According to data from the official website of the State Patent Office, China received 1.54 million patent applications, accounting for 46.4% of the global patent applications received that year in 2018 which ranks first in the world. The United States, in second place, received 597, 000 patent applications and Japan, in third place, received 314, 000, while Europe received 174, 000 applications in the same year. With the increasing popularity of domestic patent applications, there are also social demands for the evaluation of patent value. Various patent cooperation and patent transfer businesses, as well as research on patent support policies, all require the support of patent evaluation model.
In the early value evaluation, it is carried out through the regression analysis of the past transaction process which means the patent value of the actual profitability of the previous patents for the projects that have already produced patent conversions is evaluated according to the profitability model of intangible assets, or the transfer transaction price before the patent. However, the model cannot provide an effective evaluation for patents that have never had transactions and conversion cooperation. In the current patent value evaluation process from most domestic institutions, the expert subjective evaluation method is used to evaluate the corresponding value, where expert scoring method is adopted. It is too subjective and not scientific, although it can be improved by the brainstorm of a group of experts.
With the gradual promotion and application of machine learning technology and artificial intelligence technology in recent years, intelligent patent evaluation algorithms based on various artificial intelligence theories have also emerged. In particular, gray relational analysis models and rough set models as the bottom layer with the complex neural networks such as deep convolutional neural networks and multicolumn neural networks for deep and learning big data analysis at the same time is used in some research, but it still comes out with a large amount of shortcomings. As mentioned above, the reliability of the analysis results is in doubt when evaluating patents with unclear value added.
This paper intends to use the statespace method to construct a patent value evaluation model, which can evaluate the value of patents that have been converted or transferred, or that have not been converted or transferred.
1. It takes utility model patents in the field of clothing design as the research object in the paper. We’ve have adopted 100 patents that have been transformed into results as the data source where 70 patents is randomly selected for state space model construction on the actual conversion income and the remaining 30 patents is used for model validation.
For the patents mentioned above, AHP characteristic factor decomposition can be performed according to Figure 1:
In Figure 1, the audience gender (
The conditional polynomial expansion of
Where: Age means the corresponding age range of
The actual value of the patent is embodied as the entropy value of the relevant output value
Where: the higher the S value is, the higher the added value of the patent will be. On the contrary, the added value will be lower. When the entropy value is greater than 1.000, it is considered that the patent has a positive meaning for the market competitiveness of related products. Otherwise, it is negative.
However, it should be statistically correlated with the above 5 N values when the evaluation value of a patent is set as B. Then the actual evaluation model is:
Where:
Among the 70 data used to build the state space model, the actual value analysis model adopts the Hejun evaluation model. The final assessment value R comes from the product of the average patent added value in the past 1 year (12 monthly cycles) and 30, namely:
Where:
A preliminary statistical analysis of the data of the selected 70 patents revealed that the data analysis rules are shown in Table 1.
Basic Distribution Law of State Space Model Building Data
0  0, 000 RMB)  0, 000 RMB)  

X.  7  84  34 
2  572  .25  
G  4  6  68 
R  2  3  4 
In Table 1, it is found that the highest production value entropy of related patents can reach 4.197 and the lowest can reach 0.562. Among the 70 patent data used to construct the state space, some patents have a relatively positive effect on the added value of product patents, while some patents have a relatively negative effect. Therefore, the actual appraised value of some patents turns out positive, and some negative in the process of confirming B and R. According to statistics, it has 46 positive B and R, accounting for 65.7%, and 24 negative of them, accounting for 34.3% among the 70 patents
The state space model is constructed on the actual data of the above 70 patents to, form S, B, and R for the state space curve of
By substituting the remaining 30 known data into the above model, the results in Table 2 can be obtained.
Basic Distribution Law of State Space Model Construction Data
N=30  S  B (10, 000 RMB)  R (10, 000 RMB)  

X  3  52  45  
MIN  0.277  −36.743  −529.63  
AVG  1.985  −3.471  −19.68  
VAR  0.417  2.283  36.73  
R^{2}  0.996  0.997  0.995 
In Table 2, their R2 are all over 0.995, which means that the 30 verification values and the 70 modeling values are considered to fit well, although the 30 verification data have a slightly lower impact point than the 70 modeling data. According to the general laws of statistics, the state space model supported by 70 modeling data can be effectively verified by 30 verification values unexpectedly.
Take the
In Figure 2, taking the logarithm of
The value of patents is concentrated in the high
In addition to
After the data relationship constructs the state space model, its greatest application value comes from the application in the field of artificial intelligence, which can realize the patent value evaluation based on artificial intelligence machine learning based on data logic. The actual patent value estimation results after the cooperation between the patent and the institution can be obtained by inputting the patent data of the patent publication platform, and performing data integration analysis with the data of institutions that expect to transform the patent into the market. What's more, it can realize the data function through a single neural network module as shown in Figure 3.
In Figure 3, if the estimation of patent added value is carried out by a patent conversion agency, it would search for related patents on the patent platform, extract its data, and integrate the factors to form the model required. With 5 control values such as
For this neural network, it has 5 input values, all of which can be managed as separate variables in Double format. The statistical significance of the neural network aims to form a combined prediction model for patent value evaluation based on the state space model that can accommodate the previous analysis in the artificial intelligence big data space, and use the simulation projection method to fully evaluate the patent value based on this model. After the variable is input, it undergoes hidden layer functions such as logarithmic projection, polynomial function normalization, power rate statistics, and defuzzy process, and finally realizes that the neural network directly outputs the patented valueadded estimation data with RMB Yuan.
Where, the logarithmic projection process simulates the data premanagement process and the normalization process of polynomial function simulates the state space construction process in the model mentioned above in the model mentioned above; the power rate statistical process closely approximates the nature of the power rate function to the state space; the defuzzy process is used to dimensionally integrate the fuzzy data output by the neural network. The neural network architecture is shown in Table 3.
Neural Network Structure Design
Hidden layer level  Node number  Function  Node function 

1  5  Logarithmi c projection  
2  11 


3  17  Polynomial normalization  
4  29 


5  37  Power rate statistics  
6  29 


7  13  Defuzzy data output  
8  3 

In Table 3,
In this paper, the above 70 modeling data are taken as the training data of the neural network, and the 30 data are taken as the verification data. By comparing the evaluation results directly using the model in Chapter 2 with the evaluation results automatically interpreted by artificial intelligence, Table 4 can be obtained.
Value R Accuracy Comparison Table of Empirical Results of Artificial Intelligence Interpretation
[]  []  [] 

[]N=30  []Manual calculation results  []Machine learning results 
MAX  507.45  508.13 
MIN  −529.63  −527.27 
AVG  −19.68  −19.14 
VAR  36.73  33.42 
R^{2}  0.995  0.999 
In Table 4, the final interpretation results of the two interpretation modes for the R are basically the same, but the R2 of the machine learning result reaches 0.999, which is higher than the manual calculation result of 0.995, that is, the machine learning result is more consistent with the model. In view of the fact that the machine learning process does not construct a substantial state space, but only uses a neural network system that can accommodate the state space which means machine learning has better control over data than the calculation mode of manually constructing a state space model.
In the studies, the grey relational model and rough set model are used to evaluate the patent value in machine learning. However, these models all have shortcomings in the patent evaluation that has not gone through the early transformation process as discussed above. In this paper, a state space model is constructed through known patent value data, and a highdimensional (5 control variables) data projection space is maintained. After extracting data from patentrelated information and patent conversion agency information, the statespace model is projected to obtain the corresponding patent value data, especially the patent valueadded income data that the patent may generate in the institution. The basic model of patent value evaluation in this article is 30 times of the average performance of patent value added in the past 12 months, that is, the commercial value evaluation model proposed by Hejun Consulting. The significant statistical results with R2 higher than 0.995 were obtained by manually calculating the model or building a neural network machine learning architecture based on the model, which means it is suitable for patent value evaluation scenarios.
Value R Accuracy Comparison Table of Empirical Results of Artificial Intelligence Interpretation
[]  []  [] 

[]N=30  []Manual calculation results  []Machine learning results 
MAX  507.45  508.13 
MIN  −529.63  −527.27 
AVG  −19.68  −19.14 
VAR  36.73  33.42 
R^{2}  0.995  0.999 
Basic Distribution Law of State Space Model Building Data
0  0, 000 RMB)  0, 000 RMB)  

X.  7  84  34 
2  572  .25  
G  4  6  68 
R  2  3  4 
Basic Distribution Law of State Space Model Construction Data
N=30  S  B (10, 000 RMB)  R (10, 000 RMB)  

X  3  52  45  
MIN  0.277  −36.743  −529.63  
AVG  1.985  −3.471  −19.68  
VAR  0.417  2.283  36.73  
R^{2}  0.996  0.997  0.995 
Neural Network Structure Design
Hidden layer level  Node number  Function  Node function 

1  5  Logarithmi c projection  
2  11 


3  17  Polynomial normalization  
4  29 


5  37  Power rate statistics  
6  29 


7  13  Defuzzy data output  
8  3 

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