CTNF 18/924,194 CTNF 97617 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-9 and 11-19 are rejected under 35 U.S.C. 103 as being unpatentable over Bhate et al. (US Publication 20210304400 A1; hereinafter “Bhate”) in view of Zhao et al. (US publication 20110222754 A1; hereinafter “Zhao”) and Mohseni et al. (US Publication 20210142160 A1; hereinafter “Mohseni”) . In re to claim 1, Bhate teaches wherein: An inspection system, comprising: a camera (input device; [0019] discloses the use of an input device to capture image data for further processing. Further, lines 8-10 disclose that the input device may be a camera) ; and a controller including one or more processors in communication with the camera (Fig. 1 shows the camera (correspondent to the claims) in communication with the central processing unit and graphics processing unit (which are collectively understood as the one or more processors that constitute the controller)) , the controller being configured to: receive one or more images of a target (imaged area; [0020] discloses the capture of an environment that constitutes an imaged area. It is understood that this is the target) captured by the camera (Fig. 3 shows the use of the classifier and detector models that comprise the central and graphics processing units receiving an input image. Thus, showing the controller receiving the one or more captured images from the camera (each correspondent to the claims, respectively). See also [0019] lines 7-8, which disclose capture of the input frame by the input device) ; determine, using a first computer vision algorithm, at least one first prediction ([0020] discloses the use of a classifier in order to generate a probability, this prediction being understood as the first prediction) within the target and a corresponding first prediction confidence level for each of the one or more images ([0020] discloses the use of a classifier in order to generate a probability (understood as a first prediction confidence level) according to an area within the image) ; as well as to process a select input image ([0041] discloses the extraction of the input frame from a plurality of frames in a video feed); determine, using a second computer vision algorithm that is different from the first computer vision algorithm, at least one second prediction of the object of interest and a corresponding second prediction confidence level for each of the selected image ([0036] discloses that the system performs a second detection operation, being the application of the damage detector (118), that is further shown in Fig. 1(118). Additionally, [0036] discloses that the system produces confidence scores for each instance of detected damage) ; and output the at least one second prediction and the second prediction confidence level for the selected image ([0036] lines 7-14 discloses that the system provides confidence scores for each instance of detected damage alongside coordinate data) . Bhate does not explicitly teach wherein: one first prediction is of an object of interest However, in a similar field of endeavor, Zhao teaches wherein: one first prediction is of an object of interest (Fig. 2 discloses the detection of defects within an image, thus disclosing the prediction of an object of interest within an imaged area as a first prediction (by virtue of being a par to the 1 st image algorithm described in [0026])). Zhao, like Bhate, teaches a system that uses a plurality of algorithms in a sequential manner to detect defects within image data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang to perform object of interest prediction when using the first algorithm, as taught by Zhao, to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been to enable the removal of image data lacking a detectable defect to reduce the amount of processing performed by the second algorithm, as is performed in Zhao in Fig. 2 (which shows the removal of defect-free images) . Bhate, in view of Zhao, does not explicitly teach wherein: select an input for which the confidence level of the at least one first prediction is greater than or equal to a first prediction threshold value . However, in a related field of endeavor, Mohseni teaches wherein: select an input for which the confidence level of the at least one first prediction is greater than or equal to a first prediction threshold value ([0071] lines 13-15 “…based at least in part on whether a SoftMax probability of a rejector function exceeds a predetermined threshold probability value…” it is understood that the use of a predetermined threshold for a SoftMax probability calculation discloses the use of thresholding in the instant application’s claims). Mohseni, like Bhate, teaches a probability dependent methodology used to analyze input data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bhate to utilize a SoftMax threshold for the data selection, as taught by Mohseni, to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been to instill a minimum confidence in the initial prediction. In re to claim 2 [dependent on claim 1], Bhate, in view of Zhao and Mohseni, teaches wherein: the first computer vision algorithm determines the first prediction and the corresponding first prediction confidence level for all of the one or more images (Bhate [0020] discloses the use of a classifier in order to generate a probability, thus disclosing performance of the prediction and its corresponding prediction confidence level (correspondent to the claims) on the at least one image) . In re to claim 3 [dependent on claim 1], Bhate, in view of Zhao and Mohseni, teaches wherein: the at least one first prediction comprises at least one of a first classification of the object of interest or localization of the object of interest (Zhao [0030] discloses the classification of objects of interest as defect free or potentially defective. Thus, disclosing the performance of a first classification of the object of interest) . The reasons for combination are the same as provided above. In re to claim 4 [dependent on claim 1], Bhate, in view of Zhao and Mohseni, teaches wherein: the object of interest is a defect within the target (Zhao [0030] discloses the classification of objects of interest as defect free or potentially defective. Thus, disclosing the object of interest being a defect being detected) . The reasons for combination are the same as provided above. In re to claim 5 [dependent on claim 1], Bhate, in view of Zhao and Mohseni, teaches wherein: the first computer vision algorithm is at least one of a first image classification algorithm (Zhao [0030] discloses the classification of objects of interest as defect free or potentially defective. Thus, disclosing the performance of a first classification of the object of interest)) , a single shot detection algorithm, or an object tracking algorithm. The reasons for combination are the same as provided above. In re to claim 6 [dependent on claim 1], Bhate, in view of Zhao and Mohseni, teaches wherein: the second prediction comprises at least one of a second classification of the object of interest or quantification of the object of interest (Bhate Fig. 6 and Bhate [0035]-[0036] discloses the determination of the type of damage using an object detection framework. Thus, disclosing a second classification of the object of interest by the second prediction) . In re to claim 7 [dependent on claim 1], Bhate, in view of Zhao and Mohseni, teaches wherein: the second computer vision algorithm is at least one of a region proposal network, an instance segmentation algorithm (Bhate [0043] discloses, that in instances of a plurality of defects, a plurality of bounding boxes may be placed enclosing each damaged structure. Thus, disclosing an instance segmentation algorithm) , or a semantic segmentation algorithm. In re to claim 8 [dependent on claim 1], Bhate, in view of Zhao and Mohseni, teaches wherein: the camera is configured to transmit the captured one or more images to the controller immediately after capture (Bhate Fig. 1 shows a direct transmission from the input device to the controller (correspondent to the claims). Thus, the camera (correspondent to the claims) is disclosed to have direct transmission to the controller, understood to be indicative of transmission of captured images immediately to the controller) . In re to claim 9 [dependent on claim 1], Bhate, in view of Zhao and Mohseni, teaches wherein: the one or more images are sequential frames of a video captured by the camera or a time ordered sequence of still images (Bhate [0045] discloses that the captured input data is of a video feed, and as such is understood as a time ordered sequence of still images) . As to claims 11-19, they are the method performed by the system recited in claims 1-9. As such, claims 11-19 are rejected under the same reasons provided above, respectively . 07-21-aia AIA Claim s 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bhate in view of Zhao and Mohseni, in further view of Wang et al. (US Patent 10621717 B2; hereinafter “Wang”) . In re to claim 10 [dependent on claim 1], Bhate, in view of Zhao and Mohseni, does not explicitly teaches wherein: the inspection system is a borescope. However, in a similar field of endeavor, Wang teaches wherein: the inspection system is a borescope (borescope; col. 4 lines 38-41 “In at least one embodiment, the input image 106 is provided to the neural network 102 via one or more wired and/or wireless connections from a source, such as a camera or borescope.”) . Wang, like Bhate, teaches an inspection system used to determine the location of defects on a surface. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bhate, in view of Zhao and Mohseni, to examine using a specimen using a borescope, as taught by Wang, to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been to enable the system to examine specimens in difficult to reach locations for a user. As to claim 20, it is the method performed by the system recited in claim 10. As such, claim 20 is rejected under the same reasons provided above. Double Patenting 08-33 AIA The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer. Claims 1-10 and 11-20 (due to reciting similar limitations to claims 1-10, respectively) are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-10 of McCrackin et al. (US patent 12153653 B2; hereinafter McCrackin). Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are anticipated by McCrackin. US publication 20250148056 A1 US patent 12153653 B2 1. (Currently Amended) An inspection system, comprising: a camera; and a controller including one or more processors in communication with the camera, the controller being configured to: receive one or more of a target captured by the camera; determine, using a first computer vision algorithm, at least one first prediction of an object of interest within the target and a corresponding first prediction confidence level for each of the one or more images; select an image for which the confidence level of the at least one first prediction is greater than or equal to a first prediction threshold value; determine, using a second computer vision algorithm that is different from the first computer vision algorithm, at least one second prediction of the object of interest and a corresponding second prediction confidence level for the selected image; and output the at least one second prediction and the second prediction confidence level for the selected image. 1. (Currently Amended) An inspection system, comprising: a camera; and a controller including one or more processors in communication with the camera, the controller being configured to: receive a plurality of images of a target captured by the camera; determine, using a first computer vision algorithm, a first prediction of a defect within the target and a corresponding first prediction confidence level for substantially each of the plurality of images; select a subset of the plurality of images for which the confidence level of the first prediction is greater than a first prediction threshold value; determine, using a second computer vision algorithm that is different from the first computer vision algorithm, a second prediction of the defect and a corresponding second prediction confidence level for each of the selected images in the subset, wherein the second computer vision algorithm includes at least one of a regional proposal network and an instance segmentation model; and output the second prediction and the second prediction confidence level for each of the selected images in the subset. 2. The system of claim 1, wherein the first computer vision algorithm determines the first prediction and the corresponding first prediction confidence level for all of the one or more images. 2. The system of claim 1, wherein the first computer vision algorithm determines the first prediction and the corresponding first prediction confidence level for all of the plurality of images. 3. The system of claim 1, wherein the at least one first prediction comprises at least one of a first classification of the object of interest or localization of the object of interest. 3. The system of claim 1, wherein the first prediction comprises a first classification of the defect. 4. The system of claim 1, wherein the object of interest is a defect within the target. 3. The system of claim 1, wherein the first prediction comprises a first classification of the defect. 5. The system of claim 1, wherein the first computer vision algorithm is at least one of a first image classification algorithm, a single shot detection algorithm, or an object tracking algorithm. 5. The system of claim 1, wherein the first computer vision algorithm is at least one of a first image classification algorithm, a single shot detection algorithm, or an object tracking algorithm. 6. The system of claim 1, wherein the second prediction comprises at least one of a second classification of the object of interest or quantification of the object of interest. 6. The system of claim 1, wherein the second prediction comprises at least one of a second classification of the defect or quantification of the defect. 7. The system of claim 1, wherein the second computer vision algorithm is at least one of a region proposal network, an instance segmentation algorithm, or a semantic segmentation algorithm. 1. “…subset, wherein the second computer vision algorithm includes at least one of a regional proposal network and an instance segmentation model…” 8. The system of claim 1, wherein the camera is configured to transmit the captured one or more images to the controller immediately after capture. 8. The system of claim 1, wherein the camera is configured to transmit the captured plurality of images to the controller immediately after capture. 9. The system of claim 1, wherein the one or more images are sequential frames of a video captured by the camera or a time ordered sequence of still images. 9. The system of claim 1, wherein the plurality of images are sequential frames of a video captured by the camera or a time ordered sequence of still images. 10. The system of claim 1, wherein the inspection system is a borescope. 10. The system of claim 1, wherein the inspection system is a borescope. While McCrackin does not explicitly teach the selection of a singular image (regarding claim 1), the selection of a subset of images is understood to constitute the selection of an image (due to requiring image selection to produce the subset of images). As such (including the similar claim limitations shown above), Claim 1 of McCrackin anticipates the limitations of Claim 1 of the instant application. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEVIN M COOMBER whose telephone number is (571)270-0950. The examiner can normally be reached Monday - Friday 8:00am-5:00pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gregory Morse can be reached at (571) 272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /KEVIN M COOMBER/Examiner, Art Unit 2663 /GREGORY A MORSE/Supervisory Patent Examiner, Art Unit 2698 Application/Control Number: 18/924,194 Page 2 Art Unit: 2663 Application/Control Number: 18/924,194 Page 3 Art Unit: 2663 Application/Control Number: 18/924,194 Page 4 Art Unit: 2663 Application/Control Number: 18/924,194 Page 5 Art Unit: 2663 Application/Control Number: 18/924,194 Page 6 Art Unit: 2663 Application/Control Number: 18/924,194 Page 7 Art Unit: 2663 Application/Control Number: 18/924,194 Page 8 Art Unit: 2663 Application/Control Number: 18/924,194 Page 9 Art Unit: 2663 Application/Control Number: 18/924,194 Page 10 Art Unit: 2663 Application/Control Number: 18/924,194 Page 11 Art Unit: 2663 Application/Control Number: 18/924,194 Page 12 Art Unit: 2663 Application/Control Number: 18/924,194 Page 13 Art Unit: 2663 Application/Control Number: 18/924,194 Page 14 Art Unit: 2663 Application/Control Number: 18/924,194 Page 15 Art Unit: 2663
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