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
Applicant’s claim for domestic benefit under 35 U.S.C. 119(e) is acknowledged.
Information Disclosure Statement
The information disclosure statement submitted has been considered by the Examiner and made of record in the application file.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims of U.S. Patent Nos. 11790565, 12154303. Although the claims at issue are not identical, they are not patentably distinct from each other because it would have been obvious to one of ordinary skill in the art a time before the effective filing date of the claimed subject matter to arrive at the obvious claimed instant invention. See example comparison table below.
Instant Application
Pat # 11790565
Comments
1. A method for providing synthetic images using a generative adversarial network (GAN), the method comprising:
1. A method of operating a generative adversarial network (GAN), comprising:
Substantially the same.
receiving an image and class information defining a segmentation of the image into semantic classes; assigning a semantic class to each pixel of the image according to the segmentation; performing average smoothing of the class information to smooth abrupt boundaries where semantic information changes;
receiving an image having learned parameters;
Obvious to segment and classify segments of images, scale and shift to classify images. Obvious when a segment is classified, then all the pixels in the given segment take on that classification.
assigning scaling and shifting parameters to each pixel of the image based on the smoothed class information; and
using an input class of the image to determine scaling and shifting parameters in a normalization layer; and
Instant claimed subject matter is broader.
using the scaling and shifting parameters to perform batch normalization.
compressing the image using the determined scaling and shifting parameters by performing average smoothing between parameter layers and normalization layers to smooth abrupt boundaries where semantic information changes.
Instant claimed subject matter is broader.
Claims 2-8 are obvious over claims 2-20 of Pat # 11790565.
Instant Application
Pat # 11790565
Comments
9. A generative adversarial network (GAN), comprising: a processor; and a memory storing computer readable instructions that, when executed by the processor, configure the GAN to perform operations comprising: receiving an image and class information defining a segmentation of the image into semantic classes;
9. A system comprising: a processor; and a memory storing computer readable instructions that, when executed by the processor, configure the system to perform operations comprising: receiving an image having learned parameters;
Substantially the same.
assigning a semantic class to each pixel of the image according to the segmentation; performing average smoothing of the class information to smooth abrupt boundaries where semantic information changes; assigning scaling and shifting parameters to each pixel of the image based on the smoothed class information; and
using an input class of the image to determine scaling and shifting parameters in a normalization layer; and
Obvious to segment and classify segments of images, scale and shift to classify images. Obvious when a segment is classified, then all the pixels in the given segment take on that classification.
using the scaling and shifting parameters to perform batch normalization.
compressing the image using the determined scaling and shifting parameters by performing average smoothing between parameter layers and normalization layers to smooth abrupt boundaries where semantic information changes.
Instant claimed subject matter is broader.
Claims 10-16 are obvious over claims 2-20 of Pat # 11790565.
Instant Application
Pat # 11790565
Comments
17. A non-transitory computer-readable storage medium including instructions that, when executed by a computer of a generative adversarial network (GAN), cause the computer to perform operations comprising: receiving an image and class information defining a segmentation of the image into semantic classes;
17. A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to perform operations comprising: receiving an image having learned parameters;
Substantially the same. Obvious to segment and classify images.
assigning a semantic class to each pixel of the image according to the segmentation; performing average smoothing of the class information to smooth abrupt boundaries where semantic information changes; assigning scaling and shifting parameters to each pixel of the image based on the smoothed class information; and
using an input class of the image to determine scaling and shifting parameters in a normalization layer; and
Obvious to segment and classify segments of images, scale and shift to classify images. Obvious when a segment is classified, then all the pixels in the given segment take on that classification.
using the scaling and shifting parameters to perform batch normalization.
compressing the image using the determined scaling and shifting parameters by performing average smoothing between parameter layers and normalization layers to smooth abrupt boundaries where semantic information changes.
Instant claimed subject matter is broader.
Claims 18-20 are obvious over claims 2-20 of Pat # 11790565.
Instant Application
Pat # 12154303
Comments
1. A method for providing synthetic images using a generative adversarial network (GAN), the method comprising:
1. A method of operating a generative adversarial network (GAN), comprising:
Substantially the same.
receiving an image and class information defining a segmentation of the image into semantic classes; assigning a semantic class to each pixel of the image according to the segmentation; performing average smoothing of the class information to smooth abrupt boundaries where semantic information changes;
receiving an image having learned parameters;
Obvious to segment and classify segments of images, scale and shift to classify images. Obvious when a segment is classified, then all the pixels in the given segment take on that classification.
assigning scaling and shifting parameters to each pixel of the image based on the smoothed class information; and
using an input class of the image to determine scaling and shifting parameters in a normalization layer; and
Instant claimed subject matter is broader.
using the scaling and shifting parameters to perform batch normalization.
compressing the image using the determined scaling and shifting parameters by performing average smoothing between parameter layers and normalization layers.
Instant claimed subject matter is broader.
Claims 2-8 are obvious over claims 2-20 of Pat # 12154303.
Instant Application
Pat # 12154303
Comments
9. A generative adversarial network (GAN), comprising: a processor; and a memory storing computer readable instructions that, when executed by the processor, configure the GAN to perform operations comprising: receiving an image and class information defining a segmentation of the image into semantic classes;
9. A system comprising: a processor; and a memory storing computer readable instructions that, when executed by the processor, configure the system to perform operations comprising: receiving an image having learned parameters;
Substantially the same.
assigning a semantic class to each pixel of the image according to the segmentation; performing average smoothing of the class information to smooth abrupt boundaries where semantic information changes; assigning scaling and shifting parameters to each pixel of the image based on the smoothed class information; and
using an input class of the image to determine scaling and shifting parameters in a normalization layer; and
Obvious to segment and classify segments of images, scale and shift to classify images. Obvious when a segment is classified, then all the pixels in the given segment take on that classification.
using the scaling and shifting parameters to perform batch normalization.
compressing the image using the determined scaling and shifting parameters by performing average smoothing between parameter layers and normalization layers.
Instant claimed subject matter is broader.
Claims 10-16 are obvious over claims 2-20 of Pat # 12154303.
Instant Application
Pat # 12154303
Comments
17. A non-transitory computer-readable storage medium including instructions that, when executed by a computer of a generative adversarial network (GAN), cause the computer to perform operations comprising: receiving an image and class information defining a segmentation of the image into semantic classes;
17. A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to perform operations comprising: receiving an image having learned parameters;
Substantially the same. Obvious to segment and classify images.
assigning a semantic class to each pixel of the image according to the segmentation; performing average smoothing of the class information to smooth abrupt boundaries where semantic information changes; assigning scaling and shifting parameters to each pixel of the image based on the smoothed class information; and
using an input class of the image to determine scaling and shifting parameters in a normalization layer; and
Obvious to segment and classify segments of images, scale and shift to classify images. Obvious when a segment is classified, then all the pixels in the given segment take on that classification.
using the scaling and shifting parameters to perform batch normalization.
compressing the image using the determined scaling and shifting parameters by performing average smoothing between parameter layers and normalization layers.
Instant claimed subject matter is broader.
Claims 18-20 are obvious over claims 2-20 of Pat # 12154303.
Allowable Subject Matter
Claims 1-20 seem to contain allowable subject matter over prior art of record.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Notice of Reference Cited for state of the art.
Any response to this Office Action should be faxed to (571) 273-8300 or mailed to:
Commissioner for Patents
P.O. Box 1450
Alexandria, VA 22313-1450
Hand-delivered responses should be brought to
Customer Service Window
Randolph Building
401 Dulany Street
Alexandria, VA 22314
Any inquiry concerning this communication or earlier communications from the
Examiner should be directed to Fayyaz Alam whose telephone number is (571) 270-1102. The Examiner can normally be reached on Monday-Friday from 9:30am to 7:00pm.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Jeanette Parker can be reached on (571) 270-3647. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free) or 703-305-3028.
Any inquiry of a general nature or relating to the status of this application or proceeding should be directed to the receptionist/customer service whose telephone number is (571) 272-2600.
Fayyaz Alam
July 11, 2026
/FAYYAZ ALAM/
Primary Examiner, Art Unit 2646
Prosecution Insights