DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on March 14, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to because the links in Figure 12 labeled "1010E" and "1010F" are mislabeled and should be labeled "1010D" and "1010E" respectively per the specification. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
New corrected drawings in compliance with 37 CFR 1.121(d) are required in this application because of the aforementioned formalities. Applicant is advised to employ the services of a competent patent draftsperson outside the Office, as the U.S. Patent and Trademark Office no longer prepares new drawings. The corrected drawings are required in reply to the Office action to avoid abandonment of the application. The requirement for corrected drawings will not be held in abeyance.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 30 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because there is no definition of non-transient computer readable medium in the applicant’s specification. Therefore, under BRI non-transient computer readable medium could include signals making the claim signals per se. As such the claim is rejected under failing to fall into the one of the statutory categories of the patent eligible subject matter.
Claims 1-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an
abstract idea (mental process) without significantly more.
Claim 1:
Regarding claim 1, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A system comprising: a microprocessor; and a computer readable medium, coupled with the microprocessor and comprising microprocessor readable and executable instructions that, when executed by the microprocessor, cause the microprocessor to:”, and a system or machine is one of the four statutory categories of invention.
In step 2A prong 1 of the 101-analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
“analyze an Artificial Intelligence (AI) generated composition,…, to identify a snippet of the AI generated composition that is the same or similar to a snippet of a composition used to train the AI algorithm;” (this is a mental process, a person could mentally evaluate analyzing AI generated composition and identify similar snippets of AI generated compositions, see MPEP § 2106.04(a)(2)(III)),
“identify license information associated with the snippet of the composition used to train the AI algorithm;” (this is a mental process, a person could mentally evaluate identifying license information associated with a snippet of composition, see MPEP § 2106.04(a)(2)(III)),
“and generate licensing information for the AI generated composition that comprises the licensing information associated with the identified snippet of the AI generated composition” (this is a mental process, a person could mentally generate licensing information, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under the broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
In step 2A prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
“A system comprising: a microprocessor;” (Using a microprocessor is considered generic computer component being used as tool to perform functions of the judicial exception – see MPEP § 2106.05(f)),
“and a computer readable medium, coupled with the microprocessor;” (Using a computer readable medium coupled with a microprocessor is considered generic computer component being used as tool to perform functions of the judicial exception – see MPEP § 2106.05(f)),
“and comprising microprocessor readable and executable instructions that, when executed by the microprocessor, cause the microprocessor to” (Using microprocessor readable and executable instructions is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f)),
“using a similarity algorithm” (Using a similarity algorithm is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f)),
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, additional elements iv, and v recites generic computer component being used as tool to perform functions of the judicial exception, and additional elements vi and vii recites mere instructions to apply an exception using generic computer, which is not indicative of significantly more.
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 2:
Regarding claim 2, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 2 recites the following additional elements:
“The system of claim 1, wherein the similarity algorithm uses at least one of: a vector to identify the snippet of AI generated composition, a hash of the snippet of the AI generated composition, and the snippet of the AI generated composition.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using generic computer - see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 3:
Regarding claim 3, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 3 recites the following additional elements:
“The system of claim 1, wherein the licensing information comprises one or more of: information associated with a specific version of the AI algorithm, information associated with a specification version of the AI generated composition, information associated with a specific version of the composition used to train the AI algorithm, information associated with a specific time that input information was used to generate the AI generated composition, and the input information that was used to generate the AI generated composition.” (this is a mental process, a person could mentally generate the licensing information being one of information associated with a specific version of the AI algorithm, information associated with a specification version of the AI generated composition, information associated with a specific version of the composition used to train the AI algorithm, information associated with a specific time that input information was used to generate the AI generated composition, and the input information that was used to generate the AI generated composition, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 4:
Regarding claim 4, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 4 recites the following additional elements:
“The system of claim 1, wherein the license information comprises each of the following: a specific version of the AI algorithm, a specification version of the AI generated composition, a specific version of the composition used to train the AI algorithm, and input information used to generate the AI generated composition.” (this is a mental process, a person could mentally generate the licensing information comprising a specific version of the AI algorithm, a specification version of the AI generated composition, a specific version of the composition used to train the AI algorithm, and input information used to generate the AI generated composition, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 5:
Regarding claim 5, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 5 recites the following additional elements:
“The system of claim 1, wherein the licensing information comprises at least one of: information about the AI algorithm, information about the composition used to train the AI algorithm, license information about the composition used to train the AI algorithm, likely licenses associated with the identified snippet of the AI generated composition, a hash of the snippet of the AI generated composition, the snippet of the AI generated composition, a vector generated by the similarity algorithm, input information used to generate the AI generated composition, the AI generated composition, information associated with the software application, testing information, source code removal information, and release information.” (this is a mental process, a person could mentally generate the licensing information being one of information about the AI algorithm, information about the composition used to train the AI algorithm, license information about the composition used to train the AI algorithm, likely licenses associated with the identified snippet of the AI generated composition, a hash of the snippet of the AI generated composition, the snippet of the AI generated composition, a vector generated by the similarity algorithm, input information used to generate the AI generated composition, the AI generated composition, information associated with the software application, testing information, source code removal information, and release information, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 6:
Regarding claim 6, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 6 recites the following additional elements:
“The system of claim 1, wherein the licensing information comprises a likely license associated with the AI generated composition.” (this is a mental process, a person could mentally generate licensing information comprising a license associated with an AI generated composition, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 7:
Regarding claim 7, it is dependent upon claim 6, and thereby incorporates the limitations of, and corresponding analysis to claim 6. Further, claim 7 recites the following additional elements:
“The system of claim 6, wherein the likely license associated with the AI generated composition comprises a plurality of likely licenses associated with the AI generated composition that are displayed as varying percentages of the plurality of likely licenses.” (In step 2A, prong 2, this is considered insignificant extra-solution activity of mere data gathering – see MPEP § 2106.05(g)). (In step 2B, this is also considered insignificant extra-solution activity of mere data gathering, which is a well understood routine and conventional activity, see Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93).
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 8:
Regarding claim 8, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 8 recites the following additional elements:
“The system of claim 1, wherein the licensing information comprises a vector generated...and wherein the vector generated by the similarity algorithm comprises at least one of: vector information generated by the similarity algorithm for the AI generated composition and matching vector information generated … for the composition used to train the AI algorithm.” (this is a mental process, a person could mentally generate the licensing information comprising a vector generated by the similarity algorithm and the vector comprising of at least one of vector information generated by the similarity algorithm for the AI generated composition and matching vector information generated for the composition used to train the AI algorithm, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
“by the similarity algorithm” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using generic computer - see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 9:
Regarding claim 9, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 9 recites the following additional elements:
“The system of claim 1, wherein the licensing information comprises at least one of: a hash of the snippet of material generated by the AI algorithm and the snippet of material generated by the AI algorithm.” (this is a mental process, a person could mentally generate the licensing information being one of a hash of the snippet of material generated by the AI algorithm and the snippet of material generated by the AI algorithm, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 10:
Regarding claim 10, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 10 recites the following additional elements:
“The system of claim 1, wherein the licensing information is stored in a blockchain and wherein the licensing information stored in the blockchain comprises at least one of: an AI algorithm block, a training composition block, a license block, a licenses filtered out block, a user input block, an AI input block, a generated composition block, a likely license block, a copyright options block, a royalty block, a transaction block, a vector block, a hash snippet block, and a snippet block.” (In step 2A, prong 2, this is considered insignificant extra-solution activity of storing data – see MPEP § 2106.05(g)). (In step 2B, this is also considered insignificant extra-solution activity of storing data, which is a well understood routine and conventional activity, see Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 11:
Regarding claim 11, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 11 recites the following additional elements:
“The system of claim 1, wherein the licensing information is stored in a blockchain, wherein the blockchain comprises a plurality of transaction blocks that comprise a plurality of watermarks, wherein the plurality of transaction blocks comprises a chain-of-title for the AI generated composition, and wherein each of the plurality of transaction blocks identifies at least one of: a licensor, a previous owner, a borrower, and a current owner.” (In step 2A, prong 2, this is considered insignificant extra-solution activity of storing data – see MPEP § 2106.05(g)). (In step 2B, this is also considered insignificant extra-solution activity of storing data, which is a well understood routine and conventional activity, see Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 12:
Regarding claim 12, it is dependent upon claim 11, and thereby incorporates the limitations of, and corresponding analysis to claim 11. Further, claim 12 recites the following additional elements:
“The system of claim 11, wherein each of the plurality of transaction blocks that comprise the chain-of-title for the AI generated composition further comprise a hash of the AI generated composition for each copy of the AI generated composition and wherein each copy of the AI generated composition has a different watermark associated with a current owner of each copy of the AI generated composition.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using generic computer - see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 13:
Regarding claim 13, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 13 recites the following additional elements:
“The system of claim 1, wherein the licensing information is stored in a blockchain,” (In step 2A, prong 2, this is considered insignificant extra-solution activity of storing data – see MPEP § 2106.05(g)). (In step 2B, this is also considered insignificant extra-solution activity of storing data, which is a well understood routine and conventional activity, see Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011)).
“and wherein the blockchain comprises a plurality of branches that individually track individual copies of the AI generated composition and ownership of the individual copies of the AI generated composition.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using generic computer - see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 14:
Regarding claim 14, it is dependent upon claim 13, and thereby incorporates the limitations of, and corresponding analysis to claim 13. Further, claim 14 recites the following additional elements:
“The system of claim 13, wherein the plurality of branches track individual media rights for each of the individual copies of the AI generated composition.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using generic computer - see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 15:
Regarding claim 15, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 15 recites the following additional elements:
“The system of claim 1, wherein the licensing information is stored in a blockchain ” (In step 2A, prong 2, this is considered insignificant extra-solution activity of storing data – see MPEP § 2106.05(g)). (In step 2B, this is also considered insignificant extra-solution activity of storing data, which is a well understood routine and conventional activity, see Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011)).
“and wherein the blockchain tracks a maximum number of copies of the AI generated composition.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using generic computer - see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 16:
Regarding claim 16, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 16 recites the following additional elements:
“The system of claim 1, wherein the licensing information is stored in a blockchain ” (In step 2A, prong 2, this is considered insignificant extra-solution activity of storing data – see MPEP § 2106.05(g)). (In step 2B, this is also considered insignificant extra-solution activity of storing data, which is a well understood routine and conventional activity, see Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011)).
“and wherein the blockchain identifies a number of times a copy of the AI generated composition can be sold/transferred.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using generic computer - see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 17:
Regarding claim 17, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A method comprising”, and a method or process is one of the four statutory categories of invention.
In step 2A prong 1 of the 101-analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
“analyzing,…, an Artificial Intelligence (AI) generated composition,…, to identify a snippet of the AI generated composition that is the same or similar to a snippet of a composition used to train the AI algorithm;” (this is a mental process, a person could mentally evaluate analyzing AI generated composition and identify similar snippets of AI generated compositions, see MPEP § 2106.04(a)(2)(III)),
“identifying,…, license information associated with the snippet of the composition used to train the AI algorithm;” (this is a mental process, a person could mentally evaluate identifying license information associated with a snippet of composition, see MPEP § 2106.04(a)(2)(III)),
“and generating,…, licensing information for the AI generated composition that comprises the licensing information associated with the identified snippet of the AI generated composition.” (this is a mental process, a person could mentally generate licensing information, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under the broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
In step 2A prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
“by a microprocessor” (Using a microprocessor is considered generic computer component being used as tool to perform functions of the judicial exception – see MPEP § 2106.05(f)),
“using a similarity algorithm” (Using a similarity algorithm is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f)),
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, additional element iv recites generic computer component being used as tool to perform functions of the judicial exception, and additional element v recites mere instructions to apply an exception using generic computer, which is not indicative of significantly more.
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 18:
Regarding claim 18, it is dependent upon claim 17, and thereby incorporates the limitations of, and corresponding analysis to claim 17. Further, claim 18 recites the following additional elements:
“The method of claim 17, wherein the similarity algorithm uses at least one of: a vector to identify the snippet of AI generated composition, a hash of the snippet of the AI generated composition, and the snippet of the AI generated composition.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using generic computer - see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 19:
Regarding claim 19, it is dependent upon claim 17, and thereby incorporates the limitations of, and corresponding analysis to claim 17. Further, claim 19 recites the following additional elements:
“The method of claim 17, wherein the license information comprises each of the following: a specific version of the AI algorithm, a specification version of the AI generated composition, a specific version of the composition used to train the AI algorithm, and input information used to generate the AI generated composition.” (this is a mental process, a person could mentally generate the licensing information being one of information associated with a specific version of the AI algorithm, information associated with a specification version of the AI generated composition, information associated with a specific version of the composition used to train the AI algorithm, information associated with a specific time that input information was used to generate the AI generated composition, and the input information that was used to generate the AI generated composition, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 20:
Regarding claim 20, it is dependent upon claim 17, and thereby incorporates the limitations of, and corresponding analysis to claim 17. Further, claim 20 recites the following additional elements:
“The method of claim 17, wherein the licensing information comprises a likely license associated with the AI generated composition.” (this is a mental process, a person could mentally generate licensing information comprising a license associated with an AI generated composition, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 21:
Regarding claim 21, it is dependent upon claim 20, and thereby incorporates the limitations of, and corresponding analysis to claim 20. Further, claim 21 recites the following additional elements:
“The method of claim 20, wherein the likely license associated with the AI generated composition comprises a plurality of likely licenses associated with the AI generated composition that are displayed as varying percentages of the plurality of likely licenses.” (In step 2A, prong 2, this is considered insignificant extra-solution activity of mere data gathering – see MPEP § 2106.05(g)). (In step 2B, this is also considered insignificant extra-solution activity of mere data gathering, which is a well understood routine and conventional activity, see Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93).
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 22:
Regarding claim 22, it is dependent upon claim 17, and thereby incorporates the limitations of, and corresponding analysis to claim 17. Further, claim 22 recites the following additional elements:
“The method of claim 17, wherein the licensing information comprises a vector generated...and wherein the vector generated by the similarity algorithm comprises at least one of: vector information generated by the similarity algorithm for the AI generated composition and matching vector information generated … for the composition used to train the AI algorithm.” (this is a mental process, a person could mentally generate the licensing information comprising a vector generated by the similarity algorithm and the vector comprising of at least one of vector information generated by the similarity algorithm for the AI generated composition and matching vector information generated by the similarity algorithm for the composition used to train the AI algorithm, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
“by the similarity algorithm” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using generic computer - see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 23:
Regarding claim 23, it is dependent upon claim 17, and thereby incorporates the limitations of, and corresponding analysis to claim 17. Further, claim 23 recites the following additional elements:
“The method of claim 17, wherein the licensing information is stored in a blockchain and wherein the licensing information stored in the blockchain comprises at least one of: an AI algorithm block, a training composition block, a license block, a licenses filtered out block, a user input block, an AI input block, a generated composition block, a likely license block, a copyright options block, a royalty block, a transaction block, a vector block, a hash snippet block, and a snippet block.” (In step 2A, prong 2, this is considered insignificant extra-solution activity of storing data – see MPEP § 2106.05(g)). (In step 2B, this is also considered insignificant extra-solution activity of storing data, which is a well understood routine and conventional activity, see Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 24:
Regarding claim 24, it is dependent upon claim 17, and thereby incorporates the limitations of, and corresponding analysis to claim 17. Further, claim 24 recites the following additional elements:
“The method of claim 17, wherein the licensing information is stored in a blockchain, wherein the blockchain comprises a plurality of transaction blocks that comprise a plurality of watermarks, wherein the plurality of transaction blocks comprise a chain-of-title for the AI generated composition, and wherein each of the plurality of transaction blocks identifies at least one of: a licensor, a previous owner, a borrower, and a current owner.” (In step 2A, prong 2, this is considered insignificant extra-solution activity of storing data – see MPEP § 2106.05(g)). (In step 2B, this is also considered insignificant extra-solution activity of storing data, which is a well understood routine and conventional activity, see Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 25:
Regarding claim 25, it is dependent upon claim 24, and thereby incorporates the limitations of, and corresponding analysis to claim 24. Further, claim 25 recites the following additional elements:
“The method of claim 24, wherein each of the plurality of transaction blocks that comprise the chain-of-title for the AI generated composition further comprise a hash of the AI generated composition for each copy of the AI generated composition and wherein each copy of the AI generated composition has a different watermark associated with a current owner of each copy of the AI generated composition.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using generic computer - see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 26:
Regarding claim 26, it is dependent upon claim 17, and thereby incorporates the limitations of, and corresponding analysis to claim 17. Further, claim 26 recites the following additional elements:
“The method of claim 17, wherein the licensing information is stored in a blockchain,” (In step 2A, prong 2, this is considered insignificant extra-solution activity of storing data – see MPEP § 2106.05(g)). (In step 2B, this is also considered insignificant extra-solution activity of storing data, which is a well understood routine and conventional activity, see Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011)).
“and wherein the blockchain comprises a plurality of branches that individually track individual copies of the AI generated composition and ownership of the individual copies of the AI generated composition.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using generic computer - see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 27:
Regarding claim 27, it is dependent upon claim 26, and thereby incorporates the limitations of, and corresponding analysis to claim 26. Further, claim 27 recites the following additional elements:
“The method of claim 26, wherein the plurality of branches track individual media rights for each of the individual copies of the AI generated composition.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using generic computer - see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 28:
Regarding claim 28, it is dependent upon claim 17, and thereby incorporates the limitations of, and corresponding analysis to claim 17. Further, claim 28 recites the following additional elements:
“The method of claim 17, wherein the licensing information is stored in a blockchain” (In step 2A, prong 2, this is considered insignificant extra-solution activity of storing data – see MPEP § 2106.05(g)). (In step 2B, this is also considered insignificant extra-solution activity of storing data, which is a well understood routine and conventional activity, see Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011)).
“and wherein the blockchain tracks a maximum number of copies of the AI generated composition.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using generic computer - see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 29:
Regarding claim 29, it is dependent upon claim 17, and thereby incorporates the limitations of, and corresponding analysis to claim 17. Further, claim 29 recites the following additional elements:
“The method of claim 17, wherein the licensing information is stored in a blockchain” (In step 2A, prong 2, this is considered insignificant extra-solution activity of storing data – see MPEP § 2106.05(g)). (In step 2B, this is also considered insignificant extra-solution activity of storing data, which is a well understood routine and conventional activity, see Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011)).
“and wherein the blockchain identifies a number of times a copy of the AI generated composition can be sold/transferred.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using generic computer - see MPEP § 2106.05(f)). Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 30:
Regarding claim 30, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A non-transient computer readable medium having stored thereon instructions that cause a processor to execute a method,”, there is no definition of non-transient computer readable medium in the applicant’s specification. Therefore, under BRI non-transient computer readable medium could include signals making the claim signals per se. As such the claim fails to fall into the one of the statutory categories of the patent eligible subject matter.
In step 2A prong 1 of the 101-analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
“the method comprising instructions to: analyze an Artificial Intelligence (AI) generated composition,…, to identify a snippet of the AI generated composition that is the same or similar to a snippet of a composition used to train the AI algorithm;” (this is a mental process, a person could mentally evaluate analyzing AI generated composition and identify similar snippets of AI generated compositions, see MPEP § 2106.04(a)(2)(III)),
“identify license information associated with the snippet of the composition used to train the AI algorithm;” (this is a mental process, a person could mentally evaluate identifying license information associated with a snippet of composition, see MPEP § 2106.04(a)(2)(III)),
“and generate licensing information for the AI generated composition that comprises the licensing information associated with the identified snippet of the AI generated composition” (this is a mental process, a person could mentally generate licensing information, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under the broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
In step 2A prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
“A non-transient computer readable medium having stored thereon instructions” (Using a non-transient computer readable medium is considered generic computer component being used as tool to perform functions of the judicial exception – see MPEP § 2106.05(f)),
“that cause a processor to execute a method,” (Using a processor is considered generic computer component being used as tool to perform functions of the judicial exception – see MPEP § 2106.05(f)),
“using a similarity algorithm” (Using a similarity algorithm is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f)),
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, additional elements iv, and v recites generic computer component being used as tool to perform functions of the judicial exception, and additional element vi recites mere instructions to apply an exception using generic computer, which is not indicative of significantly more.
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 3, 5-6, 9, 17, 20, and 30 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Kiely M. et al, (US. Patent Application Publication 20250139205 A1) effectively filed on November 1, 2023, (hereafter Kiely).
Claim 1:
Regarding claim 1, Kiely teaches “A system comprising: a microprocessor; and a computer readable medium, coupled with the microprocessor and comprising microprocessor readable and executable instructions that, when executed by the microprocessor, cause the microprocessor to: analyze an Artificial Intelligence (AI) generated composition, using a similarity algorithm, to identify a snippet of the AI generated composition that is the same or similar to a snippet of a composition used to train the AI algorithm;”
See Kiely in paragraph [0068] describing, “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.” Further, see Kiely in paragraph [0024] describing, “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 AI 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.” Here, Kiely establishes an algorithm for comparing newly generated AI content which can be seen as generated composition and comparing it to an original composition in a database. Further, see Kiely in paragraph [0026] describing, “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.” Here, Kiely further establishes the database comprising of images used to analyze similarity. Further, see Kiely in paragraph [0027] describing, “In an embodiment, the method includes an AI training and digital profile recording methodology. In this embodiment the method includes following the unique profile assignment, a generative AI model is trained on the library or content database.” Here, Kiely further establishes the database being used to train an AI algorithm.
Further, Kiely teaches “identify license information associated with the snippet of the composition used to train the AI algorithm;”
See Kiely in paragraph [0078] describing, “At block 506, upon a generative Artificial Intelligence (AI) 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.” Here, Kiely establishes identifying original copyright works, which is interpreted as the license information, associated with a derivative of work generated by AI, which is seen as the snippet of composition, from training data which is used to train an AI algorithm.
Further, Kiely teaches “and generate licensing information for the AI generated composition that comprises the licensing information associated with the identified snippet of the AI generated composition.”
See Kiely in paragraph [0018] describing, “In an embodiment, upon a generative AI model creating a new derivate work it first commences its training process. In this embodiment, the generative AI model is built for the purpose of monitoring copyrighted data use. In this embodiment, as the generative AI model generates derivative works, it continually tracks the use of copyrighted data, ensuring no loss of information.” Here, Kiely establishes copyright information for a derivative of AI generated work being tracked, the derivative can be seen as a snippet. Further, see Kiely in paragraph [0025] describing, “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.” Here, Kiely further establishes generating copyright information.
Claim 3:
Regarding claim 3, Kiely teaches the limitations of claim 1.
Further, Kiely teaches “The system of claim 1, wherein the licensing information comprises one or more of: information associated with a specific version of the AI algorithm, information associated with a specification version of the AI generated composition, information associated with a specific version of the composition used to train the AI algorithm, information associated with a specific time that input information was used to generate the AI generated composition, and the input information that was used to generate the AI generated composition.”
See Kiely in paragraph [0025] describing, “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.” Here, Kiely establishes copyright information seen as licensing information comprising of traced influences and the newly created work which can be seen as information regarding a specific version of the composition that can be used to train an AI algorithm.
Claim 5:
Regarding claim 5, Kiely teaches the limitations of claim 1.
Further, Kiely teaches “The system of claim 1, wherein the licensing information comprises at least one of: information about the AI algorithm, information about the composition used to train the AI algorithm, license information about the composition used to train the AI algorithm, likely licenses associated with the identified snippet of the AI generated composition, a hash of the snippet of the AI generated composition, the snippet of the AI generated composition, a vector generated by the similarity algorithm, input information used to generate the AI generated composition, the AI generated composition, information associated with the software application, testing information, source code removal information, and release information.”
See Kiely in paragraph [0029] describing, “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 AI's output.” Here, Kiely establishes copyright information seen as licensing information comprising clear and accurate attribution of copyright, which can be seen as likely licenses associated with the snippet of AI generated composition.
Claim 6:
Regarding claim 6, Kiely teaches the limitations of claim 1.
Further, Kiely teaches “The system of claim 1, wherein the licensing information comprises a likely license associated with the AI generated composition.”
See Kiely in paragraph [0029] describing, “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 AI's output.” Here, Kiely establishes copyright information seen as licensing information comprising clear and accurate attribution of copyright, which can be seen as likely licenses associated with the snippet of AI generated composition.
Claim 9:
Regarding claim 9, Kiely teaches the limitations of claim 1.
Further, Kiely teaches “The system of claim 1, wherein the licensing information comprises at least one of: a hash of the snippet of material generated by the AI algorithm and the snippet of material generated by the AI algorithm.”
See Kiely in paragraph [0018] describing, “In an embodiment, upon a generative AI model creating a new derivate work it first commences its training process. In this embodiment, the generative AI model is built for the purpose of monitoring copyrighted data use. In this embodiment, as the generative AI model generates derivative works, it continually tracks the use of copyrighted data, ensuring no loss of information.” Here, Kiely establishes copyright data seen as licensing information and it is tracked with the derivative of generated work which can be seen as a snippet of material generated by the AI model or algorithm.
Claim 17:
Regarding claim 17, Kiely teaches “A method comprising: analyzing, by a microprocessor, an Artificial Intelligence (AI) generated composition, using a similarity algorithm, to identify a snippet of the AI generated composition that is the same or similar to a snippet of a composition used to train the AI algorithm;”
See Kiely in paragraph [0068] describing, “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.” Here, Kiely establishes an algorithm for comparing newly generated AI content which can be seen as generated composition and comparing it to an original composition in a database. Further, see Kiely in paragraph [0024] describing, “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 AI 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.” Further, see Kiely in paragraph [0026] describing, “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.” Here, Kiely further establishes the database comprising of images used to analyze similarity. Further, see Kiely in paragraph [0027] describing, “In an embodiment, the method includes an AI training and digital profile recording methodology. In this embodiment the method includes following the unique profile assignment, a generative AI model is trained on the library or content database.” Here, Kiely further establishes the database being used to train an AI algorithm.
Further, Kiely teaches “identifying, by the microprocessor, license information associated with the snippet of the composition used to train the AI algorithm;”
See Kiely in paragraph [0078] describing, “At block 506, upon a generative Artificial Intelligence (AI) 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.” Here, Kiely establishes identifying original copyright works, which is interpreted as the license information, associated with a derivative of work generated by AI, which is seen as the snippet of composition, from training data which is used to train an AI algorithm.
Further, Kiely teaches “and generating, by the microprocessor, licensing information for the AI generated composition that comprises the licensing information associated with the identified snippet of the AI generated composition.”
See Kiely in paragraph [0018] describing, “In an embodiment, upon a generative AI model creating a new derivate work it first commences its training process. In this embodiment, the generative AI model is built for the purpose of monitoring copyrighted data use. In this embodiment, as the generative AI model generates derivative works, it continually tracks the use of copyrighted data, ensuring no loss of information.” Here, Kiely establishes copyright information for a derivative of AI generated work being tracked, the derivative can be seen as a snippet. Further, see Kiely in paragraph [0025] describing, “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.” Here, Kiely further establishes generating the copyright information.
Claim 20:
Regarding claim 20, Kiely teaches the limitations of claim 17.
Further, Kiely teaches “The method of claim 17, wherein the licensing information comprises a likely license associated with the AI generated composition.”
See Kiely in paragraph [0029] describing, “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 AI's output.” Here, Kiely establishes copyright information seen as licensing information comprising clear and accurate attribution of copyright, which can be seen as likely licenses associated with the snippet of AI generated composition.
Claim 30:
Regarding claim 30, Kiely teaches “A non-transient computer readable medium having stored thereon instructions that cause a processor to execute a method, the method comprising instructions to: analyze an Artificial Intelligence (AI) generated composition, using a similarity algorithm, to identify a snippet of the AI generated composition that is the same or similar to a snippet of a composition used to train the AI algorithm;”
See Kiely in paragraph [0068] describing, “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.” Here, Kiely establishes an algorithm for comparing newly generated AI content which can be seen as generated composition and comparing it to an original composition in a database. Further, see Kiely in paragraph [0024] describing, “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 AI 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.” Further, see Kiely in paragraph [0026] describing, “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.” Here, Kiely further establishes the database comprising of images used to analyze similarity. Further, see Kiely in paragraph [0027] describing, “In an embodiment, the method includes an AI training and digital profile recording methodology. In this embodiment the method includes following the unique profile assignment, a generative AI model is trained on the library or content database.” Here, Kiely further establishes the database being used to train an AI algorithm.
Further, Kiely teaches “identify license information associated with the snippet of the composition used to train the AI algorithm;”
See Kiely in paragraph [0078] describing, “At block 506, upon a generative Artificial Intelligence (AI) 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.” Here, Kiely establishes identifying original copyright works, which is interpreted as the license information, associated with a derivative of work generated by AI, which is seen as the snippet of composition, from training data which is used to train an AI algorithm.
Further, Kiely teaches “and generate licensing information for the AI generated composition that comprises the licensing information associated with the identified snippet of the AI generated composition.”
See Kiely in paragraph [0018] describing, “In an embodiment, upon a generative AI model creating a new derivate work it first commences its training process. In this embodiment, the generative AI model is built for the purpose of monitoring copyrighted data use. In this embodiment, as the generative AI model generates derivative works, it continually tracks the use of copyrighted data, ensuring no loss of information.” Here, Kiely establishes copyright information being for a derivative of AI generated work being tracked, the derivative can be seen as a snippet. Further, see Kiely in paragraph [0025] describing, “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.” Here, Kiely further establishes generating the copyright information.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 2 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kiely et al., in view of Dicklin Z. et al, (US. Patent Application Publication 20250103640 A1) effectively filed on September 21, 2023, (hereafter Dicklin).
Claim 2:
Regarding claim 2, Kiely teaches the limitations of claim 1.
Kiely does not appear to explicitly teach “The system of claim 1, wherein the similarity algorithm uses at least one of: a vector to identify the snippet of AI generated composition, a hash of the snippet of the AI generated composition, and the snippet of the AI generated composition.”,
However in the same field of art, Dicklin teaches “The system of claim 1, wherein the similarity algorithm uses at least one of: a vector to identify the snippet of AI generated composition, a hash of the snippet of the AI generated composition, and the snippet of the AI generated composition.”
See Dicklin in paragraph [0031] describing, “The preparation may include inputting a document into an embedding model to generate a query embedding based on the document. The query embedding is a digital representation of the document. The preparation can also include inputting a portion of the document (e.g., a sentence, paragraph, or section of the document) into the embedding model to generate a query embedding for the portion of the document, and, similarly, the query embedding for the document portion is a digital representation of that document portion. The document pre-processing subsystem then associates these query embeddings with the document or the document portions. These embeddings are used by other portions or subsystems of the cloud-based content management platform to determine if a document or document portion is relevant to a user's actual query or predicted needs. For example, a user of the platform may submit a text prompt to the system, and the system may input the text prompt into the embedding model to obtain a query embedding for the text prompt. The text prompt's query embedding can be compared to the query embeddings associated with a document or portions of that document. If the text prompt's query embedding is sufficiently similar to a document's or a document portion's query embedding, then the platform can determine that the document or document portion is likely relevant to the user's text prompt.” Here, Dicklin establishes a platform that performs a similarity check which can be seen as a similarity algorithm for a query embedding of a document that was inputted into a MLM which is a machine learning model, in which the embedding of the inputted document can be a vector to identify a snippet of AI generated composition.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kiely with the teachings of Dicklin by using Kiely’s teachings of analyzing AI generated composition and identifying and generating license information for the composition, and incorporate with Dicklin’s teachings of a vector to identify a composition using a similarity algorithm.
One of ordinary skill in the art would be motivated to do so because by integrating Dicklin’s frameworks into the methods of Kiely, which are both in the same field of art, one of ordinary skill in the art would bring “processing documents in cloud storage for query embeddings, providing personalized generative machine learning model (MLM) prompts to users based on documents in cloud storage, real-time anticipation of user interest in information contained in documents in cloud storage, and providing generative answers that include citations to source documents in cloud storage. Processing the documents for query embeddings helps gather cloud storage documents together that are similar to each other and similar to generative MLM prompts submitted by a user. Providing personalized prompts helps preemptively generate generative MLM prompts based on recent user activity on the cloud-based content management platform.” (Dicklin, paragraph [0004]).
Claim 18:
Regarding claim 18, Kiely teaches the limitations of claim 17.
Kiely does not appear to explicitly teach “The method of claim 17, wherein the similarity algorithm uses at least one of: a vector to identify the snippet of AI generated composition, a hash of the snippet of the AI generated composition, and the snippet of the AI generated composition.”,
However in the same field of art, Dicklin teaches “The method of claim 17, wherein the similarity algorithm uses at least one of: a vector to identify the snippet of AI generated composition, a hash of the snippet of the AI generated composition, and the snippet of the AI generated composition.”
See Dicklin in paragraph [0031] describing, “The preparation may include inputting a document into an embedding model to generate a query embedding based on the document. The query embedding is a digital representation of the document. The preparation can also include inputting a portion of the document (e.g., a sentence, paragraph, or section of the document) into the embedding model to generate a query embedding for the portion of the document, and, similarly, the query embedding for the document portion is a digital representation of that document portion. The document pre-processing subsystem then associates these query embeddings with the document or the document portions. These embeddings are used by other portions or subsystems of the cloud-based content management platform to determine if a document or document portion is relevant to a user's actual query or predicted needs. For example, a user of the platform may submit a text prompt to the system, and the system may input the text prompt into the embedding model to obtain a query embedding for the text prompt. The text prompt's query embedding can be compared to the query embeddings associated with a document or portions of that document. If the text prompt's query embedding is sufficiently similar to a document's or a document portion's query embedding, then the platform can determine that the document or document portion is likely relevant to the user's text prompt.” Here, Dicklin establishes a platform that performs a similarity check which can be seen as a similarity algorithm for a query embedding of a document that was inputted into a MLM which is a machine learning model, in which the embedding of the inputted document can be a vector to identify a snippet of AI generated composition.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kiely with the teachings of Dicklin by using Kiely’s teachings of analyzing AI generated composition and identifying and generating license information for the composition, and incorporate with Dicklin’s teaching of a vector to identify a composition using a similarity algorithm.
One of ordinary skill in the art would be motivated to do so because by integrating Dicklin’s frameworks into the methods of Kiely, which are both in the same field of art, one of ordinary skill in the art would bring “processing documents in cloud storage for query embeddings, providing personalized generative machine learning model (MLM) prompts to users based on documents in cloud storage, real-time anticipation of user interest in information contained in documents in cloud storage, and providing generative answers that include citations to source documents in cloud storage. Processing the documents for query embeddings helps gather cloud storage documents together that are similar to each other and similar to generative MLM prompts submitted by a user. Providing personalized prompts helps preemptively generate generative MLM prompts based on recent user activity on the cloud-based content management platform.” (Dicklin, paragraph [0004]).
Claim(s) 4 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kiely et al., in view of Olivier P. et al, (US. Patent Application Publication 20250111276 A1) effectively filed on January 25th, 2023, (hereafter Olivier).
Claim 4:
Regarding claim 4, Kiely teaches the limitations of claim 1.
Further, Kiely teaches “The system of claim 1, wherein the license information comprises each of the following:…and input information used to generate the AI generated composition.”
See Kiely in paragraph [0025] describing, “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.” Here, Kiely establishes copyright information seen as licensing information comprising of traced influences and the newly created work in which the newly created work, unique DNA profile, and influences can be the input information used to generate AI generated composition.
Kiely does not appear to explicitly teach “a specific version of the AI algorithm, a specification version of the AI generated composition, a specific version of the composition used to train the AI algorithm,”,
However in the same field of art, Olivier teaches “a specific version of the AI algorithm, a specification version of the AI generated composition, a specific version of the composition used to train the AI algorithm,”
See Olivier in paragraph [0151] describing, “In some embodiments, the specifications comprise a user-provided application scope, whereas in other embodiments, the specifications comprise a user-provided list of objects. The specifications may also comprise a user-specified application scope, wherein the use rights in the second data block comprise information related to a licensed application scope for the respective training dataset, wherein to identify a candidate set of training datasets matching the specifications, the method comprises comparing the user-specified application scope to the licensed application scope, the candidate set of training datasets including those training datasets for which the licensed application scope in the second data block of the respective training dataset includes the user-specified application scope.” Here, Olivier establishes license information regarding a specification of a user application scope for a training dataset, which can be seen as a specific version of the composition used to train. Further, see Olivier in paragraph [0154] describing, “In some embodiments, the proposed usage of the AI training data comprises an identifier of the AI platform, wherein the use rights in the second data block of each of the training datasets identifies at least one licensed AI platform” Here, Olivier establishes a specific version of the AI algorithm, with the identifier of the AI platform used for training data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kiely with the teachings of Olivier by using Kiely’s teachings of analyzing AI generated composition and identifying and generating license information for the composition, and incorporate with Olivier’s teaching of license information comprising specification and specific version of composition.
One of ordinary skill in the art would be motivated to do so because by integrating Olivier’s frameworks into the methods of Kiely, which are both in the same field of art, one of ordinary skill in the art would bring “a method of operating a computer to build a customized training dataset for training an artificial intelligence (AI) platform, comprising: obtaining (i) specifications of training data for training the AI platform and (ii) data indicative of a proposed usage of the training data; consulting a database of training datasets, each associated with use rights, to identify a candidate set of training datasets matching the specifications; and authorizing release of a subset of the training datasets in the candidate set of training datasets based on the data indicative of the proposed usage of the AI training data and the use rights associated with the training data sets in the candidate set of training datasets.” (Olivier, paragraph [0007]).
Claim 19:
Regarding claim 19, Kiely teaches the limitations of claim 17.
Further, Kiely teaches “The system of claim 1, wherein the license information comprises each of the following:…and input information used to generate the AI generated composition.”
See Kiely in paragraph [0025] describing, “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.” Here, Kiely establishes copyright information seen as licensing information comprising of traced influences and the newly created work in which the newly created work, unique DNA profile, and influences can be the input information used to generate AI generated composition.
Kiely does not appear to explicitly teach “a specific version of the AI algorithm, a specification version of the AI generated composition, a specific version of the composition used to train the AI algorithm,”,
However in the same field of art , Olivier teaches “a specific version of the AI algorithm, a specification version of the AI generated composition, a specific version of the composition used to train the AI algorithm,”
See Olivier in paragraph [0151] describing, “In some embodiments, the specifications comprise a user-provided application scope, whereas in other embodiments, the specifications comprise a user-provided list of objects. The specifications may also comprise a user-specified application scope, wherein the use rights in the second data block comprise information related to a licensed application scope for the respective training dataset, wherein to identify a candidate set of training datasets matching the specifications, the method comprises comparing the user-specified application scope to the licensed application scope, the candidate set of training datasets including those training datasets for which the licensed application scope in the second data block of the respective training dataset includes the user-specified application scope.” Here, Olivier establishes license information regarding a specification of a user application scope for a training dataset, which can be seen as a specific version of the composition used to train. Further, see Olivier in paragraph [0154] describing, “In some embodiments, the proposed usage of the AI training data comprises an identifier of the AI platform, wherein the use rights in the second data block of each of the training datasets identifies at least one licensed AI platform” Here, Olivier establishes a specific version of the AI algorithm, with the identifier of the AI platform used for training data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kiely with the teachings of Olivier by using Kiely’s teachings of analyzing AI generated composition and identifying and generating license information for the composition, and incorporate with Olivier’s teaching of license information comprising specification and specific version of composition.
One of ordinary skill in the art would be motivated to do so because by integrating Olivier’s frameworks into the methods of Kiely, which are both in the same field of art, one of ordinary skill in the art would bring “a method of operating a computer to build a customized training dataset for training an artificial intelligence (AI) platform, comprising: obtaining (i) specifications of training data for training the AI platform and (ii) data indicative of a proposed usage of the training data; consulting a database of training datasets, each associated with use rights, to identify a candidate set of training datasets matching the specifications; and authorizing release of a subset of the training datasets in the candidate set of training datasets based on the data indicative of the proposed usage of the AI training data and the use rights associated with the training data sets in the candidate set of training datasets.” (Olivier, paragraph [0007]).
Claim(s) 7 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Kiely et al., in view of Messerly S. et al, (US. Patent Application Publication 20220160434 A1) effectively filed on November 23rd, 2021, (hereafter Messerly).
Claim 7:
Regarding claim 7, Kiely teaches the limitations of claim 6.
Further, Kiely teaches “The system of claim 6, wherein the likely license associated with the AI generated composition comprises a plurality of likely licenses associated with the AI generated composition…”
See Kiely in paragraph [0029] describing, “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 AI's output.” Here, Kiely establishes copyright information seen as licensing information comprising clear and accurate attribution of copyright, which can be seen as likely licenses associated with the snippet of AI generated composition, the plurality comes from the original artists and copyright holders implying more than one.
Kiely does not appear to explicitly teach “…that are displayed as varying percentages of the plurality of likely licenses.”,
However in the same field of art and in an analogous system, Messerly teaches “...that are displayed as varying percentages of the plurality of likely licenses.”
See Messerly in paragraph [0088] describing, “The system can display the predicted classification of target 460 and/or of a list of potential targets based on the probability or confidence score from evaluating the AI-based segmentation or classification model using image 465 as input. For example, if image 465 contains two potential targets, and the system has been trained to recognize 90 identified target blood vessels, the system can evaluate a classification probability for each of the two potential targets being each the 90 known targets.” Here, Messerly establishes a system that displays targets with a probability/confidence score associated with it based on AI-based segmentation. In an analogous system, this same behavior can be done with the targets being the licenses which are established to be in a plurality with the two potential targets, the AI-based segmentation can be interpreted as the AI generated composition, and the probability/confidence scores can be the varying percentages of the licenses that are displayed. Further, see Messerly in paragraph [0089] describing, “In an example, the system may determine with high confidence that the first potential target is a particular blood vessel, such as the SVC, and may accordingly identify the first potential target as such via display 450. The system may identify the first potential target when the probability or confidence score from the AI-based model exceeds a threshold, for example greater than 97% confidence. In another example, the system may identify that the second potential target has a 75% chance of being the right renal vein, and a 25% chance of being the right suprarenal vein, and may accordingly display both of these probabilities via display 450. The system may display multiple probabilities when the scores from the AI-based model are lower than a threshold, for example lower than 97% confidence.” Here, Messerly further establishes the displaying of the plurality of targets with their probability scores as percentages.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kiely with the teachings of Messerly by using Kiely’s teachings of analyzing AI generated composition and identifying and generating license information for the composition, and incorporate with Messerly’s teaching of displaying likely targets with associated similarity percentages/scores.
One of ordinary skill in the art would be motivated to do so because by integrating Messerly’s frameworks into the methods of Kiely, which are both in the same field of art, one of ordinary skill in the art would bring “a target recognition and needle guidance system. The target recognition and needle guidance system comprises a console including memory and a processor, and an ultrasound probe. The console is configured to instantiate a target recognition process for recognizing an anatomical target of a patient, by applying an artificial intelligence model to features of candidate targets in ultrasound-imaging data to determine a recognition score.” (Messerly, paragraph [0005]).
Claim 21:
Regarding claim 21, Kiely teaches the limitations of claim 20.
Further, Kiely teaches “The method of claim 20, wherein the likely license associated with the AI generated composition comprises a plurality of likely licenses associated with the AI generated composition…”
See Kiely in paragraph [0029] describing, “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 AI's output.” Here, Kiely establishes copyright information seen as licensing information comprising clear and accurate attribution of copyright, which can be seen as likely licenses associated with the snippet of AI generated composition, the plurality comes from the original artists and copyright holders implying more than one.
Kiely does not appear to explicitly teach “…that are displayed as varying percentages of the plurality of likely licenses.”,
However in the same field of art and in an analogous system, Messerly teaches “...that are displayed as varying percentages of the plurality of likely licenses.”
See Messerly in paragraph [0088] describing, “The system can display the predicted classification of target 460 and/or of a list of potential targets based on the probability or confidence score from evaluating the AI-based segmentation or classification model using image 465 as input. For example, if image 465 contains two potential targets, and the system has been trained to recognize 90 identified target blood vessels, the system can evaluate a classification probability for each of the two potential targets being each the 90 known targets.” Here, Messerly establishes a system that displays targets with a probability/confidence score associated with it based on AI-based segmentation. In an analogous system, this same behavior can be done with the targets being the licenses which are established to be in a plurality with the two potential targets, the AI-based segmentation can be interpreted as the AI generated composition, and the probability/confidence scores can be the varying percentages of the licenses that are displayed. Further, see Messerly in paragraph [0089] describing, “In an example, the system may determine with high confidence that the first potential target is a particular blood vessel, such as the SVC, and may accordingly identify the first potential target as such via display 450. The system may identify the first potential target when the probability or confidence score from the AI-based model exceeds a threshold, for example greater than 97% confidence. In another example, the system may identify that the second potential target has a 75% chance of being the right renal vein, and a 25% chance of being the right suprarenal vein, and may accordingly display both of these probabilities via display 450. The system may display multiple probabilities when the scores from the AI-based model are lower than a threshold, for example lower than 97% confidence.” Here, Messerly further establishes the displaying of the plurality of targets with their probability scores as percentages.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kiely with the teachings of Messerly by using Kiely’s teachings of analyzing AI generated composition and identifying and generating license information for the composition, and incorporate with Messerly’s teaching of displaying likely targets with associated similarity percentages/scores.
One of ordinary skill in the art would be motivated to do so because by integrating Messerly’s frameworks into the methods of Kiely, which are both in the same field of art, one of ordinary skill in the art would bring “a target recognition and needle guidance system. The target recognition and needle guidance system comprises a console including memory and a processor, and an ultrasound probe. The console is configured to instantiate a target recognition process for recognizing an anatomical target of a patient, by applying an artificial intelligence model to features of candidate targets in ultrasound-imaging data to determine a recognition score.” (Messerly, paragraph [0005]).
Claim(s) 8 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Kiely et al., in view of Ivankovic M. et al, (US. Patent Application Publication 20210132915 A1) effectively filed on November 6th, 2019, (hereafter Ivankovic).
Claim 8:
Regarding claim 8, Kiely teaches the limitations of claim 1.
Kiely does not appear to explicitly teach “The system of claim 1, wherein the licensing information comprises a vector generated by the similarity algorithm and wherein the vector generated by the similarity algorithm comprises at least one of: vector information generated by the similarity algorithm for the AI generated composition and matching vector information generated by the similarity algorithm for the composition used to train the AI algorithm.”,
However in the same field of art and in an analogous system, Ivankovic teaches “The system of claim 1, wherein the licensing information comprises a vector generated by the similarity algorithm and wherein the vector generated by the similarity algorithm comprises at least one of: vector information generated by the similarity algorithm for the AI generated composition and matching vector information generated by the similarity algorithm for the composition used to train the AI algorithm.”
See Ivankovic in paragraph [0012] describing, “The operations may further include, when the predicted code transformation for the target source code includes executable code, training the machine learning model on training examples including the training source code paired with corresponding training executable code resulting from compiling or interpreting the training source code. In some implementations, generating the code insight for the target source code using the machine learning model includes generating a vector representation for the target source code using the machine learning model configured to receive a set of target features extracted from the target source code as feature inputs, determining similarity scores for a pool of training source code snippets stored in the memory hardware, each similarity score associated with a corresponding training code snippet and indicating a level of similarity between the vector representation for the target source code and a respective vector representation for the corresponding training code snippet, and identifying one or more training source code snippets from the pool of training source code snippets that have similarity scores satisfying a similarity threshold as corresponding to mutations of the target source code..” Here, Ivankovic establishes generating a vector using a machine learning model in which the machine learning model also determines similarity which can make the generating by the model be seen as a vector being generated by a similarity algorithm. Ivankovic also establishes the vector comprising of matching vector information generated by the similarity algorithm used to train the algorithm with the training of the model using training source code and that training source code is matched to the vector generated by the model, seen as the similarity algorithm, which has the target source code which can be seen as the composition in an analogous system.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kiely with the teachings of Ivankovic by using Kiely’s teachings of analyzing AI generated composition and identifying and generating license information for the composition, and incorporate with Ivankovic’s teaching of generating a vector comprising matching information generated by a similarity algorithm.
One of ordinary skill in the art would be motivated to do so because by integrating Ivankovic’s frameworks into the methods of Kiely, which are both in the same field of art, one of ordinary skill in the art would bring “a method for generating code insights.”, and “The method further includes obtaining, by the data processing hardware, a machine learning model based on the tool type indicator, the machine learning model trained on training source code associated with the specified one of the code labeling type of software development tool or the code transformation type of software development tool. The method further includes generating, by the data processing hardware, the code insight for the target source code using the machine learning model. When the tool type indicator specifies that the software development tool includes the code labeling type of software development tool, the code insight for the target source code includes a predicted label for the target source code. When the tool type indicator specifies that the software development tool includes the code transformation type of software development tool, the code insight for the target source code includes a predicted code transformation for the training source code. The method further includes transmitting, by the data processing hardware, the code insight to the developer device, the code insight when received by the developer device causing a graphical user interface executing on the developer device to display the code insight on a display screen of the developer device.” (Ivankovic, paragraph [0004]).
Claim 22:
Regarding claim 22, Kiely teaches the limitations of claim 17.
Kiely does not appear to explicitly teach “The method of claim 17, wherein the licensing information comprises a vector generated by the similarity algorithm and wherein the vector generated by the similarity algorithm comprises at least one of: vector information generated by the similarity algorithm for the AI generated composition and matching vector information generated by the similarity algorithm for the composition used to train the AI algorithm.”,
However in the same field of art and in an analogous system, Ivankovic teaches “The method of claim 17, wherein the licensing information comprises a vector generated by the similarity algorithm and wherein the vector generated by the similarity algorithm comprises at least one of: vector information generated by the similarity algorithm for the AI generated composition and matching vector information generated by the similarity algorithm for the composition used to train the AI algorithm.”
See Ivankovic in paragraph [0012] describing, “The operations may further include, when the predicted code transformation for the target source code includes executable code, training the machine learning model on training examples including the training source code paired with corresponding training executable code resulting from compiling or interpreting the training source code. In some implementations, generating the code insight for the target source code using the machine learning model includes generating a vector representation for the target source code using the machine learning model configured to receive a set of target features extracted from the target source code as feature inputs, determining similarity scores for a pool of training source code snippets stored in the memory hardware, each similarity score associated with a corresponding training code snippet and indicating a level of similarity between the vector representation for the target source code and a respective vector representation for the corresponding training code snippet, and identifying one or more training source code snippets from the pool of training source code snippets that have similarity scores satisfying a similarity threshold as corresponding to mutations of the target source code..” Here, Ivankovic establishes generating a vector using a machine learning model in which the machine learning model also determines similarity which can make the generating by the model be seen as a vector being generated by a similarity algorithm. Ivankovic also establishes the vector comprising of matching vector information generated by the similarity algorithm used to train the algorithm with the training of the model using training source code and that training source code is matched to the vector generated by the model, seen as the similarity algorithm, which has the target source code which can be seen as the composition in an analogous system.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kiely with the teachings of Ivankovic by using Kiely’s teachings of analyzing AI generated composition and identifying and generating license information for the composition, and incorporate with Ivankovic’s teaching of generating a vector comprising matching information generated by a similarity algorithm.
One of ordinary skill in the art would be motivated to do so because by integrating Ivankovic’s frameworks into the methods of Kiely, which are both in the same field of art, one of ordinary skill in the art would bring “a method for generating code insights.”, and “The method further includes obtaining, by the data processing hardware, a machine learning model based on the tool type indicator, the machine learning model trained on training source code associated with the specified one of the code labeling type of software development tool or the code transformation type of software development tool. The method further includes generating, by the data processing hardware, the code insight for the target source code using the machine learning model. When the tool type indicator specifies that the software development tool includes the code labeling type of software development tool, the code insight for the target source code includes a predicted label for the target source code. When the tool type indicator specifies that the software development tool includes the code transformation type of software development tool, the code insight for the target source code includes a predicted code transformation for the training source code. The method further includes transmitting, by the data processing hardware, the code insight to the developer device, the code insight when received by the developer device causing a graphical user interface executing on the developer device to display the code insight on a display screen of the developer device.” (Ivankovic, paragraph [0004]).
Claim(s) 10-16, and 23-29 are rejected under 35 U.S.C. 103 as being unpatentable over Kiely et al., in view of Dehaeck D. et al, (US. Patent Application Publication 20170243179 A1) effectively filed on August 4th, 2016, (hereafter Dehaeck).
Claim 10:
Regarding claim 10, Kiely teaches the limitations of claim 1.
Kiely does not appear to explicitly teach “The system of claim 1, wherein the licensing information is stored in a blockchain and wherein the licensing information stored in the blockchain comprises at least one of: an AI algorithm block, a training composition block, a license block, a licenses filtered out block, a user input block, an AI input block, a generated composition block, a likely license block, a copyright options block, a royalty block, a transaction block, a vector block, a hash snippet block, and a snippet block.”,
However in the same field of art and in an analogous system, Dehaeck teaches “The system of claim 1, wherein the licensing information is stored in a blockchain and wherein the licensing information stored in the blockchain comprises at least one of: an AI algorithm block, a training composition block, a license block, a licenses filtered out block, a user input block, an AI input block, a generated composition block, a likely license block, a copyright options block, a royalty block, a transaction block, a vector block, a hash snippet block, and a snippet block.”
See Dehaeck in paragraph [0014] describing, “The system provides a secured environment for trading of digital artwork including loaning/renting of the artwork through encryption of the digital artworks, protection of copyright with invisible and online traceable watermarks, recording all activity as metadata of the work in a blockchain ledger, etc.” Here, Dehaeck explicitly establishes licensing information being stored in a blockchain with the copyright of digital artwork. Further, see Dehaeck in paragraph [0014] describing, “Also, the system records the chain of title in a blockchain based ledger for a digital artwork edition upon sale and/or a resale of the digital edition to ensure ownership authenticity of the digital artwork as an embedded part of the work.” Here, Dehaeck explicitly establishes the blockchain comprising of a chain of title for digital artwork that is sold. Further, see Dehaeck in paragraph [0038] describing, “In a preferred embodiment, blockchain technology is used for recording the chain of titles which creates a chain where any changes made to a block will change that block's hash, which must be recomputed and stored in the next block.” Here, Dehaeck explicitly establishes a hash snippet block for recording the chain of titles established to be linked to licensing information.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kiely with the teachings of Dehaeck by using Kiely’s teachings of analyzing AI generated composition and identifying and generating license information for the composition and incorporate with Dehaeck’s teachings of licensing information stored in a blockchain.
One of ordinary skill in the art would be motivated to do so because by integrating Dehaeck frameworks into the methods of Kiely, which are both in the same field of art, one of ordinary skill in the art would bring “a system and method for making digital artwork accessible to as many customers as possible without compromising on authenticity and exclusivity of the artworks.” (Dehaeck, paragraph [0005]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0006]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0007]), “a system and method for monetizing digital artworks through granting of temporary viewing rights transmitted over a computer network.” (Dehaeck, paragraph [0008]), “a system and method which enable specifying maximum number of copies of a digital artwork that can be sold or distributed to ensure exclusivity of the digital artwork.” (Dehaeck, paragraph [0009]), and “a system and method for enabling earning of recurring income from temporary access (rent/loaning) of digital artwork and sharing of the earning among artist, collector etc.” (Dehaeck, paragraph [0010]).
Claim 11:
Regarding claim 11, Kiely teaches the limitations of claim 1.
Further, Kiely teaches “…the AI generated composition…”
See Kiely in paragraph [0024] describing, “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 AI 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.” Here, Kiely establishes an algorithm for comparing newly generated AI content which can be seen as generated.
Kiely does not appear to explicitly teach “The system of claim 1, wherein the licensing information is stored in a blockchain, wherein the blockchain comprises a plurality of transaction blocks that comprise a plurality of watermarks, wherein the plurality of transaction blocks comprises a chain-of-title for…, and wherein each of the plurality of transaction blocks identifies at least one of: a licensor, a previous owner, a borrower, and a current owner.”,
However, in the same field of art and in an analogous system, Dehaeck teaches “The system of claim 1, wherein the licensing information is stored in a blockchain, wherein the blockchain comprises a plurality of transaction blocks that comprise a plurality of watermarks,”
See Dehaeck in paragraph [0014] describing, “The system provides a secured environment for trading of digital artwork including loaning/renting of the artwork through encryption of the digital artworks, protection of copyright with invisible and online traceable watermarks, recording all activity as metadata of the work in a blockchain ledger, etc.” Here, Dehaeck explicitly establishes watermarks for digital artworks, which in an analogous system can be seen as AI generated composition, that are to be loaned or rented which comprises of a transaction being done, in a blockchain. The loaned/rented artworks stored in the blockchain ledger are being interpreted as the plurality of transaction blocks.
Further, Dehaeck teaches “wherein the plurality of transaction blocks comprises a chain-of-title for…,”
See Dehaeck in paragraph [0014] describing, “Also, the system records the chain of title in a blockchain based ledger for a digital artwork edition upon sale and/or a resale of the digital edition to ensure ownership authenticity of the digital artwork as an embedded part of the work.” Here, Dehaeck explicitly establishes the blockchain comprising of a chain of title for digital artwork that is sold, which can be seen as a plurality of transactions blocks as established and the artwork is also established to be seen as AI generated composition in analogous system.
Further, Dehaeck teaches “and wherein each of the plurality of transaction blocks identifies at least one of: a licensor, a previous owner, a borrower, and a current owner.”
See Dehaeck in paragraph [0036] describing, “The Patron Index, as shown within the box 408 on the user interface 400, includes information, among others, information on the patrons who buy permanent viewing rights of the digital artwork and the unique numbers of the patron edition copies owned by the patrons etc. In a preferred embodiment, the first buyers of the patron edition copies in the primary market are referred to as Founding Patrons. In the secondary market, whenever a Founding Patron sells a digital artwork through the system, then the new owners will be listed in the Patron Index as Active Patrons. The sellers from the primary market will be listed under their respective headings in the provenance as Past Founding Patrons. Similarly, in the secondary market, sellers will be listed as Past Patrons in the Patron Index.” Here, Dehaeck explicitly establishes the digital artwork, established to be linked to a blockchain ledger with transaction blocks, identifying a previous owner with the past founding patrons and a current owner with the new owner.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kiely with the teachings of Dehaeck by using Kiely’s teachings of analyzing AI generated composition and identifying and generating license information for the composition and incorporate with Dehaeck’s teachings of licensing information stored in a blockchain.
One of ordinary skill in the art would be motivated to do so because by integrating Dehaeck frameworks into the methods of Kiely, which are both in the same field of art, one of ordinary skill in the art would bring “a system and method for making digital artwork accessible to as many customers as possible without compromising on authenticity and exclusivity of the artworks.” (Dehaeck, paragraph [0005]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0006]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0007]), “a system and method for monetizing digital artworks through granting of temporary viewing rights transmitted over a computer network.” (Dehaeck, paragraph [0008]), “a system and method which enable specifying maximum number of copies of a digital artwork that can be sold or distributed to ensure exclusivity of the digital artwork.” (Dehaeck, paragraph [0009]), and “a system and method for enabling earning of recurring income from temporary access (rent/loaning) of digital artwork and sharing of the earning among artist, collector etc.” (Dehaeck, paragraph [0010]).
Claim 12:
Regarding claim 12, Kiely in view of Dehaeck teaches the limitations of claim 11.
Further, Kiely teaches “…the AI generated composition…”
See Kiely in paragraph [0024] describing, “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 AI 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. Here, Kiely establishes an algorithm for comparing newly generated AI content which can be seen as generated.
Kiely does not appear to explicitly teach “The system of claim 11, wherein each of the plurality of transaction blocks that comprise the chain-of-title for…further comprise a hash of…for each copy of…and wherein each copy of…has a different watermark associated with a current owner of each copy of….”,
However in the same field of art and in an analogous system, Dehaeck teaches “The system of claim 11, wherein each of the plurality of transaction blocks that comprise the chain-of-title for…further comprise a hash of…for each copy of…and wherein each copy of…has a different watermark associated with a current owner of each copy of….”
See Dehaeck in paragraph [0014] describing, “Also, the system records the chain of title in a blockchain based ledger for a digital artwork edition upon sale and/or a resale of the digital edition to ensure ownership authenticity of the digital artwork as an embedded part of the work.” Here, Dehaeck explicitly establishes the blockchain comprising of a chain of title for digital artwork that is sold, which can be seen as a plurality of transactions blocks as established and the artwork is also established to be seen as AI generated composition in an analogous system. Further, see Dehaeck in paragraph [0038] describing, “In a preferred embodiment, blockchain technology is used for recording the chain of titles which creates a chain where any changes made to a block will change that block's hash, which must be recomputed and stored in the next block.” Here, Dehaeck explicitly establishes a hash for the blocks for the chain of titles which are linked to digital artwork seen as AI generated composition. Further, see Dehaeck in paragraph [0031] describing, “In some embodiments, a visible and/or an imperceptible digital watermark can be applied to a digital artwork making the digital artwork unique.” Here, Dehaeck establishes putting a watermark on a digital artwork making that piece of artwork unique implying a different watermark for each copy of the artwork.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kiely with the teachings of Dehaeck by using Kiely’s teachings of analyzing AI generated composition and identifying and generating license information for the composition and incorporate with Dehaeck’s teachings of licensing information stored in a blockchain.
One of ordinary skill in the art would be motivated to do so because by integrating Dehaeck frameworks into the methods of Kiely, which are both in the same field of art, one of ordinary skill in the art would bring “a system and method for making digital artwork accessible to as many customers as possible without compromising on authenticity and exclusivity of the artworks.” (Dehaeck, paragraph [0005]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0006]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0007]), “a system and method for monetizing digital artworks through granting of temporary viewing rights transmitted over a computer network.” (Dehaeck, paragraph [0008]), “a system and method which enable specifying maximum number of copies of a digital artwork that can be sold or distributed to ensure exclusivity of the digital artwork.” (Dehaeck, paragraph [0009]), and “a system and method for enabling earning of recurring income from temporary access (rent/loaning) of digital artwork and sharing of the earning among artist, collector etc.” (Dehaeck, paragraph [0010]).
Claim 13:
Regarding claim 13, Kiely in view of Dehaeck teaches the limitations of claim 1.
Further, Kiely teaches “…the AI generated composition…”
See Kiely in paragraph [0024] describing, “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 AI 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. Here, Kiely establishes an algorithm for comparing newly generated AI content which can be seen as generated.
Kiely does not appear to explicitly teach “The system of claim 1, wherein the licensing information is stored in a blockchain, and wherein the blockchain comprises a plurality of branches that individually track individual copies of…and ownership of the individual copies of….”,
However, in the same field of art and in an analogous system, Dehaeck teaches “The system of claim 1, wherein the licensing information is stored in a blockchain, and wherein the blockchain comprises a plurality of branches that individually track individual copies of…and ownership of the individual copies of….”
See Dehaeck in paragraph [0014] describing, “Patron Edition System provides a platform for selling a predefined number of copies of a digital artwork by an artist or a representative of the artist to patrons/collectors and subsequent reselling of the copies of the artwork by the patrons/collectors. Examples of representative of the artist include, but are not limited to, a gallery, a dealer, an intermediary etc. The feature of allowing selling of only a limited number of copies of the digital artwork ensures exclusivity of the artwork. Also, the system records the chain of title in a blockchain based ledger for a digital artwork edition upon sale and/or a resale of the digital edition to ensure ownership authenticity of the digital artwork as an embedded part of the work.” Here, Dehaeck explicitly establishes a system with a blockchain for digital artwork, which was previously established to be seen as AI generated composition, that is sold. Further, see Dehaeck in paragraph [0036] describing, “To make trading of a digital artwork through the patron edition system transparent and to facilitate verification of provenance data associated with the digital artwork, the present invention provides on the user interface a record of ownership of the patron edition copies of the digital artwork, the record being hereinafter referred to as Patron Index. The Patron Index, as shown within the box 408 on the user interface 400, includes information, among others, information on the patrons who buy permanent viewing rights of the digital artwork and the unique numbers of the patron edition copies owned by the patrons etc. In a preferred embodiment, the first buyers of the patron edition copies in the primary market are referred to as Founding Patrons. In the secondary market, whenever a Founding Patron sells a digital artwork through the system, then the new owners will be listed in the Patron Index as Active Patrons. The sellers from the primary market will be listed under their respective headings in the provenance as Past Founding Patrons. Similarly, in the secondary market, sellers will be listed as Past Patrons in the Patron Index. In the present example, Patron 1 is one of the Founding Patrons for the digital artwork. If Patron 2 buys the digital artwork from the artist then Patron 2 becomes a Founding Patron and, if Patron 2 buys it from Patron 1, then Patron 2 becomes an Active Patron and Patron 1 becomes a Past Founding Patron for the patron edition copy of the digital artwork being traded.” Here, Dehaeck establishes a tracking of copies of the digital artwork and the ownership, the Patron Index is the individual tracking of the copies and ownership. Further, see Dehaeck in Figure 4,
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. Here, the Patron Index shows individual tracking of the copies with the work number and the ownership and this individual tracking can act as the branches for tracking in the system established to comprise a blockchain for this digital artwork.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kiely with the teachings of Dehaeck by using Kiely’s teachings of analyzing AI generated composition and identifying and generating license information for the composition and incorporate with Dehaeck’s teachings of licensing information stored in a blockchain.
One of ordinary skill in the art would be motivated to do so because by integrating Dehaeck frameworks into the methods of Kiely, which are both in the same field of art, one of ordinary skill in the art would bring “a system and method for making digital artwork accessible to as many customers as possible without compromising on authenticity and exclusivity of the artworks.” (Dehaeck, paragraph [0005]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0006]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0007]), “a system and method for monetizing digital artworks through granting of temporary viewing rights transmitted over a computer network.” (Dehaeck, paragraph [0008]), “a system and method which enable specifying maximum number of copies of a digital artwork that can be sold or distributed to ensure exclusivity of the digital artwork.” (Dehaeck, paragraph [0009]), and “a system and method for enabling earning of recurring income from temporary access (rent/loaning) of digital artwork and sharing of the earning among artist, collector etc.” (Dehaeck, paragraph [0010]).
Claim 14:
Regarding claim 14, Kiely in view of Dehaeck teaches the limitations of claim 13.
Further, Kiely teaches “…track individual media rights for each of the individual copies of the AI generated composition.”
See Kiely in paragraph [0076] describing, “The method 500 includes, at block 502, the step of analysing an original copyright work to formulate a unique digital profile of the work.” Further, see Kiely in paragraph [0077] describing, “At block 504, the method includes the step of storing one or more analysed copyright works along with their digital profiles.” Further, see Kiely in paragraph [0078] describing, “At block 506, upon a generative Artificial Intelligence (AI) 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.” Here, Kiely the tracking of individual media right for individual copies of AI generated composition with the storing of the copyright works and identifying of original copyright works for the new derivative work, which is seen as the AI generated composition. The method described by Kiely comprises blocks to perform this which can be seen as the branches to perform these operations.
Kiely does not appear to explicitly teach “The system of claim 13, wherein the plurality of branches…”,
However, in the same field of art and in an analogous system, Dehaeck teaches “The system of claim 13, wherein the plurality of branches…”
See Dehaeck in paragraph [0036] describing, “To make trading of a digital artwork through the patron edition system transparent and to facilitate verification of provenance data associated with the digital artwork, the present invention provides on the user interface a record of ownership of the patron edition copies of the digital artwork, the record being hereinafter referred to as Patron Index. The Patron Index, as shown within the box 408 on the user interface 400, includes information, among others, information on the patrons who buy permanent viewing rights of the digital artwork and the unique numbers of the patron edition copies owned by the patrons etc. In a preferred embodiment, the first buyers of the patron edition copies in the primary market are referred to as Founding Patrons. In the secondary market, whenever a Founding Patron sells a digital artwork through the system, then the new owners will be listed in the Patron Index as Active Patrons. The sellers from the primary market will be listed under their respective headings in the provenance as Past Founding Patrons. Similarly, in the secondary market, sellers will be listed as Past Patrons in the Patron Index. In the present example, Patron 1 is one of the Founding Patrons for the digital artwork. If Patron 2 buys the digital artwork from the artist then Patron 2 becomes a Founding Patron and, if Patron 2 buys it from Patron 1, then Patron 2 becomes an Active Patron and Patron 1 becomes a Past Founding Patron for the patron edition copy of the digital artwork being traded.” Here, Dehaeck establishes a tracking of copies of the digital artwork and the ownership, the Patron Index is the individual tracking of the copies and ownership. Further, see Dehaeck in Figure 4,
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. Here, the Patron Index shows individual tracking of the copies with the work number and the ownership, and this individual tracking can act as the branches for tracking in the system established to comprise a blockchain for this digital artwork.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kiely with the teachings of Dehaeck by using Kiely’s teachings of analyzing AI generated composition and identifying and generating license information for the composition and incorporate with Dehaeck’s teachings of licensing information stored in a blockchain.
One of ordinary skill in the art would be motivated to do so because by integrating Dehaeck frameworks into the methods of Kiely, which are both in the same field of art, one of ordinary skill in the art would bring “a system and method for making digital artwork accessible to as many customers as possible without compromising on authenticity and exclusivity of the artworks.” (Dehaeck, paragraph [0005]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0006]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0007]), “a system and method for monetizing digital artworks through granting of temporary viewing rights transmitted over a computer network.” (Dehaeck, paragraph [0008]), “a system and method which enable specifying maximum number of copies of a digital artwork that can be sold or distributed to ensure exclusivity of the digital artwork.” (Dehaeck, paragraph [0009]), and “a system and method for enabling earning of recurring income from temporary access (rent/loaning) of digital artwork and sharing of the earning among artist, collector etc.” (Dehaeck, paragraph [0010]).
Claim 15:
Regarding claim 15, Kiely in view of Dehaeck teaches the limitations of claim 1.
Further, Kiely teaches “…the AI generated composition…”
See Kiely in paragraph [0024] describing, “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 AI 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. Here, Kiely establishes an algorithm for comparing newly generated AI content which can be seen as generated.
Kiely does not appear to explicitly teach “The system of claim 1, wherein the licensing information is stored in a blockchain and wherein the blockchain tracks a maximum number of copies of….”,
However in the same field of art and in an analogous system, Dehaeck teaches “The system of claim 1, wherein the licensing information is stored in a blockchain and wherein the blockchain tracks a maximum number of copies of….”
See Dehaeck in paragraph [0032] describing, “As shown in FIG. 3, when the option patron edition is selected by the artist/representative 121 under the field 306, the patron edition system server 102 generates only those numbers of copies of the digital artwork which the artist 121 specifies under the field 308 on the user interface 300. In the present example, the maximum number of copies to be reproduced is 5 as indicated by the artist 121 in the field 308. So, for the artist/representative 121, the patron edition system server 102 will generate only 5 copies of the patron editions from the digital artwork uploaded by the artist.” Here, Dehaeck establishes a tracking of maximum number of copies of digital artwork established to be seen as AI generated composition in an analogous system, the patron edition system is also established to consist of a blockchain for digital artwork and its licensing information.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kiely with the teachings of Dehaeck by using Kiely’s teachings of analyzing AI generated composition and identifying and generating license information for the composition and incorporate with Dehaeck’s teachings of licensing information stored in a blockchain.
One of ordinary skill in the art would be motivated to do so because by integrating Dehaeck frameworks into the methods of Kiely, which are both in the same field of art, one of ordinary skill in the art would bring “a system and method for making digital artwork accessible to as many customers as possible without compromising on authenticity and exclusivity of the artworks.” (Dehaeck, paragraph [0005]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0006]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0007]), “a system and method for monetizing digital artworks through granting of temporary viewing rights transmitted over a computer network.” (Dehaeck, paragraph [0008]), “a system and method which enable specifying maximum number of copies of a digital artwork that can be sold or distributed to ensure exclusivity of the digital artwork.” (Dehaeck, paragraph [0009]), and “a system and method for enabling earning of recurring income from temporary access (rent/loaning) of digital artwork and sharing of the earning among artist, collector etc.” (Dehaeck, paragraph [0010]).
Claim 16:
Regarding claim 16, Kiely in view of Dehaeck teaches the limitations of claim 1.
Further, Kiely teaches “…the AI generated composition…”
See Kiely in paragraph [0024] describing, “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 AI 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. Here, Kiely establishes an algorithm for comparing newly generated AI content which can be seen as generated.
Kiely does not appear to explicitly teach “The system of claim 1, wherein the licensing information is stored in a blockchain and wherein the blockchain identifies a number of times a copy of…can be sold/transferred.”,
However, in the same field of art and in an analogous system, Dehaeck teaches “The system of claim 1, wherein the licensing information is stored in a blockchain and wherein the blockchain identifies a number of times a copy of…can be sold/transferred.”
See Dehaeck in paragraph [0009] describing, “A further object of the present invention is to provide a system and method which enable specifying maximum number of copies of a digital artwork that can be sold or distributed to ensure exclusivity of the digital artwork.” Here Dehaeck explicitly establishes specifying or identifying the number of times a copy of digital artwork can be sold or distributed/transferred, the digital artwork can be AI generated composition in an analogous system. Further, see Dehaeck in paragraph [0014] describing, “Also, the system records the chain of title in a blockchain based ledger for a digital artwork edition upon sale and/or a resale of the digital edition to ensure ownership authenticity of the digital artwork as an embedded part of the work.” Here, Dehaeck establishes the system storing digital artwork information in blockchain.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kiely with the teachings of Dehaeck by using Kiely’s teachings of analyzing AI generated composition and identifying and generating license information for the composition and incorporate with Dehaeck’s teachings of licensing information stored in a blockchain.
One of ordinary skill in the art would be motivated to do so because by integrating Dehaeck frameworks into the methods of Kiely, which are both in the same field of art, one of ordinary skill in the art would bring “a system and method for making digital artwork accessible to as many customers as possible without compromising on authenticity and exclusivity of the artworks.” (Dehaeck, paragraph [0005]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0006]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0007]), “a system and method for monetizing digital artworks through granting of temporary viewing rights transmitted over a computer network.” (Dehaeck, paragraph [0008]), “a system and method which enable specifying maximum number of copies of a digital artwork that can be sold or distributed to ensure exclusivity of the digital artwork.” (Dehaeck, paragraph [0009]), and “a system and method for enabling earning of recurring income from temporary access (rent/loaning) of digital artwork and sharing of the earning among artist, collector etc.” (Dehaeck, paragraph [0010]).
Claim 23:
Regarding claim 23, Kiely teaches the limitations of claim 17.
Kiely does not appear to explicitly teach “The method of claim 17, wherein the licensing information is stored in a blockchain and wherein the licensing information stored in the blockchain comprises at least one of: an AI algorithm block, a training composition block, a license block, a licenses filtered out block, a user input block, an AI input block, a generated composition block, a likely license block, a copyright options block, a royalty block, a transaction block, a vector block, a hash snippet block, and a snippet block.”,
However in the same field of art and in an analogous system, Dehaeck teaches “The method of claim 17, wherein the licensing information is stored in a blockchain and wherein the licensing information stored in the blockchain comprises at least one of: an AI algorithm block, a training composition block, a license block, a licenses filtered out block, a user input block, an AI input block, a generated composition block, a likely license block, a copyright options block, a royalty block, a transaction block, a vector block, a hash snippet block, and a snippet block.”
See Dehaeck in paragraph [0014] describing, “The system provides a secured environment for trading of digital artwork including loaning/renting of the artwork through encryption of the digital artworks, protection of copyright with invisible and online traceable watermarks, recording all activity as metadata of the work in a blockchain ledger, etc.” Here, Dehaeck explicitly establishes licensing information being stored in a blockchain with the copyright of digital artwork. Further, see Dehaeck in paragraph [0014] describing, “Also, the system records the chain of title in a blockchain based ledger for a digital artwork edition upon sale and/or a resale of the digital edition to ensure ownership authenticity of the digital artwork as an embedded part of the work.” Here, Dehaeck explicitly establishes the blockchain comprising of a chain of title for digital artwork that is sold. Further, see Dehaeck in paragraph [0038] describing, “In a preferred embodiment, blockchain technology is used for recording the chain of titles which creates a chain where any changes made to a block will change that block's hash, which must be recomputed and stored in the next block.” Here, Dehaeck explicitly establishes a hash snippet block for recording the chain of titles established to be linked to licensing information.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kiely with the teachings of Dehaeck by using Kiely’s teachings of analyzing AI generated composition and identifying and generating license information for the composition, and incorporate with Dehaeck’s teachings of licensing information stored in a blockchain.
One of ordinary skill in the art would be motivated to do so because by integrating Dehaeck frameworks into the methods of Kiely, which are both in the same field of art, one of ordinary skill in the art would bring “a system and method for making digital artwork accessible to as many customers as possible without compromising on authenticity and exclusivity of the artworks.” (Dehaeck, paragraph [0005]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0006]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0007]), “a system and method for monetizing digital artworks through granting of temporary viewing rights transmitted over a computer network.” (Dehaeck, paragraph [0008]), “a system and method which enable specifying maximum number of copies of a digital artwork that can be sold or distributed to ensure exclusivity of the digital artwork.” (Dehaeck, paragraph [0009]), and “a system and method for enabling earning of recurring income from temporary access (rent/loaning) of digital artwork and sharing of the earning among artist, collector etc.” (Dehaeck, paragraph [0010]).
Claim 24:
Regarding claim 24, Kiely teaches the limitations of claim 17.
Further, Kiely teaches “…the AI generated composition…”
See Kiely in paragraph [0024] describing, “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 AI 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.” Here, Kiely establishes an algorithm for comparing newly generated AI content which can be seen as generated.
Kiely does not appear to explicitly teach “The method of claim 17, wherein the licensing information is stored in a blockchain, wherein the blockchain comprises a plurality of transaction blocks that comprise a plurality of watermarks, wherein the plurality of transaction blocks comprise a chain-of-title for…, and wherein each of the plurality of transaction blocks identifies at least one of: a licensor, a previous owner, a borrower, and a current owner.”,
However in the same field of art and in an analogous system, Dehaeck teaches “The system of claim 1, wherein the licensing information is stored in a blockchain, wherein the blockchain comprises a plurality of transaction blocks that comprise a plurality of watermarks,”
See Dehaeck in paragraph [0014] describing, “The system provides a secured environment for trading of digital artwork including loaning/renting of the artwork through encryption of the digital artworks, protection of copyright with invisible and online traceable watermarks, recording all activity as metadata of the work in a blockchain ledger, etc.” Here, Dehaeck explicitly establishes watermarks for digital artworks, which in an analogous system can be seen as AI generated composition, that are to be loaned or rented which comprises of a transaction being done, in a blockchain. The loaned/rented artworks stored in the blockchain ledger are being interpreted as the plurality of transaction blocks.
Further, Dehaeck teaches “wherein the plurality of transaction blocks comprises a chain-of-title for…,”
See Dehaeck in paragraph [0014] describing, “Also, the system records the chain of title in a blockchain based ledger for a digital artwork edition upon sale and/or a resale of the digital edition to ensure ownership authenticity of the digital artwork as an embedded part of the work.” Here, Dehaeck explicitly establishes the blockchain comprising of a chain of title for digital artwork that is sold, which can be seen as a plurality of transactions blocks as established and the artwork is also established to be seen as AI generated composition in analogous system.
Further, Dehaeck teaches “and wherein each of the plurality of transaction blocks identifies at least one of: a licensor, a previous owner, a borrower, and a current owner.”
See Dehaeck in paragraph [0036] describing, “The Patron Index, as shown within the box 408 on the user interface 400, includes information, among others, information on the patrons who buy permanent viewing rights of the digital artwork and the unique numbers of the patron edition copies owned by the patrons etc. In a preferred embodiment, the first buyers of the patron edition copies in the primary market are referred to as Founding Patrons. In the secondary market, whenever a Founding Patron sells a digital artwork through the system, then the new owners will be listed in the Patron Index as Active Patrons. The sellers from the primary market will be listed under their respective headings in the provenance as Past Founding Patrons. Similarly, in the secondary market, sellers will be listed as Past Patrons in the Patron Index.” Here, Dehaeck explicitly establishes the digital artwork, established to be linked to a blockchain ledger with transaction blocks, identifying a previous owner with the past founding patrons and a current owner with the new owner.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kiely with the teachings of Dehaeck by using Kiely’s teachings of analyzing AI generated composition and identifying and generating license information for the composition, and incorporate with Dehaeck’s teachings of licensing information stored in a blockchain.
One of ordinary skill in the art would be motivated to do so because by integrating Dehaeck frameworks into the methods of Kiely, which are both in the same field of art, one of ordinary skill in the art would bring “a system and method for making digital artwork accessible to as many customers as possible without compromising on authenticity and exclusivity of the artworks.” (Dehaeck, paragraph [0005]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0006]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0007]), “a system and method for monetizing digital artworks through granting of temporary viewing rights transmitted over a computer network.” (Dehaeck, paragraph [0008]), “a system and method which enable specifying maximum number of copies of a digital artwork that can be sold or distributed to ensure exclusivity of the digital artwork.” (Dehaeck, paragraph [0009]), and “a system and method for enabling earning of recurring income from temporary access (rent/loaning) of digital artwork and sharing of the earning among artist, collector etc.” (Dehaeck, paragraph [0010]).
Claim 25:
Regarding claim 25, Kiely in view of Dehaeck teaches the limitations of claim 24.
Further, Kiely teaches “…the AI generated composition…”
See Kiely in paragraph [0024] describing, “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 AI 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.” Here, Kiely establishes an algorithm for comparing newly generated AI content which can be seen as generated.
Kiely does not appear to explicitly teach “The method of claim 24, wherein each of the plurality of transaction blocks that comprise the chain-of-title for…further comprise a hash of…for each copy of…and wherein each copy of…has a different watermark associated with a current owner of each copy of….”,
However in the same field of art and in an analogous system, Dehaeck teaches “The method of claim 24, wherein each of the plurality of transaction blocks that comprise the chain-of-title for…further comprise a hash of…for each copy of…and wherein each copy of…has a different watermark associated with a current owner of each copy of….”
See Dehaeck in paragraph [0014] describing, “Also, the system records the chain of title in a blockchain based ledger for a digital artwork edition upon sale and/or a resale of the digital edition to ensure ownership authenticity of the digital artwork as an embedded part of the work.” Here, Dehaeck explicitly establishes the blockchain comprising of a chain of title for digital artwork that is sold, which can be seen as a plurality of transactions blocks as established and the artwork is also established to be seen as AI generated composition in an analogous system. Further, see Dehaeck in paragraph [0038] describing, “In a preferred embodiment, blockchain technology is used for recording the chain of titles which creates a chain where any changes made to a block will change that block's hash, which must be recomputed and stored in the next block.” Here, Dehaeck explicitly establishes a hash for the blocks for the chain of titles which are linked to digital artwork seen as AI generated composition. Further, see Dehaeck in paragraph [0031] describing, “ In some embodiments, a visible and/or an imperceptible digital watermark can be applied to a digital artwork making the digital artwork unique.” Here, Dehaeck establishes putting a watermark on a digital artwork making that piece of artwork unique implying a different watermark for each copy of the artwork.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kiely with the teachings of Dehaeck by using Kiely’s teachings of analyzing AI generated composition and identifying and generating license information for the composition, and incorporate with Dehaeck’s teachings of licensing information stored in a blockchain.
One of ordinary skill in the art would be motivated to do so because by integrating Dehaeck frameworks into the methods of Kiely, which are both in the same field of art, one of ordinary skill in the art would bring “a system and method for making digital artwork accessible to as many customers as possible without compromising on authenticity and exclusivity of the artworks.” (Dehaeck, paragraph [0005]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0006]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0007]), “a system and method for monetizing digital artworks through granting of temporary viewing rights transmitted over a computer network.” (Dehaeck, paragraph [0008]), “a system and method which enable specifying maximum number of copies of a digital artwork that can be sold or distributed to ensure exclusivity of the digital artwork.” (Dehaeck, paragraph [0009]), and “a system and method for enabling earning of recurring income from temporary access (rent/loaning) of digital artwork and sharing of the earning among artist, collector etc.” (Dehaeck, paragraph [0010]).
Claim 26:
Regarding claim 26, Kiely in view of Dehaeck teaches the limitations of claim 17.
Further, Kiely teaches “…the AI generated composition…”
See Kiely in paragraph [0024] describing, “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 AI 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.” Here, Kiely establishes an algorithm for comparing newly generated AI content which can be seen as generated.
Kiely does not appear to explicitly teach “The method of claim 17, wherein the licensing information is stored in a blockchain, and wherein the blockchain comprises a plurality of branches that individually track individual copies of…and ownership of the individual copies of….”,
However in the same field of art and in an analogous system, Dehaeck teaches “The method of claim 17, wherein the licensing information is stored in a blockchain, and wherein the blockchain comprises a plurality of branches that individually track individual copies of…and ownership of the individual copies of….”
See Dehaeck in paragraph [0014] describing, “Patron Edition System provides a platform for selling a predefined number of copies of a digital artwork by an artist or a representative of the artist to patrons/collectors and subsequent reselling of the copies of the artwork by the patrons/collectors. Examples of representative of the artist include, but are not limited to, a gallery, a dealer, an intermediary etc. The feature of allowing selling of only a limited number of copies of the digital artwork ensures exclusivity of the artwork. Also, the system records the chain of title in a blockchain based ledger for a digital artwork edition upon sale and/or a resale of the digital edition to ensure ownership authenticity of the digital artwork as an embedded part of the work.” Here, Dehaeck explicitly establishes a system with a blockchain for digital artwork, which was previously established to be seen as AI generated composition, that is sold. Further, see Dehaeck in paragraph [0036] describing, “To make trading of a digital artwork through the patron edition system transparent and to facilitate verification of provenance data associated with the digital artwork, the present invention provides on the user interface a record of ownership of the patron edition copies of the digital artwork, the record being hereinafter referred to as Patron Index. The Patron Index, as shown within the box 408 on the user interface 400, includes information, among others, information on the patrons who buy permanent viewing rights of the digital artwork and the unique numbers of the patron edition copies owned by the patrons etc. In a preferred embodiment, the first buyers of the patron edition copies in the primary market are referred to as Founding Patrons. In the secondary market, whenever a Founding Patron sells a digital artwork through the system, then the new owners will be listed in the Patron Index as Active Patrons. The sellers from the primary market will be listed under their respective headings in the provenance as Past Founding Patrons. Similarly, in the secondary market, sellers will be listed as Past Patrons in the Patron Index. In the present example, Patron 1 is one of the Founding Patrons for the digital artwork. If Patron 2 buys the digital artwork from the artist then Patron 2 becomes a Founding Patron and, if Patron 2 buys it from Patron 1, then Patron 2 becomes an Active Patron and Patron 1 becomes a Past Founding Patron for the patron edition copy of the digital artwork being traded.” Here, Dehaeck establishes a tracking of copies of the digital artwork and the ownership, the Patron Index is the individual tracking of the copies and ownership. Further, see Dehaeck in Figure 4,
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. Here, the Patron Index shows individually tracking of the copies with the work number and the ownership and this individual tracking can act as the branches for tracking in the system established to comprise a blockchain for this digital artwork.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kiely with the teachings of Dehaeck by using Kiely’s teachings of analyzing AI generated composition and identifying and generating license information for the composition, and incorporate with Dehaeck’s teachings of licensing information stored in a blockchain.
One of ordinary skill in the art would be motivated to do so because by integrating Dehaeck frameworks into the methods of Kiely, which are both in the same field of art, one of ordinary skill in the art would bring “a system and method for making digital artwork accessible to as many customers as possible without compromising on authenticity and exclusivity of the artworks.” (Dehaeck, paragraph [0005]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0006]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0007]), “a system and method for monetizing digital artworks through granting of temporary viewing rights transmitted over a computer network.” (Dehaeck, paragraph [0008]), “a system and method which enable specifying maximum number of copies of a digital artwork that can be sold or distributed to ensure exclusivity of the digital artwork.” (Dehaeck, paragraph [0009]), and “a system and method for enabling earning of recurring income from temporary access (rent/loaning) of digital artwork and sharing of the earning among artist, collector etc.” (Dehaeck, paragraph [0010]).
Claim 27:
Regarding claim 27, Kiely in view of Dehaeck teaches the limitations of claim 26.
Further, Kiely teaches “…track individual media rights for each of the individual copies of the AI generated composition.”
See Kiely in paragraph [0076] describing, “The method 500 includes, at block 502, the step of analysing an original copyright work to formulate a unique digital profile of the work.” Further, see Kiely in paragraph [0077] describing, “At block 504, the method includes the step of storing one or more analysed copyright works along with their digital profiles.” Further, see Kiely in paragraph [0078] describing, “At block 506, upon a generative Artificial Intelligence (AI) 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.” Here, Kiely the tracking of individual media right for individual copies of AI generated composition with the storing of the copyright works and identifying of original copyright works for the new derivative work, which is seen as the AI generated composition. The method described by Kiely comprises blocks to perform this which can be seen as the branches to perform these operations.
Kiely does not appear to explicitly teach “The method of claim 26, wherein the plurality of branches…”,
However, in the same field of art and in an analogous system, Dehaeck teaches “The method of claim 26, wherein the plurality of branches…”
See Dehaeck in paragraph [0036] describing, “To make trading of a digital artwork through the patron edition system transparent and to facilitate verification of provenance data associated with the digital artwork, the present invention provides on the user interface a record of ownership of the patron edition copies of the digital artwork, the record being hereinafter referred to as Patron Index. The Patron Index, as shown within the box 408 on the user interface 400, includes information, among others, information on the patrons who buy permanent viewing rights of the digital artwork and the unique numbers of the patron edition copies owned by the patrons etc. In a preferred embodiment, the first buyers of the patron edition copies in the primary market are referred to as Founding Patrons. In the secondary market, whenever a Founding Patron sells a digital artwork through the system, then the new owners will be listed in the Patron Index as Active Patrons. The sellers from the primary market will be listed under their respective headings in the provenance as Past Founding Patrons. Similarly, in the secondary market, sellers will be listed as Past Patrons in the Patron Index. In the present example, Patron 1 is one of the Founding Patrons for the digital artwork. If Patron 2 buys the digital artwork from the artist then Patron 2 becomes a Founding Patron and, if Patron 2 buys it from Patron 1, then Patron 2 becomes an Active Patron and Patron 1 becomes a Past Founding Patron for the patron edition copy of the digital artwork being traded.” Here, Dehaeck establishes a tracking of copies of the digital artwork and the ownership, the Patron Index is the individual tracking of the copies and ownership. Further, see Dehaeck in Figure 4,
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. Here, the Patron Index shows individual tracking of the copies with the work number and the ownership, and this individual tracking can act as the branches for tracking in the system established to comprise a blockchain for this digital artwork.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kiely with the teachings of Dehaeck by using Kiely’s teachings of analyzing AI generated composition and identifying and generating license information for the composition and incorporate with Dehaeck’s teachings of licensing information stored in a blockchain.
One of ordinary skill in the art would be motivated to do so because by integrating Dehaeck frameworks into the methods of Kiely, which are both in the same field of art, one of ordinary skill in the art would bring “a system and method for making digital artwork accessible to as many customers as possible without compromising on authenticity and exclusivity of the artworks.” (Dehaeck, paragraph [0005]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0006]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0007]), “a system and method for monetizing digital artworks through granting of temporary viewing rights transmitted over a computer network.” (Dehaeck, paragraph [0008]), “a system and method which enable specifying maximum number of copies of a digital artwork that can be sold or distributed to ensure exclusivity of the digital artwork.” (Dehaeck, paragraph [0009]), and “a system and method for enabling earning of recurring income from temporary access (rent/loaning) of digital artwork and sharing of the earning among artist, collector etc.” (Dehaeck, paragraph [0010]).
Claim 28:
Regarding claim 28, Kiely in view of Dehaeck teaches the limitations of claim 17.
Further, Kiely teaches “…the AI generated composition…”
See Kiely in paragraph [0024] describing, “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 AI 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. Here, Kiely establishes an algorithm for comparing newly generated AI content which can be seen as generated.
Kiely does not appear to explicitly teach “The method of claim 17, wherein the licensing information is stored in a blockchain and wherein the blockchain tracks a maximum number of copies of….”,
However in the same field of art and in an analogous system, Dehaeck teaches “The method of claim 17, wherein the licensing information is stored in a blockchain and wherein the blockchain tracks a maximum number of copies of….”
See Dehaeck in paragraph [0032] describing, “As shown in FIG. 3, when the option patron edition is selected by the artist/representative 121 under the field 306, the patron edition system server 102 generates only those numbers of copies of the digital artwork which the artist 121 specifies under the field 308 on the user interface 300. In the present example, the maximum number of copies to be reproduced is 5 as indicated by the artist 121 in the field 308. So, for the artist/representative 121, the patron edition system server 102 will generate only 5 copies of the patron editions from the digital artwork uploaded by the artist.” Here, Dehaeck establishes a tracking of maximum number of copies of digital artwork established to be seen as AI generated composition in an analogous system, the patron edition system is also established to consist of a blockchain for digital artwork and its licensing information.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kiely with the teachings of Dehaeck by using Kiely’s teachings of analyzing AI generated composition and identifying and generating license information for the composition and incorporate with Dehaeck’s teachings of licensing information stored in a blockchain.
One of ordinary skill in the art would be motivated to do so because by integrating Dehaeck frameworks into the methods of Kiely, which are both in the same field of art, one of ordinary skill in the art would bring “a system and method for making digital artwork accessible to as many customers as possible without compromising on authenticity and exclusivity of the artworks.” (Dehaeck, paragraph [0005]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0006]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0007]), “a system and method for monetizing digital artworks through granting of temporary viewing rights transmitted over a computer network.” (Dehaeck, paragraph [0008]), “a system and method which enable specifying maximum number of copies of a digital artwork that can be sold or distributed to ensure exclusivity of the digital artwork.” (Dehaeck, paragraph [0009]), and “a system and method for enabling earning of recurring income from temporary access (rent/loaning) of digital artwork and sharing of the earning among artist, collector etc.” (Dehaeck, paragraph [0010]).
Claim 29:
Regarding claim 29, Kiely in view of Dehaeck teaches the limitations of claim 17.
Further, Kiely teaches “…the AI generated composition…”
See Kiely in paragraph [0024] describing, “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 AI 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. Here, Kiely establishes an algorithm for comparing newly generated AI content which can be seen as generated.
Kiely does not appear to explicitly teach “The method of claim 17, wherein the licensing information is stored in a blockchain and wherein the blockchain identifies a number of times a copy of…can be sold/transferred.”,
However, in the same field of art and in an analogous system, Dehaeck teaches “The method of claim 17, wherein the licensing information is stored in a blockchain and wherein the blockchain identifies a number of times a copy of…can be sold/transferred.”
See Dehaeck in paragraph [0009] describing, “A further object of the present invention is to provide a system and method which enable specifying maximum number of copies of a digital artwork that can be sold or distributed to ensure exclusivity of the digital artwork.” Here Dehaeck explicitly establishes specifying or identifying the number of times a copy of digital artwork can be sold or distributed/transferred, the digital artwork can be AI generated composition in an analogous system. Further, see Dehaeck in paragraph [0014] describing, “Also, the system records the chain of title in a blockchain based ledger for a digital artwork edition upon sale and/or a resale of the digital edition to ensure ownership authenticity of the digital artwork as an embedded part of the work.” Here, Dehaeck establishes the system storing digital artwork information in blockchain.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kiely with the teachings of Dehaeck by using Kiely’s teachings of analyzing AI generated composition and identifying and generating license information for the composition and incorporate with Dehaeck’s teachings of licensing information stored in a blockchain.
One of ordinary skill in the art would be motivated to do so because by integrating Dehaeck frameworks into the methods of Kiely, which are both in the same field of art, one of ordinary skill in the art would bring “a system and method for making digital artwork accessible to as many customers as possible without compromising on authenticity and exclusivity of the artworks.” (Dehaeck, paragraph [0005]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0006]), “a system and method for helping digital artworks realize their fullest market potential.” (Dehaeck, paragraph [0007]), “a system and method for monetizing digital artworks through granting of temporary viewing rights transmitted over a computer network.” (Dehaeck, paragraph [0008]), “a system and method which enable specifying maximum number of copies of a digital artwork that can be sold or distributed to ensure exclusivity of the digital artwork.” (Dehaeck, paragraph [0009]), and “a system and method for enabling earning of recurring income from temporary access (rent/loaning) of digital artwork and sharing of the earning among artist, collector etc.” (Dehaeck, paragraph [0010]).
Conclusion
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/HASSAN RAMADAN SESAY/Examiner, Art Unit 2146
/USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146
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