Patent right confirmation and traceability system and method based on DIKWP semantic block chain

Abstract

The invention provides a patent right confirmation and traceability system and method combining a DIKWP semantic model and a block chain technology, and belongs to the technical field of block chains and intellectual property protection. According to the system, an innovation process is structurally expressed as a five-layer semantic map of data, information, knowledge, intelligence and intention through a semantic modeling module, a semantic abstract hash record of each innovation node is chained and stored through a hash storage module, and meanwhile it is ensured that a creative chain record is true, reliable and non-tampering in combination with a multi-layer semantic consistency signature mechanism. The system can also quickly retrieve similar invention records based on rich semantic information on a chain, and provides a semantic-level innovation traceability analysis function. Under the scenes of AI content generation, collaborative innovation and the like, the reliability of patent owner confirmation and the efficiency of infringement comparison can be greatly improved, the original rights and interests of the inventor are fully guaranteed, and the method has good industrial application prospects.

Description

Technical Field

[0001] This invention relates to the technical fields of blockchain, knowledge graphs, and intellectual property protection. Specifically, it relates to a patent ownership confirmation and traceability system and method based on the DIKWP semantic blockchain. This system combines a five-layer semantic model (Data, Information, Knowledge, Wisdom, Purpose, i.e., DIKWP) with blockchain technology for the confirmation and preservation of ownership of original inventions and the tracing of innovation paths. It is particularly suitable for patent protection and ownership verification in scenarios such as AI-generated content, TRIZ intelligent solution processes, and cross-system collaborative innovation. Background Technology

[0002] With the rapid development of artificial intelligence (AI), big data, and cross-disciplinary collaborative innovation, the process of generating inventions and ideas has become more complex and digitalized. AI-generated content (AIGC) has been widely used in the creation of images, texts, and other creative works, but the ownership of its copyright and the creation process are difficult to determine, posing new challenges to intellectual property rights confirmation. For example, images or articles produced by generative models often lack clear records of creators and process evidence, leading to disputes over copyright ownership and infringement determination. Traditional patent protection focuses more on the application and authorization of the final result, with relatively weak recording and confirmation of the innovation process itself, failing to fully prove the origin and development of the idea.

[0003] Several blockchain-based evidence storage technologies have been applied to the fields of digital copyright and patent protection. Blockchain, with its decentralized, tamper-proof, traceable, and timestamped characteristics, offers unique advantages for confirming and protecting digital content rights. For example, storing the hash value of a work file or invention manuscript on the blockchain can prove the existence of content at a specific point in time, ensuring the credibility of electronic evidence. The Hangzhou and Beijing Internet Courts have recognized the legal evidentiary value of blockchain-based evidence, and "blockchain + copyright" service platforms have emerged, providing copyright holders with one-stop services such as evidence storage, authentication, and tracing. However, most current on-chain evidence storage solutions only record the hash fingerprint of the work or data, lacking in-depth descriptions of the semantic information of the creation process. In other words, the blockchain typically only stores byte-level data such as transactions or files, without understanding the actual meaning represented by this data. This means that when tracing the source of an idea or understanding the inventive concept, manual analysis of the original materials is still necessary, failing to fully utilize the automatic reasoning capabilities of machines.

[0004] On the other hand, while knowledge graph technology can structurally represent knowledge and its relationships, traditional knowledge graphs are mostly centrally stored, making them susceptible to tampering. They typically only contain static relationships from data to knowledge, lacking timestamp protection and immutability guarantees for the innovation evolution process. The classic DIKW (Digital-Information-Knowledge-Wise) model divides human cognition into four layers: data, information, knowledge, and wisdom, but it does not include the representation of innovation motivation and intention. Adding "Purpose" to the top layer of the DIKW model creates the DIKWP model, which can be used to capture "goal-oriented" factors in the decision-making process. By introducing "Purpose" at the top layer, the reasons for "why do this" from raw data to executable wisdom can be more completely represented, thus integrating the contextual intentions in human innovation activities into the knowledge representation model.

[0005] Based on a framework integrating the DIKWP semantic model with blockchain, semantic content such as data, information, knowledge, wisdom, and intent can be structurally recorded on the blockchain, forming a "semantic blockchain." This DIKWP semantic blockchain not only records raw data but also explicitly records its semantic interpretation and target intent, essentially constructing a distributed semantic ledger with rich context. Each record on the chain contains content at different semantic levels and is connected through relationships, allowing tracing how data gradually evolves into information, knowledge, and wisdom. More importantly, this framework supports assigning ownership to different levels of content on the chain, realizing a new way of protecting digital intellectual property rights: for example, raw data, extracted information, derived knowledge, and even wisdom / intent can be separately marked with ownership, and rights can be allocated through tokenization mechanisms. This indicates that combining semantic modeling with blockchain notarization can more effectively solve the problems of unclear ownership and difficulty in preserving evidence of innovative achievements in the digital age.

[0006] However, there is currently no semantic blockchain-based rights confirmation scheme specifically designed for the invention and innovation process. For example, during the TRIZ intelligent invention problem-solving process, numerous intermediate innovative ideas and inspirations are generated. If these innovative nodes could be structurally recorded and rights confirmed, it would greatly facilitate proving "who proposed which idea and when" in the future. Similarly, for AI-assisted invention ideas or innovation projects completed through cross-team collaboration, a mechanism is needed to record each step of knowledge contribution to prevent difficulties in clarifying the contributions of each contributor in the event of patent ownership disputes. In view of this, it is necessary to provide a new system and method that combines the DIKWP semantic graph with blockchain technology for rights confirmation, evidence storage, and rapid traceability in the innovation process, so as to fully protect the ownership of original inventions and improve the efficiency of infringement determination and evidence preservation. Summary of the Invention

[0007] The main objective of this invention is to provide a patent ownership confirmation and traceability system and method based on the DIKWP semantic blockchain, which can record each link of innovation activities in a structured manner and utilize the immutability and time stamp reliability of blockchain to realize the full-chain evidence preservation of invention ideas, thereby solving the problems of difficulty in confirming patent ownership, easy loss or tampering of the creative chain, and difficulty in obtaining evidence of infringement in the context of AI-generated content and collaborative innovation in the prior art.

[0008] The DIKWP semantic modeling method for the innovation process provides a way to structurally represent the innovation process as a DIKWP semantic graph. This graph contains five types of nodes: Data, Information, Knowledge, Wisdom, and Purpose. These nodes are connected according to causal reasoning or logical inheritance relationships, forming a path for the evolution of innovative ideas. For example, Data nodes represent original facts or inputs; Information nodes represent the interpretation and analysis of data; Knowledge nodes represent rules and solutions derived from information; Wisdom nodes represent higher-level comprehensive decisions or solution optimizations; and Purpose nodes clearly define the motivation or goal of innovation. Through these five layers of semantic graph, the key elements and reasoning processes in the innovation chain are explicitly represented, making the thought process behind invention clear and traceable.

[0009] Semantic hash on-chain evidence storage mechanism: The aforementioned DIKWP semantic graph structure is converted into a semantic hash representation and embedded into the blockchain transaction structure to achieve full-chain evidence storage, rights confirmation, and timestamp protection of the innovation path. Specifically, whenever a new key node or knowledge achievement is generated during the innovation process, the system calculates a summary of the node and its associated semantic context, generates a corresponding semantic hash value, and then records this hash on the chain along with a blockchain transaction. The distributed ledger mechanism of the blockchain ensures that these innovation process records cannot be tampered with once they are on the chain, and each record carries a precise timestamp. Compared to merely storing the final result on the chain, this invention constructs a complete timeline of creative evolution by gradually recording semantic hash fingerprints during the innovation process, providing a credible basis for identifying the "first inventor" in the future.

[0010] A multi-layered semantic consistency signature mechanism is proposed to perform semantic verification and knowledge inheritance annotation at each node in the innovation evolution process, ensuring the unforgeability and auditability of the creative chain. Specifically, when a new DIKWP node is added at each step, the relevant contributor or system uses its digital private key to digitally sign the hash value of the current node's content and the hash pointer of the preceding node. This signature is attached to the on-chain transaction as part of the node's record. The node record also embeds a reference to its direct preceding knowledge source (such as the preceding node's hash pointer or ID) to mark knowledge inheritance. Through multi-layered signatures and on-chain references, two levels of "consistency" are guaranteed: first, vertically, each innovation step is semantically coherent and logically connected to its predecessor, and cannot be denied after being signed; second, horizontally, different expressions of the same content are consistent across the semantic levels of the DIKWP model. For example, when a knowledge node references a specific data node and information node, this correspondence is locked after signature authentication and cannot be arbitrarily changed. The aforementioned mechanism ensures that each link in the creative chain is confirmed by stakeholders. Once the entire chain is established, any attempt to tamper with existing nodes or insert forged nodes will disrupt the signature chain and can therefore be detected and rejected promptly. Simultaneously, reviewers can audit the signature and citation relationships throughout the chain to verify whether the evolution of innovative ideas is traceable and whether the semantic reasoning process is logical.

[0011] Semantic Patent Origin Algorithm: This invention also proposes a semantic origin algorithm based on on-chain DIKWP records. When patent infringement disputes or copyright disputes involving AI-generated content occur, this algorithm can quickly retrieve invention records in the blockchain that are semantically similar to the disputed content and their authorization dates, providing a reference for determining "first invention" or "first creation right." Its implementation steps include: constructing a corresponding DIKWP semantic description for the disputed invention (or suspected plagiarism), then performing semantic matching and comparison with the existing DIKWP graph on the blockchain to calculate similarity. During the matching process, not only keywords are considered, but also graph structure and causal relationships are used for deep comparison to find highly similar authorized invention records at the levels of problem, method, and effect. For each similar record found, its blockchain timestamp and authorization subject are extracted, thereby quickly locating potential prior inventions and determining who first recorded a particular idea and when. In one instance, if a copyright dispute arises involving AI-generated works with conflicting claims, this system can search the blockchain for existing DIKWP records of similar works and find the earliest on-chain time, thereby assisting in identifying the true original creator. It is evident that this semantic tracing algorithm effectively improves the intelligence level of existing technology retrieval and infringement comparison, reduces the uncertainty of manual retrieval, and provides strong technical support for patent examination and judicial rulings.

[0012] Open Integration and Platform Support: The system of this invention supports integration with external innovation platforms and data sources, including but not limited to the DIKWP-XaaS (DIKWP as a Service) platform, AI knowledge management systems, and the patent database of the State Intellectual Property Office. Through standard interfaces, the system can obtain structured innovation process data from the DIKWP as a Service platform, or share its on-chain rights confirmation records with other AI knowledge management or intellectual property platforms. Simultaneously, the system can connect to existing patent data from the Patent Office, converting publicly available prior art documents into DIKWP semantic nodes and incorporating them into the on-chain knowledge base to assist innovators in semantic-level novelty search analysis and patent layout. Access to external data also endows the system with cross-institutional recognition capabilities: for example, it can connect to official patent registration platforms to obtain rights confirmation records endorsed by credible nodes; it can also integrate with internal enterprise R&D management systems to achieve automatic on-chain backup of R&D process records. At the application level, the system provides a user-friendly visual interface, presenting on-chain records in the form of a "creative contribution graph" or timeline, facilitating user browsing and regulatory auditing. Through multi-source integration and platform-based deployment, the solution of this invention can be seamlessly embedded into the existing innovation ecosystem, forming an intellectual property protection network that links the blockchain and its off-chain components.

[0013] Compared with existing technologies, the patent ownership confirmation and traceability system and method based on the DIKWP semantic blockchain of this invention has the following beneficial effects:

[0014] Completeness and Structure of Innovation Records: This invention, for the first time, records the entire process of invention and creation in the form of a semantic graph, rather than merely saving the final result file or its hash value. Through the DIKWP model, data, information, knowledge, wisdom, and intent are expressed hierarchically, with clear connections between each level, achieving a panoramic depiction of the innovation chain. In this way, every key step in the innovation process is traceable, filling the gap in traditional patent rights confirmation that focuses only on the result while neglecting the process. Especially in AI-assisted creation and collaborative innovation, this system can accurately record the evolution of each contributor's ideas and the knowledge inheritance relationship, providing a reliable basis for subsequent patent ownership allocation.

[0015] Data Immutability and Trustworthy Timestamps: Leveraging the distributed ledger technology of blockchain, this invention ensures that the innovation process, once recorded, is protected against tampering. No one can modify the on-chain ownership record without being detected, guaranteeing the objective authenticity of the evidence. Simultaneously, each on-chain transaction is accompanied by a precise timestamp, automatically forming a timeline of innovation activities. Compared to traditional methods such as manual signing of experimental records or notarization, the timestamps provided by blockchain have globally recognized credibility. In the event of a patent dispute, all parties can directly retrieve the on-chain evidence as electronic proof without needing to additionally prove the reliability of the records. This greatly simplifies the evidence-gathering process and improves the efficiency of intellectual property rights protection.

[0016] An unforgeable innovation chain: Through a multi-layered semantic consistency signature mechanism, each innovation node is digitally endorsed by the relevant responsible party, and the chain stores hash references of its preceding and following nodes. This means that the order and content of nodes throughout the entire innovation chain are ordered and jointly confirmed by all parties. Any attempt to insert, delete, or modify records on the chain afterward will disrupt the signature chain and can therefore be detected and prevented immediately. This invention technically establishes the unforgeability of the innovation process record, eliminating the possibility of others tampering with creative results or altering the order of contributions. Simultaneously, because each node record retains complete contextual relationships and source markers, examiners can easily reconstruct the evolution of the inventive idea, thus having greater confidence in the rationality of the invention process during the novelty and inventiveness examination of patent applications.

[0017] Rapid and intelligent semantic tracing capabilities: The semantic tracing algorithm of this invention breaks through the limitations of relying solely on keyword matching, automatically understanding the deep semantics of the invention content and performing similar innovation searches. When faced with suspected plagiarism or patent conflicts, the system can quickly find semantically equivalent or similar technical solutions and their timestamps from massive on-chain records. For example, the system can discover that a certain AI-generated content already has a similar record on the chain, thereby determining the true original author. This automated semantic comparison significantly reduces the workload of manual retrieval of existing technologies, improving the objectivity of infringement determination and patent examination. Once a potential conflicting solution is detected, the system can further output the difference nodes of the two on the DIKWP graph to assist in the analysis of whether they constitute substantial similarity. The introduction of on-chain semantic retrieval capabilities upgrades intellectual property investigation from "finding evidence" to "finding knowledge," significantly improving the intelligence level of industry supervision and legal services.

[0018] Broad Commercial Prospects: This invention possesses significant industrial application value and commercial potential. On one hand, it provides a much-needed intellectual property protection tool for the booming AIGC content production and collaborative R&D needs of enterprises. Through a SaaS service, it offers content creators and independent innovation enterprises functions such as innovation process documentation, creative rights confirmation, and anti-plagiarism monitoring, directly addressing market pain points. On the other hand, this system aligns perfectly with the national "blockchain + intellectual property" development direction, serving as part of infrastructure such as copyright blockchains and patent blockchains. It can be adopted by intellectual property management departments or industry alliances to achieve cross-organizational innovation data sharing and trusted transfer. By recording creative contributions and supporting the allocation of rights based on contribution, this invention also has the potential to foster new business models, such as creative transactions based on on-chain rights certificates, knowledge crowdfunding, and rapid patent licensing. While protecting the rights of innovators, this invention also opens up new avenues for intellectual property value transformation, possessing broad commercial promotion prospects.

[0019] In summary, this invention provides a novel patent ownership verification and traceability system with a novel structural design that integrates the DIKWP semantic model into the blockchain notarization process, combining the trustworthiness of immutable data with the rich semantics of knowledge representation. The application of this solution will greatly enhance the ability to protect intellectual property rights in the AI ​​era and in a collaborative innovation environment, promoting the healthy development of the innovation ecosystem. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the overall architecture of the patent confirmation and traceability system based on the DIKWP semantic blockchain of this invention;

[0021] Figure 2 This is a schematic diagram of the structure of the DIKWP semantic graph model for the innovation process in this invention;

[0022] Figure 3 A flowchart of the innovative path chain evidence storage method provided by this invention;

[0023] Figure 4 A flowchart illustrating the semantic tracing algorithm provided by this invention in a patent dispute scenario;

[0024] Figure 5 This is a schematic diagram illustrating the deployment and data storage of the system of the present invention in a consortium blockchain environment. Detailed Implementation

[0025] The system and method of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the embodiments described herein are only for explaining the technical solutions of the present invention and are not intended to limit the scope of the present invention.

[0026] System architecture: such as Figure 1 As shown, the patent confirmation and traceability system of this invention mainly includes the following functional modules: semantic modeling module 101, hash evidence storage module 102, signature verification module 103, traceability analysis module 104, and external integration interface module 105 for data interaction. Each module can be deployed in software on a server or a node of a blockchain network to work collaboratively.

[0027] Semantic Modeling Module 101: Module 101 is used to transform data from the innovation process into a DIKWP semantic graph representation. When the system receives innovation-related input (such as logs of AI-generated content, creative notes uploaded by R&D personnel, etc.), this module first performs semantic parsing and hierarchical extraction on the original content to generate a semantic graph representation, such as... Figure 2The DIKWP model is illustrated below. Specifically, innovation activities are decomposed into five semantic nodes: Data node D represents raw facts or input data, such as experimental measurements, user needs, and initial AI-generated solutions; Information node I represents the interpretation and organization of data, such as features, patterns, or intelligence extracted from data; Knowledge node K represents reusable rules, solutions, or design principles formed based on information, such as inventive ideas obtained using the TRIZ principle; Wisdom node W represents higher-level comprehensive decisions or optimization solutions, such as strategic choices or innovative combinations of solutions for a problem; and Intent node P explicitly indicates the goal or motivation that the innovation behavior attempts to solve, such as the technical effect or specific need that the invention aims to achieve. The nodes are connected by directed edges, whose semantic relationships include "originating from," "derived from," and "achieved," used to characterize the causal reasoning chain of innovative thinking. For example, an edge from a Data node to an Information node can represent "information derived from data," and an edge from a Knowledge node to a Wisdom node represents "wise decisions formed from knowledge accumulation." Through the above methods, the semantic modeling module 101 can construct a knowledge graph structure that comprehensively describes the innovation process, laying the foundation for subsequent rights confirmation, evidence preservation, and source tracing analysis.

[0028] Hash Evidence Storage Module 102: Module 102 is responsible for converting the DIKWP semantic graph into a digital digest that can be stored on the blockchain and recording it. Specifically, this module calculates semantic hash values ​​for key content in the graph. A "semantic hash" can be understood as a digital fingerprint calculated from the content of a node and its relationships. For example, module 102 uses a hash function to comprehensively calculate the attribute values ​​of a node (text description, numerical parameters, etc.) and its connection relationships with predecessor nodes to obtain a fixed-length hash value. Whenever a new node is added or a new inference path is formed, the system generates a corresponding hash. In the preferred implementation, it can be expressed by the formula: ,in Represents a node Content, This indicates the relationship between the node and its predecessor node. The hash evidence module 102 then constructs a blockchain transaction, embedding the hash value and necessary metadata into the transaction's fields (such as transaction notes or payload), and sends it to the blockchain network for confirmation. Figure 3As shown, when the innovation process begins, the hash of the overall project intent node P can be registered on the chain first to establish the timestamp of the initial motivation. Subsequently, for each new "data discovery," new "information extraction," new "knowledge achievement," or new "decision-making scheme," module 102 generates a hash and records it on the chain. Since each new block is linked to the hash fingerprint of the previous block, the content of any stage, once recorded on the chain, is protected against tampering and has a trusted timestamp. In some embodiments, for large amounts of original data or files, a "off-chain storage + on-chain recording of hash pointers" approach can be used to reduce the storage burden on the blockchain. Even so, this system will still save the semantic summary and reference relationships of the data on the chain to ensure that even if the data itself is stored off-chain, there are sufficient semantic annotations on the chain to prove its meaning and its association with other innovation nodes. The hash notarization module 102 is compatible with mainstream blockchain platforms (such as consortium blockchains or public blockchains) and can use smart contracts to solidify semantic hashes into the block structure, thereby achieving automatic notarization of the entire innovation activity chain.

[0029] Signature Verification Module 103: Module 103 implements the aforementioned multi-layer semantic consistency signature mechanism, performing digital signature confirmation and verification for each newly added node and its associated nodes. Whenever the semantic modeling module 101 generates a new DIKWP node, the system identifies the contributing entity of the node (e.g., the ID of the developer who proposed the idea, or the AI ​​model identifier that generated the content) and the previous link referenced by the node. Then, the corresponding entity uses its digital private key to digitally sign the "hash value of the current node" and the "hash pointer of the previous node". This signature is attached to the on-chain transaction as part of the node's block record. The signature verification module 103 is responsible for checking the signature validity and hierarchical consistency of each record on the chain, including but not limited to: whether the signature was indeed generated by the declared entity and verified by its public key, whether the hash of the previous node referenced in the node record exists and has not been tampered with, and whether the semantic content of the current node is logically consistent with the content of its previous node (an automatic verification method is to compare the node type and the associated edge type, such as a data node should be followed by an information node instead of jumping directly to a smart node, etc.). If an invalid signature or inconsistent semantic association is found, module 103 will reject the record from being added to the chain or mark it as abnormal. In subsequent audits, the signature verification module 103 can also re-traverse the entire innovation chain, verifying the signature at each step to ensure the authenticity and reliability of the innovation chain. For example, in collaborative innovation scenarios, different researchers sign their respective contribution nodes, forming a multi-participant innovation chain. The signature verification module ensures that every contribution recorded on the chain is genuine and clearly identified, preventing future tampering with the author or misattribution. This multi-layered digital signature also constitutes irrefutable proof: no participant can deny that they have signed and endorsed the contribution recorded on the chain, thus clarifying their respective responsibilities.

[0030] Source tracing analysis module 104: Module 104 is used to execute semantic source tracing algorithms when needed, retrieving and reasoning about the accumulated innovation records on the blockchain. Users (such as patent examiners, legal professionals, or innovators themselves) can submit query requests through this module, which include the target invention or creation content to be compared (in the form of text description, image features, DIKWP semantic graph, etc.). Module 104 first parses the target content into a DIKWP semantic representation, and then searches for similar subgraphs or related records in the on-chain database. During the search, semantic embedding and graph matching algorithms are used to measure the similarity between the target and each record on the blockchain in terms of node semantic type, node content, and structural pattern. For example, if the target is a description of a technical solution, the system will extract several "knowledge nodes" and "information nodes," and attach an "intent node" to represent the problem to be solved. Then, it will compare it with thousands of invention DIKWP graphs on the blockchain to see if there are similar knowledge solutions under the same intent. If a record with a high degree of matching is found, the source tracing analysis module 104 will extract information such as the confirmation time and holder of the corresponding record and generate a source tracing analysis report. Figure 4 As shown, in the event of a patent dispute, after the user inputs a description of the disputed technical solution, the system finds several records with high similarity rankings on the blockchain, and marks the earliest one belonging to a certain inventor and whose record date is earlier than the application date of the disputed patent, thus indicating the possible existence of related prior art. In addition, the source tracing analysis module 104 also supports semantic difference comparison: given two innovation records, the system can output their difference nodes on the DIKWP graph, helping to determine the innovation points of the new solution relative to existing solutions. Module 104 fully utilizes the rich semantic information stored on the blockchain to achieve intelligent innovation source tracing and patent analysis, which is more comprehensive and efficient than manual retrieval methods.

[0031] External Integration Interface Module 105: Module 105 provides a standard data exchange interface, enabling this system to interact with other platforms and databases. On one hand, it allows external systems to call the services of this system. For example, an enterprise's internal innovation management software can send internal creative data to the semantic modeling module 101 for rights confirmation and on-chain storage through the interface. An AI content generation platform can also automatically submit generated content and its source data to this system for notarization through the interface. On the other hand, interface module 105 can access external knowledge and data sources, including patent literature databases (obtaining publicly available background technical knowledge in a specific technical field and converting it into on-chain knowledge layer nodes for reference) and existing copyright blockchain platform data (introducing historical works' rights confirmation records to avoid duplicate notarization). For example, by connecting to the patent data of the State Intellectual Property Office, the system can periodically synchronize new authorized patents, record them as knowledge nodes on the chain, and mark the official authorization time, thereby forming an authoritative existing technology chain within the system. Furthermore, by connecting to the DIKWP-PaaS cloud service platform, the system can leverage its domain ontology and semantic analysis tools to enhance its understanding and processing capabilities for innovative content in specific industries. The interface module also implements access control and permission management to ensure that external data access and on-chain data access meet security and compliance requirements. Through open integration, the system of this invention can be embedded in various innovative activity scenarios to form an ecosystem-level intellectual property protection solution.

[0032] Example: Ownership and Source Tracing of AI-Generated Content

[0033] To better understand the workflow of this invention, the following specific scenario illustrates the system's operation. Assume a user creates a design sketch using an AI model and gradually improves it to form a product solution for an invention. Traditionally, users find it difficult to prove the originality of the sketch and the solution, as well as the evolutionary relationship between them. However, using this invention's system, when generating the initial sketch, the user first stores the AI ​​model's input prompt as a data node D0 and the output sketch image as an information node I0 on the blockchain for verification—the blockchain records their corresponding hashes and timestamps. Next, the user analyzes the sketch to obtain a design principle (as a knowledge node K1) and a clear target purpose (as an intent node P1); this information is also extracted by the semantic modeling module and recorded on the blockchain. Then, the user has another AI model optimize the design principle represented by K1 to obtain an improved solution (forming a new knowledge node K2, associated with a wisdom layer decision node W1); the system records the reasoning process from K1 to K2 and the AI ​​model's contribution in this process, and the AI ​​model or the developer performs digital signature authentication on this node. In this way, every step of the entire creative process is recorded on the blockchain with a timestamp. If others later question the design and claim it's not the user's original work, the user can retrieve the on-chain records as strong evidence: the blockchain clearly shows the evolution from the initial sketch to the final design, with each stage having a clear timestamp and signature, proving that the user did indeed gradually create the invention and completed it before any competitors. If the opposing party also presents similar designs as counter-evidence, the system can use its source tracing analysis function to compare the DIKWP graphs of both parties, quickly discovering that the user's records at key knowledge nodes are earlier than the opposing party's corresponding records, thus strongly supporting the user's claim to prior creation rights.

[0034] It should be noted that the system of this invention can be deployed in a consortium blockchain environment and jointly maintained by the nodes of all participating innovation parties, ensuring the credibility of the ledger process and the privacy and security of the data from a technical implementation perspective. Figure 5As shown. For confidential innovation projects, a "chain hash + off-chain encrypted storage" approach can be used to protect sensitive content, allowing authorized participants to decrypt and view it only when necessary, thus achieving a balance between protecting innovation details and publicly disclosing proof of ownership. Furthermore, the method of this invention is not only applicable to the patent field but can also be extended to intellectual property-related scenarios such as tracing the creation of copyrighted works, preventing plagiarism detection, and preserving trade secrets. Some existing projects have preliminarily verified the feasibility of putting the creation process on the blockchain. For example, recording the entire process of digital artworks from initial inspiration to the final product using blockchain is known as a "digital creation passport," used to solve the problem of work ownership authentication in the AI ​​era. Building upon this, this invention introduces semantic graphs and multi-layered signature mechanisms, making the recording of the innovation process more structured and reliable, and can be seen as a refinement and enhancement of the aforementioned concepts.

[0035] In summary, through the above modules and processes, this invention details the system composition and working mechanism for patent ownership confirmation and traceability based on the DIKWP semantic blockchain. Figure 2 The five-layer semantic model shown clearly expresses the important elements and logical connections in the innovation process; utilizing Figure 3 The on-chain evidence storage and signature mechanism shown ensures that the records of each innovation node possess tamper-proof and verifiable authority; leveraging Figure 4 The semantic retrieval process shown can quickly find clues to relevant invention records in complex on-chain data. It is foreseeable that this invention can significantly improve the reliability and efficiency of proving ownership of innovative achievements, providing a powerful technical means to protect the rights of inventors.

Claims

1. A patent right confirmation method based on a DIKWP semantic blockchain, characterized in that, Comprising the following steps: (1) Model the invention innovation process semantically as a DIKWP semantic graph containing data, information, knowledge, wisdom and intent nodes; (2) When a new key node or innovation result is generated in the innovation process, calculate the semantic hash value of the node and record the hash value on the chain through the blockchain transaction to give an accurate timestamp and tamper-proof protection; (3) Use the digital signature of the corresponding contributor to sign the hash value of each new node and its predecessor hash pointer, and store the signature information in the corresponding blockchain record to authenticate the node content and its inheritance relationship; (4) Repeat steps (1) to (3) to record the evolution of each node in the innovation process in sequence until a blockchain record covering the entire invention evolution chain is formed.

2. The method of claim 1, wherein, The step of semantically modeling the innovation process as a DIKWP semantic graph includes: decomposing the elements in the innovation process into five types of semantic nodes: data, information, knowledge, wisdom and intent, and connecting the nodes through directed relationships of causal reasoning or logical inheritance to form a semantic network that can represent the evolution of innovation ideas.

3. The method of claim 1, wherein, The digital signature is generated by the private key of the corresponding innovation contributor, which is used to sign the hash value of the node generated by the contributor and the hash reference of the previous link, and the signature is verified by the public key on the blockchain to check the validity of the signature; Through this signature mechanism, the order and relationship of the innovation nodes on the chain can be ensured to be tamper-proof, and any unauthorized node insertion or modification will result in signature verification failure and be rejected from the chain.

4. The method of claim 1, wherein, For large volumes of raw data or files, the original content is stored off-chain and the corresponding hash fingerprint is recorded on-chain, and the hash and semantic node reference relationship is saved in the blockchain record, to reduce the storage burden on the chain while ensuring that the on-chain record contains sufficient semantic evidence.

5. The method of claim 1, wherein, The blockchain uses a consortium chain architecture, which is maintained by multiple innovation participant nodes in a distributed ledger, and all on-chain records are only accessible to authorized nodes, ensuring the sharing of authenticated data and privacy security.

6. A method for tracing innovation based on DIKWP semantic blockchain, characterized in that, Comprising the following steps: (1) Receive a query request about the target invention or content to be detected, parse and represent the target content as a DIKWP semantic graph; (2) Retrieve similar innovation record subgraphs in the pre-constructed blockchain semantic database in terms of node type, content and structure, and calculate the similarity; (3) When at least one on-chain innovation record with a similarity higher than a preset threshold is retrieved, obtain the authentication timestamp and ownership information of the record, generate a traceability analysis report, and indicate the most relevant prior innovation record and its authentication time; (4) If no on-chain record meeting the similarity requirement is retrieved, generate a traceability report to indicate that there is no known highly similar record in the blockchain evidence database.

7. The method of claim 6, wherein, After retrieving similar innovation records, further comprising: comparing the semantic differences between the target DIKWP graph and the DIKWP graph of the similar records, outputting the difference nodes or difference links at the data, information, knowledge, wisdom and intention levels to assist in judging the similarities and differences between the two technical solutions and the innovation points.

8. A patent right confirmation and traceability system based on a DIKWP semantic blockchain, characterized in that, Comprise: A semantic modeling module (101) for semantic parsing of important information in the invention and innovation process, generating a DIKWP semantic graph structure containing data, information, knowledge, wisdom and intention nodes; a hash storage module (102) for calculating semantic hash values of key nodes in the DIKWP semantic graph, and storing the hash of each node with the transaction record in the blockchain ledger to store and authorize each stage of the innovation process; A signature verification module (103) for digitally signing and verifying each newly generated semantic node record, the signature being generated by the node contributor based on the current node hash and its predecessor hash pointer, and stored with the blockchain record to ensure the credibility and non-repudiation of the innovation link node sequence; a traceability analysis module (104) for performing semantic retrieval on the chain based on user-provided queries, comparing the similarity between the DIKWP representation corresponding to the query and the stored DIKWP semantic graph in the blockchain, finding matching prior innovation records and outputting traceability results containing timestamp and holder information; an external integration interface module (105) for providing a data interaction interface to integrate external innovation platforms and databases, including importing innovation process data into the semantic modeling module from the outside, and sharing the chain-based authorization and storage results with external patent databases or knowledge management systems.

9. The system of claim 8, wherein, The DIKWP semantic graph constructed by the semantic modeling module (101) includes five layers of semantic nodes: data layer nodes represent raw input or factual data, information layer nodes represent the interpretation and analysis of data, knowledge layer nodes represent the principles of schemes induced from information, wisdom layer nodes represent comprehensive decision-making or strategy selection, and intention layer nodes represent the motivation or target of innovation activities. Each node is related to each other according to the cause-effect or logical relationship, and can completely represent the evolution link of the innovation idea from problem to solution.

10. The system of claim 8, wherein, The traceability analysis module (104) is configured to parse the target content submitted by the user into a DIKWP semantic node representation, and search for similar innovation records in the blockchain ledger based on the semantic structure; when there are records with a similarity exceeding a predetermined threshold, extract the authorization timestamp and ownership information of the corresponding record to generate a report, which is used to determine the novelty or precedence of the target content relative to the chain record.

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