CN108764363A - A kind of patented technology Life Cycle Analysis - Google Patents

A kind of patented technology Life Cycle Analysis Download PDF

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CN108764363A
CN108764363A CN201810570771.2A CN201810570771A CN108764363A CN 108764363 A CN108764363 A CN 108764363A CN 201810570771 A CN201810570771 A CN 201810570771A CN 108764363 A CN108764363 A CN 108764363A
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principal component
life cycle
analysis
data
technology life
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郎利影
康爱红
张永健
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Hebei University of Engineering
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques

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Abstract

The invention belongs to patent analysis technical fields, relate more specifically to a kind of patented technology Life Cycle Analysis being combined based on constituent analysis and K arest neighbors.The sorting technique is in such a way that feature extraction and sorting algorithm are combined, devise the grader based on principal component analysis and nearest neighbor algorithm, to multiple index analysis of technology life cycle, effective judgment technology degree of innovation, to integrated forecasting technology future developing trend.The redundancy and the complex nature of the problem of index have been lacked in this method letter significantly, to improve the accuracy and speed of patented technology life cycle analysis.

Description

A kind of patented technology Life Cycle Analysis
Technical field
The invention belongs to patent analysis technical fields, relate more specifically to one kind and are mutually tied with K arest neighbors based on constituent analysis The patented technology Life Cycle Analysis of conjunction.
Background technology
Include many recessive technical information in patent information, reveals out by carrying out mining analysis to it, can analyze The development grain of technology, science determines the direction of interprise's intensive management and effectively promotes patent exploitation efficiency, has and preferably may be used Letter property and accuracy.Using life cycle residing for patent information judgment technology and forecast analysis is carried out, is the hot spot of current research.
Technology life cycle is the use for describing a technology, is developed from base application science derivative, generally by skill Art life cycle is divided into four-stage：Budding period, growth stage, maturity period and decline phase.The patent information in each stage can Show different features, therefore the development trend that can master a skill by the stage residing for technology life cycle makes science and determines Plan.Currently used analysis method technology life cycle has s curve methods, diagrammatic representation technology life cycle, relative indicatrix method, opposite Five kinds of methods of growth rate method and TCT, this five kinds of methods are all based on the analysis of single index, such as the Shen of amount of the application for patent or patentee Please quantity change with time trend, in fact, in fact influence the index of patented technology life cycle not only the two indexs, The also such as many indexs of Patent classificating number, inventor's quantity, scientific and technological references numbers etc., they are to patented technology Life Cycle The influence of phase also can not be ignored, and have more reasonability to the research of multi objective.
The usury pellet of Southwest Jiaotong University in 2011 has made the patented technology life cycle research of multi objective, is used in text Nearest neighbor classifier analytical technology life cycle information, it is relatively simple, but arest neighbors sorting algorithm belongs to inertia algorithm, Memory overhead is big, and calculation amount is larger when classifying to test sample, it may appear that sample imbalance problem.
Invention content
Place, the present invention propose a kind of patented technology life cycle sorting technique in view of the shortcomings of the prior art, The sorting technique devises in such a way that feature extraction and sorting algorithm are combined based on principal component analysis and nearest neighbor algorithm Grader, to multiple index analysis of technology life cycle, effective judgment technology degree of innovation, to integrated forecasting technology not Carry out development trend.
To achieve the above object, the present invention adopts the following technical scheme that：
A kind of patented technology Life Cycle Analysis, it is characterised in that：
S1, patent file data are collected；
S2, n patent index is chosen, all patent files is analyzed according to n patent index, obtained following original Data X, m are the time,
S3, smoothing processing, First Year and m annual datas remain unchanged, the data after moving average method calculatingDefinition It is as follows：
S4, normalized, by the data difference divided by its maximum value in each year after smoothing processing, normalized Data set definition afterwardsFor：
S5, data after normalization are calculatedCovariance matrix：S=(sij)n*n, wherein
S6, the eigenvalue λ for seeking covariance matrix S1≥λ2≥…λn>0 and corresponding standardization orthogonal eigenvectors V:
Feature vector V=(a1,a2,……,an),
S7, variance contribution ratio t is calculated using characteristic valuei：Each characteristic value divided by characteristic value summation,
I.e.：
S8, cumulative proportion in ANOVA G (r) is calculated：The sum of first variance contribution ratio, the first two variance contribution ratio ... ..., The sum of all variance contribution ratios,
I.e.：
S9, principal component number r is chosen, and calculates each principal component scores in r principal component：The corresponding feature of principal component to Amount is multiplied by standardized data battle array,
Fi=a1iY1+a2i Y2+…+aniYn, i=1,2 ..., r.
The technical program further optimizes, and further includes step S10, according to principal component scores, utilizes K nearest neighbor classifiers Classify to principal component scores, determines the stage residing for patent life cycle.
The technical program further optimizes, and described further includes step S10, and the principal component scores of selection are drawn line chart, And according to the four stage signature analysis line charts of technology life cycle.
The technical program further optimizes, and the step S8 chooses principal component counting method and is：Accumulate contribution rate G (r) When >=85%, the minimum value of r.
It is different from the prior art, above-mentioned technical proposal has the following advantages that：
1. the use of Principal Component Analysis can be a small number of overall targets multi objective dimensionality reduction, patent life cycle point be improved The speed of analysis.
2. the sorting technique classifies to test sample data using K nearest neighbor classifiers, patent Life Cycle is improved The precision of phase analysis.
Description of the drawings
Fig. 1 is traditional patent multi objective development trend line chart；
Fig. 2 is nano biological sensor principal component scores figure；
Fig. 3 is thin film transistor liquid crystal display technology principal component scores figure；
Fig. 4 is triniscope display technology principal component scores figure.
Specific implementation mode
For the technology contents of technical solution, construction feature, the objects and the effects are described in detail, below in conjunction with specific reality It applies example and attached drawing is coordinated to be explained in detail.
A present invention preferably embodiment, a kind of patented technology statement periodic classification method, it includes the following steps：
S1, patent file data are collected,
Collect data：The embodiment is the analysis and research to patent data life cycle, it is therefore desirable to from suitable patent Patent data is extracted in database.Patent database has free and two classes of business.But due to the language of free patent database and As a result format is various, therefore cannot extract target variable well, so the achievement data of this article is mainly derived from business patent Database --- Derwent Innovations Index database (abbreviation DII).
It is that research object carries out patented technology with nano biological sensor (Nano-biosensor serves, abbreviation NBS) The research of life cycle, period are 1985-2008.In addition instruction of two similar prior techniques as K- Nearest Neighbor Classifiers is chosen Practice collection：Thin film transistor liquid crystal display technology (Thin FilmTransistor Liquid Crystal Display, referred to as TFT-LCD)：The budding period of 1978-1990, the growth stage of 1991-2007；Triniscope display technology (Cathode RayTube, abbreviation CRT)：The maturity period of 1973-2000, the decline phase of 2001-2008.
S2, n patent index is chosen, all patent files is analyzed according to n patent index, obtained following original Data X, m are the time,
As shown in fig.1, being traditional patent multi objective development trend line chart.Technology lifes of the Fig. 1 based on patent document It orders in cycle analysis model study paper (usury is red, Southwest Jiaotong University's master thesis, 2011) using patent tradition The processing of multi-target analysis method.
Traditional patented technology life cycle is mostly referred to the research of patent applicant and number of applications two indices Marking the less extraction of data influences the objectivity of experimental result, therefore this article influences the factor in patent period by investigation, from DII In be extracted 13 patent indexes for patented technology life cycle research.13 indexs are respectively：Amount of the application for patent (indicator 1), priority patent amount (indicator 2), house journal's power people's quantity (indicator 3), machine Structure patentee quantity (indicator 4), inventor's quantity (indicator 5), science citation quantity (indicator 6), Patent Citation quantity (indicator 7), IPC quantity (indicator 8), IPC top5 quantity (indicator 9), IPC top10 quantity (indicator 10), MC (indicator 11), MC top5 quantity (indicator 12), MC Top10 quantity (indicator 13).
The present invention is using nano biological sensor data as embodiment one, and n is the time in the embodiment, and m is patent index, because This, n=24, m=13 obtain raw data matrix X, as follows：
S3, smoothing processing, First Year and the n-th annual data remain unchanged, the data after moving average method calculatingDefinition is such as Under：
Data matrix is obtained after raw data matrix X smoothing processingsIt is as follows：
S4, normalized, by the data difference divided by its maximum value in each year after smoothing processing, normalized Data set definition afterwardsFor：
Data matrixData matrix is obtained after normalizedIt is as follows：
S5, data after normalization are calculatedCovariance matrix：S=(sij)n*n, wherein
Calculate data matrixCovariance matrix S, be worth as follows：
S6, the eigenvalue λ for seeking covariance matrix S1≥λ2≥…λn>0 and corresponding standardization orthogonal eigenvectors V：
Feature vector V=(a1,a2,……,an),
Characteristic value [the λ of embodiment covariance matrix S1,λ2..., λ13]=[1.424067108,0.043200726, 0.003943523,0.000556867,0.000126844,0.000104319,0.000031284,0.000014929, 0.000010130,0.000004721,0.000003165,0.000001953,0.000000626]
The value that the embodiment standardizes orthogonal eigenvectors V is as follows：
S7, variance contribution ratio t is calculated using characteristic valuei：Each characteristic value divided by characteristic value summation, i.e.,：
The embodiment [λ1,λ2..., λ13] corresponding variance contribution ratio be [0.967393390788461, 0.0293469992819856,0.0026789034687487,0.000378289555526965, 0.00008616738865728,0.0000708655354652318,0.0000212519998825547, 0.0000101413639684064,0.00000688135532971816,0.00000320704875106824, 0.00000215026524039108,0.00000132644715443356,0.00000042550082805501]。
S8, cumulative proportion in ANOVA G (r) is calculated：The sum of first variance contribution ratio, the first two variance contribution ratio ... ..., The sum of all variance contribution ratios, i.e.,：
The embodiment calculates cumulative proportion in ANOVA：0.967393390788,0.996740390070, 0.999419293539,0.999797583095,0.999883750483,0.999954616019,0.999975868019, 0.999986009383,0.999992890738,0.999996097787,0.999998248052,0.999999574,1.
S9, principal component number r is chosen, and calculates each principal component scores in r principal component：The corresponding feature of principal component to Amount is multiplied by standardized data battle array,
Fi=a1iY1+a2iY2+…+aniYn, i=1,2 ..., r.
Principal component, which is meant, represents original multiple indexs with a small number of indexs of synthesization, plays the role of dimensionality reduction, due to Principal component scores are the materializations of principal component, and thus principal component scores are exactly the specific evaluation score of principal component.
Choose principal component number r：It is general to be chosen (accumulation contribution rate >=85%) using accumulation contribution rate.
Since first principal component contribution rate is 0.967393390788, noticeably greater than 0.85, therefore only need to extract in the example First main at r=1.
Embodiment two
The embodiment is with thin film transistor liquid crystal display technology, and n is the time in embodiment, and m is patent index, therefore, n= 30, m=13, raw data matrix X is obtained, it is as follows：
Data matrix is obtained after raw data matrix X smoothing processingsIt is as follows：
Data matrixData matrix is obtained after normalizedIt is as follows：
Calculate data matrixCovariance matrix S, be worth as follows：
Characteristic value [the λ of two covariance matrix S of the embodiment1,λ2..., λ13]=[0.0000000102, 0.0000000406,0.0000048089,0.0000087108,0.0000109586,0.0000439284, 0.0001075078,0.0002337249,0.0010010296,0.0017922160,0.0048910841, 0.0441366666,1.1860855354]
The value of the standardization orthogonal eigenvectors V of the embodiment two is as follows：
Two [λ of the embodiment1,λ2..., λ13] corresponding variance contribution ratio be [0.9578212047,0.0356424844, 0.0039497860,0.0014473007,0.0008083796,0.0001887441,0.0000868177, 0.0000354743,0.0000088496,0.0000070344,0.0000038834,0.0000000328, 0.0000000083]
The embodiment two calculates cumulative proportion in ANOVA：0.957821205,0.993463689,0.997413475, 0.998860776,0.999669155,0.9998579,0.999944717,0.999980192,0.999989041, 0.999996076,0.999999959,0.999999992,1.
Since first principal component contribution rate is 0.9578212047, noticeably greater than 0.85, therefore the need to be only extracted in the example One main at r=1.
Embodiment three
The embodiment is with triniscope display technology, and n is the time in embodiment, and m is patent index, therefore, n=36, m =13, raw data matrix X is obtained, it is as follows：
Time Index 1 Index 2 Index 3 Index 4 Index 5 Index 6 Index 7 Index 8 Index 9 Index 10 Index 11 Index 12 Index 13
1973 17 292 94 12 21 14 162 61 259 270 62 9 10
1974 22 285 84 8 36 12 158 60 247 251 73 12 12
1975 58 332 101 11 142 17 171 71 260 275 76 13 16
1976 67 421 121 14 317 14 189 76 320 345 155 26 32
1977 59 435 114 15 474 23 196 75 316 342 146 32 47
1978 88 468 125 22 630 33 215 92 323 377 235 31 80
1979 138 361 112 22 551 49 212 88 291 312 247 21 88
1980 233 589 138 14 608 41 219 93 530 550 285 26 152
1981 481 821 148 6 839 54 213 100 748 768 366 240 441
1982 1033 1048 179 11 1122 84 288 109 955 985 548 510 802
1983 1011 974 183 14 636 61 266 116 893 913 419 93 541
1984 1009 1013 202 9 581 57 252 125 942 962 490 83 324
1985 1264 1254 241 7 651 65 329 126 1149 1188 575 161 389
1986 1444 1386 271 12 751 83 353 120 1265 1294 673 221 401
1987 1332 1258 250 10 761 89 333 127 1136 1180 636 211 412
1988 1346 1263 283 8 809 66 325 125 1165 1199 643 266 490
1989 1269 1158 255 9 882 78 346 106 1080 1104 664 299 490
1990 1277 1194 265 10 878 70 351 120 1096 1118 831 623 761
1991 1327 1193 236 15 702 52 328 121 1133 1153 818 725 803
1992 1424 1259 246 8 767 82 373 125 1201 1216 807 813 874
1993 1310 1162 219 5 809 73 362 118 1090 1110 752 814 867
1994 1418 1281 304 8 961 85 432 146 1131 1163 947 879 940
1995 1555 1363 312 9 1036 76 451 152 1187 1234 1051 939 1008
1996 2061 1851 332 6 1245 77 540 154 1699 1754 1080 1284 1349
1997 1727 1476 285 8 1288 65 438 151 1365 1405 973 1088 1123
1998 1649 1439 283 2 1235 77 410 149 1327 1369 901 1064 1099
1999 1731 1536 264 4 1331 65 435 119 1441 1475 997 1068 1153
2000 1917 1673 301 9 1671 65 501 127 1576 1598 1016 1059 1186
2001 2177 1918 317 17 1739 85 544 147 1746 1802 1218 1514 1587
2002 1606 1382 318 11 1419 41 413 144 1241 1286 1224 1118 1144
2003 1142 906 275 16 1127 43 291 123 800 840 1074 713 730
2004 851 651 218 20 1138 42 227 100 544 597 1007 455 471
2005 722 564 225 27 1072 36 182 103 439 496 993 328 335
2006 783 677 271 26 1400 31 165 123 473 589 1477 463 471
2007 686 498 277 27 1265 32 132 115 314 412 1480 379 380
2008 455 305 178 12 746 12 66 98 190 244 988 216 219
Data matrix is obtained after raw data matrix X smoothing processingsIt is as follows：
Data matrixData matrix is obtained after normalizedIt is as follows：
Calculate data matrixCovariance matrix S, be worth as follows：
Characteristic value [the λ of three covariance matrix S of the embodiment1, λ2..., λ13]=[0.000002781,0.000022738, 0.000096266,0.000177450,0.000291382,0.001175813,0.001776267,0.005921460, 0.006516496,0.013468914,0.036388953,0.113305315,0.756048397]
The value of the standardization orthogonal eigenvectors V of the embodiment three is as follows：
Three [λ of the embodiment1, λ2..., λ13] corresponding variance contribution ratio be [0.808441700,0.121157246, 0.038910666,0.014402295,0.006968082,0.006331810,0.001899360,0.001257295, 0.000311574,0.000189747,0.000102938,0.000024313,0.000002974]
The embodiment three calculates cumulative proportion in ANOVA：0.8084417,0.929598945,0.968509612, 0.982911907,0.989879988,0.996211798,0.998111159,0.999368454,0.999680028, 0.999869775,0.999972713,0.999997026,1.
Since the sum of first and second variance contribution ratios are 0.929598945, noticeably greater than 0.85, therefore need to carry in the example Take two it is main at r=2.
The variance contribution ratio of first characteristic value is calculated according to characteristic value：The first of thin film transistor liquid crystal display technology The contribution rate of characteristic value is about 95.78%, and the contribution rate of the First Eigenvalue of triniscope display technology is about： 80.864%, the contribution rate of the First Eigenvalue of nano biological sensor is about：96.74%.
In order to more completely extract the information of original index, generally most when choosing contribution rate of accumulative total and reaching 85% or more Few principal component number.When triniscope display technology chooses a principal component, contribution rate is less than 85%, so needing again Second principal component, is chosen, contribution rate is accumulated at this time and meets the requirements to 92.98%.
In order to construct K- Nearest Neighbor Classifiers, training set and test set will choose same amount of principal component number, so Two principal components are extracted to thin film transistor liquid crystal display technology and nano biological sensor.
It is each to calculate separately thin film transistor liquid crystal display technology, triniscope display technology and nano biological sensor From the principal component scores of two principal components, as shown in the following table 1 to table 2.
1 thin film transistor liquid crystal display technology first principal component score 1 of table and Second principal component, score 2
2 triniscope display technology first principal component score 1 of table and Second principal component, score 2
3 first principal component score 1 of table and Second principal component, score 2
Thin film transistor liquid crystal display technology, triniscope display technology and nanometer are drawn out according to upper table 1 to table 3 The line chart of biosensor.As shown in Figures 2 to 4, respectively nano biological sensor principal component scores figure, thin film transistor (TFT) LCD technology principal component scores figure and triniscope display technology principal component scores figure.Score 1 and score 2 in every width figure Respectively first principal component score and Second principal component, score.
66 instructions are constructed with two principal component scores of thin film transistor liquid crystal display technology and triniscope display technology Practice collection data, according to thin film transistor liquid crystal display technology (TFT-LCD)：1978-1990 budding periods, the 1991-2007 year's harvest Long-term and triniscope display technology (CRT)：1973-2000 maturity periods, the existing proof system of 2001-2008 decline phases Make point 66 training set data labels, as shown in table 4.Wherein 1 it is the budding period, 2 be the growth stage, 3 be the maturity period and 4 is decline Phase.
4 66 training set data labels of table
Nano biological sensor is tested after building K- nearest neighbor classifiers according to the above training set data and training set label 24 score datas, show that the label of 24 years (1985-2008) is as shown in table 5 below.
5 24 test set labels of table
1885 1986 1987 1988 1989 1990
1 1 1 1 1 1
1991 1992 1993 1994 1995 1996
1 1 1 1 1 1
1997 1998 1999 2000 2001 2002
2 2 2 2 2 2
2003 2004 2005 2006 2007 2008
2 2 2 2 2 2
Technology life cycle analytical model research one of the usury pellet of the result and Southwest Jiaotong University based on patent document The result of study of text is completely the same.Illustrating can be rationally anti-by the method that principal component analysis extraction feature and K arest neighbors are combined Reflect actual conditions.In addition, the redundancy and the complex nature of the problem of index have been lacked in this method letter significantly, to improve patented technology The accuracy and speed of life cycle analysis.
It should be noted that herein, relational terms such as first and second and the like are used merely to a reality Body or operation are distinguished with another entity or operation, are deposited without necessarily requiring or implying between these entities or operation In any actual relationship or order or sequence.Moreover, the terms "include", "comprise" or its any other variant are intended to Non-exclusive inclusion, so that process, method, article or terminal device including a series of elements include not only those Element, but also include other elements that are not explicitly listed, or further include for this process, method, article or end The intrinsic element of end equipment.In the absence of more restrictions, being limited by sentence " including ... " or " including ... " Element, it is not excluded that there is also other elements in process, method, article or the terminal device including the element.This Outside, herein, " being more than ", " being less than ", " being more than " etc. are interpreted as not including this number；" more than ", " following ", " within " etc. understandings It includes this number to be.
Although the various embodiments described above are described, once a person skilled in the art knows basic wounds The property made concept, then additional changes and modifications can be made to these embodiments, so example the above is only the implementation of the present invention, It is not intended to limit the scope of patent protection of the present invention, it is every to utilize equivalent structure made by description of the invention and accompanying drawing content Or equivalent process transformation, it is applied directly or indirectly in other relevant technical fields, the patent for being similarly included in the present invention Within protection domain.

Claims (4)

1. a kind of patented technology Life Cycle Analysis, it is characterised in that：
S1, patent file data are collected；
S2, n patent index is chosen, all patent files is analyzed according to n patent index, obtain following initial data X, m are the time,
S3, smoothing processing, First Year and m annual datas remain unchanged, the data after moving average method calculatingIt is defined as follows：
S4, normalized, respectively divided by its maximum value in each year by the data after smoothing processing, after normalized Data set definitionFor：
S5, data after normalization are calculatedCovariance matrix：S=(sij)n*n, wherein
S6, the eigenvalue λ for seeking covariance matrix S1≥λ2≥…λn>0 and corresponding standardization orthogonal eigenvectors V:
Feature vector V=(a1,a2,……,an),
S7, variance contribution ratio t is calculated using characteristic valuei：Each characteristic value divided by characteristic value summation,
I.e.：
S8, cumulative proportion in ANOVA G (r) is calculated：The sum of first variance contribution ratio, the first two variance contribution ratio ... ... own The sum of variance contribution ratio,
I.e.：
S9, principal component number r is chosen, and calculates each principal component scores in r principal component：The corresponding feature vector of principal component multiplies With standardized data battle array,
Fi=a1iY1+a2i Y2+…+aniYn, i=1,2 ..., r.
2. a kind of patented technology Life Cycle Analysis as described in claim 1, it is characterised in that：
Further include step S10, according to principal component scores, is classified to principal component scores using K nearest neighbor classifiers, determine that patent is given birth to Order the stage residing for the period.
3. a kind of patented technology Life Cycle Analysis as described in claim 1, it is characterised in that：
Described further includes step S10, the principal component scores of selection is drawn line chart, and according to four stages of technology life cycle spy Sign analysis line chart.
4. a kind of patented technology Life Cycle Analysis as described in claim 1, it is characterised in that：
The step S8 chooses principal component counting method：When accumulating contribution rate G (r) >=85%, the minimum value of r.
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