Patent retrieval: a literature review <s> Patent quality assessment <s> The use of patents in economic research has been seriously hindered by the fact that patents vary enormously in their importance or value, and hence, simple patent counts cannot be informative about innovative output. The purpose of this article is to put forward patent counts weighted by citations as indicators of the value of innovations, thereby overcoming the limitations of simple counts. The empirical analysis of a particular innovation (Computed Tomography scanners) indeed shows a close association between citation-based patent indices and independent measures of the social value of innovations in that field. Moreover, the weighting scheme appears to be nonlinear (increasing) in the number of citations, implying that the informational content of citations rises at the margin. As in previous studies, simple patent counts are found to be highly correlated with contemporaneous RD however, here the association is within a field over time rather than cross-sectional. <s> BIB001 </s> Patent retrieval: a literature review <s> Patent quality assessment <s> Patent counts are very imperfect measures of innovative output. This paper discusses how additional data-the number of years a patent is renewed and the number of countries in which protection for the same invention is sought - can be used to improve on counts in studies which require a measure of the extent of innovation. A simple renewal based weighting scheme is proposed which may remove half of the noise in patent counts as a measure of innovative output. The paper also illustrates how these data can be used to estimate the value of the proprietary rights created by the patent laws. The parameters estimated in this analysis can be used to answer a series of questions related to the value of patents. We illustrate with estimates of how the value of patent protection would vary under alternative legal rules and renewal fees, and with estimates of the international flows of returns from the patent system. Recent progress in the development of databases has increased the potential for this type of analysis. <s> BIB002 </s> Patent retrieval: a literature review <s> Patent quality assessment <s> Through a survey, economic value estimates were obtained on 962 inventions made in the United States and Germany and on which German patent renewal fees were paid to full-term expiration in 1995. A search of subsequent U.S. and German patents yielded a count of citations to those patents. Patents renewed to full term were significantly more valuable than patents allowed to expire before their full term. The higher an invention's economic value estimate was, the more the relevant patent was subsequently cited. <s> BIB003 </s> Patent retrieval: a literature review <s> Patent quality assessment <s> We explore the usefulness of patent citations as a measure of the "importance" of a firm's patents, as indicated by the stock market valuation of the firm's intangible stock of knowledge. Using patents and citations for 1963--1995, we estimate Tobin's q equations on the ratios of R&D to assets stocks, patents to R&D, and citations to patents. We find that each ratio significantly affects market value, with an extra citation per patent boosting market value by 3%. Further findings indicate that "unpredictable" citations have a stronger effect than the predictable portion, and that self-citations are more valuable than external citations. <s> BIB004 </s> Patent retrieval: a literature review <s> Patent quality assessment <s> Intellectual Properties (IP), such as patents and trademarks, are one of the most critical assets in today’s enterprises and research organizations. They represent the core innovation and differentiators of an organization. When leveraged effectively, they not only protect a business from its competition, but also generate significant opportunities in licensing, execution, long term research and innovation. In certain industries, e. g., Pharmaceutical industry, patents lead to multi-billion dollar revenue per year. In this paper, we present a holistic information mining solution, called SIMPLE, which mines large corpus of patents and scientific literature for insights. Unlike much prior work that deals with specific aspects of analytics, SIMPLE is an integrated and end-to-end IP analytics solution which addresses a wide range of challenges in patent analytics such as the data complexity, scale, and nomenclature issues. It encompasses techniques for patent data processing and modeling, analytics algorithms, web interface and web services for analytics service delivery and end-user interaction. We use real-world case studies to demonstrate the effectiveness of SIMPLE. <s> BIB005 </s> Patent retrieval: a literature review <s> Patent quality assessment <s> In recent years, the number of patents filed by the business enterprises in the technology industry are growing rapidly, thus providing unprecedented opportunities for knowledge discovery in patent data. One important task in this regard is to employ data mining techniques to rank patents in terms of their potential to earn money through licensing. Availability of such ranking can substantially reduce enterprise IP (Intellectual Property) management costs. Unfortunately, the existing software systems in the IP domain do not address this task directly. Through our research, we build a patent ranking software, named COA (Claim Originality Analysis) that rates a patent based on its value by measuring the recency and the impact of the important phrases that appear in the "claims" section of a patent. Experiments show that COA produces meaningful ranking when comparing it with other indirect patent evaluation metrics--citation count, patent status, and attorney's rating. In reallife settings, this tool was used by beta-testers in the IBM IP department. Lawyers found it very useful in patent rating, specifically, in highlighting potentially valuable patents in a patent cluster. In this article, we describe the ranking techniques and system architecture of COA. We also present the results that validate its effectiveness. <s> BIB006 </s> Patent retrieval: a literature review <s> Patent quality assessment <s> The paper uses two hand-collected datasets to implement a novel research design for analyzing the precursors to patent quality. Operationalizing patent "quality" as legal validity, the paper analyzes the relation between Federal Circuit decisions on patent validity and three sets of data about the patents: quantitative features of the patents themselves, textual analysis of the patent documents, and data collected from the prosecution histories of the patents. The paper finds large and statistically significant relations between ex post validity and both textual features of the patents and ex ante aspects of the prosecution history (especially prior art submissions and the existence of internal patent office appeals before issuance). The results demonstrate the importance of refocusing analysis of patent quality on replicable indicators like validity, and the value that more comprehensive collection of prosecution history data can have for improving the output of the patent prosecution process. <s> BIB007 </s> Patent retrieval: a literature review <s> Patent quality assessment <s> There is a growing literature that aims at assessing the private value of knowledge assets and patents. It has been shown that patents and their quality as measured by citations received by future patents contribute significantly to the market value of firms beyond their R&D stocks. This paper goes one step further and distinguishes between different types of forward citations patents can receive at the European Patent Office. While a patent can be cited as non-infringing state of the art, it can also be cited because it threatens the novelty of patent applications ('blocking citations'). Empirical results from a market value model for a sample of large, R&D-intensive U.S., European and Japanese firms show that patents frequently cited as blocking references have a higher economic value for their owners than patents cited for nonblocking reasons. This finding adds to the patent value literature by showing that different types of patent citations carry different information on the economic value of patents. The result further suggests that the total number of forward citations can be an imprecise measure of patent value. <s> BIB008 </s> Patent retrieval: a literature review <s> Patent quality assessment <s> Patents are of crucial importance for businesses, because they provide legal protection for the invented techniques, processes or products. A patent can be held for up to 20 years. However, large maintenance fees need to be paid to keep it enforceable. If the patent is deemed not valuable, the owner may decide to abandon it by stopping paying the maintenance fees to reduce the cost. For large companies or organizations, making such decisions is difficult because too many patents need to be investigated. In this paper, we introduce the new patent mining problem of automatic patent maintenance prediction, and propose a systematic solution to analyze patents for recommending patent maintenance decision. We model the patents as a heterogeneous time-evolving information network and propose new patent features to build model for a ranked prediction on whether to maintain or abandon a patent. In addition, a network-based refinement approach is proposed to further improve the performance. We have conducted experiments on the large scale United States Patent and Trademark Office (USPTO) database which contains over four million granted patents. The results show that our technique can achieve high performance. <s> BIB009 </s> Patent retrieval: a literature review <s> Patent quality assessment <s> The number of patents filed each year has increased dramatically in recent years, raising concerns that patents of questionable validity are restricting the issuance of truly innovative patents. For this reason, there is a strong demand to develop an objective model to quantify patent quality and characterize the attributes that lead to higher-quality patents. In this paper, we develop a latent graphical model to infer patent quality from related measurements. In addition, we extract advanced lexical features via natural language processing techniques to capture the quality measures such as clarity of claims, originality, and importance of cited prior art. We demonstrate the effectiveness of our approach by validating its predictions with previous court decisions of litigated patents. <s> BIB010 </s> Patent retrieval: a literature review <s> Patent quality assessment <s> Current patent systems face a serious problem of declining quality of patents as the larger number of ap- plications make it difficult for patent officers to spend enough time for evaluating each application. For building a better patent system, it is necessary to define a public consensus on the quality of patent applications in a quantitative way. In this article, we tackle the problem of assessing the quality of patent applications based on machine learning and text mining techniques. For each patent application, our tool automatically computes a score called patentability, which indicates how likely it is that the application will be approved by the patent office. We employ a new statis- tical prediction model to estimate examination results (approval or rejection) based on a large data set including 0.3 million patent applications. The model computes the patentability score based on a set of feature variables including the text contents of the specification documents. Experimental results showed that our model outperforms a conven- tional method which uses only the structural properties of the documents. Since users can access the estimated result through a Web-browser-based GUI, this system allows both patent examiners and applicants to quickly detect weak applications and to find their specific flaws. <s> BIB011 </s> Patent retrieval: a literature review <s> Patent quality assessment <s> Patents are critical for a company to protect its core technologies. Effective patent mining in massive patent databases can provide companies with valuable insights to develop strategies for IP management and marketing. In this paper, we study a novel patent mining problem of automatically discovering core patents (i.e., patents with high novelty and influence in a domain). We address the unique patent vocabulary usage problem, which is not considered in traditional word-based statistical methods, and propose a topic-based temporal mining approach to quantify a patent's novelty and influence. Comprehensive experimental results on real-world patent portfolios show the effectiveness of our method. <s> BIB012 </s> Patent retrieval: a literature review <s> Patent quality assessment <s> Effective patent valuation is important for patent holders. Forward patent citations, widely used in assessing patent value, have been considered as reflecting knowledge flows, just like paper citations. However, patent citations also carry legal implication, which is important for patent valuation. We argue that patent citations can either be technological citations that indicate knowledge transfer or be legal citations that delimit the legal scope of citing patents. In this paper, we first develop citation-network based methods to infer patent quality measures at either the legal or technological dimension. Then we propose a probabilistic mixture approach to incorporate both the legal and technological dimensions in patent citations, and an iterative learning process that integrates a temporal decay function on legal citations, a probabilistic citation network based algorithm and a prediction model for patent valuation. We learn all the parameters together and use them for patent valuation. We demonstrate the effectiveness of our approach by using patent maintenance status as an indicator of patent value and discuss the insights we learned from this study. <s> BIB013
Assessing the technical quality and importance of inventions is very important to patent owners because it allows them to: -Better utilize their IP management costs by automated recommendation of patent maintenance decisions. -Better determine the novelty and originality of their patents. -Maximize licensing revenues by automatic estimation of the patent value. Because there is no ground truth for quality measurements, performance evaluation of quality assessment techniques is usually based on indicators such as correlation with patent forward citations, maintenance status history, court rulings (if any), and/or patent reexamination history (if any). Some early work scored patents using their metadata such as citations count, maintenance history, global prosecution efforts BIB002 , and even manually by patent attorneys. Automated patent quality assessment has gained more traction in recent years though. Citation analysis has been and still a main technique for patent valuation BIB008 BIB004 BIB003 BIB001 BIB013 . Wang et al. BIB013 proposed a probabilistic mixture approach to predict whether a topic patent will be renewed at different renewal periods. The method first divides the citations into two groups, technological and legal. From each group, different features reflecting the technological richness, technological influence, legal patent scope, and legal blocking power of each patent are combined. The authors subsequently built a binary classifier using these probabilistic features. Evaluation is performed by comparing the model's predictions against the renewal decisions of a collection of patents. While proved effective, estimating patent value as a binary outcome might not be practical, especially if a patent owner needs to prioritize his maintenance decisions of multiple patents. Quality assessment based on the lexical features of the patent text was also explored in the literature BIB011 BIB009 BIB010 . Liu et al. BIB010 proposed a graphical model to estimate patent quality as a latent variable. The model utilized lexical features extracted from the patent text such as claims n-grams age and popularity, lexical alignment between the claims and the description, number of dependent and independent claims, number of reported classes when filing the patent, and other features. The authors also incorporated measurements such as forward citations count, court decisions, and reexamination records. It is clear that court decisions are only available for small number of patents which might not allow building a robust model. Jin et al. BIB009 modeled the patent maintenance decision as recommendation problem where patents were represented as multi-modal heterogeneous information network. The model utilized several metadata features, lexical features such as unique words and lengths of different sections, as well as inventor and assignee profile features. Experimental results showed high prediction accuracy on a large number of USPTO patents. Hu et al. BIB012 proposed a time-based topic model which ranks patents novelty and influence based on whether the dominant topics in patent's prior art (for novelty) or forward art (for influence) are still active topics. The authors also proposed using time decay function to address the problem of old patents having less prior art and more forward art than newer patents and vice versa. Results showed high correlation between assigned ranks and forward citations count. Hido et al. BIB011 proposed a scoring model which assigned a patentability score to each patent and thus can be utilized to determine whether it will be granted. First, the authors extracted textual features such as word frequency, word age, and syntactic complexity (e.g., number of sentences). Then, they trained a classifier using previous patent office decisions as ground truth. Though results showed the model effectiveness, the utilized syntactic complexity features are all extracted from the topic patent and thus could be good predictors for the writing quality not patentability potential. The correlation between patent claims novelty and patent value using lexical analysis of patent text has been analyzed in previous studies BIB005 BIB006 . Hasan et al. BIB006 proposed an IRbased ranking tool which analyzes patent claims for originality. The technique first extracts key terms and phrases from the claims text using syntactic patterns and then looks for usage patterns backward to determine their novelty, and forward to determine their influence. The method considers usage patterns only through user-defined time window. It is also keywordbased and hence will fail to capture key phrases that are semantically similar and subsequently might give inaccurate scores. Along the efforts of using patent legal data for quality assessment, Mann and Underweiser BIB007 utilized prosecution histories, court decisions, and patent textual features to analyze patent quality. The analysis suggested that patent examination records would be very helpful in better discriminating high-quality patents from low-quality patents and possibly improve the examination process as a whole.