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 .
Response to Remark(s)
Applicant's amendment filed March 13th, 2026 have been fully entered and considered. Applicant’s amendment to claim 1 have overcome each and every claim objection previously set forth in the Non-Final Office Action mailed on December 18th, 2025. Regarding the arguments to the previous prior art rejections, the examiner respectfully finds the arguments to be non-persuasive, see response to remarks section below. Accordingly, this action is made final.
Status of Claims
Claims 1-20 are pending, claim 6 has been amended. Claims 1-20 remains rejected.
Response to Argument(s)
Claim Objection:
In page 9 of the remarks, the Applicants stated an amendment has made to overcome the claim objection to claim 6, claim 6’s objection has been withdrawn.
Prior Art 102 and 103 rejections:
In pages 9-11 of the remarks, the Applicants argue that the proposed Muddala (previously proposed as the 102 reference for the 102 rejection of the independent claims 1, 7 and 20) does not teach or suggest the features of the independent claims:
“refining the respective inpainting region based….(ii) the respective feature of the two or more features overlapping with the non-inpainting region”
In support of the above argument, the Applicants assert that, basing on the Office Action’s rejection, this particular feature of the claims, as mentioned above, was previously mapped to Muddala’s “Figure 1 and page 7, last par., wherein the overlapped DDP include the mislabeled of the background point and the foreground point into the disocclusion area [non-inpainting region],” thus the Examiner’s rejection is premised on the “disocclusion area” supposedly being the ”non-inpainting region” in Muddala.
However, the Applicants argue that Muddala’s abstract teach away that, in Muddala’s abstract, it teaches “inpainting methods aim to fill these disocclusions….” And Muddala’s page 1 of 19, suggests that “their disocclusions are occluded regions in the original view that are revealed in the virtual view as a result of warping, hence, Muddala discloses “inpainting methods….aim to fill these disocclusions,” therefore, the Applicants find that Muddala does not disclose the features of the claims as stated above, in a sense that since Muddala’s disocclusion areas are disclosed to be filled in, hence, it cannot possibly be analogous to “non-inpainting region” in the claims.
Examiner’s reply:
The examiner respectfully finds the Applicants’ arguments to be non-persuasive regarding the teaching of the proposed Muddala on the features of the claims as stated above. Moreover, the examiner mapped the recited “non-inpainting region” to the disclosed “disocclusion areas” of Muddala to be analogous and consistent. Importantly, the Applicants are respectfully reminded that the claims are construed based on their BRI (broadest reasonable interpretation) in light of the specification, hence, the recited “non-inpainting region” shares the same scope with Muddala’s disocclusion areas, since Muddala’s disocclusion areas as disclosed in Muddala’s abstract being used a method that would result in no disocclusions to be appearing in the rendered data, to overcome the limits of the previous methods in the field that aim to fill these disocclusions by producing plausible texture content causing spatial and temporal inconsistencies.
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Muddala’s abstract stating that their invention does not fill in the disocclusion areas like previous methods, which would result in spatial and temporal inconsistencies, rather the invention aims to fill in occlusions such that no disocclusions appear when the data is rendered to a virtual view.
Furthermore, Muddala’s disocclusion areas occur between pixels that are neighboring have distinct difference in depth, and that become separated in the warped image. In other words, background and foreground regions touching result in disocclusion areas in the warped image.
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Muddala’s section 2.1.1.
In Muddala, their proposed method aims to perform an image synthesis method for extrapolating virtual views in a way that there are no disocclusions appear and views are consistent in the spatial and temporal domain.
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Muddala’s page 4 of 19, 1st column.
Therefore, what is being inpainted here are occlusions and not disocclusions, the occlusions are being inpainted during the production of the layered depth image, so that no disocclusions would appear when the layered depth image is rendered to the virtual view, therefore, the invention aims to result in no disocclusions, in other words, no regions to be filled in hole at the step of rendering the layered depth image to the virtual view, which is analogous to non-inpainting regions.
Therefore, the prior art rejections remain. See 102 and 103 rejections below for more details.
Claim Rejections - 35 USC § 102
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.
(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.
Claims 1, 7-8, 15-16 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Suryanarayana M. Muddala et. al. (“Spatio-Temporal Consistent Depth-Image-Based Rendering using Layered Depth Image and Inpainting, Feb. 2016, EURASIP Journal on Image and Video Processing, Vol. 2016, Article Number 9” hereinafter as “Muddala”).
Regarding claim 1, Muddala discloses a system comprising: a computing device, comprising: one or more processors; a memory; and a non-transitory computer readable medium having instructions stored thereon that when executed by a processor cause performance of a set of functions, wherein the set of functions comprises (Page 3, last par., discloses the area of the invention is in computer vision which indicates the use of a computer, which includes a processor to execute instructions [functions] stored in a memory and a RAM/ROM [non-transitory computer readable medium] to execute out these functions of the invention stored in a program): receiving an image from an image capture device (Fig. 3 illustrated the input to the processing is an image which is received from an camera [image capture device] according to Fig. 1); determining a mask for the image (FIG. 3 illustrated a mask generation for the input image), wherein the mask comprises (i) one or more inpainting regions that each designate a portion of the image to be inpainted (The mask generated in FIG. 3 includes occlusion region [region to be inpainted]), and (ii) a non-inpainting region that is not to be inpainted (Moreover, FIG. 3 shows that the mask includes both regions of the occlusion [shaded white areas] and regions not to be inpainted [other than the shaded white areas, which is the black area] including occlusion areas and disocclusion areas such as shown in FIG. 4); determining a depth representation comprising a pixelwise depth estimation for a scene represented by the image (Fig.3 illustrates a depth representation “Depth(1…N_t)” which represents the depth estimate for each pixel [pixelwise depth estimation]), wherein a respective inpainting region in the mask comprises pixels that represent two or more features of the scene (Page 7 of section 2.2 discloses pixels in the DDP [constituent of the mask] includes background and foreground labeling [two features]), wherein the two or more features have different depth estimates in the depth representation (Page 7 of section 2.2, discloses the background and the foreground regions have different depth estimates), and wherein a respective feature of the two or more features overlaps with the non-inpainting region (Page 7 of section 2.2, discloses the DDPs must overlap and produce inconsistent foreground-background labeling, such that the pixel-labeled background in one DDP might be labeled foreground in another DDP; such as further shown in FIG. 4); refining the respective inpainting region based on (i) the two or more features having different depth estimates (Section 2.1.3 discloses the occlusion identification to correctly identify background and foreground and inpaint the occluded areas [refining as claimed] including the mislabeled background and foreground points with overlapped labelled points in overlapped DDP as previously discussed in Page 7 of section 2.2), and (ii) the respective feature of the two or more features overlapping with the non-inpainting region (As shown in FIG. 4 and disclosed in Page 7 of section 2.2, the overlapped DDP include the mislabeled of the background point and the foreground point into the disocclusion area [non-inpainting region]); and inpainting the image in accordance with refining the respective inpainting region such that the portion of the respective feature that overlaps with the non-inpainting region is not inpainted (The inpainting [hole-filling] such as illustrated in FIG. 3 to fille in the occluded region, example of the occluded image is shown in FIG. 5.d; the process of the filling is further disclosed in 2.4.3 wherein the filling happens so that only background pixels in the target patch by removing foreground pixels in order to avoid artifacts at foreground objects; therefore, the inpainting process here is to make sure that the foreground region is excluded in the process so that the foreground region is not to be having artifacts [not needed for filling of to be inpainted]; in another sense, the overlapped DDP is now corrected labeled and not needed to be inpainted further in the corrected patch as disclosed in 2.4.3).
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Muddala’s Fig. 3 occlusion mask includes regions to be inpainted.
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Muddala’s Figure 4 of the overlapped DDP wherein the pixel-labeled background is labeled as foreground in the disocclusion area [non-inpainting region]
Regarding claim 7, Muddala discloses a method comprising: receiving an image from an image capture device (Fig. 3 illustrated the input to the processing is an image which is received from an camera [image capture device] according to Fig. 1); determining a mask for the image (FIG. 3 illustrated a mask generation for the input image), wherein the mask comprises (i) one or more inpainting regions that each designate a portion of the image to be inpainted (The mask generated in FIG. 3 includes occlusion region [region to be inpainted]), and (ii) a non-inpainting region that is not to be inpainted (Moreover, FIG. 3 shows that the mask includes both regions of the occlusion [shaded white areas] and regions not to be inpainted [other than the shaded white areas, which is the black area] including occlusion areas and disocclusion areas such as shown in FIG. 4); determining a depth representation comprising a pixelwise depth estimation for a scene represented by the image (Fig.3 illustrates a depth representation “Depth(1…N_t)” which represents the depth estimate for each pixel [pixelwise depth estimation]), wherein a respective inpainting region in the mask comprises pixels that represent two or more features of the scene (Page 7 of section 2.2, discloses pixels in the DDP [constituent of the mask] includes background and foreground labeling [two features]), wherein the two or more features have different depth estimates in the depth representation (Page 7 of section 2.2, discloses the background and the foreground regions have different depth estimates), and wherein a respective feature of the two or more features overlaps with the non-inpainting region (Page 7 of section 2.2, discloses the DDPs mist overlap and produce inconsistent foreground-background labeling, such that the pixel-labeled background in one DDP might be labeled foreground in another DDP; such as further shown in FIG. 4); refining the respective inpainting region based on (i) the two or more features having different depth estimates (Section 2.1.3 discloses the occlusion identification to correctly identify background and foreground and perform inpainting on the occluded areas [refining as claimed] including the mislabeled background and foreground points with overlapped labelled points in overlapped DDP as previously discussed in Page 7 of section 2.2), and (ii) the respective feature of the two or more features overlapping with the non-inpainting region (As shown in FIG. 4 and disclosed in Page 7 of section 2.2, the overlapped DDP include the mislabeled of the background point and the foreground point into the disocclusion area [non-inpainting region]); and inpainting the image in accordance with refining the respective inpainting region such that the portion of the respective feature that overlaps with the non-inpainting region is not inpainted (The inpainting [hole-filling] such as illustrated in FIG. 3 to fille in the occluded region, example of the occluded image is shown in FIG. 5.d; the process of the filling is further disclosed in 2.4.3 wherein the filling happens so that only background pixels in the target patch by removing foreground pixels in order to avoid artifacts at foreground objects; therefore, the inpainting process here is to make sure that the foreground region is excluded in the process so that the foreground region is not to be having artifacts [not needed for filling of to be inpainted]; in another sense, the overlapped DDP is now corrected labeled and not needed to be inpainted further in the corrected patch as disclosed in 2.4.3).
Regarding claim 8, Muddala discloses the method of claim 7, wherein refining the respective inpainting region is performed concurrently with determining the depth representation for the image (As shown in Muddala’s FIG. 3, the texture map processing and the depth map processing is happened parallel, or concurrently).
Regarding claim 15, Muddala discloses the method of claim 7, wherein the two or more features comprises a foreground feature and a background feature (Page 7 of section 2.2, discloses the background and the foreground regions have different depth estimates), wherein the foreground feature has a first depth (The depth of the foreground is analogous to “a first depth”), wherein the background feature has a second depth (The depth of the background is analogous to “second depth”), wherein the first depth is less than the second depth (The depth of the foreground is always less than the depth of the background as disclosed in page 6, 1st par. and illustrated in fig. 4), and wherein refining the respective inpainting region comprises adjusting the inpainted region to omit the foreground feature based on the first depth being less than the second depth (Section 2.1.3 discloses the occlusion identification to correctly identify background and foreground and perform inpainting on the occluded areas [refining as claimed] including the mislabeled background and foreground points with overlapped labelled points in overlapped DDP as previously discussed in Page 7 of section 2.2; as the threshold images are created according to Page 7 of section 2.2, done by adjusting the pixel of the occlusion depth by thresholding when it determines that the occlusion belongs to the background, hence, the thresholding is to omit the foreground feature processing, and that the background depth is determined to be background since its larger than the depth of foreground, as discussed previously).
Regarding claim 16, Muddala discloses the method of claim 7, wherein refining the respective inpainting region comprises removing at least a portion of the respective feature that overlaps with the non-inpainted region from the respective inpainting region (Page 2, 2nd par., discloses the problem of the invention trying to solve is to remove overlapping [called artifacts] of warped image, moreover, the removing is done by pixels in the overlap regions according to page 10, 2nd column, 1st par., and page 8, 1st par.).
Regarding claim 20, Muddala discloses a non-transitory computer readable medium having instructions stored thereon that when executed by a processor cause performance of a set of functions, wherein the set of functions comprises (Page 3, last par., discloses the area of the invention is in computer vision which indicates the use of a computer, which includes a processor to execute instructions [functions] stored in a memory and a RAM/ROM [non-transitory computer readable medium] to execute out these functions of the invention stored in a program): receiving an image from an image capture device (Fig. 3 illustrated the input to the processing is an image which is received from an camera [image capture device] according to Fig. 1); determining a mask for the image (FIG. 3 illustrated a mask generation for the input image), wherein the mask comprises (i) one or more inpainting regions that each designate a portion of the image to be inpainted (The mask generated in FIG. 3 includes occlusion region [region to be inpainted]), and (ii) a non-inpainting region that is not to be inpainted (Moreover, FIG. 3 shows that the mask includes both regions of the occlusion [shaded white areas] and regions not to be inpainted [other than the shaded white areas, which is the black area] including occlusion areas and disocclusion areas such as shown in FIG. 4); determining a depth representation comprising a pixelwise depth estimation for a scene represented by the image (Fig.3 illustrates a depth representation “Depth(1…N_t)” which represents the depth estimate for each pixel [pixelwise depth estimation]), wherein a respective inpainting region in the mask comprises pixels that represent two or more features of the scene (Page 7 of section 2.2, discloses pixels in the DDP [constituent of the mask] includes background and foreground labeling [two features]), wherein the two or more features have different depth estimates in the depth representation (Page 7 of section 2.2, discloses the background and the foreground regions have different depth estimates), and wherein a respective feature of the two or more features overlaps with the non-inpainting region (Page 7 of section 2.2, discloses the DDPs mist overlap and produce inconsistent foreground-background labeling, such that the pixel-labeled background in one DDP might be labeled foreground in another DDP; such as further shown in FIG. 4); refining the respective inpainting region based on (i) the two or more features having different depth estimates (Section 2.1.3 discloses the occlusion identification to correctly identify background and foreground and perform inpainting on the occluded areas [refining as claimed] including the mislabeled background and foreground points with overlapped labelled points in overlapped DDP as previously discussed in Page 7 of section 2.2), and (ii) the respective feature of the two or more features overlapping with the non-inpainting region (As shown in FIG. 4 and disclosed in Page 7 of section 2.2, the overlapped DDP include the mislabeled of the background point and the foreground point into the disocclusion area [non-inpainting region]); and inpainting the image in accordance with refining the respective inpainting region such that the portion of the respective feature that overlaps with the non-inpainting region is not inpainted (The inpainting [hole-filling] such as illustrated in FIG. 3 to fille in the occluded region, example of the occluded image is shown in FIG. 5.d; the process of the filling is further disclosed in 2.4.3 wherein the filling happens so that only background pixels in the target patch by removing foreground pixels in order to avoid artifacts at foreground objects; therefore, the inpainting process here is to make sure that the foreground region is excluded in the process so that the foreground region is not to be having artifacts [not needed for filling of to be inpainted]; in another sense, the overlapped DDP is now corrected labeled and not needed to be inpainted further in the corrected patch as disclosed in 2.4.3).
Claim Rejections - 35 USC § 103
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.
Claims 2-4 and 9-12 are rejected under 35 U.S.C. 103 as being unpatentable over Suryanarayana M. Muddala et. al. (“Spatio-Temporal Consistent Depth-Image-Based Rendering using Layered Depth Image and Inpainting, Feb. 2016, EURASIP Journal on Image and Video Processing, Vol. 2016, Article Number 9” hereinafter as “Muddala”) in view of Peter Amon et. al. (“US 2022/0076117 A1” hereinafter as “Amon”).
Regarding claim 2, Muddala discloses the system of claim 1, wherein refining the respective inpainting region comprises applying the image and the mask to output a refined mask comprising the refined respective inpainting region (As discussed above in claim 1, by using the input and the mask of FIG. 3 and Fig. 4 a refined mask is output including refined respective inpainting region [according to section 2.4.3 wherein the refined region including the generated threshold image]).
However, Muddala does not explicitly disclose applying a machine learning model to the image and the mask.
In the same field of image inpainting (par. [0047], Amon), Amon discloses applying a machine learning model to the image and the mask (paragraphs [0010]-[0011] discloses using machine learning model to the image to obtain background and foreground images classified for the image, including the image and the mask of the objects according to par. [0011]; therefore, the process of Muddala as discussed is to output a result include processing of the detection of the object and classify the object regions as foreground or background which is now taught by Amon to include using machine learning model).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Muddala to obtaining an image input and a mask of the input image and performing refining respective inpainting region of the input image.
Wherein Muddala’s obtaining of the refined mask comprising the refined respective inpainting region can be modified to be applying a machine learning model to the image and a mask of the image to output a refined mask comprising the refined respective inpainting region as taught by Amon.
The combination of the arts would arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to use machine learning model to classify image regions more effectively by refining the regions in the classification task (abstract, Amon).
Regarding claim 3, Muddala in view of Amon discloses the system of claim 2, wherein the computing device and the machine learning model are part of a server system (Muddala, as discussed above, discloses the processing as part of a computer vision area, hence, includes using of a computer [server system] such as disclosed in page 3, last par.).
Regarding claim 4, Muddala in view of Amon, wherein Muddala discloses the system of claim 2, wherein refining the respective inpainting region is further based on designating the respective feature of the two or more features as foreground object (Muddala discloses, the inpainting [hole-filling] such as illustrated in Muddala’s FIG. 3 to fille in the occluded region, example of the occluded image is shown in Muddala’s FIG. 5.d; the process of the filling is further disclosed in Muddala’s 2.4.3 wherein the filling happens so that only background pixels in the target patch by removing foreground pixels in order to avoid artifacts at foreground objects; therefore, the inpainting process here is to make sure that the foreground region is excluded in the process so that the foreground region is not to be having artifacts [not needed for filling of to be inpainted]; in another sense, the overlapped DDP is now corrected labeled and not needed to be inpainted further in the corrected patch as disclosed in Muddala’s 2.4.3; wherein the process of inpainting/filling is based on determining that certain object if foreground object according to Fig. 3 and Fig. 4).
However, Muddala does not explicitly disclose the set of functions further comprising: training the machine learning model (i) to identify a plurality of objects within the scene, and (ii) to designate each object as a foreground object or a background object; and applying the machine learning model to the image to designate the respective feature of the two or more features being as a foreground object.
In the same field of image inpainting (par. [0047], Amon), Amon discloses the set of functions further comprising: training the machine learning model (i) to identify a plurality of objects within the scene, and (ii) to designate each object as a foreground object or a background object (Amon, par. [0024] discloses the training includes training the segmentation part to obtain segmentation of objects as to be classified as foreground and background objects later); and applying the machine learning model to the image to designate the respective feature of the two or more features being as a foreground object (Par. [0024] discloses the segmentation part to identify the objects [two or more features] and then perform classifying the objects as background or foreground by using further machine learning classifier according to paragraphs [0022]-[0023]).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Muddala to perform refining the respective inpainting region is further based on designating the respective feature of the two or more features as foreground object.
Wherein Muddala’s performing of refining the respective inpainting region is further based on designating the respective feature of the two or more features as foreground object can be modified to be based on training a machine learning model (i) to identify a plurality of objects within the scene, and (ii) to designate each object as a foreground object or a background object; and applying the machine learning model to the image to designate the respective feature of the two or more features being as a foreground object as taught by Amon.
The combination of the arts would arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to use machine learning model to classify image regions more effectively by refining the regions in the classification task (abstract, Amon).
Regarding claim 9, Muddala discloses the method of claim 8, wherein refining the respective inpainting region concurrently with determining the depth representation for the image comprises applying to the image and to the mask, wherein the machine learning model outputs a refined mask comprising the refined respective inpainting region (By using the input and the mask of FIG. 3 and Fig. 4 a refined mask is output including refined respective inpainting region [according to section 2.4.3 wherein the refined region including the generated threshold image]).
However, Muddala does not explicitly disclose applying a machine learning model to the image and the mask.
In the same field of image inpainting (par. [0047], Amon), Amon discloses applying a machine learning model to the image and the mask (paragraphs [0010]-[0011] discloses using machine learning model to the image to obtain background and foreground images classified for the image, including the image and the mask of the objects according to par. [0011]; therefore, the whole process of Muddala as discussed above in claims 7 and 8 to output a result include processing of the detection of the object and classify the object regions as foreground or background which is now taught by Amon to include using machine learning model).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Muddala to obtaining an image input and a mask of the input image and performing refining respective inpainting region of the input image.
Wherein Muddala’s obtaining of the refined mask comprising the refined respective inpainting region can be modified to be applying a machine learning model to the image and a mask of the image to output a refined mask comprising the refined respective inpainting region as taught by Amon.
The combination of the arts would arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to use machine learning model to classify image regions more effectively by refining the regions in the classification task (abstract, Amon).
Regarding claim 10, Muddala in view of Amon discloses the method of claim 9.
However, Muddala does not explicitly disclose wherein the machine learning model determines both the depth representation and the refined respective inpainting region.
In the same field of image inpainting (par. [0047], Amon), Amon discloses wherein the machine learning model determines both the depth representation and the refined respective inpainting region (Amon teaches using machine learning to determine depth representation and inpainting according to Amon’s par. [0047]; moreover, since the objects are determined by using a machine learning as discussed in Amon’s par. [0016] therefore, the objects identified in Amon using the machine learning model is used for both the depth representation of the object and the refined threshold image of Muddala such as shown in Muddala’s Fig. 3 and Fig. 4).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Muddala to obtaining an image input and a mask of the input image and performing refining respective inpainting region of the input image.
Wherein Muddala’s obtaining of the refined mask comprising the refined respective inpainting region can be modified to be applying a machine learning model to the image and a mask of the image to output a refined mask comprising the refined respective inpainting region, wherein the machine learning model determines both the depth representation and the refined respective inpainting region as taught by Amon.
The combination of the arts would arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to use machine learning model to classify image regions more effectively by refining the regions in the classification task (abstract, Amon).
Regarding claim 11, Muddala discloses the method of claim 7, wherein refining the respective inpainting region comprises applying the image and the mask to output a refined mask comprising the refined respective inpainting region (By using the input and the mask of FIG. 3 and Fig. 4 a refined mask is output including refined respective inpainting region [according to section 2.4.3 wherein the refined region including the generated threshold image]).
However, Muddala does not explicitly disclose applying a machine learning model to the image and the mask.
In the same field of image inpainting (par. [0047], Amon), Amon discloses applying a machine learning model to the image and the mask (paragraphs [0010]-[0011] discloses using machine learning model to the image to obtain background and foreground images classified for the image, including the image and the mask of the objects according to par. [0011]; therefore, the whole process of Muddala as discussed above in claim 7 to output a result include processing of the detection of the object and classify the object regions as foreground or background which is now taught by Amon to include using machine learning model).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Muddala to obtaining an image input and a mask of the input image and performing refining respective inpainting region of the input image.
Wherein Muddala’s obtaining of the refined mask comprising the refined respective inpainting region can be modified to be applying a machine learning model to the image and a mask of the image to output a refined mask comprising the refined respective inpainting region as taught by Amon.
The combination of the arts would arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to use machine learning model to classify image regions more effectively by refining the regions in the classification task (abstract, Amon).
Regarding claim 12, Muddala in view of Amon discloses the method of claim 11, wherein refining the respective inpainting region is further based on designating the respective feature of the two or more features as foreground object (Muddala discloses, the inpainting [hole-filling] such as illustrated in Muddala’s FIG. 3 to fille in the occluded region, example of the occluded image is shown in Muddala’s FIG. 5.d; the process of the filling is further disclosed in Muddala’s 2.4.3 wherein the filling happens so that only background pixels in the target patch by removing foreground pixels in order to avoid artifacts at foreground objects; therefore, the inpainting process here is to make sure that the foreground region is excluded in the process so that the foreground region is not to be having artifacts [not needed for filling of to be inpainted]; in another sense, the overlapped DDP is now corrected labeled and not needed to be inpainted further in the corrected patch as disclosed in Muddala’s 2.4.3; wherein the process of inpainting/filling is based on determining that certain object if foreground object according to Fig. 3 and Fig. 4).
However, Muddala does not explicitly disclose the method further comprising: training the machine learning model (i) to identify a plurality of objects within the scene, and (ii) to designate each object as a foreground object or a background object; and applying the machine learning model to the image to designate the respective feature of the two or more features being as a foreground object.
In the same field of image inpainting (par. [0047], Amon), Amon discloses the method further comprising: training the machine learning model (i) to identify a plurality of objects within the scene, and (ii) to designate each object as a foreground object or a background object (Amon, par. [0024] discloses the training includes training the segmentation part to obtain segmentation of objects as to be classified as foreground and background objects later); and applying the machine learning model to the image to designate the respective feature of the two or more features being as a foreground object (Par. [0024] discloses the segmentation part to identify the objects [two or more features] and then perform classifying the objects as background or foreground by using further machine learning classifier according to paragraphs [0022]-[0023]).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Muddala to perform refining the respective inpainting region is further based on designating the respective feature of the two or more features as foreground object.
Wherein Muddala’s performing of refining the respective inpainting region is further based on designating the respective feature of the two or more features as foreground object can be modified to be based on training a machine learning model (i) to identify a plurality of objects within the scene, and (ii) to designate each object as a foreground object or a background object; and applying the machine learning model to the image to designate the respective feature of the two or more features being as a foreground object as taught by Amon.
The combination of the arts would arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to use machine learning model to classify image regions more effectively by refining the regions in the classification task (abstract, Amon).
Claims 5-6 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Suryanarayana M. Muddala et. al. (“Spatio-Temporal Consistent Depth-Image-Based Rendering using Layered Depth Image and Inpainting, Feb. 2016, EURASIP Journal on Image and Video Processing, Vol. 2016, Article Number 9” hereinafter as “Muddala”) in view of Peter Amon et. al. (“US 2022/0076117 A1” hereinafter as “Amon”) and Nikita Dvornik et. al. (“On the Importance of Visual Context for Data Augmentation in Scene Understanding, June 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 43, Issue 61” hereinafter as “Dvornik”).
Regarding claim 5, Muddala in view of Amon discloses the system of claim 2.
However, Muddala does not explicitly disclose the set of functions further comprising: obtaining a plurality of training images; adding an image feature to each of the training images; creating a plurality of training masks corresponding to the plurality of training images, wherein each training mask comprises an image feature region comprising an initial outline of the added feature; augmenting each feature region by adjusting the initial outline of the added feature; and after augmenting each feature region of the plurality of masks, training the machine learning model using the plurality of training images and the plurality of masks using the initial outline of the added feature for inpainting each training image using a corresponding mask.
In the same field of image inpainting (par. [0047], Amon), Amon discloses the set of functions further comprising: obtaining a plurality of training images (Amon, par. [0021] discloses the training include obtaining a number of training images being fed to the feature extractor); adding an image feature to each of the training images (Amon, par. [0021] discloses the feature extractor to extract features from the training images; furthermore, par. [0043] discloses the training images may include images that have same scene is combined or augmented with objects or objects of predetermined types, in other word, the object being extracted the features can be added into the training images to create augmented images to be part of the training images); creating a plurality of training masks corresponding to the plurality of training images (Amon, par. [0043] discloses generation of training images include augmenting the starting image with another instance of an object of a predetermined type, and for a plurality of images would include the use of a plurality of objects of predetermined types; moreover, these instances are represented by pixel map according to par. [0047] which is analogous to “mask”), wherein each training mask comprises an image feature region comprising an initial outline of the added feature (as disclosed in Amon’s par. [0043], the portion of the another instance of object can be understood as the image feature including an initial of the added feature as claimed; moreover, these objects are represented by object outline according to [0132]); augmenting each feature region by adjusting the initial outline of the added feature (Amon, par. [0043-0045] discloses the training images are generated synthetically by augmenting the instances of objects into the starting image, the instances of objects here are analogous to the initial of the added feature which are being adjusted areas of which pixel or area percentage to be augmented into the image [according to par. 0043]); and after augmenting each feature region of the plurality of masks, training the machine learning model using the plurality of training images (Amon, par. [0046] discloses the synthetic augmented training images are used to train the machine learning model) and the plurality of masks using the initial outline of the added feature for inpainting each training image using a corresponding mask (Amon, par. [0047] discloses the augmented instances of object are used as annotations for supervised machine learning; wherein the augmenting can be used a inpainting technique to add the instances of the image into the starting image).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Muddala to have a system of performing image refinement. Wherein Muddala’s system can be modified to further perform obtaining a plurality of training images; adding an image feature to each of the training images; creating a plurality of training masks corresponding to the plurality of training images, wherein each training mask comprises an image feature region comprising an initial outline of the added feature; augmenting each feature region by adjusting the initial outline of the added feature; and after augmenting each feature region of the plurality of masks, training the machine learning model using the plurality of training images and the plurality of masks using the initial outline of the added feature for inpainting each training image using a corresponding mask as taught by Amon. The combination of the arts would arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to use machine learning model to classify image regions more effectively by refining the regions in the classification task (abstract, Amon).
However, Muddala in view of Amon does not explicitly disclose initial outline of the added feature as ground truth.
In the same field of data augmentation (title, Dvornik) Dvornik discloses initial outline of the added feature as ground truth (Section 4.6 discloses the objects being augmented into the image is also the ground truth for training the neural network model).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Muddala in view of Amon to have a system that performs augmenting each feature region of the plurality of masks, training the machine learning model using the plurality of training images and the plurality of masks using the initial outline of the added feature for inpainting each training image using a corresponding mask. wherein Amon’s the initial outline of the added feature can be taught as ground truth as taught by Dvornik to arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to perform training of the model more efficiently (abstract and section 4.6, Dvornik).
Regarding claim 6, Muddala in view of Amon and Dvornik discloses the method of claim 5, further comprising: using the machine learning model to predict an inpainted depth estimate of each augmented feature region (As discussed above in Muddala, the inpainting [hole-filling] such as illustrated in FIG. 3 to fille in the occluded region, example of the occluded image is shown in FIG. 5.d; the process of the filling is further disclosed in 2.4.3 wherein the filling happens so that only background pixels in the target patch by removing foreground pixels in order to avoid artifacts at foreground objects; therefore, the inpainting process here is to make sure that the foreground region is excluded in the process so that the foreground region is not to be having artifacts [not needed for filling of to be inpainted]; in another sense, the overlapped DDP is now corrected labeled and not needed to be inpainted further in the corrected patch as disclosed in 2.4.3; moreover, Amon further discloses [0010-0011] discloses using machine learning model to the image to obtain background and foreground images classified for the image, including the image and the mask of the objects according to [0011]; therefore, the whole process of Muddala as discussed above in claim 11 to output a result include processing of the detection of the object and classify the object regions as foreground or background which is now taught by Amon to include using machine learning model); and refining each augmented feature region based at least in part on the inpainted depth estimate of each augmented feature region (As discussed in Muddala, as discussed above in claim 11, by using the input and the mask of FIG. 3 and Fig. 4 a refined mask is output including refined respective inpainting region [according to section 2.4.3 wherein the refined region including the generated threshold image]; moreover, Amon, par. [0047] discloses the augmented instances of object are used as annotations for supervised machine learning; wherein the augmenting can be used a inpainting technique to add the instances of the image into the starting image).
However, Muddala does not explicitly disclose while training the machine learning model, applying each respective mask to a depth estimate of each corresponding training image.
In the same field of image inpainting (par. [0047], Amon), Amon discloses while training the machine learning model, applying each respective mask to a depth estimate of each corresponding training image (Amon, [0039] discloses the training images used for training includes objects at different depth levels, wherein each objects have their map [each respective mask as claimed, here each respective mask has no antecedent basis hence, can be understood to be any map or mask of object respectively as taught in the art]).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Muddala to perform refining the respective inpainting region is further based on designating the respective feature of the two or more features as foreground object.
Wherein Muddala’s system can be modified to be based on a using a machine learning model to perform the image refinement, and while training the machine learning model, applying each respective mask to a depth estimate of each corresponding training image as taught by Amon.
The combination of the arts would arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to use machine learning model to classify image regions more effectively by refining the regions in the classification task (abstract, Amon).
Regarding claim 13, Muddala in view of Amon discloses the method of claim 11 further comprising: obtaining a plurality of training images; adding an image feature to each of the training images; creating a plurality of training masks corresponding to the plurality of training images, wherein each training mask comprises an image feature region comprising an initial outline of the added feature; augmenting each feature region by adjusting the initial outline of the added feature; and after augmenting each feature region of the plurality of masks, training the machine learning model using the plurality of training images and the plurality of masks using the initial outline of the added feature for inpainting each training image using a corresponding mask.
In the same field of image inpainting (par. [0047], Amon), Amon discloses the set of functions further comprising: obtaining a plurality of training images (Amon, par. [0021] discloses the training include obtaining a number of training images being fed to the feature extractor); adding an image feature to each of the training images (Amon, par. [0021] discloses the feature extractor to extract features from the training images; furthermore, par. [0043] discloses the training images may include images that have same scene is combined or augmented with objects or objects of predetermined types, in other word, the object being extracted the features can be added into the training images to create augmented images to be part of the training images); creating a plurality of training masks corresponding to the plurality of training images (Amon, par. [0043] discloses generation of training images include augmenting the starting image with another instance of an object of a predetermined type, and for a plurality of images would include the use of a plurality of objects of predetermined types; moreover, these instances are represented by pixel map according to par. [0047] which is analogous to “mask”), wherein each training mask comprises an image feature region comprising an initial outline of the added feature (as disclosed in Amon’s par. [0043], the portion of the another instance of object can be understood as the image feature including an initial of the added feature as claimed; moreover, these objects are represented by object outline according to [0132]); augmenting each feature region by adjusting the initial outline of the added feature (Amon, par. [0043-0045] discloses the training images are generated synthetically by augmenting the instances of objects into the starting image, the instances of objects here are analogous to the initial of the added feature which are being adjusted areas of which pixel or area percentage to be augmented into the image [according to par. 0043]); and after augmenting each feature region of the plurality of masks, training the machine learning model using the plurality of training images (Amon, par. [0046] discloses the synthetic augmented training images are used to train the machine learning model) and the plurality of masks using the initial outline of the added feature for inpainting each training image using a corresponding mask (Amon, par. [0047] discloses the augmented instances of object are used as annotations for supervised machine learning; wherein the augmenting can be used a inpainting technique to add the instances of the image into the starting image).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Muddala to have a system of performing image refinement. Wherein Muddala’s system can be modified to further perform obtaining a plurality of training images; adding an image feature to each of the training images; creating a plurality of training masks corresponding to the plurality of training images, wherein each training mask comprises an image feature region comprising an initial outline of the added feature; augmenting each feature region by adjusting the initial outline of the added feature; and after augmenting each feature region of the plurality of masks, training the machine learning model using the plurality of training images and the plurality of masks using the initial outline of the added feature for inpainting each training image using a corresponding mask as taught by Amon. The combination of the arts would arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to use machine learning model to classify image regions more effectively by refining the regions in the classification task (abstract, Amon).
However, Muddala in view of Amon does not explicitly disclose initial outline of the added feature as ground truth.
In the same field of data augmentation (title, Dvornik) Dvornik discloses initial outline of the added feature as ground truth (Section 4.6 discloses the objects being augmented into the image is also the ground truth for training the neural network model).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Muddala in view of Amon to have a system that performs augmenting each feature region of the plurality of masks, training the machine learning model using the plurality of training images and the plurality of masks using the initial outline of the added feature for inpainting each training image using a corresponding mask. wherein Amon’s the initial outline of the added feature can be taught as ground truth as taught by Dvornik to arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to perform training of the model more efficiently (abstract and section 4.6, Dvornik).
Regarding claim 14, Muddala in view of Amon and Dvornik discloses the method of claim 13, further comprising: using the machine learning model to predict an inpainted depth estimate of each augmented feature region (As discussed above in Muddala, the inpainting [hole-filling] such as illustrated in FIG. 3 to fille in the occluded region, example of the occluded image is shown in FIG. 5.d; the process of the filling is further disclosed in 2.4.3 wherein the filling happens so that only background pixels in the target patch by removing foreground pixels in order to avoid artifacts at foreground objects; therefore, the inpainting process here is to make sure that the foreground region is excluded in the process so that the foreground region is not to be having artifacts [not needed for filling of to be inpainted]; in another sense, the overlapped DDP is now corrected labeled and not needed to be inpainted further in the corrected patch as disclosed in 2.4.3; moreover, Amon further discloses [0010-0011] discloses using machine learning model to the image to obtain background and foreground images classified for the image, including the image and the mask of the objects according to [0011]; therefore, the whole process of Muddala as discussed above in claim 11 to output a result include processing of the detection of the object and classify the object regions as foreground or background which is now taught by Amon to include using machine learning model); and refining each augmented feature region based at least in part on the inpainted depth estimate of each augmented feature region (As discussed in Muddala, by using the input and the mask of FIG. 3 and Fig. 4 a refined mask is output including refined respective inpainting region [according to section 2.4.3 wherein the refined region including the generated threshold image]; moreover, Amon, par. [0047] discloses the augmented instances of object are used as annotations for supervised machine learning; wherein the augmenting can be used a inpainting technique to add the instances of the image into the starting image).
However, Muddala does not explicitly disclose while training the machine learning model, applying each respective mask to a depth estimate of each corresponding training image.
In the same field of image inpainting (par. [0047], Amon), Amon discloses while training the machine learning model, applying each respective mask to a depth estimate of each corresponding training image (Amon, [0039] discloses the training images used for training includes objects at different depth levels, wherein each objects have their map [each respective mask as claimed, here each respective mask has no antecedent basis hence, can be understood to be any map or mask of object respectively as taught in the art]).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Muddala to perform refining the respective inpainting region is further based on designating the respective feature of the two or more features as foreground object.
Wherein Muddala’s system can be modified to be based on a using a machine learning model to perform the image refinement, and while training the machine learning model, applying each respective mask to a depth estimate of each corresponding training image as taught by Amon.
The combination of the arts would arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to use machine learning model to classify image regions more effectively by refining the regions in the classification task (abstract, Amon).
Claims 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Suryanarayana M. Muddala et. al. (“Spatio-Temporal Consistent Depth-Image-Based Rendering using Layered Depth Image and Inpainting, Feb. 2016, EURASIP Journal on Image and Video Processing, Vol. 2016, Article Number 9” hereinafter as “Muddala”) in view of Rohit R. Ranade et. al. (“US 2021/0125307 A1” hereinafter as “Ranade”).
Regarding claim 17, Muddala discloses the method of claim 7, further comprising: determining a foreground of the inpainted image and a background of the inpainted image (As shown in FIG. 3 and FIG. 4, the inpainted image has clear background and foreground determined).
However, Muddala does not explicitly disclose applying a shallow depth of field to the inpainted image based on the one or more inpainting regions in the mask.
In the same field of image inpainting ([0050], Ranade) Ranade discloses applying a shallow depth of field to the inpainted image based on the one or more inpainting regions in the mask (paragraphs [0050]-[0051] discloses, after the image is being inpainted [hole filled], apply shallow depth of field of the image based on the inpainting regions as discussed above in claim 7 according to Muddala, here Ranade further teaches to apply the shallow depth of field to the inpainted image).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Muddala to perform inpainting image based on background and foreground determination.
Wherein Muddala’s inpainting method can be modified to be applying a shallow depth of field to the inpainted image based on the one or more inpainting regions in the mask as taught by Ranade to arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to obtain image through inpainting more efficiently with finalizing the image with missing information (paragraphs [0049]-[0051], Ranade).
Regarding claim 18, Muddala in view of Ranade discloses the method of claim 17.
However, Muddala does not explicitly disclose wherein applying the shallow depth of field to the inpainted image comprises: determining a number of image artifacts in the inpainted image, wherein each image artifact corresponds to an inpainted region of the inpainted image; determining that the number of image artifacts exceeds a threshold number; and applying the shallow depth of field to the image based on the number of image artifacts exceeding the threshold number.
In the same field of image inpainting (par. [0050], Ranade) Ranade discloses wherein applying the shallow depth of field to the inpainted image comprises: determining a number of image artifacts in the inpainted image (Ranade, par. [0009] discloses the final image [which is the final image result of the applying the shallow depth of field as disclosed in paragraphs [0050]-[0051] includes a window averaging filer being applied to the occluded areas in the fused image, which is further disclosed in par. [0126] which is using a kernel size to filter out occluded areas and par. [0125] if there is additional pixel locations, if there is additional pixel locations then perform the window averaging filter, therefore, there is a determination of number of additional pixel locations), wherein each image artifact corresponds to an inpainted region of the inpainted image (Ranade, the occluded areas indicates the areas to be inpainted according to paragraphs [0050]-[0051]); determining that the number of image artifacts exceeds a threshold number (as discussed previously, as there is additional pixel locations, that the number of them exceeds one, then perform the window averaging filter); and applying the shallow depth of field to the image based on the number of image artifacts exceeding the threshold number (the window averaging filter and the shallow depth of field being performed as a process to obtain the final image according to par. [0009]).
Thus, it would have been obvious for a person of ordinary skill in the art before the effective filing date to modify Muddala to perform inpainting image based on background and foreground determination.
Wherein Muddala’s inpainting method can be modified to be applying a shallow depth of field to the inpainted image based on the one or more inpainting regions in the mask, wherein applying the shallow depth of field to the inpainted image comprises: determining a number of image artifacts in the inpainted image, wherein each image artifact corresponds to an inpainted region of the inpainted image; determining that the number of image artifacts exceeds a threshold number; and applying the shallow depth of field to the image based on the number of image artifacts exceeding the threshold number as taught by Ranade to arrive at the claimed invention discussed above. Such a modification is the result of combing prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to obtain image through inpainting more efficiently with finalizing the image with missing information (paragraphs [0049]-[0051], Ranade).
Regarding claim 19, Muddala in view of Ranade discloses the method of claim 17, wherein applying the shallow depth of field to the inpainted image comprises: detecting one or more image artifacts corresponding to one or more inpainted regions of the inpainted regions (Muddala, page 7, last par., discloses the background and the foreground regions have different depth estimates; the depth of the foreground is always less than the depth of the background as disclosed in page 6, 1st par. and illustrated in fig. 4; section 2.1.3 discloses the occlusion identification to correctly identify background and foreground and inpainting on the occluded areas [refining as claimed] including the mislabeled background and foreground points with overlapped labelled points in overlapped DDP as previously discussed in page 7, last par.; as the threshold images are created according to page 7, 2nd column, 1st par, done by adjusting the pixel of the occlusion depth by thresholding when it determines that the occlusion belongs to the background, hence, the thresholding is to omit the foreground feature processing, and that the background depth is determined to be background since its larger than the depth of foreground, as discussed previously); comparing, based on the depth representation, a depth of each image artifacts to a foreground depth of the inpainted imagen (as discussed previously, the occlusion mask is determined that the foreground and background depth of each image contains artifacts; as disclosed in Muddala’s page 2, 1st par., and figure 2a); and applying the shallow depth of field to the inpainted image based on determining that the depth of each image artifact is greater than the foreground depth (as the processing to obtain the inpainted image is taught in Ranade to apply the shallow depth of field).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/PHUONG HAU CAI/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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