The transformative development of artificial intelligence technology is reshaping the innovation landscape in the Asia-Pacific region. As countries successively launch AI development strategies, regional AI patent applications are growing by over 40% annually, with the industry scale exceeding $200 billion. In this wave, intellectual property protection faces unprecedented challenges. Issues such as the patentability of algorithmic innovations, determination of data ownership, and AI ethical constraints urgently need to be resolved, as traditional protection systems struggle to fully adapt to the needs of new technological developments.

Against the backdrop of accelerated AI innovation deployment in countries like Japan, South Korea, and Singapore, establishing systematic intellectual property protection solutions has become a key issue for enterprises going global. Companies need to deeply understand the regional policy environment, innovate protection tools and methods, and establish dynamic response mechanisms. Through forward-looking planning and refined management, they must ensure core technology security while promoting the orderly flow of innovative elements to achieve sustainable development.

Regional AI Intellectual Property Trends

1.1 Policy Evolution Trends

The Asia-Pacific region is experiencing profound changes in AI intellectual property policies. Japan took the lead in revising its “Patent Examination Guidelines” in 2023, explicitly including AI-generated content within the scope of protection and establishing an “AI invention fast-track examination channel.” This channel shortens the AI-related patent examination cycle to 6 months, 60% faster than regular examinations. Meanwhile, the Japan Patent Office issued the “AI Intellectual Property Protection White Paper,” proposing to establish an AI algorithm protection classification system, which will be officially implemented by the end of 2024.

The Korean Intellectual Property Office launched the “Innovation Technology Priority Examination System” for AI fields, focusing on supporting patent applications for core technologies such as machine learning, deep learning, and neural networks. In 2023, Korea’s new AI-related patent applications increased by 45% year-on-year, with domestic enterprises accounting for over 65%. The government plans to invest 200 billion won in 2024 to support SMEs’ AI intellectual property layout, expected to drive related investments to reach 1 trillion won.

Singapore, as a regional innovation center, is focused on creating an AI intellectual property protection pilot zone. The “Smart Innovation 2.0 Plan” was launched in early 2024, establishing an AI Intellectual Property Service Center in the Jurong Innovation District. The center integrates professional service resources such as patent agency, legal consulting, and asset evaluation to provide one-stop solutions for enterprises. The Intellectual Property Office of Singapore, in conjunction with the Agency for Science, Technology and Research, launched an AI patent funding scheme, providing up to 70% patent application fee subsidies for key technological breakthrough projects.

1.2 Industry Development Pattern

The Asia-Pacific AI industry shows diversified development trends. Japan has established leading advantages in industrial robotics and autonomous driving, with industry leaders like Toyota and Sony holding over 10,000 AI patents each. Japan’s AI industry scale reached 15 trillion yen in 2023 and is expected to exceed 25 trillion yen by 2025. The industry clustering effect is significant, with the Tokyo-Yokohama AI Innovation Corridor gathering over 3,000 innovative entities, accounting for 45% of the country’s annual patent applications.

Korea has developed distinctive advantages in AI chips based on its semiconductor industry foundation. Samsung Electronics leads the “AI Chip Industry Alliance,” integrating upstream and downstream resources to promote technological innovation. In 2023, Korea’s AI chip-related patent applications increased by 55% year-on-year, accounting for 12% of global applications. The government is promoting the construction of an “AI Semiconductor Innovation Park,” planning to cultivate 100 specialized and innovative enterprises by 2025, driving employment for 100,000 people.

Singapore focuses on developing AI services and building a regional innovation platform. The number of new AI enterprise registrations in the first quarter of 2024 increased by 80% year-on-year, with foreign-invested enterprises accounting for over 40%. Singapore’s Agency for Science, Technology and Research leads the “AI Technology Transfer Alliance” to promote the industrialization of R&D results. Singapore’s AI service exports are expected to exceed 5 billion Singapore dollars in 2024, a 35% increase from the previous year.

1.3 Risk Challenge Analysis

As AI technology accelerates its evolution, intellectual property protection faces new challenges. First is the increased difficulty in rights confirmation, with standards for determining the originality of AI-generated content not yet unified. Japan and Korea have adopted a “human-machine collaboration” recognition approach in practice, requiring clear disclosure of AI tool usage, but implementation effects remain to be tested. Second is the complexity of infringement forms, with disputes over the legality of data use and algorithm reference in AI model training processes.

Cross-border protection faces severe challenges. Differences in AI intellectual property protection rules among countries increase corporate compliance costs. Taking cross-border data flow as an example, Japan adopts a relatively open policy, while Korea implements strict controls, requiring enterprises to establish differentiated compliance systems. Additionally, the rapid iteration of AI technology leads to mismatches between patent protection periods and innovation cycles, questioning the effectiveness of traditional protection tools.

Talent competition is intensifying, with rising risks of core technology leakage. The talent mobility rate in the Asia-Pacific AI field exceeded 25% in 2023, far higher than other technical fields. Enterprises face trade secret protection pressure in talent management, especially potential significant losses from the departure of technical personnel involved in core aspects such as algorithm optimization and model training. Meanwhile, the development of open-source communities accelerates technology diffusion, requiring enterprises to find a balance between open innovation and protection control.

Infrastructure development lag constrains industry development. Despite increased investment by various countries, the AI intellectual property service system still has shortcomings. Professional talent reserves are insufficient, especially the scarcity of compound talents proficient in both AI technology and intellectual property. Data infrastructure construction is inadequate, with cross-border data flow facing technical barriers. Furthermore, intellectual property financial service innovation is insufficient to meet enterprises’ rapid development needs.

Regional coordination mechanisms need improvement. Currently, there is a lack of unified AI intellectual property protection standards, with insufficient policy coordination among countries. Cooperation mechanisms under the RCEP framework have not fully played their role, and cross-border law enforcement efficiency needs improvement. Enterprises need to deal with multiple sets of rules in regional layout, increasing operating costs. It is recommended to strengthen regional policy coordination and promote the establishment of unified protection standards and enforcement mechanisms.

Algorithm Protection System Construction

2.1 Core Technology Rights Confirmation Strategy

Multi-level rights confirmation strategies are becoming mainstream in Asia-Pacific AI algorithm protection practices. Patent protection, as the primary means of core technology rights confirmation, requires enterprises to accurately grasp application strategies. The latest examination standards issued by the Japan Patent Office clarify that AI algorithms with technical features can obtain patent protection, focusing on examining their specific solutions to technical problems. Enterprises should detail the technical features of algorithms in patent application documents, highlighting their specific implementation methods in particular application scenarios.

The Korean Intellectual Property Office launched the “AI Algorithm Patent Classification Guide,” dividing algorithm innovation into three levels: basic infrastructure, model optimization, and application implementation. Enterprises can adopt differentiated patent layout strategies according to different levels of technological innovation. For basic algorithm innovation, a “multi-point layout” strategy is recommended, forming protection networks through series of patent applications. At the model optimization level, focus on protecting technical solutions with substantial improvements. For specific application innovations, emphasis should be placed on commercial value and market prospects.

In choosing rights confirmation methods, the characteristics of different protection tools need comprehensive consideration. Besides patent protection, copyright registration has important value for source code protection. The Intellectual Property Office of Singapore launched an “AI Works Copyright Registration Platform,” supporting online registration of algorithm source code and providing blockchain evidence services. For algorithm innovations difficult to protect through patents, enterprises can consider establishing protection foundations through copyright registration. Meanwhile, strengthen trade secret protection and implement strict confidentiality measures for core algorithms.

2.2 Trade Secret Management Mechanism

Trade secret protection is a crucial aspect of algorithm innovation management. Enterprises need to establish a full-process confidentiality management system covering research and development, use, transfer, and other aspects. Japan’s Ministry of Economy, Trade and Industry issued the “AI Trade Secret Protection Guidelines,” proposing the protection concept of “classification by level, dynamic management.” It is recommended that enterprises classify algorithms by level, adopting differentiated protection measures for different levels. Core algorithms should implement highest-level protection, strictly control access rights, and maintain complete usage records.

Physical isolation and technical encryption are basic means of protecting algorithm trade secrets. Korean large enterprises generally adopt a “dual isolation” strategy, namely physical environment isolation and network environment isolation. Regarding physical environment, core algorithm R&D and operating environments should be strictly separated from other areas, implementing access control and video surveillance. For network environment, build independent development networks, prohibiting connection with external networks. Meanwhile, implement encrypted storage and transmission of algorithm codes, establishing access control mechanisms.

Personnel management is a key link in trade secret protection. Enterprises should sign confidentiality agreements with relevant personnel, clarifying confidentiality obligations and non-competition requirements. Singapore enterprises generally adopt a “knowledge dispersion” strategy, decomposing core algorithms into multiple modules, with different teams responsible for development and maintenance, avoiding single personnel mastering complete technical solutions. Establish departure personnel management mechanisms, timely recover relevant permissions to prevent technology leakage.

2.3 Cross-border Rights Protection Paths

As AI technology cross-border flow becomes increasingly frequent, establishing efficient rights protection mechanisms becomes an important issue facing enterprises. First, prepare for rights protection, including improving evidence preservation mechanisms and establishing technical monitoring systems. Japanese enterprises generally adopt a “dual-track” evidence preservation strategy, simultaneously conducting notarization preservation and blockchain preservation. For technical monitoring, use AI tools to continuously scan similar technologies in the market, timely detecting potential infringement behaviors.

Recognition standards for AI algorithm infringement are gradually forming in various countries’ judicial practices. The Korean Supreme Court recently clarified that judging algorithm infringement requires examining the substantial content of technical solutions, not just formal expressions. When discovering suspected infringement, enterprises should timely collect evidence and hire professional institutions for technical comparison. If infringement is confirmed, multiple rights protection methods can be adopted, such as administrative complaints and civil litigation.

Regional cooperation mechanisms provide new paths for cross-border rights protection. The intellectual property working group established under the RCEP framework is promoting enforcement collaboration among member states. Enterprises can seek cross-border enforcement support through this mechanism to improve rights protection efficiency. The Singapore International Arbitration Centre specifically established an “AI Technology Dispute Arbitration Tribunal,” providing a professional platform for cross-border dispute resolution. It is recommended that enterprises include arbitration clauses in contracts to reserve mechanisms for potential dispute resolution.

Moreover, industry self-discipline is an important means of protecting algorithm rights. The Japan AI Industry Alliance issued the “Algorithm Trading Self-discipline Convention,” advocating member enterprises to comply with intellectual property rules and build a healthy competitive environment. Korea promotes the establishment of an “AI Technology Trading Credit System,” evaluating enterprises’ intellectual property protection performance as an important reference for business cooperation. Enterprises should actively participate in industry self-discipline and protect rights through collective action.

Data Rights Protection Solutions

3.1 Data Asset Classification Management

As a key element of AI development, data classification management directly affects enterprises’ compliance risks and commercial value. The “Data Economy Guidelines 2024” issued by Japan’s Ministry of Economy, Trade and Industry proposes a “four-quadrant classification method,” classifying data assets from two dimensions: data sensitivity and commercial value. High-sensitivity, high-value data requires the strictest protection measures, including encrypted storage, access control, and usage tracking. For low-sensitivity, high-value data, the focus is on establishing comprehensive development and utilization mechanisms to maximize value.

The Korea Data Industry Promotion Agency launched a “Data Asset Evaluation System,” dividing AI training data into three levels: basic data, industry data, and proprietary data. Basic data mainly comes from the public domain, which enterprises can freely use but need to pay attention to data quality control. Industry data is usually built jointly by industry alliances, with participating enterprises needing to follow unified usage rules. Proprietary data is enterprises’ core asset, often having unique commercial value, requiring strict management systems.

Based on data classification, establishing corresponding management mechanisms is crucial. Singapore’s Data Governance Committee recommends enterprises adopt a “layered and graded” management model, formulating differentiated management strategies for different types of data. Establish data management committees at the organizational level to coordinate data classification and usage decisions. Deploy data security tools at the technical level to achieve refined management. Meanwhile, establish data asset accounts and regularly assess data value and risk status.

3.2 Compliance Risk Prevention

With the continuous improvement of data protection regulations in various countries, enterprises face stricter compliance requirements. Japan’s 2024 revision of the “Personal Information Protection Law” strengthens compliance requirements for AI training data. When using personal data to train AI models, enterprises need to ensure the legality of data acquisition and clearly inform data subjects of usage purposes. Establish “data compliance files” to record data sources, processing procedures, and usage situations to respond to regulatory inspections.

Korea’s “Data Industry Development Law” proposes specific requirements for data trading activities. Enterprises need to conduct compliance assessments for data transactions, focusing on reviewing the legality of data sources, reasonability of transaction pricing, and adequacy of security protection measures. Establish data transaction contract templates, clarifying rights and obligations of both parties, especially data usage scope and confidentiality requirements. Meanwhile, introduce third-party evaluation institutions to conduct independent reviews of major data transactions.

Cross-border data flow faces complex compliance environments. Singapore adopts a relatively open policy, allowing free data flow but requiring enterprises to take adequate protection measures. Enterprises need to assess target countries’ data protection levels and choose appropriate data transfer mechanisms. Establish cross-border data flow accounts to record data transfer time, scale, and purpose. Regularly conduct compliance audits to timely discover and rectify issues.

3.3 Value Realization Models

Data value realization is the core objective of enterprise data strategy. Japan launched the “data bank” model, where professional institutions integrate various data resources to provide standardized services for AI enterprises. Enterprises can gain returns through data custody, processing, and trading. Japan’s data trading market scale reached 200 billion yen in 2023 and is expected to exceed 500 billion yen by 2025. The data bank model lowers the threshold for SMEs to acquire high-quality data, promoting industry innovation.

Korea explores the “data cooperative” model, encouraging enterprises to establish data sharing alliances. Member enterprises jointly invest data resources, forming scale effects and enhancing data value. Alliances set unified data pricing standards to protect member interests. Through establishing revenue distribution mechanisms, mobilize enterprise participation enthusiasm. By 2024, Korea has established 15 industry-level data cooperatives, covering key areas such as manufacturing, finance, and healthcare.

Singapore focuses on developing the “data service” model, cultivating professional data service providers. These enterprises provide value-added services such as data cleaning, labeling, and analysis to help AI enterprises improve data quality. Establish data service evaluation systems for third-party certification of service quality. Encourage data service innovation and support enterprises in developing new service models. Singapore’s data service market scale is expected to reach 2 billion Singapore dollars in 2024.

Data value assessment is an important foundation for monetization. The Japan Data Circulation Promotion Council issued the “Data Value Assessment Guidelines,” proposing a comprehensive evaluation model. Quantitatively evaluate data value from dimensions such as data quality, market demand, and application potential. Establish dynamic pricing mechanisms to adjust data prices according to market changes. Meanwhile, introduce insurance mechanisms to provide risk protection for data transactions.

Data asset securitization becomes a new value realization path. The Korean Financial Supervisory Service approved the establishment of “data asset investment trusts,” allowing high-quality data assets to be securitized. Enterprises can activate stock data assets by issuing data income right securities. Establish strict risk control mechanisms to protect investor interests. This innovation greatly enhances data asset liquidity, providing new financing channels for enterprises.

Ethical Framework and Compliance Construction

4.1 Ethical Assessment System

AI ethical assessment has become an essential component of corporate compliance. Japan’s Ministry of Economy, Trade and Industry, together with major technology companies, released the “AI Ethics Practice Guidelines 2024,” establishing a “three-layer, five-dimensional” assessment framework. The three layers refer to technical, application, and impact layers; the five dimensions include fairness, transparency, security, privacy protection, and social responsibility. Companies need to conduct ethical assessments at the initial stage of product development and use the assessment results as a crucial basis for decision-making. According to recent statistics, over 200 Japanese companies have adopted this framework for self-assessment.

The “AI Ethics Risk Assessment Tool” developed by the Korea Institute of Science and Technology has been widely adopted. This tool uses quantitative scoring methods to conduct comprehensive assessments from dimensions such as technical characteristics, application scenarios, and impact scope. Assessment results are classified into three levels: low-risk, medium-risk, and high-risk, corresponding to different management requirements. High-risk AI systems must undergo independent third-party assessment and establish continuous monitoring mechanisms. Companies can optimize product design and reduce ethical risks based on assessment results.

Singapore launched the “AI Ethics Certification Program” to encourage companies to voluntarily undergo ethical assessment certification. The certification standards cover algorithmic fairness, decision interpretability, privacy protection, and other aspects, adopting a progressive assessment model. Companies can choose basic, advanced, or leading-level certification based on their circumstances. Certified companies will enjoy government procurement priority and other policy support. As of the third quarter of 2024, 157 companies have obtained certification.

4.2 Responsibility Boundary Delineation

Clear delineation of AI system responsibility boundaries is significant for risk management and dispute resolution. Japan’s “AI Liability Act” clearly stipulates the scope of responsibilities for developers, operators, and users. Developers mainly bear product quality responsibility and need to ensure algorithm design meets technical standards and ethical requirements. Operators are responsible for system operational safety and establishing emergency response mechanisms. Users need to use the system reasonably within authorized scope, avoiding abuse or misuse.

The Korean Industrial Standards Commission released the “AI System Responsibility Allocation Guidelines,” proposing the principle of “layered responsibility.” Vertically, responsibilities are divided into decision-making, management, and execution layers. The decision-making layer mainly consists of corporate executives responsible for strategic decisions and resource support. The management layer includes project leaders and technical experts who bear specific implementation responsibilities. The execution layer consists of operators who need to strictly follow operating procedures. Horizontally, it clarifies different departments’ scope of responsibilities and establishes collaboration mechanisms.

Liability insurance has become an important tool for companies to avoid risks. The Monetary Authority of Singapore promotes the establishment of an “AI Liability Insurance Pool” to provide specialized insurance services for companies. Insurance coverage includes algorithmic errors, data breaches, decision-making errors, and other aspects. Premiums are determined based on company size, application scenarios, and risk levels. Fast-track claims channels have been established to improve risk handling efficiency. In the first quarter of 2024, over 500 companies have purchased AI liability insurance.

4.3 Dispute Resolution Mechanism

The complexity of AI disputes requires establishing specialized dispute resolution mechanisms. Japan established an “AI Dispute Mediation Center,” adopting a dual-track system of “technology + law.” Mediators consist of AI experts and legal experts who can accurately understand technical issues and legal points. Online mediation models are implemented to improve processing efficiency. Statistics show that 312 dispute cases were handled in 2023, with a mediation success rate of 85%. Mediation results have legal binding force and can be submitted to courts for enforcement.

Korea launched the “AI Dispute Fast-Track Procedure,” developing standardized solutions for different types of disputes. For algorithmic fairness disputes, companies are required to provide decision-making bases and technical explanations for expert committee evaluation. Data usage disputes focus on reviewing authorization procedures and usage scope. A burden of proof reversal system has been established, requiring companies to prove their compliance. Procedural requirements are simplified to shorten processing cycles, with general cases controlled within 30 days.

Regional cooperation provides new ideas for cross-border dispute resolution. The Singapore International Arbitration Centre has established an “AI Dispute Resolution Alliance” with arbitration institutions in Japan, Korea, and other countries to promote unified arbitration rules. A “one-stop” service model is adopted, allowing parties to initiate arbitration at any member institution and enjoy full network support. An expert resource sharing mechanism has been established to provide cross-border evidence collection, technical authentication, and other professional services. In 2024, 86 cross-border dispute cases have been successfully handled.

Social supervision is also an important means of dispute prevention. The Japan AI Industry Association established an “Ethics Complaint Platform” to accept complaints and reports about AI systems from all sectors of society. A hierarchical processing mechanism has been established, with general complaints handled by companies themselves and major complaints triggering special investigations. Complaint handling reports are regularly published for social supervision. Industry self-discipline is promoted through complaint handling to encourage companies to improve products and services.

Corporate Practice Path

5.1 Organizational Structure Optimization

Organizational transformation in the AI era has become inevitable. The “Enterprise Digital Transformation Guidelines 2024” issued by Japan’s Ministry of Economy, Trade and Industry recommends companies build “matrix” organizational structures. In addition to traditional functional departments, AI innovation centers are established to coordinate technology R&D and application promotion. Innovation centers operate on a project basis, forming cross-departmental teams based on business needs. Decision-making efficiency is improved through flat management to promote resource integration. Surveys show that companies adopting this model have improved innovation efficiency by over 40%.

Korean large enterprises generally establish Chief AI Officers (CAIO), forming a “troika” with CTOs and CIOs. CAIOs are mainly responsible for AI strategy planning, ethical governance, and risk control, ensuring corporate AI development direction meets regulatory requirements and social expectations. AI governance committees are established to regularly evaluate development strategies and implementation effects. Dedicated compliance teams conduct daily supervision and guidance. Professional staff are allocated according to enterprise scale, with large enterprises generally having no less than 10 people.

Singapore promotes a “hub-style” organizational model, encouraging companies to establish AI innovation laboratories. Laboratories serve as hubs for technological innovation and talent cultivation, connecting internal business departments with external partners. An open innovation model is adopted to enhance innovation capabilities through project cooperation, technology introduction, and talent exchange. Achievement transformation mechanisms are established to promote technology industrialization. As of 2024, over 100 companies in Singapore have established professional innovation laboratories.

5.2 Management Process Reengineering

AI applications require systematic restructuring of traditional management processes. Toyota’s “AI + Lean Management” model in Japan has gained widespread recognition. AI technology is deeply integrated with lean principles to achieve intelligent and refined production processes. Digital twin systems are established for real-time monitoring and optimization of production operations. Algorithm analysis identifies efficiency bottlenecks for continuous process improvement. Practice shows this model can improve production efficiency by 30% and reduce quality costs by 25%.

Korea’s Hyundai Group implements an “agile” management model, breaking traditional departmental barriers. A “two-speed IT” architecture is adopted, managing stability business and innovative business separately. Innovative business uses iterative development models to quickly respond to market demands. An OKR assessment system is established to strengthen goal orientation and result assessment. An “internal entrepreneurship” mechanism encourages employee innovation. In 2023, the group incubated 156 AI innovation projects internally, with a commercialization rate of 35%.

Process digitalization is an important handle for management transformation. Singapore Telecom Group developed an “Intelligent Process Platform” to achieve business process automation and intelligence. RPA technology handles standardized business to free up human resources. Intelligent decision-making systems provide data support and suggested solutions. Process monitoring mechanisms track execution in real-time. Statistics show process automation rate has reached 75%, reducing operating costs by 40%.

5.3 Talent Pipeline Development

Talent is the core element of AI development. Japan launched the “AI Talent Cultivation Plan 2024,” building a multi-level cultivation system. For high-end talent, joint training is conducted with top universities. For technical backbones, professional certification and continuing education are provided. Basic staff receive universal training to improve AI literacy. A talent evaluation system links skill levels with compensation. It is expected to train 100,000 AI professionals by 2025.

Korea implements an “AI Talent Strategic Reserve” plan, establishing industry-academia-research joint training mechanisms. Companies provide internship positions and project opportunities, universities are responsible for theoretical teaching, and research institutes conduct frontier research. Talent development funds support outstanding students and researchers. A mentoring system promotes experience inheritance. Incentive mechanisms are improved to attract and retain core talent. In 2024, 5,000 reserve talents have entered enterprises.

Singapore adopts a “capability map” model to plan talent development paths. Detailed capability standards are formulated according to position requirements, clarifying knowledge and skill requirements for different levels. Personalized development plans support employee career growth. International certification systems are introduced to enhance talent market competitiveness. Flexible employment mechanisms attract global talent. Compensation systems are improved and equity incentives implemented to enhance talent attractiveness.

Facing AI era opportunities and challenges, Asia-Pacific enterprises need to build systematic intellectual property protection and compliance governance systems. First, they must accurately grasp regional policy trends and formulate development strategies that meet local requirements. Second, establish complete algorithm protection mechanisms and strengthen core technology management. Third, emphasize data asset management and explore value realization models. Fourth, improve ethical governance frameworks and clarify responsibility boundaries. Fifth, promote organizational transformation and cultivate professional talent teams. Companies are recommended to adopt the following strategies: first, establish professional IP management teams to strengthen risk prevention; second, increase R&D investment to cultivate independent innovation capabilities; third, deepen international cooperation to promote technical standard mutual recognition; fourth, improve internal control systems to strengthen compliance management; fifth, emphasize talent cultivation to build innovation ecosystems. Through systematic layout and continuous efforts, enterprises will win initiative and competitive advantages in the AI development wave.

Conclusion

In today’s deepening digital economy, AI technology innovation has become an important battlefield for enterprises participating in international competition. For companies planning to enter or already deployed in the Asia-Pacific market, establishing a complete AI intellectual property protection system is not only related to building technological competitive advantages but also fundamental to achieving long-term stable development. Through scientific layout strategies, enterprises can effectively control risks and promote technological innovation to gain initiative in intense market competition.

Against the background of accelerating regional integration, enterprises need to establish systematic thinking and coordinate the balance between technological innovation and compliance protection. This requires enterprises not only to strengthen internal management and optimize protection strategies but also to actively participate in regional cooperation and promote the formation of a positive innovation ecosystem. By building a multi-level, three-dimensional protection system, enterprises can both protect their own rights and contribute to regional innovative development, achieving a win-win development pattern.