Patent value evaluation system for orthopedic medical instrument and diagnosis and treatment equipment

Abstract

The technical scheme of the invention discloses an orthopedic medical instrument and diagnosis and treatment equipment patent value evaluation system, which is characterized by comprising an orthopedic medical instrument and diagnosis and treatment equipment evaluation reference standard data acquisition module; an orthopedic medical instrument and diagnosis and treatment equipment patent data integration module; a multi-source data feature construction module; an orthopedic medical instrument and diagnosis and treatment equipment patent comprehensive value evaluation model; and an evaluation and result output module. According to the system disclosed by the invention, traditional patent evaluation indexes such as law, technology and economy are combined with use effects, clinical research data, registration and approval processes, supervision policies, production processes and processes and market dynamics to form a dynamic evaluation and prospect prediction model. Meanwhile, a multi-source data acquisition and machine learning algorithm is introduced, comprehensive analysis of patent technology innovation, clinical adaptability and market potential is realized, and an exclusive patent value evaluation system for orthopedic medical instruments and diagnosis and treatment equipment is constructed.

Description

Technical Field

[0001] This invention relates to a system for evaluating the patent value of patents for orthopedic medical devices and diagnostic equipment. Background Technology

[0002] Orthopedic medical devices and diagnostic equipment play an irreplaceable role in clinical diagnosis and treatment, encompassing multiple aspects such as endoscopes, surgical tools, imaging equipment (e.g., MRI, CT, C-arm machines), and functional testing equipment (e.g., ECG monitoring, ultrasound). These devices and equipment not only provide a precise operating platform for orthopedic surgery but also offer crucial support in the imaging diagnosis, perioperative management, and rehabilitation follow-up of diseases. With the rapid development of minimally invasive surgery, digital orthopedics, robot-assisted surgery, and artificial intelligence imaging, the technological iteration speed of orthopedic medical devices and diagnostic equipment has accelerated significantly, and the number of corresponding patent applications has shown a continuous upward trend. However, the quality of patents varies greatly, and some achievements are difficult to commercialize. How to scientifically identify patents with high technological content and clinical value has become an urgent problem to be solved.

[0003] Current patent valuation systems primarily focus on three dimensions: legal, technological, and economic. The legal dimension mainly concerns the stability of patent rights, the scope of protection, and enforceability. The technological dimension assesses the innovativeness, clinical applicability, and safety and effectiveness of medical devices, such as structural optimization, improved image clarity, and levels of automation and intelligence. The economic dimension typically uses three asset valuation methods: cost approach, market approach, and income approach, quantifying value by comparing the market size of the medical device, competing products, and expected future revenue. While these evaluation methods provide a relatively complete framework for patent valuation, their application in the orthopedic specialty remains insufficient.

[0004] Current evaluation systems for orthopedic medical devices and diagnostic equipment suffer from the following main problems: First, evaluation indicators tend to be general, lacking coverage of orthopedic clinical scenarios, such as the role of imaging equipment in intraoperative navigation and postoperative follow-up, and the adaptability of surgical tools to different surgical procedures. Second, data resources are limited; clinical usage data, equipment reliability monitoring, and hospital procurement data are difficult to obtain fully, restricting the accurate assessment of patent value. Third, existing methods still rely mainly on weighted scoring and static analysis, lacking utilization of multi-source data and dynamic prediction. Fourth, evaluation entities are fragmented; research institutions tend to focus on technology and applicability research, evaluation companies emphasize asset returns, and medical institutions pay more attention to clinical suitability, lacking a comprehensive evaluation platform that crosses entities and dimensions. These shortcomings result in limited practical application of current evaluation results in technology transfer, investment and financing decisions, and medical institution procurement.

[0005] In the patent evaluation process for orthopedic medical devices and diagnostic equipment, special consideration must be given to the characteristics of the field. On the one hand, the patent value of devices and equipment depends not only on technical parameters but also on lifecycle management factors such as clinical validation, registration approval, product certification, and post-market adverse event monitoring. On the other hand, the safety of device materials, the rationality of structural design, the accuracy and stability of imaging and functional testing equipment, and their suitability for clinicians' operating habits and patient compliance should be emphasized. Simultaneously, the decisive impact of external factors such as national centralized procurement policies, hospital bidding systems, access to medical insurance catalogs, and price negotiations on commercialization prospects must also be considered. Summary of the Invention

[0006] The present invention aims to solve the following technical problems:

[0007] (1) The existing patent evaluation index system has weak data capture capabilities and cannot match and analyze the patent data of orthopedic medical devices and diagnostic equipment with clinical guidelines, clinical trials, registration and approval systems, clinical use effects, product improvements and other content.

[0008] (2) The existing patent evaluation indicators are not broad enough, and lack quantitative evaluation indicators in terms of electrical performance, system reliability, total cost of ownership, evidence level, biocompatibility and cybersecurity of orthopedic medical devices and diagnostic equipment.

[0009] (3) The evaluation methods of the existing patent evaluation index system for orthopedic medical devices and diagnostic equipment are not advanced enough. They cannot integrate multi-source data from home and abroad with advanced index calculation methods, resulting in slow processing efficiency and low accuracy of results.

[0010] To address the aforementioned technical problems, the present invention discloses a patent value assessment system for orthopedic medical devices and diagnostic equipment, characterized by comprising:

[0011] The data acquisition module for the evaluation reference standard of orthopedic medical devices and diagnostic equipment is used to acquire and structure key data indicators related to the patent to be evaluated from multi-source heterogeneous data. The multi-source heterogeneous data comes from literature, clinical guidelines and expert consensus related to orthopedic medical devices and diagnostic equipment.

[0012] The orthopedic medical device and diagnostic equipment patent data integration module is used to efficiently and accurately collect patent-related data of the patents to be evaluated from multiple heterogeneous data sources, and to perform in-depth cleaning, sorting and feature extraction.

[0013] The multi-source data feature construction module is used to construct multi-source data features of the patent to be evaluated based on the key data indicators obtained by the orthopedic medical device and diagnostic equipment evaluation reference standard data acquisition module and the patent-related data obtained by the orthopedic medical device and diagnostic equipment patent data integration module, forming a comprehensive feature vector that is finally used as input for the subsequent comprehensive value assessment model of orthopedic medical device and diagnostic equipment patents;

[0014] The comprehensive value assessment model for patents of orthopedic medical devices and diagnostic equipment takes the multi-source data features D obtained by the multi-source data feature construction module as input, and uses six sub-models to calculate six dimensions: clinical and scientific research, policy and regulation, patent legal value, production and use, patent technology value and patent economic value. The evaluation data of each dimension is calculated, and then the outputs of the six sub-models are fused through a gating network model.

[0015] The evaluation and results output module is used to analyze, interpret, and visualize the results output by the comprehensive value assessment model for orthopedic medical devices and diagnostic equipment patents, and finally generate an intuitive patent value assessment report for all patents to be evaluated.

[0016] Preferably, the key data indicators include family size / geographical coverage data, legal status / litigation risk data, claim coverage, patent citation network analysis data, core patent expiration date, international standard adoption data, industry technical specification compliance data, improvement in major endpoints, 10-year revision rate, functional score change value, adverse event incidence rate, complication rate reduction, international guideline recommendation strength data, indication coverage consistency data, CE / FDA / NMPA certification validity data, label indication compliance data, inclusion in the national medical insurance catalog data, reimbursement ratio / out-of-pocket amount, historical winning bid price trend data, procurement volume market share data, target patient penetration rate, unit price × usage prediction model, BOM cost structure breakdown data, industry average gross profit comparison data, tertiary hospital coverage rate, channel cooperation stability data, satisfaction score, pain relief VAS score, ease of operation score, and intraoperative adaptation problem incidence rate.

[0017] Preferably, the data acquisition module for the evaluation reference standard of orthopedic medical devices and diagnostic equipment systematically sorts out the collected data and extracts text through regular expressions and rule matching, or uses Docano for manual annotation combined with natural language processing tools such as SpaCy for entity recognition and relation extraction, transforming unstructured policy text into structured data.

[0018] Preferably, the orthopedic medical device and diagnostic equipment patent data integration module includes a multi-dimensional heterogeneous data acquisition unit for orthopedic medical device and diagnostic equipment patents and a patent data cleaning and sorting unit for orthopedic medical device and diagnostic equipment, wherein:

[0019] The multi-source heterogeneous data acquisition unit for orthopedic medical devices and diagnostic equipment patents is used to acquire multi-source heterogeneous data indicators of all patents in the field of orthopedic medical devices and diagnostic equipment related to the patent to be evaluated.

[0020] The patent data cleaning and sorting unit for orthopedic medical devices and diagnostic equipment is used to perform in-depth cleaning, format conversion and structural integration of the original patent data obtained by the multi-dimensional heterogeneous data acquisition unit for orthopedic medical devices and diagnostic equipment.

[0021] Preferably, the patent data indicators collected by the orthopedic medical device and diagnostic equipment patent heterogeneous data acquisition unit include applicant, inventor, patent type, patent field, full text of technical specification, publication number, priority, patent family information, legal status, citation relationship, transfer and licensing records, litigation information, communication standard, designated country patent number, and expected expiration date.

[0022] Preferably, the data cleaning process adopted by the orthopedic medical device and diagnostic equipment patent data cleaning and sorting unit specifically includes the following steps:

[0023] Step 1, Data Retrieval and Preliminary Storage: The collected original patent data is initially stored according to fields;

[0024] Step 2, Data Exploration: Perform descriptive statistical analysis on the data to identify missing values, outliers, duplicates, and inconsistent formats;

[0025] Step 3, Field Cleaning: Deduplicat key fields, standardize their format, and correct any errors;

[0026] Step 4, Code and Category Mapping: Map the category codes in the original database to a unified category system;

[0027] Step 5, Ontology and Lexical Standardization: Standardize professional terms and normalize synonyms for text fields such as patent specifications and abstracts;

[0028] Step 6: Structured text extraction: Extract semi-structured or structured information from the patent text using rule matching, regular expressions, or natural language processing techniques.

[0029] Preferably, the multi-source data feature is represented as D, then:

[0030]

[0031] in, For feature data that can represent the value of patented technology, For characteristic data that can represent the economic value of a patent, For feature data that can represent the legal value of a patent, In order to represent the characteristic data of production and use, In order to represent the characteristic data of policies and regulations, To be able to represent characteristic data of clinical and research settings.

[0032] Preferably, the comprehensive value assessment model for the patents of orthopedic medical devices and diagnostic equipment is represented by E. β (·), then we have:

[0033]

[0034] Among them, G β (·) represents the computational model, D tech (·) represents a sub-model used to obtain data for evaluating the value dimension of patented technologies. eco (·) represents a sub-model used to obtain data for evaluating the economic value dimension of patents, D legal (·) represents a sub-model used to obtain data for evaluating the legal value dimension of patents. m&s (·) represents the sub-model used to obtain evaluation data for the production and usage dimensions, D p &s (·) represents the sub-model used to obtain evaluation data for policy and regulatory dimensions, D c&r (·) is a sub-model used to obtain evaluation data for clinical and research dimensions.

[0035] Preferably, sub-model D tech (·) Based on the rules set in the table below, the final patent technology value dimension evaluation data is obtained from the aspects of risk management closed loop, usability engineering, software life cycle, electrical safety and EMC, reprocessing / sterilization / packaging, network security and interoperability, system performance and reliability;

[0036]

[0037] Based on the table above, sub-model D tech (·) Obtain the final evaluation score D for the patent technology value dimension. tech The calculation process used is as follows:

[0038]

[0039] Where: the score for the secondary indicator is represented by S. iThis indicates that i = 1, 2, 3, 4, ..., 7, which correspond to the scores of the seven secondary indicators in the table above: S1: Risk Management Closed-Loop Score, S2: Availability Engineering Score, S3: Software Lifecycle Score, S4: Electrical Safety and EMC Score, S5: Reprocessing / Sterilization / Packaging Score, S6: Cybersecurity and Interoperability Score, and S7: System Performance and Reliability Score; W i C is the weight of the score of the i-th secondary indicator; cor For correction factors;

[0040] Sub-model D eco (·) Based on the rules set in the table below, the final evaluation data of the patent economic value dimension is obtained from the aspects of market size and growth, total cost of ownership, medical insurance / payment accessibility, centralized procurement / bidding procurement adaptation, price and competitive landscape, international market expansion, investment / cooperation attractiveness, and penetration rate;

[0041]

[0042] Based on the table above, sub-model D eco (·) Obtain the final patent economic value dimension score D eco The calculation process used is as follows:

[0043]

[0044] Where: the score for the secondary indicator is represented by S. i The values ​​i = 1, 2, 3, 4, ..., 8 correspond to the scores of the eight secondary indicators in the table above: S1: Market size and growth score, S2: Total cost of ownership (TCO) score, S3: Healthcare / payment accessibility score, S4: Centralized procurement / bidding procurement matching score, S5: Price and competitive landscape score, S6: International market expansion score, S7: Investment / cooperation attractiveness score, and S8: Penetration rate score.

[0045] Sub-model D legal (·) Based on the rules set in the table below, the final evaluation data of the patent legal value dimensions are obtained from the aspects of patent family breadth and geographical coverage, remaining protection period and expiration structure, claim coverage, procedural robustness / legal status, citation influence, FTO, litigation / opposition risk, and licensing / transaction potential;

[0046]

[0047] Based on the table above, sub-model D legal (·) Obtain the final patent legal value dimension score D legal The calculation process used is as follows:

[0048]

[0049] Where: the score for the secondary indicator is represented by S. i The values ​​i = 1, 2, 3, 4, ..., 8 correspond to the scores of the eight secondary indicators in the table above: S1: Patent family breadth and geographic coverage score, S2: Remaining protection period and expiration structure score, S3: Claim coverage score, S4: Procedural robustness / legal status score, S5: Citation influence score, S6: FTO score, S7: Litigation / opposition risk score, and S8: Licensing / transaction potential score.

[0050] Sub-model D m&s (·) Based on the rules set in the table below, the final production and use dimension evaluation data are obtained from the aspects of channels and services, reprocessing / sterilization compliance, biocompatibility, post-market surveillance and vigilance, PROMs / functional scores, and quality system consistency.

[0051]

[0052] Based on the table above, sub-model D m&s (·) Obtain the final production and usage dimension score D m&s The calculation process used is as follows:

[0053]

[0054] Where: the score for the secondary indicator is represented by S. i The values ​​i = 1, 2, 3, 4, ..., 6 correspond to the scores of the six secondary indicators in the table above: S1: Channel and service score, S2: Reprocessing / sterilization compliance score, S3: Biocompatibility score, S4: Post-market surveillance and vigilance score, S5: PROMs / functional rating score, and S6: Quality system consistency score.

[0055] Sub-model D p&s (·) Based on the rules set in the table below, the final policy and regulatory evaluation data will be obtained from the aspects of registration path and status, UDI / traceability, cybersecurity compliance, ethics and privacy compliance, standard / guideline adoption, and innovation / priority review;

[0056]

[0057] Based on the table above, sub-model D p&s (·) Obtain the final policy and regulatory dimension score D p&s The calculation process used is as follows:

[0058]

[0059] Where: the score for the secondary indicator is represented by S. iThis means that i = 1, 2, 3, 4, ..., 6, which correspond to the scores of the 6 secondary indicators in the table above: S1: Registration path and status score, S2: UDI / traceability score, S3: Cybersecurity compliance score, S4: Ethics and privacy compliance score, S5: Standard / guideline adoption score, and S6: Innovation / priority review score.

[0060] Sub-model D c&r (·) Based on the rules set in the table below, the final clinical and research dimension evaluation data are obtained from the aspects of evidence level and consistency, RWE / registration data, revision / reoperation rate, complications / adverse events, accuracy / consistency, academic visibility, and guideline / consensus adoption.

[0061]

[0062] Based on the table above, sub-model D c&r (·) Obtain the final clinical and research dimension score D c&r The calculation process used is as follows:

[0063]

[0064] Where: the score for the secondary indicator is represented by S. i Let i = 1, 2, 3, 4, ..., 7, which correspond to the scores of the seven secondary indicators in the table above: S1: Level of Evidence and Consistency Score, S2: RWE / Registry Data Score, S3: Revision / Reoperation Rate Score, S4: Complication / Adverse Event Score, S5: Accuracy / Consistency Score, S6: Academic Visibility Score, and S7: Guideline / Consensus Adoption Score.

[0065] Preferably, the evaluation and result output module includes a value ranking and grade division unit, a result visualization unit, and a patent value analysis report generation unit, wherein:

[0066] The value ranking and classification unit is used to rank the comprehensive value scores of all patents to be evaluated output by the comprehensive value assessment model for orthopedic medical devices and diagnostic equipment in percentile order, and calculates the percentile ranking of each sample in descending order.

[0067] The results visualization unit generates multi-dimensional patent value analysis charts based on the evaluation results output by the value ranking and grade division unit and / or the comprehensive value evaluation model for orthopedic medical devices and diagnostic equipment patents.

[0068] The patent value analysis report generation unit generates a systematic patent value analysis report for orthopedic medical devices and diagnostic equipment based on the evaluation results output by the value ranking and grading unit and / or the comprehensive value assessment model for orthopedic medical devices and diagnostic equipment.

[0069] The system disclosed in the present invention combines traditional patent evaluation indicators such as law, technology, and economy with usage effects, clinical research data, registration and approval processes, regulatory policies, production processes and procedures, and market dynamics to form a dynamic evaluation and forward-looking prediction model. At the same time, the present invention introduces multi-source data collection and machine learning algorithms to achieve comprehensive analysis of the innovation, clinical adaptability, and market potential of patent technologies, and constructs a patent value evaluation system exclusive to orthopedic medical devices and diagnostic and treatment equipment. By combining patent value evaluation with the promotion of achievement transformation, the scientific nature, adaptability, and practicality of the evaluation results are ensured, providing strong support for the research and development, investment, and policy formulation of orthopedic medical devices and diagnostic and treatment equipment.

[0070] Through the value evaluation system disclosed in the present invention, the multi-dimensional value composition of patents for orthopedic medical devices and diagnostic and treatment equipment can be systematically revealed, covering key indicators such as the innovation of device structure, clinical adaptability, operation safety, imaging performance, registration and approval, and policy environment.

[0071] Compared with traditional general evaluation methods, the evaluation index system established in the present invention has stronger pertinence and advancement. It can not only capture the core technical features of orthopedic surgical tools, imaging diagnostic equipment, and functional monitoring instruments, but also reflect their true value in the process of clinical application, policy supervision, and market access. This system helps investors identify high-value patents among similar technologies, promotes scientific research institutions and enterprises to optimize their R & D directions, and also provides a clear transformation orientation for medical institutions and entrepreneurs, thus realizing the scientific evaluation and forward-looking judgment of the patent value in the field of orthopedic medical devices and diagnostic and treatment equipment. Brief Description of the Drawings

[0072] Figure 1 Schematically shows the working process of the system disclosed in the present invention. Detailed Embodiments

[0073] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. In addition, it should be understood that after reading the content taught in the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present invention. <于

[0074] As Figure 1 shown, a patent value evaluation system for orthopedic medical devices and diagnostic and treatment equipment disclosed in an embodiment of the present invention includes a module for obtaining evaluation reference standard data of orthopedic medical devices and diagnostic and treatment equipment, a module for integrating patent data of orthopedic medical devices and diagnostic and treatment equipment, a module for constructing multi-source data features, a comprehensive value evaluation model for patents of orthopedic medical devices and diagnostic and treatment equipment, and a module for evaluation and result output.

[0075] The data acquisition module for the evaluation reference standard of orthopedic medical devices and diagnostic equipment is used to acquire and structure key data indicators from multi-source heterogeneous data related to orthopedic medical devices and diagnostic equipment, including relevant literature, clinical guidelines, and expert consensus, to reflect the guiding significance and application potential of the patent technology in clinical practice.

[0076] In one preferred embodiment of the present invention, the key data indicators acquired by the orthopedic medical device and diagnostic equipment evaluation reference standard data acquisition module are shown in Table 1 below.

[0077] Table 1. Data on Evaluation Reference Standards for Orthopedic Medical Devices and Diagnostic Equipment

[0078]

[0079] As shown in Table 1 above, the data sources include literature and academics, guidelines and standards, registration and real-world data, regulation and labeling, patents and legal matters, economics and channels, and other aspects. Among them, literature and academics include WOS, PubMed / PMC, and conference proceedings; guidelines and standards include AAOS / NICE / WHO / Chinese Medical Association, ISO / ASTM / YY / group standards; registration and real-world data include joint registrations such as NJR and AOANJRR, hospital HIS / claims (compliance and anonymization); regulation and labeling data include FDA (510(k) / DeNovo / PMA, MAUDE), EUDAMED; patents and legal matters include WIPO, INPADOC, USPTO / EPO / CNIPA, and Darts-IP; economics and channels include centralized procurement portals, medical insurance catalogs, bidding announcements, financial annual reports / prospectuses; and other aspects include relevant information obtained from official authoritative websites such as the website of the National Medical Products Administration, the China Medical Device Information Network, and the website of the National Healthcare Security Administration.

[0080] The data acquisition module for the evaluation reference standard of orthopedic medical devices and diagnostic equipment systematically sorts out the collected data and extracts text through regular expressions and rule matching, or uses Docano for manual annotation combined with natural language processing tools such as SpaCy for entity recognition and relation extraction, transforming unstructured policy texts into structured data.

[0081] The orthopedic medical device and diagnostic equipment patent data integration module is used to efficiently and accurately collect patent-related data of the patents to be evaluated from multiple heterogeneous data sources, and perform in-depth cleaning, sorting, and feature extraction to provide high-quality input for the upper-level evaluation model. In this embodiment of the invention, the orthopedic medical device and diagnostic equipment patent data integration module further includes an orthopedic medical device and diagnostic equipment patent multi-source heterogeneous data acquisition unit and an orthopedic medical device and diagnostic equipment patent data cleaning and sorting unit.

[0082] The orthopedic medical device and diagnostic equipment patent multi-source heterogeneous data acquisition unit is used to acquire multi-source heterogeneous data indicators of all patents in the field of orthopedic medical devices and diagnostic equipment related to the patent to be evaluated. In one preferred embodiment of the invention, the orthopedic medical device and diagnostic equipment patent multi-source heterogeneous data acquisition unit mainly uses API interfaces to batch filter and collect series of patent data related to orthopedic medical devices and diagnostic equipment from professional patent data platforms such as the Incopat database and the PatSnap database. In another preferred embodiment of the invention, the orthopedic medical device and diagnostic equipment patent multi-source heterogeneous data acquisition unit uses the patent number as a search condition and searches open databases such as Google Patents as a supplement to ensure data comprehensiveness.

[0083] The patent data indicators collected by the orthopedic medical device and diagnostic equipment patent multi-dimensional heterogeneous data acquisition unit include, but are not limited to: applicant, inventor, patent type, patent field (such as IPC classification number), full text of technical specification, publication number, priority, patent family information, legal status, citation relationship (cited / referenced), assignment / licensing records, litigation information, communication standards, designated country patent number, expected expiration date, and other fields.

[0084] The patent data cleaning and sorting unit for orthopedic medical devices and diagnostic equipment is used to perform in-depth cleaning, format conversion and structural integration of the original patent data obtained by the multi-dimensional heterogeneous data acquisition unit for orthopedic medical devices and diagnostic equipment patents, to ensure the high quality and usability of the data, and to provide standardized input for subsequent feature extraction.

[0085] In a preferred embodiment of the present invention, the data cleaning process employed by the orthopedic medical device and diagnostic equipment patent data cleaning and sorting unit specifically includes the following steps:

[0086] Step 1, Data Retrieval and Preliminary Storage: The collected original patent data is initially stored according to fields;

[0087] Step 2, Data Exploration: Perform descriptive statistical analysis on the data to identify missing values, outliers, duplicates, and inconsistent formats;

[0088] Step 3, Field Cleaning: Deduplicat key fields (e.g., duplicate patent records), standardize formats (e.g., standardize date formats and unify text encoding), and correct error values;

[0089] Step 4, Code and Classification Mapping: Map the classification codes (such as IPC classifications) in the original database to a unified classification system;

[0090] Step 5, Ontology and Lexical Standardization: Standardize professional terms and normalize synonyms for text fields such as patent specifications and abstracts;

[0091] Step 6: Structured text extraction: Extract semi-structured or structured information (such as technical solutions, main effects, etc.) from the patent text using rule matching, regular expressions, or natural language processing (NLP) techniques (such as keyword extraction and dependency parsing).

[0092] The multi-source data feature construction module is used to construct multi-source data features of the patent to be evaluated based on the key data indicators obtained by the orthopedic medical device and diagnostic equipment evaluation reference standard data acquisition module and the patent-related data obtained by the orthopedic medical device and diagnostic equipment patent data integration module.

[0093] The multi-source data feature construction module provides a unified data view that integrates all dimensions of features of the patent to be evaluated. Among them, each data item can be logically associated through the patent ID or other related identifiers to form a comprehensive feature vector that is ultimately used as input for the comprehensive value assessment model of orthopedic medical devices and diagnostic equipment patents.

[0094] In a preferred embodiment of the present invention, the multi-source data features constructed by the multi-source data feature construction module are represented as D, then:

[0095]

[0096] in, For feature data that can represent the value of patented technology, For characteristic data that can represent the economic value of a patent, For feature data that can represent the legal value of a patent, ("m" stands for "manufacturing", and "s" stands for "using") which represents the characteristic data that can represent production and use. To represent the characteristic data of policies and regulations ("p" stands for "policies" and "s" stands for "supervision"), ("c" stands for "clinic", and "r" stands for "research") These are characteristic data that can represent clinical and research data.

[0097] The comprehensive value assessment model for orthopedic medical device and diagnostic equipment patents is used to calculate the final value score of orthopedic medical device and diagnostic equipment patents. The model takes multi-source data features D obtained from the multi-source data feature construction module as input, and uses six sub-models to calculate the evaluation data for each of the six dimensions: clinical and research, production and use, policy and regulation, patent legal value, patent technological value, and patent economic value. Finally, a gating network model is used to fuse the outputs of the six sub-models.

[0098] The comprehensive value assessment model for patents in orthopedic medical devices and diagnostic equipment is represented by E. β (·), then we have:

[0099]

[0100] Among them, G β (·) represents the computational model, D tech (·) represents a sub-model used to obtain data for evaluating the value dimension of patented technologies. eco (·) represents a sub-model used to obtain data for evaluating the economic value dimension of patents, D legal (·) represents a sub-model used to obtain data for evaluating the legal value dimension of patents. m&s (·) represents the sub-model used to obtain evaluation data for the production and usage dimensions, D p &s (·) represents the sub-model used to obtain evaluation data for policy and regulatory dimensions, D c&r (·) is a sub-model used to obtain evaluation data for clinical and research dimensions.

[0101] In E β (D) Model: Sub-model D tech (·) Based on the rules set in Table 2 below, the final patent technology value dimension evaluation data is obtained from the aspects of risk management closed loop, usability engineering, software life cycle, electrical safety and EMC, reprocessing / sterilization / packaging, network security and interoperability, system performance and reliability.

[0102] Table 2 Sub-model D tech Scoring criteria and data extraction methods

[0103]

[0104] Based on Table 2 above, sub-model D tech (·) Obtain the final evaluation score D for the patent technology value dimension. tech The calculation process used is as follows:

[0105]

[0106] Where: the score for the secondary indicator is represented by S. i This indicates that i = 1, 2, 3, 4, ..., 7, which correspond to the scores of the seven secondary indicators in Table 2 above. Specifically: S1: Risk Management Closed-Loop Score, S2: Availability Engineering Score, S3: Software Lifecycle Score, S4: Electrical Safety and EMC Score, S5: Reprocessing / Sterilization / Packaging Score, S6: Cybersecurity and Interoperability Score, and S7: System Performance and Reliability Score; W i C is the weight of the score of the i-th secondary indicator; cor The correction coefficient is used to adjust the scores of the primary indicators to suit actual needs or further standardize the data. Each secondary indicator has a score S. i and weight W i All scores are given in Table 2 above. The score for each secondary indicator is obtained by comprehensively evaluating each sub-item, ranging from 0 to 10.

[0107] Sub-model D eco (·) Based on the rules set in Table 3 below, the final patent economic value dimension evaluation data is obtained from the aspects of market size and growth, total cost of ownership (TCO), medical insurance / payment accessibility, centralized procurement / bidding procurement adaptation, price and competitive landscape, international market expansion, investment / cooperation attractiveness, and penetration rate.

[0108] Table 3 Sub-model D eco Scoring criteria and data extraction methods

[0109]

[0110] Based on Table 3 above, sub-model D eco (·) Obtain the final patent economic value dimension score D eco The calculation process used is as follows:

[0111]

[0112] Where: the score for the secondary indicator is represented by S. i This indicates that i = 1, 2, 3, 4, ..., 8, corresponding to the scores of the eight secondary indicators in Table 3 above. Specifically: S1: Market size and growth score, S2: Total Cost of Ownership (TCO) score, S3: Healthcare / Payment accessibility score, S4: Centralized procurement / Bidding procurement matching score, S5: Price and competitive landscape score, S6: International market expansion score, S7: Investment / cooperation attractiveness score, and S8: Penetration rate score; W i C is the weight of the score of the i-th secondary indicator; cor The correction coefficient is used to adjust the scores of the primary indicators to suit actual needs or further standardize the data. Each secondary indicator has a score S. i and weight W iAll scores are given in Table 3 above. The score for each secondary indicator is obtained by comprehensively evaluating each sub-item, ranging from 0 to 10.

[0113] Sub-model D legal (·) Based on the rules set in Table 4 below, the final evaluation data of the patent legal value dimensions are obtained from the aspects of patent family breadth and geographical coverage, remaining protection period and expiration structure, claim coverage, procedural robustness / legal status, citation influence, FTO, litigation / opposition risk, and licensing / transaction potential.

[0114] Table 4 Sub-model D legal Scoring criteria and data extraction methods

[0115]

[0116] Based on Table 4 above, sub-model D legal (·) Obtain the final patent legal value dimension score D legal The calculation process used is as follows:

[0117]

[0118] Where: the score for the secondary indicator is represented by S. i Indicated by i = 1, 2, 3, 4, ..., 8, these correspond to the scores of the eight secondary indicators in Table 4 above, specifically: S1: Patent family breadth and geographic coverage score; S2: Remaining protection period and expiration structure score; S3: Claim coverage score; S4: Procedural robustness / legal status score; S5: Citation impact score; S6: FTO score; S7: Litigation / opposition risk score; S8: Licensing / transaction potential score; W i C is the weight of the score of the i-th secondary indicator; cor The correction coefficient is used to adjust the scores of the primary indicators to suit actual needs or further standardize the data. Each secondary indicator has a score S. i and weight W i All scores are given in Table 4 above. The score for each secondary indicator is obtained by comprehensively evaluating each sub-item, ranging from 0 to 10.

[0119] Sub-model D m&s (·) Based on the rules set in Table 5 below, the final production and use dimension evaluation data are obtained from the aspects of channels and services, reprocessing / sterilization compliance, biocompatibility, post-market surveillance and vigilance, PROMs / functional scores, and quality system consistency.

[0120] Table 5 Sub-model D m&s Scoring criteria and data extraction methods

[0121]

[0122] Based on Table 5 above, sub-model D m&s (·) Obtain the final production and usage dimension score D m&s The calculation process used is as follows:

[0123]

[0124] Where: the score for the secondary indicator is represented by S. i This indicates that i = 1, 2, 3, 4, ..., 6, corresponding to the scores of the six secondary indicators in Table 5 above, specifically: S1: Channel and Service Score, S2: Reprocessing / Sterilization Compliance Score, S3: Biocompatibility Score, S4: Post-Market Monitoring and Vigilance Score, S5: PROMs / Functional Rating Score, and S6: Quality System Consistency Score; W i C is the weight of the score of the i-th secondary indicator; cor The correction coefficient is used to adjust the scores of the primary indicators to suit actual needs or further standardize the data. Each secondary indicator has a score S. i and weight W i All scores are given in Table 5 above. The score for each secondary indicator is obtained by comprehensively evaluating each sub-item, ranging from 0 to 10.

[0125] Sub-model D p&s (·) Based on the rules set in Table 6 below, the final policy and regulatory dimension evaluation data are obtained from the aspects of registration path and status, UDI / traceability, cybersecurity compliance, ethics and privacy compliance, standard / guideline adoption, and innovation / priority review.

[0126] Table 6 Sub-model D p&s Scoring criteria and data extraction methods

[0127]

[0128] Based on Table 6 above, sub-model D p&s (·) Obtain the final policy and regulatory dimension score D p&s The calculation process used is as follows:

[0129]

[0130] Where: the score for the secondary indicator is represented by S. i This indicates that i = 1, 2, 3, 4, ..., 6, corresponding to the scores of the six secondary indicators in Table 6 above, specifically: S1: Registration Path and Status Score, S2: UDI / Traceability Score, S3: Cybersecurity Compliance Score, S4: Ethics and Privacy Compliance Score, S5: Standard / Guideline Adoption Score, and S6: Innovation / Priority Review Score; W iIt is the weight of the score of the o-th secondary indicator; C cor The correction coefficient is used to adjust the scores of the primary indicators to suit actual needs or further standardize the data. Each secondary indicator has a score S. i and weight W i All scores are given in Table 6 above. The score for each secondary indicator is obtained by comprehensively evaluating each sub-item, ranging from 0 to 10.

[0131] Sub-model D c&r (·) Based on the rules set in Table 7 below, the final clinical and research dimension evaluation data are obtained from the aspects of evidence level and consistency, RWE / registration data, revision / reoperation rate, complications / adverse events, accuracy / consistency, academic visibility, and guideline / consensus adoption.

[0132] Table 7 Sub-model D c&r Scoring criteria and data extraction methods

[0133]

[0134] Based on Table 7 above, sub-model D c&r (·) Obtain the final clinical and research dimension score D c&r The calculation process used is as follows:

[0135]

[0136] Where: the score for the secondary indicator is represented by S. i This indicates that i = 1, 2, 3, 4, ..., 7, corresponding to the scores of the seven secondary indicators in Table 7 above, specifically: S1: Level of Evidence and Consistency Score, S2: RWE / Registry Data Score, S3: Revision / Reoperation Rate Score, S4: Complication / Adverse Event Score, S5: Accuracy / Consistency Score, S6: Academic Visibility Score, and S7: Guideline / Consensus Adoption Score; W i C is the weight of the score of the i-th secondary indicator; cor The correction coefficient is used to adjust the scores of the primary indicators to suit actual needs or further standardize the data. Each secondary indicator has a score S. i and weight W i All scores are given in Table 7 above. The score for each secondary indicator is obtained by comprehensively evaluating each sub-item, ranging from 0 to 10.

[0137] E, a comprehensive value assessment model for patents of orthopedic medical devices and diagnostic equipment. β(·) To validate and evaluate the effectiveness and reliability of the comprehensive value score calculated by the model, an independent validation set can be constructed. For each patent evaluated in this validation set, multiple field experts can be invited to conduct independent subjective scoring or ranking to establish a "true" or "expert consensus" evaluation benchmark. Subsequently, the comprehensive value evaluation model E for orthopedic medical devices and diagnostic equipment patents will be compared. β The performance of the model is evaluated by assessing the consistency between the calculated comprehensive value score and the expert scores or ranking results. Specifically, the Spearman correlation coefficient between the comprehensive value score output by the model and the expert scores is calculated to measure their consistency in ranking; at the same time, nonparametric rank correlation coefficients such as Kendall's Tau can also be used for auxiliary verification.

[0138] The evaluation and results output module is used to analyze, interpret, and visualize the results output by the comprehensive value assessment model for orthopedic medical devices and diagnostic equipment patents, and finally generate an intuitive patent value assessment report for all patents to be evaluated.

[0139] In a preferred embodiment of the present invention, the evaluation and result output module further includes a value ranking and grade division unit, a result visualization unit, and a patent value analysis report generation unit.

[0140] The Value Ranking and Grading Unit is used to rank the comprehensive value scores of all patents to be evaluated from the comprehensive value assessment model for orthopedic medical devices and diagnostic equipment by percentile (PR). The percentile ranking for each sample is calculated in descending order. Based on the percentile ranking, the Value Ranking and Grading Unit further divides all patents to be evaluated into different evaluation levels, including: High Value (Top 20%): PRi ≥ 80%, Good Value (20%–50%): 50% ≤ PRi < 80%, Medium Value (50%–80%): 20% ≤ PRi < 50%, and Low Value (Bottom 20%): PRi < 20%.

[0141] The results visualization unit generates multi-dimensional patent value analysis charts based on the evaluation results output by the value ranking and grading unit and / or the comprehensive value assessment model for orthopedic medical devices and diagnostic equipment patents. In a preferred embodiment of this invention, the patent value analysis charts may include: a radar chart: used to display the score distribution of a single patent across six dimensions: clinical and research, production and use, policy and regulation, patent legal value, patent technological value, and patent economic value; a scatter plot / bubble chart: used to show the distribution of patents across two core dimensions (such as technological innovation vs. market potential), and can be overlaid with color or size to reflect the total value; a bar chart / line chart: used to display trend analysis results, such as the annual changes in patent value in a specific technical field; and a heat map—used to display the distribution of patent value among different technical subfields or applicants.

[0142] The patent value analysis report generation unit generates a systematic patent value analysis report for orthopedic medical devices and diagnostic equipment based on the evaluation results output by the value ranking and grading unit and / or the comprehensive value assessment model for orthopedic medical device and diagnostic equipment patents. In a preferred embodiment of this invention, the report includes: the purpose of the evaluation, an overview of the model methodology, key evaluation results (e.g., a list of high-value patents, the distribution of patent numbers at each grade), key insights (e.g., the technical characteristics of high-value patents, market trends), recommendations (e.g., technology layout, investment direction), and detailed charts and data support.

Claims

1. An orthopedic medical instrument and diagnosis and treatment equipment patent value evaluation system, characterized in that, Comprise: Orthopaedic medical devices and diagnosis and treatment equipment evaluation reference standard data acquisition module, for obtaining and structuring key data indicators related to the patent to be evaluated from multi-source heterogeneous data, wherein the multi-source heterogeneous data is derived from orthopaedic medical devices and diagnosis and treatment equipment related literature, clinical guidelines, expert consensus; Orthopaedic medical devices and diagnosis and treatment equipment patent data integration module, for efficiently and accurately collecting patent-related data of the patent to be evaluated from multiple heterogeneous data sources, and performing deep cleaning, carding and feature extraction; Multi-source data feature construction module, for constructing multi-source data features of the patent to be evaluated based on the key data indicators obtained by the orthopaedic medical devices and diagnosis and treatment equipment evaluation reference standard data acquisition module and the patent-related data obtained by the orthopaedic medical devices and diagnosis and treatment equipment patent data integration module, forming a comprehensive feature vector for subsequent orthopaedic medical devices and diagnosis and treatment equipment patent comprehensive value evaluation model input; Orthopaedic medical devices and diagnosis and treatment equipment patent comprehensive value evaluation model, taking the multi-source data features D obtained by the multi-source data feature construction module as input, using six sub-models to calculate the six dimensions of clinical and scientific research, policy and supervision, patent legal value, production and use, patent technology value and patent economic value, respectively, to calculate the dimension evaluation data, and then fuse the outputs of the six sub-models through a gating network model; Evaluation and result output module, for analyzing, interpreting and visualizing the results output by the orthopaedic medical devices and diagnosis and treatment equipment patent comprehensive value evaluation model, and finally generating an intuitive patent value evaluation report for all patents to be evaluated.

2. The orthopedic medical instrument and diagnosis and treatment equipment patent value assessment system of claim 1, wherein, The key data indicators include family size / territorial coverage data, legal status / litigation risk data, claim coverage, patent citation network analysis data, core patent expiration date, international standard adoption data, industry technical specification compliance data, main endpoint improvement amplitude, 10-year revision rate, functional score change value, adverse event rate, complication reduction ratio, international guideline recommendation intensity data, indication coverage consistency data, CE / FDA / NMPA certification effectiveness data, label indication compliance data, national medical insurance directory inclusion data, payment proportion / self-pay amount, historical winning price trend data, procurement quantity market share data, target patient penetration rate, unit price x dosage prediction model, BOM cost structure disassembly data, industry average gross profit comparison data, tertiary hospital coverage rate, channel cooperation stability data, satisfaction score, pain relief VAS score, operation convenience score, and intraoperative adaptation problem incidence.

3. The orthopedic medical instrument and diagnosis and treatment equipment patent value assessment system of claim 1, wherein, The orthopaedic medical devices and diagnosis and treatment equipment evaluation reference standard data acquisition module systematically carding the collected data, and through regular expression and rule matching for text extraction, or using Doccano for manual annotation combined with SpaCy and other natural language processing tools for entity recognition and relationship extraction, converting unstructured policy text into structured data.

4. The orthopedic medical instrument and diagnosis and treatment equipment patent value assessment system of claim 1, wherein, The orthopedic medical instrument and diagnosis and treatment equipment patent data integration module comprises an orthopedic medical instrument and diagnosis and treatment equipment patent multi-element heterogeneous data acquisition unit and an orthopedic medical instrument and diagnosis and treatment equipment patent data cleaning and carding unit, wherein: The orthopedic medical instrument and diagnosis and treatment equipment patent multi-element heterogeneous data acquisition unit is used to acquire multi-source heterogeneous data indexes of all orthopedic medical instrument and diagnosis and treatment equipment field patents related to the patent to be evaluated; The orthopedic medical instrument and diagnosis and treatment equipment patent data cleaning and carding unit is used to deeply clean, format convert and structure integrate the original patent data obtained by the orthopedic medical instrument and diagnosis and treatment equipment patent multi-element heterogeneous data acquisition unit.

5. The orthopedic medical instrument and diagnosis and treatment equipment patent value assessment system of claim 4, wherein, The patent data indexes collected by the orthopedic medical instrument and diagnosis and treatment equipment patent multi-element heterogeneous data acquisition unit include applicant, inventor, patent type, patent field, technical specification full text, publication number, priority, patent family information, legal status, citation relationship, transfer license record, litigation information, communication standard, designated national patent number, and expected expiration date.

6. The orthopedic medical instrument and diagnosis and treatment equipment patent value assessment system of claim 4, wherein, The data cleaning process adopted by the orthopedic medical instrument and diagnosis and treatment equipment patent data cleaning and carding unit specifically comprises the following steps: Step 1, data retrieval and preliminary storage: the collected original patent data is preliminarily stored according to the field; Step 2, data exploration: descriptive statistical analysis is performed on the data to identify missing values, outliers, duplicates and inconsistent formats; Step 3, field cleaning: key fields are de-duplicated, format unified and error value corrected; Step 4, code and classification mapping: mapping the classification codes in the original database to a unified classification system; Step 5, ontology and vocabulary standardization: standardizing professional vocabulary and synonym normalization processing for text fields such as patent specification and abstract; Step 6, structured text extraction: using rule matching, regular expression or natural language processing technology to extract semi-structured or structured information from patent text.

7. The orthopedic medical instrument and diagnosis and treatment equipment patent value assessment system of claim 1, wherein, The multi-source data feature is represented as D, then: wherein D p"t$％t t " c$ characteristic data capable of representing the economic value of the patent, characteristic data capable of representing the economic value of the patent, characteristic data capable of representing the legal value of the patent, characteristic data capable of representing production and use, characteristic data capable of representing policy and regulation, characteristic data capable of representing clinical and scientific research.

8. The orthopedic medical instrument and diagnosis and treatment equipment patent value assessment system of claim 7, wherein, The orthopedic medical instrument and diagnosis and treatment equipment patent comprehensive value evaluation model is expressed as E p (·), then: Wherein, G β (·) is a calculation model, D t$c( (·) is a sub-model for obtaining patent technology value dimension evaluation data, D $c+ (·) is a sub-model for obtaining patent economic value dimension evaluation data, D l$g"l (·) is a sub-model for obtaining patent legal value dimension evaluation data, D ,&. (·) is a sub-model for obtaining production and use dimension evaluation data, D p&. (·) is a sub-model for obtaining policy and supervision dimension evaluation data, D c&0 (·) is a sub-model for obtaining clinical and scientific research dimension evaluation data.

9. The orthopedic medical instrument and diagnostic equipment patent value assessment system of claim 8, wherein, Sub-model D t$c( (·) Obtain the final patent technology value dimension evaluation data from the aspects of risk management closed loop, availability engineering, software life cycle, electrical safety and EMC, reprocessing / sterilization / packaging, network security and interoperability, system performance and reliability based on the rules set in the following table; Based on the above table, sub-model D t$c( (·) Obtain the final patent technology value dimension evaluation score D t$c( The calculation process adopted is: wherein: the secondary index score is represented by Si, wherein i = 1, 2, 3, 4, …, 7, corresponding to the 7 secondary index scores in the above table, S4: Risk Management Closed Loop Score, S5: Usability Engineering Score, S6: Software Life Cycle Score, S7: Electrical Safety and EMC Score, S8: Reprocessing / Sterilization / Packaging Score, S9: Network Security and Interoperability Score, S2: System Performance and Reliability Score; Wi is the weight of the i-th secondary index score; C c+0 is a correction factor; Sub-model D $c+ (·) Based on the rules set in the table below, the final patent economic value dimension evaluation data is obtained from market size and growth, total cost of ownership, medical insurance / payment accessibility, collection / recruitment adaptation, price and competition pattern, international market expansion, investment / cooperation attractiveness, penetration rate. Based on the above table, sub-model D $c+ (·) Obtain the final patent economic value dimension score D $c+ The calculation process used is: wherein: the secondary indicator score is represented by Si, i = 1, 2, 3, 4, …, 8, corresponding to the eight secondary indicator scores in the table above, S4: market size and growth score, S5: total cost of ownership (TCO) score, S6: insurance / payment accessibility score, S7: centralized procurement / invitation procurement fit score, S8: price and competition landscape score, S9: international market expansion score, S2: investment / cooperation attractiveness score, S : : penetration rate score; Sub-model D l$g"l (·) Based on the rules set in the table below, the final patent legal value dimension evaluation data is obtained from the patent family breadth and regional coverage, remaining protection period and expiration structure, claim coverage, procedure robustness / legal status, citation influence, FTO, litigation / opposition risk, and licensing / trading potential. Based on the above table, sub-model D l$g"l (·) Obtain the final patent legal value dimension score D l$g"l The calculation process used is: wherein: the secondary indicator score is represented by Si, i = 1, 2, 3, 4, …, 8, corresponding to the eight secondary indicator scores in the table above, S4: patent family breadth and geographical coverage score, S5: remaining term and expiration structure score, S6: claim coverage score, S7: procedural robustness / law status score, S8: citation impact score, S9: FTO score, S2: litigation / objection risk score, S : : licensing / trading potential score; Submodel D ,&. (·) Obtain final production and use dimension evaluation data from the channel and service, reprocessing / sterilization compliance, biocompatibility, post-market monitoring and alertness, PROMs / functional scores, quality system consistency based on the rules set in the following table; Based on the above table, sub-model D ,&. (·) Obtain the final production and use dimension score D ,&. The calculation process used is: Wherein: the secondary index score is represented by S1, i=1, 2, 3, 4, …, 6, respectively corresponding to the secondary index scores in the above table, S4: channel and service score, S5: reprocessing / sterilization compliance score, S6: biocompatibility score, S7: post-marketing monitoring and alert score, S8: PROMs / function score score, S9: quality system consistency score; Submodel D p&. (·) Obtain the final policy and regulatory dimension evaluation data from the registration path and status, UDI / tracing, network security compliance, ethics and privacy compliance, standard / guideline adoption, innovation / priority review, based on the rules set in the following table; Based on the above table, sub-model D p&. (·) Obtain the final policy and regulatory dimension score D p&. The calculation process used is: Wherein: the secondary index score is represented by S1, i=1, 2, 3, 4, …, 6, respectively corresponding to the secondary index scores in the above table, S4: registration path and status score, S5: UDI / tracing score, S6: network security compliance score, S7: ethics and privacy compliance score, S8: standard / guideline adoption score, S9: innovation / accelerated approval score; Submodel D c&0 (·) Obtain the final clinical and scientific dimension evaluation data from the evidence level and consistency, RWE / registration data, revision / reoperation rate, complications / adverse events, precision / consistency, academic visibility, guideline / consensus adoption based on the rules set in the following table; Based on the above table, sub-model D c&0 (·) Obtain final clinical and research dimension scores D c&0 The calculation process used is: Wherein: the secondary index score is represented by S1, i=1, 2, 3, 4, …, 7, corresponding to the seven secondary index scores in the above table respectively, S4: evidence level and consistency score, S5: RWE / registration data score, S6: revision / reoperation rate score, S7: complication / adverse event score, S8: precision / consistency score, S9: academic visibility score, S2: guideline / consensus adoption score.

10. The orthopedic medical instrument and diagnostic equipment patent value assessment system of claim 1, wherein, The evaluation and result output module includes a value ranking and grade division unit, a result visualization unit, and a patent value analysis report generation unit, wherein: The value ranking and grade division unit is used to perform percentile ranking on the comprehensive value score results of all patents to be evaluated output by the orthopedic medical instrument and diagnosis and treatment equipment patent comprehensive value evaluation model, and calculate the percentile ranking of each sample in descending order; The result visualization unit generates multi-dimensional patent value analysis charts according to the evaluation results output by the value ranking and grade division unit and / or the orthopedic medical instrument and diagnosis and treatment equipment patent comprehensive value evaluation model; The patent value analysis report generation unit forms a system orthopedic medical instrument and diagnosis and treatment equipment patent value analysis report according to the evaluation results output by the value ranking and grade division unit and / or the orthopedic medical instrument and diagnosis and treatment equipment patent comprehensive value evaluation model.

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