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 .
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1 and 15-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al. (Learning 3D Photography Videos via Self-supervised Diffusion on Single Images, 21 February 2023, arXiv, Pages 1-10), hereinafter “Wang”.
Regarding claim 1, Wang teaches:
A method performed by an electronic device (See the Abstract.), comprising:
acquiring a first image comprising at least a first region and a second region (See Vi in Stage 2 in Fig. 5 on page 6. The pixels of the man and motorcycle together meet the claimed “second region” and the pixels of the background meet the claimed “first region”.) a target object to be moved in the first image from the second region to the first region (See the man and motorcycle indicated as input (for outpainting) in Stage 1 of Fig. 5.), and the first region after the target object is moved (See Vi+1 in Stage 2 in Fig. 5.); and
performing target object removal processing on the first image using a first artificial intelligence (AI) network (See the Vi+1 result in Fig. 5 and page 3, paragraph bridging the left and right columns: “Towards the real application of animation, we further present a novel task: out-animation, which requires the model to generate a video that extends the space and time of input objects (or selected parts of an image).” The examiner asserts that this spatial and temporal extension meets the claimed “target object removal processing” because the repositioning of the man and motorcycle in Vi to Vi+1 results in the modification of the original pixels to background.) based on guidance information related to at least one of the first region and the second region (See the text prompt “A man in a jean jacket riding a motorcycle on a road” in Fig. 5.),
wherein the second region is a region of the target object in the first image in which the target object is located prior to the removal processing (See the pixels of the man and motorcycle in Vi in Stage 2 of Fig. 5.).
Regarding claim 15, Wang teaches:
The method according to 1, wherein the performing the target object removal processing on the first image using the first AI network comprises: performing the target object removal processing using the first AI network based on the target object and the second image to obtain a first removal processing result, the second image corresponding to the first image after the first region and the second region are removed from the first image; and repeating the operation of performing the target object removal processing using the first AI network based on the target object to obtain a removal processing result based on a determination that a set condition is reached (See the Vi+1 result in Fig. 5 and page 3, paragraph bridging the left and right columns: “Towards the real application of animation, we further present a novel task: out-animation, which requires the model to generate a video that extends the space and time of input objects (or selected parts of an image).” The examiner asserts that this spatial and temporal extension meets the claimed “obtain a first removal processing result, the second image corresponding to the first image after the first region and the second region are removed from the first image”, as explained before, and the “repeating” is met by the operations in the diffusion model (M-UNet in Fig. 5).).
Regarding claim 16, Wang teaches:
The method according to claim 1, wherein the target object removal processing comprises normalization processing, and the normalization processing comprises: splitting a plurality of input features into a first preset number of first feature groups; combining the first feature groups to obtain corresponding second feature groups (See the residual block, self-attention layer, and cross-attention layer in Fig. 4.); and performing normalization processing on the second feature groups (See the masked enhanced block (MEB) in Fig. 4 and page 5, right column: “We utilize two stacked spatially normalization layers to embed the masked image features ˜ ˜ zi and the mask ˜ ˜ Mi for enhancing the spatial information, and add the timestep t embedding as normal, as shown in Fig. 4 (b).”).
Regarding claim 17, Wang teaches:
The method according to claim 16, wherein the performing normalization processing on the second feature groups comprises: performing convolution processing on the second feature groups; performing normalization processing on second feature groups after the convolution processing; and fusing second feature groups after the normalization processing (See Fig. 4(b) and page 5, right column: “We formulate the stacked spatially nor malization layers as follows:
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where the fi−1 and fi are the input and output features. γ(·) and β(·) are the convolution layers to map latent features or masks into high-level spatially-adaptive features and add them with input features.”).
Regarding claim 18, Wang teaches:
The method according to claim 1, wherein the first AI network is a Diffusion network (See the diffusion model in Fig. 2.)..
Wang teaches the electronic device of claim 19 for the reasons given in the treatment of claim 1.
Wang teaches the non-transitory computer-readable storage medium of claim 20 for the reasons given in the treatment of claim 1.
Allowable Subject Matter
Claims 2-14 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. The prior art of record, individually or in combination, does not disclose or suggest in claim 2: “obtaining a first repair result of performing the target object removal processing on the first image using the first AI network; performing correction processing on the first repair result based on the guidance information related to at least one of the first region and the second region to obtain a removal processing result of performing the target object removal processing on the first image.”
Inpainting, meeting the claimed “first repair result”, is well-known in the art; however, the limitation “performing correction processing on the first repair result based on the guidance information related to at least one of the first region and the second region to obtain a removal processing result of performing the target object removal processing on the first image” is not disclosed.
Dependent claims 3-14 are dependent on claim 2 and include the same allowable subject matter.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN S LEE whose telephone number is (571)272-1981. The examiner can normally be reached 11:30 AM - 7:30 PM.
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/Jonathan S Lee/Primary Examiner, Art Unit 2677
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