated approach to identifying breakthrough innovations but also explores the impact of a firm's technological search strategy on them.

Deep learning-based automatic action extraction from structured chemical synthesis procedures.

This article proposes a methodology that uses machine learning algorithms to extract actions from structured chemical synthesis procedures, thereby bridging the gap between chemistry and natural language processing. The proposed pipeline combines ML algorithms and scripts to extract relevant data from USPTO and EPO patents, which helps transform experimental procedures into structured actions. This pipeline includes two primary tasks: classifying patent paragraphs to select chemical procedures and converting chemical procedure sentences into a structured, simplified format. We employ artificial neural networks such as long short-term memory, bidirectional LSTMs, transformers, and fine-tuned T5. Our results show that the bidirectional LSTM classifier achieved the highest accuracy of 0.939 in the first task, while the Transformer model attained the highest BLEU score of 0.951 in the second task. The developed pipeline enables the creation of a dataset of chemical reactions and their procedures in a structured format, facilitating the application of AI-based approaches to streamline synthetic pathways, predict reaction outcomes, and optimize experimental conditions. Furthermore, the developed pipeline allows for creating a structured dataset of chemical reactions and procedures, making it easier for researchers to access and utilize the valuable information in synthesis procedures.

Assessment of patentability by means of semantic patent analysis – A mathematical-logical approach

To obtain patent protection, a patent must fulfill statutory patentability requirements examined by a patent office. Such examinations are mostly performed manually and are quite time-consuming. Therefore, we suggest a computer-based process for the assessment of patentability by means of a mathematical-logical approach comparing patents with semantic structures. In order to make such an assessment, we compare the feature combinations of patent claims with the pertinent prior art. For proof of concept, the process has been tested successfully on an US-application claiming a method for raising a crane boom which can be categorized as non-patentable with regard to the requirement of non-obviousness. The result is consistent with that of a USPTO patent examiner, which underpins that at least under certain conditions not only patent examiners but also applicants and third parties can assess the chance and scope of protection for claimed inventions and patent applications with regard to patentability by our process.

Supervised Contrastive Learning for Interpretable Long-Form Document Matching

Recent advancements in deep learning techniques have transformed the area of semantic text matching (STM). However, most state-of-the-art models are designed to operate with short documents such as tweets, user reviews, comments, and so on. These models have fundamental limitations when applied to long-form documents such as scientific papers, legal documents, and patents. When handling such long documents, there are three primary challenges: (i) the presence of different contexts for the same word throughout the document, (ii) small sections of contextually similar text between two documents, but dissimilar text in the remaining parts (this defies the basic understanding of “similarity”), and (iii) the coarse nature of a single global similarity measure which fails to capture the heterogeneity of the document content. In this article, we describe CoLDE : Co ntrastive L ong D ocument E ncoder—a transformer-based framework that addresses these challenges and allows for interpretable comparisons of long documents. CoLDE uses unique positional embeddings and a multi-headed chunkwise attention layer in conjunction with a supervised contrastive learning framework to capture similarity at three different levels: (i) high-level similarity scores between a pair of documents, (ii) similarity scores between different sections within and across documents, and (iii) similarity scores between different chunks in the same document and across other documents. These fine-grained similarity scores aid in better interpretability. We evaluate CoLDE on three long document datasets namely, ACL Anthology publications, Wikipedia articles, and USPTO patents. Besides outperforming the state-of-the-art methods on the document matching task, CoLDE is also robust to changes in document length and text perturbations and provides interpretable results. The code for the proposed model is publicly available at https://github.com/InterDigitalInc/CoLDE .

Firms as Influencers: Shaping Industries through Search and Collaboration

Firms often engage in shaping the various dimensions of an industry's competitive landscape. While the actions by which firms are able to shape an industry has received increased attention, research has yet to examine the knowledge antecedents of firms’ shaping behavior; that is the knowledge that allows firms to create a persistent change to the competitive landscape. This paper takes a first step at quantitatively examining the information processing antecedents of industry shaping by employing a novel methodology using computational linguistic tools to provide evidence on how firms can engage in industry shaping. Specifically, I examine how a firm’s shaping of the positioning space depends on its depth and breadth knowledge search and the extent to which they collaborate with other firms. I study this question using machine learning techniques in combination with 2.4 million USPTO patents and 585,133 firm descriptions. The results show that search breadth and depth are positively associated with industry shaping while the effect of collaboration is contingent on a firm's level of breadth and depth. Overall, the findings suggest that depth enables a structural understanding of the environment allowing other firms to learn from the focal firm in collaborative relationships, enhancing their credibility. Meanwhile, breadth lowers the level of expertise, decreasing the amount of useful knowledge that other firms learn from collaboration.

How public investment fuels innovation: Clues from government-subsidized USPTO patents

• This study is among the first attempts to depict a full picture of the public-subsidized innovation scheme. • This study proposes a multi-dimension framework for learning public-subsidized innovation schemes—the innovation promotor, innovator, and innovation area. • Government agencies are more and more involved in collaborative relationships. • Non-government innovators have shown more powerful vitality than government innovators. • Federal agencies vary in preferences to select subsidized recipients and technological fields. Considerably little effort has been devoted to systematically exploring the landscape of government-funded innovation schemes, leaving policymakers and academics little empirical evidence with which to grasp the overall situation of national R&D funding strategies. In the United States (US), patents with a government right or financial interest are flagged, offering a window through which we can examine the landscape of today's patterns of government support for innovation. We conduct the examination from the perspectives of the leading government agencies, the promotor-recipient relationship, and the common concerns of different participants. The results show that the R&D project funding by federal agencies has, among other factors, contributed to the growth in the number of patent records. The Departments of Defense (DoD), Energy (DoE), and Health and Human Services (HHS), and the National Science Foundation (NSF) have issued the most funding and hold rights to more patent records than any other agencies. Moreover, reforms to the US patent system made 40 years ago to afford more protection to non-government assignees appear to have significantly impacted patenting activity. Universities, companies, and individuals have become far more active in fulfilling government-funded projects, with remarkable innovations as a result. In recent years, funding priorities have shifted toward research in biomedicine, fields related to human life, and computer science. However, the four dominant agencies responsible for most government funding differ in their investment priorities and frequently adjust which areas of innovation they support. This study is among the first attempts to support the landscape of the public-invested innovation using the full government-subsidized patent dataset. The analyses in this paper are instructive in understanding the dynamics of US government funding for innovation and informing effective policymaking around innovation.

"Skunky" Cannabis: Environmental Odor Troubleshooting and the "Need-for-Speed".

Although the “skunky” odor characteristic of cannabis has been widely referenced, its cause has been historically misassigned to unspecified “skunky terpenes”. Recent reports from two independent research groups, the Koziel team (March and April 2021) and Oswald team (August and November 2021), have corrected this misassignment by linking the “skunky” character of industrial hemp and cannabis to 3-methyl-2-butene-1-thiol (321MBT). A recent USPTO patent application review clearly indicated that the Oswald team should take full credit for the discovery of this link with respect to cannabis. However, the August 19, 2021 publication of their patent application appears to be their formal public disclosure of 321MBT as the primary source odorant which is responsible for the targeted “skunky” odor. This date is well after the March and April 2021 public disclosures by the Koziel team for the 321MBT/“skunky” odor link relative to both cannabis and industrial hemp. This Viewpoint summarizes the investigative strategy leading to the public disclosure of this historically elusive link. It is presented from the perspective of the rapid multidimensional–gas chromatography–mass spectrometry–olfactometry (i.e., MDGC-MS-O) based odorant-prioritization “screening” approach, as applied by the Koziel team.

Patent citing patterns of foreign firms: an analysis of Indian IT services industry

ABSTRACT This study captures the origin and pattern of foreign knowledge inflows to the Indian IT services industry using patent citation data. It examines the patterns of backward patent citations by the US origin and non-US origin foreign IT services firms operating in India using the USPTO patent citation data from 2013 to 2017. To analyze the citing patterns of firms, we classified backward citations into two categories: self-citations and non-self-citations. Our study finds that patents granted to the US origin firms hold a relatively higher number of backward citations and a higher average number of inventors. It indicates that the Indian IT services industry receives more complex technology inflows from the US origin firms compared to non-US origin firms. It also shows a positive relationship between self-citations and lowers patent grant lag. Further, this study suggests that more self-citations by the US origin firms reinforce more patents that develop a patent cycle for a firm. Therefore, the US origin firms in the Indian IT services industry dominate in patenting activities at USPTO.

Automation and related technologies: a mapping of the new knowledge base

Using the entire population of USPTO patent applications published between 2002 and 2019, and leveraging on both patent classification and semantic analysis, this paper aims to map the current knowledge base centred on robotics and AI technologies. These technologies are investigated both as a whole and distinguishing core and related innovations, along a 4-level core-periphery architecture. Merging patent applications with the Orbis IP firm-level database allows us to put forward a twofold analysis based on industry of activity and geographic location. In a nutshell, results show that: (i) rather than representing a technological revolution, the new knowledge base is strictly linked to the previous technological paradigm; (ii) the new knowledge base is characterised by a considerable—but not impressively widespread—degree of pervasiveness; (iii) robotics and AI are strictly related, converging (particularly among the related technologies and in more recent times) and jointly shaping a new knowledge base that should be considered as a whole, rather than consisting of two separate GPTs; (iv) the US technological leadership turns out to be confirmed (although declining in relative terms in favour of Asian countries such as South Korea, China and, more recently, India).

Disclosure and Subsequent Innovation: Evidence from the Patent Depository Library Program

How important is access to patent documents for subsequent innovation? We examine the expansion of the USPTO Patent Library system after 1975. Patent libraries provided access to patents before the Internet. We find that after patent library opening, local patenting increases by 8–20 percent relative to similar regions. Additional analyses suggest that disclosure of technical information drives this effect: inventors increasingly take up ideas from outside their region, and the effect is strongest in technologies where patents are more informative. We thus provide evidence that disclosure plays an important role in cumulative innovation. (JEL D83, K11, O31, O34, R11)