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 .
Priority
Acknowledgement is made of Applicant’s claim of priority from the US provisional application 63/618,192 filed on 01/05/2024.
Drawings
The 2-page drawings have been considered and placed on record in the file.
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.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter as follows. Considering claim 1, it is directed to a “machine-readable storage medium”. Applicant’s specification excludes the use of transitory propagating signals only from some embodiments with respect to a computer-readable storage media— ¶0102: “In some embodiments, the computer-readable storage media are tangible storage devices that do not include a transitory propagating signal”. However, the specification does not exclude the use of transitory propagating signals such as carrier waves for the recited machine-readable storage medium. The broadest reasonable interpretation of a claim drawn to a machine-readable storage medium typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media. See Subject Matter Eligibility of Computer Readable Media, 1351 OG 212 (26 Jan 2010). See MPEP 2111.01. Signals are nothing but the physical characteristics of a form of energy, and as such is nonstatutory natural phenomena. See, e.g., In re Nuitjen, 500 F. 3d 1346, 1357 (Fed. Cir. 2007)(slip. op. at 18)("A transitory, propagating signal like Nuitjen's is not a process, machine, manufacture, or composition of matter.' … Thus, such a signal cannot be patentable subject matter."). Thus, claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. In order to overcome this rejection, Applicant may amend the independent claim 1 by inserting the term “non-transitory” before “machine-readable storage medium”.
Claim Rejections - 35 USC § 103
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. (US 2022/0198332 A1) in view of Ciftci et al. (US 2021/0209388 A1).
Regarding claim 1, Yang teaches, A method for improved generative model image attribution, (Yang, ¶0005: “decentralized attribution of generative models, and/or methods”) comprising: accessing, by a processor, a machine-readable storage medium containing instructions executable by the processor to: (Yang, ¶0006: “computer-readable media having instructions encoded thereon, the instructions, when executed by a processor, being operable to”) transform an initial distribution of a user-end generative model into a latent distribution; (Yang, ¶0043: a linear approximation can capture the perturbation from x.sub.0=G(z:θ0) to x=G(z:θ) for latent z”; and ¶0009: “key-dependent generators for individual users”) (Yang, ¶0038: “adding a fixed and bounded perturbation δ to the output of the root model”) according to, a user-specific key, (Yang, ¶0076: “user creates a key-dependent generative model for which the outputs can be correctly attributed”) and a predetermined perturbation strength (Yang, ¶0038: “a fixed and bounded perturbation δ”) to embed a fingerprinted latent variable directly into the user-end generative model (Yang, ¶0024: “Analyzing fingerprints in generated images”; ¶0029: “P.sub.z is the latent distribution”; and ¶0030: “generative models can achieve distinguishability individually and attributability”) such that the user-end generative model generates images with model-specific fingerprints. (Yang, ¶0076: “user creates a key-dependent generative model for which the outputs can be correctly attributed”). However, Yang does not explicitly teach, apply a principal component analysis on the latent distribution to obtain a plurality of latent semantic dimensions.
In an analogous field of endeavor, Ciftci teaches apply a principal component analysis (Ciftci, ¶0034: “Principal Component Analysis is a dimension reduction technique… achieved by forming new variables which are linear combinations of the variables in the original data set”) on the latent distribution to obtain a plurality of latent semantic dimensions (Ciftci, ¶0103: “encoder ‘encodes’ the data from a high dimensional space into a latent (hidden) representation space z, which is lower dimensionality”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Yang using the teachings of Ciftci to introduce a principal component analysis. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of simplifying a distribution of multi-dimensional data for efficient management. Therefore, it would have been obvious to combine the analogous arts Yang and Ciftci to obtain the invention in claim 1.
Regarding claim 2, Yang in view of Ciftci teaches, The method for improved generative model image attribution of claim 1, wherein the initial distribution is a Gaussian distribution. (Yang, ¶0127: “we start by noting that ϕ.sup.TΣϕ′ has zero mean and is normally distributed”).
Regarding claim 3, Yang in view of Ciftci teaches, The method for improved generative model image attribution of claim 1, wherein in the model is pretrained. (Yang, ¶0067: “use pre-trained PGAN and CycleGAN”).
Regarding claim 4, Yang in view of Ciftci teaches, The method for improved generative model image attribution of claim 1, wherein the subset of the plurality of semantic dimensions comprise an orthonormal dimension and a complementary dimension. (Yang, ¶0141: “keys may further be orthogonal or opposite to each other and belong to a subspace dependent on the data distribution”; ¶0042: “Normalized keys: The system considers data-compliant keys” and ¶0067: “orthogonal keys abound due to the high-dimensionality of the output space, for which decentralized attribution is achieved”).
Regarding claim 5, Yang in view of Ciftci teaches, The method for improved generative model image attribution of claim 1, wherein the perturbations applied along the subset of the plurality of latent dimensions are linear perturbations. (Yang, ¶0043: “linear approximation can capture the perturbation from x.sub.0=G(z:θ0) to x=G(z:θ) for latent z”).
Regarding claim 6, Yang in view of Ciftci teaches, The method for improved generative model image attribution of claim 1, wherein the perturbations are further applied along the subset of the plurality of latent dimensions according to a random seed. (Ciftci, ¶0043: “the generator is seeded with randomized input that is sampled from a predefined latent space”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Yang in view of Ciftci using the additional teachings of Ciftci to introduce a random seed. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of assigning unique identifiable perturbations for different users. Therefore, it would have been obvious to combine the analogous arts Yang and Ciftci to obtain the invention in claim 6.
Regarding claim 7, Yang in view of Ciftci teaches, The method for improved generative model image attribution of claim 1, wherein the latent semantic dimensions are imperceptible dimensions (Ciftci, ¶0103: “a latent (hidden) representation space z, which is lower dimensionality”) based on latent variables of the latent distribution. (Ciftci, ¶0056: “hidden variables in high-dimensional data. As such, it belongs to what is called the latent variable class of models”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Yang in view of Ciftci using the additional teachings of Ciftci to introduce imperceptible variables. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of identifying a user of a generative model through an encoded hidden variable in the output of the model. Therefore, it would have been obvious to combine the analogous arts Yang and Ciftci to obtain the invention in claim 7.
Regarding claim 8, Yang in view of Ciftci teaches, The method for improved generative model image attribution of claim 1, wherein the model-specific fingerprints are semantically meaningful perturbations to a generated image (Yang, ¶0074: “classifier can achieve robustness against post-processed images by fine-tuning the classifier using post-processed images”) such that an attribution accuracy of the model-specific fingerprints is resistant to postprocesses. (Yang, ¶0023: “robust attributability against adversarial post-processes of model outputs”).
Regarding claim 9, Yang in view of Ciftci teaches, The method for improved generative model image attribution of claim 8, wherein the postprocesses include noising, blurring, reformatting to .jpeg, or a combination thereof. (Yang, ¶0035: “robust attributability under post-processes including image blurring, cropping, noising, JPEG conversion, and a combination of all”).
Regarding claim 10, Yang in view of Ciftci teaches, The method for improved generative model image attribution of claim 1, wherein the method further comprises: decoding, by the processor, (Yang, ¶0005: “processor verifies a GAN of the plurality of GANs based upon an output”) the user-specific key from a generated image. (Yang, ¶0038: “key-dependent generative model G.sub.ϕ achieves distinguishable output distribution”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MEHRAZUL ISLAM whose telephone number is (571)270-0489. The examiner can normally be reached Monday-Friday: 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Saini Amandeep can be reached on (571) 272-3382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MEHRAZUL ISLAM/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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