Traditional Invention and Innovation Theory 1946-TRIZ Does Not Adapt to the Digital Era

-Innovative problem-solving methods combining DIKWP model and classic TRIZ

Purpose driven Integration of data, information, knowledge, and wisdom Invention and creation methods: DIKWP-TRIZ

(Chinese people's own original invention and creation methods:DIKWP - TRIZ)

Intellectual Property Protection in the Post-Patenting Era

Yucong Duan, Shiming Gong

DIKWP-AC Artificial Consciousness Laboratory

AGI-AIGC-GPT Evaluation DIKWP (Global) Laboratory

World Association of Artificial Consciousness

(Email：duanyucong@hotmail.com)

Catalogue

Abstract

1 Decentralization of intellectual property protection

2 AI-assisted Innovation and Intellectual Property Analysis

3 Real-time updated intellectual property database

4 Personalized intellectual property protection strategy

5 Public participation in intellectual property innovation ecology

Conclusion

摘要

1 知识产权保护的去中心化

2 AI辅助的创新和知识产权分析

3 实时更新的知识产权数据库

4 个性化的知识产权保护策略

5 公众参与的知识产权创新生态

结论

Reference

Abstract

With the rapid development of artificial intelligence (AI) and digital technology, the world is welcoming the post-patent application era, which is characterized by fundamental changes in the mechanism of intellectual property protection and innovation promotion. This paper discusses how decentralization technology, AI-assisted innovation, real-time updated database, personalized protection strategy and public participation can jointly reshape the intellectual property protection framework and innovation promotion model in this new era. We analyze how these changes can improve the efficiency and transparency of intellectual property management, promote wider innovation cooperation, and challenge the traditional methods of intellectual property protection. This paper points out that in order to adapt to these changes, it is necessary to build a more flexible, transparent and collaborative ecosystem for intellectual property protection and innovation promotion, so as to cope with the continuous changes in future technological development and market demand.

In the post-patent application era, the change of intellectual property protection and innovation promotion will be a challenge and opportunity issue. With the rapid development and wide application of artificial intelligence (AI), blockchain, big data and other technologies, the traditional intellectual property protection mechanism, especially the patent application system, is facing great reform pressure. The progress of these technologies can not only greatly promote innovation activities and improve R&D efficiency, but also redefine the management and protection of intellectual property rights. The following are possible changes in intellectual property protection and innovation promotion in the post-patent application era.

1 Decentralization of intellectual property protection

With the application of blockchain technology, intellectual property protection may develop towards decentralization. The non-tampering and transparency characteristics of blockchain can be used to create a distributed intellectual property registration and management system, which makes the process of registration, verification and protection of innovation achievements more efficient and transparent. In such a system, each innovation can be uniquely identified and recorded in the blockchain, thus providing an indisputable innovation certificate, reducing the occurrence of intellectual property disputes and reducing management costs.

2 AI-assisted Innovation and Intellectual Property Analysis

The application of artificial intelligence technology will greatly affect the innovation process and intellectual property analysis. AI can help researchers quickly screen and analyze a large number of documents and patent information, and identify potential research areas and innovations. At the same time, AI can also be used to automatically monitor and analyze the use of intellectual property rights in the market, and help rights holders find and prevent potential infringements, thus protecting their intellectual property rights more effectively.

3 Real-time updated intellectual property database

The future intellectual property database may become more dynamic and updated in real time. Using cloud computing and big data technology, we can collect and update global intellectual property data in real time, and provide researchers and enterprises with the latest market and technology trend information. This can not only accelerate the R&D process, but also help enterprises to better avoid intellectual property risks and promote innovation based on market demand.

4 Personalized intellectual property protection strategy

In the post-patent application era, the intellectual property protection strategy may become more personalized and flexible. By analyzing the specific needs and market environment of enterprises, combined with the predictions and suggestions of AI technology, enterprises can customize intellectual property protection strategies that are more suitable for their own development. This strategy includes not only traditional patent protection, but also diversified protection methods such as copyright, trademark and trade secrets, and the commercialization of intellectual property rights through technology licensing and cooperative development.

5 Public participation in intellectual property innovation ecology

Intellectual property protection and innovation promotion in the post-patent application era may also pay more attention to public participation. Through the open innovation platform, enterprises and research institutions can invite users to participate in the innovation process and jointly develop and improve new products and technologies. This model can not only accelerate the innovation process, but also collect user feedback in the early stage and improve the market adaptability of products and technologies. At the same time, public participation can increase the transparency of intellectual property innovation and public support for scientific research, and establish a healthier and more active innovation ecosystem.

Conclusion

Intellectual property protection and innovation in the post-patent application era will bring many opportunities and challenges. The decentralized intellectual property protection mechanism, AI-assisted innovation process, real-time updated database, personalized protection strategy and innovation ecology of public participation will jointly promote the development of intellectual property management in a more efficient, transparent and open direction. These changes can not only promote innovation activities, improve research and development efficiency, but also provide more solid protection for intellectual property rights. In order to adapt to these changes, enterprises, research institutions and the government need to constantly explore and adapt to the new intellectual property management model and jointly build a healthier, more active and inclusive innovation ecosystem.

摘要

随着人工智能（AI）和数字化技术的飞速发展，全球正迎来后专利申请时代，这一时代特征是知识产权保护和创新促进机制的根本变革。本文探讨了在这一新时代下，去中心化技术、AI辅助创新、实时更新的数据库、个性化保护策略以及公众参与如何共同重塑知识产权保护框架和创新促进模式。我们分析了这些变革如何提高知识产权管理的效率和透明度，促进更广泛的创新合作，以及对传统知识产权保护方法的挑战。本文提出，为了适应这些变革，需要构建一个更加灵活、透明和协作的知识产权保护和创新促进生态系统，以应对未来技术发展和市场需求的不断变化。

在后专利申请时代，知识产权保护和创新促进的变革将是一个颇具挑战和机遇的议题。随着人工智能（AI）、区块链、大数据等技术的快速发展和广泛应用，传统的知识产权保护机制，特别是专利申请系统，面临着重大的变革压力。这些技术的进步不仅能够极大地促进创新活动，提高研发效率，还能够重新定义知识产权的管理和保护方式。以下是后专利申请时代知识产权保护与创新促进可能发生的变革。

1 知识产权保护的去中心化

随着区块链技术的应用，知识产权保护可能朝向去中心化的方向发展。区块链的不可篡改性和透明性特征可以用来创建一个分布式的知识产权登记和管理系统，使创新成果的登记、验证和保护过程更加高效和透明。在这样的系统中，每项创新成果都可以被唯一标识和记录在区块链上，从而提供一个不可争议的创新证明，减少知识产权纠纷的发生，并降低管理成本。

2 AI辅助的创新和知识产权分析

人工智能技术的应用将极大地影响创新过程和知识产权分析。AI可以帮助研究人员快速筛选和分析大量的文献和专利信息，识别潜在的研究领域和创新点。同时，AI还可以用于自动监测和分析市场上的知识产权使用情况，帮助权利人发现和防止潜在的侵权行为，从而更有效地保护自身的知识产权。

3 实时更新的知识产权数据库

未来的知识产权数据库可能会变得更加动态和实时更新。利用云计算和大数据技术，可以实现对全球知识产权数据的即时收集和更新，为研究人员和企业提供最新的市场和技术趋势信息。这不仅可以加速研发过程，还可以帮助企业更好地规避知识产权风险，促进基于市场需求的创新。

4 个性化的知识产权保护策略

在后专利申请时代，知识产权保护策略可能会变得更加个性化和灵活。通过分析企业的具体需求和市场环境，结合AI技术的预测和建议，企业可以定制出更加适合自身发展的知识产权保护策略。这种策略不仅包括传统的专利保护，还可能包括版权、商标、商业秘密等多元化的保护方式，以及通过技术许可和合作开发等方式实现知识产权的商业化。

5 公众参与的知识产权创新生态

后专利申请时代的知识产权保护和创新促进也可能更加注重公众参与。通过开放创新平台，企业和研究机构可以邀请广大用户参与到创新过程中，共同开发和改进新产品和技术。这种模式不仅可以加速创新过程，还可以在早期阶段收集用户反馈，提高产品和技术的市场适应性。同时，公众参与可以增加知识产权创新的透明度和公众对科学研究的支持度，建立起更加健康和活跃的创新生态系统。

结论

后专利申请时代的知识产权保护与创新促进变革将带来诸多机遇和挑战。去中心化的知识产权保护机制、AI辅助的创新过程、实时更新的数据库、个性化保护策略以及公众参与的创新生态等变革，将共同推动知识产权管理向更加高效、透明和开放的方向发展。这些变革不仅能够促进创新活动，提高研发效率，还能够为知识产权提供更加坚实的保护。为了适应这些变革，企业、研究机构和政府需要不断探索和适应新的知识产权管理模式，共同构建一个更加健康、活跃和包容的创新生态系统。

Reference

[1] Duan Y. Which characteristic does GPT-4 belong to? An analysis through DIKWP model. DOI: 10.13140/RG.2.2.25042.53447. https://www.researchgate.net/publication/375597900_Which_characteristic_does_GPT-4_belong_to_An_analysis_through_DIKWP_model_GPT-4_shishenmexinggeDIKWP_moxingfenxibaogao. 2023.

[2] Duan Y. DIKWP Processing Report on Five Personality Traits. DOI: 10.13140/RG.2.2.35738.00965. https://www.researchgate.net/publication/375597092_wudaxinggetezhide_DIKWP_chulibaogao_duanyucongYucong_Duan. 2023.

[3] Duan Y. Research on the Application of DIKWP Model in Automatic Classification of Five Personality Traits. DOI: 10.13140/RG.2.2.15605.35047. https://www.researchgate.net/publication/375597087_DIKWP_moxingzaiwudaxinggetezhizidongfenleizhongdeyingyongyanjiu_duanyucongYucong_Duan. 2023.

[4] Duan Y, Gong S. DIKWP-TRIZ method: an innovative problem-solving method that combines the DIKWP model and classic TRIZ. DOI: 10.13140/RG.2.2.12020.53120. https://www.researchgate.net/publication/375380084_DIKWP-TRIZfangfazongheDIKWPmoxinghejingdianTRIZdechuangxinwentijiejuefangfa. 2023.

[5] Duan Y. The Technological Prospects of Natural Language Programming in Large-scale AI Models: Implementation Based on DIKWP. DOI: 10.13140/RG.2.2.19207.57762. https://www.researchgate.net/publication/374585374_The_Technological_Prospects_of_Natural_Language_Programming_in_Large-scale_AI_Models_Implementation_Based_on_DIKWP_duanyucongYucong_Duan. 2023.

[6] Duan Y. The Technological Prospects of Natural Language Programming in Large-scale AI Models: Implementation Based on DIKWP. DOI: 10.13140/RG.2.2.19207.57762. https://www.researchgate.net/publication/374585374_The_Technological_Prospects_of_Natural_Language_Programming_in_Large-scale_AI_Models_Implementation_Based_on_DIKWP_duanyucongYucong_Duan. 2023.

[7] Duan Y. Exploring GPT-4, Bias, and its Association with the DIKWP Model. DOI: 10.13140/RG.2.2.11687.32161. https://www.researchgate.net/publication/374420003_tantaoGPT-4pianjianjiqiyuDIKWPmoxingdeguanlian_Exploring_GPT-4_Bias_and_its_Association_with_the_DIKWP_Model. 2023.

[8] Duan Y. DIKWP language: a semantic bridge connecting humans and AI. DOI: 10.13140/RG.2.2.16464.89602. https://www.researchgate.net/publication/374385889_DIKWP_yuyanlianjierenleiyu_AI_deyuyiqiaoliang. 2023.

[9] Duan Y. The DIKWP artificial consciousness of the DIKWP automaton method displays the corresponding processing process at the level of word and word granularity. DOI: 10.13140/RG.2.2.13773.00483. https://www.researchgate.net/publication/374267176_DIKWP_rengongyishide_DIKWP_zidongjifangshiyiziciliducengjizhanxianduiyingdechuliguocheng. 2023.

[10] Duan Y. Implementation and Application of Artificial wisdom in DIKWP Model: Exploring a Deep Framework from Data to Decision Making. DOI: 10.13140/RG.2.2.33276.51847. https://www.researchgate.net/publication/374266065_rengongzhinengzai_DIKWP_moxingzhongdeshixianyuyingyongtansuocongshujudaojuecedeshendukuangjia_duanyucongYucong_Duan. 2023.

Data can be regarded as a concrete manifestation of the same semantics in our cognition. Often, Data represents the semantic confirmation of the existence of a specific fact or observation, and is recognised as the same object or concept by corresponding to some of the same semantic correspondences contained in the existential nature of the cognitive subject's pre-existing cognitive objects. When dealing with data, we often seek and extract the particular identical semantics that labels that data, and then unify them as an identical concept based on the corresponding identical semantics. For example, when we see a flock of sheep, although each sheep may be slightly different in terms of size, colour, gender, etc., we will classify them into the concept of "sheep" because they share our semantic understanding of the concept of "sheep". The same semantics can be specific, for example, when identifying an arm, we can confirm that a silicone arm is an arm based on the same semantics as a human arm, such as the same number of fingers, the same colour, the same arm shape, etc., or we can determine that the silicone arm is not an arm because it doesn't have the same semantics as a real arm, which is defined by the definition of "can be rotated". It is also possible to determine that the silicone arm is not an arm because it does not have the same semantics as a real arm, such as "rotatable".

Information, on the other hand, corresponds to the expression of different semantics in cognition. Typically, Information refers to the creation of new semantic associations by linking cognitive DIKWP objects with data, information, knowledge, wisdom, or purposes already cognised by the cognising subject through a specific purpose. When processing information, we identify the differences in the DIKWP objects they are cognised with, corresponding to different semantics, and classify the information according to the input data, information, knowledge, wisdom or purpose. For example, in a car park, although all cars can be classified under the notion of 'car', each car's parking location, time of parking, wear and tear, owner, functionality, payment history and experience all represent different semantics in the information. The different semantics of the information are often present in the cognition of the cognitive subject and are often not explicitly expressed. For example, a depressed person may use the term "depressed" to express the decline of his current mood relative to his previous mood, but this "depressed" is not the same as the corresponding information because its contrasting state is not the same as the corresponding information. However, the corresponding information cannot be objectively perceived by the listener because the contrasting state is not known to the listener, and thus becomes the patient's own subjective cognitive information.

Knowledge corresponds to the complete semantics in cognition. Knowledge is the understanding and explanation of the world acquired through observation and learning. In processing knowledge, we abstract at least one concept or schema that corresponds to a complete semantics through observation and learning. For example, we learn that all swans are white through observation, which is a complete knowledge of the concept "all swans are white" that we have gathered through a large amount of information.

Wisdom corresponds to information in the perspective of ethics, social morality, human nature, etc., a kind of extreme values from the culture, human social groups relative to the current era fixed or individual cognitive values. When dealing with Wisdom, we integrate this data, information, knowledge, and wisdom and use them to guide decision-making. For example, when faced with a decision-making problem, we integrate various perspectives such as ethics, morality, and feasibility, not just technology or efficiency.

Purpose can be viewed as a dichotomy (input, output), where both input and output are elements of data, information, knowledge, wisdom, or purpose. Purpose represents our understanding of a phenomenon or problem (input) and the goal we wish to achieve by processing and solving that phenomenon or problem (output). When processing purposes, the AI system processes the inputs according to its predefined goals (outputs), and gradually brings the outputs closer to the predefined goals by learning and adapting.

Yucong Duan, male, currently serves as a member of the Academic Committee of the School  of Computer Science and Technology at Hainan University. He is a professor and doctoral supervisor and is one of the first batch of talents selected into the South China Sea Masters Program of Hainan Province and the leading talents in Hainan Province. He graduated from the Software Research Institute of the Chinese Academy of Sciences in 2006, and has successively worked and visited Tsinghua University, Capital Medical University, POSCO University of Technology in South Korea, National Academy of Sciences of France, Charles University in Prague, Czech Republic, Milan Bicka University in Italy, Missouri State University in the United States, etc. He is currently a member of the Academic Committee of the School of Computer Science and Technology at Hainan University and he is the leader of the DIKWP (Data, Information, Knowledge, Wisdom, Purpose) Innovation Team at Hainan University, Distinguished Researcher at Chongqing Police College, Leader of Hainan Provincial Committee's "Double Hundred Talent" Team, Vice President of Hainan Invention Association, Vice President of Hainan Intellectual Property Association, Vice President of Hainan Low Carbon Economy Development Promotion Association, Vice President of Hainan Agricultural Products Processing Enterprises Association, Director of Network Security and Informatization Association of Hainan Province, Director of Artificial Intelligence Society of Hainan Province, Visiting Fellow, Central Michigan University, Member of the Doctoral Steering Committee of the University of Modena. Since being introduced to Hainan University as a D-class talent in 2012, He has published over 260 papers, included more than 120 SCI citations, and 11 ESI citations, with a citation count of over 4300. He has designed 241 serialized Chinese national and international invention patents (including 15 PCT invention patents) for multiple industries and fields and has been granted 85 Chinese national and international invention patents as the first inventor. Received the third prize for Wu Wenjun's artificial intelligence technology invention in 2020; In 2021, as the Chairman of the Program Committee, independently initiated the first International Conference on Data, Information, Knowledge and Wisdom - IEEE DIKW 2021; Served as the Chairman of the IEEE DIKW 2022 Conference Steering Committee in 2022; Served as the Chairman of the IEEE DIKW 2023 Conference in 2023. He was named the most beautiful technology worker in Hainan Province in 2022 (and was promoted nationwide); In 2022 and 2023, he was consecutively selected for the "Lifetime Scientific Influence Ranking" of the top 2% of global scientists released by Stanford University in the United States. Participated in the development of 2 international standards for IEEE financial knowledge graph and 4 industry knowledge graph standards. Initiated and co hosted the first International Congress on Artificial Consciousness (AC2023) in 2023.

Prof. Yucong Duan

DIKWP-AC Artificial Consciousness Laboratory

AGI-AIGC-GPT Evaluation DIKWP (Global) Laboratory

DIKWP research group, Hainan University

duanyucong@hotmail.com