Intellectual property transaction data processing system and method

Abstract

The invention relates to the technical field of intellectual property transaction management, and discloses an intellectual property transaction data processing system and method.The system comprises a multi-dimensional collection module and an intelligent management module, the multi-dimensional collection module obtains transaction management data of patents, trademarks and works and classifies the transaction management data to form a data set, and the intelligent management module sets a monitoring period and stores the monitoring period; according to the method, hot trends of patent transactions in different technical fields are analyzed, hot point indexes are generated, all subclass patents under different technical departments and different large classes have comparability, competitive situations of market shares of different industries are analyzed, competitive indexes are generated, all similar group trademarks under different Niss large classes have comparability, and all similar group trademarks under different Niss large classes have comparability. The system analyzes commercial values of different copyright works and generates premium indexes, so that different types of copyright works have comparability and high multi-dimensional monitoring sensitivity, the intelligent management module sets a fixed threshold value, outputs corresponding intellectual property management suggestions, helps market participants scientifically evaluate asset values, and has a good efficient operation management effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intellectual property transaction management, in particular to an intellectual property transaction data processing system and method. BACKGROUND

[0002] Intellectual property transactions are the key to the circulation and transformation of knowledge and technology value, including the buying and selling, licensing, etc. of patents, trademarks, copyrights, etc. In patent transactions, the patent owner transfers the ownership or use right of the patent, not only to obtain economic returns to encourage innovation, but also to enable the assignee to quickly acquire technology to enhance competitiveness. Trademark transactions are active, and well-known trademarks have high value. Enterprises can purchase brands to open up markets, reduce costs, and optimize brand resource allocation. Copyright transactions involve literature, art, software, etc. Creators can earn income through licensing transactions, which promotes cultural creation. Intellectual property is usually traded through professional platforms, which provide information integration, value assessment, and other services. Perfect laws and regulations also provide protection for transactions, ensuring the rights and interests of all parties.

[0003] In the prior art, a kind of intellectual property transaction data processing method is disclosed in the patent with publication number "CN115438382A", which is applicable to the field of data processing. The method determines the unique display state of the to-be-processed intellectual property transaction data by combining the data identification information and the supply side identification information of the to-be-processed intellectual property transaction data, the intellectual property transaction data in the intellectual property transaction platform database, and the supply side permission authentication condition. From the data level, the uniqueness of each supply side publishing intellectual property transaction information can be guaranteed, which is convenient for the supply side to manage its intellectual property transaction data in the later period, and also guarantees the uniqueness of the platform displaying intellectual property transaction information to the demand side. Not only is it convenient for the demand side to view information and improve transaction matching efficiency, but it also greatly reduces the management process of repeated data by the platform operator. At the same time, it also helps to encourage the supply side to actively publish intellectual property transaction information and promote the prosperity of the intellectual property transaction market. While focusing on protecting the interests of both parties in intellectual property transactions, this method fails to effectively consider the optimization management of the transaction market, resulting in insufficient market dynamic monitoring. It cannot track the trends of popular patent technologies, trademark competition patterns, and copyright value fluctuations in real time, and the industry analysis lags behind, resulting in low operational response efficiency. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides an intellectual property transaction data processing system and method, which has the advantages of high multi-dimensional monitoring sensitivity and efficient operational management effect, and solves the problems of poor dynamic monitoring capability and low operational response efficiency of traditional intellectual property transaction data processing methods.

[0005] To achieve the above-mentioned purpose, the present application provides an intellectual property transaction data processing system, which comprises a multi-dimensional acquisition module and an intelligent management module,

[0006] The multi-dimensional acquisition module is composed of a patent data unit, a trademark data unit and a work data unit, the patent data unit acquires a patent data set by connecting a big data platform through a network, the patent data set includes transaction management data of all patents, the trademark data unit acquires a trademark data set by connecting a big data platform through a network, the trademark data set includes transaction management data of all trademarks, and the work data unit acquires a work data set by connecting a big data platform through a network, and the work data set includes transaction management data of all works.

[0007] The intelligent management module is composed of a hot spot analysis unit, a market analysis unit, a value evaluation unit and a property right management unit, the hot spot analysis unit is provided with a fixed monitoring period , combined with the patent data set, the hot spot index is generated by analyzing the hot trends of patent transactions in different technical fields , the market analysis unit analyzes the competition situation of market share in different industries according to the trademark data set, and generates the corresponding competition index , the value evaluation unit analyzes the commercial value of different copyright works according to the work data set, and generates the corresponding premium index , the property right management unit is provided with a fixed value hot spot threshold , a competition threshold and a premium threshold , combined with the hot spot index , the competition index and the premium index , the corresponding intellectual property management suggestion is output.

[0008] The multi-dimensional acquisition module of the application standardizes and fuses fragmented data, provides data support for real-time capture of market changes, and the intelligent management module adopts three-level weight superposition to calculate the hot spot index , quickly identifies the patent technology explosion field and guides the research and development resource tilt, at the same time, based on two-level weight calculation competition index , accurately positioning the blue ocean market to optimize the enterprise brand layout, calculating the premium index , quantitatively evaluating the commercial value of copyright, and timely warning the risk of infringement.

[0009] As a preferred technical scheme of the application, the patent data set includes the IPC classification number and the transaction time point of each patent, wherein the IPC classification number is composed of department, large category, small category and large group / small group, the department and small category are represented by letters, the large category and large group / small group are represented by numbers, the IPC classification number is the unique identifier for quickly positioning the technical field, and the transaction time point is used to clearly define the key time node of knowledge delivery or transfer, so that the time sequence of the transaction process is clear and traceable.

[0010] As a preferred technical solution of the present application, the trademark data set includes the Nice classification number and the registration date of each trademark, wherein the Nice classification number is composed of a large category, a similar group and a number, and the large category, the similar group and the number are all represented by numbers, the Nice classification number is a unique identifier for quickly positioning the category of the trademark, and the registration date is used to determine the key time node for the validity of the intellectual property trademark, both of which together ensure that the time sequence of the transaction process is clear and traceable.

[0011] As a preferred technical solution of the present application, the work data set includes the copyright name, the transaction time point and the transaction price of each work, and in a single copyright range, there are often various forms of works such as novels, TV series, movies and plays, and through the copyright name, the commercial value of the overall copyright can be more accurately evaluated.

[0012] As a preferred technical solution of the present application, the hot spot index The calculation process is as follows,

[0013] (S11) According to the patent data set, the number of transactions of A, B, C, D, E, F, G and H patents in the monitoring period is calculated, and the number of transactions of A patents is marked as , the number of transactions of B patents is marked as , the number of transactions of C patents is marked as , the number of transactions of D patents is marked as , the number of transactions of E patents is marked as , the number of transactions of F patents is marked as , the number of transactions of G patents is marked as , and the number of transactions of H patents is marked as ;

[0014] (S12) The percentage of the number of transactions of A patents in the total number of transactions is calculated ;

[0015] (S13) According to the IPC classification number and the transaction time point, the number of transactions of each large category patent in A in the monitoring period is calculated, and the number of large categories in A is marked as , the number of transactions of the first large category patent in A is marked as , , and the percentage of the number of transactions of the first large category patent in the number of transactions of A patents is calculated ;

[0016] (S14) Statistically analyze the monitoring period according to IPC classification number and transaction time point. Within, Part A Within each major category, the number of transactions for each subcategory of patents, and the number of transactions for Part A. The number of subcategories within each major category is denoted as Part A Of the major categories, the first The number of transactions for each subclass of patents is denoted as , Then calculate the first The number of patent transactions in each sub-category was in the [number]th Percentage of patent transactions in each major category ;

[0017] (S15) Based on (S12) to (S14), the monitoring cycle is calculated using a weighted formula. Within, Part A Of the major categories, the first Hotspot Index of Small Patent Categories ;

[0018] (S16) Based on (S11) to (S15), calculate the monitoring cycle sequentially. Within each major category (B, C, D, E, F, G, and H), the hotspot index of each subcategory of patents. This results in the hotspot index of all subcategories of patents under different technology departments and major categories. The comparability provides a key indicator for subsequently judging popular trends in patent transactions.

[0019] As a preferred technical solution of the present invention, the competition index The calculation process is as follows:

[0020] (S21) Based on the trademark dataset, the monitoring periods are calculated according to the Nice Classification number and registration date. Within the categories, the number of registered trademarks from Class 1 to Class 45 are marked as follows: ;

[0021] (S22) Calculate the first Percentage of trademark registrations by major category in the total number of registrations 1 ;

[0022] (S23) Statistical analysis of monitoring cycles based on Nice Classification number and registration date. within, no. Within the major category, the number of registrations for each similar group, and the number of registrations for the first similar group. The number of similar groups in a major category is denoted as , will the In the category, the registration number of the similar group is marked as , The percentage of the registration number of the similar group in the registration number of the category is calculated again ;

[0023] (S24) According to (S22)-(S23), the competition index of the similar group trademark in the category is calculated in the monitoring period ;

[0024] (S25) According to (S21)-(S24), the competition index of each similar group trademark in each category is calculated in the monitoring period , so that the competition indexes of all similar group trademarks under different NICE categories are comparable, and a key index is provided for subsequent judgment of the competition situation of the market share of the industry.

[0025] As a preferred technical solution of the present application, the premium index The calculation process is as follows,

[0026] (S31) According to the work data set, the number of transactions of each copyright in the monitoring period is counted according to the copyright name and the transaction time point of the work, and is marked as , , indicating the total number of copyrights with work transactions;

[0027] (S32) The percentage of the number of transactions of the first copyright in the total number of transactions of the work is calculated , ;

[0028] (S33) According to the copyright name, the transaction price of each copyright in the monitoring period is counted, and the transaction price of the first copyright is marked as , , indicating the number of transactions of the first copyright, and the average price of the first copyright in the monitoring period ;

[0029] ​​​​​​​​​​​​​(S34) According to (S32)-(S33), the monitoring period is calculated by a weighting formula The copyright transaction premium index

[0030] (S35) According to (S31)-(S34), the monitoring period is calculated in sequence The copyright transaction premium index The copyright transaction premium index The copyright transaction premium index

[0031] As a preferred technical solution of the present application, when the hotspot index ≥ hot spot threshold The corresponding field of patent technology development trend is significant, and it is suggested that the enterprises in the corresponding field adjust the patent R&D direction in time to avoid the enterprises blindly following the technology trend, which helps the enterprises to accurately position the high potential technology branch, and when the competition index ≥ competition threshold The market share of the corresponding industry is competitive, and it is suggested that the enterprises in the corresponding industry focus on the submarket in time to avoid the enterprises blindly following the market trend, which helps the enterprises to optimize the brand marketing strategy.

[0032] As a preferred technical solution of the present application, when the premium index ≥ premium threshold The commercial value of the corresponding copyright is significantly higher than the industry level, and it is suggested that the enterprises of the corresponding copyright strengthen the infringement monitoring measures in time to prevent the diversion of profits caused by piracy behavior, and provide a pricing benchmark reference for subsequent copyright transactions.

[0033] The present application provides a method for processing intellectual property transaction data, comprising the following steps,

[0034] Step 1: Connect the big data platform through the network, obtain all patent, trademark and copyright transaction management data, and classify and form patent data set, trademark data set and copyright data set;

[0035] Step 2: Set a fixed monitoring period , and analyze the hot trends of patent transactions in different technical fields in combination with the patent data set to generate the corresponding hotspot index

[0036] Step 3: According to the trademark data set, analyze the competition situation of market share in different industries to generate the corresponding competition index

[0037] ​​​​Step four; according to the work data set, the commercial value of different copyright works is analyzed, and the corresponding premium index is generated ;

[0038] Step five; set fixed numerical hot spot threshold , competition threshold and premium threshold , combined with hot spot index , competition index and premium index , output corresponding intellectual property management suggestions.

[0039] Compared with the prior art, the present application provides an intellectual property transaction data processing system and method, which has the following beneficial effects:

[0040] 1、The present application acquires all patent, trademark and work transaction management data through the multi-dimensional acquisition module network connected to the big data platform, and classifies and forms patent data set, trademark data set and work data set, and the intelligent management module sets a fixed monitoring period , combined with the patent data set, the hot trend of patent transaction in different technical fields is analyzed, and the corresponding hot spot index is generated , so that the hot spot indexes of all small category patents under different technical departments and different large categories are comparable, the intelligent management module analyzes the competition situation of different industry market shares according to the trademark data set, and generates the corresponding competition index , so that the competition indexes of all similar groups of trademarks under different large categories are comparable, the intelligent management module analyzes the commercial value of different copyright works according to the work data set, and generates the corresponding premium index , so that the premium indexes of different types of copyright works are comparable, and the multi-dimensional monitoring sensitivity is high.

[0041] 2、The present application sets fixed numerical hot spot threshold , competition threshold and premium threshold through the intelligent management module , combined with hot spot index , competition index and premium index , output corresponding intellectual property management suggestions, which can help market participants to scientifically evaluate asset value, avoid transaction risks and accurately grasp investment opportunities, promote intellectual property from "passive protection" to "active value-added", and has good effect of efficient operation and management.

[0042] Fig. 1 The present application is a system flowchart;

[0043] Fig. 2 A schematic diagram of the method steps of the present application. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work are within the protection scope of the present application.

[0045] Embodiment 1

[0046] Please refer to Figs. 1-2 The present application provides an intellectual property transaction data processing system, comprising a multi-dimensional acquisition module and an intelligent management module,

[0047] The multi-dimensional acquisition module is composed of a patent data unit, a trademark data unit and a work data unit, the patent data unit collects a patent data set through a network connection of a big data platform, the patent data set includes transaction management data of all patents, the patent data set includes an IPC classification number and a transaction time point of each patent, wherein the IPC classification number is composed of a department, a large category, a small category and a large group / small group, the department and the small category are represented by letters, and the large category and the large group / small group are represented by numbers;

[0048] The trademark data unit collects a trademark data set through a network connection of a big data platform, the trademark data set includes transaction management data of all trademarks, the trademark data set includes a Nice classification number and a registration date of each trademark, wherein the Nice classification number is composed of a large category, a similar group and a number, and the large category, the similar group and the number are represented by numbers;

[0049] The work data unit collects a work data set through a network connection of a big data platform, the work data set includes transaction management data of all works, the work data set includes a copyright name, a transaction time point and a transaction price of each work;

[0050] The intelligent management module is composed of a hot spot analysis unit, a market analysis unit, a value evaluation unit and a property right management unit, the hot spot analysis unit is provided with a fixed time length of a monitoring period combined with the patent data set, the hot trend of patent transactions in different technical fields is analyzed to generate a corresponding hot spot index The market analysis unit analyzes the competition situation of market shares in different industries according to the trademark data set to generate a corresponding competition index The value evaluation unit analyzes the commercial value of different copyright works according to the work data set to generate a corresponding premium index The property right management unit is provided with a fixed numerical hot spot threshold , a competition threshold , and a premium threshold , combined with a hot spot index , a competition index , and a premium index , output corresponding intellectual property management recommendations;

[0051] Specifically, the hot spot threshold is used to measure the degree of attention of patent technology in the market, the competition threshold is used to measure the degree of competition of brand trademarks in transactions, and the premium threshold is used to measure whether the copyright transaction price has exceeded the normal market value, the hot spot threshold , the competition threshold , and the premium threshold are all based on reasonable data settings of intellectual property transaction markets, which can help market participants to scientifically evaluate asset value, avoid transaction risks and accurately grasp investment opportunities;

[0052] When the hot spot index ≥ hot spot threshold , it indicates that the development trend of patent technology in the corresponding field is significant, and it is recommended that enterprises in the corresponding field adjust their patent R&D direction in a timely manner, when the competition index ≥ competition threshold , it indicates that the market share of the corresponding industry is competitive, and it is recommended that enterprises in the corresponding industry focus on the submarket in a timely manner, when the premium index ≥ premium threshold , it indicates that the commercial value of the corresponding copyright is significantly higher than the industry level, and it is recommended that the enterprise of the corresponding copyright strengthen the infringement monitoring measures in a timely manner.

[0053] The intellectual property transaction data processing method comprises the following steps,

[0054] Step one: connect the big data platform through the network, obtain all patent, trademark and work transaction management data, and classify them into patent data set, trademark data set and work data set;

[0055] Step two: set a fixed monitoring period , combined with the patent data set, analyze the hot trend of patent transactions in different technical fields, and generate the corresponding hot spot index ;

[0056] Step three: according to the trademark data set, analyze the competition situation of market share in different industries, and generate the corresponding competition index ;

[0057] Step four: according to the work data set, analyze the commercial value of different copyright works, and generate the corresponding premium index High sensitivity of multidimensional monitoring;

[0058] Step 5: Set a fixed hotspot threshold value. Competition threshold and premium threshold Combined with the hot topic index Competition Index and premium index It provides corresponding intellectual property management suggestions, resulting in efficient operation and management with excellent results.

[0059] In this embodiment, the multi-dimensional acquisition module collects structured transaction management data of patents, trademarks, and copyrights, solving the problem of data silos in intellectual property transactions. The intelligent management module analyzes the hot trends of patent transactions in different technical fields, the competitive landscape of market share in different industries, and the commercial value of different copyrighted works, transforming human experience-based decision-making into data-driven decision-making, realizing the automated output of management suggestions, significantly reducing the cost of enterprise intellectual property management, improving asset operation efficiency, and promoting the transformation of intellectual property from "passive protection" to "proactive value-added".

[0060] Example 2

[0061] Please refer to Table 1 for the experimental data on the hotspot index. This example is based on the explanation of Example 1. Specifically, the hotspot index... The calculation process is as follows;

[0062] (S11) Based on the patent dataset, the monitoring period is calculated according to the IPC classification number and the transaction time point. The data includes the number of patent transactions in sections A, B, C, D, E, F, G, and H, with the number of transactions for section A patents marked as... The number of patent transactions in Part B is marked as The number of patent transactions in Part C is marked as The number of patent transactions in Part D is marked as The number of patent transactions in Part E is marked as The number of patent transactions in Part F is marked as The number of patent transactions in Part G is marked as The number of patent transactions in Part H is marked as ;

[0063] (S12) Calculate the percentage of patent transactions in Part A in the total number of transactions. Its expression is as follows:

[0064]

[0065] In the formula, Indicates the monitoring period The total number of patent transactions within the country;

[0066] (S13) Statistically analyze the monitoring period according to IPC classification number and transaction time point. Within Section A, the number of transactions for each major category of patents is listed, and the number of major categories in Section A is marked as follows: In part A, the first The number of transactions for each major category of patents is marked as follows: , Then calculate the first The percentage of patent transactions by each major category in the total number of patent transactions in Part A. Its expression is as follows:

[0067]

[0068] (S14) Statistically analyze the monitoring period according to IPC classification number and transaction time point. Within, Part A Within each major category, the number of transactions for each subcategory of patents, and the number of transactions for Part A. The number of subcategories within each major category is denoted as Part A Of the major categories, the first The number of transactions for each subclass of patents is denoted as , Then calculate the first The number of patent transactions in each sub-category was in the [number]th Percentage of patent transactions in each major category Its expression is as follows:

[0069]

[0070] (S15) Calculate the monitoring cycle Within, Part A Of the major categories, the first Hotspot Index of Small Patent Categories Its expression is as follows:

[0071]

[0072] In the formula, This indicates the weight of the percentage of patent transactions in Part A within the total number of transactions. Indicates that for the first The percentage weight of each major category of patent transactions in the total number of patent transactions in Part A. Indicates that for the first The number of patent transactions in each sub-category was in the [number]th The percentage weight of each major category of patent transactions. , and are constants, , representing according to , and weights, the hot spot index of the patent in the i-th small class in the j-th big class in the monitoring period is calculated.

[0073] (S16) According to (S11)-(S15), the hot spot index of the patent in each small class in each big class of B, C, D, E, F, G and H in the monitoring period is calculated in turn.

[0074] Patent transaction quantity in A part (number) Total transaction quantity (number) Percentage of patent transaction quantity in A part in total transaction quantity (%) Patent transaction quantity in the i-th major category (number) Percentage of patent transaction quantity in the i-th major category in A part (%) Sub-category patent transaction quantity (number) Percentage of sub-category patent transaction quantity in the i-th major category (%) Hot spot index Sub-category patent 1 1000 5000 20 400 40 119 29.75 0.28925 Sub-category patent 2 1000 5000 20 400 40 186 46.5 0.3395 Sub-category patent 3 1000 5000 20 400 40 95 23.75 0.27125

[0075] Table 1 Hot spot index experimental data

[0076] In the table, the small class patent 1, the small class patent 2 and the small class patent 3 in the i-th big class of A are selected as experimental objects, the monitoring period is set to one year, , , ,

[0077] The hot spot threshold is set to 0.3, and it is judged that the hot spot index of the small class patent 2 in the i-th big class of A ≥ hot spot threshold , indicating that the development trend of the corresponding field of patent technology is significant, and it is suggested that the enterprises in the corresponding field should adjust the patent research and development direction in time.

[0078] In this embodiment, by focusing on the IPC small class level, the specific technical points that are most active and most concerned in the current market can be identified very finely, and the department-level weight , the big class-level weight and the small class-level weight are flexibly configured, so that the hot spot indexes of all small class patents under different technical departments and different big classes are comparable.

[0079] Embodiment 3

[0080] Please refer to Table 2 competitive index experimental data, this embodiment is based on the explanation and description of embodiment 1, specifically, the competitive index calculation process is as follows.

[0081] ​​​​​​(S21) According to the trademark dataset, the number of trademarks registered in each class in the monitoring period is counted according to the Nice classification number and the registration date. The number of trademarks registered in each of the first to 45th classes is marked as ;

[0082] (S22) The percentage of the number of trademarks registered in the first class in the total number of trademarks registered in the monitoring period is calculated , 1 , and the expression is as follows:

[0083]

[0084] In the formula, represents the total number of trademarks registered in the monitoring period;

[0085] (S23) The number of trademarks registered in each similarity group in the first class in the monitoring period is counted according to the Nice classification number and the registration date, and the number of similarity groups in the first class is marked as . The number of trademarks registered in the first similarity group in the first class is marked as , and the percentage of the number of trademarks registered in the first similarity group in the first class is calculated , and the expression is as follows:

[0086]

[0087] (S24) The competition index of the first similarity group of trademarks in the first class in the monitoring period is calculated , and the expression is as follows:

[0088]

[0089] In the formula, represents the weight of the percentage of the number of trademarks registered in the first class in the total number of trademarks registered, represents the weight of the percentage of the number of trademarks registered in the first similarity group in the first class, and are constants, and , represents the number of trademarks registered in the first​​​​ and weight, the competition index of each similar group trademark in each category in the monitoring period is calculated. is calculated.

[0090] (S25) According to (S21)-(S24), the competition index of each similar group trademark in each category in the monitoring period is calculated in turn.

[0091] Trademark registration quantity in the x-th major category (number) Total registration quantity (number) Percentage of trademark registration quantity in the x-th major category in total registration quantity (%) Similar group registration quantity (number) Percentage of similar group registration quantity in the x-th major category (%) Competitive index Similar group 4 300 6000 5 128 42.67 0.27602 Similar group 5 300 6000 5 82 27.33 0.18398 Similar group 6 300 6000 5 90 30 0.2

[0092] Table 2 Competition index experimental data

[0093] In the table, the first category, similar group 4, similar group 5 and similar group 6 are selected as experimental objects, and the monitoring period is set to one year, , .

[0094] The competition threshold is set to 0.2, and it is judged that the competition index of similar group 5 and similar group 6 in the first category is ≥ the competition threshold , indicating that the corresponding industry market share competition is fierce, and it is suggested that the enterprises in the corresponding industry focus on the submarket in time.

[0095] In this embodiment, by focusing on the level of NICE similar group, the specific goods or service fields with the most intensive registration activities and the most intense competition in the current market can be quickly identified, and the category level weight and the similar group level weight are flexibly configured, so that the competition indexes of all similar group trademarks under different NICE categories are comparable.

[0096] Embodiment 4

[0097] Please refer to Table 3 Premium index experimental data, this embodiment is based on the explanation and description of embodiment 1, specifically, the premium index calculation process is as follows;

[0098] (S31) According to the work data set, the number of work transactions of each copyright in the monitoring period is counted according to the copyright name and work transaction time point, and is marked as , respectively, indicating the total number of copyrights with work transactions;

[0099] ​​​(S32) Calculate the percentage of the number of transactions of the first type of copyright in the total number of transactions of the copyright

[0100]

[0101]

[0102] (S33) Calculate the price of the copyright of each type of copyright in the monitoring period, and mark the price of the copyright of the first type of copyright as

[0103]

[0104]

[0105] (S34) Calculate the premium index of the copyright of the first type of copyright in the monitoring period

[0106]

[0107]

[0108] ​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​(S35) According to (S31)-(S34), the monitoring period is calculated in turn The premium index of each copyright transaction .

[0109] Copyright work transaction quantity (number) Total work transaction quantity (number) Percentage of copyright work transaction quantity in total work transaction quantity Work transaction frequency (times) Total work transaction amount (yuan) Work transaction average price (yuan) Premium index Copyright A 16 1600 1 3 28000 9333.33 6533.334 Copyright B 8 1600 0.5 2 12000 6000 4200.0015 Copyright C 92 1600 5.75 14 240000 17142.86 12000.01625

[0110] Table 3 premium index experimental data

[0111] In the table, copyright A, copyright B and copyright C are selected as experimental objects, and the monitoring period is set to one year, , ;

[0112] The premium threshold is set to 10000, and it is judged that the premium index of copyright C ≥ the premium threshold , indicating that the commercial value of the corresponding copyright is significantly higher than the industry level, and it is recommended that the enterprise of the corresponding copyright promptly strengthen the infringement monitoring measures.

[0113] In this embodiment, not only the market breadth of the commercial value of the copyright is measured by the transaction share, but also the market depth is evaluated in combination with the price level, so that the premium indexes of different types of copyright works are comparable, thereby helping the creators to accurately judge their market positioning and value potential.

[0114] In the above embodiments, only one or more feasible schemes are given, which do not represent the optimal scheme, and the size of the threshold is set for convenience of comparison. The size of the threshold depends on the number of sample data and the base number set by the person skilled in the art for each group of sample data; as long as it does not affect the proportional relationship of the parameters and the quantized values, and the size of the weight can be determined by the person skilled in the art according to each sample data and the process of multiple experiments. The above formulas are all dimensionless values.

[0115] Although the embodiments of the present application have been shown and described, it can be understood by those of ordinary skill in the art that various changes, modifications, replacements and variations can be made to these embodiments without departing from the principles and spirits of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. An intellectual property transaction data processing system, characterized by: Includes a multi-dimensional data acquisition module and an intelligent management module. The multi-dimensional acquisition module consists of a patent data unit, a trademark data unit, and a copyright data unit. The patent data unit collects patent datasets by connecting to a big data platform via a network. The patent datasets include transaction management data for all patents. The trademark data unit collects trademark datasets by connecting to a big data platform via a network. The trademark datasets include transaction management data for all trademarks. The copyright data unit collects copyright datasets by connecting to a big data platform via a network. The copyright datasets include transaction management data for all copyrights. The intelligent management module consists of a hotspot analysis unit, a market analysis unit, a value assessment unit, and a property rights management unit. The hotspot analysis unit is configured with a fixed monitoring period. Furthermore, by combining patent datasets, we can analyze popular trends in patent transactions across different technological fields and generate corresponding hotspot indices. The market analysis unit analyzes the competitive landscape of market share in different industries based on the trademark dataset and generates corresponding competition indices. The value assessment unit analyzes the commercial value of different copyrighted works based on the copyright dataset and generates corresponding premium indices. The property management unit is equipped with a fixed hotspot threshold value. Competition threshold and premium threshold Combined with the hot topic index Competition Index and premium index Provide corresponding intellectual property management suggestions.

2. The intellectual property transaction data processing system according to claim 1, characterized in that: The patent dataset includes the IPC classification number and transaction time of each patent. The IPC classification number consists of a department, a major class, a minor class, and a major group / minor group. The department and minor class are represented by letters, while the major class and major group / minor group are represented by numbers.

3. The intellectual property transaction data processing system according to claim 1, characterized in that: The trademark dataset includes the Nice Classification number and registration date for each trademark. The Nice Classification number consists of a class, similar group, and number, all of which are represented by numbers.

4. The intellectual property transaction data processing system according to claim 1, characterized in that: The copyright dataset includes the copyright name, transaction time, and transaction price for each work.

5. The intellectual property transaction data processing system according to claim 1, characterized in that: The hot topic index The calculation process is as follows: (S11) Based on the patent dataset, the monitoring period is calculated according to the IPC classification number and the transaction time point. The data includes the number of patent transactions in sections A, B, C, D, E, F, G, and H, with the number of transactions for section A patents marked as... The number of patent transactions in Part B is marked as The number of patent transactions in Part C is marked as The number of patent transactions in Part D is marked as The number of patent transactions in Part E is marked as The number of patent transactions in Part F is marked as The number of patent transactions in Part G is marked as The number of patent transactions in Part H is marked as ; (S12) Calculate the percentage of patent transactions in Part A in the total number of transactions. ; (S13) Statistically analyze the monitoring period according to IPC classification number and transaction time point. Within Section A, the number of transactions for each major category of patents is listed, and the number of major categories in Section A is marked as follows: In part A, the first The number of transactions for each major category of patents is marked as follows: , Then calculate the first The percentage of patent transactions by each major category in the total number of patent transactions in Part A. ; (S14) Statistically analyze the monitoring period according to IPC classification number and transaction time point. Within, Part A Within each major category, the number of transactions for each subcategory of patents, and the number of transactions for Part A. The number of subcategories within each major category is denoted as follows: Part A Of the major categories, the first The number of transactions for each subclass of patents is denoted as , Then calculate the first The number of patent transactions in each sub-category was in the [number]th Percentage of patent transactions in each major category ; (S15) Based on (S12) to (S14), the monitoring cycle is calculated using a weighted formula. Within, Part A Of the major categories, the first Hotspot Index of Small Patent Categories ; (S16) Based on (S11) to (S15), calculate the monitoring cycle sequentially. Within each major category (B, C, D, E, F, G, and H), the hotspot index of each subcategory of patents. .

6. The intellectual property transaction data processing system according to claim 1, characterized in that: The competition index The calculation process is as follows: (S21) Based on the trademark dataset, the monitoring periods are calculated according to the Nice Classification number and registration date. Within the categories, the number of registered trademarks from Class 1 to Class 45 are marked as follows: ; (S22) Calculate the first Percentage of trademark registrations by major category in the total number of registrations 1 ; (S23) Statistically analyze the monitoring cycles according to the Nice Classification number and registration date. within, no. Within the major category, the number of registrations for each similar group, and the number of registrations for the first similar group. The number of similar groups in a major category is denoted as , will the In the major category, the first The number of registrations for similar groups is marked as , Then calculate the first The number of similar groups registered in the th Percentage of registrations in major categories ; (S24) Based on (S22) to (S23), the monitoring cycle is calculated using a weighted formula. within, no. In the major category, the first Competition index of similar trademark groups ; (S25) Based on (S21) to (S24), calculate the monitoring cycle sequentially. Within each major category, the competition index of each similar group of trademarks. .

7. The intellectual property transaction data processing system according to claim 1, characterized in that: The premium index The calculation process is as follows: (S31) Based on the copyright dataset, the monitoring period is calculated separately according to the copyright name and the time of copyright transaction. Within, the number of copyright transactions for each type is marked as follows: , This indicates the total number of copyrights that have been traded. (S32) Calculate the first The percentage of copyright transactions in the total number of copyright transactions. , ; (S33) Statistically analyze the monitoring periods according to the copyright name. Within, the price of each copyright transaction, and the first The price of a copyrighted work is marked as , Indicates the first The number of copyright transactions is used to calculate the monitoring period. within, no. Average transaction price of copyrighted works ; (S34) Based on (S32) to (S33), the monitoring cycle is calculated using a weighted formula. within, no. Premium index for copyright transactions ; (S35) Based on (S31) to (S34), calculate the monitoring cycle sequentially. Within, the premium index for each type of copyright transaction. .

8. The intellectual property transaction data processing system according to claim 1, characterized in that: The hot topic index ≥Hotspot Threshold When the competition index indicates a significant development trend in the corresponding field of patent technology, it is recommended that companies in the corresponding field adjust their patent research and development direction in a timely manner. ≥ Competition Threshold When this occurs, it indicates that the corresponding industry market share is highly competitive, and it is recommended that companies in the corresponding industry focus on niche markets in a timely manner.

9. The intellectual property transaction data processing system according to claim 1, characterized in that: The premium index ≥Premium Threshold When this occurs, it indicates that the commercial value of the corresponding copyright is significantly higher than the industry average, and it is recommended that the companies holding the corresponding copyright promptly strengthen their infringement monitoring measures.

10. A method for processing intellectual property transaction data, applied to the intellectual property transaction data processing system described in any one of claims 1-9, characterized in that: Including the following step, Step 1: Connect to the big data platform via the network to obtain all transaction management data of patents, trademarks and copyrights, and classify them into patent datasets, trademark datasets and copyright datasets; Step 2: Set a fixed monitoring period. Furthermore, by combining patent datasets, we can analyze popular trends in patent transactions across different technological fields and generate corresponding hotspot indices. ; Step 3: Based on the trademark dataset, analyze the competitive landscape of market share in different industries and generate corresponding competition indices. ; Step 4: Based on the copyright dataset, analyze the commercial value of different copyrighted works and generate corresponding premium indices. ; Step 5: Set a fixed hotspot threshold value. Competition threshold and premium threshold Combined with the hot topic index Competition Index and premium index Provide corresponding intellectual property management suggestions.

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