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
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. 18846052, filed on 09/11/2024.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/11/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 1 and 8 objected to because of the following informalities: Claim 1 recites “the face recognition machine learning network at the face recognition system”, which should recite “the face recognition machine learning network of the face recognition system”. Claim 1 also recites, “structured in plurality of style layers” which should recite “structured in a plurality of style layers”. Claim 8 recites “applying the mapping machine learning network on the variation vector of semantic parameters” which should recite “applying the mapping machine learning network to the variation vector of semantic parameters”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-15 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, claim 1 recites several elements that lack clarity, rendering the claim indefinite. First, the nature of “a style-based generative adversarial network” is not sufficiently described in the claim. This is being interpreted generally in view of Paragraphs 47-49. However, these Paragraphs also lack clarity on the exact nature of style-based GAN being claimed relative to prior work. Secondly, and similarly, “a vector of style space parameters” is not sufficiently described in the claim, rendering the element indefinite. The Specification at Paragraphs 56 describes the vector of style space parameters as relating to attributes including “hair, pose, illumination and much more”. The vector of style space parameters is being interpreted accordingly, although “much more” is also indefinite, so the vector of style space parameters is being interpreted as hair, pose, and illumination. Thirdly, claim 1 recites “generating a variation vector of style space parameters for an input facial image by applying the trained mapping machine learning network”. Here, “applying” is vague and thus renders the limitation indefinite. This is being interpreted in view of the further limitations in claims 8 and 9.
Claims 2-15 are rejected as dependent on claim 1.
Claim 8 recites “a vector of semantic parameters”, which has improper antecedence and is being interpreted as, “a second vector of semantic parameters”, as this vector appears to be distinct from the “vector of semantic parameters” recited in claim 1.
Claim 9 is rejected as dependent on claim 8.
Claim 9 recites the following antecedence issues: a) “the style space parameters of the input facial image”, which is being interpreted as a new element; b) “the attributes modified”, which is being interpreted as a new element; and c) “the modified semantic parameters”, which is being interpreted as a new element.
Claim 12 recites the following antecedence issues: a) “A face recognition system comprising:”, which is being interpreted as “The face recognition system comprises:”. The preamble should also be amended to reference claim 1, as the claim is being interpreted as dependent on claim 1 in view of the last limitation; b) “a face recognition machine learning network” which is being interpreted as “the face recognition machine learning network”.; and c) “a database”, which is being interpreted as “a second database” as it appears to be distinct from the database of claim 1.
Claims 13-14 are rejected as dependent on claim 12.
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 15 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 for reciting “a computer program comprising instructions.” The Specification does not define this element to exclude software, and the broadest reasonable interpretation of the element encompasses software per se, thus rendering the claims as a whole non-statutory for failing to be limited to one of the four statutory categories of invention.
Allowable Subject Matter
Claims 1-15 are rejected under 35 USC 112(b), claims 1 and 8 are objected to under Claim Objections, and claim 15 is rejected under 35 USC 101. These claims would be allowable if amended to overcome these rejections and objections. Note that the below indication of allowable subject matter is in view of the interpretations of claim 1 with respect to rejections under 35 USC 112(b).
The following is a statement of reasons for the indication of allowable subject matter: With respect to claim 1, as well as all dependent claims, in addition to other limitations in the claims the Prior Art of Record fails to teach, disclose or render obvious the applicant' s invention as claimed, in particular:
Claim 1 recites, “A computerized method of generating one or more of training data and testing data for a face recognition machine learning network applied by a face recognition system comprising:
defining a semantic parameter space, wherein the semantic parameter space comprises a vector of semantic parameters associated with a facial image, wherein the semantic parameters comprise face model parameters, situation parameters, and additional parameters, wherein each semantic parameter is associated with an attribute of the facial image;
training a mapping machine learning network to transform the vector of semantic parameters to a vector of style space parameters of a style-based generative adversarial network, wherein the vector of style space parameters is structured in plurality of style layers with each style layer having a plurality of channels, wherein each channel controls an attribute of the facial image and each style layer is associated with a layer of the style-based generative adversarial network;
generating a variation vector of style space parameters for an input facial image by applying the trained mapping machine learning network;
feeding the style-based generative adversarial network with the variation vector of style space parameters to generate a variation facial image for the input facial image;
storing the variation facial image in a database; and
one or more of training and testing the face recognition machine learning network at the face recognition system by using a plurality of variation facial images stored in the database.”
Tewari (StyleRig: Rigging StyleGAN for 3D Control over Portrait Images) teaches portrait image generation using parameters of a semantic parameter space with a pretrained StyleGAN. However, the semantic space parameter vectors are not mapped to a style space parameter vector, and the output portrait images are not stored for use in the training or testing of a face recognition system. Wu (StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation) teaches image generation models involving a “style space S” for manipulating visual attributes in images. However, style space S vectors are not generated by a mapping from a semantic parameter space and the outputs are never stored for use in the training or testing of a face recognition system. Mitra (US20220028139A1) discloses an image modification strategy wherein semantic attributes are transformed to modified feature vectors, wherein an output image includes a change to a target attribute while retaining preserved attributes from the initial image. However, this reference does not disclose the mapping of these semantic vectors to style space parameter vectors with the claimed vector structure, nor the use of the altered images for training or testing of a facial recognition system. Karras (US20210150187A1) teaches an image synthesis model wherein latent code is mapped to an intermediate latent code as an appearance vector, which is subsequently used for image reconstructed for display. However, none of these references expressly disclose the bolded references above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AARON JOSEPH SORRIN whose telephone number is (703)756-1565. The examiner can normally be reached Monday - Friday 9am - 5pm.
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/AARON JOSEPH SORRIN/Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
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