CTNF 18/945,136 CTNF 97451 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. Drawings 06-37 AIA The drawings were received on 11/12/2024 . These drawings are accepted . 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. 08-34 AIA Claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 15, 21, 22, 27 and 28 of U.S. Patent No. 12183063 (application # 16/917,289) . Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows: 18/945,136 16/917,289 Claim 1. A method comprising: obtaining image data representing an image depicting a field of view of one or more sensors of a machine; generating, based at least on using one or more convolutional layers of one or more neural networks to process the image data, feature map data corresponding to a down-sampled version of the image; (the claimed feature map data is understood to be the same as 16917289’ target cells in light of instant specifications [0037]) generating output data using one or more output layers of the one or more neural networks to process the feature map data; and causing the machine to perform one or more operations based at least on the output data. Claim 15. (Currently Amended) …region of an image… with a sensor used to generate the image…of a machine Claim 22…convolutional layers of the neural network to generate the downsampled version of the grid. And Claim 21…grid of source areas in a region of an image Claim 21… mapping the source areas to target cells of the downsampled version of the grid, groups of pixels corresponding to the source areas of the grid (16917289 states [0028] “each cell of the downsampled version of the region may represent one or more elements of a feature map of output of a convolutional layer of the DNN”) Claim 21…generate output data using one or more machine learning models and based at least on a downsampled version of a grid of source areas in a region of an image… And Claim 22…wherein the one or more machine learning models include a neural network… And Claim 27…downsampling includes, based at least on the mapping…one or more pixel values from the source areas to mapped one or more pixels of the target cells Claim 28…providing output data of one or more machine learning models, generated using the downsampled version of the grid, to a control component of a machine… . 08-34 AIA Claim 2 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 21 and 22 of U.S. Patent No. 12183063 (application # 16/917,289) . Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows: 18/945,136 16/917,289 Claim 2. The method of claim 1, wherein the one or more convolutional layers non-uniformly down-sample a region of the image to generate the feature map data. Claim 22…. downsampled using one or more convolutional layers of the neural network Claim 21… groups of pixels corresponding to the source areas of the grid such that the target cells of the downsampled version of the grid have a lower resolution than the source areas of the grid mapped to the target cells, wherein sizes corresponding to resolutions of the source areas of the grid linearly decrease as proximity increases between locations associated with the source areas and a location associated with a sensor used to generate the image… . 08-34 AIA Claim 3 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 31 and 35 of U.S. Patent No. 12183063 (application # 16/917,289) . Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows: 18/945,136 16/917,289 Claim 3. The method of claim 1, wherein: one or more first portions of the down-sampled version of the image correspond to one or more first numbers of pixels of the image, one or more second portions of the down-sampled version of the image correspond to one or more second numbers of pixels of the image, the one or more second numbers different from the first number, and the one or more first portions and the one or more second portions have equivalent or substantially equivalent resolutions. (the claimed equivalent or substantially equivalent is understood to mean each target cell may be a single pixel in light of instant specifications [0037]) Claim 35… downsampling comprises… aggregating groups of pixels corresponding to the source areas into target cells associated with the downsampled version of the grid… … one or more first pixel values for one of more first target cells are determined using less pixels than one or more second pixel values for one or more second target cells… Claim 31… for each target cell of the target cells, a single source area [[cell]] of the source areas is mapped to the target cell, the target cell represents a pixel of the downsampled version of the grid, and a portion of the image that represents the pixel is computed from image data that represents the single source area cell mapped with the target cell . 08-34 AIA Claim 4 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 21 and 24 of U.S. Patent No. 12183063 (application # 16/917,289) . Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows: 18/945,136 16/917,289 Claim 4. The method of claim 1, further comprising: updating one or more dilation parameters associated with one or more kernels of the one or more convolutional layers of the one or more neural networks, wherein the updating of the one or more dilation parameters causes a resolution of the down-sampled version of the image to linearly increase along at least one direction relative to a location associated with the sensor. Claim 24… adjusting dilation factors of one or more kernels of one or more convolutional layers. Claim 24… grid is defined based at least on adjusting dilation factors… Claim 21… target cells of the downsampled version of the grid have a lower resolution than the source areas of the grid mapped to the target cells, wherein sizes corresponding to resolutions of the source areas of the grid linearly decrease as proximity increases between locations associated with the source areas and a location associated with a sensor used to generate the image . 08-34 AIA Claim 5 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 21 and 26 of U.S. Patent No. 12183063 (application # 16/917,289) . Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows: 18/945,136 16/917,289 Claim 5. The method of claim 1, wherein the down-sampled version of the image comprises a down-sampled version of a region of interest within the image. Claim 21… downsampled version of a grid of source areas in a region of an image Claim 26… wherein the region is a region of interest identified within the image 08-34 AIA Claim 6 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 24 and 25 of U.S. Patent No. 12183063 (application # 16/917,289) . Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows: 18/945,136 16/917,289 Claim 6. The method of claim 5, further comprising: updating one or more dilation parameters associated with one or more kernels of the one or more convolutional layers of the one or more neural networks, wherein the updating of the one or more dilation parameters adjusts at least one of a size or a shape of the region of interest. Claim 24… adjusting dilation factors of one or more kernels of one or more convolutional layers. Claim 24… wherein the grid is defined based at least on adjusting dilation factors… Claim 25… geometries of the source areas are defined along a direction based at least on a ratio between opposing sides of the trapezoidal shape . 08-34 AIA Claim 7 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 22 of U.S. Patent No. 12183063 (application # 16/917,289) . Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows: 18/945,136 16/917,289 Claim 7. The method of claim 1, wherein the one or more convolutional layers correspond to one or more convolutional streams of the one or more neural networks, the one or more convolutional streams associated with the one or more sensors. Claim 22… one or more convolutional layers of the neural network… Regarding Claim 7: Claim 22 of Patent no. 12183063 (application # 16/917,289) Wen et al teaches the limitations of independent claim 1 of the instant application, but fails to disclose one or more convolutional streams of the one or more neural networks, the one or more convolutional streams associated with the one or more sensors. However Urtasun et al US20200160559 teaches [0073] “in both streams, a feature pyramid network (FPN) can apply 1×1 convolution” and Fig. 2 shows two separate Convolution streams for each one or more sensor. PNG media_image1.png 365 612 media_image1.png Greyscale It would have been obvious to persons of ordinary skill in the art before the effective filing date of the claimed invention to modify Wen have convolution streams for each sensor as taught by Urtasun to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been for (Urtasun et al Abstract “training of a machine-learned model ensemble …to learn to perform more accurate multi-sensor 3D object detection.”) 08-34 AIA Claim 8 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 15 and 21 of U.S. Patent No. 12183063 (application # 16/917,289) . Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows: 18/945,136 16/917,289 Claim 8. A system comprising: one or more processors to: generate, based at least on using one or more machine learning models to process an image depicting a field of view of a sensor of a machine, a down-sampled representation of the image; and cause the machine to perform one or more operations based at least on output data generated responsive to the one or more machine learning models processing the down-sampled version of the image. Claim 21… generate output data using one or more machine learning models and based at least on … a region of an image, Claim 15. (Currently Amended) …region of an image… with a sensor used to generate the image…of a machine Claim 15… a grid of source areas in a region of an image… downsampling the grid of the source areas to generate a downsampled version of the grid Claim 15… providing output data of one or more machine learning models, generated using the downsampled version of the grid, to a control component of a machine . 08-34 AIA Claim 9 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 22 of U.S. Patent No. 12183063 (application # 16/917,289) . Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows: 18/945,136 16/917,289 Claim 9. The system of claim 8, wherein the one or more machine learning models include one or more neural networks. Claim 22… wherein the one or more machine learning models include a neural network 08-34 AIA Claim 10 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 15 and 22 of U.S. Patent No. 12183063 (application # 16/917,289) . Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows: 18/945,136 16/917,289 Claim 10. The system of claim 9, wherein the generation of the down-sampled version of the image is based at least on using one or more convolutional layers of the one or more neural networks to process the image. Claim 22… machine learning models include a neural network and at least a portion of the grid is downsampled using one or more convolutional layers of the neural network to generate the downsampled version of the grid…. And Claim 15… a grid of source areas in a region of an image 08-34 AIA Claim 11 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 22, 24, 25 and 27 of U.S. Patent No. 12183063 (application # 16/917,289) . Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows: 18/945,136 16/917,289 Claim 11. The system of claim 10, the one or more processors further to: update one or more dilation parameters associated with one or more kernels of the one or more convolutional layers of the one or more neural networks to define one or more source areas in the image, wherein the generation of the down-sampled version of the image is based at least on the one or more convolutional layers processing the image to down-sample one or more pixels within the one or more source areas. Claim 24. (Previously Presented) The processor of claim 21, wherein the grid is defined based at least on adjusting dilation factors of one or more kernels of one or more convolutional layers. And Claim 25… source areas are defined Claim 22…grid is downsampled using one or more convolutional layers of the neural network to generate the downsampled version of the grid. Claim 27…downsampling includes, based at least on the mapping, at least one of:linearly interpolating one or more pixel values from the source areas to mapped one or more pixels 08-34 AIA Claim 12 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 21 and 22 of U.S. Patent No. 12183063 (application # 16/917,289) . Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows: 18/945,136 16/917,289 Claim 12. The system of claim 8, wherein the one or more machine learning models non-uniformly down-sample different portions of the image to generate the down-sampled version of the image. Claim 22…. downsampled using one or more convolutional layers of the neural network Claim 21… groups of pixels corresponding to the source areas of the grid such that the target cells of the downsampled version of the grid have a lower resolution than the source areas of the grid mapped to the target cells, wherein sizes corresponding to resolutions of the source areas of the grid linearly decrease as proximity increases between locations associated with the source areas and a location associated with a sensor used to generate the image…. (is understood to be the same as the claimed non-uniformly down-sample different portions of the image in light of instant specifications [0037]) 08-34 AIA Claim 13 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 35 of U.S. Patent No. 12183063 (application # 16/917,289) . Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows: 18/945,136 16/917,289 Claim 13. The system of claim 8, wherein one or more pixels of the down-sampled version of the image correspond to one or more down-sampled groups of pixels of the image. Claim 35… downsampling comprises determining, based at least on aggregating groups of pixels corresponding to the source areas into target cells associated with the downsampled version of the grid, pixel values for the target cells 08-34 AIA Claim 14 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 21 and 26 of U.S. Patent No. 12183063 (application # 16/917,289) . Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows: 18/945,136 16/917,289 Claim 14. The system of claim 8, wherein the down-sampled version of the image comprises a down-sampled version of a region of interest from within the image. Claim 21… downsampled version of a grid of source areas in a region of an image Claim 26… wherein the region is a region of interest identified within the image 08-34 AIA Claim 15 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 22, 24 and 25 of U.S. Patent No. 12183063 (application # 16/917,289) . Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows: 18/945,136 16/917,289 Claim 15. The system of claim 14, wherein the one or more machine learning models includes one or more neural networks, the one or more processors further to: update one or more dilation parameters associated with one or more kernels of one or more convolutional layers of the one or more neural networks, wherein the update to the one or more dilation parameters adjusts at least one of a size or a shape of the region of interest. Claim 22…one or more machine learning models include a neural network Claim 24… adjusting dilation factors of one or more kernels of one or more convolutional layers. Claim 24… wherein the grid is defined based at least on adjusting dilation factors… Claim 25… geometries of the source areas are defined along a direction based at least on a ratio between opposing sides of the trapezoidal shape . 08-34 AIA Claim 16 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 15 of U.S. Patent No. 12183063 (application # 16/917,289) . Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows: 18/945,136 16/917,289 Claim 16. The system of claim 8, wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing one or more simulation operations; a system for performing one or more deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources. Claim 15.. to a control component of a machine. Regarding Claim 16: Claim 15 of Patent no. 12183063 (application # 16/917,289) Wen et al teaches the limitations of independent claim 8 of the instant application, but fails to disclose control system for an autonomous machine. However Urtasun et al US20200160559 teaches [0019] “autonomous vehicle perception and control.”. It would have been obvious to persons of ordinary skill in the art before the effective filing date of the claimed invention to modify Wen have control system for an autonomous vehicle as taught by Urtasun to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been for (Urtasun et al Abstract “training of a machine-learned model ensemble …to learn to perform more accurate multi-sensor 3D object detection.”) 08-34 AIA Claim 17 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 15, 19, 21 and 22 of U.S. Patent No. 12183063 (application # 16/917,289) . Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows: 18/945,136 16/917,289 Claim 17. One or more processors comprising: processing circuitry to perform one or more operations associated with a machine based at least on output data of one or more neural networks, the output data generated based at least on using one or more output layers of the one or more neural networks to process feature data representing a down-sampled representation of an image, (the claimed feature map data is understood to be the same as 16917289’ target cells in light of instant specifications [0037]) the down-sampled representation of the image generated based at least on one or more convolutional layers of the one or more neural networks non-uniformly down-sampling a region of the image. Claim 15. (Currently Amended) A system comprising:one or more processing devices to perform operations comprising:… …machine learning models, generated using the downsampled version of the grid, to a control component of a machine. Claim 19… neural network. Claim 21… mapping the source areas to target cells of the downsampled version of the grid, groups of pixels corresponding to the source areas of the grid Claim 22… include a neural network and at least a portion of the grid is downsampled using one or more convolutional layers of the neural network to generate the downsampled version of the grid. Claim 21… groups of pixels corresponding to the source areas of the grid such that the target cells of the downsampled version of the grid have a lower resolution than the source areas of the grid mapped to the target cells, wherein sizes corresponding to resolutions of the source areas of the grid linearly decrease as proximity increases between locations associated with the source areas and a location associated with a sensor used to generate the image… . 08-34 AIA Claim 18 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 15 of U.S. Patent No. 12183063 (application # 16/917,289) . Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows: 18/945,136 16/917,289 Claim 18. The one or more processors of claim 17, wherein the output data includes a path through an environment and the one or more operations are performed by the machine to follow the path. Claim 15… to a control component of a machine. Regarding Claim 18: Claim 15 of Patent no. 12183063 (application # 16/917,289) Wen et al teaches the limitations of independent claim 17 of the instant application, but fails to disclose operations are performed by the machine to follow the path. However Urtasun et al US20200160559 teaches [0059] “The motion planning system 128 can determine a motion plan and generate motion plan data 134 for the vehicle 102 based at least in part on the prediction data 132 (and/or other data)…. motion planning system 128 can determine that the vehicle 102 can perform a certain action (e.g., pass an object) without increasing the potential risk to the vehicle 102 and/or violating any traffic laws (e.g.,…lane boundaries…).” It would have been obvious to persons of ordinary skill in the art before the effective filing date of the claimed invention to modify Wen have a output data include a path for the autonomous vehicle to follow as taught by Urtasun to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been for (Urtasun et al Abstract “training of a machine-learned model ensemble …to learn to perform more accurate multi-sensor 3D object detection.”) 08-34 AIA Claim 19 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 21 and 24 of U.S. Patent No. 12183063 (application # 16/917,289) . Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows: 18/945,136 16/917,289 Claim 19. The one or more processors of claim 17, wherein one or more dilation parameters associated with one or more kernels of the one or more convolution layers define one or more sources areas within the region of the image, and one or more sizes corresponding to one or more resolutions of the one or more source areas decrease as a function of distance between the one or more sources areas and a location of a sensor used to generate the image. Claim 24… grid is defined based at least on adjusting dilation factors of one or more kernels of one or more convolutional layers. grid has a trapezoidal shape and geometries of the source areas are defined along a direction based at least on a ratio between opposing sides of the trapezoidal shape. Claim 21… sizes corresponding to resolutions of the source areas of the grid linearly decrease as proximity increases between locations associated with the source areas and a location associated with a sensor used to generate the image . 08-34 AIA Claim 20 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 15 of U.S. Patent No. 12183063 (application # 16/917,289) . Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows: 18/945,136 16/917,289 Claim 20. The one or more processors of claim 17, wherein the one or more processors are comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing one or more simulation operations; a system for performing one or more deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources. Claim 15.. to a control component of a machine. Regarding Claim 20: Claim 15 of Patent no. 12183063 (application # 16/917,289) Wen et al teaches the limitations of independent claim 17 of the instant application, but fails to disclose control system for an autonomous machine. However Urtasun et al US20200160559 teaches [0019] “autonomous vehicle perception and control.”. It would have been obvious to persons of ordinary skill in the art before the effective filing date of the claimed invention to modify Wen have control system for an autonomous vehicle as taught by Urtasun to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been for (Urtasun et al Abstract “training of a machine-learned model ensemble …to learn to perform more accurate multi-sensor 3D object detection.”) Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure : Nariyambut Murali et al US20170200063 teaches downsampling and changing the resolution of the image to help with road scene image recognition utilizing a convolution neural network and may teach other aspects of the claims. Any inquiry concerning this communication or earlier communications from the examiner should be directed to OWAIS MEMON whose telephone number is (571)272-2168. The examiner can normally be reached M-F (7:00am - 4:00pm) CST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /OWAIS I MEMON/Examiner, Art Unit 2663 Application/Control Number: 18/945,136 Page 2 Art Unit: 2663 Application/Control Number: 18/945,136 Page 3 Art Unit: 2663 Application/Control Number: 18/945,136 Page 4 Art Unit: 2663 Application/Control Number: 18/945,136 Page 5 Art Unit: 2663 Application/Control Number: 18/945,136 Page 6 Art Unit: 2663 Application/Control Number: 18/945,136 Page 7 Art Unit: 2663 Application/Control Number: 18/945,136 Page 8 Art Unit: 2663 Application/Control Number: 18/945,136 Page 9 Art Unit: 2663 Application/Control Number: 18/945,136 Page 10 Art Unit: 2663 Application/Control Number: 18/945,136 Page 11 Art Unit: 2663 Application/Control Number: 18/945,136 Page 12 Art Unit: 2663 Application/Control Number: 18/945,136 Page 13 Art Unit: 2663 Application/Control Number: 18/945,136 Page 14 Art Unit: 2663 Application/Control Number: 18/945,136 Page 15 Art Unit: 2663 Application/Control Number: 18/945,136 Page 16 Art Unit: 2663 Application/Control Number: 18/945,136 Page 17 Art Unit: 2663 Application/Control Number: 18/945,136 Page 18 Art Unit: 2663 Application/Control Number: 18/945,136 Page 19 Art Unit: 2663 Application/Control Number: 18/945,136 Page 20 Art Unit: 2663 Application/Control Number: 18/945,136 Page 21 Art Unit: 2663 Application/Control Number: 18/945,136 Page 22 Art Unit: 2663 Application/Control Number: 18/945,136 Page 23 Art Unit: 2663 Application/Control Number: 18/945,136 Page 24 Art Unit: 2663 Application/Control Number: 18/945,136 Page 25 Art Unit: 2663 Application/Control Number: 18/945,136 Page 26 Art Unit: 2663 Application/Control Number: 18/945,136 Page 27 Art Unit: 2663 Application/Control Number: 18/945,136 Page 28 Art Unit: 2663 Application/Control Number: 18/945,136 Page 29 Art Unit: 2663 Application/Control Number: 18/945,136 Page 30 Art Unit: 2663 Application/Control Number: 18/945,136 Page 31 Art Unit: 2663 Application/Control Number: 18/945,136 Page 32 Art Unit: 2663 Application/Control Number: 18/945,136 Page 33 Art Unit: 2663 Application/Control Number: 18/945,136 Page 34 Art Unit: 2663 Application/Control Number: 18/945,136 Page 35 Art Unit: 2663 Application/Control Number: 18/945,136 Page 36 Art Unit: 2663 Application/Control Number: 18/945,136 Page 37 Art Unit: 2663 Application/Control Number: 18/945,136 Page 38 Art Unit: 2663 Application/Control Number: 18/945,136 Page 39 Art Unit: 2663 Application/Control Number: 18/945,136 Page 40 Art Unit: 2663 Application/Control Number: 18/945,136 Page 41 Art Unit: 2663 Application/Control Number: 18/945,136 Page 42 Art Unit: 2663
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