Medical patent conversion public service system

Abstract

The invention provides a medical patent conversion public service system, which comprises a public service end system, a user end system and a display module, and is characterized in that the public service end system comprises a user registration and login module, a data processing module and a database; the user registration and login module is used for user registration, user information modification and user login; the database is used for storing data; the data processing module is used for processing the data in the database; the user end system comprises a hospital end system and an enterprise end system, the hospital end system and the enterprise end system perform data interaction, to-be-converted patents meeting specific conditions are screened out, and matching of the hospital end and the enterprise end is performed; and the public service end system, the hospital end system and the enterprise end system are all displayed on the terminal equipment through the display module. According to the method, the to-be-converted patents can be intelligently, objectively and accurately screened in batches, and personalized matching of the hospital side and the enterprise side can be quickly and accurately carried out.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and particularly to a public service system for medical patent transformation. Background Art

[0002] In the field of medical technology innovation, patent transformation is the core link connecting basic research and clinical application. The existing medical patent transformation work has the following defects:

[0003] (1) There is a serious patent information barrier between medical institutions and production enterprises, and it is impossible to quickly conduct personalized supply-demand matching;

[0004] (2) Most of the existing patent transformation feasibility evaluations rely on people's subjective experience, not only with low transformation efficiency but also lacking objective quantitative evaluation criteria,

[0005] (3) Facing the large number of existing medical patents in Chinese medical institutions, there is an urgent need for a technical solution that can quickly, accurately, intelligently, and batch-screen convertible patents and match medical institutions with enterprises. Summary of the Invention

[0006] To solve the above technical problems, the present application provides a public service system for medical patent transformation, which includes:

[0007] A public service end system, which includes a user registration and login module, a data processing module, and a database; the user registration and login module is used for user registration, user information modification, and user login; the database is used for storing data; the data processing module is used for processing the data in the database;

[0008] A user end system, which includes a hospital end system and an enterprise end system. The hospital end system and the enterprise end system perform data interaction to screen out the patents to be transformed that meet specific conditions and perform matching between the hospital end and the enterprise end;

[0009] A display module. The public service end system, the hospital end system, and the enterprise end system are all displayed on the terminal device through the display module.

[0010] Furthermore, the data processing module includes a technical portrait analysis module and a user portrait analysis module. The technical portrait analysis module is used for classifying the patents by technical portrait, and the user portrait analysis module is used for classifying the registered hospital end users and enterprise end users by user portrait; the database includes a patent database and a local database. The patent database is used for storing patent literature information and its corresponding technical portrait information, and the local database is used for storing user registration information and user portrait information.

[0011] Furthermore, the hospital end system includes the following modules:

[0012] A retrieval module, which is used to screen out several patents with specific technical portraits from the patents whose all patent applicants in the server database are this hospital;

[0013] A transformation prospect evaluation module, which is used to evaluate the transformation prospects of all patents under a specific technical portrait, and screen out several potential patents with transformation prospects;

[0014] A patent price analysis module, which is used to analyze the prices of several potential patents with transformation prospects, and screen out the potential patents that meet the expected transformation price conditions as target transformation patents;

[0015] A medical enterprise matching module, which is used to automatically or according to user selection send a technical transformation supply intention notice of the target transformation patent to the matching target enterprise end. The technical transformation supply intention notice includes the target transformation patent information and its corresponding intended transformation price. The target enterprise end includes the enterprise end whose business scope matches the specific technical portrait and the enterprise end that has issued a technical transformation demand intention notice of the same technology as the specific technical portrait; it is also used to send a consent or rejection message notice to the medical enterprise matching module of the enterprise end after receiving the technical transformation demand intention notice sent by the enterprise end system;

[0016] A local database, which is used to store the output data of the retrieval module, the transformation prospect evaluation module, the patent price analysis module and the medical enterprise matching module;

[0017] A user center, which is used to view the data and user information in the local database.

[0018] Furthermore, the enterprise end system includes the following modules:

[0019] A retrieval module, which is used to screen out all patents that meet the specific technical portrait from the database;

[0020] A transformation prospect evaluation module, which is used to evaluate the transformation prospects of all patents that meet the specific technical portrait, and screen out several potential patents with transformation prospects;

[0021] A patent price analysis module, which is used to analyze the prices of several potential patents with transformation prospects as the intended transformation price;

[0022] Medical-Enterprise Matching Module. The Medical-Enterprise Matching Module is used to take all potential patents with transformation prospects and / or the patents corresponding to the technology transformation supply intentions of the same specific technology portrait already released by the hospital side as target transformation patents, and automatically or according to user selection, send the technology transformation demand intention notices of the target transformation patents to the hospital sides of all the valid right holders corresponding to the respective target transformation patents. The technology transformation demand intention notice includes the target transformation patent information and its corresponding intended transformation price; it is also used to send a message notice of consent or rejection to the Medical-Enterprise Matching Module of the hospital side after receiving the transformation intention notice sent by the hospital side.

[0023] Local Database. The Local Database is used to store the output data of the Retrieval Module, the Transformation Prospect Evaluation Module, the Patent Price Analysis Module, and the Medical-Enterprise Matching Module.

[0024] User Center. The User Center is used to view the data and user information in the Local Database.

[0025] Furthermore, the presentation forms of the Medical Patent Transformation Public Service System include, but are not limited to, any one or a combination of multiple of App, web page, WeChat official account, WeChat mini-program, enterprise WeChat, and DingTalk.

[0026] Furthermore, the method for mutual matching between the hospital-side system and the enterprise-side system includes the following steps:

[0027] S1: The hospital-side system and the enterprise-side system use the Retrieval Module to screen out all patents that meet the specific technology portrait from the server-side database.

[0028] S2: The hospital-side system and the enterprise-side system use the Transformation Prospect Evaluation Module to evaluate the transformation prospects of all patents under the specific technology portrait, and screen out a number of potential patents with transformation prospects.

[0029] S3: The hospital-side system and the enterprise-side system use the Patent Price Analysis Module to analyze the prices of a number of potential patents with transformation prospects, and screen out the potential patents that meet the expected transformation price conditions as target transformation patents.

[0030] S4: The hospital-side system and the enterprise-side system use the Medical-Enterprise Matching Module to automatically or according to user selection send the technology transformation intention notices of the target transformation patents to the matching objects, and the matching objects send message notices of consent or rejection.

[0031] Furthermore, for the Medical Patent Transformation Public Service System, the user-side system further includes a transformation service agency-side system, and the transformation service agency-side system conducts data interaction with the hospital-side system and the enterprise-side system respectively.

[0032] The above-mentioned transformation service institution-side system includes, but is not limited to, the information system of financial institutions providing pledge financing in medical transformation, the information system of accounting firms / auditing firms providing intangible asset evaluation, the information system of intermediary service institutions with enterprise or hospital demand projects (non-users of hospital-side systems and enterprise-side systems), the information system of institutions that can provide cell experiments / animal experiments / device effect verification, etc.

[0033] After adopting the above technical solution, the present application has the following beneficial technical effects:

[0034] 1. The present application classifies patents by technical portraits and classifies users by user portraits.

[0035] 2. The screening process of the convertible patents in the present application includes two criteria: firstly, a preliminary screening is carried out using a convertible prospect evaluation model, and then a secondary screening of the intended conversion price is carried out using a price analysis model, which can batch, intelligently, objectively, and accurately screen out the patents to be converted.

[0036] 3. The medical-enterprise quick matching module of the present application can quickly and accurately perform personalized supply-demand matching between the hospital side and the enterprise side. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is the overall architecture diagram of the public service system for medical patent transformation.

[0038] Figure 2 It is the overall architecture diagram of the public service side system.

[0039] Figure 3 It is the overall architecture diagram of the hospital side system and the enterprise side system.

[0040] Figure 4 It is the workflow diagram of the public service system for medical patent transformation.

[0041] Figure 5 It is the trend chart of the conversion probability distribution of un-converted patents of domestic medical institutions.

[0042] Figure 6 It is the trend chart of the conversion probability distribution of un-converted patents of foreign medical institutions.

[0043] Figure 7 It is the external data verification result chart of the prediction model for the industrialization potential of medical institution patents.

[0044] Figure 8 It is the user-side system architecture including the transformation service institution-side system. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] The advantages of the present invention will be further elaborated below in conjunction with the accompanying drawings and specific embodiments. Those skilled in the art should understand that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the protection scope of the present invention.

[0046] See Figure 1 , this embodiment provides a public service system for medical patent transformation, including: a public service end system, a user end system, and a display module.

[0047] The public service end system is used to provide public basic services related to patent transformation for the user end system, such as databases, user registration, technology profiling, and user profiling analysis, etc.

[0048] The user end system includes a hospital end system and an enterprise end system. Data interaction occurs between the hospital end system and the enterprise end system to find matching objects that meet the conditions for patent transformation with each other.

[0049] The public service end system, the hospital end system, and the enterprise end system are all displayed on the terminal device through the display module. Exemplarily, the presentation methods of the system include any one or a combination of multiple of App, web page, WeChat official account, WeChat mini program, enterprise WeChat, and DingTalk.

[0050] See Figure 2 , the public service end system includes a database and a data processing module. Among them, the data processing module includes a technology profiling analysis module and a user profiling analysis module. The technology profiling analysis module is used to classify the technology profiles of patents. Based on the innovative technologies concerned by the inventors, using methods such as Mann-Kendall trend analysis, heat map (Python), text clustering, and Gompertz curve, etc., from dimensions such as the overall technology overview, geographical distribution, technical key points, and technology life cycle, a panoramic portrait of the technology is drawn, so that inventors (medical staff) can quickly, comprehensively, and directly grasp the current situation and future development trends of the technology at home and abroad. The user profiling analysis module uses a radar chart to classify the user profiles of registered users from dimensions such as the user institution situation, scientific research strength, financial strength, personnel situation, institutional risks, and product situation. Among them, the database includes a patent database and a local database; the patent database is used to store patent documents and their corresponding technology profile information. The local database is used to store all other data except patent documents and technology profile information, such as user registration information, user profiles, etc.

[0051] See Figure 3 and Figure 4, both the hospital - side system and the enterprise - side system include the following modules, but their working processes are different: retrieval module, transformation prospect evaluation module, patent price analysis module, medical - enterprise matching module, local database, and user center.

[0052] The patent price analysis module is supported by medical data. Under the guidance of the factor distribution theory, it uses methods such as the Gompertz curve and the revenue sharing model to construct a medical patent market price evaluation model that reflects the characteristics of inventors (medical staff), optimizes the model parameters, establishes an evaluation mechanism, and provides medical patent market price analysis services. The client - side index input module is used for users to input relevant index data information of the medical patent technology to be evaluated for a specific disease category and store it in the input index client - side database. The relevant index data information includes the disease category targeted by the medical patent technology to be evaluated, the disease harm degree data and disease treatment cost data of this category of diseases, the professional qualifications data of the inventor, the technical achievement data of the inventor, the technology factor data, the patent factor data, the comprehensive strength data of the hospital, the technology application prospect data, and the policy influence data; it uses methods such as the Gompertz curve and the revenue sharing model to calculate the single - disease data related to the technology based on the disease harm degree data and disease treatment cost of the medical patent technology to be evaluated, calculates the first - inventor characteristic data of the technology based on the professional qualifications data and technical achievement data of the inventor, calculates the technology quality data based on the technology factor data and patent factor data, calculates the technology transformation prospect based on the comprehensive strength data of the hospital, the technology application prospect data, and the policy influence data, and the output module is used to output the price of the medical patent technology to be evaluated.

[0053] Revenue sharing model formula:

[0054] is the technology sharing rate, that is, the ratio of the economic benefits contributed by the medical patent technology to the overall medical product.

[0055] is the technology sharing rate adjustment value, that is, the coefficient of the medical patent clinical economic value evaluation index system formed based on clinical diagnosis and treatment data in the early stage, further highlighting the ratio of the contribution of patent technologies for different disease categories to the economic benefits generated by the overall medical product.

[0056] is the economic value life cycle of the medical patent, that is, the period from the birth of the medical patent technology to the loss of its use value, creating economic benefits for the hospital and patients.

[0057] is the expected revenue generated by the overall medical product formed based on the medical patent technology in the t - th year.

[0058] is the discount rate, that is, the time value discount considered for the future income amount of the overall medical product formed based on medical patent technology.

[0059] The medical enterprise quick matching module is an automated deep processing system for medical patent information that uses key technologies such as TF-IDF (Term Frequency-Inverse Document Frequency) algorithm, vector space model, cosine similarity, and latent semantic analysis. It accurately docks medical patent technologies with industrialization prospects with the R & D directions of enterprises. Based on the keywords in the patent abstract or claims, it matches with the main products and business scopes of each enterprise in the platform enterprise database, realizes the quick matching of hospital patent technologies with the enterprise innovation R & D directions, and improves the accuracy and efficiency of the innovation R & D docking between medical enterprises. Based on the TF-IDF algorithm, vector space model, and latent semantic analysis, a word vector library for medical institution patent technologies and a word vector library for enterprise R & D directions are established, the high-frequency important words related to innovation R & D in the two word vector libraries are determined, and cosine similarity is used to achieve the accurate matching of medical institution patent technologies with the main R & D directions of enterprises.

[0060] The transformation prospect evaluation module uses a transformation prospect evaluation model to predict whether a patent has transformation potential. Preferably, the transformation prospect evaluation model is a random forest model. In actual use, only the data of 12 industrialization potential indicators of the current medical patent to be transformed needs to be input into the already constructed and trained random forest model, and the transformability probability of the current medical institution patent to be transformed can be automatically obtained. When the transformability probability is greater than or equal to a preset threshold (such as 70%), it is determined that the current medical patent to be transformed has transformation potential; otherwise, it does not have transformation potential.

[0061] The construction method of the random forest model includes:

[0062] Step 1: Data sampling: Randomly select 20% of the data from the original patent dataset as the training set for each tree, and the remaining patent data as the internal validation set;

[0063] Step 2: Feature selection: During the training process of each tree, randomly select a part of the features for splitting node selection;

[0064] Step 3: Construct decision trees: Based on the selected features, construct decision trees, and each tree grows as much as possible without pruning;

[0065] Step 4: Prediction: For each new input sample, each tree will give a prediction result; use random grid search to perform hyperparameter search on the random forest model to obtain the optimal parameter model. The optimal model hyperparameters are: the maximum number of iterations of the weak learner is 50, the maximum depth of the decision tree is 7, the minimum number of samples required for further partitioning of internal nodes is 130, and the minimum number of samples of leaf nodes is 30;

[0066] Step 5: Result summary: For classification problems, the simple voting method, that is, majority voting, is usually adopted. The final prediction result is the majority vote of all decision tree prediction results.

[0067] The verification methods of the random forest model include:

[0068] Step S1: The prediction effect of the internal verification model is good.

[0069] Based on the internal validation set, the random forest model is internally validated from four aspects: precision, recall, F1 value, and AUC area.

[0070] The precision of the random forest model is 0.871, the recall is 0.874, the F1 score is 0.869, and the AUC area is 0.900.

[0071] Step S2: External verification based on the data of untransformed patent samples.

[0072] The results obtained by the prediction model for the industrialization potential of medical institution patents are consistent with the existing literature and general experience.

[0073] Use the random forest model to evaluate the industrialization potential of untransformed authorized invention patents at home and abroad in the sample library, output the evaluation results of each patent, that is, the conversion probability, draw a bar chart of the convertibility of authorized invention patents of medical institutions at home and abroad, and fit the data. If the conversion probabilities of authorized invention patents of medical institutions at home and abroad both show a logarithmic curve distribution trend, it indicates that the random forest model has prediction accuracy.

[0074] On the basis of selecting the optimal machine learning classification model, the random forest model is used to predict the transformation prospects of untransformed authorized invention patents in domestic and foreign medical institutions in the sample library, and the prediction results of each patent, that is, the transformation probability, are output. According to the output transformation probability and referring to the 10-point standard evaluation method, 10 layers of patent transformation probability thresholds are set, and the results are shown in Table 1. It can be found from Table 1 that among the untransformed patents, 1% (516 pieces) of the authorized invention patents of medical institutions have a transformation prospect at level 10, and their transformation probability exceeds 90%. The content involved in this part of the patents can be considered as the medical technology with the most development prospects and industrialization potential. Generally speaking, the proportion of patents with a level of 7 or above, that is, a transformation probability of more than 70%, is only 4.788% (2072 / 43275). It can be seen from the perspective of transformation that the number of patents with better transformation prospects in medical institutions is relatively small. At the same time, the proportion of patents at levels 1 to 3 accounts for 70.084% (30329 / 43275), which reflects that the patent transformation probability of most medical institutions is relatively low and the transformation is relatively difficult. There is a risk that nearly 70% of the authorized invention patents of medical institutions will become "permanent sleeping patents". Further stratify the data. If patents with a level of 7, that is, a transformation probability of 70% and above, are defined as high-value patents, the proportion of high-value patents in foreign medical institutions (7.603%) is higher than that in domestic medical institutions (4.788%).

[0075] Table 1 Prediction results of transformation probability of untransformed patents of medical institutions

[0076]

[0077] On the basis of predicting the transformation prospects of medical institution patents, a bar chart of the transformability of untransformed authorized invention patents of domestic and foreign medical institutions is drawn, and the data is fitted. From Figure 5 and Figure 6 it can be seen that the transformability probabilities of authorized invention patents of domestic and foreign medical institutions show a logarithmic curve distribution trend. The logarithmic function equation is fitted to the transformability probability curve of authorized invention patents of domestic medical institutions, and the result is: , , and the logarithmic function equation is fitted to the transformability probability curve of authorized invention patents of foreign medical institutions, and the result is: , , Both are greater than 0.9, indicating that the fitting degree of the two curves is good. Moreover, the steepness of the logarithmic curve of the patent conversion probability of foreign medical institutions is higher than that of domestic medical institutions. However, the patent conversion of domestic and foreign medical institutions also faces the same problem, that is, most patents cannot be converted. Some studies have shown that the patent value usually shows a logarithmic normal curve distribution trend (see: NAIR S S, MATHEW M, NAG D. Dynamics between patent latent variables and patent price[J]. Technovation, 2011, 31(12): 648-654.), that is, in a certain field, only a small part of the high-value patents with conversion prospects, and the value of most patents is low. The results obtained by the patent industrialization potential prediction model of this application are consistent with the existing literature and general experience.

[0078] Step S3: External verification based on the converted patent sample data

[0079] Based on the established prediction model, the conversion probability of 368 pieces of medical institution patent conversion data containing transfer prices was predicted. It was found that as the conversion amount increased, the conversion probability also increased, which was in line with the actual situation.

[0080] To further verify the effectiveness of the model, 368 pieces of patent data converted from 18 medical institutions in Shanghai were collected. Each patent included the conversion contract amount. 12 key influencing factor indicators of the patent industrialization potential of the corresponding 368 patented technologies were collected to form an external data verification set for verifying the effectiveness of the medical institution patent industrialization potential prediction model established in this application. The verification results showed that the average conversion probability of 368 pieces of patent data was 0.608, which was greater than 0.5, and the F1 value of the prediction model was 0.959, indicating that the model had a good evaluation effect for the overall external data. Stratify the data according to the conversion probability in Table 1 to form the abscissa. The main coordinate axis (blue bar chart) is the average conversion contract amount (in ten thousand yuan) of the patented technologies contained in each level, and the secondary coordinate axis (orange line chart) is the average conversion probability of each level, forming Figure 7 , from Figure 7 it can be found that as the conversion amount increases, the conversion probability also increases.

[0081] The working process of the hospital-end system is as follows:

[0082] (1) The hospital-end users and enterprise-end users register and log in on the server.

[0083] (2) The server pre-uses the technology portrait analysis module to perform technology image analysis on the patents stored in the database, uses the user portrait analysis module to perform user portrait analysis on hospital users and enterprise users, and stores the technology portrait and user portrait data in the database of the server;

[0084] (3) The hospital uses the retrieval module to screen out several patents with specific technology portraits from the patents whose applicants are this hospital in the server database;

[0085] (4) Use the transformation prospect evaluation module to evaluate the transformation prospects of all patents under a specific technology portrait, and screen out several potential patents with transformation prospects;

[0086] (5) Use the patent price analysis module to analyze the prices of several potential patents with transformation prospects, and screen out the potential patents that meet the expected transformation price conditions as the target transformation patents;

[0087] (6) Use the medical-enterprise matching module to automatically or according to user selection send the technology transformation supply intention notice of the target transformation patent to the target enterprise side. The technology transformation supply intention notice includes the target transformation patent information and its corresponding intended transformation price. The target enterprise side includes the enterprise side whose business scope matches the specific technology portrait and the enterprise side that has issued a technology transformation demand intention notice identical to the specific technology portrait. After receiving the transformation intention notice, the medical-enterprise matching module of the target enterprise side sends a message notice of consent or rejection to the medical-enterprise matching module of the hospital side.

[0088] The working process of the enterprise-side system is as follows:

[0089] (1) Hospital users and enterprise users register and log in on the server;

[0090] (2) The server pre-uses the technology portrait analysis module to perform technology image analysis on the patents stored in the database, uses the user portrait analysis module to perform user portrait analysis on hospital users and enterprise users, and stores the technology portrait and user portrait data in the database of the server;

[0091] (3) The enterprise uses the retrieval module to screen out all patents that meet the specific technology portrait from the database;

[0092] (4) Use the transformation prospect evaluation module to evaluate the transformation prospects of all patents that meet the specific technology portrait, and screen out several potential patents with transformation prospects;

[0093] (5) Use the patent price analysis module to analyze the prices of several potential patents with transformation prospects as the intended transformation price;

[0094] (6) Use the medical-enterprise matching module to notify the patents corresponding to the technical transformation supply intentions of all potential patents with transformation prospects and / or the same specific technical portraits already published on the hospital side as the target transformation patents, and automatically or according to user selection, send the technical transformation demand intention notifications of the target transformation patents to the hospital sides of all the valid right holders corresponding to the respective target transformation patents. The technical transformation demand intention notification includes the target transformation patent information and its corresponding intended transformation price. After receiving the technical transformation demand intention notification, the medical-enterprise matching module on the hospital side sends a message notification of consent or rejection to the medical-enterprise matching module on the enterprise side.

[0095] As Figure 8 shown, the user-side system further includes a transformation service agency-side system, and the transformation service agency-side system conducts data interaction with the hospital-side system and the enterprise-side system respectively.

[0096] The transformation service agency-side system includes, but is not limited to, the information system of a financial institution providing pledge financing in medical transformation, the information system of an accounting firm / auditing firm providing intangible asset evaluation, the information system of an intermediary service agency with its own enterprise or hospital demand projects (non-users of the hospital-side system and the enterprise-side system), the information system of an institution that can provide cell experiments / animal experiments / effect verification of medical devices, etc.

[0097] It should be noted that the embodiments of the present invention have good implementability and do not impose any form of limitation on the present invention. Any person skilled in the art may use the disclosed technical content to modify or transform it into an equivalent effective embodiment. However, as long as it does not depart from the technical solution of the present invention, any modification, equivalent change or modification made to the above embodiments based on the technical essence of the present invention still falls within the scope of the technical solution of the present invention.

Claims

1. A public service system for medical patent transformation, characterized in that, Including: A public server system, which includes a user registration and login module, a data processing module, and a database; The user registration and login module is used for user registration, user information modification, and user login; the database is used for storing data; the data processing module is used for processing the data in the database; A user terminal system, which includes a hospital terminal system and an enterprise terminal system. The hospital terminal system and the enterprise terminal system conduct data interaction, screen out the patents to be transformed that meet specific conditions, and perform matching between the hospital terminal and the enterprise terminal; both the hospital terminal and the enterprise terminal include a retrieval module, a transformation prospect evaluation module, a patent price analysis module, and a hospital-enterprise quick matching module; A display module. The public server system, the hospital terminal system, and the enterprise terminal system are all displayed on the terminal device through the display module.

2. The medical patent transformation public service system according to claim 1, wherein, The data processing module includes a technology portrait analysis module and a user portrait analysis module. The technology portrait analysis module is used for classifying the patents by technology portrait, and the user portrait analysis module is used for classifying the registered hospital terminal users and enterprise terminal users by user portrait; the database includes a patent database and a local database. The patent database is used for storing patent literature information and its corresponding technology portrait information, and the local database is used for storing user registration information and user portrait information.

3. The medical patent transformation public service system according to claim 2, wherein The hospital terminal system includes the following modules: A retrieval module, which is used for screening out several patents with a specific technology portrait from all the patents whose patent applicants are this hospital in the server database; A transformation prospect evaluation module, which is used for evaluating the transformation prospects of all the patents under a specific technology portrait, and screening out several potential patents with transformation prospects; A patent price analysis module, which is used for analyzing the prices of several potential patents with transformation prospects, and screening out the potential patents that meet the expected transformation price conditions as the target transformation patents; A hospital-enterprise quick matching module. The hospital-enterprise quick matching module is used for automatically or according to the user's selection to send a technology transformation supply intention notice of the target transformation patent to the matching target enterprise terminal. The technology transformation supply intention notice includes the target transformation patent information and its corresponding intended transformation price. The target enterprise terminal includes an enterprise terminal whose business scope matches the specific technology portrait and an enterprise terminal that has issued a technology transformation demand intention notice with the same technology portrait; It is also used for sending a consent or rejection message notice to the hospital-enterprise quick matching module of the enterprise terminal after receiving the technology transformation demand intention notice sent by the enterprise terminal system; A local database, which is used for storing the output data of the retrieval module, the transformation prospect evaluation module, the patent price analysis module, and the hospital-enterprise quick matching module; A user center, which is used for viewing the data and user information in the local database.

4. The medical patent transformation public service system according to claim 2, wherein, The enterprise terminal system includes the following modules: A retrieval module, which is used for screening out all the patents that meet a specific technology portrait from the database; A transformation prospect evaluation module, which is used for evaluating the transformation prospects of all the patents that meet a specific technology portrait, and screening out several potential patents with transformation prospects; A patent price analysis module, which is used to analyze the prices of a number of potential patents with transformation prospects as the intended transformation prices; A medical enterprise matching module, which is used to take all potential patents with transformation prospects and / or the patents corresponding to the technology transformation supply intention notices of the same specific technology portrait already released by the hospital side as target transformation patents, and automatically or according to user selection send the technology transformation demand intention notices of the target transformation patents to the hospital sides of all the valid right holders corresponding to the target transformation patents respectively. The technology transformation demand intention notices include the target transformation patent information and its corresponding intended transformation price; it is also used to send a message notice of consent or rejection to the medical enterprise matching module of the hospital side after receiving the transformation intention notice sent by the hospital side; A local database, which is used to store the output data of the retrieval module, the transformation prospect evaluation module, the patent price analysis module and the medical enterprise matching module; A user center, which is used to view the data and user information in the local database.

5. The medical patent transformation public service system according to any one of claims 1 to 4, characterized in that Medicine The presentation modes of the patent transformation public service system include any one or a combination of multiple of App, web page, WeChat official account, WeChat mini-program, enterprise WeChat and DingTalk.

6. The medical patent transformation public service system according to any one of claims 1 to 4, characterized in that The method for mutual matching between the hospital side system and the enterprise side system includes the following steps: S1: The hospital side system and the enterprise side system use the retrieval module to screen out all patents that meet the specific technology portrait from the server database; S2: The hospital side system and the enterprise side system use the transformation prospect evaluation module to evaluate the transformation prospects of all patents under the specific technology portrait, and screen out a number of potential patents with transformation prospects; S3: The hospital side system and the enterprise side system use the patent price analysis module to analyze the prices of a number of potential patents with transformation prospects, and screen out the potential patents that meet the expected transformation price conditions as target transformation patents; S4: The hospital side system and the enterprise side system use the medical enterprise matching module to automatically or according to user selection send the technology transformation intention notices of the target transformation patents to the matching objects, and the matching objects send message notices of consent or rejection.

7. The medical patent transformation public service system according to any one of claims 1 to 4, characterized in that The user side system further includes a transformation service agency side system, and the transformation service agency side system conducts data interaction with the hospital side system and the enterprise side system respectively.

8. The medical patent transformation public service system according to claim 7, wherein The transformation service agency side system includes a financial institution information system that provides pledge financing in medical transformation, an accountant or examiner firm information system that provides intangible asset evaluation, an intermediary service agency information system that brings its own enterprise or hospital demand projects, and an institution information system that provides cell experiments or animal experiments or verification of the effects of medical devices.

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