CTNF 18/264,862 CTNF 87360 DETAILED ACTION Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim s 5 and 7-12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 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) 1-2, 6, 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. US 2021/0124960 hereinafter referred to as Lee in view of Kirigan et al. US 2021/0124350 hereinafter referred to as Kirigan in view of Zhan et al. US 2018/0108146 hereinafter referred to as Zhan . In regards to claim 1 , Lee teaches: “An information processing apparatus comprising: a recognition unit configured to perform recognition processing on the basis of a point cloud output from a photodetection ranging unit using a frequency modulated continuous wave to determine a designated area in a real object” Lee [0013] teaches there is provided an object recognition method in which an object of interest in a field of view (FoV) is recognized by acquiring time of flight (ToF) data for a plurality of points within the FoV and a plurality of point intensity data from a sensor configured to receive the reflected light by projecting the FoV with a laser, which includes acquiring point cloud data which corresponds to points distributed in the FoV. Lee paragraph [0077] teaches the LiDAR unit 1200 may acquire the distance data using a frequency modulated continuous wave (FMCW) method or a phase shift method. “the photodetection ranging unit being configured to output the point cloud … and three-dimensional coordinates of the point cloud on the basis of a reception signal reflected by an object and received” Lee paragraph [0078] teaches The LiDAR unit 1200 may calculate distance data or depth data for the FoV 200 using the ToF data or may generate a two-dimensional (2D) image, a three-dimensional (3D) image, or a video corresponding to the FoV 200. Specifically, the LiDAR unit 1200 may generate a point cloud image or a depth map. “and configured to output three-dimensional recognition information including information indicating the determined designated area.” Lee Figure 4 and paragraph [0082] teaches the object recognition method may include acquiring, by the analysis unit 1400, data for the FoV 200 from the LiDAR unit 1200 (S1100), acquiring distance data using the data for the FoV 200 (S1200), acquiring data of interest from the distance data using ROI data (S1300), and performing object recognition using an ANN (S1400). Lee does not explicitly teach: “including velocity information” Kirigan paragraph [0030] teaches one or more machine learning models can be trained to recognize agents within the environment 300 based on the captured sensor data, such as image data capture by optical cameras of the vehicle 301, point clouds captured by a LiDAR system in the vehicle 301, and radar data captured by a radar system in the vehicle 301, to name some examples. Kirigan Figure 3A teaches that agents can be a car or bicycle (object). Figure 3A, inter alia, teaches that a velocity is associated with these agents. It would have been obvious for a person with ordinary skill in the art before the invention was effectively filed to have modified Lee in view of Kirigan to have included the features of “including velocity information” for enabling the vehicle to determine its surroundings so that it may safely navigate to target destinations or assist a human driver, if one is present, with doing the same (Kirigan [0002]). Lee/Kirigan do not explicitly teach: “and a correction unit configured to correct three- dimensional coordinates of the designated area in the point cloud on the basis of the three-dimensional recognition information output by the recognition unit” Zhan paragraph [0047] teaches the correcting the point cloud segmentation and tracking results by using the feature object recognition and tracking results in the above-mentioned step 204 may include: adjusting 3D point cloud space coordinates to be consistent with those in a space coordinates system of the camera, so that a point observed through the camera can match a point on the laser radar; and then determining whether an object and a movement trajectory of the object in the point cloud segmentation and tracking result match an object and a movement trajectory of the object in the feature object recognition and tracking result obtained for the video content and determining the matching degree between them. It would have been obvious for a person with ordinary skill in the art before the invention was effectively filed to have modified Lee/Kirigan in view of Zhan to have included the features of “and a correction unit configured to correct three- dimensional coordinates of the designated area in the point cloud on the basis of the three-dimensional recognition information output by the recognition unit” because due to the insufficient density of point cloud data and the change in angle during the collection of point cloud data, it is rather difficult and time-consuming for annotation personnel to perform point cloud annotation, and the accuracy of annotation is undesirable. Therefore, it is necessary to improve the efficiency of annotating point cloud data (Zhan [0004]). In regards to claim 2 , Lee/Kirigan/Zhan teach all the limitations of claim1 and further teach: “wherein the correction unit corrects the three-dimensional coordinates of the designated area using the three-dimensional coordinates based on the point cloud previously output by the photodetection ranging unit” Zhan Figure 2 illustrates that the point cloud is collected in step 201 and processed (segmented and tracked) in step 202. Therefore, in order for the point cloud to be processed it must have been output by the sensor to the components that segment and track. The correction occurs in step 204 which happens after the processing. It would have been obvious for a person with ordinary skill in the art before the invention was effectively filed to have modified Lee/Kirigan in view of Zhan to have included the features of “wherein the correction unit corrects the three-dimensional coordinates of the designated area using the three-dimensional coordinates based on the point cloud previously output by the photodetection ranging unit” because due to the insufficient density of point cloud data and the change in angle during the collection of point cloud data, it is rather difficult and time-consuming for annotation personnel to perform point cloud annotation, and the accuracy of annotation is undesirable. Therefore, it is necessary to improve the efficiency of annotating point cloud data (Zhan [0004]). In regards to claim 6 , Lee/Kirigan/Zhan teach all the limitations of claim1 and further teach: “wherein the real object is a moving body, and the designated area is a surface of the moving body in a measurement direction measured by the photodetection ranging unit” Kirigan paragraph [0030] teaches one or more machine learning models can be trained to recognize agents within the environment 300 based on the captured sensor data, such as image data capture by optical cameras of the vehicle 301, point clouds captured by a LiDAR system in the vehicle 301, and radar data captured by a radar system in the vehicle 301, to name some examples. Kirigan Figure 3A teaches that agents can be a car or bicycle (object). Figure 3A, inter alia, teaches that a velocity is associated with these agents. The Examiner interprets that objects with a velocity are moving and that LIDAR is a collection of data received from a reflection of the surface of the object. It would have been obvious for a person with ordinary skill in the art before the invention was effectively filed to have modified Lee in view of Kirigan to have included the features of “wherein the real object is a moving body, and the designated area is a surface of the moving body in a measurement direction measured by the photodetection ranging unit” for enabling the vehicle to determine its surroundings so that it may safely navigate to target destinations or assist a human driver, if one is present, with doing the same (Kirigan [0002]). In regards to claim 13 , Lee/Kirigan/Zhan teach all the limitations of claim 1 and claim 13 contains similar limitations. Therefore, claim 13 is rejected for similar reasoning as applied to claim 1. In regards to claim 14 , Lee/Kirigan/Zhan teach all the limitations of claim 1 and claim 13 contains similar limitations. Therefore, claim 13 is rejected for similar reasoning as applied to claim 1 . 07-21-aia AIA Claim (s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Kirigan in view of Zhan in view of Baker et al. US 2020/0400821 hereinafter referred to as Baker . In regards to claim 3 , Lee/Kirigan/Zhan teach all the limitations of claim 1 but do not explicitly teach: “wherein the correction unit predicts and corrects the three-dimensional coordinates of the designated area on the basis of velocity information indicated by the point cloud” Baker paragraph [0004] teaches the method includes determining a corrected velocity vector for the Doppler LIDAR system based on the raw point cloud data. In some implementations, the method includes producing revised point cloud data that is corrected for the velocity of the Doppler LIDAR system. It would have been obvious for a person with ordinary skill in the art before the invention was effectively filed to have modified Lee/Kirigan/Zhan in view of Baker to have included the features of “wherein the correction unit predicts and corrects the three-dimensional coordinates of the designated area on the basis of velocity information indicated by the point cloud” to achieve acceptable range accuracy and detection sensitivity, direct long range LIDAR systems (Baker [0051]) . 07-21-aia AIA Claim (s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Kirigan in view of Zhan in view of Haltom US 2021/0245701 hereinafter referred to as Haltom . In regards to claim 4 , Lee/Kirigan/Zhan teach all the limitations of claim 1 but do not explicitly teach: “wherein the real object is a person, and the designated area is an arm or a foot of the person” Haltom paragraph [0060] teaches deep learning model can include a PointNet, a 3D CNN on voxelized volumetric grids of a point cloud in the frustum, a 2D CNN on a bird's eye view projection of a point cloud in the frustum, a recurrent neural network on a sequence of 3D points from close to distant, and/or the like. In some instances, generating an object characterization can include performing an object recognition/classification process through which an object type is determined based on geometric features determined for an object within the attention region and comparing the geometric features to a database of stored geometric features (e.g., stored via database 322 and/or memory element(s) 406) that represent human objects (e.g., pedestrians, cyclists, etc.) and non-human objects (e.g., bridge abutments, traffic cones, buildings, etc.) in order to determine/classify objects as human or non-human object types. In some instances, geometric features for human objects can include unobstructed/whole views of a person and/or portions of a person, such as a head, a torso/abdomen, legs, arms, etc. It would have been obvious for a person with ordinary skill in the art before the invention was effectively filed to have modified Lee/Kirigan/Zhan in view of Haltom to have included the features of “wherein the real object is a person, and the designated area is an arm or a foot of the person” because the ability to protect a pedestrian as much as possible during a collision is critical (Haltom [0003]). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL E TEITELBAUM, Ph.D. whose telephone number is (571)270-5996. The examiner can normally be reached 8:30AM-5:00PM EST. 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. /MICHAEL E TEITELBAUM, Ph.D./Primary Examiner, Art Unit 2422 Application/Control Number: 18/264,862 Page 2 Art Unit: 2422 Application/Control Number: 18/264,862 Page 3 Art Unit: 2422 Application/Control Number: 18/264,862 Page 4 Art Unit: 2422 Application/Control Number: 18/264,862 Page 5 Art Unit: 2422 Application/Control Number: 18/264,862 Page 6 Art Unit: 2422 Application/Control Number: 18/264,862 Page 7 Art Unit: 2422 Application/Control Number: 18/264,862 Page 8 Art Unit: 2422 Application/Control Number: 18/264,862 Page 9 Art Unit: 2422