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 .
Claims 1-30 is/are pending.
Drawings are accepted.
IDS is/are considered.
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, 7, 13, 19, 25 is/are rejected under 35 U.S.C. 102(a)(2) as being unpatentable over Remine et al. (US 2020/0192389).
As to claim 1:
Remine discloses:
A processor (Abstract, ¶0014, 0054 apparatus with processor having circuitries), comprising: one or more circuits to use one or more neural networks to generate one or more third images comprising one or more third objects based, at least in part, on one or more first objects within one or more first images and one or more second objects within one or more second images.
(See at least ¶0032, 0035-0036, 0045-0048, 0009 using one or more GANs, the one or more processor to receive one or more images of the real world scene, receive also one or more input images of objects to be incorporated to the real world scene image, generate one or more images of said real world scene including said objects to be incorporated. In particular, pose(s) of objects (tree, street, surface) in the real-world scene image is/are determined by a pose estimator component. The objects received in the one or more input images is/are incorporated to the real-world scene such that their poses are based on real world scene’s poses, specifically to coherently/realistically match the pose/orientation of the objects/structures of the real world scene images per example described in at least ¶0030-0031, thus generating as output one or more composite images of the world scene now having the input objects inserted in a coherent manner, for example the generated image now have more trees inserted with correct orientation and location on sidewalks )
As to claim 7:
Remine discloses:
A system comprising: one or more processors (Abstract, ¶0014, 0054 – system with processor having circuitries) to use one or more neural networks to generate one or more third images comprising one or more third objects based, at least in part, on one or more first objects within one or more first images and one or more second objects within one or more second images. (See at least ¶0032, 0035-0036, 0045-0048, 0009 using one or more GANs, the one or more processor to receive one or more images of the real world scene, receive also one or more input images of objects to be incorporated to the real world scene image, generate one or more images of said real world scene including said objects to be incorporated. In particular, pose(s) of objects (tree, street, surface) in the real-world scene image is/are determined by a pose estimator component. The objects received in the one or more input images is/are incorporated to the real-world scene such that their poses are based on real world scene’s poses, specifically to coherently/realistically match the pose/orientation of the objects/structures of the real world scene images per example described in at least ¶0030-0031, thus generating as output one or more composite images of the world scene now having the input objects inserted in a coherent manner, for example the generated image now have one or more trees inserted with correct orientation and location on sidewalks )
As to claim 13:
Remine discloses:
A method comprising: using one or more neural networks to generate one or more third images comprising one or more third objects based, at least in part, on one or more first objects within one or more first images and one or more second objects within one or more second images.
(See at least ¶0032, 0035-0036, 0045-0048, 0009 using one or more GANs, the one or more processor to receive one or more images of the real world scene, receive also one or more input images of objects to be incorporated to the real world scene image, generate one or more images of said real world scene including said objects to be incorporated. In particular, pose(s) of objects (tree, street, surface) in the real-world scene image is/are determined by a pose estimator component. The objects received in the one or more input images is/are incorporated to the real-world scene such that their poses are based on real world scene’s poses, specifically to coherently/realistically match the pose/orientation of the objects/structures of the real world scene images per example described in at least ¶0030-0031, thus generating as output one or more composite images of the world scene now having the input objects inserted in a coherent manner, for example the generated image now have one or more trees inserted with correct orientation and location on sidewalks )
As to claim 19:
Remine discloses:
A non-transitory computer-readable storage medium having stored thereon a set of instructions, which if performed by one or more processors, (Abstract, ¶0014-0015, 0054 – CRM with processor having circuitries) cause the one or more processors to at least: use one or more neural networks to generate one or more third images comprising one or more third objects based, at least in part, on one or more first objects within one or more first images and one or more second objects within one or more second images.
(See at least ¶0032, 0035-0036, 0045-0048, 0009 using one or more GANs, the one or more processor to receive one or more images of the real world scene, receive also one or more input images of objects to be incorporated to the real world scene image, generate one or more images of said real world scene including said objects to be incorporated. In particular, pose(s) of objects (tree, street, surface) in the real-world scene image is/are determined by a pose estimator component. The objects received in the one or more input images is/are incorporated to the real-world scene such that their poses are based on real world scene’s poses, specifically to coherently/realistically match the pose/orientation of the objects/structures of the real world scene images per example described in at least ¶0030-0031, thus generating as output one or more composite images of the world scene now having the input objects inserted in a coherent manner, for example the generated image now have one or more trees inserted with correct orientation and location on sidewalks )
As to claim 25:
Remine discloses:
An image generation system, comprising: one or more processors to use one or more neural networks(Abstract, ¶0014-0015, 0054 – generative system with processor having circuitries) to generate one or more third images comprising one or more third objects based, at least in part, on one or more first objects within one or more first images and one or more second objects within one or more second images, wherein one or more poses of the one or more third objects in the one or more third images is determined with respect to the one or more second objects; (See at least ¶0032, 0035-0036, 0045-0048, 0009 using one or more GANs, the one or more processor to receive one or more images of the real world scene, receive also one or more input images of objects to be incorporated to the real world scene image, generate one or more images of said real world scene including said objects to be incorporated. In particular, pose(s) of objects (tree, street, surface) in the real-world scene image is/are determined by a pose estimator component. The objects received in the one or more input images is/are incorporated to the real-world scene such that their poses are based on real world scene’s poses, specifically to coherently/realistically match the pose/orientation of the objects/structures of the real world scene images per example described in at least ¶0030-0031, thus generating as output one or more composite images of the world scene now having the input objects inserted in a coherent manner, for example the generated image now have one or more trees inserted with correct orientation and location on sidewalks )
and memory for storing network parameters for the one or more neural networks. (¶0059, Fig. 3, ¶0004, memory to store instructions and model components)
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, 4-6, 8, 10-12, 14, 16-18, 20, 22-24, 26, 28-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Remine et al. (US 2020/0192389) in view of Lee et al. (US 2020/0074707).
As to claims 2, 8, 14, 20 and 26:
Remine discloses all limitations of claim 1/7/13/19 and 25, however is silent on the one or more neural networks include one or more variational autoencoders (VAEs) to determine features for the first objects and the second objects and encode those features to a latent space to act as a constraint in generating the one or more third images comprising the one or more third objects.
Lee discloses a system/method for inserting objects into an existing image in which the one or more neural networks 2include one or more variational autoencoders (VAEs) to determine features for the first 3objects and the second objects and encode those features to a latent space to act as a 4constraint in adding the one or more first objects to the image. (See at least ¶0018, 0019, also, 0026-0028 using at least a VAE, features of the object and background are analyzed, to generate a vector in a latent space that is used to generate location/scale of the object to be added in the scene).
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the GAN system of Remine to include one or more variational autoencoders (VAEs) to determine features for the first 3objects and the second objects and encode those features to a latent space to act as a 4constraint in adding the one or more first objects to the image. Given that Remine uses GAN that generates/render object per abstract, as analogous with the GAN of Lee. A VAE allows for advantage of accurate injection by providing specific location/scale of an object in a scene (Lee, ¶0026).
As to claims 4, 10, 16, 22 and 28:
Remine in view of Lee discloses all limitations of claim 2/8/14/20/26, wherein the one or more neural networks include a generative network to determine one or more potential poses for the one or more third objects based at least in part upon object types of the one or more first objects and with respect to features of the one or more second objects, wherein information for the one or more potential poses is to be encoded into the latent space. (Remine, 0032, 0035-0036, determine potential poses by accessing also classes of objects using GAN. Lee, as discussed in above, discloses determining potential placements that maintain contextual coherence with the scene’s features per ¶0018-0019 , which is encoded in latent space, and See at least ¶0018, 0019, also, 0026-0028 using at least a VAE, features of the object and background are analyzed, to generate a vector in a latent space that is used to generate placement/scale of the object to be added in the scene.)
As to claims 5, 11, 17, 23 and 29:
Remine in view of Lee discloses all limitations of claim 4/10/16/22/28, wherein the one or more neural networks include a neural network to determine one or more potential positions for the one or more third objects based at least in part upon the object types and the one or more potential poses of the one or more first objects, and with respect to the features of the one or more second objects, wherein information for the one or more potential positions is to be encoded into the latent space. (Remine, ¶0035, “the pose estimator 1031 can estimate or determine the position and/or rotational orientation of the real-world tree. The image generator 103 is configured to insert the second image into the images of the real-world scene based on the pose of the real-world object in the second image. In this way, the image generator produces the images of the real-world scene including the simulated object and further including the real-world object. For example, the image generator can generate images of the real-world street including the simulated tree and the real-world tree”, ¶0031, “ insert the image of the simulated object into at least one of the segments based on the object classes to which the segments are assigned”. Lee discloses determining potential placements, which include position and orientation, that maintain contextual coherence with the scene’s features per ¶0018-0019, which is encoded in latent space - See at least ¶0018, 0019, also, 0026-0028 using at least a VAE, features of the object and background are analyzed, to generate a vector in a latent space that is used to generate placement/scale of the object to be added in the scene).
As to claims 6, 12, 18, 24 and 30:
Remine in view of Lee discloses all limitations of claims 5/11/17/23/29, wherein the one or more neural networks include a generative adversarial network (GAN) to generate one or more output images comprising the one or more third objects of the one or more third images, wherein the one or more third objects have different poses or positions in the one or more output images, the poses and positions to be selected from the one or more potential poses and the one or more potential positions determined from the latent space. (Lee, See at least ¶0018-0019, using neural network (Generative adversarial network) model add a desired object into a desired position of a captured real world scene image. See also Remine, ¶0035, “the pose estimator 1031 can estimate or determine the position and/or rotational orientation of the real-world tree. The image generator 103 is configured to insert the second image into the images of the real-world scene based on the pose of the real-world object in the second image. In this way, the image generator produces the images of the real-world scene including the simulated object and further including the real-world object. For example, the image generator can generate images of the real-world street including the simulated tree and the real-world tree”)
Claim(s) 3, 9, 15, 21 and 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Remine et al. (US 2020/0192389) in view of Lee et al. (US 2020/0074707) in view of Kopf (Mixture of Expert Variational Autoencoder for Clustering and Generating from Similarity-based Representation (01-2020) (IDS entry) and in further view of Irsoy et al. (Unsupervised feature extraction with autoencoder trees” (2017) – IDS entry.
As to claims 3, 9, 15, 21 and 27:
Remine in view of Lee discloses all limitations of claims 2/8/14/20/26, however is silent on the one or more neural networks 2include a gating network to select the one or more VAEs from a set of VAEs each trained 3for a different class of object, the gating network to select the one or more VAEs using a 4hierarchical mixture-of-experts approach.
Kopf discloses a gating network to select the one or more VAEs from a set of VAEs each trained 3for a different class of object (See Abstract, see page 3, a cluster I is gated to a corresponding expert (VAE), note that an expert has sole expertise in a particular class of object).
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the system/method of Lee/Remine to incorporate the feature of gating network to select VAEs as such implementation show superior clustering performance of the model on real world data (See page 2 of Kopf).
None of the above further discloses using hierarchical mixture of expert approach.
Irsoy, however, in a related field of endeavor discloses in Abstract, page 64, Section 3 through page 65, which discusses a soft decision node to direct instance to its branches according to different probability as given a gating function (gating network) in a hierarchical mixture of expert approach. Also Fig. 1, left column of page 64 discusses the gating function.
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the system/method of Lee/Remine to incorporate the feature of using hierarchical mixture of expert approach to select VAEs as such implementation improved operational accuracy (Irsoy page 71 - Conclusion)
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.
Claim(s) 1-30 is/are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-30 of copending Application No. 16/922,214.
As to claim 1:
A processor (comprising: one or more circuits to use one or more neural networks to generate one or more third images comprising one or more third objects based, at least in part, on one or more first objects within one or more first images and one or more second objects within one or more second images.
(See the reference claim 1, “One or more processors, comprising: circuitry to: cause one or more neural networks to use one or more classes of generate, for one or more objects depicted with a first pose in one or more first images and one or more other objects in one or more second images to determine a second pose of the one or more objects compatible with one or more appearances of the one or more other objects, the one or more neural networks using, as input, the one or more first images and the one or more second images to generate the second pose for the one or more objects to be added to the one or more second images; and use the one or more neural networks to add the one or more objects to the one or more second images, the added one or more objects in the one or more second images having the second pose, the second pose being different from the first pose”. The reference claim 1 is more descriptive, and capturing the concept of generating an output image that has objects of the first and second input images, wherein the poses of first objects are based on poses of the second objects of the second image inputs)
As to claim 7:
A system comprising: one or more processors to use one or more neural networks to generate one or more third images comprising one or more third objects based, at least in part, on one or more first objects within one or more first images and one or more second objects within one or more second images. (See the reference claim 7, with similar reasoning as applied in claim 1, i.e. The reference claim is more descriptive, and capturing the concept of generating an output image that has objects of the first and second input images, wherein the poses of first objects are based on poses of the second objects of the second image inputs)
As to claim 13:
A method comprising: using one or more neural networks to generate one or more third images comprising one or more third objects based, at least in part, on one or more first objects within one or more first images and one or more second objects within one or more second images.
(See the reference claim 13, with similar reasoning as applied in claim 1, i.e. The reference claim is more descriptive, and capturing the concept of generating an output image that has objects of the first and second input images, wherein the poses of first objects are based on poses of the second objects of the second image inputs)
As to claim 19:
A non-transitory computer-readable storage medium having stored thereon a set of instructions, which if performed by one or more processors, cause the one or more processors to at least: use one or more neural networks to generate one or more third images comprising one or more third objects based, at least in part, on one or more first objects within one or more first images and one or more second objects within one or more second images.
(See the reference claim 19, with similar reasoning as applied in claim 19, i.e. The reference claim is more descriptive, and capturing the concept of generating an output image that has objects of the first and second input images, wherein the poses of first objects are based on poses of the second objects of the second image inputs)
As to claim 25:
An image generation system, comprising: one or more processors to use one or more neural networks to generate one or more third images comprising one or more third objects based, at least in part, on one or more first objects within one or more first images and one or more second objects within one or more second images, wherein one or more poses of the one or more third objects in the one or more third images is determined with respect to the one or more second objects; and memory for storing network parameters for the one or more neural networks.
(See the reference claim 25, with similar reasoning as applied in claim 1, i.e. The reference claim is more descriptive, and capturing the concept of generating an output image that has objects of the first and second input images, wherein the poses of first objects are based on poses of the second objects of the second image inputs)
As to claims 2, 8, 14, 20 and 26, wherein the one or more neural networks include one or more variational autoencoders (VAEs) to determine features for the first objects and the second objects and encode those features to a latent space to act as a constraint in generating the one or more third images. (See the respective claims 2, 8, 14, 20 and 26, near verbatim recitation albeit trivial differences in ways of referring the inputs and output images)
As to claims 3, 9, 15, 21 and 27,wherein the one or more neural networks include a gating network to select the one or more VAEs from a set of VAEs each trained for a different class of object, the gating network to select the one or more VAEs using a hierarchical mixture-of-experts approach. (See the respective claims 3, 9, 15, 21 and 27, near verbatim recitation)
As to claims 4, 10, 16, 22 and 28, wherein the one or more neural networks include a generative network to determine one or more potential poses for the one or more third objects based at least in part upon object types of the one or more first objects and with respect to features of the one or more second objects, wherein information for the potential poses is to be encoded into the latent space. (See the respective claims 4, 10, 16, 22 and 28, near verbatim recitation albeit trivial differences in ways of referring the inputs and output images)
As to claims 5, 11, 17, 23 and 29, wherein the one or more neural networks include a neural network to determine one or more potential positions for the one or more third objects based at least in part upon the object types and the one or more potential poses of the one or more first objects, and with respect to the features of the one or more second objects, wherein information for the one or more potential positions is to be encoded into the latent space.
(See the respective claims 5, 11, 17, 23 and 29, near verbatim recitation albeit trivial differences in ways of referring the inputs and output images)
As to claims 6, 12, 18, 24 and 30, wherein the one or more neural networks include a generative adversarial network (GAN) to generate one or more output images comprising the one or more third objects of the one or more third images, wherein the one or more third objects have different poses or positions in the output images, the poses and positions to be selected from the one or more potential poses and the one or more potential positions determined from the latent space. (See the respective claims 6, 12, 18, 24 and 30, near verbatim recitation albeit trivial differences in ways of referring the inputs and output images)
Although the independent claims at issue are not identical, they are not patentably distinct from each other because the reference independent claims is effectively still is directed generating an output image that has objects of the first and second input images, wherein the poses of first objects are based on poses of the second objects of the second image inputs in a different phrasing. Dependent claims are otherwise near verbatim with respect to their respective reference claims.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gope et al. (US 2008/0181507) - In an embodiment, the background of an image is replaced with another background. In an embodiment, the foreground is extracted by identifying the background based on an image of the background without any foreground. In an embodiment, the foreground is extracted by identifying portions of the image that have characteristics that are expected to be associated with the background and characteristics that are expected to be associated with foreground. In an embodiment any of the images can be still images. In an embodiment, any of the images are video images.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QUAN M HUA whose telephone number is (571)270-7232. The examiner can normally be reached 10:30-6:30.
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/QUAN M HUA/Primary Examiner, Art Unit 2645
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