DETAILED ACTION
Election/Restrictions
Applicant's election with traverse of Group 1, Claims 15-25 and 29-34 in the reply filed on 13 August 2026 is acknowledged. The traversal is on the ground(s) that there is no burden in examining the different species. This is not found persuasive because Groups II and III have features excluded from Group I, and features excluded from each other. That is, each group requires a different set of references. Each different set of references requires a distinct search.
The requirement is still deemed proper and is therefore made FINAL. Claim 15 is generic. Claims including all the features of an allowable generic claim shall be rejoined.
Claims 26-28 and 35-36 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected groups, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 13 August 2026.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 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.
Claim(s) 15-17, 21-24, 30-31, 33-34 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US PG Publication 2022/0343102) in view of Liu (US PG Publication 2018/0039853).
Regarding Claim 15, Wang (US PG Publication 2022/0343102) discloses a camera apparatus (smart imaging system 100 [0035]; imaging device 104, Fig. 1 [0035]) for monitoring and/or controlling a work sequence in a working environment (intended use; imaging device captures an image of a target object as part of an industrial assembly line [0064]), wherein the camera apparatus comprises:
at least one image sensor (imaging assembly 126 [0037], [0040]) in which a plurality of picture elements are arranged (image data comprises pixel data [0034]) and which is configured to record a sequence of image data (configure the imaging device 104 to capture and analyze images [0036]) of at least one region of the working environment (in accordance with the machine vision job [0036]; objects/environment present within the imaging device FOV [0064]), wherein the recording of the image data takes place during the work sequence (intended use),
at least one detection unit (method 400, assign visual indicia to ROI 408, identify ROI 418, Fig. 4, [0066], [0074]; adaptive ROI application 116 [0037] and pattern recognition application 128 [0041]; processors 118 may process the image data or datasets captured [0041]) that is configured to detect one or more objects (visual indicia [0037]) in the image data (within the captured image [0037]),
at least one control unit (imaging device 104 may include one or more processors 118 [0039]) that is configured to control image recording parameters (imaging device 104 may increase the aperture 204, its own configuration to capture images with optimal characteristics in the determined adaptive ROI [0051], exposure length [0053]) of the image sensor (aperture 204 of imaging device 104 [0050]),
wherein the detection unit is configured to determine a region of interest (determined adaptive ROI [0051]) on the basis of the objects detected in the image data (representative of the objects/environment present within the imaging device FOV [0064]),
wherein the control unit is configured to control at least one image recording parameter (optimal brightness, sharpness [0051]) on the basis of the region of interest (in the determined adaptive ROI [0051]) determined by the detection unit (method 400, assign visual indicia to ROI 408, identify ROI 418, Fig. 4, [0066], [0074]).
Wang does not disclose, but Liu (US PG Publication 2018/0039853) teaches wherein the at least one image recording parameter comprises a cropping parameter (When the proposal box 15 is applied to the image 10, the neural networks 200 crops the target region image corresponding to the proposal box 15 [0028]) that limits the recording of image data by the image sensor (resized target image 16 is transmitted [0028]) to an image section that corresponds to the region of interest (region proposal network (RPN) 400 is applied to the image 10 to generate a proposal box 15, part of the image 10 encompassed by the proposal box 15 is referred to as a target region image [0025]).
One of ordinary skill in the art before the application was filed would have been motivated to crop the image of Wang to the ROI and resize the ROI, as in Liu, because Liu teaches that the standard machine learning models for object detection take in a fixed sized input [0025], and converting the ROI patches to the fixed sized input enables the system to take advantage of existing detection models, saving resources.
Regarding Claim 16, Wang (US PG Publication 2022/0343102) discloses the camera apparatus according to claim 15, wherein the control unit is configured to determine the at least one image recording parameter for the next recording of image data (capture a subsequent image that ideally includes pixels within the ROI that have increased/optimized brightness and sharpness values, capture subsequent image data following the adjustment of the imaging parameters [0079]) on the basis of the region of interest (brightness and sharpness of ROI pixels 420, Fig. 4) determined by the detection unit (method 400, assign visual indicia to ROI 408, identify ROI 418, Fig. 4, [0066], [0074]) in the image data of the previous recording (i.e., image preceding the “subsequent image” [0079]).
Regarding Claim 17, Wang (US PG Publication 2022/0343102) discloses the camera apparatus according to claim 15.
Wang does not disclose, but Liu (US PG Publication 2018/0039853) teaches wherein the resolution of the image data is constant and can be selected and/or preset between a minimum value and a maximum value (the predetermined identical size may be 227x227 (224x224 for VGG16) patches, pixels [0025]).
One of ordinary skill in the art before the application was filed would have been motivated to crop the image of Wang to the ROI and resize the ROI, as in Liu, because Liu teaches that the standard machine learning models for object detection take in a fixed sized input [0025], and converting the ROI patches to the fixed sized input enables the system to take advantage of existing detection models, saving resources.
Regarding Claim 21, Wang (US PG Publication 2022/0343102) discloses the camera apparatus according to claim 15, wherein the image recording parameters comprise the exposure time for the recording of the image data by the image sensor (exposure length [0053]).
Regarding Claim 22, Wang (US PG Publication 2022/0343102) discloses the camera apparatus according to claim 15, wherein the detection unit (method 400, assign visual indicia to ROI 408, identify ROI 418, Fig. 4, [0066], [0074]) comprises a neural network (adaptive ROI algorithm model including CNN [0061]) that is configured to determine a region of interest on the basis of the objects detected in the image data (trained to detect ROIs [0063]).
Regarding Claim 23, Wang (US PG Publication 2022/0343102) discloses a method for monitoring and/or controlling (smart imaging system 100 [0035]; imaging device 104, Fig. 1 [0035]) a work sequence (imaging device captures an image of a target object as part of an industrial assembly line [0064]) in a working environment (environment present within the imaging device FOV [0064]) by a camera apparatus (imaging device 104, Fig. 1 [0035]), wherein the camera apparatus comprises at least one image sensor (imaging device 104, Fig. 1 [0035] having imaging assembly 126 [0037], [0040]), at least one detection unit (method 400, assign visual indicia to ROI 408, identify ROI 418, Fig. 4, [0066], [0074]; adaptive ROI application 116 [0037] and pattern recognition application 128 [0041]; processors 118 may process the image data or datasets captured [0041]) and at least one control unit (method 400, assign visual indicia to ROI 408, identify ROI 418, Fig. 4, [0066], [0074]; adaptive ROI application 116 [0037] and pattern recognition application 128 [0041]; processors 118 may process the image data or datasets captured [0041]) that is configured to control image recording parameters of the image sensor (imaging device 104 may increase the aperture 204, its own configuration to capture images with optimal characteristics in the determined adaptive ROI [0051], exposure length [0053]), wherein a plurality of picture elements (image data comprises pixel data [0034]) are arranged in the image sensor (inherent) and the image sensor records a sequence of image data (imaging device 104 to capture and analyze images [0036]) of at least one region of the working environment (objects/environment present within the imaging device FOV [0064]),
the detection unit detects objects (assign visual indicia to ROI 408, identify ROI 418, Fig. 4, [0066], [0074]; visual indicia [0037]) in the image data (visual indicia within the captured image [0037]) and determines a region of interest (determined adaptive ROI [0051]) within the working environment on the basis of the objects detected in the image data (representative of the objects/environment present within the imaging device FOV [0064]),
the control unit controls at least one image recording parameter (increase the aperture 204 [0050]-[0051], exposure length [0053]; optimal brightness, sharpness [0051]) on the basis of the region of interest (in the determined adaptive ROI [0051]) determined by the detection unit (method 400, assign visual indicia to ROI 408, identify ROI 418, Fig. 4, [0066], [0074]).
Wang does not disclose, but Liu (US PG Publication 2018/0039853) teaches wherein the at least one image recording parameter comprises a cropping parameter (When the proposal box 15 is applied to the image 10, the neural networks 200 crops the target region image corresponding to the proposal box 15 [0028]) that limits the recording of image data by the image sensor (resized target image 16 is transmitted [0028]) to an image section that corresponds to the region of interest (region proposal network (RPN) 400 is applied to the image 10 to generate a proposal box 15, part of the image 10 encompassed by the proposal box 15 is referred to as a target region image [0025]).
One of ordinary skill in the art before the application was filed would have been motivated to crop the image of Wang to the ROI and resize the ROI, as in Liu, because Liu teaches that the standard machine learning models for object detection take in a fixed sized input [0025], and converting the ROI patches to the fixed sized input enables the system to take advantage of existing detection models, saving resources.
Regarding Claim 24, Wang (US PG Publication 2022/0343102) discloses the method according to claim 23, wherein the control unit determines image recording parameters for the next recording of image data capture a subsequent image that ideally includes pixels within the ROI that have increased/optimized brightness and sharpness values, capture subsequent image data following the adjustment of the imaging parameters [0079]) on the basis of the region of interest (brightness and sharpness of ROI pixels 420, Fig. 4) determined by the detection unit (method 400, assign visual indicia to ROI 408, identify ROI 418, Fig. 4, [0066], [0074]) in the image data of the previous recording (i.e., image preceding the “subsequent image” [0079]).
Regarding Claim 30, Wang (US PG Publication 2022/0343102) discloses the camera apparatus according to claim 15, wherein the control unit is configured to adaptively change said at least one image recording parameter (capture a subsequent image that ideally includes pixels within the ROI that have increased/optimized brightness and sharpness values, capture subsequent image data following the adjustment of the imaging parameters [0079]) on the basis of the region of interest (brightness and sharpness of ROI pixels 420, Fig. 4) determined by the detection unit (method 400, assign visual indicia to ROI 408, identify ROI 418, Fig. 4, [0066], [0074]).
Regarding Claim 31, Wang (US PG Publication 2022/0343102) discloses the camera apparatus according to claim 16, wherein the control unit is configured to adaptively change the at least one image recording parameter for the next recording of image data (capture a subsequent image that ideally includes pixels within the ROI that have increased/optimized brightness and sharpness values, capture subsequent image data following the adjustment of the imaging parameters [0079]) on the basis of the region of interest determined by the detection unit (method 400, assign visual indicia to ROI 408, identify ROI 418, Fig. 4, [0066], [0074]) in the image data of the previous recording (i.e., image preceding the “subsequent image” [0079]).
Regarding Claim 33, Wang (US PG Publication 2022/0343102) discloses the method according to claim 23, wherein the control unit adaptively changes said at least one image recording parameter (capture a subsequent image that ideally includes pixels within the ROI that have increased/optimized brightness and sharpness values, capture subsequent image data following the adjustment of the imaging parameters [0079]) on the basis of the region of interest (brightness and sharpness of ROI pixels 420, Fig. 4) determined by the detection unit (method 400, assign visual indicia to ROI 408, identify ROI 418, Fig. 4, [0066], [0074]).
Regarding Claim 34, Wang (US PG Publication 2022/0343102) discloses the method according to claim 24, wherein the control unit adaptively changes the image recording parameters for the next recording of image data (capture a subsequent image that ideally includes pixels within the ROI that have increased/optimized brightness and sharpness values, capture subsequent image data following the adjustment of the imaging parameters [0079]) on the basis of the region of interest (brightness and sharpness of ROI pixels 420, Fig. 4) determined by the detection unit (method 400, assign visual indicia to ROI 408, identify ROI 418, Fig. 4, [0066], [0074]) in the image data of the previous recording (i.e., image preceding the “subsequent image” [0079]).
Claim(s) 18, 29, 32 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US PG Publication 2022/0343102) in view of Liu (US PG Publication 2018/0039853) and Pieper (US PG Publication 2022/0141450).
Regarding Claim 18, Wang (US PG Publication 2022/0343102) discloses the camera apparatus according to claim 15.
Wang does not disclose, but Pieper (US PG Publication 2022/0141450) teaches wherein the image recording parameters comprise a binning parameter that summarizes a number of adjacent picture elements of the image sensor (in response to detecting reduced contrast, activate binning [0161]).
One of ordinary skill in the art would have been motivated to activate binning in the camera of Wang in response to detecting poor lighting in the region of interest because binning is a known camera setting for improving dynamic range (lighting), and Wang desires to achieve the best lighting and sharpness of the ROI by adjusting camera settings [0051].
Regarding Claim 29, Wang (US PG Publication 2022/0343102) discloses the camera apparatus according to claim 15.
Wang does not disclose, but Pieper (US PG Publication 2022/0141450) teaches wherein the recording of the image data takes place continuously at a presettable or preset frame rate (the camera types may be capable of any image capture rate, such as 60 frames per second (fps), 120 fps, 240 fps [0200]).
One of ordinary skill in the art would be motivated to operate the camera of Wang at a known frame rate because many modern cameras operate at fixed frame rates, therefore it is standard to operate a camera at a fixed frame rate.
Regarding Claim 32, Wang (US PG Publication 2022/0343102) discloses the method according to claim 23.
Wang does not disclose, but Pieper (US PG Publication 2022/0141450) teaches wherein the image sensor records a sequence of image data of at least one region of the working environment continuously at a presettable or preset frame rate (the camera types may be capable of any image capture rate, such as 60 frames per second (fps), 120 fps, 240 fps [0200]).
One of ordinary skill in the art would be motivated to operate the camera of Wang at a known frame rate because many modern cameras operate at fixed frame rates, therefore it is standard to operate a camera at a fixed frame rate.
Claim(s) 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US PG Publication 2022/0343102) in view of Liu (US PG Publication 2018/0039853) and Bessel (US Patent 6,275,252).
Regarding Claim 19, Wang (US PG Publication 2022/0343102) discloses the camera apparatus according to claim 15.
Wang does not disclose, but Bessel (US Patent 6,275,252) teaches wherein the image recording parameters comprise a spatial filter (spatial filter adapted based on lens position information, Column 5 lines 55-end) parameter that spatially averages the image data recorded by the image sensor (inherent in definition of “filter”).
One of ordinary skill in the art before the application was filed would have been motivated to add an adaptive spatial filter to the image of Wang because filters improve image fidelity and preserve edges, improving image quality for subsequent image processing.
Regarding Claim 20, Wang (US PG Publication 2022/0343102) discloses the camera apparatus according to claim 15.
Wang does not disclose, but Bessel (US Patent 6,275,252) teaches wherein the image recording parameters comprise a temporal filter parameter (spatial filter adapted based on lens position information, Column 5 lines 55-end) that temporally averages a sequence of image data consecutively recorded by the image sensor (inherent in definition of “filter”).
One of ordinary skill in the art before the application was filed would have been motivated to add an adaptive spatial filter to the image of Wang because filters improve image fidelity and preserve edges, improving image quality for subsequent image processing.
Claim(s) 25 is rejected under 35 U.S.C. 103 as being unpatentable over Wang (US PG Publication 2022/0343102) in view of Liu (US PG Publication 2018/0039853) and Chaung (US PG Publication 2012/0169842).
Regarding Claim 25, Wang (US PG Publication 2022/0343102) discloses the method according to claim 23, wherein the image sensor records the entire region of the working environment with a maximum resolution at the start of the method (representative of the objects/environment present within the imaging device FOV [0064]; since this image is before the ROI is identified, it is the full image).
Wang does not disclose but Chaung (US PG Publication 2012/0169842) teaches stores the recording as a reference image (underlying fisheye reference image [0088]).
One of ordinary skill in the art before the application was filed would have been motivated to supplement with registration to an overview/reference image, as in Chaung, because Chuang teaches that registration enables the user of the vision system to back-project objects detected by the camera into their physical location in the environment [0074], improving spatial awareness and understanding.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20090122164 A1 - ROI setting method with selecting, enlarging, and reducing ROI
US 20240428186 A1 - crop a region of interest to deliver to the deep learning module
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/SHADAN E HAGHANI/Examiner, Art Unit 2485
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