• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Modeling the dynamics of firms’technological impact?

    2021-12-22 06:51:54ShuqiXu徐舒琪ManuelSebastianMarianiandLinyuan呂琳媛
    Chinese Physics B 2021年12期

    Shuqi Xu(徐舒琪) Manuel Sebastian Mariani and Linyuan L¨u(呂琳媛)

    1Yangtze Delta Region Institute(Huzhou)&Institute of Fundamental and Frontier Sciences,University of Electronic Science and Technology of China,Huzhou 313001,China

    2University Research Priority Program on Social Networks,University of Zurich,CH-8050 Zurich,Switzerland

    3Beijing Computational Science Research Center,Beijing 100193,China

    Keywords: firm technological impact,patent analysis,impact dynamics

    1. Introduction

    Complexity science methods have been recently applied to understand the dynamics of impact along creative careers.[1–7]In particular, researchers have unveiled remarkable regularities through the quantitative analysis of individuals’ creative works (such as scientists’ articles and directors’ movies). These works found that the career trajectories of scientists,[1,8]artists,[8]directors,[6,8]and best-seller authors,[6,9]among others, are determined by a uniform pattern called random-impact rule: the most important works occur randomly within an individual’s work sequence. Sinatraet al.emphasized that the ability of an individual to produce high-impact works is independent of his/her career stage,[1]based on which they developed theQmodel,where the impact of an individual’s works is determined by a random factor,reflecting the potential of the idea,and a parameterQ,representing the individual’s capability to improve the work.According to the model,high-impact discoveries require a combination of an excellent idea and highQ. These methods and findings are mainly focused on individuals’creative careers,and they have not been extended to model the impact of organizations,such as firms.

    Existing evidence for firms suggests discrepancies among the impact dynamics of firms and individuals. Vast management literature acknowledged that firm age is an essential determinant of a firm’s impact.[10,11]And it is commonly recognized that young firms tend to present higher capability of innovation.[12,13]As a consequence, firm age might be negatively related to the impact of its technology. Recently,Xuet al.[14]provided initial evidence for systematic differences between temporal patterns of the highest-impact works in scientists’careers and firms’lifecycles.However,this work focused on the empirical evidence,and it did not propose a dynamical model to describe firms’impact evolution. How to modify existing impact dynamical models to accurately reproduce the observed firms’impact patterns?

    To answer this question, we analyze the patent licensing histories of worldwide firms from the U.S. Patent and Trademark Office(USPTO)and the European Patent Office(EPO).We propose a new dynamical model to capture the observed dynamic regularities. Specifically,by analyzing the evolution of average patent impact within firms’lifecycles, we observe a constantly decreasing trend, which indicates that firms tend to receive diminishing returns from their patents as they are issued more patents.We show that theQmodel is unable to capture firms’empirical impact patterns. Motivated by this finding,we generalize theQmodel by incorporating the property that patents’impact depends not only on the firm’s capability and the potential of the patent idea, but also on the patent’s position within the firm’s lifecycle. Therefore, we propose a time-varying returns model to reproduce the observed patterns of the dynamics of firms’impact. We show that for both two analyzed datasets, the proposed model matches the empirical results more accurately than the state-of-art random-impact model andQmodel. Our study sheds light on modeling the patterns of firms’impact dynamics,which can provide useful insights for managerial practices.

    2. Method

    2.1. Patent data of firms

    Patent activity is usually considered as a rich source to reflect firms’impact in terms of technology.[15–17]In this study,we investigate two large patent datasets: (i) The record of the patents granted by the EPO to 163565 firms from 1978 to 2016,covering 772127 patents. Note that the original data involves all types of applicants which are not limited to firms.In the analysis, we identify firm applicants by specific name identifiers, see Appendix A for details. (ii) The patent data from the USPTO to 7440 firms from 1926 to 2017,amounting to 2458402 patents.[18,19]Only patents whose applicant is a listed firm on the US stock market are included in the data.For each patent, the two datasets both include patent number,issuance date,names and ids of applicant firms,and a list of patent references. Therefore,we can identify the applicant firms of each patent, and based on the reference information,we can calculate the citation count of each patent at any given time. We summarize the main properties of the two studied datasets in Table 1.

    Table 1. Datasets description.

    EPO and USPTO are regarded as the most representative and highly reliable databases for patent analysis and have been used in many information science and cross-disciplinary studies.[20–23]Table 1 demonstrates that at the patent level,USPTO contains more patents than EPO. Kimet al.[20]argued that the United States is considered as one of the world’s largest commercial and technological markets,and for this reason, international firms tend to file patents in the USPTO to claim their rights to protect the innovation in the US. As for the EPO data, it contains rich information on technological innovations mostly related to firms located in European countries. At the firm level, the USPTO data involves fewer firms than the EPO data, because all firms in the USPTO data are limited to listed firms,whereas there is no such restriction for firms in the EPO data.

    We further show the cumulative distribution of the number of issued patents for firms in the two datasets in Fig.1(a).We observe that firms’ patenting activities is heavier in the USPTO than in the EPO. In the EPO data, 61.5% of firms have been issued only one patent, 93.9% of firms have been issued no more than 10 patents. For the USPTO data,there are only 13.44%of firms that have been issued one patent in total,82.34% of firms have been issued no more than 100 patents.One of the main reasons for this difference is that the listed firms usually have an advantage over ordinary firms in terms of resource and strength(for example,in terms of finance and innovation),which allows them to issue more patents.

    Table 2 reports the top five firms issued the largest number of patents in the two datasets. In the EPO data, the top firms are mostly from Japan,the United States,and European countries; as for the USPTO data, most of the top firms are from the United States and Japan.

    In the following, we only consider patents issued up to 2006. In this way, we have at least a 10-year time window to observe their impact. Furthermore, the research subjects of this study are firms, we need to focus on firms with sufficiently active patenting activities. For this reason, we limit our firm-level analysis to firms that have been issued at least 20 patents for the EPO data,which refers to 3548 firms,and at least 15 patents for the USPTO data,which filters 2819 firms(see Table 1). Our conclusions are robust with respect to alternative choices of the filter standard of analyzed firms (see Appendix C for the results of filtering firms based on their number of years of patenting activities).

    Table 2. Top firms by the number of issued patents. Time range refers to the first and last patent issuance year.

    Fig.1. Cumulative distribution of firms’patent number and technological impact. (a) Cumulative distribution of the number of issued patents for firms in the EPO and USPTO data. (b)–(c)Cumulative distribution of TI for firms in EPO(b)and USPTO(c)data,as well as the impact thresholds that distinguish the three firm groups of impact.

    2.2. Quantifying technological impact

    Patent’s technological impact. To evaluate the novelty of the innovation seeking patent protection, patent applicants are required to disclose the prior knowledge on which they relied,[24]represented by citations to prior patents. Citations are proposed by the patent applicant, and subsequently checked and corrected by the patent examiner.[25,26]Because of this, patents’ citation count is the most used metric for patents’ technological impact.[23,27]Despite its wide use, citation count suffers from a serious age bias by favoring older patents over more recent ones.[28–30]The main reason is that it takes time for a patent to accumulate citations, and the preferential attachment (also known as the “rich-get-richer”mechanism)[31]further amplifies discrepancies in the citation count of patents issued at different times.[32,33]The age bias makes it unreliable to directly compare the citation count of patents issued in different years.[34]To overcome this issue,following Ref. [14], we measure a given patent’s technological impact in terms of its normalized citation count (NC),which corresponds to its ranking by citation count when compared against all patents issued in the same year. In this way,NC ∈[0,1)close to one(zero)corresponds to high(low)impact. Normalized citation count can effectively suppress the age bias held by citation count,[14]thus allowing us to compare on the same scale the impact of patents of different ages.

    Fig.2. An illustration of two firms’lifecycles. Each dot represents a patent,and the x-axis refers to the issuance year of patents,y-axis refers to the technological impact(NC)of each patent,red dots denote breakthrough patents(patents with the highest NC). (a) Firm CANON’s lifecycle from the EPO data. (b)Firm APPLE’s lifecycle from the USPTO data.

    Firm’s technological impact and classification. Motivated by previous works,[14,35,36]a firm’s lifecycle is defined as a time-ordered sequence of its issued patents (see Fig. 2).And we define a given firm’s technological impact(TI)as the impact of its highest-impact patent, which we refer to as the firm’s “breakthrough”. Based on the technological impact of all analyzed firms,we classify firms into three groups:we consider the top 10%firms as high-impact firms,the bottom 30%firms as low-impact firms, and the remaining 60% firms as medium-impact firms, see Figs.1(b)and 1(c)for the classification details.

    2.3. Modeling impact dynamics

    To describe the temporal dynamics of the technological impact in firms’ patenting lifecycles, we resort to three models to reproduce patents’impact.

    State-of-the-art models and their shortcomings. We consider two models from previous works:[1]the randomimpact model and theQ-model. The random-impact model assumes that the impact of each of a firm’s issued patents is a random variable. Specifically,the model keeps a given firm’s total patent number unchanged, and it extracts the impact of each of the firm’s patents from the impact values of patents issued in that issuance year(without replacement). According to the random-impact model,the only difference between two firms is their patent productivity: a productive firm is more likely to have a breakthrough patent with high technological impact merely because of a larger number of attempts with the same likelihood to have high impact.

    TheQmodel assumes that each firm has an capability valueQ,which is unique to the firm and stable throughout the firm’s lifecycle.Qcaptures the capability of firms to improve(Q>1)or reduce(Q<1)the impact of a patent. Specifically,in the model, a firmαrandomly chooses a patent ideaiwith potential impactri, and it modulates the idea according to itsQ,resulting in a patent of impact,

    whereriis a patent-dependent random factor which is lognormal distributed with mean 1,identical for all firms.

    The above two models were both motivated by the empirical findings on scientists’careers,[1]in which the highestimpact works are randomly distributed among individuals’careers. For firms, the validity of the two models has not been examined.

    Time-varying returns model. In theQmodel, a firm’s early and late patents have the same probability to be the breakthrough patents, and the potential impact(r)of a patent idea itself and the firm’s capability (Q) are the only two factors that determine the impact of a patent,independently of the patent’s order within the applicant firm’s lifecycle. However,in management literature,many studies[12,13,37]have also uncovered the role of age played in firms’technological quality.These works argued that as firms become more experienced,the cost and benefits brought by innovation change,which suggests that firms may receive time-varying returns from their patents depending on the stage in their lifecycles. Inspired by these findings, based on theQmodel, we propose a timevarying returns model which assumes that a firm will enhance or diminish the potential impact of a patent based on its inherent ability combined with its age,quantified by its number of previously issued patents. In formulas, the impact of patentifrom firmαis modeled as

    whereΦis a time-varying function representing the influence of firm age on patent impact,ni ∈{1,...,Nα}is the position of patentiwithin the time-ordered sequence of firmα’s patents,andNαis the total patent number issued to firmα. In the next section,we will show that a monotonously decreasing function exhibits an accurate fit to the empirical data,which we refer to as a diminishing returns effect.

    3. Results

    3.1. Productivity and impact dynamics in firms’patenting lifecycles

    Fig. 3. Temporal dynamics of patent productivity and technological impact during firms’ lifecycles. The panels show the temporal trends of the total number of issued patents(a),(b)and cumulative technological impact(c),(d)for the three different groups of firms. Only firms with at least 20-year lifetime are involved in this analysis. Error bars refer to the standard error of the mean. The results show that there are sustained differences across the three impact groups of firms,and the growing trends of productivity and impact are well-described by sigmoidal and power-law functions,respectively,for both datasets. The fitting parameters are shown along the curves.

    3.2. Timing pattern of the firms’breakthrough patents

    We focus here on the recent finding uncovered by the recent study[14]that breakthrough patents (highest-impact patents)tend to occur early within a firm’s patenting lifecycle.This finding is revealed by the ratioN?/Nbetween the relative position of a firm’breakthrough patent,N?, and the total number of patents issued to the firm,N; a low (high) value of this ratio indicates that the breakthrough tends to be among the early(late)patents by the firm.[14]The results on the EPO and USPTO data are shown in Fig. 4. We find that the probability that a firm is issued its breakthrough patent is indeed not constant over time. A firm’s technological breakthrough is more likely to be among the firm’s earliest patents, regardless of firm’s group of impact,represented by an early peak ofP(N?/N). This is true for both the EPO and USPTO datasets;the same is not true for reshuffled lifecycles constructed from the original data by randomizing the temporal order of each firm’s patents (see the light lines in Fig. 4). These findings are consistent with the previous findings by Xuet al..[14]In addition, Fig. 4 reports that the peak for high-impact firms is slightly higher than that for the medium and low-impact firms.While through statistical tests,we find only the difference between the high-impact and low-impact firms in the USPTO data is significant,which demonstrates that the timing pattern of top-impact firms’breakthrough patents is similar to that of ordinary firms’ breakthroughs. So in the following, we will construct a unified model for all analyzed firms. In Table 3,we further provide specific impact values(TI)and relative positions of breakthrough patents(N?/N)for a few high-impact firms that are involved in both the EPO and USPTO data. For some of them,such as MOTOROLA MOBILITY and INTERNATIONAL BUSINESS MACHS, the breakthrough patents in the two datasets exhibit similar temporal positions. While there also exist firms that demonstrate gaps in terms of the timing of their breakthrough patents in the EPO and USPTO data,such as EASTMAN KODAK and EXXON MOBIL.This difference confirms firms’heterogeneity in terms of patenting activities in different patent offices, under which the uniform early timing has more credibility.

    The observed early peak ofP(N?/N) can find explanations from previous management literature which claimed that young firms tend to present a higher probability of innovation than mature ones.[12,13]The underlying reason could be that as firms age, benefits from technological progress diminish,[13]and therefore, patents from old firms are more likely to be the extension and improvement of their established technologies,[39]which tend to hold relatively less impact compared with those from new entrants.The result proves that patent impact is correlated with the patent’s temporal order among the applicant firm’s lifecycle, and provides initial evidence in favor of a time-varying mechanism,i.e.,firms will obtain varying returns from patents at different stages in their lifecycles. This is radically different from the random-impact phenomenon in scientific and artistic careers, for which the return is stable.

    Fig.4. Distribution of the temporal order of firms’highest-impact patents.We show the probability distribution of the relative order(N?/N)of firms’technological breakthroughs, and the same distribution for randomized patenting lifecycles (denoted by the light lines) for high-, medium-, and low-impact firms, for the EPO and USPTO data, respectively. The results represent the average of 200 times statistics(to take the“paralleling”breakthroughs with the same impact value into consideration) and randomized firms’ lifecycles. The early-stage peak of the distributions for all three groups of firms indicates that firms are more likely to achieve their highestimpact patent at the beginning of their patenting activity, contradicting the random-impact rule.

    3.3. Dynamical model of firms’technological impact

    To further understand and model the dynamics of firms’impact, we then analyze the average impact of patents issued to firms in different phases of lifecycles. In Fig. 5, we show patents’ average impact as a function of their temporal orderwithin firms’ lifecycles. We find an approximately linear decreasing trend, which indicates that firms tend to obtain diminishing returns from patents as they are issued more patents.Based on this finding,in the time-varying returns model shown in Eq.(2),we assume a linearly-decreasing function to model the phenomenon of the diminishing returns

    Table 3. The technological impact and timing to achieve the breakthrough patents of several high-impact firms. Here we select firms that have records in both the EPO and USPTO data.

    whereβandγare parameters to be estimated based on the empirical data. Whenβ=1,γ=0(no-varying scenario),the time-varying returns model reduces to theQ-model.

    Fig.5.Average impact of patents as a function of their temporal order within the applicant firms’lifecycle. We divide each firm’s issued patents into 20 equally-sized groups according to the patents’issuance date. By aggregating all the analyzed firms,each dot represents the average impact of firms’patents that belong to the corresponding group. For example, the first dot from the left represents the average impact of firms’earliest 5%patents. Error bars refer to the standard errors of the mean. The results show that for both the two datasets, there is a linearly decreasing trend of patent impact as the patent order increases(via linear fit),which suggests a monotonously decreasing function for the time-varying returns model.

    Assuming the proposed model, we firstly sum over the impact values of a firm’s all patents,and empirically estimate each firm’sQbased on Eq. (2) through the approximate formula

    whereIαis the total patent impact of firmα,Pαdenotes the set of patents issued to firmα. Subsequently, we generate each patent’s simulated impact according to Eq.(2),and compare the simulated temporal pattern of firms’ breakthrough patents(i.e.,the probability distribution ofN?/N)with the corresponding pattern in the empirical data.

    To calibrate the model parameters to the data, we apply a maximum likelihood approach. Specifically, we consider the Jensen Shannon divergence (DJS)[40]to measure the statistical “distance” between the observed temporal distribution,Pdata(N?/N), and the model-generated distribution,Pmodel(N?/N), which is the symmetric version of the Kullback–Leibler divergence.[1,41]It is defined as

    whereM=(Pdata+Pmodel)/2 is the mixed distribution. Then the likelihood function is defined as the fraction of modelgenerated distributions that are sufficiently close to the empirical one among 100 times simulations, i.e., the fraction of the simulated distributions such thatDJS<0.002 for the EPO data andDJS<0.005 for the USPTO data,in which the thresholds are chosen based on theDJSresults in each dataset. The threshold is a little larger concerning the USPTO data than the EPO data considering theDJSresults are overall larger for the former. The value of the likelihood function ranges from 0(no simulation fulfills the threshold) to 1 (all of the simulations fulfill the threshold). We choose the optimal parameter pair(?β,?γ)that maximizes the likelihood to generate a model whose temporal pattern is sufficiently close to the empirical one. The results are shown in Figs.6(a)and 6(b),in which the color of each box refers to the value of the likelihood function.Figures 6(a) and 6(b) demonstrate that the optimal combination of parameters(denoted by the box with green contour)is ?β=0.9, ?γ=0.06 for the EPO data, and ?β=0.9, ?γ=0.08 for the USPTO data, which are similar to the slope of the fitted line in Fig. 5. We also observe that theQmodel corresponding to the box with pink contour achieves 0 in terms of the likelihood on both datasets,namely,none of the 100 times simulations fulfills the related thresholds. This reveals that to accurately model firms’ impact dynamics, the role of the diminishing returns effect cannot be ignored.

    Fig. 6. Comparison of the empirical and model-based temporal patterns of firms’ breakthrough patents. (a), (b) The likelihood that the timing distribution of breakthrough patents(P(N?/N))generated by the time-varying returns model is sufficiently close to the empirical one(see Subsection 3.3 for details)among 100 times simulations for different parameter combinations. The parameter pairs that lead to the maximum likelihood are ?β =0.9, ?γ =0.06 for the EPO data (a), and ?β =0.9, ?γ =0.08 for the USPTO data (b), which are corresponding to the boxes with green contour. The Q model(corresponding to the box denoted by the pink contour)achieves the lowest likelihood. (c),(d)A comparison of the empirical and model-based distributions P(≤N?/N)of the breakthrough patents’temporal positions of firms. We consider two state-of-art models and the proposed time-varying returns model,which uses the optimal parameter pairs as mentioned above. The results indicate that the empirical results in the two datasets are well reproduced by the time-varying returns model.

    Figures 6(c) and 6(d) show the fitting performance of the proposed model with the optimal parameters against the random-impact model and theQmodel for the EPO and USPTO datasets. The result reveals that the proposed timevarying returns model can match the empirical curve much more accurately. While the random-impact model andQmodel assume that a patent’s impact does not depend on its temporal position among its applicant firm’s patents,thus fail to generate the concave timing distribution of breakthrough patents observed in the empirical data. The results further confirm the discrepancy of the timing pattern of the highestimpact works for firms and individuals(such as scientists and artists[1,6,8]). Besides, we also notice that although our proposed model outperforms the random-impact model andQmodel by a wide margin, it cannot fit the empirical results perfectly, especially for the USPTO data. This could be due to the heterogeneity in firms’impact dynamics and model parameters,which will be validated in future works.

    4. Conclusion and discussion

    In this work, we modeled the evolution mechanism of firms’ technological impact within lifecycles. The USPTO and EPO patenting history data of applicant firms on a long timescale enable us to quantitatively trace and analyze the dynamic patterns of diverse firms worldwide. By treating each firm as a time-ordered sequence of its issued patents,we quantified the technological impact of their patents through a timenormalized citation measure, based on which we studied the dynamics of firms’ patent productivity, technological impact,and the timing pattern of the highest-impact patents. There are three main takeaways from this work.Firstly,we provided evidence that firms tend to have their technological breakthroughs at the early stage within their lifecycles, which contradicts the random-impact rule that governing individual careers,and confirms previous findings.[14]Secondly,we found that the decrease of firms’average patent impact along with the patenting lifecycles is accurately matched by a linear function. Thirdly,most importantly, inspired by these findings, we proposed a time-varying returns model, which is proved to capture the temporal distributions of firms’ technological breakthroughs more accurately than two state-of-art dynamical models.

    A potential limitation of our approach is that our analysis assumes that all firms follow a uniform pattern. Yet different firms might have different trajectories, and the “average” behavior might not hold for all of them. Thus, our results could be not informative for specific types of firms. In future research,we will explore multiple patterns held by distinct types and industries of firms. Identifying drivers of the temporal position of firms’technological breakthroughs is also worth further research: The proposed model assumes that the time-varying functionΦis given, which leads to the important problem of identifying its determinants at the firm level.Besides,incorporating our insights into real-world investment and policy decisions would need careful integration with a variety of signals beyond patent analysis.Addressing these questions will help us identify the best path for firm development and further catch a glimpse of the secret of business success.

    To conclude,this paper enriches the understanding of firm impact dynamics.It provides the first step towards new models to describe the observed,firm-specific empirical patterns. The results can offer insights into technological innovation management for firms.

    Acknowledgment

    We thank Luciano Pietronero, Emanuele Pugliese, and Andrea Zaccaria for providing us with the EPO patent data and enlightening discussions.

    Appendix A: Filtering firm applicants in the

    EPO data

    As the research target in this paper is firm and not all applicants involved in the EPO data are firms,we remove some of the patent data that can be sure not assigned to firms. In detail,we identify four kinds of applicants based on their names and the information of their patents’inventors according to the following rules. (i) Firm applicants. If an applicant has the firm-related strings (refer to the first row in Table A1) in its name,this applicant is identified as a firm. (ii)Individual applicants. For each patent issued to a given applicant,if all its inventors also appear in the assignee field,and the applicant’s name does not contain any firm-related string,this applicant is identified as an individual applicant.[42](iii)Educational applicants. If the applicant does not have any firm-related string in its name but has the education-related strings (refer to the second row in Table A1), it is considered as an educational institution. (iv)Other applicants. If none of the above conditions is satisfied,the applicant belongs to this set. To exclude the patent data that is irrelevant to firms,we remove the patents issued to individual applicants and educational applicants,and leave the remaining data for the study. In total, we exclude 15.5%applicants and 4.3%patents,note that few of such applicants have larger than 20 patents, so the filtering has little influence on our analysis.

    Table A1. The identification strings in applicants’names that we apply to filter applicants in the EPO data.

    Fig.B1. Distribution of the parameter values(b and η)in the fitting of productivity and impact dynamics(see Fig.3)for firms in the(a),(c)EPO and(b),(d)USPTO data.

    As for the USPTO data,in view of Koganet al.[18,19]have made the extraction based on the Center for Research in Security Prices(CRSP)database, we do not implement any applicant filter on this data.

    Appendix B: Distribution of the fitting parameters in Fig.3

    Based on Fig. 3, we further show the distribution of the fitting parameters of the productivity growth and impact growth for the three groups of firms in Fig. B1. The results reveal that in terms of productivity, the development of high-impact firms (〈bhigh〉=0.33 in EPO,〈bhigh〉=0.24 in USPTO)is faster than that of low-impact firms(〈blow〉=0.36 in EPO,〈blow〉= 0.31 in USPTO). In terms of technological impact, for low-impact firms, the growth is steady, with〈ηlow〉=0.88 in EPO, and〈ηlow〉=0.82 in USPTO.The increase of other firms is much faster, for whom the averageηis bigger,see Fig.B1.

    Appendix C: Robustness check of the main results

    In the main text, we consider firms that have been issued at least a given number of patents. To guarantee the robustness of our main results, we further add analysis under a different filter criterion for firms in both datasets, i.e.,the analyzed firms need to have long-lasting active patenting activities.[1,5,14]Specifically, for EPO data, we only consider firms that have at least 10-year patenting activity and have been issued to at least one patent every two years,which ends up with 1495 firms; for USPTO data, firms need to have at least 15-year patenting activity and have been issued to at least one patent every three years,involving 1007 firms. The main results under the new filter criterion are shown in Fig. C1,which demonstrate the robustness of our results.

    Fig. C1. Robustness check of the main results, in which we restrict firms’ patenting activities to be long-lasting and active. The results are consistent with the conclusions in the main text. (a), (b) Distribution of the temporal order of firms’ highest-impact patents, which is similar to Fig. 4. (c), (d) Comparison of the empirical and model-based temporal patterns of firms’ breakthrough patents, which is similar to Fig. 6. The optimal parameters for the EPO data are ?β =0.8, ?γ =0.06,and for the USPTO data,they are ?β =0.9, ?γ =0.08.

    欧美精品人与动牲交sv欧美| 日韩av不卡免费在线播放| 国产男女超爽视频在线观看| 午夜福利在线免费观看网站| 精品人妻熟女毛片av久久网站| 免费在线观看完整版高清| 不卡视频在线观看欧美| 亚洲激情五月婷婷啪啪| 国产精品人妻久久久影院| videossex国产| 超碰97精品在线观看| 国产午夜精品一二区理论片| 色94色欧美一区二区| 丝袜人妻中文字幕| 国产亚洲午夜精品一区二区久久| 国产精品嫩草影院av在线观看| 女人高潮潮喷娇喘18禁视频| 成人亚洲精品一区在线观看| 免费在线观看黄色视频的| 黄片播放在线免费| 久久精品夜色国产| 一区二区日韩欧美中文字幕| 亚洲av欧美aⅴ国产| av又黄又爽大尺度在线免费看| 精品久久久精品久久久| 国产乱人偷精品视频| 亚洲中文av在线| av免费观看日本| 久久人人爽av亚洲精品天堂| 久久久精品区二区三区| 亚洲,欧美精品.| 成人毛片60女人毛片免费| 一本色道久久久久久精品综合| 好男人视频免费观看在线| 亚洲精品国产av蜜桃| 91精品三级在线观看| 亚洲精品,欧美精品| av天堂久久9| 少妇人妻精品综合一区二区| 国产午夜精品一二区理论片| 亚洲精品久久久久久婷婷小说| 高清av免费在线| 亚洲国产色片| 日韩精品免费视频一区二区三区| 亚洲精品aⅴ在线观看| 又粗又硬又长又爽又黄的视频| 在现免费观看毛片| 国产xxxxx性猛交| 亚洲欧美精品自产自拍| av又黄又爽大尺度在线免费看| 高清欧美精品videossex| 涩涩av久久男人的天堂| 你懂的网址亚洲精品在线观看| 七月丁香在线播放| 色播在线永久视频| 国产日韩欧美视频二区| 丰满饥渴人妻一区二区三| av线在线观看网站| 九九爱精品视频在线观看| 青春草视频在线免费观看| 汤姆久久久久久久影院中文字幕| 日韩av在线免费看完整版不卡| 一区二区三区精品91| 日本午夜av视频| 国产国语露脸激情在线看| 99久久中文字幕三级久久日本| 有码 亚洲区| 丰满迷人的少妇在线观看| av国产精品久久久久影院| 高清欧美精品videossex| 亚洲精品成人av观看孕妇| 自线自在国产av| 男女无遮挡免费网站观看| 亚洲在久久综合| 在线看a的网站| 69精品国产乱码久久久| 多毛熟女@视频| 亚洲五月色婷婷综合| 免费看不卡的av| 久久99精品国语久久久| xxxhd国产人妻xxx| 精品99又大又爽又粗少妇毛片| 性高湖久久久久久久久免费观看| 久久这里只有精品19| 人妻人人澡人人爽人人| 精品国产一区二区三区四区第35| 天天操日日干夜夜撸| 亚洲精品国产一区二区精华液| 免费日韩欧美在线观看| 巨乳人妻的诱惑在线观看| 欧美精品一区二区大全| 丰满乱子伦码专区| 丰满饥渴人妻一区二区三| 老汉色av国产亚洲站长工具| 国产国语露脸激情在线看| 色播在线永久视频| 天天影视国产精品| 美女大奶头黄色视频| 亚洲欧洲精品一区二区精品久久久 | 亚洲精品视频女| 成人影院久久| 老汉色∧v一级毛片| www.av在线官网国产| 久久久久精品久久久久真实原创| 欧美日韩国产mv在线观看视频| 精品亚洲成国产av| 高清黄色对白视频在线免费看| 国产精品 欧美亚洲| 人妻系列 视频| 人人妻人人爽人人添夜夜欢视频| 99热国产这里只有精品6| videossex国产| 巨乳人妻的诱惑在线观看| 久久久亚洲精品成人影院| 中国国产av一级| 国产一级毛片在线| 波野结衣二区三区在线| 精品久久蜜臀av无| 侵犯人妻中文字幕一二三四区| 亚洲精品一二三| 一区在线观看完整版| 美女福利国产在线| 国产精品久久久av美女十八| 亚洲精品视频女| 日本爱情动作片www.在线观看| 午夜精品国产一区二区电影| 熟女电影av网| 精品午夜福利在线看| 国产黄色视频一区二区在线观看| 熟女av电影| 久久精品久久久久久噜噜老黄| 丰满少妇做爰视频| 国产精品香港三级国产av潘金莲 | 黄色配什么色好看| 18禁裸乳无遮挡动漫免费视频| av女优亚洲男人天堂| 亚洲精品一二三| 国产欧美日韩综合在线一区二区| 99久国产av精品国产电影| 两个人免费观看高清视频| 满18在线观看网站| 中文精品一卡2卡3卡4更新| 亚洲av在线观看美女高潮| 欧美人与善性xxx| a级毛片在线看网站| 一级毛片 在线播放| 亚洲精品国产av成人精品| 丁香六月天网| 看免费成人av毛片| 亚洲精品,欧美精品| 91久久精品国产一区二区三区| 欧美 亚洲 国产 日韩一| 国产 精品1| 精品午夜福利在线看| 精品国产国语对白av| 亚洲图色成人| 波野结衣二区三区在线| 视频区图区小说| 2018国产大陆天天弄谢| av在线播放精品| 久久久久久人人人人人| 黄片小视频在线播放| 大香蕉久久网| 一区在线观看完整版| 人人妻人人添人人爽欧美一区卜| 婷婷色av中文字幕| 美女高潮到喷水免费观看| av片东京热男人的天堂| 99久久精品国产国产毛片| 精品一区二区三卡| 最近最新中文字幕大全免费视频 | 1024香蕉在线观看| 国产黄色视频一区二区在线观看| 国产在线一区二区三区精| 精品国产乱码久久久久久男人| 欧美日韩视频精品一区| 高清av免费在线| 少妇猛男粗大的猛烈进出视频| 丝袜在线中文字幕| 亚洲人成网站在线观看播放| 制服丝袜香蕉在线| 精品午夜福利在线看| 亚洲欧美精品综合一区二区三区 | 国产熟女午夜一区二区三区| 高清不卡的av网站| 成年av动漫网址| 久久人妻熟女aⅴ| 777米奇影视久久| 久久精品国产综合久久久| 亚洲少妇的诱惑av| 岛国毛片在线播放| av在线老鸭窝| 久久国产精品大桥未久av| 国产黄频视频在线观看| 欧美精品av麻豆av| 亚洲欧美一区二区三区国产| 少妇被粗大的猛进出69影院| 久久精品人人爽人人爽视色| 欧美日韩精品成人综合77777| 亚洲欧洲精品一区二区精品久久久 | 亚洲精品久久久久久婷婷小说| 激情五月婷婷亚洲| 免费看av在线观看网站| 18+在线观看网站| 国产 一区精品| 欧美日韩视频高清一区二区三区二| 亚洲欧美一区二区三区国产| 一区二区日韩欧美中文字幕| 久久国产精品大桥未久av| 日韩中文字幕视频在线看片| 99久久综合免费| 久久精品国产亚洲av天美| 午夜福利一区二区在线看| 成年av动漫网址| 中文字幕人妻丝袜制服| 午夜久久久在线观看| 成年人午夜在线观看视频| 久久这里有精品视频免费| 视频在线观看一区二区三区| 久久午夜综合久久蜜桃| 欧美 日韩 精品 国产| 亚洲av电影在线进入| 日韩制服骚丝袜av| 日韩欧美精品免费久久| 免费黄频网站在线观看国产| 日本-黄色视频高清免费观看| 国产国语露脸激情在线看| 97在线视频观看| 中文字幕亚洲精品专区| 黄色配什么色好看| 人人妻人人澡人人看| 一本久久精品| 国产一区有黄有色的免费视频| 午夜福利网站1000一区二区三区| 欧美中文综合在线视频| 久久久久久免费高清国产稀缺| 久久精品国产亚洲av涩爱| 99久久中文字幕三级久久日本| 春色校园在线视频观看| 日韩电影二区| 亚洲美女黄色视频免费看| 国产成人免费无遮挡视频| 一个人免费看片子| 久久精品国产亚洲av高清一级| 爱豆传媒免费全集在线观看| 中文乱码字字幕精品一区二区三区| 亚洲精品久久成人aⅴ小说| 午夜日本视频在线| 亚洲国产精品一区三区| 久久久久久久国产电影| 最近中文字幕2019免费版| 国产成人精品婷婷| 人妻系列 视频| 亚洲男人天堂网一区| 亚洲精品中文字幕在线视频| 另类亚洲欧美激情| 大香蕉久久网| 亚洲av欧美aⅴ国产| 欧美中文综合在线视频| 曰老女人黄片| 久久韩国三级中文字幕| 三级国产精品片| 亚洲,欧美精品.| 欧美日韩精品成人综合77777| 日本爱情动作片www.在线观看| 久久久久久久国产电影| 久久久久久久久久人人人人人人| 日本vs欧美在线观看视频| 欧美日韩国产mv在线观看视频| 国产一区有黄有色的免费视频| 男的添女的下面高潮视频| 亚洲欧洲精品一区二区精品久久久 | 欧美精品一区二区大全| 亚洲精品国产av成人精品| 黄色一级大片看看| 色视频在线一区二区三区| 久热这里只有精品99| 性少妇av在线| 久久久久久人人人人人| 久久精品亚洲av国产电影网| 国产 精品1| 国产有黄有色有爽视频| 啦啦啦视频在线资源免费观看| 日韩在线高清观看一区二区三区| 国产亚洲午夜精品一区二区久久| 日本欧美视频一区| 久久久精品国产亚洲av高清涩受| 婷婷成人精品国产| 2022亚洲国产成人精品| 亚洲欧美中文字幕日韩二区| 久久久久久久久久人人人人人人| 高清不卡的av网站| 十八禁网站网址无遮挡| 久久久久人妻精品一区果冻| 五月伊人婷婷丁香| 在线观看www视频免费| 美女大奶头黄色视频| 亚洲国产最新在线播放| 日韩,欧美,国产一区二区三区| 久久久久人妻精品一区果冻| 日韩电影二区| 久久久久久伊人网av| 少妇人妻精品综合一区二区| av有码第一页| 亚洲伊人久久精品综合| videossex国产| 男女午夜视频在线观看| 日韩,欧美,国产一区二区三区| 波多野结衣一区麻豆| 国产av国产精品国产| 在线亚洲精品国产二区图片欧美| 香蕉丝袜av| 国产日韩一区二区三区精品不卡| 大片免费播放器 马上看| 精品人妻在线不人妻| 精品人妻在线不人妻| 美女国产视频在线观看| 中文字幕精品免费在线观看视频| 国产毛片在线视频| 久久久精品国产亚洲av高清涩受| 少妇猛男粗大的猛烈进出视频| 欧美在线黄色| 波多野结衣一区麻豆| 日本欧美国产在线视频| 中文字幕人妻丝袜一区二区 | 亚洲国产av新网站| 一二三四在线观看免费中文在| 90打野战视频偷拍视频| 黑人猛操日本美女一级片| 乱人伦中国视频| 这个男人来自地球电影免费观看 | 下体分泌物呈黄色| 亚洲,欧美精品.| 国产一区亚洲一区在线观看| 麻豆精品久久久久久蜜桃| 成人亚洲精品一区在线观看| 色哟哟·www| 久久人人97超碰香蕉20202| 人成视频在线观看免费观看| 精品少妇久久久久久888优播| 国产免费又黄又爽又色| 美女主播在线视频| 亚洲国产色片| 岛国毛片在线播放| 国产亚洲欧美精品永久| 丝袜在线中文字幕| 最近最新中文字幕大全免费视频 | 欧美亚洲 丝袜 人妻 在线| 国产免费一区二区三区四区乱码| 亚洲人成网站在线观看播放| 亚洲av成人精品一二三区| 少妇人妻精品综合一区二区| 欧美最新免费一区二区三区| 免费av中文字幕在线| 久久精品国产a三级三级三级| 亚洲精品日韩在线中文字幕| 精品久久久精品久久久| 久久亚洲国产成人精品v| 中国三级夫妇交换| 青春草国产在线视频| 丝袜美腿诱惑在线| www.精华液| 老熟女久久久| 超色免费av| 黄色 视频免费看| 精品一区二区三卡| 汤姆久久久久久久影院中文字幕| videossex国产| 蜜桃在线观看..| 日韩一卡2卡3卡4卡2021年| 少妇 在线观看| 色94色欧美一区二区| av一本久久久久| 国产深夜福利视频在线观看| 男女啪啪激烈高潮av片| 国产精品久久久久久av不卡| 久热这里只有精品99| 最新中文字幕久久久久| 最近2019中文字幕mv第一页| 人人妻人人澡人人爽人人夜夜| 国产精品三级大全| 婷婷成人精品国产| 边亲边吃奶的免费视频| 一区二区三区四区激情视频| 日韩电影二区| 久久这里只有精品19| 亚洲五月色婷婷综合| 欧美最新免费一区二区三区| 国产视频首页在线观看| 在线观看人妻少妇| 一级片免费观看大全| 久久国产亚洲av麻豆专区| 一区二区日韩欧美中文字幕| 大码成人一级视频| 亚洲伊人色综图| 成人手机av| 不卡av一区二区三区| 日本黄色日本黄色录像| xxx大片免费视频| 久久精品国产a三级三级三级| 国产黄频视频在线观看| 丝袜美足系列| 亚洲男人天堂网一区| 午夜久久久在线观看| 涩涩av久久男人的天堂| 波多野结衣一区麻豆| 免费黄色在线免费观看| 肉色欧美久久久久久久蜜桃| 人人妻人人添人人爽欧美一区卜| 人妻 亚洲 视频| 美女国产视频在线观看| 久久久精品区二区三区| 国产成人精品久久二区二区91 | 欧美精品一区二区免费开放| 国产精品嫩草影院av在线观看| 女人久久www免费人成看片| 中文字幕最新亚洲高清| 日本欧美视频一区| 久久这里只有精品19| 亚洲成人一二三区av| 边亲边吃奶的免费视频| 一级毛片我不卡| 美女高潮到喷水免费观看| 青春草亚洲视频在线观看| 在线观看一区二区三区激情| 亚洲美女视频黄频| 超碰97精品在线观看| 日韩av不卡免费在线播放| 高清欧美精品videossex| 免费大片黄手机在线观看| 成人影院久久| 成年女人在线观看亚洲视频| 美女国产视频在线观看| 国产精品久久久久久精品古装| 性少妇av在线| 欧美成人午夜精品| 人人妻人人澡人人爽人人夜夜| 中国国产av一级| 在线观看免费视频网站a站| 日韩中文字幕视频在线看片| 一级a爱视频在线免费观看| 少妇的逼水好多| 美女主播在线视频| 国产精品嫩草影院av在线观看| 尾随美女入室| 成人国语在线视频| 又黄又粗又硬又大视频| 亚洲精品在线美女| 亚洲精品aⅴ在线观看| 美国免费a级毛片| 亚洲中文av在线| 天美传媒精品一区二区| 亚洲人成网站在线观看播放| 国产成人精品婷婷| 新久久久久国产一级毛片| 欧美bdsm另类| 老汉色∧v一级毛片| 国产黄色免费在线视频| 成人漫画全彩无遮挡| 精品一品国产午夜福利视频| 国产精品亚洲av一区麻豆 | 国产 精品1| 国产成人精品久久二区二区91 | 中文字幕最新亚洲高清| 国产免费又黄又爽又色| 日韩大片免费观看网站| 新久久久久国产一级毛片| 日韩不卡一区二区三区视频在线| 欧美亚洲日本最大视频资源| 久久精品亚洲av国产电影网| 少妇人妻精品综合一区二区| 伊人亚洲综合成人网| av不卡在线播放| av.在线天堂| 91精品伊人久久大香线蕉| 亚洲中文av在线| 亚洲 欧美一区二区三区| 人人妻人人爽人人添夜夜欢视频| 国产精品一区二区在线不卡| 国产午夜精品一二区理论片| 一边亲一边摸免费视频| 亚洲欧美色中文字幕在线| 中国三级夫妇交换| 伊人久久国产一区二区| 如日韩欧美国产精品一区二区三区| 一本久久精品| 日韩熟女老妇一区二区性免费视频| xxxhd国产人妻xxx| 久久精品国产亚洲av涩爱| 亚洲国产精品一区三区| 国产精品国产av在线观看| 欧美少妇被猛烈插入视频| 日韩精品免费视频一区二区三区| 亚洲av福利一区| 精品亚洲成a人片在线观看| 精品国产一区二区久久| 久久久久久人妻| 人人妻人人澡人人看| 久久久久久人妻| 国产国语露脸激情在线看| 18禁动态无遮挡网站| 国产 一区精品| 妹子高潮喷水视频| av免费观看日本| 中文字幕亚洲精品专区| 少妇被粗大猛烈的视频| 三上悠亚av全集在线观看| 久久精品国产自在天天线| 亚洲美女搞黄在线观看| 亚洲国产av影院在线观看| 亚洲精品一区蜜桃| 亚洲av.av天堂| 91成人精品电影| 亚洲av.av天堂| 老女人水多毛片| 日韩中文字幕视频在线看片| 日本欧美视频一区| 国产男女内射视频| 在线观看免费视频网站a站| 夫妻午夜视频| 国产av一区二区精品久久| 亚洲精品国产色婷婷电影| 日韩视频在线欧美| 欧美bdsm另类| 一级a爱视频在线免费观看| 久久国产精品大桥未久av| 丰满迷人的少妇在线观看| 日韩视频在线欧美| 久久精品久久久久久噜噜老黄| 亚洲国产色片| 国产免费现黄频在线看| 亚洲综合色惰| 天天躁日日躁夜夜躁夜夜| av视频免费观看在线观看| 欧美精品av麻豆av| 国产精品香港三级国产av潘金莲 | 99re6热这里在线精品视频| 国产片内射在线| 高清视频免费观看一区二区| 黄色怎么调成土黄色| 国产精品二区激情视频| 国产精品一区二区在线不卡| 亚洲欧美成人精品一区二区| 在线观看三级黄色| 成年动漫av网址| 国产精品免费大片| av在线app专区| 一级毛片 在线播放| 丝袜喷水一区| av又黄又爽大尺度在线免费看| 亚洲精品久久成人aⅴ小说| 伦理电影免费视频| 国产成人午夜福利电影在线观看| 9色porny在线观看| 看免费成人av毛片| 美女福利国产在线| 美女脱内裤让男人舔精品视频| 色婷婷久久久亚洲欧美| 日韩人妻精品一区2区三区| 在线免费观看不下载黄p国产| 欧美亚洲 丝袜 人妻 在线| 婷婷成人精品国产| 又大又黄又爽视频免费| 日本91视频免费播放| 一级片'在线观看视频| 国产午夜精品一二区理论片| 国产成人av激情在线播放| 黄片小视频在线播放| 国产av国产精品国产| 国产精品av久久久久免费| 国产精品熟女久久久久浪| 欧美精品一区二区大全| 97精品久久久久久久久久精品| 如日韩欧美国产精品一区二区三区| 熟妇人妻不卡中文字幕| 精品一区二区免费观看| 男人添女人高潮全过程视频| 激情五月婷婷亚洲| 中文字幕人妻丝袜一区二区 | 一本久久精品| 母亲3免费完整高清在线观看 | 大香蕉久久网| 日韩免费高清中文字幕av| 亚洲精品aⅴ在线观看| 欧美成人午夜精品| 亚洲美女黄色视频免费看| 18禁观看日本| 99热网站在线观看| 免费少妇av软件| 伦理电影免费视频| 国产在线视频一区二区| 嫩草影院入口| 国产成人精品久久久久久| 一二三四在线观看免费中文在| 成人手机av| 十八禁高潮呻吟视频| 国产激情久久老熟女| 国产不卡av网站在线观看| 日日啪夜夜爽| 七月丁香在线播放| 巨乳人妻的诱惑在线观看| 91久久精品国产一区二区三区| 国产在线视频一区二区| 性色avwww在线观看| 人成视频在线观看免费观看| 少妇熟女欧美另类| 久久精品国产自在天天线| 午夜福利视频在线观看免费| 91久久精品国产一区二区三区| 日本黄色日本黄色录像| 天天躁日日躁夜夜躁夜夜| 母亲3免费完整高清在线观看 | 久久鲁丝午夜福利片| av国产久精品久网站免费入址| 国产女主播在线喷水免费视频网站|