DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Specification
2. The Amendments to the Specification filed on 09/29/2023 have been accepted and made of record.
Claim Rejections - 35 USC § 102
3. 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.
4. 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.
5. Claims 1-2, 5-7, 9, 11, 14-18 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Govindarao et al. (US-PGPUB 2017/0374269).
Regarding claim 1, Govindarao discloses a method (see figs. 3A-3C and figs. 7-10) comprising:
receiving a plurality of depth values corresponding to a plurality of areas depicted in an image captured by an image sensor (Scene 300 is captured by an image sensor embodied in the camera 208/210. The viewfinder depth map 320 of the scene 300 illustrates depth for objects in the scene 300 of fig. 3A, using a region 322 representing depth for the lady 302, a region 324 representing depth for the lady 304, a region 326 representing depth for the man 306, a region 328 representing depth for the shrubs 308, a region 329 representing depth for the tree trunk 310, a region 320 representing depth of the background; the regions have different patterns to indicate varying depths; see figs. 3A-3B and paragraphs 0076-0077);
generating a depth histogram categorizing each depth value of the plurality of depth values into a depth value range of a plurality of depth value ranges (The depth information of the viewfinder depth map 320 is represented using the disparity histogram 330. An X-axis represents the disparity values (inversely proportional to depth) from 0 to 250, where a disparity value 0 represents an object at a farthest depth from the camera and a disparity value 250 represents an object at a closest depth from the camera. The height of a vertical bar along the Y-axis represents a size of an object at a corresponding disparity (or depth) in the scene 300. A vertical bar 332 represent the lady 302, a vertical bar 334 represent the lady 304, a vertical bar 336 represent the man 306, a vertical bar 338 represent the shrubs 308, a vertical bar 340 represent the tree trunk 310, and a vertical bar 342 represents the background of the scene 300; see fig. 3C and paragraph 0078);
determining an autofocus distance based on the depth histogram; and causing the image sensor to capture an image based on the autofocus distance (The method facilitates selection of objects from the plurality of objects based on depth information of the objects in the viewfinder depth map. The objects are selected if the objects are greater than a threshold percentage of image size in the viewfinder depth map. The method facilitates capture of two or more images of the scene by at least adjusting focus of a camera corresponding to the depth information of the selected objects; see figs. 7-10 and paragraph 0100-0122).
Regarding claim 2, Govindarao discloses everything claimed as applied above (see claim 1). In addition, Govindarao discloses wherein causing the image sensor to capture the image based on the autofocus distance comprises: adjusting a focus setting of the image sensor based on the autofocus distance, wherein the image sensor comprises a lens (Adjusting focus of a camera corresponding to the depth information of the selected object; see paragraphs 0102-0103 and figs. 7-10. As the depth of the object is already known from the viewfinder depth map, a focus position (lens position) of a camera can be determined such that if an image is captured by setting the focus of the camera at the focus position, the object is in focus in the captured image; see paragraphs 0050, 0052. Camera module 122/208/210 includes lens 0037, 0039, 0045, 0076).
Regarding claim 5, Govindarao discloses everything claimed as applied above (see claim 1). In addition, Govindarao discloses the method is executed based on receiving an indication of an application starting use of the image sensor (The operations of the method can include semi-automatic fashion, including an interaction of the user via user interface presentations for the execution of the imaging applications; see paragraph 0129, 0033, 0035, 0043).
Regarding claim 6, Govindarao discloses everything claimed as applied above (see claim 1). In addition, Govindarao discloses determining the autofocus distance based on the depth histogram comprises: determining one or more peaks of the depth histogram (The one or more objects having sizes greater than the threshold percentage of image size can be selected based on analyzing peaks of various disparities in the disparity histogram; see paragraph 0050), wherein each of the one or more peaks is associated with a range of depth values; selecting a peak from the one or more peaks, wherein the selected peak is associated with a minimum range of depth values of the one or more peaks (The objects are selected based on analyzing the disparity histogram to determine significant peaks. A peak can be determined as a significant peak if number of pixels is greater than a threshold percentage, for example 5% or 10%, of pixels (or the image size) in the image. If number of images that can be captured or processed in the focal stack capture or the burst capture is fixed at 3, then three most significant peaks can be picked from the disparity histogram; see paragraph 0056); and determining the autofocus distance based on the selected peak (The method facilitates capture of two or more images of the scene by at least adjusting focus of a camera corresponding to the depth information of the selected objects; see figs. 7-10 and paragraph 0100-0122, 0050, 0056).
Regarding claim 7, Govindarao discloses everything claimed as applied above (see claim 1). In addition, Govindarao discloses wherein determining the autofocus distance based on the depth histogram comprises: determining one or more peaks of the depth histogram (The objects are selected based on analyzing the disparity histogram to determine significant peaks; see paragraphs 0050, 0056), wherein each peak is associated with a peak height (Height of a vertical bar along the Y-axis represents a size of an object at a corresponding disparity (or depth) in the scene 300. So, based on the heights of the vertical bars, sizes of different objects at corresponding disparity values may be determined; see paragraph 0078); selecting a peak from the one or more peaks based on the selected peak being associated with a maximum peak height of the one or more peaks (A peak can be determined as a significant peak if number of pixels is greater than a threshold percentage, for example 5% or 10%, of pixels (or the image size) in the image. If number of images that can be captured or processed in the focal stack capture or the burst capture is fixed at 3, then three most significant peaks can be picked from the disparity histogram; see paragraph 0056); and determining the autofocus distance based on the selected peak (The method facilitates capture of two or more images of the scene by at least adjusting focus of a camera corresponding to the depth information of the selected objects; see figs. 7-10 and paragraph 0100-0122, 0050, 0056).
Regarding claim 9, Govindarao discloses everything claimed as applied above (see claim 1). In addition, Govindarao discloses wherein determining the autofocus distance based on the depth histogram is not based on one or more regions of interest associated with the image captured by the image sensor (The focus of the camera can be adjusted by using statistics of the viewfinder depth map to augment a plurality of statistics generated by a statistics engine for improving quality of the two or more images; see paragraphs 0052, 0107).
Regarding claim 11, Govindarao discloses everything claimed as applied above (see claim 1). In addition, Govindarao discloses generating the depth histogram comprises: determining a count of the plurality of depth values; based at least on the count of the plurality of depth values, determining the plurality of depth value ranges, wherein the depth histogram is determined based on the plurality of depth value ranges (The depth information of the viewfinder depth map 320 is represented using the disparity histogram 330. An X-axis represents the disparity values and Y-axis represents number of pixels occupied by objects at corresponding disparity values. The height of a vertical bar along the Y-axis represents a size of an object at a corresponding disparity (or depth) in the scene 300; see figs. 3A-3C and paragraph 0078).
Regarding claim 14, Govindarao discloses everything claimed as applied above (see claim 1). In addition, Govindarao discloses each depth value of the plurality of depth values is associated with a confidence value, wherein the method further comprises: verifying that the confidence value associated with each depth value of the plurality of depth values is greater than a threshold confidence value (The objects are selected based on analyzing the disparity histogram to determine significant peaks. For instance, a peak may be determined as a significant peak if number of pixels is greater than a threshold percentage, for example 5% or 10%, of pixels (or the image size) in the image. The significant peaks can be determined automatically once the threshold percentage is set. The objects corresponding to the threshold percentage of the image size are thereby selected; see paragraph 0056).
Regarding claim 15, Govindarao discloses everything claimed as applied above (see claim 14). In addition, Govindarao discloses removing each depth value with an associated confidence value less than the threshold confidence value from the plurality of depth values (Objects greater than a threshold percentage of image size in the viewfinder depth map are selected, the non-selected objects are disregarded for focusing purposes; see paragraphs 0056, 0050).
Regarding claim 16, Govindarao discloses everything claimed as applied above (see claim 1). In addition, Govindarao discloses each depth value of the plurality of depth values is associated with a confidence value and a classification of an area represented by the depth value, wherein the method further comprises: verifying that the depth value associated with each depth value of the plurality of depth values is greater than a threshold confidence value associated with the classification of the area represented by the depth value (The objects are selected based on analyzing the disparity histogram to determine significant peaks. For instance, a peak may be determined as a significant peak if number of pixels is greater than a threshold percentage, for example 5% or 10%, of pixels (or the image size) in the image. The significant peaks can be determined automatically once the threshold percentage is set. The objects corresponding to the threshold percentage of the image size are thereby selected; see paragraph 0056. Objects meeting the threshold requirement are classified as object for focusing purposes).
Regarding claim 17, Govindarao everything claimed as applied above (see claim 1). In addition, Govindarao discloses determining the autofocus distance based on the depth histogram comprises: selecting a depth value range from the plurality of depth value ranges; determining an average depth value based on the selected depth value range; and determining the autofocus distance based on the average depth value (The method facilitates capture of two or more images of the scene by at least adjusting focus of a camera corresponding to the depth information of the selected objects; see figs. 7-10 and paragraph 0100-0122).
Regarding claim 18, Govindarao discloses a computing system (see figs. 1-2) comprising:
a control system (Controller 108/processor 202; see figs. 1-2 and paragraphs 0034-0035, 0041) configured to:
receive a plurality of depth values corresponding to a plurality of areas depicted in an image captured by an image sensor (Scene 300 is captured by an image sensor embodied in the camera 208/210. The viewfinder depth map 320 of the scene 300 illustrates depth for objects in the scene 300 of fig. 3A, using a region 322 representing depth for the lady 302, a region 324 representing depth for the lady 304, a region 326 representing depth for the man 306, a region 328 representing depth for the shrubs 308, a region 329 representing depth for the tree trunk 310, a region 320 representing depth of the background; the regions have different patterns to indicate varying depths; see figs. 3A-3B and paragraphs 0076-0077);
generate a depth histogram categorizing each depth value of the plurality of depth values into a depth value range of a plurality of depth value ranges (The depth information of the viewfinder depth map 320 is represented using the disparity histogram 330. An X-axis represents the disparity values (inversely proportional to depth) from 0 to 250, where a disparity value 0 represents an object at a farthest depth from the camera and a disparity value 250 represents an object at a closest depth from the camera. The height of a vertical bar along the Y-axis represents a size of an object at a corresponding disparity (or depth) in the scene 300. A vertical bar 332 represent the lady 302, a vertical bar 334 represent the lady 304, a vertical bar 336 represent the man 306, a vertical bar 338 represent the shrubs 308, a vertical bar 340 represent the tree trunk 310, and a vertical bar 342 represents the background of the scene 300; see fig. 3C and paragraph 0078);
determine an autofocus distance based on the depth histogram; and causing the image sensor to capture an image based on the autofocus distance (The method facilitates selection of objects from the plurality of objects based on depth information of the objects in the viewfinder depth map. The objects are selected if the objects are greater than a threshold percentage of image size in the viewfinder depth map. The method facilitates capture of two or more images of the scene by at least adjusting focus of a camera corresponding to the depth information of the selected objects; see figs. 7-10 and paragraph 0100-0122).
Regarding claim 20, Govindarao discloses a non-transitory computer readable medium (Memory 128/204 stores information to implement the functions of 100/200; see figs. 1-2 and paragraphs 0038, 0040) storing program instructions executable by one or more processors (Controller 108/processor 202; see figs. 1-2 and paragraphs 0034-0035, 0041) to cause the one or more processors to perform operations comprising:
receiving a plurality of depth values corresponding to a plurality of areas depicted in an image captured by an image sensor (Scene 300 is captured by an image sensor embodied in the camera 208/210. The viewfinder depth map 320 of the scene 300 illustrates depth for objects in the scene 300 of fig. 3A, using a region 322 representing depth for the lady 302, a region 324 representing depth for the lady 304, a region 326 representing depth for the man 306, a region 328 representing depth for the shrubs 308, a region 329 representing depth for the tree trunk 310, a region 320 representing depth of the background; the regions have different patterns to indicate varying depths; see figs. 3A-3B and paragraphs 0076-0077);
generating a depth histogram categorizing each depth value of the plurality of depth values into a depth value range of a plurality of depth value ranges (The depth information of the viewfinder depth map 320 is represented using the disparity histogram 330. An X-axis represents the disparity values (inversely proportional to depth) from 0 to 250, where a disparity value 0 represents an object at a farthest depth from the camera and a disparity value 250 represents an object at a closest depth from the camera. The height of a vertical bar along the Y-axis represents a size of an object at a corresponding disparity (or depth) in the scene 300. A vertical bar 332 represent the lady 302, a vertical bar 334 represent the lady 304, a vertical bar 336 represent the man 306, a vertical bar 338 represent the shrubs 308, a vertical bar 340 represent the tree trunk 310, and a vertical bar 342 represents the background of the scene 300; see fig. 3C and paragraph 0078);
determining an autofocus distance based on the depth histogram; and causing the image sensor to capture an image based on the autofocus distance (The method facilitates selection of objects from the plurality of objects based on depth information of the objects in the viewfinder depth map. The objects are selected if the objects are greater than a threshold percentage of image size in the viewfinder depth map. The method facilitates capture of two or more images of the scene by at least adjusting focus of a camera corresponding to the depth information of the selected objects; see figs. 7-10 and paragraph 0100-0122).
Claim Rejections - 35 USC § 103
6. 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.
7. 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.
8. Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Govindarao in view of Stavely et al. (US-PGPUB 2013/0002940).
Regarding claim 3, Govindarao discloses everything claimed as applied above (see claim 1). In addition, Govindarao discloses after causing the image sensor to capture the image based on the autofocus distance, executing a further autofocus process to determine a refined autofocus distance, wherein generating the depth histogram and determining the autofocus distance are associated with a first average execution duration, wherein executing the further autofocus process to determine the refined autofocus distance is associated with a second average execution duration (The apparatus 200 is caused to generate the video where the one or more objects are in continuous autofocus in different image frames of the video. Herein, the ‘continuous autofocus’ refers to a mode of the camera in which one or more objects in the scene can be continuously focused upon against a changing background (the one or more objects are moving). The first image frame is captured at the time instance t(n) by setting the focus of the camera at the first focus position f1 based on the depth map (or disparity map) received at the time instance t(n−1). A second focus position f2 is determined based on the disparity D2 of the object, where the disparity D2 is obtained from the second depth map received at the time instance t(n). The change in focus positions of the object from f1 to f2 between two captures is thereby determined based on the change in disparity D1 to D2 and using one or more other image statistics. For performing the continuous autofocus, the apparatus 200 is caused to adjust the focus of the camera based on the determined focus position, for example the second focus position f2, to capture an image frame, for example a second image frame, of the video; see paragraphs 0057, 0059-0060, 0081-0084).
However, Govindarao does not expressly disclose the first average execution duration is less than the second average execution duration.
On the other hand, Stavely discloses the first average execution duration is less than the second average execution duration (The stereoscopic autofocus system can provide a relatively quick coarse autofocus adjustment. Then, the digital autofocus system provides a finer focus adjustment; see paragraph 0006).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Govindarao and Stavely to provide the first average execution duration is less than the second average execution duration for the purpose of providing a relatively quicker and more reliable autofocus total processing rather than just implementing a single non-changeable autofocus processing.
Regarding claim 4, Govindarao discloses everything claimed as applied above (see claim 1). In addition, Govindarao discloses wherein the method further comprises: after causing the image sensor to capture the image based on the autofocus distance, executing a further autofocus process to determine a refined autofocus distance, wherein generating the depth histogram and determining the autofocus distance are associated with a first average processing power, wherein executing the further autofocus process to determine the refined autofocus distance is associated with a second average processing power (The apparatus 200 is caused to generate the video where the one or more objects are in continuous autofocus in different image frames of the video. Herein, the ‘continuous autofocus’ refers to a mode of the camera in which one or more objects in the scene can be continuously focused upon against a changing background (the one or more objects are moving). The first image frame is captured at the time instance t(n) by setting the focus of the camera at the first focus position f1 based on the depth map (or disparity map) received at the time instance t(n−1). A second focus position f2 is determined based on the disparity D2 of the object, where the disparity D2 is obtained from the second depth map received at the time instance t(n). The change in focus positions of the object from f1 to f2 between two captures is thereby determined based on the change in disparity D1 to D2 and using one or more other image statistics. For performing the continuous autofocus, the apparatus 200 is caused to adjust the focus of the camera based on the determined focus position, for example the second focus position f2, to capture an image frame, for example a second image frame, of the video; see paragraphs 0057, 0059-0060, 0081-0084).
However, Govindarao does not expressly disclose the first average processing power is less than the second average processing power.
Nevertheless, Stavely discloses the first average processing power is less than the second average processing power (The stereoscopic autofocus system can provide a relatively quick coarse autofocus adjustment. Then, the digital autofocus system provides a finer focus adjustment; see paragraph 0006. A faster coarse processing uses less processing time and thus less power consumption).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Govindarao and Stavely to provide the first average processing power is less than the second average processing power for the purpose of providing a relatively quicker and more reliable autofocus total processing rather than just implementing a single non-changeable autofocus processing.
9. Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Govindarao in view of Schlaudraff et al. (US-PGPUB 2022/0277441).
Regarding claim 8, Govindarao discloses everything claimed as applied above (see claim 1). In addition, Govindarao discloses determining the autofocus distance based on the depth histogram is based on applying a machine model to the depth histogram (The instructions may specifically configure the processor 202 to perform the algorithms and/or operations described herein when the instructions are executed. The processor 202 can be a processor of a specific device, for example, a mobile terminal or network device adapted for employing embodiments by further configuration of the processor 202 by instructions for performing the algorithms described herein; see paragraph 0041).
However, Govindarao does not expressly disclose machine learning models.
On the other hand, Schlaudraff discloses determining the autofocus distance based on the depth is based on applying a machine learning model to the depth (The input image and/or the output image may comprise depth data which are representative of the focus distance of a focal plane, in which the respective input image was recorded. The image apparatus may be configured to control the autofocus objective depending on the depth data. Using software-based autofocus that can be used both for finding and holding the focus and executing the method by a neural network device or a machine learning device trained by input and output image data; see paragraphs 0017, 0113-0114, 0125).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Govindarao and Schlaudraff to provide determining the autofocus distance based on the depth histogram is based on applying a machine learning model to the depth histogram for the purpose of providing a quick and reliable autofocus processing.
10. Claim 10, 12, 13 and 19 is rejected under 35 U.S.C. 103 as being unpatentable over Govindarao in view of Katayama (US-PGPUB 2018/0070006).
Regarding claim 10, Govindarao discloses everything claimed as applied above (see claim 1). In addition, Govindarao discloses wherein generating the depth histogram comprises: determining a range of depth values of the plurality of depth values (The regions in map 320 have different patterns to indicate varying depths; see figs. 3A-3B and paragraphs 0076-0077) and determining the depth histogram based on the plurality of depth value ranges (The depth information of the viewfinder depth map 320 is represented using the disparity histogram 330; see figs. 3B-3C and paragraph 0078).
However, Govindarao does not expressly discloses based at least on the range of depth values, determining the plurality of depth value ranges to evenly divide the range of depth values.
On the other hand, Katayama discloses based at least on the range of depth values, determining the plurality of depth value ranges to evenly divide the range of depth values (As illustrated in FIG. 4A, the screen during imaging (object image 401) displayed on the display unit 112 includes objects 403, 404 and 405. The distance map corresponding to the object image 401 is a distance map 402 illustrated in FIG. 4B. In the distance map 402, distance data 406, 407 and 408 indicate distances to the objects 403, 404 and 405, respectively. The system control unit 110 compiles the numbers of unit areas in the area 421 for each distance indicated by the distance data and arranges the numbers of unit areas for each distance in ascending order from the minimum value to the maximum value of distance. For example, the numbers of unit areas corresponding to each distance in the area 421 of FIG. 4B are as illustrated in Table 1, and the numbers are converted into a graph to obtain the distance histogram illustrated in FIG. 5; see figs. 4A-7 and paragraphs 0056-0060. Katayama discloses determining a plurality of distance ranges and evenly distributing the distance ranges in the histogram).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Govindarao and Katayama to provide based at least on the range of depth values, determining the plurality of depth value ranges to evenly divide the range of depth values for the purpose of effectively displaying significant distance information to the user, thus accurately executing focus on objects intended by the user.
Regarding claim 12, Govindarao discloses everything claimed as applied above (see claim 1). However, Govindarao does not expressly disclose a dual pixel sensor.
On the other hand, Katayama discloses receiving the plurality of depth values corresponding to the plurality of areas depicted in the image is based on receiving data collected from a dual pixel sensor (The imaging sensor 103 can divide and import signals of pixels of an imaging surface to acquire phase difference information for AF (auto focus) of each pixel. Based on the phase difference information acquired from the imaging sensor 103, the distance information generation unit 105 generates the information of distance to the object being imaged at predetermined resolving power. The distance information generation unit 105 generates a distance map based on the focus detection signal; see paragraphs 0038, 0047, 0028-0029).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Govindarao and Katayama to provide a dual pixel sensor for the purpose of improving the detection of the focus detection.
Regarding claim 13, Govindarao and Katayama disclose everything claimed as applied above (see claim 12). However, Govindarao does not disclose a dual pixel sensor.
Nevertheless, Katayama discloses the plurality of depth values are a plurality of disparity values determined based on the data collected from the dual pixel sensor (Based on the phase difference information acquired from the imaging sensor 103, the distance information generation unit 105 generates the information of distance to the object being imaged at predetermined resolving power. The distance information generation unit 105 generates a distance map based on the focus detection signal; see paragraphs 0038, 0047, 0028-0029).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Govindarao and Katayama to provide a dual pixel sensor for the purpose of improving the detection of the focus detection.
Regarding claim 19, Govindarao discloses everything claimed as applied above (see claim 18). However, Govindarao does not disclose a phase detection sensor.
On the other hand, Katayama discloses the control system is further configured to determine the plurality of depth values based on data collected from a phase detection sensor (The imaging sensor 103 can divide and import signals of pixels of an imaging surface to acquire phase difference information for AF (auto focus) of each pixel. Based on the phase difference information acquired from the imaging sensor 103, the distance information generation unit 105 generates the information of distance to the object being imaged at predetermined resolving power. The distance information generation unit 105 generates a distance map based on the focus detection signal; see paragraphs 0038, 0047, 0028-0029).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Govindarao and Katayama to provide a phase detection sensor for the purpose of improving the detection of the focus detection.
Citation of Pertinent Art
11. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Tzur (US Patent 9,501,834) discloses an image is chosen automatically, based on which image has the highest depth map histogram count.
Feng et al. (US-PGPUB 2022/0237813) discloses a face or object detection algorithm may be used to determine potential objects of interest and the focal depths of those potential objects of interest compared to peaks on a histogram of depth data to determine if the potential objects are far enough apart in distance to trigger capturing additional images.
Kameyama (US-PGPUB 2016/0225167) discloses the image processing unit 205 divides the range of the subject distances based on the distribution of the pixel values of the distance image 42 (i.e., distribution of the subject distances), extracts a corresponding area of the captured image for each of the ranges of the subject distances, and generates depth-divided images 43. One depth-divided image can be selected based on one or more of conditions, such as a predetermined specific subject being shown, a predetermined specific subject being in focus, a subject having a certain size or more being shown, a subject being shown near the center of the image, and the like.
Contact Information
12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CYNTHIA CALDERON whose telephone number is (571)270-3580. The examiner can normally be reached M-F 9:00 AM-5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, TWYLER HASKINS can be reached at (571)272-7406. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CYNTHIA CALDERON/Primary Examiner, Art Unit 2639 07/22/2026
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