METHOD AND SYSTEM FOR OPTIMIZING COPYRIGHT PROTECTION IN A GENERATIVE ARTIFICIAL INTELLIGENCE (AI) SYSTEM

Abstract

A method of optimizing copyright protection within an Artificial Intelligence (AI) system, includes analysing an original copyright work to formulate a unique creative digital DNA profile of the work; storing one or more analysed copyright works, along with their digital creative DNA profiles; and upon a generative Artificial Intelligence (AI) model creating a new derivate work, tracing reference training data of the derivative work to identify one or more original copyright works that have informed the new derivative work. A system for optimizing copyright protection within an Artificial Intelligence (AI) system, includes a means of analysing an original copyright work to formulate a unique creative digital DNA profile of the work; a means of storing one or more analysed copyright works along with their creative digital DNA profiles; and a means of, upon a generative Artificial Intelligence (AI) model creating a new derivate work, tracing reference training data of the derivative work to identify one or more original copyright works that have informed the new derivative work.

Description

[0001] A METHOD OF, AND A SYSTEM FOR, OPTIMIZING COPYRIGHT PROTECTION WITHIN A GENERATIVE ARTIFICIAL INTELLIGENCE (Al) SYSTEM

[0002] The present invention relates to a method of, and a system for, optimizing copyright protection within a generative Artificial Intelligence (Al) system.

[0003] Background to the Invention

[0004] It will be appreciated by those in the industry, that the uneasy overlap between generative Artificial Intelligence (Al) and copyright law may result in several complex problems developing for copyright holders as well as technology users and developers. More particularly, the use of generative Artificial Intelligence (Al) causes significant problems in identifying and enforcing copyright.

[0005] As such, it will be appreciated that there is a desperate need for a plan that considers the legal and ethical implications of technology, to make sure Al develops in the right way, i.e., that it respects copyright. Copyright holders claim that Al often makes use of data that is copyrighted, without permission or payment. This is also a problem ethically, because it's important to respect the rights of people who create original work. The problem on the one hand is that generative Al has the potential to develop amazing new things. On the other hand, there need to be mechanisms in place to ensure that it does not infringe upon copyright holders through these developments.

[0006] One of the most pressing challenges lies in the inherent working of Al models. Generative Al learns and replicates patterns in human-created copyrighted data, raising questions about infringement and rights of use.

[0007] Further, there's significant ambiguity about the ownership of Al-generated content. For instance, should the copyright be assigned to the Al developers, the original data creators, or the Al itself. This has resulted in many legal battles, an example being the dispute over the ChatGPT model's output. More and more, the issue of unlicensed content arises, as Al can unwittingly use copyrighted data during its learning process, potentially leading to infringement claims. In the US, copyright law doesn't currently extend to works solely created by a machine. This becomes contentious when considering works that have been generated without substantial human input. An illustrative case is the US Copyright Office's decision to grant a registration for a comic book generated with Al's assistance. This matter is currently under review before the Copyright Office to determine the extent of actual human involvement.

[0008] The gravity of the issue lies in the substantial legal implications. The Al industry is grappling with an alarming increase in lawsuits filed by copyright holders against Al companies, signaling a strenuous conflict between technological innovation and intellectual property rights. The intricacy of detecting copyright infringements, coupled with the challenges posed by compliance with intellectual property laws, complicate the issue.

[0009] There is an urgent need for innovative solutions and improved legal frameworks that can effectively address these issues, balancing the rights of copyright holders with the progress of Al technologies.

[0010] There is also an urgent need for an invention and method for analyzing and protecting all aspects of copyright associated with the arts. For example, in recorded music, there are music structures and music performances of musical instruments and indeed the performance and singing of the human voice which importantly contributes the copyright. In this instance, the human Voice, herein referred to as a Voice DNA Protection System which safeguards the unique vocal characteristics of individuals against unauthorized cloning, exploitation, or misuse. The Voice DNA system utilizes advanced biometric analysis techniques to generate digital profiles of individuals' vocal patterns, providing a robust mechanism for voice authentication, copyright protection, and forensic analysis in cases of infringement. Also described here are methods to also detect other aspects of a musical recording copyright which relates to the musical structure of a piece (recorded and written), it’s related instrument parts and the recordings of those instruments and parts.

[0011] Summary of the Invention

[0012] According to a first aspect of the invention, there is provided a method of optimizing copyright protection within an Artificial Intelligence (Al) system, including one or more of the following steps: analysing an original copyright work to formulate a unique creative digital DNA profile of the work; storing one or more analysed copyright works along with their creative digital DNA profiles; and upon a generative Artificial Intelligence (Al) model creating a new derivate work, tracing reference training data of the derivative work to identify one or more original copyright works that have informed the new derivative work.

[0013] In an embodiment, the step of storing one or more analysed copyright works includes the step of establishing a content structure within the Artificial Intelligence (Al) system. In this embodiment, the content structure outlines a central repository, such as a database, of the copyright works. In an example of this embodiment, each of the copyright works in the database will be automatically examined in detail before being allocated a distinct creative digital DNA profile. In an embodiment, original copyright works are preserved, monitored, and faithfully transmitted. In this embodiment, copyright holders for each piece of data are meticulously identified and documented, involving extensive crossreferencing and verification for accuracy.

[0014] In this embodiment, the database is provided in the form of a databank for all copyright holders and the unique creative digital DNA profiles associated with their copyright works. In this embodiment, the creative digital DNA profile and copyright database may be licensed to a third party, such as a developer for use in training their Al model. In this embodiment, the system is accessed by third-party Al developers and used to keep new generative works safe from copyright infringement.

[0015] In an embodiment of the invention, upon the Artificial Intelligence (Al) system creating a new derivate work, the method includes the step of identifying all the associated artists and rights holders who have influenced the new derivative work, to enable the artists and rights holders to be attributed as copyright beneficiaries of the derivate work.

[0016] In an embodiment, upon a generative Al model creating a new derivate work it first commences its training process. In this embodiment, the generative Al model is built for the purpose of monitoring copyrighted data use. In this embodiment, as the generative Al model generates derivative works, it continually tracks the use of copyrighted data, ensuring no loss of information. In an embodiment, the generative Al model is operable to provide details of the original copyright holders. In this embodiment, the model ensures that parent works are duly acknowledged and credited, to thereby preserve the rights and ensure recognition of the original creators and rights holders.

[0017] In some embodiments, a methodology, algorithm or set of criteria for copyright works identification can be provided within an application tool. In some embodiments, copyright works identification is facilitated through auto-training which includes one or more feedback loops. Thus, upon identifying use of a copyright work, the copyright classification information (or reference data information related to what constitutes copyright work) can be fed back (e.g., a recursive loop) to the auto- training process for subsequent copyright identifications. Some embodiments comprise an automated learning model. Some embodiments comprise a simplified method for configuring data overlap for false positives vs. false negatives. Some embodiments comprise a slider or other control for adjusting parameters of interest. In some embodiments, the new derivate work can be uploaded to the Al model and its origin accurately traced back the copyright work which influenced the Al model in initially creating the new derivative work.

[0018] In an embodiment of the invention, the unique creative digital DNA profile will be allocated to the respective copyright holder(s) work. In this embodiment, each of the copyright holder(s) is provided with the option of providing training permission for their copyright work, in terms of which their digitally DNA profiled work is made available for Al model training. In this embodiment, only copyright works with training permissions will be eligible for training. In this embodiment, digital profiles without training permission will be prevented from being accessed for Al model training purposes. In this embodiment, all new derivate works derived from the Al systems will have copyright holder permissions. In this embodiment, all new derivate works will be attributed to the correct copyright holder(s) via a lineage check by documenting the lineage at the time of generation of the new derivative work, thus accurately tracing development right back to the original rights holders and their works that influenced the Al model. In this embodiment, copyright holder(s) are provided with assurance that if their copyright work is used for training the Al model, they will own or co-own the copyright to any derivate work that can be proven to have been influenced by their original work via the unique digital profile attributed to it. In this embodiment, the copyright holder(s) can be provided with ownership, either whole or partial, and a potential share of revenues from royalties or the exploitation of the new derivative work.

[0019] In an embodiment, the Al model employs a sophisticated algorithm to scrutinize digital Master Works, allowing for a unique digital creative DNA profile to be created for each of the copyright works it scrutinizes albeit a literary, artistic or musical work such as music, books, photo images, paintings, films, sculpture, and the like.

[0020] In an embodiment, method includes employing a generative or creative Al model, resembling a large language model (LLM), which generates new content. In the above embodiment, the Al model is trained on the library of books while considering the unique digital profile of each work. In this embodiment, the Al model actively tracks and records creative digital influences during content generation.

[0021] In an embodiment, the method includes employing a tracing and attribution algorithm. In this embodiment, the method includes comparing the unique digital profile of newly generated Al content with that of original works in the database. In this embodiment, the algorithm is operable to attribute influences and potential copyright ownership based on identified similarities.

[0022] In an embodiment, the method includes employing a copyright attribution report generator. In this embodiment, the copyright attribution report generator compiles essential information, including the newly created work, its unique digital creative DNA profile, and traced influences to generate a copyright attribution report. In this embodiment, the report serves as a comprehensive record for copyright attribution purposes.

[0023] In an embodiment, the method includes employing an algorithmic analysis and profile assignment methodology. In this embodiment, the method includes analysing a database of several million images. In this embodiment, each image is assigned a unique digital creative DNA profile, such as a digital fingerprint, that encapsulates the images distinct artistic elements.

[0024] In an embodiment, the method includes an Al training and digital profile recording methodology. In this embodiment the method includes following the unique profile assignment, a generative Al model is trained on the library or content database. In this embodiment, the Al model is operable to learn and assimilate the digital profile of each image. In this example embodiment, as the Al model generates new art, it simultaneously records the creative digital influences from the original images it was trained on.

[0025] In an embodiment, the method includes tracing and attribution of digital creative DNA profiles. In this embodiment, a tracing and attribution algorithm is employed to compare the unique digital profiles of Al-generated images with the original images' digital profile in the database. In this embodiment, the algorithm is operable to identify and pinpoint the sources of influence in the new work, thus establishing a definitive creative DNA lineage of unique digital profiles from original works to new works.

[0026] In an embodiment, the method includes generating a copyright attribution report. The copyright attribution report generator includes a compilation of all the information into a comprehensive report. In this embodiment, the report provides a clear and accurate attribution of copyright to the original artists or copyright holders whose works influenced the Al's output.

[0027] According to a second aspect of the invention, there is provided a system for optimizing copyright protection within an Artificial Intelligence (Al) system, including one or more of the following steps: a means of analysing an original copyright work to formulate a unique digital creative DNA profile of the work; a means of storing one or more analysed copyright works along with their digital creative DNA profiles; and a means of, upon a generative Artificial Intelligence (Al) model creating a new derivate work, tracing reference training data of the derivative work to identify one or more original copyright works that have informed the new derivative work.

[0028] In an example embodiment of the invention, the system analyses each book within a database, assigning a unique digital profile to each work. It evaluates literary elements like theme, style, character development, and plot structure to generate distinctive identifiers.

[0029] In an embodiment, the system accesses a database containing a broad range of video clips or movies. In this embodiment, the system analyses the video / film content stored in the database, breaking down each video / film into its essential elements, and assigning each a unique digital creative DNA profile. In this embodiment, the essential elements may include plot, character development, cinematography, sound design, actor voice dialog and more.

[0030] In an embodiment, the system traces the Al-generated work's creative DNA back to its original sources in the database. In this embodiment, the system is operable to accurately attribute copyright to the original creators and copyright holders, based on the creative DNA tracing.

[0031] In an embodiment, the system includes a creative digital profile analysis algorithm. In this embodiment, the algorithm is operable to analyse each patent within a database, assigning a unique digital profile to each patent. In this embodiment, is operable to evaluate novel and inventive elements of the patents and associated citations.

[0032] In an embodiment, the system includes a creative Al model, resembling a large language model (LLM), which generates new content. In this embodiment, the Al model is trained on the database of patents while considering the unique digital profile of each invention. In this embodiment, it actively tracks and records unique digital profile influences during new and novel invention generation.

[0033] In an embodiment, the system includes a tracing and attribution algorithm. In this embodiment, the tracing and attribution algorithm compares the unique digital profile of a newly generated Al invention with that of prior art in the database. In this embodiment, the algorithm is operable to attribute influences and potential inventive ownership based on identified similarities.

[0034] In an embodiment, the system includes a copyright attribution report generator. In this embodiment, the copyright attribution report generator compiles essential information, including the newly created novel invention, its unique digital profile, and traced prior art influences. In this embodiment, the report serves as a comprehensive record for inventor attribution purposes.

[0035] In an embodiment, the system includes a methodology for scanning 3-dimensional (3D) sculptures and other 3-dimensional art for compilation of or more 3-dimensional maps into a data bank. In this embodiment, each 3-dimensional map is assigned a unique digital Creative DNA profile.

[0036] In an embodiment, the system includes a generative Al 3-dimensional printing model. In this embodiment, the Al model is used to create new 3-dimensional art through 3D printing. In this embodiment, the Al model is operable to enable 3D mapping of the generated work to be analysed and influencing copyright to be assigned to the new work.

[0037] In an embodiment, the system includes a Voice DNA generation module responsible for capturing and analyzing the distinctive vocal characteristics of individuals. This module employs advanced signal processing algorithms to extract key vocal parameters, including pitch, tone, timbre, and pronunciation, and encode them into digital Voice DNA profiles, safeguarding against unauthorized cloning, imitation, or exploitation by third parties. Vocal DNA technology offers unprecedented capabilities for voice cloning and dubbing. By analyzing the vocal DNA of actors, singers, voice artists or any human voice, music artists, actors, filmmakers and content creators can protect their song performances in a music copyright and dialog performances in film or radio plays etc from being exploited or cloned by generative Al without permissions.

[0038] Alternatively singing artists and actors can permit their voices to be used in the training of generative Al for dubbing purposes or even to generate entirely new performances, expanding the possibilities for creative storytelling and localization allowing also for attribution and compensation when their voices have been replicated by an Al.

[0039] The present invention addresses another critical problem within the realm of copyright management. The system allows for the input of derivative works generated by any third- party generative Al system. Upon analysis, the system can identify the copyrights involved and their respective holders, providing accurate attribution regardless of when the derivative work was created.

[0040] Additionally, the system can determine whether the copyrights that influenced the Al during the creation of the derivative had received proper training permissions. This crucial capability clarifies the ongoing debate surrounding the opt-out argument, demonstrating that once a model has been trained on the relevant copyright, opting out becomes effectively impossible. Brief of the drawings

[0041] These and other features of this invention will become apparent from the following description of one example described with reference to the accompanying drawings in which:

[0042] Figure 1 shows a system for optimizing copyright protection within an Artificial Intelligence (Al) system, in accordance with an aspect of the invention; and

[0043] Figure 2 shows a system for optimizing copyright protection within an Artificial Intelligence (Al) system, in accordance with an aspect of the invention; and

[0044] Figure 3 shows a system for optimizing copyright protection within an Artificial Intelligence (Al) system, in accordance with an aspect of the invention; and

[0045] Figure 4 shows a computer within which a set of instructions, for causing the computer to perform any one or more of the methodologies described herein, in accordance with aspects of the invention; and

[0046] Figure 5 shows a method of optimizing copyright protection within an Artificial Intelligence (Al) system, in accordance with aspects of the invention.

[0047] Detailed

[0048] The following description of the invention is provided as an enabling teaching of the invention. Those skilled in the relevant art will recognise that many changes can be made to the embodiment described, while still attaining the beneficial results of the present invention. It will also be apparent that some of the desired benefits of the present invention can be attained by selecting some of the features of the present invention without utilising other features. Accordingly, those skilled in the art will recognise that modifications and adaptions to the present invention are possible and can even be desirable in certain circumstances and are a part of the present invention. Thus, the following description is provided as illustrative of the principles of the present invention and limitation thereof. In Figure 1 , a system for optimizing copyright protection within an Artificial Intelligence (Al) system, in accordance with an aspect of the invention, is generally described with reference to numeral 100.

[0049] In use, the system 100 includes a master work 102, which is uploaded to the system 100. In turn, the system 100 analyses the master work 102 and creates a unique digital fingerprint (akin to DNA) 104 for the master work 102. The associated digital profile 104 is linked to the human authors and copyright owners of the master work 102.

[0050] The original master work’s unique digital fingerprint or digital profile 104 is stored in a databank 106 for retrieval during lineage assessment and search at the time of Al generating new or derivative works.

[0051] In Figure 2, a system for optimizing copyright protection within an Artificial Intelligence (Al) system, in accordance with an aspect of the invention, is generally described with reference to numeral 200.

[0052] In use, a generative Artificial Intelligence (Al) model 202 sends a request to the databank 204 for a profiled data set.

[0053] The system 200 then compiles all relevant digital profiles matching the request 206. The system then creates a training data set 208 of only allowable profiled works.

[0054] The system 200 sends the requested training data set 208 to the generative Al model 402.

[0055] In Figure 3, a system for optimizing copyright protection within an Artificial Intelligence (Al) system, in accordance with an aspect of the invention, is generally described with reference to numeral 300.

[0056] In accordance with embodiments, one of the aspects of the invention is its ability to assess works generated by Al models which have previously been trained on both copyright works and other data, the origin of which precede this invention. An example is any Al model currently generating new or derivative works that cannot show any lineage to the original works which the Al model referenced during the generation of the new work. The system 300 includes a training data set 302, a generative Al model 304, a new derivate work (that does not show any lineage to the original works referenced) 306, a system verification 308, a genetic lineage search 310, a list of lineage holders 312, new copyright 314.

[0057] In the system 300, the data training set 302 is used to train a generative Al model 304. The generative Al model 304 outputs a new work 306. The new work 306 is sent to the system 300 for verification 308. The system 300 analyses the new work 306 and conducts an automatic search 310, tracing back the lineage of the new work 306 to its source of origin 312.

[0058] A profile of the original copyright holders whose works were used as an influence in the Al generated work is extracted and an assignment token is created.

[0059] New copyright is then assigned to the new work 314 with the names of the original copyright holders as beneficiaries of the new work.

[0060] In Figure 4, a computer within which a set of instructions, for causing the computer to perform any one or more of the methodologies described herein, may be executed. In accordance with embodiments of the invention, the computer is generally described with reference to numeral 400.

[0061] According to some embodiments, a computer 400 is disclosed which comprises: one or more processors; and a non-transitory computer-readable memory having stored therein computer-executable instructions, that when executed by the one or more processors, cause the one or more processors to perform actions comprising: analysing an original copyright work to formulate a unique digital profile of the work, storing one or more analysed copyright works along with their digital profiles, and upon a generative Artificial Intelligence (Al) model creating a new derivate work, tracing reference training data of the derivative work to identify one or more original copyright works that have informed the new derivative work.

[0062] In a networked deployment, the computer 400 may operate in the capacity of a server or a client machine in server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The computer 400 may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a network router, switch or bridge, or any computer 400 capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that computer 400. Further, while only a single computer 400 is illustrated, the term "computer" shall also be taken to include any collection of computers that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

[0063] The example computer system 400 includes a processor 402 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory 404 and a static memory 406, which communicate with each other via a bus 408. The computer 200 may further include a video display unit 410 (e.g., a liquid crystal display (LCD)). The computer 200 also includes an alphanumeric input device 412 (e.g., a keyboard), a user interface (Ul) navigation device 414 (e.g., a mouse), a disk drive unit 416, a signal generation device 418 (e.g., a speaker) and a network interface device 420.

[0064] The disk drive unit 416 includes a computer-readable medium 422 on which is stored one or more sets of instructions and data structures (e.g., software 424) embodying or utilising any one or more of the methodologies or functions described herein. The software 424 may also reside, completely or at least partially, within the main memory 404 and / or within the processor during execution thereof by the computer system 400, the main memory and the processor also constituting computer-readable media. To this end, for clarity, please note that where the software 424 is not located in the main memory 404 and / or within the processor during execution thereof by the computer system 400, it will be in a cloud-based or remote storage location and may be executed directly from there.

[0065] The software 424 may further be transmitted or received over a network 426 via the network interface device 420 utilising any one of several well-known transfer protocols (e.g., HTTP, FTP).

[0066] In some embodiments the computer-readable medium 422 for carrying out the above- mentioned technical steps of the framework’s functionality, is non-transitory in nature. The non-transitory computer-readable medium 422 has tangibly stored thereon, or tangibly encoded thereon, software 424 that when executed by a device (e.g., application server, messaging server, email server, ad server, content server and / or client device, and the like) cause at least one processor to perform a method for optimizing copyright protection within an Artificial Intelligence (Al) system. In accordance with one or more embodiments, a system is provided that comprises one or more computer systems 400 configured to provide functionality in accordance with such embodiments. In accordance with one or more embodiments, functionality is embodied in steps of a method performed by at least one computer. In accordance with one or more embodiments, software 424, program code (or program logic) executed by a processor(s) of a computer system 400 to implement functionality in accordance with one or more such embodiments is embodied in, by and / or on a non-transitory computer-readable medium 422.

[0067] While the computer-readable medium 422 is shown in an example embodiment to be a single medium, the term "computer-readable medium" should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) that store the one or more sets of instructions. The term "computer- readable medium" shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the computer system 400 and that cause the computer system 400 to perform any one or more of the methodologies of the present embodiments, or that is capable of storing, encoding or carrying data structures utilised by or associated with such a set of instructions. The term "computer-readable medium" shall accordingly be taken to include, but not be limited to, solid-state memories and optical and magnetic media as well as cloud storage options (such as Amazon Webservices ™, Microsoft Azure ™, and the like).

[0068] In Figure 5, in accordance with the first aspect of the invention, the method of optimizing copyright protection within an Artificial Intelligence (Al) system, is generally described with reference to numeral 500.

[0069] The method 500 includes, at block 502, the step of analysing an original copyright work to formulate a unique digital profile of the work.

[0070] At block 504, the method includes the step of storing one or more analysed copyright works along with their digital profiles. At block 506, upon a generative Artificial Intelligence (Al) model creating a new derivate work, tracing reference training data of the derivative work to identify original copyright works that have informed the new derivative work.

[0071] It is to be understood that the invention is not limited to the specific details described herein which are given by way of example only and that various modifications and alterations are possible without departing from the scope of the invention as defined in the appended claims.

Claims

1. A method for optimizing copyright protection in an Artificial Intelligence (AI) system, comprising the following steps: analysis of an original copyrighted work to formulate a unique creative digital DNA profile of the work; preserving one or more analyzed copyrighted works together with their digital creative DNA profiles; and after a new derivative work has been created by a generative Artificial Intelligence (AI) model, tracing the original training data of that derivative work to identify one or more original copyrighted works that contributed to the new derivative work.

2. The method according to claim 1, wherein the step of storing one or more analyzed works protected by copyright comprises the step of establishing the structure of the content in the Artificial Intelligence (AI) system.

3. The method of claim 2, wherein the content structure defines a central repository, such as a database, of copyrighted works.

4. A method according to any of the preceding paragraphs, wherein each of the copyrighted works in the database is automatically examined in detail before being assigned a separate creative digital DNA profile.

5. The method according to any of paragraphs 2-4, in which the database is constructed in the form of a data bank for all copyright holders and unique digital creative DNA profiles associated with their copyrighted works.

6. The method of any one of paragraphs 2-5, wherein the creative digital DNA profile and copyright database may be licensed to a third party, such as a developer, for use in training its AI model.

7. The method of claim 6, wherein the system is available to third party AI developers and can be used to maintain newly generated works in a state of protection against copyright infringement.

8. A method according to any of the preceding paragraphs in which original works protected by copyright are preserved, controlled and accurately transmitted.

9. A method according to any of the preceding paragraphs, wherein the rights holders for each piece of data are carefully identified and documented, accompanied by extensive cross-checking and verification.

10. The method of any of the preceding claims, wherein when the Artificial Intelligence (AI) system creates a new derivative work, the method comprises the step of identifying all associated artists and copyright holders who have influenced the new derivative work so that the artists and copyright holders can be listed as beneficiaries of the copyright in the derivative work.

11. The method of any one of the preceding claims, wherein when the generative AI model creates a new derivative work, it first begins a learning process.

12. The method according to any of the preceding claims, wherein the generative AI model is constructed for the purpose of monitoring the use of copyrighted data.

13. The method of any one of the preceding claims, wherein, when the generative AI model generates derivative works, it continuously monitors the use of copyrighted data to ensure that no information is lost.

14. The method of any of the preceding claims, wherein the generative AI model can provide details regarding the copyright holders of the original data.

15. A method according to any of the preceding claims, wherein the model ensures that the original works of authorship are properly acknowledged and credited, thereby preserving the rights and providing recognition to the original creators and rights holders.

16. The method according to any of the preceding claims, wherein the methodology, algorithm or set of criteria for identifying copyrights can be built into an application tool.

17. A method according to any of the preceding claims, wherein the identification of copyrighted works is carried out by means of an automatic learning system comprising one or more feedback loops.

18. The method of claim 17, wherein, upon identifying a use of a copyrighted work, the copyright classification information (or background information regarding what constitutes a copyrighted work) may be fed back (e.g., in a recursive loop) to the automatic learning process for subsequent copyright identification.

19. The method according to any one of the preceding claims, wherein the AI ​​model is in the form of an automatic learning model.

20. The method of any one of the preceding claims, wherein a simplified method for configuring data overlay for the ratio of false positives to false negatives is provided.

21. The method according to any of the preceding claims, in which a slider or other control is provided for adjusting the parameters of interest.

22. The method of any of the preceding claims, wherein a new derivative work can be loaded into the AI ​​model and the origin of that work is precisely traced back to the copyrighted work that influenced the AI ​​model when the new derivative work was originally created.

23. A method according to any of the preceding paragraphs, wherein the unique creative digital DNA profile is assigned to the relevant copyright holder(s).

24. A method according to any of the preceding paragraphs, wherein each copyright holder(s) is given the option to grant permission for the use of their copyrighted work for training the AI, in the sense of which of their works equipped with creative digital DNA profiles are made available for training the AI ​​model.

25. A method according to any of the preceding paragraphs, wherein only copyrighted works with teaching permissions may be selected for teaching.

26. A method according to any of the preceding paragraphs, which prevents access to creative digital DNA profiles that do not have training authorization for use in training AI models.

27. A method according to any of the preceding paragraphs, wherein all new derivative works obtained from AI systems will have the permissions of the copyright holders.

28. The method of any of the preceding claims, wherein new derivative works are attributed to the proper copyright holder(s) by verifying the lineage of the work by documenting that lineage during the generation of such new derivative work, thereby accurately tracing the development back to the original copyright holders and their works that influenced the AI ​​model.

29. A method according to any of the preceding paragraphs, wherein the copyright holder(s) are guaranteed that if their copyrighted work is used to train an AI model, they will own or co-own the copyright in any derivative work that can be shown to have been influenced by the original work of those copyright holders, through the use of a unique creative digital DNA profile assigned to that work.

30. A method according to any of the preceding paragraphs in which the copyright holder(s) may obtain, in whole or in part, ownership of, and a potential share of, royalty income or exploitation of, the new derivative work.

31. The method according to any of the preceding claims, wherein the AI ​​model uses a sophisticated algorithm to thoroughly analyze digital patterns, allowing it to create a unique creative digital DNA profile for each of the copyrighted works, it carries out a thorough analysis regardless of whether it is a literary, artistic or musical work, such as music, books, photographs, paintings, films, sculptures, etc.

32. The method of any one of the preceding claims, wherein the method comprises using a generative or creative AI model resembling a large language model (LLM) that generates new content.

33. The method of claim 32, wherein the AI ​​model is trained on a library of books, taking into account the unique creative digital DNA profile of each work.

34. The method of claim 32 or 33, wherein the AI ​​model actively monitors and records creative digital influences during the content generation process.

35. The method according to any of the preceding claims, wherein the method also comprises using an algorithm to track and determine authorship.

36. The method according to claim 35, wherein the method comprises comparing the unique creative digital DNA profile of the newly created content using AI with the profiles of the original works from the database.

37. The method according to claim 35 or 36, wherein the algorithm is capable of identifying sources of influence and potential copyright holders based on the identified similarities.

38. The method of any one of the preceding claims, wherein the method comprises using a copyright attribution report generator.

39. The method of claim 38, wherein the copyright attribution report generator compiles material information, including a newly created work, its unique creative digital DNA profile, and tracked influences to generate a copyright attribution report.

40. The method of claim 39, wherein said report serves as a complete record for copyright purposes.

41. The method according to any one of the preceding claims, wherein the method comprises using an arithmetic analysis and profile assignment technique.

42. The method of claim 41, wherein the method comprises analyzing a database of several million images.

43. The method of claim 42, wherein each image is assigned a unique creative digital DNA profile, such as a digital fingerprint, that incorporates various artistic elements of the images.

44. The method according to claim 43, wherein the method comprises a method for training AI and recording a creative digital DNA profile.

45. The method according to any one of the preceding claims, wherein the method comprises training a generative AI model based on a digital library or content database.

46. ​​The method of claim 45, wherein the AI ​​model can learn and assimilate the DNA profile of each image.

47. The method of claim 45 or 46, wherein, when the AI ​​model generates a new work, it simultaneously records the creative digital influence from the original images on which the model was trained.

48. The method of any one of the preceding claims, wherein the method comprises tracking and attributing creative digital DNA profiles.

49. The method of claim 48, which uses a tracking and attribution algorithm to compare unique creative digital profiles of AI-generated images with digital profiles of original images stored in a database.

50. The method according to claim 48 or 49, wherein said algorithm allows for the identification and precise indication of sources of influence in a new work, thereby establishing a clear line of unique creative digital DNA profiles from the original work to new works.

51. The method of any one of the preceding claims, wherein the method comprises the step of generating a copyright attribution report.

52. The method of claim 51, wherein the copyright attribution report comprises a compilation of all information into a single, detailed and comprehensive report.

53. The method according to paragraph 51 or 52, wherein said report provides clear and accurate attribution of copyright to the original creators or copyright holders whose works influenced the AI ​​output.

54. A system for optimizing copyright protection in an Artificial Intelligence (AI) system, comprising: means of analyzing an original copyrighted work to formulate a unique creative digital DNA profile for that work; means for storing one or more analyzed copyrighted works together with their creative digital DNA profiles; and means for, after the generative Artificial Intelligence (AI) model creates a new derivative work, to trace the original training data for the derivative work to identify one or more original copyrighted works that contributed to the new derivative work.

55. The system of claim 54, wherein the system analyzes each book in the database, assigning a unique creative digital DNA profile to each work.

56. The system of claim 55, wherein the system evaluates literary elements such as theme, style, character development, and composition of a work to generate distinctive identifiers.

57. The system of any one of claims 54-56, wherein the system accesses a database containing a wide range of video clips or movies.

58. The system of any one of paragraphs 54-57, wherein the system analyzes the content of videos / movies stored in the database, breaks down each video / movie into its essential elements and assigns each a unique creative digital DNA profile.

59. The system of claim 58, wherein such essential elements may be plot, character development, cinematography, sound design, voice dialogue of actors, etc.

60. The system of any one of paragraphs 54-59, wherein the system traces the creative DNA profile of the AI-generated work back to the original sources in the database.

61. The system of claim 60, wherein the system is capable of accurately attributing copyright back to the original creators and rights holders based on digital tracking.

62. The system of claim 61, wherein the system comprises an algorithm for analyzing creative digital DNA profiles.

63. The system of claim 62, wherein the system comprises a creative AI model that mimics a large language model (LLM) that generates new content.

64. The system according to claim 62 or 63, in which the AI ​​model is trained on a database of patents, taking into account the unique creative digital DNA profile of each invention.

65. The system of any one of paragraphs 62-64, wherein the AI ​​model actively monitors and records the influence of unique creative digital DNA profiles in the process of generating new and novel inventions.

66. A system according to any of the preceding claims, wherein the algorithm is capable of analyzing each patent in the database, assigning a unique creative digital DNA profile to each patent.

67. The system of claim 66, wherein the system is capable of evaluating innovations and inventive step elements in patents and related references.

68. The system of claim 66 or 67, wherein the system comprises a tracking and attribution algorithm.

69. The system of any one of paragraphs 66-68, wherein the tracking and attribution algorithm compares the unique creative digital DNA profile of the newly created AI invention with profiles of known patents in the database.

70. The system according to any of paragraphs 66-69, the algorithm is capable of determining sources of influence and potential copyright holders of the invention based on the identified similarities.

71. The system of any of the preceding claims, wherein the system comprises a copyright attribution report generator.

72. The system of claim 71, wherein the copyright attribution report generator compiles material information including a newly created invention, its unique creative digital DNA profile, and traceable influences of known patents.

73. The system of claim 71 or 72, wherein the report serves as a complete record for the purposes of identifying the inventor.

74. A system according to any one of the preceding claims, wherein the system uses a technique for scanning 3-dimensional (3D) sculptures and other 3-dimensional works of art to compile one or more 3-dimensional maps in a database.

75. The system of claim 74, wherein each 3-dimensional map is assigned a unique creative digital DNA profile.

76. The system of claim 74 or 75, wherein the system comprises a generative AI model for 3D printing.

77. The system according to any of paragraphs 74-76, in which said AI model is used to create a new 3-dimensional work by means of 3D printing.

78. The system according to any of paragraphs 74-77, in which such AI model allows for the analysis of a 3D map of a generated work and the assignment of influencing copyrights to a new work.