DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/08/2025, 07/22/2025 and 09/19/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Cornell et al. US PG-Pub(US 20210118274 A1) in view of Ikenoue US PG-Pub(US 20120114173 A1).
Regarding Claim 1, Cornell teaches an image processing method, comprising: acquiring a first image and a second image of a target area(¶[0030] discloses using a camera to capture a first and second image of a cargo container), wherein a plurality of objects placed in a piled form exist in the target area(Fig 2A shows a plurality of cargo pallets all piled up in the cargo container), and the first image and the second image correspond to different image acquisition moments(Fig. 4 step 404 discloses receiving a first cargo image at a first time and step 410 discloses receiving a second cargo image at a second time point.) ; determining first information and second information according to the first image and the second image (¶[0030], “ In some embodiments, for example, the server 210 and/or the user device 202 may identify a first box label 232a-1 of the first box 230a-1 (e.g., based on input received from the camera 216a and/or the sensor 216b) and execute an Optical Character Recognition (OCR) algorithm to identify alphanumeric data depicted on the first box label 232a-1.”, discloses using OCR to determine alphanumeric data pertaining to the cargo containers.), wherein the first information represents a change status in external contours of the plurality of objects between the first image and the second image(¶[0029], “According to some embodiments, any data descriptive of any of the objects 230a-1, 230a-2, 230c-1, 230c-2, 230a-c, 232a-1, 232a-2, 232c, 234, 236, 238, such as recognized indicia thereof, location, size, shape, quantity, color, etc., may be stored in a database or memory device 240. The server 210 (and/or the user device 202) may comprise or be in communication with the memory device 240”, ¶[0029] discloses the shape and size of the objects are compared and stored in memory.), and the second information represents a change status in internal textures of the plurality of objects between the first image and the second image(¶[0031], “According to some embodiments, the comparison of the attributes may be utilized to lookup, calculate, and/or otherwise compute quantitative metrics regarding the cargo 230a-c. Based on the magnitude of the differences in dimensions/measurements, textures, heat signatures, colors, and/or locations, for example, the AI code 242 may (i) identify an instance of a missing pallet 260a”, as disclosed in this section textures of the object are compared to determine any change);
Cornell does not explicitly teach determining whether any of the plurality of objects is moved according to the first information and the second information.
Ikenoue teaches determining whether any of the plurality of objects is moved according to the first information and the second information. ([0057] “FIG. 4 shows a detailed configuration of the tracking apparatus 14 according to the present embodiment. The tracking apparatus 14 includes an image acquiring unit 20 for acquiring input image data input from the image pickup apparatus 12, an image storage unit 24 for storing the input image data or contour image data, a contour image generator 22 for generating a contour image from input image data, a tracking start/end determining unit 28 for determining the start and end of tracking, a tracking process unit 26 for performing a tracking process using a particle filter, a result storage unit 36 for storing final tracking result data”, as disclosed in this section of the prior art image data is processed and tracking data is determined to see if the object has moved from the original position.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Cornell with Ikenoue in order to determine if any object has moved from the data acquired. One skilled in the art would have been motivated to modify Cornell in this manner in order for tracking a target object in an input image. (Ikenoue, ¶[0001])
Regarding Claim 17, claim 17 is considered an apparatus claim substantially corresponding to claim 1. Please see the discussion of claim 1 above for a discussion of similar limitations. Furthermore, Cornell teaches an electronic device(See, ¶[0042]), comprising: a processor(See, ¶0066]) and a memory for storing a computer program executable on the processor(See, ¶0066]), wherein the processor is configured to execute steps of an image processing method when running the computer program(See, ¶0066]);
Regarding Claim 18, Claim 18 is considered an storage medium claim substantially corresponding to claim 1. Please see the discussion of claim 1 above for a discussion of similar limitations. Furthermore, Cornell teachesA non-transitory storage medium(See, ¶0080]), storing a computer program, wherein the computer program(See, ¶[0080]), when being executed by a processor, implements steps of an image processing method(See, ¶0066]);
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Cornell et al. US PG-Pub(US 20210118274 A1) in view of Ikenoue US PG-Pub(US 20120114173 A1) in view of Langer et al. ("Robust and Efficient Object Change Detection by Combining Global Semantic Information and Local Geometric Verification", as cited by the applicant in the IDS filed on 10/08/2025).
Regarding Claim 2, while the combination of Cornell and Ikenoue teach the image processing method according to claim 1, they do not explicitly teach wherein determining the first information according to the first image and the second image comprises: binarizing the first image by using a first model to obtain a third image and binarizing the second image by using the first model to obtain a fourth image, wherein the first model is trained by using a semantic segmentation algorithm, pixel values of pixels corresponding to the plurality of objects in the third image and the fourth image are first values, and pixel values of pixels other than the pixels corresponding to the plurality of objects are second values; and determining the first information by comparing the fourth image with the third image.
Langer teaches wherein determining the first information according to the first image and the second image comprises: binarizing the first image by using a first model to obtain a third image and binarizing the second image by using the first model to obtain a fourth image (Page 8455, Right Col, Last Paragraph, “Our approach leverages the idea of change detection but applies the operation locally. Since object candidates are already generated from the full scene using semantic information, it is no longer necessary to perform global change detection. It is sufficient to apply the operation in local regions around the initial candidates. This is a two stage process. First, for each detected object, we extract the supporting plane in its surrounding. It is aligned to the nearest horizontal plane in the reference map (plane plane alignment) by applying the Iterative Closest Point (ICP) algorithm”, as disclosed in this section of the prior art objects are extracted and binarized as seen in figure 2.);
wherein the first model is trained by using a semantic segmentation algorithm, pixel values of pixels corresponding to the plurality of objects in the third image and the fourth image are first values, and pixel values of pixels other than the pixels corresponding to the plurality of objects are second values and determining the first information by comparing the fourth image with the third image (Page 8454, Right Col, Last paragraph, “A. Object Detection from Global Semantic Context We consider 3D reconstructions of entire rooms to be independent of single camera perspectives and robot trajecto ries. From the global reconstruction, semantic information is exploited to discover new objects. Semantic segmentation has received most attention in the computer vision community for pixel-wise classification of images and the rise of deep learning, in particular CNNs, has drastically improved re sults [18], [19]. The introduction of the ScanNet dataset [20] has enabled the transition to apply semantic segmentation to dense 3D reconstructions of indoor scenes. In this work, semantic segmentation generates class labels for all vertices in the 3D reconstruction.”, discloses performing semantic segmentation to determine a class label for the object in the image.);
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Cornell and Ikenoue with Langer in order to perform semantic segmentation on the images. One skilled in the art would have been motivated to modify Cornell and Ikenoue in this manner in order for efficient detection of novel objects in 3D reconstructions of indoor environments. (Langer, Abstract)
Allowable Subject Matter
Claims 3-15 and 19-21 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 following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 3, the primary reason for indication of allowable subject matter is that the prior art fails to teach or reasonably suggest “determining the first information by comparing the fourth image with the third image comprises: determining a plurality of first coefficients according to the third image and the fourth image, wherein each first coefficient represents whether a pixel value of one pixel point in the third image is identical to a pixel value of a corresponding one pixel point in the fourth image; and determining the first information by using the plurality of first coefficients.” It is noted that the examiner has not found any other prior art to anticipate or obviate the quoted claim limitations
Claims 4-5 are allowable by virtue of dependency on an objected claim.
Regarding Claim 6, the primary reason for indication of allowable subject matter is that the prior art fails to teach or reasonably suggest “wherein determining the second information according to the first image and the second image comprises: binarizing the first image by using a second model to obtain a fifth image; and binarizing the second image by using the second model to obtain a sixth image, wherein the second model is trained by using an edge detection algorithm, pixel values of pixel points corresponding to edges in the fifth image and the sixth image are first values, and pixel values of pixel points corresponding to non-edges are second values; and determining the second information by using at least the fifth image and the sixth image.” It is noted that the examiner has not found any other prior art to anticipate or obviate the quoted claim limitations.
Claims 7-11 and 21 are allowable by virtue of dependency.
Regarding Claim 12, the primary reason for indication of allowable subject matter is that the prior art fails to teach or reasonably suggest “wherein determining whether any of the plurality of objects is moved according to the first information and the second information comprises: in a case where the first information representing that a change occurs in the external contours of the plurality of objects between the first image and the second image, and/or where the second information representing that a change occurs in the internal textures of the plurality of objects between the first image and the second image, determining that at least one of the plurality of objects is moved; or, in a case where the first information represents that no change occurs in the external contours of the plurality of objects between the first image and the second image, and the second information represents that no change occurs in the internal textures of the plurality of objects between the first image and the second image, determining that none of the plurality of objects is moved.” It is noted that the examiner has not found any other prior art to anticipate or obviate the quoted claim limitations.
Regarding Claim 13, the claim is allowable by virtue of dependency.
Regarding Claim 14, the primary reason for indication of allowable subject matter is that the prior art fails to teach or reasonably suggest “acquiring a first image and a second image of a target area comprises: acquiring a ninth image and a tenth image of a first area, wherein the first area at least comprises the target area, and the ninth image and the tenth image correspond to different image acquisition moments; determining at least one second area in the first area according to the ninth image and the tenth image, wherein a plurality of objects placed in a piled form exist in the second area; and determining a target area from the at least one second area, and cropping the ninth image and the tenth image based on the target area to obtain the first image and the second image.” It is noted that the examiner has not found any other prior art to anticipate or obviate the quoted claim limitations.
Regarding Claim 15, the claim is allowable by virtue of dependency.
Regarding Claim 19, the primary reason for indication of allowable subject matter is that the prior art fails to teach or reasonably suggest “wherein determining whether any of the plurality of objects is moved according to the first information and the second information comprises: in a case where the first information representing that a change occurs in the external contours of the plurality of objects between the first image and the second image, and/or where the second information representing that a change occurs in the internal textures of the plurality of objects between the first image and the second image, determining that at least one of the plurality of objects is moved; or, in a case where the first information represents that no change occurs in the external contours of the plurality of objects between the first image and the second image, and the second information represents that no change occurs in the internal textures of the plurality of objects between the first image and the second image, determining that none of the plurality of objects is moved.” It is noted that the examiner has not found any other prior art to anticipate or obviate the quoted claim limitations.
Regarding Claim 20, the primary reason for indication of allowable subject matter is that the prior art fails to teach or reasonably suggest “acquiring a first image and a second image of a target area comprises: acquiring a ninth image and a tenth image of a first area, wherein the first area at least comprises the target area, and the ninth image and the tenth image correspond to different image acquisition moments; determining at least one second area in the first area according to the ninth image and the tenth image, wherein a plurality of objects placed in a piled form exist in the second area; and determining a target area from the at least one second area, and cropping the ninth image and the tenth image based on the target area to obtain the first image and the second image. ” It is noted that the examiner has not found any other prior art to anticipate or obviate the quoted claim limitations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kashima et al. US PG-Pub(US 20160343143 A1) discloses using an edge detection algorithm with pixel data to determine non-edges and edges in ¶[0015]-¶[0017].
Chen et al. US PG-Pub(US 20210049397 A1) discloses performing semantic segmentation to determine a probability map of a target object in ¶[0008].
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAN D HOANG whose telephone number is (571)272-4344. The examiner can normally be reached Monday-Friday 8-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JOHN M VILLECCO can be reached at 571-272-7319. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HAN HOANG/Primary Examiner, Art Unit 2661
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