CTFR 17/906,562 CTFR 98143 DETAILED ACTION 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. Applicant’s response to the Non-final Office Action dated 11/05/2025, filed with the office on 01/29/2026, has been entered and made of record. 12-151 AIA 26-51 12-51 Status of Claims Claims 1-18 are pending. Claims 1, 3-6, 10, 14, 17 and 18 are amended. Response to Arguments Applicant’s amendment of the independent claims which has altered the scope of the claims of the instant application, has necessitated the new ground(s) of rejection presented in this office action with respect to claims of the instant application. Accordingly, in response to Applicant’s arguments that are merely directed to the amended independent claims, new analyses have been presented below, which make Applicant’s arguments moot. In response to applicant's argument presented in page 15, first paragraph, that Examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin , 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). The broadest reasonable interpretation of constantly rotating a pixel position in an image includes changing pixel position on an image by an angle of rotation which is disclosed in Kobayashi, ¶0009: “change in an on-image position of a pixel including the feature value or the pixel value of the acquired images along a time lapse, and generating information on correspondence between the position of the pixel in each of the images and an angle of rotation of the inspection target from the position change information”. Therefore, it would have been obvious to a person skilled in the art to apply an angular rotation to determine the position of a pixel representing a target feature of interest based on the position change information. Therefore, Applicant’s arguments are not found persuasive. Consequently, THIS ACTION IS MADE FINAL. 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 1, 2, 5-9, 11-14 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Ando (US 2016/0379075 A1), in view Dharia et al. (US 2021/0278257 A1) and in further view of Berkovich et al. (US 11,888,002 B2) . Regarding claim 1, Ando teaches, An information processing device, comprising: a central processing unit (CPU) configured to: (Ando, ¶0021: “an image recognition device 10 is configured to include a pixel signal processing unit 100”) acquire imaging information; (Ando, ¶0006: “an image captured by a camera”) set at least one pixel position for acquisition of sampling pixel for each divided region of a plurality of divided regions of the imaging information, (Ando, ¶0051: “image recognition processing for the image input from the image reading section 113 for each region expressed by the partial image information input from the partial image setting section 114, that is, for each partial image”; the sampling pixel is interpreted as pixels in “each region expressed by the partial image information”) wherein the each divided region is obtained by division of the imaging information, (Ando, ¶0048: “dividing the region of an image into a plurality of regions.”) the imaging information includes: first imaging information corresponding to a first divided region of the plurality of divided regions , and second imaging information corresponding to a second divided region of the plurality of divided regions , (Ando, ¶0078: “the partial images B10 and B16, among the 16 partial images B1 to B16 obtained by block division, are detection candidates”; B10 is considered as the first divided region and B16 is considered as the second divided region) each of the first divided region and the second divided region includes a plurality of pixels, (Ando, ¶0055: “Each of the pixels has a color corresponding to the divided range of each color space obtained by dividing pixels in a partial image into color spaces”) the at least one pixel position comprises: a first pixel position for the first imaging information, and a second pixel position for the second imaging information, the first pixel position is set based on a first base pixel in the first divided region, the second pixel position is set based on a second base pixel in the second divided region, and the first pixel position for the first imaging information is different from the second pixel position for the second imaging information; generate a sampling image including the sampling pixel for the each divided region; calculate a first feature amount of the generated sampling image; (Ando, ¶0055: “the feature amount calculation section 1151 calculates a simple feature amount”) perform a recognition processing operation based on the first feature amount of the sampling image; (Ando, ¶0055: “reduce power consumption due to a reduction in the number of pixels of the low resolution image S2 but also to shorten the total processing time in the image recognition device”) and output a recognition processing result of the recognition processing operation, (Ando, ¶0051: “determination section 115 outputs the result of image recognition processing in each partial image”). However, Ando does not explicitly teach, wherein the second imaging information is acquired after the first imaging information in time series and the at least one pixel position comprises: a first pixel position for the first imaging information, and a second pixel position for the second imaging information, the first pixel position is set based on a first base pixel in the first divided region, the second pixel position is set based on a second base pixel in the second divided region, and the first pixel position for the first imaging information is different from the second pixel position for the second imaging information; generate a sampling image including the sampling pixel for the each divided region . In an analogous field of endeavor, Dharia teaches, wherein the second imaging information is acquired after the first imaging information in time series. (Dharia, ¶0146: “operations may be repeated over time based on additional reflected pulse data, additional full-resolution image(s), and/or the ROI images to select other ROIs that focus on other features of interest”; second imaging information is interpreted as an additional region of interest). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ando using the teachings of Dharia to introduce capturing additional images. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of identifying additional features that wasn’t identified in the first image. Therefore, it would have been obvious to combine the analogous arts Ando and Dharia to obtain the above-described limitations in claim 1. However, the combination of Ando and Dharia does not explicitly teach, the at least one pixel position comprises: a first pixel position for the first imaging information, and a second pixel position for the second imaging information, the first pixel position is set based on a first base pixel in the first divided region, the second pixel position is set based on a second base pixel in the second divided region, and the first pixel position for the first imaging information is different from the second pixel position for the second imaging information; generate a sampling image including the sampling pixel for the each divided region . In another analogous field of endeavor, Berkovich teaches, the at least one pixel position comprises: a first pixel position for the first imaging information, and a second pixel position for the second imaging information, (Berkovich, col. 20, lines 43-44: “array of pixel cells 712 is divided into sampling groups (e.g., group 750”) the first pixel position is set based on a first base pixel in the first divided region, the second pixel position is set based on a second base pixel in the second divided region, (Berkovich, col. 20, lines 44-47: “sampling groups (e.g., group 750) each including multiple pixel cells, and from each group only one pixel cell (e.g., pixel cell 752) is enabled or allowed to transmit pixel data for the subsequent frame”) and the first pixel position for the first imaging information is different from the second pixel position for the second imaging information; (Berkovich, Fig. 7c: the selected pixel (i.e. pixel 752) is in a different position compared to the other selected pixel positions) generate a sampling image (Berkovich, co. 28, lines 49-51: “perform a sub-sampling operation (e.g., only a subset of pixel cells are turned on and/or are enabled to output pixel data), to reduce the frame rate”) including the sampling pixel for the each divided region . (Berkovich, col. 20, lines 52-55: “a case where array of pixel cells 712 performs a sub-sampling operation, controller 706 can control image processor 704 to process pixel data from one pixel cell from each sampling group”). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ando in view of Dharia using the teachings of Berkovich to introduce setting pixels with positions relative to a base pixel. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of only processing the selected pixels to reduce computation load. Therefore, it would have been obvious to combine the analogous arts Ando, Dharia and Berkovich to obtain the invention in claim 1. Regarding claim 2, Ando in view of Dharia and in further view of Berkovich teaches, The information processing device according to claim 1, wherein the CPU is further configured to: perform a machine learning process (Ando, ¶0058: “classifier prepared in advance in the classifier applying section 1152, for example, a supervised learning machine”) by use of a recurrent neural network (RNN) (Dharia, ¶0123: “detect objects sensed by multiple pixel groups… implement a recurrent neural network (RNN)”) based on a first sampling pixel set in the first imaging information and a second sampling pixel set in the second imaging information; (Ando, ¶0051: “image recognition processing for the image input from the image reading section 113 for each region expressed by the partial image information input from the partial image setting section 114, that is, for each partial image”) and perform the recognition processing operation based on result of the machine learning process. (Ando, ¶0058: “As a classifier… a support vector machine (SVM) can be considered”; recognition is interpreted as classification). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ando in view of Dharia and in further view of Berkovich using the additional teachings of Dharia to introduce a recurrent neural network. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of identifying features in a sequence of images. Therefore, it would have been obvious to combine the analogous arts Ando, Dharia and Berkovich to obtain the invention in claim 2. Regarding claim 5, Ando in view of Dharia and in further view of Berkovich teaches, The information processing device according to claim 1, wherein the CPU is further configured to set the at least one pixel position in the each divided region, based on the acquisition of the imaging information and an instruction from outside of the information processing device . (Ando, ¶0060: “it is possible to update the image recognition according to the user's preference”; outside instruction is interpreted as user input). Regarding claim 6, Ando in view of Dharia and in further view of Berkovich teaches, The information processing device according to claim 1, wherein the CPU is further configured to set pixel positions included in the each divided region as the at least one pixel position. (Ando, ¶0048: “The partial image setting section 114 sets a partial image by dividing the region of the image input from the image reading section 113 into at least one region for which image recognition processing is to be performed”). Regarding claim 7, Ando in view of Dharia and in further view of Berkovich teaches, The information processing device according to claim 1, wherein the CPU is further configured to set all pixel positions included in the imaging information as the at least one pixel position. (Ando, ¶0051: “image recognition processing for the image input from the image reading section 113 for each region expressed by the partial image information… for each partial image”). Regarding claim 8, Ando in view of Dharia and in further view of Berkovich teaches, The information processing device according to claim 1, wherein the CPU is further configured to: accumulate the first feature amount; (Ando, ¶0053: “for each partial image set by the partial image setting section 114, the feature amount calculation section 1151 calculates the feature amount of a subject included in the partial image”) perform the recognition processing operation based on the accumulated first feature amount (Ando, ¶0051: “image recognition processing for the image input from the image reading section 113 for each region expressed by the partial image information”; the total feature amount of the first partial image is interpreted as the accumulated first feature amount) and output the recognition processing result of the recognition processing operation. (Ando, ¶0051: “determination section 115 outputs the result of image recognition processing in each partial image”). Regarding claim 9, Ando in view of Dharia and in further view of Berkovich teaches, The information processing device according to claim 8, wherein the CPU is further configured to perform recognition processing -operation based a second feature amount obtained (Ando, ¶0053: “for each partial image set by the partial image setting section 114, the feature amount calculation section 1151 calculates the feature amount of a subject included in the partial image”) by an integrating operation of a plurality of accumulated feature amounts, and the plurality of accumulated feature amounts comprises the accumulated first feature amount. (Ando, ¶0073: “the feature amount calculation section 1151 calculates the feature amount of a subject… for each partial image expressed by the partial image”; the total feature amount of the first partial image is interpreted as the accumulated feature amount). Regarding claim 11, Ando in view of Dharia and in further view of Berkovich teaches, The information processing device according to claim 8, wherein the CPU is further configured to: select a fifth feature amount from a plurality of accumulated feature amounts, based on a specific condition, (Ando, ¶0094: “determines whether or not a subject expressed by the feature amount calculated by the feature amount calculation section 1151 is the detection target subject”; the specific condition is interpreted as the presence of the target subject/object) wherein the plurality of accumulated feature amounts comprises the accumulated first feature amount; and perform the recognition processing operation based on the fifth feature amount. (Ando, ¶0051: “image recognition processing for the image input from the image reading section 113 for each region expressed by the partial image information”; the total feature amount of the first partial image is interpreted as the accumulated first feature amount). Regarding claim 12, Ando in view of Dharia and in further view of Berkovich teaches, The information processing device according to claim 11, wherein the CPU is further configured to perform the recognition processing operation based on most recent feature amount in time series among the plurality of accumulated feature amounts. (Dharia, ¶0140: “process discussed with respect to FIG. 6A may be repeated, with new ROIs being selected over time based on additional radiometric image(s), additional full-resolution low-light image(s), and/or feature attribute(s) 614”). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ando in view of Dharia and in further view of Berkovich using the additional teachings of Dharia to introduce additional feature attributes. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of accumulating features over time and computing the recognition result the latest accumulated features. Therefore, it would have been obvious to combine the analogous arts Ando, Dharia and Berkovich to obtain the invention in claim 12. Regarding claim 13, Ando in view of Dharia and in further view of Berkovich teaches, The information processing device according to claim 8, wherein the CPU is further configured to discard a sixth feature amount, (Ando, ¶0014: “determines whether or not a subject expressed by the feature amount calculated by the feature amount calculation section is the detection target subject according to the classification score”; classification score indicates whether or not a feature amount belong to a target subject to identify which feature amount to consider for the recognition results) corresponding to a specific condition, from a plurality of accumulated feature amounts and the plurality of feature amounts comprises the accumulated first feature amount. (Ando, ¶0094: “determines whether or not a subject expressed by the feature amount calculated by the feature amount calculation section 1151 is the detection target subject”; the specific condition is interpreted as the absence of the target subject/object). Regarding claim 14, Ando in view of Dharia and in further view of Berkovich teaches, The information processing device according to claim 1, wherein the CPU is further configured to perform the recognition processing operation on the first feature amount of the sampling image based on training data (Ando, ¶0058: “classification score indicating the detection target subject likeliness according to the similarity with training data by comparing the feature amount of the subject”) for each pixel of the plurality of pixels corresponding to the at least one pixel position of the each divided region. (Ando, ¶0055: “each pixel in a partial image has can be considered”; divided region is interpreted as a partial region). Regarding claim 16, Ando in view of Dharia and in further view of Berkovich teaches, The information processing device according to claim 1, wherein the CPU is further configured to make an exposure condition for acquisition of the first imaging information different from an exposure condition for acquisition of the second imaging information. (Dharia, ¶0158: “capture additional image data that is of higher quality (e.g., has a better exposure, magnification, white balance, etc.) than the initial image data captured”). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ando in view of Dharia and in further view of Berkovich using the additional teachings of Dharia to introduce capturing additional image with higher exposure. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of a brighter image with more details. Therefore, it would have been obvious to combine the analogous arts Ando, Dharia and Berkovich to obtain the invention in claim 16. Regarding claim 17 it recites a method with steps corresponding to the elements of the device recited in claim 1. Therefore, the recited steps of the method claim 17 are mapped to the proposed combination in the same manner as the corresponding elements in device claim 1. Additionally, the rationale and motivation to combine Ando, Dharia and Berkovich presented in rejection of claim 1, apply to this claim. Ando additionally teaches, An information processing method comprising: (Ando, ¶0017: “an image recognition method includes:”). Regarding claim 18 it recites a computer-readable medium storing a program with instruction steps corresponding to the elements of the device recited in claim 1. Therefore, the recited instruction steps of the program claim 18 are mapped to the proposed combination in the same manner as the corresponding elements in device claim 1. Additionally, the rationale and motivation to combine Ando, Dharia and Berkovich presented in rejection of claim 1, apply to this claim. Dharia additionally teaches, A non-transitory computer-readable medium having stored thereon, computer-executable instructions which, when executed by a computer, cause the computer to execute operations, the operations comprising: (Dharia, ¶0005: “a non-transitory computer readable storage medium is provided having stored thereon instructions that, when executed by a computing device, cause the computing device to perform operations”). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ando in view of Dharia and in further view of Berkovich using the additional teachings of Dharia to introduce a non-transitory computer readable storage medium. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of storing and applying the image feature recognition processing algorithm on a computer. Therefore, it would have been obvious to combine the analogous arts Ando, Dharia and Berkovich to obtain the invention in claim 18 . 07-21-aia AIA Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Ando (US 2016/0379075 A1), in view Dharia et al. (US 2021/0278257 A1), in further view of Berkovich et al. (US 11,888,002 B2) and still in further view of Kobayashi (US 2018/0158183 A1) . Regarding claim 3, Ando in view of Dharia and in further view of Berkovich teaches, The information processing device according to claim 1, wherein the CPU is further configured to . However, the combination of Ando, Dharia and Berkovich does not explicitly teach, rotate, in a constant cycle the at least one pixel position in the each divided region, based on the acquisition of the imaging information. In an analogous field of endeavor, Kobayashi teaches, rotate, in a constant cycle the at least one pixel position in the each divided region, based on the acquisition of the imaging information. (Kobayashi, ¶0009: “change in an on-image position of a pixel including the feature value or the pixel value of the acquired images along a time lapse, and generating information on correspondence between the position of the pixel in each of the images and an angle of rotation of the inspection target from the position change information”). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ando in view of Dharia and in further view of Berkovich using the teachings of Kobayashi to introduce rotating pixel positions. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of identifying a target feature from the rotating pixel information. Therefore, it would have been obvious to combine the analogous arts Ando, Dharia, Berkovich and Kobayashi to obtain the invention in claim 3 . 07-21-aia AIA Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Ando (US 2016/0379075 A1), in view Dharia et al. (US 2021/0278257 A1), in further view of Berkovich et al. (US 11,888,002 B2) and still in further view of Alesiani et al. (US 2019/0147621 A1) . Regarding claim 4, Ando in view of Dharia and in further view of Berkovich teaches, The information processing device according to claim 1, wherein the CPU is further configured to . However, the combination of Ando, Dharia and Berkovich does not explicitly teach, arbitrarily set the at least one pixel position in the each divided region, based on the acquisition of the imaging information. In an analogous field of endeavor, Alesiani teaches, arbitrarily set the at least one pixel position in the each divided region, based on the acquisition of the imaging information. (Alesiani, ¶0044: “a dense set of particles (image pixel points) are sampled at random according to some distribution. The distribution can be guided by the image classification or other information”). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ando in view of Dharia and in further view of Berkovich using the teachings of Alesiani to introduce random sampling of pixel positions. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of identifying additional features from the imaging information. Therefore, it would have been obvious to combine the analogous arts Ando, Dharia, Berkovich and Alesiani to obtain the invention in claim 4 . 07-21-aia AIA Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Ando (US 2016/0379075 A1), in view Dharia et al. (US 2021/0278257 A1), in further view of Berkovich et al. (US 11,888,002 B2) and still in further view of Sajjadi et al. (US 2020/0293796 A1) . Regarding claim 10, Ando in view of Dharia and in further view of Berkovich teaches, The information processing device according to claim 8, wherein the CPU is further configured to: update an internal state of a deep neural network (DNN); integrate the first feature amount with at least one third feature amount accumulated until immediately before the acquisition of the imaging information, to obtain a fourth feature amount , wherein the at least on third feature amount is related to the updated internal state ; and perform the recognition processing operation based on the fourth feature amount. (Ando, ¶0076: “image recognition processing for an image of the next frame is performed again from the first image recognition process”). However, the combination of Ando, Dharia and Berkovich does not explicitly teach, update an internal state of a deep neural network (DNN); integrate the first feature amount with at least one third feature amount accumulated until immediately before the acquisition of the imaging information, to obtain a fourth feature amount , wherein the at least on third feature amount is related to the updated internal state . In an analogous field of endeavor, Sajjadi teaches, update an internal state of a deep neural network (DNN); (Sajjadi, ¶0079: “the entry point location to the intersection may then be back-projected into previous images (e.g., instance) of the sensor data 102 to train the machine learning model(s) 104”) integrate the first feature amount with at least one third feature amount accumulated until immediately before the acquisition of the imaging information, (Sajjadi, ¶0077: “temporal filtering (e.g., statistical filtering) may be performed over multiple predictions corresponding to previous consecutive instances of the sensor data 102”) to obtain a fourth feature amount , wherein the at least on third feature amount is related to the updated internal state . (Sajjadi, ¶0032: “distance may be encoded as the distance to the intersection for training the DNN. As such, the entry point location to the intersection may then be back-projected into previous instances of sensor data to train the DNN”). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ando in view of Dharia and in further view of Berkovich using the teachings of Sajjadi to introduce combining features of multiple frames. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of a increasing the chance of detecting the object if interest. Therefore, it would have been obvious to combine the analogous arts Ando, Dharia, Berkovich and Sajjadi to obtain the invention in claim 10 . 07-21-aia AIA Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Ando (US 2016/0379075 A1), in view Dharia et al. (US 2021/0278257 A1)), in further view of Berkovich et al. (US 11,888,002 B2) and still in further view of Mandal et al. (US 2018/0189587 A1) . Regarding claim 15, Ando in view of Dharia and in further view of Berkovich teaches, The information processing device according to claim 1, CPU is further configured to . However, the combination of Ando, Dharia and Berkovich does not explicitly teach, set a specific pixel position for calculation of a specific feature amount in a specific sampling pixel different from the sampling pixel; and perform the recognition processing operation based on the specific sampling pixel. In an analogous field of endeavor, Mandal teaches, set a specific pixel position for calculation (Mandal, ¶0078: “selecting new combinations of pixel addresses that are to be used in a sliding window buffer”) of a specific feature amount in a specific sampling pixel (Mandal, ¶0079: “address generator 305 include or otherwise specify one or a plurality of pixel addresses”) different from the sampling pixel; (Mandal. ¶0190: “tracking controller 818 may identify one or more regions of interest within new image data (e.g., a new image frame provided by an image sensor”) and perform the recognition processing operation based on the specific sampling pixel. (Mandal, ¶0191: “perform feature detection operations on only the region(s) of interest identified by tracking controller”). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ando in view of Dharia and in further view of Berkovich using the teachings of Mandal to introduce selecting different sampling pixels in a new image. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of optimizing pixel-based feature detection. Therefore, it would have been obvious to combine the analogous arts Ando, Dharia, Berkovich and Mandal to obtain the invention in claim 15. Conclusion 07-39 AIA 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 MEHRAZUL ISLAM whose telephone number is (571)270-0489. The examiner can normally be reached Monday-Friday: 8am-5pm. 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, Saini Amandeep can be reached at (571) 272-3382. 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. /MEHRAZUL ISLAM/Examiner, Art Unit 2662 /AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662 Application/Control Number: 17/906,562 Page 2 Art Unit: 2662 Application/Control Number: 17/906,562 Page 3 Art Unit: 2662 Application/Control Number: 17/906,562 Page 4 Art Unit: 2662 Application/Control Number: 17/906,562 Page 6 Art Unit: 2662 Application/Control Number: 17/906,562 Page 7 Art Unit: 2662 Application/Control Number: 17/906,562 Page 8 Art Unit: 2662 Application/Control Number: 17/906,562 Page 9 Art Unit: 2662 Application/Control Number: 17/906,562 Page 10 Art Unit: 2662 Application/Control Number: 17/906,562 Page 11 Art Unit: 2662 Application/Control Number: 17/906,562 Page 12 Art Unit: 2662 Application/Control Number: 17/906,562 Page 13 Art Unit: 2662 Application/Control Number: 17/906,562 Page 14 Art Unit: 2662 Application/Control Number: 17/906,562 Page 15 Art Unit: 2662 Application/Control Number: 17/906,562 Page 16 Art Unit: 2662 Application/Control Number: 17/906,562 Page 18 Art Unit: 2662 Application/Control Number: 17/906,562 Page 19 Art Unit: 2662 Application/Control Number: 17/906,562 Page 20 Art Unit: 2662 Application/Control Number: 17/906,562 Page 21 Art Unit: 2662 Application/Control Number: 17/906,562 Page 22 Art Unit: 2662 Application/Control Number: 17/906,562 Page 23 Art Unit: 2662 Application/Control Number: 17/906,562 Page 24 Art Unit: 2662
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