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 .
DETAILED ACTION
The United States Patent & Trademark Office appreciates the application that is submitted by the inventor/assignee. The United States Patent & Trademark Office reviewed the following application and has made the following comments below.
Priority
Applicant’s claim for provisional application is recognized: 12/20/2023
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(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained through the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived 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(a) 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.
Claim(s) 1-4, 6, 9-12, 14, 16-19 are rejected under 35 U.S.C 103 as being anticipated by Bauer et al (U.S. Patent Pub. No 2024/0346690 A1, hereinafter referred to as Bauer) in view of Yoshimochi et al (U.S. Patent Pub. No 2025/0104379 A1, hereinafter referred to as Yoshimochi).
In regards to Claim 1, Bauer teaches a computer-implemented method for reducing latency in capturing three-dimensional (3D) images, the method comprising: configuring, by a computer (paragraph 69, Bauer teaches a computer), a camera to capture a two-dimensional (2D) image of a scene (paragraph 53, paragraph 87 Figure 6, Bauer teaches configuring a digital camera to obtain 2D images of the scanned area.); performing a machine learning-based object-detection operation on the 2D image (paragraph 35, paragraph 68, paragraph 90, Bauer teaches using a machine learning model to detect an object in the 2D image) to generate a number of bounding boxes (paragraph 88, paragraph 90, Bauer teaches using a object-detection based machine learning model to generate a bounding box), wherein a respective bounding box corresponds to an object in the scene (paragraph 88, paragraph 90, Bauer teaches a bounding box corresponding to the object of interest in the scene.)areas based on the generated bounding boxes (paragraph 90, paragraph 140, paragraph 143, Bauer teaches setting an object of interest in correlation with a generated bounding box.); and
Bauer does not explicitly disclose configuring the camera to operate in region of interest mode and configuring the camera to capture one or more 3D images of the scene while operating in ROI mode.
Yoshimochi is in the same field of art of obtaining an image of a region of interest. Further, Yoshimochi teaches configuring the camera to operate in region of interest (ROI) mode (paragraph 96, paragraph 114, Yoshimochi teaches configuring the camera to receive indication of ROI and generate the image data based on the ROI, examiner interprets this as the camera operating in ROI mode) and Yoshimochi teaches configuring the camera to capture one or more 3D images (paragraph 54, Yoshimochi teaches configuring the camera to capture 3D images) of the scene while operating in ROI mode (paragraph 96, paragraph 114, Yoshimochi teaches configuring the camera to receive indication of ROI and generate the image data based on the ROI, examiner interprets this as the camera operating in ROI mode).
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 Bauer by modifying the camera in Bauer to capture three-dimensional images while operating in ROI mode that is taught by Yoshimochi, to make the invention that uses a machine learning model to identify regions of interest based on bounding boxes in two dimensional images (Bauer) and incorporating a three-dimensional camera to capture three-dimensional images of the regions of interest while operating in ROI mode (Yoshimochi); thus, one of ordinary skill in the art would be motivated to combine the references since processing an ROI of an image at full resolution rather than the entire image can conserve power, bandwidth, and processing time of the system (paragraph 33, Yoshimochi).
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.
In regards to claim 2, Bauer in view of Yoshimochi discloses wherein, further comprising performing a machine learning-based image-segmentation operation on the 2D image (paragraph 129, paragraph 134, Yoshimochi teaches using a machine learning based image segmentation operation on the 2D image) to determine types of objects in the scene (paragraph 134, paragraph 136, Yoshimochi teaches using a machine learning segmentation operation to identify objects in the scene).
In regards to claim 3, Bauer in view of Yoshimochi discloses wherein, setting the one or more ROI areas (paragraph 33, Yoshimochi teaches setting ROI areas) comprises determining whether a bounding box is an ROI area based on an object type corresponding to the bounding box (paragraph 88, paragraph 90, Bauer teaches determining an object of interest based on the associated label and bounding box).
In regards to claim 4, Bauer in view of Yoshimochi discloses wherein, configuring the camera to capture one or more 3D images (paragraph 54, Yoshimochi teaches configuring the camera to capture 3D images) comprises turning on a structured light projector (paragraph 54, Yoshimochi teaches activating a structured light projector) and configuring the camera to capture images of the scene under illumination of the structured light projector (paragraph 54, Yoshimochi teaches configuring the camera to capture images under the illumination of the structured light projector).
In regards to claim 6, Bauer in view of Yoshimochi discloses wherein, setting the one or more ROI areas (paragraph 33, Yoshimochi teaches setting ROI areas) comprises sending to the camera (paragraph 114, Yoshimochi teaches sending ROI indicator to the camera), via a Serial Peripheral Interface (SPI) interface (paragraph 71, Yoshimochi teaches using a Serial Peripheral Interface), position and size of each ROI area (paragraph 105, Yoshimochi teaches a ROI indicator that indicates a position and size of an ROI in the image).
In regards to claim 9, Bauer teaches a computer-vision system, comprising: a camera to capture a two-dimensional (2D) image of a scene (paragraph 53, paragraph 87, Figure 6, Bauer teaches using a digital camera to obtain 2D images of the scanned area.); a camera-control unit (paragraph 55, paragraph 56, Bauer teaches using a controller that controls the camera.); and a machine learning-based object-detection unit (paragraph 88, paragraph 90, Bauer teaches using a machine learning-based object detection model) to perform an object-detection operation on the 2D image to generate a number of bounding boxes (paragraph 90, Bauer teaches using a machine learning-based object detection model to generate a bounding box), wherein a respective bounding box corresponds to an object in the scene (paragraph 90, Bauer teaches generating a bounding box that corresponds to the object of interest.); wherein the camera-control unit (paragraph 55, paragraph 56, Bauer teaches using a controller that controls the camera) is to: (paragraph 90, paragraph 140, paragraph 143, Bauer teaches setting an object of interest in correlation with a generated bounding box.); and
Bauer does not explicitly disclose a camera-control unit to configure the camera to operate in a region of interest (ROI) mode and configure the camera to capture one or more 3D images of the scene while operating in ROI mode.
Yoshimochi is in the same field of art of obtaining an image of a region of interest. Further, Yoshimochi teaches wherein a camera control unit (paragraph 91, Yoshimochi teaches using a camera control interface to control the operations of the camera) is to configure the camera to operate in region of interest mode (paragraph 96, paragraph 114, Yoshimochi teaches configuring the camera to receive indication of ROI and generate the image data based on the ROI) and configure the camera to capture one or more 3D images (paragraph 54, Yoshimochi teaches configuring the camera to capture 3D images) of the scene while operating in ROI mode (paragraph 96, paragraph 114, Yoshimochi teaches configuring the camera to receive indication of ROI and generate the image data based on the ROI, examiner interprets this as the camera operating in ROI mode).
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 Bauer by modifying the camera and camera-control unit in Bauer to capture three-dimensional images while operating in ROI mode that is taught by Yoshimochi, to make the invention that uses a machine learning model to identify regions of interest based on bounding boxes in two dimensional images (Bauer) and incorporating a three-dimensional camera to capture three-dimensional images of the regions of interest (Yoshimochi); thus, one of ordinary skill in the art would be motivated to combine the references since processing the region of interest of an image at full resolution rather than the entire image can conserve power, bandwidth, and processing time of the system (paragraph 33, Yoshimochi).
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.
In regards to claim 10, Bauer in view of Yoshimochi discloses wherein, further comprising a machine learning-based image-segmentation unit (paragraph 129, paragraph 134, Yoshimochi teaches using a machine learning based image segmentation model) to perform an image-segmentation operation on the 2D image (paragraph 134, paragraph 136, Yoshimochi teaches using a machine learning segmentation operation on the two-dimensional image) to determine types of objects in the scene (paragraph 134, paragraph 136, Yoshimochi teaches using a machine learning segmentation operation to identify objects in the scene).
In regards to claim 11, Bauer in view of Yoshimochi discloses wherein, while setting the ROI areas, the camera-control unit (paragraph 55, paragraph 56, Bauer teaches using a controller that controls the camera) is to determine whether a bounding box is an ROI area (paragraph 118, Bauer teaches determining an object of interest from a bounding box) based on an object type corresponding to the bounding box (paragraph 88, paragraph 90, Bauer teaches using the associated label with the corresponding bounding box).
In regards to claim 12, Bauer in view of Yoshimochi discloses wherein, while configuring the camera to capture one or more 3D images (paragraph 54, Yoshimochi teaches configuring the camera to capture 3D images), the camera-control unit (paragraph 91, Yoshimochi teaches a camera control unit that controls the operations of the image sensor and ISP) is to turn on a structured light projector (paragraph 54, Yoshimochi teaches activating a structured light projector) and configure the camera to capture images of the scene under illumination of the structured light projector (paragraph 54, Yoshimochi teaches configuring the camera to capture images under the illumination of the structured light projector).
In regards to claim 14, Bauer in view of Yoshimochi discloses wherein, the camera-control unit (paragraph 91, Yoshimochi teaches a camera control unit that controls the operations of the image sensor and ISP) comprises a Serial Peripheral Interface (SPI) interface (paragraph 71, Yoshimochi teaches a Serial Peripheral Interface.); and wherein, while setting the one or more ROI areas, the camera-control unit (paragraph 91, Yoshimochi teaches a camera control unit that provides the camera with the ROI indicator) is to send the position and size of each ROI area (paragraph 105, Yoshimochi teaches a ROI indicator consisting of the position and size of an ROI) to the camera via the SPI interface (paragraph 71, Yoshimochi teaches using a Serial Peripheral Interface).
In regards to claim 16, Bauer discloses a non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for reducing latency in capturing three-dimensional(paragraph 53, Figure 6, Bauer teaches configuring a digital camera to obtain 2D images of the scanned area.); performing a machine learning-based object-detection operation (paragraph 68, paragraph 83, paragraph 90, Bauer teaches using a machine learning-based object detection model) on the 2D image to generate a number of bounding boxes (paragraph 88, paragraph 90, Bauer teaches generating a bounding box based on objects in the image), wherein a respective bounding box corresponds to an object in the scene (paragraph 88, paragraph 90, Bauer teaches generating a bounding box that corresponds to an object of interest in the scene.); (paragraph 90, paragraph 140, paragraph 143, Bauer teaches setting an object of interest in correlation with a generated bounding box.); and
Bauer does not explicitly disclose configuring the camera to operate in region of interest mode and configuring the camera to capture one or more 3D images of the scene while operating in ROI mode.
Yoshimochi is in the same field of art of obtaining an image of a region of interest. Further, Yoshimochi teaches configuring the camera to operate in region of interest (ROI) mode (paragraph 96, paragraph 114, Yoshimochi teaches configuring the camera to receive indication of ROI and generate the image data based on the ROI, examiner interprets this as the camera operating in ROI mode) and Yoshimochi teaches configuring the camera to capture one or more 3D images (paragraph 54, Yoshimochi teaches configuring the camera to capture 3D images) of the scene while operating in ROI mode. (paragraph 96, paragraph 114, Yoshimochi teaches configuring the camera to receive indication of ROI and generate the image data based on the ROI, examiner interprets this as the camera operating in ROI mode).
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 Bauer by modifying the camera in Bauer to capture three-dimensional images while operating in ROI mode that is taught by Yoshimochi, to make the invention that uses a machine learning model to identify regions of interest based on bounding boxes in two dimensional images (Bauer) and incorporating a three-dimensional camera to capture three-dimensional images of the regions of interest while operating in ROI mode (Yoshimochi); thus, one of ordinary skill in the art would be motivated to combine the references since processing an ROI of an image at full resolution rather than the entire image can conserve power, bandwidth, and processing time of the system (paragraph 33, Yoshimochi).
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.
In regards to claim 17, Bauer in view of Yoshimochi discloses wherein, the method further comprises performing a machine learning-based image-segmentation operation on the 2D image (paragraph 129, paragraph 134, Yoshimochi teaches using a machine learning based image segmentation operation on the 2D image) to determine types of objects in the scene (paragraph 134, paragraph 136, Yoshimochi teaches using a machine learning segmentation operation to identify objects in the scene), and wherein setting the one or more ROI areas comprises determining whether a bounding box is an ROI area based on an object type corresponding to the bounding box (paragraph 88, paragraph 90, Bauer teaches determining an object of interest based on the associated label and bounding box).
In regards to claim 18, Bauer in view of Yoshimochi discloses wherein, configuring the camera to capture one or more 3D images (paragraph 54, Yoshimochi teaches configuring the camera to capture 3D images) comprises turning on a structured light projector (paragraph 54, Yoshimochi teaches activating a structured light projector) and configuring the camera to capture images of the scene under illumination of the structured light projector (paragraph 54, Yoshimochi teaches configuring the camera to capture images under the light source).
In regards to claim 19, Bauer in view of Yoshimochi discloses wherein, setting the one or more ROI areas comprises sending to the camera (paragraph 91, Yoshimochi teaches sending the ROI indicator to the camera), via a Serial Peripheral Interface (SPI) interface (paragraph 71, Yoshimochi teaches a Serial Peripheral Interface), position and size of each ROI area (paragraph 105, Yoshimochi teaches a Roi indicator consisting of the position and size of an ROI).
Claim(s) 5, and 13 are rejected under 35 U.S.C 103 as being anticipated by Bauer et al (U.S. Patent Pub. No 2024/0346690 A1, hereinafter referred to as Bauer) in view of Yoshimochi et al (U.S. Patent Pub. No 2025/0104379 A1, hereinafter referred to as Yoshimochi) further in view of Mahbub et al (U.S. Patent. No 11381743 B1, hereinafter referred to as Mahbub).
In regards to claim 5, Bauer in view of Yoshimochi, teaches the computer-implemented method of claim 1 that performs the machine learning-based object-detection operation to generate a number of bounding boxes.
Bauer in view of Yoshimochi does not explicitly teach wherein performing the machine learning-based object-detection operation comprises applying a You Only Look Once (YOLO) algorithm.
Mahbub is in the same field of the art of capturing an image of a region of interest. Further, Mahbub teaches wherein performing the machine learning-based object-detection operation comprises applying a You Only Look Once (YOLO) algorithm. (Col 39, lines 15-35, Mahbub teaches a deep learning-based detection model that includes the You only look once (YOLO) algorithm).
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 Bauer in view of Yoshimochi by modifying the machine learning-based object-detection operation taught by Bauer in view of Yoshimochi to include a You only look once algorithm taught by Mahbub to make the invention that uses a machine learning-based object-detection operation (Bauer in view of Yoshimochi) and applying a You only look once algorithm (Mahbub); thus, one of ordinary skill in the art would be motivated to combine the references since the reduced number of images captured for a target region of interest can reduce power consumption, latency, and processing bandwidth of the device (Mahbub, Col. 10 lines 21-40).
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.
In regards to claim 13, Bauer in view of Yoshimochi, teaches the computer-implemented method of claim 9 that performs the machine learning-based object-detection operation to generate a number of bounding boxes.
Bauer in view of Yoshimochi does not explicitly teach wherein performing the machine while performing the machine learning-based object-detection operation the machine learning-based object-detection unit is to apply a You Only Look Once (YOLO) algorithm.
Mahbub is in the same field of the art of capturing an image of a region of interest. Further, Mahbub teaches wherein while performing the machine learning-based object-detection operation (Col 39, lines 15-35, Mahbub teaches performing a machine learning-based object-detection operation) the machine learning-based object-detection unit is to apply a You Only Look Once (YOLO) algorithm (Col 39, lines 15-35, Mahbub teaches using deep learning-based detector to apply a You only look once (YOLO) network).
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 Bauer in view of Yoshimochi by modifying the machine learning-based object-detection operation taught by Bauer in view of Yoshimochi to apply a You only look once algorithm taught by Mahbub to make the invention that uses a machine learning-based object-detection operation (Bauer in view of Yoshimochi) and applying a You only look once algorithm (Mahbub); thus, one of ordinary skill in the art would be motivated to combine the references since the reduced number of images captured for a target region of interest can reduce power consumption, latency, and processing bandwidth of the device (Mahbub, Col. 10 lines 21-40).
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.
Claim(s) 7 is rejected under 35 U.S.C 103 as being anticipated by Bauer et al (U.S. Patent Pub. No 2024/0346690 A1, hereinafter referred to as Bauer) in view of Yoshimochi et al (U.S. Patent Pub. No 2025/0104379 A1, hereinafter referred to as Yoshimochi) further in view of Naoki et. Al (U.S. Patent Pub. No 2022/0245828 A1, hereinafter referred to as Naoki).
In regards to claim 7, Bauer in view of Yoshimochi, teaches the computer-implemented method of claim 1 that configures the camera to capture 3D images.
Bauer in view of Yoshimochi does not explicitly teach wherein further comprising configuring the camera to generate an invalid frame before capturing the 3D images.
Naoki is in the same field of the art of capturing images of regions of interest.
Further, Naoki teaches wherein further comprising configuring the camera (paragraph 95, Naoki teaches a camera) to generate an invalid frame before capturing the 3D images (paragraph 128, paragraph 146, Naoki teaches generating an invalid frame before capturing images).
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 Bauer in view of Yoshimochi by modifying the camera to capture 3D images taught by Bauer in view of Yoshimochi to configure the camera to generate an invalid frame before capturing an image taught by Naoki to make the invention that configures a camera to generate an invalid frame (Naoki) before capturing 3D images (Bauer in view of Yoshimochi); thus, one of ordinary skill in the art would be motivated to combine the references to adjust the timing of generating image data (Naoki, 128)
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.
Allowable Subject Matter
Claim 8 is 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.
Claim 15 is 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.
Claim 20 is 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.
Claims 8, 15, 20 are objected to, since no prior art teach while the machine learning object detection is performing a function, the system generates an invalid frame to assist in image processing.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL ANTHONY DAVIS whose telephone number is (571)272-0170. The examiner can normally be reached on M-F 7:30am-4pm.
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/MICHAEL ANTHONY DAVIS III/Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674
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