DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Applicant’s amendment was received on 3/18/26 and has been entered and made of record. Currently, claims 1-20 are pending.
Drawings
The drawings were received on 3/18/26. These drawings are accepted.
Applicant’s amendment to the specification and to Fig. 15 has overcome the objection set forth in the previous Office Action and has therefore been withdrawn.
Response to Arguments
Applicant's arguments filed 3/18/26 have been fully considered but they are not persuasive.
The applicant asserts Raghu et al. (US 2016/0176358) fails to disclose the limitations set forth in claims 2, 3, 10, 11, 17, and 18. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). As such, the Examiner disagrees with the applicant’s assertion and the combination of Felner et al. (US 2022/0374638) and Raghu disclose that which is set forth in claims 2, 3, 10, 11, 17, and 18. Particularly, Felner discloses a recognition program 118 that detects features and objects present in a captured image. A detection program 122 detects when concentrated or otherwise bright light is affecting the ability of a camera to capture images that can be used for image recognition. The light interference detection program 122 detects high light intensity portions, such as bright spots from the sun or headlights. The recognition program 118 detects features and objects present in a captured image and can send notifications to vehicle computing device 116 to perform an action, such as braking, steering, etc. (paras 22-24, 41, 60-62, and 80). Felner also discloses the light interference detection program 122 may generate a disparity image from received images or may use an already generated disparity image for performing the light interference detection herein. For instance, the light interference detection program 122 may determine areas of the received images that exceed a brightness threshold and compare those areas with areas of the disparity image for determining whether the disparity information in these areas is below a threshold for detecting light interference. Further, the light interference detection program 122 may determine whether the detected light interference is affecting the camera image sensor(s) in an area of interest, such as an area in the direction of travel, in the roadway, or other areas that may be necessary for safe navigation (para 54). Light interference may correspond to a bright spot as an area of white pixels or of local brightness on the standard visual captured image 502 and as an area of low or no disparity data on the disparity image 504 (e.g., below a threshold level as compared to a remainder of the disparity image 504 (para 62). The system may identify one or more regions of relatively high brightness in the grayscale reduced noise image. For example, the system may examine individual pixels of the image for comparison with a threshold level of brightness and may perform clustering on pixels that exceed the brightness threshold to determine any bright spots in the image (para 80). The threshold values would thus create maximum and minimum brightness values.
Raghu discloses, as is known in the art, a digital image is a matrix (a two-dimensional array) of pixels. The value of each pixel is proportional to the brightness of the corresponding point in the scene; its value is often derived from the output of an A/D converter 103. The matrix of pixels, is typically square and may be thought of as an image as N×N m-bit pixels where N is the number of points along the axes and m controls the number of brightness values. Using m bits gives a range of 2.sup.m values, ranging from 0 to 2.sup.m−1. For example, if m is 8 this would provide brightness levels ranging between 0 and 255, which may be displayed as black and white, respectively, with shades of grey in between, similar to a greyscale image (para 117).
Raghu was cited for the relationship between pixels, bit range, that make up a digital image and brightness. Felner discloses the selecting of a portion or portions of a digital image having a high light intensity or bright spot. Felner further uses brightness threshold values to determine a high light intensity portion or bright spot. The threshold values would be boundaries for the selected potion of the digital image. Therefore, the combination of Felner and Raghu disclose that set forth in claims 2, 3, 10, 11, 17, and 18.
Claim Rejections - 35 USC § 102
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1, 5-7, 9, 13-16, and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Felner et al. (US 2022/0374638).
Regarding claims 1 and 16, Felner discloses a non-transitory computer-readable medium or media comprising one or more sequences of instructions which, when executed by at least one processor, causes steps to be performed and a computer-implemented method for image processing comprising:
selecting a first portion of image data of an input image, in which the first portion of image data represents a high light intensity portion of the image data and the remaining image data comprises a lower light intensity portion of the image data (see Fig. 5 and paras 23-24, 30, 44, 54, and 59, recognition program 118 detects features and objects present in a captured image of lower light intensity than light interference detection program 122, which detects high light intensity portions, such as bright spots);
using at least a part of the first portion of image data for bright object detection or object detection and classification in the input image (see paras 54, 60-62, and 80, light interference detection program 122, detects high light intensity portions, such as bright spots from the sun or headlights); and
using at least part of the lower light intensity portion of the image data in one or more image processing applications or tasks (see paras 23 and 41, recognition program 118 detects features and objects present in a captured image and can send notifications to vehicle computing device 116 to perform an action, such as braking, steering, etc.).
Regarding claim 9, Felner discloses a system comprising:
one or more processors (see paras 31 and 33, vehicle computing device 116 may include one or more processors); and
a non-transitory computer-readable medium or media comprising one or more sets of instructions which, when executed by at least one of the one or more processors (see paras 31 and 34, computer-readable media 204), causes steps to be performed comprising:
selecting a first portion of image data of an input image, in which the first portion of image data represents a high light intensity portion of the image data and the remaining image data comprises a lower light intensity portion of the image data (see Fig. 5 and paras 23-24, 30, 44, 54, and 59, recognition program 118 detects features and objects present in a captured image of lower light intensity than light interference detection program 122, which detects high light intensity portions, such as bright spots);
using at least a part of the first portion of image data for bright object detection or object detection and classification in the input image (see paras 54, 60-62, and 80, light interference detection program 122, detects high light intensity portions, such as bright spots from the sun or headlights); and
using at least part of the lower light intensity portion of the image data in one or more image processing applications or tasks (see paras 23 and 41, recognition program 118 detects features and objects present in a captured image and can send notifications to vehicle computing device 116 to perform an action, such as braking, steering, etc.).
Regarding claims 5, 13, and 20, Felner further discloses wherein the step of using at least part of the lower light intensity portion of the image data in one or more image processing applications comprises: using all of the image data in one or more image processing applications (see paras 23 and 41, recognition program 118 detects features and objects present in a captured image and can send notifications to vehicle computing device 116 to perform an action, such as braking, steering, etc.).
Regarding claims 6 and 14, Felner further discloses wherein the step of using at least part of the lower light intensity portion of the image data in one or more image processing applications comprises: using at least part of the lower light intensity portion of image data but none of the first portion of image data in one or more image processing applications (see paras 23, 41, and 83-84, recognition program 118 detects features and objects present in a captured image and can send notifications to vehicle computing device 116 to perform an action, such as braking, steering, etc., if the light interference detection program 122 does not detect light intensity above a threshold then no bright spots are deemed to be found).
Regarding claims 7 and 15, Felner further discloses wherein at least one of the one or more image processing applications or tasks comprises at least one of: a simultaneous localization and mapping (SLAM) process; an object detection and classification process; distortion correction; cropping; stitching; depth extraction; and three-dimensional (3D) construction (see paras 23-24, 27, and 58, recognition program 118 detects features and objects present in a captured image and performs mapping, object detection and classification, depth extraction and 3D construction).
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 2-4, 8, 10-12, and 17-19 are rejected under 35 U.S.C. 103(a) as being unpatentable over Felner as applied to claims 1, 9, and 16 above, and further in view of Raghu et al. (US 2016/0176358).
Regarding claims 2, 10, and 17 Felner does not disclose expressly wherein the first portion of image data of an input image comprises: selecting data within a bit range of the image data, in which one boundary of the bit range corresponds to a maximum or minimum.
Raghu discloses wherein the first portion of image data of an input image comprises: selecting data within a bit range of the image data, in which one boundary of the bit range corresponds to a maximum or minimum (see paras 117-118, bit ranges can be selected, such as 8-bit).
Regarding claim 8, Felner does not disclose expressly wherein the image data represents one channel of the input image and a first portion is selected for each channel of the input image.
Raghu discloses wherein the image data represents one channel of the input image and a first portion is selected for each channel of the input image (see paras 117-118, 8-bit channels for RGB can be utilized).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the bit range of the image data, as described by Raghu, with the system of Felner.
The suggestion/motivation for doing so would have been to provide more or less image detail depending on the processing capability.
Therefore, it would have been obvious to combine Raghu with Felner to obtain the invention as specified in claims 2, 8, 10, and 17.
Regarding claims 3, 11, and 18, Raghu further discloses wherein the step of selecting data within a bit range of the image data, in which one boundary of the bit range corresponds to a maximum or minimum comprises: given that the image data is represented by a bit depth defined by an upper limit value and a lower limit value, selecting the upper limit value or the lower limit value of the bit depth as the maximum or the minimum that defines one of the boundaries of the bit range of the first portion of image data (see paras 117-118, bit ranges can be selected, such as 8-bit).
Regarding claims 4, 12, and 19, Raghu further discloses wherein the step of selecting data within a bit range of the image data, in which one boundary of the bit range corresponds to a maximum or minimum comprises: selecting a maximum data value or a minimum data value of the input image as the maximum or the minimum that defines one of the boundaries of the bit range of the first portion of image data (see paras 117-118, bit ranges can be selected, such as 8-bit).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK R MILIA whose telephone number is (571) 272-7408. The examiner can normally be reached Monday-Friday, 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Akwasi Sarpong can be reached at 571-270-3438. The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARK R MILIA/ Primary Examiner, Art Unit 2681
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