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
Response to Amendment
Applicant's arguments filed 12/22/2025 have been fully considered but they are not persuasive.
The amendment concerns the image ROI in the process being stored on a non-volatile storage medium. Applicant in pg. 6 of remarks asserts that the copy of the ROI of the image would have to be stored in RAM, which is a volatile storage medium. Applicant in pg. 7 argues that intermediate processing data, such as the ROI is typically stored in fast, volatile memory. Georgis teaches using hard disk drives or solid-state drives, which are forms of non-volatile memory. Additionally, the applicant argues that only the DNN model, the Bayer-domain image data, and the LPD/LPR results are stored in the non-volatile memory as taught in Georgis [0040]: “The memory 204 may include suitable logic, circuitry, and interfaces that may be configured to store the program instructions to be executed by the circuitry 202. The memory 204 may further be configured to store the DNN model 110, the acquired Bayer-domain image data, and the LPD/LPR results. Examples of implementation of the memory 204 may include, but are not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Hard Disk Drive (HDD), a Solid-State Drive (SSD), a CPU cache, and/or a Secure Digital (SD) card.”. However, applicant does not cite any place where Georgis teaches against also storing the ROI in non-volatile memory. Additionally, Georgis teaches that the ROI is based on of the system that are stored in non-volatile memory, such as the LPD result, the Bayer-domain image data, and the DNN. Georgis [0034]-[0035]: “[0034] The system 102 may extract a first region-of-interest (RoI) 120 based on the extracted LPD result. The first RoI 120 may include a first license plate image of the first license plate 118 associated with the first vehicle 114. For example, the first RoI 120 may include a first license plate image that may include a license plate number “7MYK778”. Details of the extraction of the first RoI 120 are further provided, for example, in FIG. 3A.
[0035] In accordance with an embodiment, the system 102 may debayer the extracted first RoI 120. The extracted first RoI 120 may be debayered for reconstruction of a color image from the Bayer-domain image data. In an alternate embodiment, the DNN model 110 may be configured to debayer the extracted first RoI 120. Further, the debayered first RoI 120 may be processed based on application of a sequence of Image Signal Processing (ISP) operations on the debayered first RoI 120. Details of the debayering process of the first RoI 120 and the application of ISP operations are further provided, for example, in FIG. 3A.” Thus, Georgis does teach the ROI, which is the partial image, can be stored in non-volatile memory.
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.
Claims 1, and 3 are rejected under 35 U.S.C. 103 as being unpatentable over Georgis (Pub No. US 20220171981) in view of Daly et al. (Pub No. US 5948038 A).
As per claim 1, Georgis (US 20220171981 A1) teaches the claimed:
1. An image recording device comprising: a first image capturing module comprising a first lens and a first sensor, (Georgis [0023]: The image sensor 108 may include suitable logic, circuitry, interfaces, and/or code that may be configured to acquire Bayer-domain image data of the scene 112 that may include at least a first license plate 118 of the first vehicle 114. The image sensor 108 may be arranged with suitable optical instruments, such as lenses and actuators for the lenses to focus on a scene and/or a particular object-of-interest in the scene.”)
the first sensor catching a light beam passing through the first lens and (Georgis [0091]: “The lens assembly 608 may include one or more optical lenses that may be required to focus a beam of light on an aperture of the image sensor 604 to enable the image sensor 604 to acquire the Bayer-domain image data of a scene”).
providing a first image (Georgis in figure 1 where it shows the first image 112 as being a picture of the vehicle with its license plate. Also, please see Georgis in [0019] “The system 102 may include suitable logic, circuitry, and interfaces that may be configured to determine a license plate number of the first vehicle 114 based on Bayer-domain image data of a scene (such as the scene 112) which includes a vehicle (such as the first vehicle 114)”); a processor (Georgis in figure 2, circuitry 202) selecting a first partial area of the first image, copying image data of the first partial area and providing a first partial image (Georgis [0067]: “At 402, copies of the first RoI 310A may be generated. In accordance with an embodiment, the circuitry 202 may be configured to generate copies of the extracted first RoI 310A. For example, the circuitry 202 may generate four copies of the first RoI 310A. The generated copies of the first RoI 310A may include the license plate number “7MYK778” of the first license plate 118 of the first vehicle 114.” The extracted ROI is the partial image).
Georgis alone does not explicitly teach the remaining claim limitations.
However, Georgis in combination with Daly teaches the claimed:
and adjusting the first image and generating a first adjusted image (Daly teaches this feature in col 6, lines 12-19 “In a specific embodiment of the invention, the traffic violation processing method includes the step of resolution reduction, to convert the initial high resolution digital image to a lower resolution image. As used herein, the term "low resolution image" is a relative term which refers to any traffic image after its original resolution has been reduced. Preferably, the resolution of one or more of the digitized images (e.g., the traffic scene image or the vehicle image) is reduced to provide a corresponding low resolution image”. In this passage, the “resolution reduction” corresponds to the claimed adjustment of the first image.).
and a non-volatile memory storing the first partial image and at least one of the first image and the first adjusted image (Georgis in figure 1 shows the first image 112 and towards the end of [0087]: “… The image-capture device 602 may further include a lens assembly 608, a memory 610, the DNN model 110, and the camera serial interface 612.”) -- (Georgis in [0067] generates a copy of the ROI image (first partial image). In order to do this processing, Georgis would have to store the first partial image somewhere. Georgis allows using forms of non-volatile memory to store the DNN, the image data, and the results of the image detection. Georgis [0040]: “The memory 204 may include suitable logic, circuitry, and interfaces that may be configured to store the program instructions to be executed by the circuitry 202. The memory 204 may further be configured to store the DNN model 110, the acquired Bayer-domain image data, and the LPD/LPR results. Examples of implementation of the memory 204 may include, but are not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Hard Disk Drive (HDD), a Solid-State Drive (SSD), a CPU cache, and/or a Secure Digital (SD) card.” Hard Disk Drives and Solid-State Drives are examples of non-volatile memory. Georgis teaches that the ROI is based aspects stored in non-volatile memory, such as the LPD result and the DNN. Georgis [0034]-[0035]: “[0034] The system 102 may extract a first region-of-interest (RoI) 120 based on the extracted LPD result. The first RoI 120 may include a first license plate image of the first license plate 118 associated with the first vehicle 114. For example, the first RoI 120 may include a first license plate image that may include a license plate number “7MYK778”. Details of the extraction of the first RoI 120 are further provided, for example, in FIG. 3A.
[0035] In accordance with an embodiment, the system 102 may debayer the extracted first RoI 120. The extracted first RoI 120 may be debayered for reconstruction of a color image from the Bayer-domain image data. In an alternate embodiment, the DNN model 110 may be configured to debayer the extracted first RoI 120. Further, the debayered first RoI 120 may be processed based on application of a sequence of Image Signal Processing (ISP) operations on the debayered first RoI 120. Details of the debayering process of the first RoI 120 and the application of ISP operations are further provided, for example, in FIG. 3A.” Thus, the extracted ROI can be in the same non-volatile memory as other features.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to reduce the resolution of the first image as taught by Daly with the system of Georgis. Daly states that the motivation to combine is: “Advantageously, this resolution reduction step reduces the digital storage requirements for that image” (Daly in col 6, lines 23-25).
As per claim 3, Georgis alone does not explicitly teach the claimed limitations.
However, Georgis in combination with Daly teaches the claimed:
3. The image recording device according to claim 1, wherein the processor generates the first adjusted image by reducing pixels of the first image (Daly teaches this feature in col 6, lines 12-19 “In a specific embodiment of the invention, the traffic violation processing method includes the step of resolution reduction, to convert the initial high resolution digital image to a lower resolution image. As used herein, the term "low resolution image" is a relative term which refers to any traffic image after its original resolution has been reduced. Preferably, the resolution of one or more of the digitized images (e.g., the traffic scene image or the vehicle image) is reduced to provide a corresponding low resolution image”. In this passage, the resolution reduction results in reduced pixels of the first image.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to reduce the pixels of the first image as taught by Daly with the system of Georgis. The motivation of claim 1 is incorporated herein.
Claims 2, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Georgis in view of Daly et al. in further view of Xue (US 20120069181 A1).
As per claim 2, Georgis in combination with Daly does not explicitly teach the claimed limitations.
However, Georgis and Daly in combination with Xue teaches the claimed:
2. The image recording device according to claim 1, wherein the first partial area is selected fixedly from an image corresponding to a specific angle ranged from a horizontal direction in which the first image capturing module captures images to both sides expanded from the horizontal direction; (Xue [0176]: “The object identification unit 18 extracts candidate points of a road side-end edge portion, which is an identification target object, by using the edge image prepared by the binarization process (step S24). In this extraction process, at first, a plurality of processing lines is set for the edge image processed by the binarization process. In an example embodiment, each of the processing lines is a line composed of pixels aligned and extending in one horizontal direction in the edge image processed by the binarization process. The direction of processing line is not required to be a horizontal direction. For example, the direction of processing line can be a vertical direction or slanted direction. Further, each of processing lines can be composed of pixels with a same number of pixels or different numbers of pixels.” The lines at a vertical or slanted direction extended from a horizontal direction correspond to the specific angles ranged from a horizontal direction.)
or, the first partial area is selected based on an intersection at which two lane lines extend in the first image. (Xue describes identification of objects including lane lines or crossing areas that could be intersections. Crossing happen generally at intersections and line identification will identify intersections. Xue [0012]: “ Specifically, the object identification device extracts boundaries or edges of an identification target object and then identifies the identification target object, defined by the edges, in the captured image area. The identification target objects may be road side-end obstacles such as side walls, guardrails/crash barriers, telegraph poles/utility poles, streetlamps/streetlights, stepped portions such as pedestrian crossings at the road side-end, in-front vehicles, lane markings, or the like.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the first partial area selection techniques as taught by Xue with the system of Georgis and Daly in order to obtain partial images based on horizon and boarders of the road scene and the characteristics of the road that might be of interest.
As per claim 11, it is similar in scope to claim 2 and thus is rejected under the same rationale. However, it is also dependent on claim 6 and is rejected by the same art. The claims describe similar concepts with different language. “…or two edges of the first image disposed along a horizontal direction are substantially tangent to two perimeters of the first sensor disposed along the horizontal direction” corresponds to “from an image corresponding to a specific angle ranged from a horizontal direction in which the first image capturing module captures images to both sides expanded from the horizontal direction”. The tangent from the perimeters disposed along a horizontal direction correspond to the angle ranged from a horizontal direction.
Claims 4, 6-8, and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Georgis in view of Daly and further in view of Meler et al. (US 2018/0262683 A1).
As per claim 4, Georgis alone does not explicitly teach the claimed limitations.
However, Georgis in combination with Meler teaches the claimed:
4. The image recording device according to claim 1, further comprising a
second image capturing module capturing a second image, wherein the processor stitches the first adjusted image and the second image and generates an integrated image. (Meler [0004]: “In a first aspect, the subject matter described in this specification can be embodied in systems that include a first image sensor configured to capture a first image and a second image sensor configured to capture a second image. The systems include a processing apparatus that is configured to receive the first image from the first image sensor; receive the second image from the second image sensor; stitch the first image and the second image to obtain a stitched image; identify an image portion of the stitched image that is positioned on a stitching boundary of the stitched image;.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the stitching as taught by Meler with the system of Georgis and Daly in order to allow show the user multiple photographs of the vehicle at the same time by essentially merging several smaller photographs together by way of the stitching process.
As per claim 7, is similar in scope to claims 3 and 4 and thus is rejected under the same rationale.
As per claim 6, Georgis teaches the claimed:
6. An image recording device comprising: a first image capturing module comprising a first sensor, the first sensor catching light beam and providing a first image; (Georgis [0091]: “The lens assembly 608 may include one or more optical lenses that may be required to focus a beam of light on an aperture of the image sensor 604 to enable the image sensor 604 to acquire the Bayer-domain image data of a scene”. Georgis in figure 1 and in [0019] where it shows the first image 112 as being a picture of the vehicle with its license plate captured using “Image Sensor 108”);
a processor generating a first partial image with the first image, and/or (Georgis in [0039]: “The circuitry 202 may include one or more specialized processing units, which may be implemented as a separate processor”. Georgis [0067]: “At 402, copies of the first RoI 310A may be generated. In accordance with an embodiment, the circuitry 202 may be configured to generate copies of the extracted first RoI 310A. For example, the circuitry 202 may generate four copies of the first RoI 310A. The generated copies of the first RoI 310A may include the license plate number “7MYK778” of the first license plate 118 of the first vehicle 114.” The extracted ROI is the partial image).
Georgis alone does not teach the claimed limitation.
However, Georgis in combination with Meler teaches the claimed:
a second image capturing module catching light beam and providing a second image (Meler [0052]: “and receive a second image from the second image sensor 216. The processing apparatus 212 may be configured to perform image signal processing (e ).”).
generating a second partial image with the second image (As mentioned above, Meler teaches of a second image in [0052]. Georgis teaches of extracting multiple license plate images, e.g. please see Georgis in [0081]: “At 506, a second RoI 502 may be extracted. In accordance with an embodiment, the circuitry 202 may be configured to extract the second RoI 502 that may include a second license plate image of a second license plate 504, such as “XYZ789” of the second vehicle 116”.
The claimed feature is taught when this second partial image is extracted using the second image of Meler (e.g. in Meler in [0052]). In this instance, Georgis discloses the second ROI corresponds to claimed second partial image).
and stitching the first image and the second image to generate an integrated image. (Meler [0005]: “In a second aspect, the subject matter described in this specification can be embodied in methods that include receiving a first image from a first image sensor; receiving a second image from a second image sensor; stitching the first image and the second image to obtain a stitched image; identifying an image portion of the stitched image that is positioned on a stitching boundary of the stitched image;”).
a non-volatile memory storing the first partial image and at least one of the first image, the second image, the second partial image, the integrated image, the first adjusted image and the second adjusted image. (Georgis allows using forms of non-volatile memory to store the DNN, the image data, and the results of the image detection. Georgis [0040]: “The memory 204 may include suitable logic, circuitry, and interfaces that may be configured to store the program instructions to be executed by the circuitry 202. The memory 204 may further be configured to store the DNN model 110, the acquired Bayer-domain image data, and the LPD/LPR results. Examples of implementation of the memory 204 may include, but are not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Hard Disk Drive (HDD), a Solid-State Drive (SSD), a CPU cache, and/or a Secure Digital (SD) card.” Hard Disk Drives and Solid-State Drives are examples of non-volatile memory. Georgis teaches that the ROI is based aspects stored in non-volatile memory, such as the LPD result and the DNN. Georgis [0034]-[0035]: “[0034] The system 102 may extract a first region-of-interest (RoI) 120 based on the extracted LPD result. The first RoI 120 may include a first license plate image of the first license plate 118 associated with the first vehicle 114. For example, the first RoI 120 may include a first license plate image that may include a license plate number “7MYK778”. Details of the extraction of the first RoI 120 are further provided, for example, in FIG. 3A.
[0035] In accordance with an embodiment, the system 102 may debayer the extracted first RoI 120. The extracted first RoI 120 may be debayered for reconstruction of a color image from the Bayer-domain image data. In an alternate embodiment, the DNN model 110 may be configured to debayer the extracted first RoI 120. Further, the debayered first RoI 120 may be processed based on application of a sequence of Image Signal Processing (ISP) operations on the debayered first RoI 120. Details of the debayering process of the first RoI 120 and the application of ISP operations are further provided, for example, in FIG. 3A.” Thus, the extracted ROI can be in the same non-volatile memory as other features.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the image stitching as taught by Meler with the system of Georgis in order to combine the images into one image that has both parts.
As per claim 8, Georgis alone does not explicitly teach the claimed limitations. However, Georgis in combination with Meller teaches the claimed:
8. The image recording device according to claim 6, wherein the processor adjusts the first image and/or the second image before stitching the first image and the second image for a width of the first image being substantially the same with a width of the second image. (Meler [0087]: “here P_composite is a pixel value in the composite image, P_bottom is a corresponding pixel value from the bottom image, P_top is a corresponding pixel value from the top image, and b is a blending ratio that varies vertically across a blending region along the stitching boundary. For example, b may be 1 below a bottom edge of the blending region, b may be at or near 0.5 for pixels right at the stitching boundary, and b may decease to zero at a top edge of the blending region. Operation of blending pixels from the top image and the bottom image (e.g., such as described by Eqn. 1) may be referred to as a weighted average. In some implementations, a blending operation may be effectuated using one or more pixel masks. By way of an illustration, a mask may include an array of values, wherein a value of 1 may be used to select a pixel at a corresponding location from the top image; a value of 0 may be used to select a pixel from the bottom image; and a value between 0 and 1 may be used to average or blend pixels from the top and bottom images. The mask array may be configured based on dimensions of a blending region, for example, having a width (in number of pixels) equal to the length of the stitching boundary and a height (in number of pixels) equal thickness of the blending region. In general, the blending region need not correspond exactly to extent of the seam or the image portions. passed to a machine learning module. For example, the thickness of the blending region may be less than the thickness of the seam. In the example stitched image 610, the blending region coincides with the image portions from the seam that extend from an upper boundary 650 to a lower boundary 652. The height of the blending region in this example is the distance (in number of pixels) between the upper boundary 650 and the lower boundary 652 (i.e., 8 pixels in this example).”
Meler figure 6 shows two images being stitched together one over the other along a horizontal seam and having the same width so that the stitching matches.).
It would have been obvious to one of ordinary skill in the art at the time of filing to align the widths of the stitched images as taught by Meler with the system of Georgis modified by Meler to ensure that the combine image is symmetrical and looks like one unified image.
As per claim 13, Georgis alone does not teach the claimed features.
However, Georgis in combination with Meler teaches the claimed:
13. The image recording device according to claim 6, wherein the processor calculates image data of an overlapping area of the first image and the second image, and stitches the first image and the second image to generate the integrated image with 360-degree panorama. (Meler [0107]: When the stitching is finalized, the resulting composite image (e.g., a panoramic or spherical image) may be subject to additional image processing (e.g., output projection mapping and/or encoding in a compressed format) to generate an output image (e.g., a still image or frame of video). In some implementations, the composite image may be the final output image.”)
It would have been obvious to one of ordinary skill in the art to use the stitching to generate a 360-degree panorama because the stitching would help connect several photographs taken by different vantage points. This stitching could help join them into resembling one larger photograph instead.
As per claim 14, Georgis teaches the claimed:
14. The image recording device according to claim 6, wherein the first image capturing module is a fisheye lens, and the second image capturing module is a fisheye lens or a wide-angle lens. (Georgis [0091]: ”The lens assembly 608 may be utilized by the image-capture device 602 to eliminate or reduce optical aberrations that may arise while capturing the Bayer-domain image data 304A. Examples of the one or more optical lenses in the lens assembly 608 may include, but are not limited to, a standard lens, a telephoto lens, a wide angle lens, a fish eye lens, a macro lens, a tilt-shift lens, a prime lens and a zoom lens.” The reference describes a variety of lenses that could be used for the image capturing modules, including wide angle and fisheye. It would be obvious that one could be a fish eye and the other could be a fish-eye or a wide-angle.).
Claims 5 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Georgis in view of Daly and further in view of Meler in further view of Van Dusen et al. (US Pub 2017/0353748 A1).
As per claim 5, Georgis alone does not teach all the claimed features.
However, Georgis and Daley teaches the claimed:
5 The image recording device according to claim 4, wherein the first image has mlxnl pixels, the second image has m2xn2 pixels, the first partial image has moxno pixels, the first adjusted image has a xbin1 pixels, m1, m2, mo, n1, n2, n0,
(For example, Daley in col 4, lines 15-23 talks about the first image (larger traffic image) having a pixel size of 3000x2000 pixels and a first partial image having a size of 300x200; Daley teaches of the size of the first adjusted image having less pixels, e.g. it may be 768x512 pixels for the low-resolution image as mentioned in col 6, lines 40-42 of Daley).
a1m1 and b1n1 are positive integers, and a1 and b1 are positive numbers less than one (Van Dusen [0057]: “The scaled image resolution is an image scale factor (a value less than one) times the input image resolution. The image scale factor may be calculated using the input width, input height, and/or input pixel number corresponding to the input image resolution and the respective width limit, height limit, and/or pixel number limit corresponding to the image resolution limit.” a1m1 and b1n1 are positive integers when the scale factors selected by Van Dusen in [0057] result in multiplying even sized width (m1) or height (n1) values in the image).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the image dimension specifications as taught by Daly with the system of Georgis modified in order to clarify the required dimensions of the individual images and the combined images, as well as to convey that the reduction in resolution is a reduction in pixel dimension.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the scale factors less than 1 as taught by Van Dusen with the system of Georgis as modified by Daly because using these values is a mathematically known way in which to uniformly lower the resolution size along a given dimension or dimensions of an image when scaling down.
As per claim 9, it is similar in scope to claim 5 and is thus rejected under the same rationale. Claim 9 differs in that it also discloses a second partial image that has a different set of whole number pixel dimensions that are also positive integers. However, claim 9 is dependent on claim 6 and inherits the second image from Meler.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Georgis in view of Daly and further in view of Meler and further in view of Broggi (US 20210142055 A1).
As per claim 12, Georgis in view of Meler does not explicitly teach the claimed limitations. However, Georgis in view of Meler and further in view of Broggi teaches the claimed.
12. The image recording device according to claim 6, wherein the processor recognizes two lane lines from the first image/the second image, (Broggi analyzes 2 images captured and sticked together [0056]: “The video pipeline 156 may be configured to encode video frames captured by each of the capture devices 102a-102n. In some embodiments, the video pipeline 156 may be configured to perform video stitching operations to stitch video frames captured by each of the lenses 112a-112n to generate the panoramic field of view (e.g., the panoramic video frames).” Broggi identifies 2 lane lines that could be from two different images in the video that are stiches together [0134]: “Referring to FIG. 5, a diagram illustrating an example implementation of capturing video of vehicles from a stationary mounting location is shown. An example scenario 400 is shown. The example scenario 400 may comprise an intersection 402. The intersection 402 may comprise two roadways meeting. The intersection 402 may comprise a lane 404 and a lane 406 of a first road intersecting with a lane 408 and a lane 410 of a second road.”).
calculates an intersection at which the two lane lines extend, and determines the first partial image/the second partial image with the intersection. (Broggi [0134]: “The intersection 402 may comprise two roadways meeting. The intersection 402 may comprise a lane 404 and a lane 406 of a first road intersecting with a lane 408 and a lane 410 of a second road. The lane 404 may be for traveling in an opposite direction to the lane 406 on the first road and the lane 408 may be for traveling in an opposite direction to the lane 410 on the second road.” The images that are stitched together to provide a field of view can show and identify 2 lane lines that form an intersection in one of the partial images being combined).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the recognition of lane line intersections in two stitched-together images as taught by Broggi with the partial images being stitched together Georgis modified by Meler in order to analyze two combined images and the road elements in both of them to find an intersection in the partial image of one of the two.
Conclusion
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/THOMAS JOHN FOSTER/Examiner, Art Unit 2616
/HAI TAO SUN/Primary Examiner, Art Unit 2616