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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/27/2025 is 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 § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
2.) Claim(s) 1-2, 6-8, 12-15, 18-19 and 21-22 is/are rejected under 35 U.S.C. 102 (a1) (a2) as being anticipated by Nash et al. (US Pub No.: 2017/0230585A1).
Regarding Claim 1, Nash et al. disclose a computer-implemented method (A multi-camera device may include two asymmetric cameras disposed to image a target scene. The multi-camera device further includes a processor coupled to a memory component and a display, the processor configured to retrieve an image generated by a first camera from the memory component, retrieve an image generated by a second camera from the memory component, receive input corresponding to a preview zoom level, retrieve spatial transform information and photometric transform information from memory, modify at least one image received from the first and second cameras by the spatial transform and the photometric transform, and provide on the display a preview image comprising at least a portion of the at least one modified image and a portion of either the first image or the second image based on the preview zoom level, Abstract; Figures 3-5), comprising:
displaying, by a display screen of a computing device, an initial preview of a scene being captured by a first image capturing device of the computing device, wherein the first image capturing device is operating within a first range of focal lengths (A camera configured for wide-angle image capture and a camera configured for telephoto image capture, and transitioning between a view displayed from one camera and then the other. The two cameras are positioned and configured to generally image the same target scene. A transition can be performed by “fading” (transitioning) a spatial alignment and a photometric alignment between the images produced by the two cameras using an intelligent state machine. The state machine is configured to display a view from one camera and transition to the view from the other camera as an imaging parameter changes, for example, a desired zoom level. Using a spatial transform and a photometric transform, a “preview” image presented to a user can be indistinguishable regardless of which camera is being used to provide (or serve) the image(s). The first camera 115 is the main camera and has a wide angle lens, for example, having a focal length of 3.59 mm. The second camera is an auxiliary camera and has telephoto lens having a focal length of 6 mm, Paragraphs 0003-0004, 0032-0035);
detecting, by the computing device, a zoom operation predicted to cause the first image capturing device to reach a limit of the first range of focal lengths (desired zoom level changes) (A transition can be performed by “fading” (transitioning) a spatial alignment and a photometric alignment between the images produced by the two cameras using an intelligent state machine. The state machine is configured to display a view from one camera and transition to the view from the other camera as an imaging parameter changes, for example, a desired zoom level. The transition can include using a spatial transform (for example, that models spatial alignment differences between images from the two cameras) and/or a photometric transform (for example, that models color and/or intensity differences between images from the two cameras), Paragraphs 0003-0004);
in response to the detecting, activating a second image capturing device of the computing device to capture a zoomed preview of the scene, wherein the second image capturing device is configured to operate within a second range of focal lengths (As mentioned above, a transition can be performed by “fading” (transitioning) a spatial alignment and a photometric alignment between the images produced by the two cameras using an intelligent state machine. The state machine is configured to display a view from one camera and transition to the view from the other camera as an imaging parameter changes, for example, a desired zoom level. The transition can include using a spatial transform (for example, that models spatial alignment differences between images from the two cameras) and/or a photometric transform (for example, that models color and/or intensity differences between images from the two cameras), Paragraphs 0003-0004; Figures 3-5);
updating a geometry-based warping transformation based on a comparison of respective image features from the initial preview and the zoomed preview (The transition can include using a spatial transform (for example, that models spatial alignment differences between images from the two cameras) and/or a photometric transform (for example, that models color and/or intensity differences between images from the two cameras), Paragraphs 0003-0004. Static calibration 412 may be performed using a known target scene, for example, a test target. In some examples, static calibration may be performed “at the factory” as an initial calibration step of a multi-camera device. Aspects of static calibration are further described, for example, in FIG. 5. Parameters determined from static calibration 412 may be stored in memory to be subsequently used for spatial alignment 440 and/or for photometric alignment 455, Paragraph 0063. The spatial alignment module 655 may use previously determined alignment information (for example, retrieving such information from a memory component). The previously determined alignment information may be used as a starting point for spatial alignment of images provided by the two cameras. The spatial alignment module 655 can include a feature detector 642 and a feature matcher 646. The feature detector 642 may include instructions (or functionality) to detect features (or keypoints) in each of image A 605 and image B 610 based on criteria that may be predetermined, by one or more of various feature detection techniques, Paragraphs 0070-0071; Figures 4-6);
aligning the zoomed preview with the initial preview by applying the updated warping transformation, wherein the updated warping transformation reduces one or more viewing artifacts caused by a change in a field of view when transitioning from the initial preview to the zoomed preview (The spatial alignment module 655. In various embodiments, the spatial alignment module 655 may be implemented in software, hardware, or a combination of software and hardware. The spatial alignment module 655 may use previously determined alignment information (for example, retrieving such information from a memory component). The previously determined alignment information may be used as a starting point for spatial alignment of images provided by the two cameras. The spatial alignment module 655 can include a feature detector 642 and a feature matcher 646. The feature detector 642 may include instructions (or functionality) to detect features (or keypoints) in each of image A 605 and image B 610 based on criteria that may be predetermined, by one or more of various feature detection techniques known to a person of ordinary skill in the art. The feature matcher 646 match the identified features in image A 605 to image B 610 using a feature matching technique, for example, image correlation. In some embodiments, the images to be aligned may be partitioned into blocks, and feature identification and matching may be performed on a block-to-block level, Paragraphs 0070-0071; Figures 3-5); and
displaying, by the display screen of the computing device, the aligned zoomed preview of the image captured by the second image capturing device while operating within the second range of focal lengths (Retrieve a first image from the memory component; retrieve a second image from the memory component; determine a spatial transform, the spatial transform including information to spatially align pixels of the first image and corresponding pixels of the second image, and save the spatial transform in the memory component; determine a photometric transform, the photometric transform including information of differences in color and intensity between pixels of the first image and corresponding pixels of the second image, and save the photometric transform in the memory component; receive input corresponding to a next preview zoom level; retrieve information of the spatial transform and the photometric transform from memory; modify at least one of the retrieved first and second images using the spatial transform information and the photometric transform information based on the next preview zoom level; and provide on the display a preview image, the display image comprising an image from the first camera, an image from the second camera, or an image that comprises a portion of the modified image and a portion of an image from the first camera or the second camera, based on the next preview zoom level, Claim 1, Paragraphs 0070-0071, 0079-0080).
With regard to Claim 2, Nash et al. disclose the method of claim 1, wherein the comparison of the respective image features further comprises: detecting one or more visual features in the initial preview and the zoomed preview; and generating, based on the one or more visual features, a visual correspondence between the initial preview and the zoomed preview (The regions can be matched to determine regions depicting corresponding features between the images, that is, to determine which regions in the images depict the same feature. The regions depicting corresponding features can be matched spatially and as to intensity. The spatial or intensity correspondence between regions depicting corresponding features can permit accurate matching using the regions, leading, for example, to accurate downstream image alignment and/or depth map construction. Spatial alignment or equalization of intensity values between corresponding regions can accommodate the structure of keypoints included in the regions. In some examples, the histogram of each corresponding region can be analyzed to determine spatial intensity variation, and a spatial mapping between the intensities of the corresponding regions can be performed to provide equalized intensity that is adapted to local structure content such as distinctive features. Spatial alignment module 355 can include feature detector 357 including instructions that configure the image processor 320 to detect distinctive features, or keypoints, in the image data. Such features can correspond to points in the images that can be matched with a high degree of accuracy. For example, distinctive features may be characterized at least partly by the presence or sharpness of edges or lines, corners, ridges, or blobs differing in properties, for example, size, shape, dimension, brightness or color compared to surrounding pixel regions. Generally, object or feature recognition may involve identifying points of interest (also called keypoints) in an image and/or localized features around those keypoints for the purpose of feature identification, Paragraphs 0039-0041, 0048-0050).
Regarding Claim 6, Nash et al. disclose the method of claim 1, wherein the updating of the geometry-based warping transformation utilizes frame-based data comprising one or more of an image, a pre-crop of the image, a scene depth, or a calibration parameter respectively associated with the first image capturing device and the second image capturing device (Using the transformation parameters 550, a mapping 560 can be generated relating the images from the camera 520 to the images from camera 530 or vice versa. The mapping 560 and transformation parameters 550 can be stored in a memory 570 of the multi-camera device 510, or a memory component that is not part of the multi-camera device 510. As the multi-camera device 510 is subjected to wear and tear and other factors affecting its initial factor calibration, the embodiments described herein can be used to refine, readjust or tune the transformation parameters 550 and the mapping 560. For example, the spatial alignment and intensity equalization embodiments described herein can be applied dynamically as the multi-camera device 510 is being used by a user to account for shift in transformation parameters 550 and mapping 560, Paragraph 0068).
In regard to Claim 7, Nash et al. disclose the method of claim 6, wherein the calibration parameter comprises an auto-focus distance (These parameters can include a scaling factor. The scaling factor can be defined as roughly the ratio of the focal lengths of the two asymmetric cameras 520 and 530. The two asymmetric cameras 520 and 530 have different focal length and magnification, in order to map or juxtapose their images on each other, a scaling factor can be determined. Other parameters of the transformation 550 can include a viewpoint matching matrix, principal offset, geometric calibration and other parameters relating the images of the camera 520 to the camera 530, Paragraphs 0067-0068).
With regard to Claim 8, Nash et al. disclose the method of claim 1, wherein the applying of the updated warping transformation is performed on each frame of the initial preview and a corresponding frame of the zoomed preview in a side-by-side comparison (Image A 405 from a first camera and image B 410 from a second camera are received and static calibration 412 is performed. Although referred to for convenience as image A 405 and image B 410, image A 405 may refer to a series of images from the first camera of the multi-camera device. spatial alignment 440 further spatially aligns image A and image B, mapping pixels from image A to corresponding pixels of image B. In other words, spatial alignment 440 may determine a pixel or a plurality of pixels in image A that represent the same feature as a corresponding pixel of pixels in image B, Paragraphs 0063-0064; Figures 4-6).
Regarding Claim 12, Nash et al. disclose the method of claim 1, further comprising: transitioning, by the computing device and based on the updated warping transformation, from the first image capturing device to the second image capturing device (The two cameras are positioned and configured to generally image the same target scene. A transition can be performed by “fading” (transitioning) a spatial alignment and a photometric alignment between the images produced by the two cameras using an intelligent state machine. The state machine is configured to display a view from one camera and transition to the view from the other camera as an imaging parameter changes, for example, a desired zoom level. The transition can include using a spatial transform (for example, that models spatial alignment differences between images from the two cameras) and/or a photometric transform (for example, that models color and/or intensity differences between images from the two cameras), Paragraphs 0002-0003, 0091-0092).
With regard to Claim 13, Nash et al. disclose the method of claim 1, wherein the updating of the geometry-based warping transformation further comprises: reducing jitter by applying temporal feature matching and tracking (Spatial alignment module 355 may include instructions that configure the image processor 320 to perform spatial alignment on captured image data. For example, each of the first camera 315 and second camera 316 may capture an image depicting the target scene according to each camera's different parameters and characteristics. As discussed above, images generated of the same target scene from the first camera 315 and second camera 316 may differ due to discrepancies in sensor gains, roll-offs, pitch, yaw, sensitivity, field of view, white balance, geometric distortion, and noise sensitivities, differences between the lenses in the first camera 115 and the second camera 116, and on-board image signal conditioning. In order to perform accurate spatial alignment of the images, spatial alignment module 355 may configure the image processor 320 to detect corresponding features between the images from the first camera 315, estimate an appropriate transformation (or mapping between the corresponding regions) and perform region matching producing images which can be accurately juxtaposed on top of each other. Additionally, the spatial alignment module 355 may configure the image processor 320 to align the two images even when corresponding features between images cannot be detected, Paragraphs 0048-0056).
Regarding Claim 14, Nash et al. disclose a computing device (A multi-camera device may include two asymmetric cameras disposed to image a target scene. The multi-camera device further includes a processor coupled to a memory component and a display, the processor configured to retrieve an image generated by a first camera from the memory component, retrieve an image generated by a second camera from the memory component, receive input corresponding to a preview zoom level, retrieve spatial transform information and photometric transform information from memory, modify at least one image received from the first and second cameras by the spatial transform and the photometric transform, and provide on the display a preview image comprising at least a portion of the at least one modified image and a portion of either the first image or the second image based on the preview zoom level, Abstract; Figures 3-5), comprising:
a display screen (Display 325, Paragraph 0059; Figure 3);
a first image capturing device configured to operate within a first range of focal lengths (The first camera 115 is the main camera and has a wide angle lens, for example, having a focal length of 3.59 mm. The second camera is an auxiliary camera and has telephoto lens having a focal length of 6 mm, Paragraphs 0003-0004, 0032-0035);
a second image capturing device configured to operate within a second range of focal lengths (The second camera is an auxiliary camera and has telephoto lens having a focal length of 6 mm, Paragraphs 0003-0004, 0032-0035);
one or more processors (Image processor 320 and device processor 360, Paragraphs 0057-0060; Figure 3); and
data storage, wherein the data storage has stored thereon computer-executable instructions that, when executed by the one or more processors (Device processor 360 may write data to storage module 310, for example data representing captured images, image alignment data, intensity value data, and the like. While storage module 310 is represented graphically as a traditional disk device, those with skill in the art would understand that the storage module 310 may be configured as any storage media device. For example, the storage module 310 may include a disk drive, such as a floppy disk drive, hard disk drive, optical disk drive or magneto-optical disk drive, or a solid state memory such as a FLASH memory, RAM, ROM, and/or EEPROM. the storage module 310 may include a ROM memory containing system program instructions stored within the multi-camera device 300, Paragraph 0060; Figure 3), cause the mobile device to carry out functions comprising:
displaying, by the display screen, an initial preview of a scene being captured by a first image capturing device of the computing device, wherein the first image capturing device is operating within a first range of focal lengths (A camera configured for wide-angle image capture and a camera configured for telephoto image capture, and transitioning between a view displayed from one camera and then the other. The two cameras are positioned and configured to generally image the same target scene. A transition can be performed by “fading” (transitioning) a spatial alignment and a photometric alignment between the images produced by the two cameras using an intelligent state machine. The state machine is configured to display a view from one camera and transition to the view from the other camera as an imaging parameter changes, for example, a desired zoom level. Using a spatial transform and a photometric transform, a “preview” image presented to a user can be indistinguishable regardless of which camera is being used to provide (or serve) the image(s). The first camera 115 is the main camera and has a wide angle lens, for example, having a focal length of 3.59 mm. The second camera is an auxiliary camera and has telephoto lens having a focal length of 6 mm, Paragraphs 0003-0004, 0032-0035);
detecting, by the computing device, a zoom operation predicted to cause the first image capturing device to reach a limit of the first range of focal lengths (desired zoom level changes) (A transition can be performed by “fading” (transitioning) a spatial alignment and a photometric alignment between the images produced by the two cameras using an intelligent state machine. The state machine is configured to display a view from one camera and transition to the view from the other camera as an imaging parameter changes, for example, a desired zoom level. The transition can include using a spatial transform (for example, that models spatial alignment differences between images from the two cameras) and/or a photometric transform (for example, that models color and/or intensity differences between images from the two cameras), Paragraphs 0003-0004);
in response to the detecting, activating a second image capturing device of the computing device to capture a zoomed preview of the scene, wherein the second image capturing device is configured to operate within a second range of focal lengths (As mentioned above, a transition can be performed by “fading” (transitioning) a spatial alignment and a photometric alignment between the images produced by the two cameras using an intelligent state machine. The state machine is configured to display a view from one camera and transition to the view from the other camera as an imaging parameter changes, for example, a desired zoom level. The transition can include using a spatial transform (for example, that models spatial alignment differences between images from the two cameras) and/or a photometric transform (for example, that models color and/or intensity differences between images from the two cameras), Paragraphs 0003-0004; Figures 3-5);
updating a geometry-based warping transformation based on a comparison of respective image features from the initial preview and the zoomed preview (The transition can include using a spatial transform (for example, that models spatial alignment differences between images from the two cameras) and/or a photometric transform (for example, that models color and/or intensity differences between images from the two cameras), Paragraphs 0003-0004. Static calibration 412 may be performed using a known target scene, for example, a test target. In some examples, static calibration may be performed “at the factory” as an initial calibration step of a multi-camera device. Aspects of static calibration are further described, for example, in FIG. 5. Parameters determined from static calibration 412 may be stored in memory to be subsequently used for spatial alignment 440 and/or for photometric alignment 455, Paragraph 0063. The spatial alignment module 655 may use previously determined alignment information (for example, retrieving such information from a memory component). The previously determined alignment information may be used as a starting point for spatial alignment of images provided by the two cameras. The spatial alignment module 655 can include a feature detector 642 and a feature matcher 646. The feature detector 642 may include instructions (or functionality) to detect features (or keypoints) in each of image A 605 and image B 610 based on criteria that may be predetermined, by one or more of various feature detection techniques, Paragraphs 0070-0071; Figures 4-6);
aligning the zoomed preview with the initial preview by applying the updated warping transformation, wherein the updated warping transformation reduces one or more viewing artifacts caused by a change in a field of view when transitioning from the initial preview to the zoomed preview (The spatial alignment module 655. In various embodiments, the spatial alignment module 655 may be implemented in software, hardware, or a combination of software and hardware. The spatial alignment module 655 may use previously determined alignment information (for example, retrieving such information from a memory component). The previously determined alignment information may be used as a starting point for spatial alignment of images provided by the two cameras. The spatial alignment module 655 can include a feature detector 642 and a feature matcher 646. The feature detector 642 may include instructions (or functionality) to detect features (or keypoints) in each of image A 605 and image B 610 based on criteria that may be predetermined, by one or more of various feature detection techniques known to a person of ordinary skill in the art. The feature matcher 646 match the identified features in image A 605 to image B 610 using a feature matching technique, for example, image correlation. In some embodiments, the images to be aligned may be partitioned into blocks, and feature identification and matching may be performed on a block-to-block level, Paragraphs 0070-0071; Figures 3-5); and
displaying, by the display screen of the computing device, the aligned zoomed preview of the image captured by the second image capturing device while operating within the second range of focal lengths (Retrieve a first image from the memory component; retrieve a second image from the memory component; determine a spatial transform, the spatial transform including information to spatially align pixels of the first image and corresponding pixels of the second image, and save the spatial transform in the memory component; determine a photometric transform, the photometric transform including information of differences in color and intensity between pixels of the first image and corresponding pixels of the second image, and save the photometric transform in the memory component; receive input corresponding to a next preview zoom level; retrieve information of the spatial transform and the photometric transform from memory; modify at least one of the retrieved first and second images using the spatial transform information and the photometric transform information based on the next preview zoom level; and provide on the display a preview image, the display image comprising an image from the first camera, an image from the second camera, or an image that comprises a portion of the modified image and a portion of an image from the first camera or the second camera, based on the next preview zoom level, Claim 1, Paragraphs 0070-0071, 0079-0080).
With regard to Claim 15, Nash et al. disclose the computing device of claim 14, wherein the functions for the comparison of the respective image features further comprise: detecting one or more visual features in the initial preview and the zoomed preview; and generating, based on the one or more visual features, a visual correspondence between the initial preview and the zoomed preview (The regions can be matched to determine regions depicting corresponding features between the images, that is, to determine which regions in the images depict the same feature. The regions depicting corresponding features can be matched spatially and as to intensity. The spatial or intensity correspondence between regions depicting corresponding features can permit accurate matching using the regions, leading, for example, to accurate downstream image alignment and/or depth map construction. Spatial alignment or equalization of intensity values between corresponding regions can accommodate the structure of keypoints included in the regions. In some examples, the histogram of each corresponding region can be analyzed to determine spatial intensity variation, and a spatial mapping between the intensities of the corresponding regions can be performed to provide equalized intensity that is adapted to local structure content such as distinctive features. Spatial alignment module 355 can include feature detector 357 including instructions that configure the image processor 320 to detect distinctive features, or keypoints, in the image data. Such features can correspond to points in the images that can be matched with a high degree of accuracy. For example, distinctive features may be characterized at least partly by the presence or sharpness of edges or lines, corners, ridges, or blobs differing in properties, for example, size, shape, dimension, brightness or color compared to surrounding pixel regions. Generally, object or feature recognition may involve identifying points of interest (also called keypoints) in an image and/or localized features around those keypoints for the purpose of feature identification, Paragraphs 0039-0041, 0048-0050).
In regard to Claim 18, Nash et al. disclose the computing device of claim 14, wherein the updating of the geometry-based warping transformation utilizes frame-based data comprising one or more of an image, a pre-crop of the image, a scene depth, or a calibration parameter respectively associated with the first image capturing device and the second image capturing device (Using the transformation parameters 550, a mapping 560 can be generated relating the images from the camera 520 to the images from camera 530 or vice versa. The mapping 560 and transformation parameters 550 can be stored in a memory 570 of the multi-camera device 510, or a memory component that is not part of the multi-camera device 510. As the multi-camera device 510 is subjected to wear and tear and other factors affecting its initial factor calibration, the embodiments described herein can be used to refine, readjust or tune the transformation parameters 550 and the mapping 560. For example, the spatial alignment and intensity equalization embodiments described herein can be applied dynamically as the multi-camera device 510 is being used by a user to account for shift in transformation parameters 550 and mapping 560, Paragraph 0068).
Regarding Claim 19, Nash et al. disclose the computing device of claim 14, wherein the functions for applying of the updated warping transformation are performed on each frame of the initial preview and a corresponding frame of the zoomed preview in a side-by-side comparison (Image A 405 from a first camera and image B 410 from a second camera are received and static calibration 412 is performed. Although referred to for convenience as image A 405 and image B 410, image A 405 may refer to a series of images from the first camera of the multi-camera device. spatial alignment 440 further spatially aligns image A and image B, mapping pixels from image A to corresponding pixels of image B. In other words, spatial alignment 440 may determine a pixel or a plurality of pixels in image A that represent the same feature as a corresponding pixel of pixels in image B, Paragraphs 0063-0064; Figures 4-6).
With regard to Claim 21, Nash et al. disclose the computing device of claim 14, wherein the functions for the updating of the geometry-based warping transformation further comprise: reducing jitter by applying temporal feature matching and tracking (Spatial alignment module 355 may include instructions that configure the image processor 320 to perform spatial alignment on captured image data. For example, each of the first camera 315 and second camera 316 may capture an image depicting the target scene according to each camera's different parameters and characteristics. As discussed above, images generated of the same target scene from the first camera 315 and second camera 316 may differ due to discrepancies in sensor gains, roll-offs, pitch, yaw, sensitivity, field of view, white balance, geometric distortion, and noise sensitivities, differences between the lenses in the first camera 115 and the second camera 116, and on-board image signal conditioning. In order to perform accurate spatial alignment of the images, spatial alignment module 355 may configure the image processor 320 to detect corresponding features between the images from the first camera 315, estimate an appropriate transformation (or mapping between the corresponding regions) and perform region matching producing images which can be accurately juxtaposed on top of each other. Additionally, the spatial alignment module 355 may configure the image processor 320 to align the two images even when corresponding features between images cannot be detected, Paragraphs 0048-0056).
Computer program storing Claim 22 corresponds to method claim 1 and device claim 14 and is also rejected as discussed in the above rejections to the claims (Also see Paragraphs 0060, 0107).
3.) Allowable Subject Matter
Claims 3-5, 9-11, 16-17 and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PRITHAM DAVID PRABHAKHER whose telephone number is (571)270-1128. The examiner can normally be reached Monday to Friday 8:00 am to 5:00 pm EST.
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Pritham David Prabhakher
Patent Examiner
Pritham.Prabhakher@uspto.gov
/PRITHAM D PRABHAKHER/Primary Examiner, Art Unit 2638
Prosecution Insights