Historical Patents Research Articles

A General Methodology for Technology Opportunity Discovery Based on Opportunity Evaluation and Optimization

Technology opportunities are important drivers of technological advances. Consequently, methods for technology opportunity discovery (TOD) are proposed to discover new types of technology opportunities and to design criteria for defining and evaluating technology opportunities, providing R&D teams and innovators with a plethora of inventive ideas. However, current TOD methods have some common limitations. First, the criteria for defining technology opportunities are typically restrictive, thus may exclude some promising candidates. Second, most criteria for evaluating opportunities lack empirical evidence. In this article, we propose a general methodology for discovering technology opportunities that addresses these limitations. We create a less restrictive technology opportunity space (TOS), built evaluation models for each candidate by learning from historical data, and use optimization techniques to search the TOS for the best technology opportunities. We then implement the proposed methodology in a case study that discovered firm-specific technology opportunities in neural network technology. We present technology opportunities as connected subnetworks of subject–action–object based knowledge networks; designed industry-level, firm-specific and patent-specific evaluation criteria; use random forest to develop the evaluation model from historical patents; and apply ant colony optimization to find the best opportunities. The case shows the feasibility and effectiveness of the general methodology for TOD.

A patent infringement early-warning methodology based on intuitionistic fuzzy sets: A case study of Huawei

In the era of technology-driven economies, patent infringement has become one of the main risks faced by companies, which exists in all stages of technological innovation. However, the increasing size of patent information as well as the inherent fuzziness of patent infringement risk make the early warning of this risk a knowledge-intensive engineering activity. In this study, a novel patent infringement early warning methodology based on intuitionistic fuzzy sets (IFSs) is proposed to accurately evaluate and classify patent infringement risk for its management. First, a hierarchical indicator system of the methodology is established, including indicators of regional judicial and administrative protection. Then entropy weights for IFSs and intuitionistic fuzzy weighted geometric (IFWG) operators are utilized to objectively and automatically aggregate indicator data on early warning patents and their similar patents to evaluate IFS results, which is a multi-layer data processing structure. Finally, the normalized Euclidean distances are used to classify risk levels. In a case study, Huawei's historical patents are taken as the test data, and the methodology is verified by comparing the output results and classification with the actual litigation status. Managerial implications for design engineers and patent attorneys are discussed corresponding to various technological innovation stages.

Sequential patent trading recommendation using knowledge-aware attentional bidirectional long short-term memory network (KBiLSTM)

With the rapid development of the patent marketplace, patent trading recommendation is required to mitigate the technology searching cost of patent buyers. Current research focuses on the recommendation based on existing patents of a company; a few studies take into account the sequential pattern of patent acquisition activities and the possible diversity of a company’s business interests. Moreover, the profiling of patents based on solely patent documents fails to capture the high-order information of patents. To bridge the gap, we propose a knowledge-aware attentional bidirectional long short-term memory network (KBiLSTM) method for patent trading recommendation. KBiLSTM uses knowledge graph embeddings to profile patents with rich patent information. It introduces bidirectional long short-term memory network (BiLSTM) to capture the sequential pattern in a company’s historical records. In addition, to address a company’s diverse technology interests, we design an attention mechanism to aggregate the company’s historical patents given a candidate patent. Experimental results on the United States Patent and Trademark Office (USPTO) data set show that KBiLSTM outperforms state-of-the-art baselines for patent trading recommendation in terms of F1 and normalised discounted cumulative gain (nDCG). The attention visualisation of randomly selected company intuitively demonstrates the recommendation effectiveness.

Historical patent data: A practitioner's guide

AbstractI provide a primer on six recent large‐scale historical patent data sets for use in innovation research. I discuss how each data set is constructed, the types of patent information included in each, and the quality and completeness of each. Throughout, I emphasize when our knowledge of the history of invention is dependent on the data source used and provide recommendations about which data set is most likely to be best for different contexts. Overall, these data sets paint a remarkably consistent picture of the history of U.S. invention. When the data sets do disagree, these differences tend to be minor, although I highlight some important exceptions. I further describe several “niche” historical patent data sets that allow researchers to study institutional contexts that cannot be studied using modern data. Finally, I discuss features of patent data that are not available for the historical patents but are available for modern patents.

Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks

Discovering and seizing technology opportunities is key to innovation at all levels. However, there are several open issues in the existing research into the discovery of technology opportunities, such as the insufficient specification of technology opportunities, defining the features of opportunities in a way that may lead to the exclusion of some valuable opportunities, and a lack of empirical support for evaluation criteria. This study proposes a new approach to technology opportunity discovery that attempts to address these issues. Our approach uses patents as a data source and constructs domain knowledge networks (DKNs) automatically based on the syntactic dependencies of technological words. We represent technology opportunities as connected sub-networks within DKNs and use a regression analysis of historical patents to obtain significant variables that affect the value of technology opportunities. These are then used to form an objective function for searching for and interpreting optimal opportunities. Ant colony optimization is applied to discover the optimal set of technology opportunities. The feasibility and effectiveness of the proposed approach are demonstrated by empirical research into a technology for measuring mechanical vibrations or sound waves by electromagnetic means.

Demand Shocks, Procurement Policies, and the Nature of Medical Innovation: Evidence from Wartime Prosthetic Device Patents

We analyze wartime prosthetic device patents to investigate how demand and procurement policy can shape medical innovation. We use machine learning tools to develop new data describing the aspects of medical and mechanical innovations that are emphasized in patent documents. Our analysis of historical patents yields three primary facts. First, we find that the U.S. Civil War and World War I led to substantial increases in the quantity of prosthetic device patenting relative to patenting in other medical and mechanical technology classes. Second, we find that the Civil War led inventors to focus broadly on improving aspects of the production process, while World War I did not, consistent with the United States applying a more cost-conscious procurement model during the Civil War. Third, we find that inventors emphasized dimensions of product quality (e.g., a prosthetic’s appearance or comfort) that aligned with differences in buyers’ preferences, as described in the historical record, across wars. We conclude that procurement environments can significantly shape the scientific problems with which inventors engage, including the choice to innovate on quality or cost.

Comparing Historical Patent Datasets

I provide a primer on six recent large-scale historical patent datasets for use in innovation research. I discuss how each dataset is constructed, the types of patent information included in each, and the quality and completeness of each. Throughout, I emphasize when our knowledge of the history of invention is dependent on the data source used and provide recommendations about which dataset is most likely to be best for different contexts. Overall, these datasets paint a remarkably consistent picture of the history of U.S. invention. When the datasets do disagree, these differences tend to be minor, although I highlight some important exceptions. I further describe several niche historical patent datasets that allow researchers to study institutional contexts that cannot be studied using modern patent data. Finally, I discuss features of patent data that are not available for the historical patents but are available for modern patents.

Patent analysis and classification prediction of biomedicine industry: SOM-KPCA-SVM model

This paper proposed the application of a combinatorial model of machine learning to patent quality classification and forecasting in the biomedical industry. The model consists of three methods: Self-Organizing Map (SOM), Kernel Principal Component Analysis (KPCA) and Support Vector Machine (SVM), and names it SOM-KPCA-SVM model. The model proposed in this paper is implemented in two steps. First, the SOM groups the patent data and defines the patent level. Second, the patent data is reduced by KPCA to decrease noise, and SVM is applied to KPCA’s patent data to derive the classification results. The study collected 11,251 biopharmaceutical patent data from the patent transaction news. After training the patent quality model, 2196 historical patents were used to verify the performance of the training model. The accuracy of the match between experimental results and actual transaction status reached 84.13%. Therefore, the proposed patent quality method as a preliminary screening solution automatically and effectively evaluates the quality of patents. This method saves valuable time for reviewing experts, facilitates the rapid identification of high-quality patents, and can be used for the development of commercialization and mass customization of products.

Technological Opportunity and the Locus of Innovation: Airmail, Aircraft, and Local Capabilities

This paper explores how innovation is jointly shaped by exposure to technological opportunities and local capabilities. We exploit a quasi-natural experiment, the establishment of the United States Post Office’s Airmail routes, to analyze how a new technological opportunity spurred innovations in aircraft technology between 1915 and 1935. Using a novel dataset of historical patents, we find that the introduction of an airmail route into a county results in a 93% increase in aircraft-related patents in that county, on average. We also explore the role of local capabilities and find that different types of local knowledge have a heterogeneous effect on patent quality. We find that treated areas with more users and tinkerers produce more low-quality patents, whereas treated areas with more specialists and workers with higher education produce more high- quality patents. This research contributes to existing literature by refining our understanding of how technological opportunities lead to advances in early- stage technologies.

Quality assurance and evaluation of change for patent metadata

ABSTRACTThe quality of metadata greatly impacts access to information resources, affecting both precision and recall of the search results. Metadata quality assurance is especially important for unique materials that are not widely available outside of the specialized digital repositories that provide access to them. One example of such unique and valuable materials is digitized historical patents. This paper describes a case study of the metadata management for a digital collection of U.S. patents issued in the 19th‐early 20th centuries, discusses the results of the evaluation of metadata change intended to support the metadata quality assurance for this collection and presents plans for further research. We expect the study to make a contribution to understanding of metadata management and metadata quality assurance and the role of metadata change in these processes.

From Ideas to Trade

This paper studies how cross-country differences in the allocation of technology affect bilateral trade. Do both the stock and dispersion of technology matter for exports? We build on Eaton and Kortum (2002) and develop a Ricardian model where the process of innovation determines both the stock and dispersion of technology in each country. Like in the EK model, a higher stock of technology and lower relative input costs foster exports. Unlike in the EK model, a country's overall comparative advantage depends on the country-specific dispersion of technology which governs the advantage of having lower input costs. In particular, a smaller technological dispersion benefits countries with lower input costs since trade is determined by these and not technological differences. The opposite is true for a high dispersion. To test our model we create a novel dataset of historical patents and use these to construct our technology variables. Our results confirm our model's predictions and indicate that technological innovation matters for trade through both the stock of patents and their dispersion across the economy.

Unveiling the geography of historical patents in the United States from 1836 to 1975.

It is clear that technology is a key driver of economic growth. Much less clear is where new technologies are produced and how the geography of U.S. invention has changed over the last two hundred years. Patent data report the geography, history, and technological characteristics of invention. However, those data have only recently become available in digital form and at the present time there exists no comprehensive dataset on the geography of knowledge production in the United States prior to 1975. The database presented in this paper unveils the geography of historical patents granted by the United States Patent and Trademark Office (USPTO) from 1836 to 1975. This historical dataset, HistPat, is constructed using digitalized records of original patent documents that are publicly available. We describe a methodological procedure that allows recovery of geographical information on patents from the digital records. HistPat can be used in different disciplines ranging from geography, economics, history, network science, and science and technology studies. Additionally, it is easily merged with post-1975 USPTO digital patent data to extend it until today.

Historical Patents as Inspiration for Digital Textile and Apparel Design

This research used historically informed analysis as a framework to examine studio design practice. Discovery of a historical patent for a coat made from a single pattern piece led to an examination of United States coat and jacket patents to identify past patternmaking explorations that could inform current design practice, with a focus on those with one pattern piece. These patterns allowed large expanses of cloth for surface design, especially for creation of engineered digital prints on a single canvas, and opened up avenues for creative experimentation. The research objectives were to analyze the patterns for shape and accuracy of fit, to evaluate them as a beginning point to inform future patternmaking and design approaches, to engineer digital prints for prototypes, and to create aesthetically appealing designs that could be produced with minimal fabric waste. Digital copies of the patterns were created, tested and compared, with a focus on evaluation of critical fit points and the potential for further creative development. Prototype designs were generated from the patent patterns, with some faithful to the original patent and others moving away from the patentee’s intentions, although keeping the pattern shape. Digital prints were then created and engineered for the patterns. This investigation also reoriented traditional apparel design practice by placing pattern manipulation at the beginning of the design process, and made design development contingent upon research objectives. At the same time it confirmed that research through design is not linear, and that studio practice also informed the objectives.

NonDestructive Evaluation of Carbon Fiber Reinforced Polymers with Ultrasonics and Infrared Thermography: An Overview on Historical Steps and Patents

The increasing use of composites in the transport industry and specifically in the aerospace field calls for nondestructive evaluation techniques for quality control during manufacturing and in service. Therefore, the availability of techniques which are effective to assess a material is free of slag inclusions, delamination and porosity is a crucial point. Of course, no technique is exhaustive alone; some are more effective for detection of surface discontinuities, others for outlining of buried shallow defects and others for detection of deeper anomalies. Then, an integration of two, or more, techniques may be the optimal solution especially for nondestructive evaluation of aerospace parts. The attention of this review is focused on two techniques: ultrasonic testing, which is commonly used, and infrared thermography, which was recently recognized amongst standards, for nondestructive testing and evaluation of composite materials in the aeronautical industry. For each of the two techniques, historical steps are traced with particular attention to the patents produced. In particular, the advantages coming from an integration of infrared thermography with ultrasonics and some other techniques are also discussed.

A patent quality analysis for innovative technology and product development

Enterprises evaluate intellectual property rights and the quality of patent documents in order to develop innovative products and discover state-of-the-art technology trends. The product technologies covered by patent claims are protected by law, and the quality of the patent insures against infringement by competitors while increasing the worth of the invention. Thus, patent quality analysis provides a means by which companies determine whether or not to customize and manufacture innovative products. Since patents provide significant financial protection for businesses, the number of patents filed is increasing at a fast pace. Companies which cannot process patent information or fail to protect their innovations by filing patents lose market competitiveness. Current patent research is needed to estimate the quality of patent documents. The purpose of this research is to improve the analysis and ranking of patent quality. The first step of the proposed methodology is to collect technology specific patents and to extract relevant patent quality performance indicators. The second step is to identify the key impact factors using principal component analysis. These factors are then used as the input parameters for a back-propagation neural network model. Patent transactions help judge patent quality and patents which are licensed or sold with intellectual property usage rights are considered high quality patents. This research collected 283 patents sold or licensed from the news of patent transactions and 116 patents which were unsold but belong to the technology specific domains of interest. After training the patent quality model, 36 historical patents are used to verify the performance of the trained model. The match between the analytical results and the actual trading status reached an 85% level of accuracy. Thus, the proposed patent quality methodology evaluates the quality of patents automatically and effectively as a preliminary screening solution. The approach saves domain experts valuable time targeting high value patents for R&D commercialization and mass customization of products.

Diamond Tools with Diamond Grits Set in a Predetermined Pattern

Although diamond tools have been used for over a century, the diamond grits distribution in the matrix is not uniform. This is because the large and light diamond grits tend to segregate from the small and heavy metal powder during the mixing process, hence diamond distribution in the diamond tools is intrinsically heterogeneous. As a result, the cutting performance of the diamond tools cannot be optimized. In 1997, Dr. James Chien-Min Sung applied two historical patents that can allow the design of diamond distribution according to a predetermined pattern. As the result, the life of diamond tools may be doubled; and the cutting speed, may also be twice as high. The three-dimensional saw segments with arrayed diamond grits were made back in 1999 with the improved performance as predicted. The Sung invention can allow the diamond tools industry to make ideal saw segment that has variable diamond size and diamond separation at different regions. Conventional diamond saws contain diamond grits that are distributed randomly in a metal matrix, as a result, their cutting speeds are slow and their sawing lives are short. In 1997, Dr. James C. Sung applied new patents that revealed revolutionary technology for making diamond tools with diamond grits set in a predetermined pattern. The diamond placement design was first appeared in a series of DiaGrid® products, such as wire saws and grinding wheels. In 1999, DiaGrid® pad conditioners was introduced and it has since become the world's standard for dressing pads, particularly those used for chemical mechanical planarization of semiconductor devices. In 2005, Shinhan adapted the idea and produced saw segments with diamond grits set in a predetermined pattern, their results confirmed that the sawing speed and the life were significantly improved over conventional designs.