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 .
Preliminary Amendment
Applicant submitted a preliminary amendment on 3/26/2024. The Examiner acknowledges the amendment and has reviewed the claims accordingly.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 3/26/2024, 7/16/2025, and 10/22/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered and attached 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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-3, 11-17, and 25-29 are rejected under 35 U.S.C. 103 as being unpatentable over Usikov (U.S. Patent Pub. No. 2019/0279371) in view of Ehmann (U.S. Patent Pub. No. 2015/0054974).
Regarding Claim 1, Usikov teaches an apparatus for image segmentation, the apparatus comprising:
memory; and one or more processors coupled to the memory, the one or more processors being configured to (Fig. 2, 202 Image processor and 204 Memory:)
obtain a frame capturing a scene (¶19 The sensor circuitry 104 may comprise suitable logic, circuitry, interfaces, and/or code that may be configured to concurrently capture the depth map and the color image of a same scene;)
generate, based on the frame, a first segmentation map comprising a target segmentation mask identifying a target of interest and one or more background masks identifying one or more background regions of the frame; and (¶18 The image-processing apparatus 102 may be configured to extract the object-of-interest from the color image based on a refined object mask with a refined object boundary; ¶22 The scene may comprise one or more foreground objects, for example, an object-of-interest that is to be segmented; ¶23 The RGB image 110a includes a first foreground object 118a, a second foreground object 120a, and a background 116a represented in a RGB color channel.)
Usikov does not explicitly disclose generate a second segmentation map comprising the first segmentation map with the one or more background masks filtered out, the one or more background masks being filtered from the first segmentation map based on a depth map associated with the frame.
Ehmann is in the same field of art of image analysis. Further, Ehmann teaches generate a second segmentation map comprising the first segmentation map with the one or more background masks filtered out, the one or more background masks being filtered from the first segmentation map based on a depth map associated with the frame (¶58 At 590, the method includes generating a refined segmentation map by refining, on a pixel-by-pixel basis, the initial segmentation map based on the history of segmentation information; ¶37 the auxiliary information has made the automatic detection and segmentation of humans viable in real-time, where the background is no longer the main limiting factor. In systems utilizing depth sensors, virtually all of the requirements imposed on the background by the RGB-based segmentation algorithms are removed, and the segmentation can be carried out more reliably and more quickly, even under conditions that would be normally adverse for RGB segmentation, such as dynamic backgrounds, rapid lighting changes, low color contrast etc. For example, this is accomplished by performing a fast object-of-interest detection and segmentation on the depth data, and then refining its results in the RGB domain.)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Usikov by generating a second segmentation map with a background removed that is taught by Ehmann; thus, one of ordinary skilled in the art would be motivated to combine the references to reduce segmentation errors and unwanted artifacts (Ehmann ¶5).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 2, Usikov in view of Ehmann discloses the apparatus of claim 1, wherein, to generate the second segmentation map, the one or more processors are configured to:
based on a comparison of the first segmentation map with the depth map, determine a threshold difference between respective depth values associated with the one or more background masks and respective depth values associated with the target segmentation mask identifying the target of interest (Usikov, ¶24 The image-processing apparatus 102 may be configured to exclude a plurality of depth values greater than a threshold depth value by the depth thresholding operation. For example, all pixels located less than a certain meter in depth (such as 1.5 depth value) from the sensor circuitry 104 may be considered as belonging to the foreground object(s) and accordingly object mask(s) may be generated. The threshold depth value corresponds to a maximum depth value associated with pixels of the first object mask of the object-of-interest, such as the first foreground object 118a.)
Regarding Claim 3, Usikov in view of Ehmann discloses the apparatus of claim 2, wherein, to generate the second segmentation map, the one or more processors are configured to:
based on the threshold difference between respective depth values associated with the one or more background masks and respective depth values associated with the target segmentation mask identifying the target of interest, remove the one or more background masks from the first segmentation map (Usikov, ¶24 The image-processing apparatus 102 may be configured to exclude a plurality of depth values greater than a threshold depth value by the depth thresholding operation. For example, all pixels located less than a certain meter in depth (such as 1.5 depth value) from the sensor circuitry 104 may be considered as belonging to the foreground object(s) and accordingly object mask(s) may be generated. The threshold depth value corresponds to a maximum depth value associated with pixels of the first object mask of the object-of-interest, such as the first foreground object 118a.)
Regarding Claim 11, Usikov in view of Ehmann discloses the apparatus of claim 1, wherein the one or more processors are configured to: generate, based on the frame and the second segmentation map, a modified frame (Ehmann, ¶59 At 595, the method includes performing RGB segmentation on a corresponding reference frame of the RGB image video based on the refined segmentation map. For example, the temporally coherent segmentation unit 530 is configured to generate a final segmentation map by performing RGB segmentation based on the refined segmentation map generated from secondary, auxiliary information. A more detailed description of the processes employed by the segmentation unit 530 is provided in relation to FIGS. 12 and 13.)
The reasons for combining Usikov and Ehmann are similar to that stated in the rejection of claim 1. In addition, this same reasoning is pertinent and applicable to the rejections of claim 12 below.
Regarding Claim 12, Usikov in view of Ehmann discloses the apparatus of claim 11, wherein the modified frame includes at least one of a visual effect, an extended reality effect, an image processing effect, a blurring effect, an image recognition effect, an object detection effect, a computer graphics effect, a chroma keying effect, and an image stylization effect (Ehmann, ¶59 At 595, the method includes performing RGB segmentation on a corresponding reference frame of the RGB image video based on the refined segmentation map. For example, the temporally coherent segmentation unit 530 is configured to generate a final segmentation map by performing RGB segmentation based on the refined segmentation map generated from secondary, auxiliary information. A more detailed description of the processes employed by the segmentation unit 530 is provided in relation to FIGS. 12 and 13; object detection is performed.)
Regarding Claim 13, Usikov in view of Ehmann discloses the apparatus of claim 1, further comprising an image capture device, wherein the frame is generated by the image capture device (Usikov, ¶18 Examples of the image-processing apparatus 102 may include, but are not limited to, a digital camera, a camcorder, a head-mounted device (HMD), a surveillance equipment, a smartphone, a smart-glass, a virtual reality-, mixed reality-, or an augmented reality-based device, a computing device, and/or other consumer electronic (CE) devices.)
Regarding Claim 14, Usikov in view of Ehmann discloses the apparatus of claim 1, wherein the apparatus comprises a mobile device (Usikov, ¶18 Examples of the image-processing apparatus 102 may include, but are not limited to, a digital camera, a camcorder, a head-mounted device (HMD), a surveillance equipment, a smartphone, a smart-glass, a virtual reality-, mixed reality-, or an augmented reality-based device, a computing device, and/or other consumer electronic (CE) devices.)
Regarding claim 15, claim 15 has been analyzed with regard to claim 1 and is rejected for the same reasons of obviousness as used above.
Claim 16 recites limitations similar to claim 2 and is rejected under the same rationale and reasoning.
Claim 17 recites limitations similar to claim 3 and is rejected under the same rationale and reasoning.
Claim 25 recites limitations similar to claim 11 and is rejected under the same rationale and reasoning.
Claim 26 recites limitations similar to claim 12 and is rejected under the same rationale and reasoning.
Regarding claim 27, claim 27 has been analyzed with regard to claim 1 and is rejected for the same reasons of obviousness as used above as well as in accordance with Usikov further teaching on: A non-transitory computer-readable medium having stored thereon instructions that executed by one or more processors (Usikov, ¶73 a non-transitory computer readable medium and/or storage medium, where there is stored therein, a set of instructions executable by a machine and/or a computer)
Claim 28 recites limitations similar to claim 2 and is rejected under the same rationale and reasoning.
Claim 29 recites limitations similar to claim 3 and is rejected under the same rationale and reasoning.
Claims 8-10 and 22-24 are rejected under 35 U.S.C. 103 as being unpatentable over Usikov (U.S. Patent Pub. No. 2019/0279371) in view of Ehmann (U.S. Patent Pub. No. 2015/0054974) in view of Guo (U.S. Patent Pub. No. 2022/0057806).
Regarding Claim 8, Usikov in view of Ehmann teaches the apparatus of claim 1.
Usikov in view of Ehmann does not explicitly disclose wherein the frame comprises a monocular frame generated by a monocular image capture device.
Guo is in the same field of art of image analysis. Further, Guo teaches wherein the frame comprises a monocular frame generated by a monocular image capture device (Guo, ¶17 the detection system may implement a neural network model with minimal training that processes red, green, and blue (RGB) values from a monocular camera sensor to generate a depth map and a segmentation map in parallel using a network and data processing flow.)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Usikov in view of Ehmann by using a monocular camera for a neural network to produce a segmentation and depth map that is taught by Guo; thus, one of ordinary skilled in the art would be motivated to combine the references to effectively detect unknown and complex objects (Guo ¶4).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 9, Usikov in view of Ehmann in view of Guo discloses the apparatus of claim 1, wherein the first segmentation map and the second segmentation map are generated using one or more neural networks (Guo, ¶17 the detection system may implement a neural network model with minimal training that processes red, green, and blue (RGB) values from a monocular camera sensor to generate a depth map and a segmentation map in parallel using a network and data processing flow.)
The reasons for combining Usikov, Ehmann, and Guo are similar to that stated in the rejection of claim 8. In addition, this same reasoning is pertinent and applicable to the rejections of claim 10 below.
Regarding Claim 10, Usikov in view of Ehmann in view of Guo discloses the apparatus of claim 1, wherein the one or more processors are configured to generate the depth map using a neural network (Guo, ¶17 the detection system may implement a neural network model with minimal training that processes red, green, and blue (RGB) values from a monocular camera sensor to generate a depth map and a segmentation map in parallel using a network and data processing flow.)
Claim 22 recites limitations similar to claim 8 and is rejected under the same rationale and reasoning.
Claim 23 recites limitations similar to claim 9 and is rejected under the same rationale and reasoning.
Claim 24 recites limitations similar to claim 10 and is rejected under the same rationale and reasoning.
Allowable Subject Matter
Claims 4-7 and 18-21 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.
Regarding claims 4 and 18, no prior art teaches wherein the depth map comprises a set of depth
masks associated with depth values corresponding to pixels of the frame, and wherein, to generate the second segmentation map, the one or more processors are configured to:
based on a comparison of the first segmentation map with the depth map, determine an overlap between the target segmentation mask identifying the target of interest and one or more depth masks from the set of depth masks in the depth map;
based on the overlap, keep the target segmentation mask identifying the target of interest; and
based on an additional overlap between the one or more background masks and one or more additional depth masks from the set of depth masks, filter the one or more background masks from the first segmentation map.
Regarding claims 6 and 20, no prior art teaches wherein, to generate the second segmentation map, the one or more processors are configured to:
determine intersection-over-union (IOU) scores associated with depth regions from the depth map and predicted masks from the first segmentation map;
based on the IOU scores, match the depth regions from the depth map with the predicted masks from the first segmentation map, the predicted masks comprising the target segmentation mask identifying the target of interest and the one or more background masks identifying the one or more background regions of the frame; and
filter the one or more background masks from the first segmentation map based on a determination that one or more IOU scores associated with the one or more background masks are below a threshold.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUSTIN BILODEAU whose telephone number is (571)272-1032. The examiner can normally be reached 9am-5pm.
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/DUSTIN BILODEAU/Examiner, Art Unit 2664
/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664
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