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 Arguments
Applicants arguments/amendments filed on 5/11/2026 have been entered and made of record.
The examiner notes that applicants that applicant’s statement of substance of the interview does not fully reflect the examiner recollection. See Examiner Interview Summary mailed 4/22/206. As stated in the summary: “The examiner noted that the third option may overcome 35 U.S.C. 101 since claim 7 was not rejected, however further consideration may be needed in light of the fact the intervening claims were not incorporated. The examiner believes claim 7 by itself is unlikely to be allowable based on 35 U.S.C. 102/ 103. The examiner suggests amending claims 4 and 7 into the independent claims however such an amendment would require further search and consideration.”
Applicant’s arguments, see page 10 , filed 5/11/2026, with respect to 35 U.S.C. 101 have been fully considered and are persuasive. The rejection of claims 1-6, 11-15 and 20 has been withdrawn in light of the amendments.
Applicant's arguments filed 5/11/2026 with respect to 35 U.S.C. 102 and 103 have been fully considered but they are not persuasive.
Applicant argues “However, Qi fails to disclose or suggest the features of determining, "for each pixel
image in the plurality of pixel images, a probability value of a probability that a respective pixel image corresponds to the object based on the input image," and obtaining "a first classification map by classifying each pixel image in the plurality of pixel images of the obtained input image as one of an object region corresponding to the object and a background region corresponding to the background of the object based on an arrangement of the plurality of pixel images and a result of comparing a preset first reference probability value with the determined probability value for each pixel image," as recited in claim 1.
Thus, for at least these reasons, claim 1, and claims depending therefrom, are patentably distinguishable over Qi.”
The examiner notes that applicant merely states that newly amended features and states that Qi does not disclose these newly amended features. Applicant does little to explain why these new features are not taught The examiner partly disagrees for the reasons articulated in the rejection below. Qi discloses some of the recited features in part while a new ground of rejection is used to reject the features in their entirety. As such applicants arguments fail to address the new grounds of rejection below.
The remaining arguments with respect to 35 U.S.C. 102 and 103 rely on the same reasoning
Claim Objections
Claim 11 objected to because of the following informalities: “the determined probability value for each pixel image” should read a determined probability value for each pixel image. Appropriate correction is required.
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.
Claim(s) 1-3, 11 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over QI et al US 2024/0394893 in view of Gu US 20140056472 A1.
Re claim 1 Qi discloses An electronic apparatus comprising: a memory storing at least one instruction; and at least one processor configured to execute the at least one instruction to ( see paragraph 7 “An example apparatus can include memory and one or more processors coupled to the memory, the one or more processors being configured to:” note that the invention may be implemented by a processor and memory):
obtain an input image by capturing an object and a background of the object through a camera (see paragraph 40 note that images are obtained of a camera see paragraph 49 note that the images can have a foreground and a background) the input image comprising a plurality of pixel images; ( see paragraph 46 note that the image is composed of pixels also see paragraph 59 note that applicant uses the term “pixel image” in a way that corresponds to pixels of the image [(see paragraph 68 of the specification])
determine, for each pixel image in the plurality of pixel images, a value representative of whether that a respective pixel image corresponds to the object based on the input image (see paragraph 59 and 60 “Moreover, the adaptive Gaussian thresholding can be used to select frame regions having depth values that differ from the depth values of surrounding/background pixels by a threshold amount. For example, in some cases, the adaptive Gaussian thresholding can identify one or more depth values of a target of interest in the depth map 404, and set a depth threshold or range used to subtract regions/pixels/objects in the depth map 404 that do not correspond to and/or are connected to the target of interest in the depth map 404” note that a value is determined for each pixel and compared to a threshold to determine if the pixel is a object pixel or a background pixel )
obtain a first classification map by classifying each pixel image in the plurality of pixel images of the obtained input image as one of an object region corresponding to the object and a background region corresponding to the background of the object (see paragraph 60-62 note that each pixel may be classified as foreground or background forming a mask) based on an arrangement of the plurality of pixel images and a result of comparing a preset first reference value with the determined value for each pixel image; (see paragraph 59 and 60 “Moreover, the adaptive Gaussian thresholding can be used to select frame regions having depth values that differ from the depth values of surrounding/background pixels by a threshold amount. For example, in some cases, the adaptive Gaussian thresholding can identify one or more depth values of a target of interest in the depth map 404, and set a depth threshold or range used to subtract regions/pixels/objects in the depth map 404 that do not correspond to and/or are connected to the target of interest in the depth map 404” note that a value is determined for each pixel and compared to a threshold to determine if the pixel is a object pixel or a background pixel )
pre-process the first classification map to obtain a second classification map in which a noise region in the first classification map is removed (see paragraph 63 note that a foreground mask can have noise removed by performing dilation and erosion);
and obtain an object image corresponding to the object, based on the first classification map and the second classification map, by using the noise region in the first classification map and information about a distance between the camera and the object (see paragraph 65-67 note that the matching between the segmentation map and the depth map is used to generate final segmentation output see paragraph 74 and 75 note that the segmented frame with depth filtering has a segmented target of interest 612 without background elements 610 see paragraph 61-63 note that the noise filtered version of the foreground mask of the depth map [second classification map] is used in the generation of the final segmentation , this is is based on then noise region and the foreground mask prior to noise removal meaning that the final segmentation map will also be based on these features).
While Qi uses a thresholding operation to determine whether pixel are foreground or background Qi does not expressly disclose determining a probability value for each pixel or a reference probability value Gu disclose determining a probability value for each pixel and comparing a preset first reference probability value with the determined probability value for each pixel image (see paragraph 63 and 64 note that probabilities are calculated for each pixel to determined background and foreground and compared to a threshold probability). The motivation to combine is “Moreover, embodiments detailed herein may permit for a more accurate identification of foreground objects to be performed” See paragraph 37. One of ordinary skill in the art could have easily used the teachings of Gu to modify Qi to include foreground/background probabilities. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Qi and Gu to reach the aforementioned advantage.
Re claim 2 Qi discloses obtain a final classification map, based on the first classification map and the second classification map, by using the noise region in the first classification map and the information about the distance between the camera and the object; (see paragraph 65-67 note that the matching between the segmentation map and the depth map is used to generate final segmentation output including a segmentation map without shapes below a threshold see paragraph 74 and 75 note that the segmented frame with depth filtering has a segmented target of interest 612 without background elements 610 see paragraph 61 note that the noise filtered version of the foreground object is used in the generation of the final segmentation therefore it is based on then noise region and the foreground mask prior to erosion ). and obtain the object image by applying the final classification map to the input image see figure 6 and paragraphs 74 and 75 note that a segmented frame with segmented object 612 is generated)
Re claim 3 QI discloses wherein the second classification map is a classification map obtained by performing a morphology process on the first classification map. (see paragraph 63 note that a noise removed foreground mask is generated by applying morphological operations like dilation and erosion).
Re claim 11 Qi discloses an operating method of an electronic apparatus, the operating method comprising
obtain an input image by capturing an object and a background of the object through a camera (see paragraph 40 note that images are obtained of a camera see paragraph 49 note that the images can have a foreground and a background) the input image comprising a plurality of pixel images; ( see paragraph 46 note that the image is composed of pixels also see paragraph 59 note that applicant uses the term “pixel image” in a way that corresponds to pixels of the image [(see paragraph 68 of the specification])
obtain a first classification map by classifying each pixel image in the plurality of pixel images of the obtained input image as one of an object region corresponding to the object and a background region corresponding to the background of the object (see paragraph 60-62 note that each pixel may be classified as foreground or background forming a mask) based on an arrangement of the plurality of pixel images and a result of comparing a preset first reference value with the determined value for each pixel image; (see paragraph 59 and 60 “Moreover, the adaptive Gaussian thresholding can be used to select frame regions having depth values that differ from the depth values of surrounding/background pixels by a threshold amount. For example, in some cases, the adaptive Gaussian thresholding can identify one or more depth values of a target of interest in the depth map 404, and set a depth threshold or range used to subtract regions/pixels/objects in the depth map 404 that do not correspond to and/or are connected to the target of interest in the depth map 404” note that a value is determined for each pixel and compared to a threshold to determine if the pixel is a object pixel or a background pixel )
pre-process the first classification map to obtain a second classification map in which a noise region in the first classification map is removed (see paragraph 63 note that a foreground mask can have noise removed by performing dilation and erosion);
and obtain an object image corresponding to the object, based on the first classification map and the second classification map, by using the noise region in the first classification map and information about a distance between the camera and the object (see paragraph 65-67 note that the matching between the segmentation map and the depth map is used to generate final segmentation output see paragraph 74 and 75 note that the segmented frame with depth filtering has a segmented target of interest 612 without background elements 610 see paragraph 61-63 note that the noise filtered version of the foreground mask of the depth map [second classification map] is used in the generation of the final segmentation , this is is based on then noise region and the foreground mask prior to noise removal meaning that the final segmentation map will also be based on these features).
While Qi uses a thresholding operation to determine whether pixel are foreground or background Qi does not expressly disclose determining a probability value for each pixel or a reference probability value Gu disclose determining a probability value for each pixel and comparing a preset first reference probability value with the determined probability value for each pixel image (see paragraph 63 and 64 note that probabilities are calculated for each pixel to determined background and foreground and compared to a threshold probability). The motivation to combine is “Moreover, embodiments detailed herein may permit for a more accurate identification of foreground objects to be performed” See paragraph 37. One of ordinary skill in the art could have easily used the teachings of Gu to modify Qi to include foreground/background probabilities. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Qi and Gu to reach the aforementioned advantage.
Re claim 20 Qi discloses A non-transitory computer-readable recording medium having instructions stored therein, which when executed by a processor in an electronic apparatus cause the processor to perform (see paragraph 6 “a non-transitory computer-readable medium is provided for segmentation with monocular depth estimation. An example non-transitory computer-readable medium can include instructions that, when executed by one or more processors, cause the one or more processors to”) the operating method of claim 11 (see rejection of claim 11).
Claim(s) 2 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over QI et al US 2024/0394893 in view Gu US 20140056472 A1 in further view of Zhang et al 20240004924 A1.
Re claim 2 Qi discloses obtain a final classification map, based on the first classification map and the second classification map, by using the noise region in the first classification map and the information about the distance between the camera and the object; (see paragraph 65-67 note that the matching between the segmentation map and the depth map is used to generate final segmentation output including a segmentation map without shapes below a threshold see paragraph 74 and 75 note that the segmented frame with depth filtering has a segmented target of interest 612 without background elements 610 see paragraph 61 note that the noise filtered version of the foreground object is used in the generation of the final segmentation therefore it is based on then noise region and the foreground mask prior to erosion )
Qi and Gu do not expressly disclose and obtain the object image by applying the final classification map to the input image. Zhang discloses obtain the object image by applying the final classification map to the input image (see paragraph 136 extracts the foreground object from the reference image 824 utilizing a segmentation mask generated via a segmentation operation and combines the foreground object with the input digital image 822 utilizing a compositing operation. Note that a segmentation mask may be applied to the reference image to extract the foreground object). The motivation to combine is to generates a composite image 826 by combining one of the foreground objects from the reference image 824 with the background of the input digital image 822 (e.g., by inserting the foreground object into the input digital image 822) (See paragraph 136). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Qi GU and Zhang to reach the aforementioned advantage.
Re claim 12 Qi discloses obtaining a final classification map, based on the first classification map and the second classification map, by using the noise region in the first classification map and the information about the distance between the camera and the object; (see paragraph 65-67 note that the matching between the segmentation map and the depth mask is used to generate final segmentation output including a segmentation map without shapes below a threshold see paragraph 74 and 75 note that the segmented frame with depth filtering has a segmented target of interest 612 without background elements 610 see paragraph 61 note that the noise filtered version of the foreground object is used in the generation of the final segmentation therefore it is based on then noise region and the foreground mask prior to erosion ).
Qi and Gu do not expressly disclose and wherein the obtaining of the object image comprises obtaining the object image by applying the final classification map to the input image. Zhang discloses wherein the obtaining of the object image comprises obtaining the object image by applying the final classification map to the input image (see paragraph 136 extracts the foreground object from the reference image 824 utilizing a segmentation mask generated via a segmentation operation and combines the foreground object with the input digital image 822 utilizing a compositing operation. Note that a segmentation mask may be applied to the reference image to extract the foreground object). The motivation to combine is to generates a composite image 826 by combining one of the foreground objects from the reference image 824 with the background of the input digital image 822 (e.g., by inserting the foreground object into the input digital image 822) (See paragraph 136). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Qi Gu and Zhang to reach the aforementioned advantage.
Allowable Subject Matter
Claim 4-6,8-10 and 13-5 and 17-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEAN T MOTSINGER whose telephone number is (571)270-1237. The examiner can normally be reached 9AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chineyere Wills-Burns can be reached at (571) 272-9752. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SEAN T MOTSINGER/Primary Examiner, Art Unit 2673
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