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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: Fig. 1, 42. 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. 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.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: Fig. 2, S230. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) 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. 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.
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.
The broadest reasonable interpretation of a claim drawn to a computer readable medium 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 MPEP 2111.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. § 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter) and Interim Examination Instructions for Evaluating Subject Matter Eligibility Under 35 U.S.C. § 101, Aug. 24, 2009; p. 2.
Claim 12 is drawn to such a computer readable medium that covers both transitory and non-transitory embodiments but may be amended to narrow the claim to cover only statutory embodiments by adding the limitation "non-transitory" to the claim.
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)(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 and 8-12 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Zheng et al. (US 12,430,727).
Regarding claims 1, 11, and 12, Zheng discloses a computer program stored in a computer-readable medium, an apparatus for generating semantic images using condition image diffusion, the apparatus comprising: one or more processors (processor unit 205); a network interface (Fig. 1, image processing apparatus 110 can communicate with other devices via cloud 115); a memory (memory unit 210) for loading a computer program executed by the processors; and a storage for storing large-capacity network data (database 120) and the computer program, and a method of generating semantic images using condition image diffusion, by an apparatus including a processor and a memory, the method comprising the steps of:
(a) training an image generation model by inputting N-th learning data (N is a random positive integer) (see Fig. 10 and col 10 lines 46-60, and col 18 lines 15-41, a diffusion model receives training images);
(b) inputting input data for generating semantic images into the trained image generation model (see Fig. 10 and col 10 lines 46-60, and col 18 lines 15-41, a diffusion model adds noise to images and generates a set of intermediate semantic images); and
(c) outputting one or more semantic images generated by the image generation model according to the input data, wherein the input data is a condition image frame (Layout) corresponding to an input condition, and through the condition image frame, classes, which are one or more objects included in a semantic image to be generated, are classified into each category by the object, and a different number is assigned to pixel areas occupied by the classes for each classified category (see Fig. 10 and col 7 line 62-col 8 line 24, col 8 lines 53-64, col 18 lines 42-57, and col 19 lines 37-45, a pixel diffusion model can be trained with noisy images using a perceptual loss, reverse diffusion is performed to acquire predicted image and image features that are then compared to actual image and image features to output a final semantic image).
Regarding claim 2, Zheng further discloses wherein step (a) includes the steps of: (a-1) generating N'-th learning data by adding noise to the N-th learning data through selection of any one among first to fourth noise addition methods (see Fig. 10 and col 10 lines 46-60, and col 18 lines 15-41, a diffusion model receives training images);
(a-2) predicting noise applied to the N'-th learning data by inputting the generated N'-th learning data into the image generation model (see col 18 lines 24-57, a diffusion model adds noise to images and generates a set of intermediate semantic images); and
(a-3) comparing the noise added at step (a-1) with the noise predicted at step (a-2), setting a difference thereof as a loss function, and learning by applying gradient descent (see col 18 lines 24-57, a pixel diffusion model can be trained with noisy images using a perceptual loss, reverse diffusion is performed to acquire predicted image and image features that are then compared to actual image and image features to output a final semantic image, gradient descent is utilized) .
Regarding claim 3, Zheng further discloses after step (a-3), the steps of: (a-4) determining whether the set loss function converges to a minimum value; (a-5) returning to step (a-1) when a result of the determination at step (a-4) is NO; and (a-6) terminating learning of the image generation model when the result of the determination at step (a-4) is YES (see Fig. 9 and col 15 line 36-col 16 line 62, a pixel diffusion model can be trained with noisy images using a perceptual loss, an iterative process is performed).
Regarding claim 8, Zheng further discloses wherein the N-th learning data is an N-th real image, and in this case, step (a-2) includes the steps of: (a-2-1) inputting the generated N′-th learning data into the image generation model; (a-2-2) inputting N-th input condition data, which is obtained by adding noise to an N-th condition image frame corresponding to the N-th real image through any one among the first to fourth noise addition methods, into the image generation model; and (a-2-3) predicting noise applied to the N′-th learning data by reflecting the input N-th input condition data (see Fig. 10 and col 10 lines 46-60, and col 18 lines 15-57, a diffusion model receives training images, the diffusion model adds noise to images and generates a set of intermediate semantic images, a pixel diffusion model can be trained with noisy images using a perceptual loss, reverse diffusion is performed to acquire predicted image and image features that are then compared to actual image and image features to output a final semantic image, gradient descent is utilized).
Regarding claim 9, Zheng further discloses wherein step (b) includes the steps of: (b-1) randomly generating noise data of a size the same as that of the semantic image desired to be generated, and inputting the noise data into the image generation model as input data; and (b-2) generating input condition data by adding noise to a condition image frame for generating a semantic image through any one of the first to fourth noise addition methods, and inputting the input condition data into the image generation model (see col 3 lines 34-45, col 4 lines 4-11, col 10 lines 46-60, the diffusion model is trained using an adaptively-blurred perceptual loss by applying a Gaussian kernel of different sizes to the predicted images and ground-truth images).
Regarding claim 10, Zheng further discloses wherein the image generation model includes an encoder unit and a decoder unit, and further includes a condition image frame utilization unit including one or more convolution layers that acquire feature values from the condition image frame and calculate a scale value and a bias value by passing the feature values, wherein the calculated scale value and bias value are acquired from the input data, and input into the batch normalized feature values to be calculated (see Figs. 3 and 5 and col 10 lines 46-60, col 11 line 66-col 12 line 8, and col 12 lines 43-48, a forward diffusion process 315 acts as an encoder, a reverse diffusion process 325 acts as a decoder, convolution layers are utilized).
Allowable Subject Matter
Claims 4-7 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. To further show the state of the art please refer to the attached Notice of References Cited.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK R MILIA whose telephone number is (571) 272-7408. 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, Akwasi Sarpong can be reached at 571-270-3438. The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARK R MILIA/ Primary Examiner, Art Unit 2681