CTFR 18/678,658 CTFR 95647 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. Response to Arguments Claim Rejections - 35 USC § 101: 07-37 AIA Applicant's arguments filed 2/22/2026 have been fully considered but they are not persuasive. Amendments to independent claims 1, 7 and 13 does not overcome the rejection. The amendments do not contain elements beyond the abstract idea that integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. Merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”. Applicant’s specification as filed does recite elements that integrate the exception into a practical application, although not recited in the claimed invention. For example, [0064] The autonomous driving control device 138 may provide autonomous driving control for avoiding collisions with the objects 11, 12, and 13 through an expanded field of view, based on cooperative recognition of the cooperative recognition device 136 or [0066] the autonomous driving control device 138 may control autonomous driving, based on the established generalized strategy. For these reasons, the rejection is maintained. Claim Rejections - 35 USC § 103: 07-37 AIA Applicant's arguments filed 2/22/2026 have been fully considered but they are not persuasive. Applicant argues Shrivastava does not disclose or suggest the claimed concept of calculating a "safety point" that is proximate to a collision point and restricted to a current driving lane of an autonomous vehicle or suggest constraints "defined by the time to collision, the collision point, and the safety point in a coordinate system having a movement-distance axis and a time axis". Examiner respectfully disagrees . In this case, Shrivastava et al. (US 20250178642 A1; hereinafter Shrivastava) discloses calculating a "safety point" that is proximate to a collision point and restricted to a current driving lane of an autonomous vehicle. As shown in Fig 7, the shortest lateral clearance point 706A interpreted as the safety point and the most constraining point 706B as the collision point (e.g., see at least, Fig 7, [100] point 706A is the shortest lateral clearance point…706B is the most constraining point of the agent with respect to the baseline, being used to generate spatial clearance constraints). The driving lane is shown (e.g., [0100] The distance 710 (e.g., between point 704A and point 704B) is for example the distance in which the AV interacts with the obstacle along the lane). Shrivastava further discloses constraints "defined by the time to collision, the collision point, and the safety point in a coordinate system having a movement-distance axis and a time axis" (e.g., see at least, [0131] FIG. 11 illustrates…station-lateral constraint and station time constraint analysis based on sensor data associated with an agent that is moving toward a road along which the AV is moving). PNG media_image1.png 565 666 media_image1.png Greyscale For these reasons, the rejection is maintained . Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1 and 3-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim 1 is directed to a method of generating trajectory constraints for collision avoidance in autonomous vehicles (i.e., a process). Therefore, claim 1 is within at least one of the four statutory categories. Regarding Prong I of the Step 2A analysis in the 2019 PEG , the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. Claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites: A method of generating trajectory constraints for collision avoidance in autonomous vehicles in an infrastructure cooperative autonomous driving system, the method comprising: receiving, via an on-board communication device, a broadcast message from an edge infrastructure, the broadcast message comprising a traveler information message (TIM) having an extended field containing a predicted future trajectory of a first object; predicting, via a processor, a future trajectory of a second object sensed by a sensor device of an autonomous vehicle calculating, via the processor, a time to collision and a collision point based on a comparison between a target trajectory of the autonomous vehicle and the predicted future trajectories of the first object and the second object calculating, via the processor, a safety point on the target trajectory that is proximate to the collision point but restricted to a current driving lane of the autonomous vehicle based on lane information included in a road map and the collision point; and generating, via the processor, trajectory constraints defined by the time to collision, the collision point, and the safety point in a coordinate system with a movement- distance axis and a time axis The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. But for the recitation of “via the processor” nothing in the claim elements precludes the step from practically being performed in the mind For example, “predicting…” in the context of this claim encompasses a person looking at data collected and forming a simple judgement and is not too complex to be performed with the aid of pen and paper. Additionally, “calculating” describes a mathematical relationship and falls in the mathematical concepts grouping. Accordingly, the claim recites at least one abstract idea. Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”. In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”: A method of generating trajectory constraints for collision avoidance in autonomous vehicles in an infrastructure cooperative autonomous driving system, the method comprising: receiving, via an on-board communication device, a broadcast message from an edge infrastructure, the broadcast message comprising a traveler information message (TIM) having an extended field containing a predicted future trajectory of a first object; predicting, via a processor, a future trajectory of a second object sensed by a sensor device of an autonomous vehicle calculating, via the processor, a time to collision and a collision point based on a comparison between a target trajectory of the autonomous vehicle and the predicted future trajectories of the first object and the second object calculating, via the processor, a safety point on the target trajectory that is proximate to the collision point but restricted to a current driving lane of the autonomous vehicle based on lane information included in a road map and the collision point; and generating, via the processor, trajectory constraints defined by the time to collision, the collision point, and the safety point in a coordinate system with a movement- distance axis and a time axis. For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application. Regarding the additional limitations of “receiving…” and “ generating …”, the examiner submits that this limitation is insignificant extra-solution activity that merely use a computer (processor) to perform the process. In particular, the receiving steps from external sources are recited at a high level of generality (i.e. as a general means of gathering data for use in the predicting and calculating steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The generating step is recited at a high level of generality (i.e. as a general means of processing and transmitting data from predicting and calculating steps ), and amounts to mere post solution, which is a form of insignificant extra-solution activity. Lastly, the claim merely describes how to generally “apply” the otherwise mental judgements in a generic or general purpose vehicle control environment. The method of generating trajectory constraints for collision avoidance in autonomous vehicles and merely automates the predicting and calculating steps. Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular vehicle navigation or control problem, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Regarding Step 2B of the 2019 PEG , representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the comparing and calculating … amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “receiving…” and “ generating …”; the examiner submits that these limitations are insignificant extra-solution activity. Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well understood, routine, conventional activity in the field. The additional limitations of “receiving…” and “ generating …” are well-understood, routine, and conventional activities, and the specification does not provide any indication that the processor is anything other than a conventional computer network component. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, the claim is not patent eligible. Same analysis applied to independent claims 7 and 13. Dependent claims 3-6, 8-12 and 14-17 do not recite any further limitations that cause the claim to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 3-6, 8-12 and 14-17 are not patent eligible under the same rationale as provided for in the rejection of Claim 1. Therefore, claims 1 and 3-17 are ineligible under 35 USC §101. 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 and 3-17 are rejected under 35 U.S.C. 103 as being unpatentable over Shrivastava et al. (US 20250178642 A1; hereinafter Shrivastava ) in view of Sharman Banjade et al. (US 20240214786 A1; hereinafter Sharman Banjade) . Regarding claim 1 , Shrivastava teaches a method of generating trajectory constraints for collision avoidance in an infrastructure cooperative autonomous driving system (see at least, [0073] determining…constraints for autonomous vehicle route planning; Fig 1), PNG media_image2.png 515 737 media_image2.png Greyscale the method comprising: predicting, via a processor, a future trajectory of a second object sensed by a sensor device of an autonomous vehicle (see at least, [0103] FIG. 9 illustrates an autonomous vehicle 906 moving across a lane, which obtains sensor data….indicative of….a second agent (e.g., a vehicle and/or a pedestrian and/or a bicyclist)….For example, 904A, 904B, 904C is a second agent (e.g., an obstacle) at T seconds, T+1 seconds and T+2 seconds, respectively), calculating, via the processor, a safety point on the target trajectory that is proximate to the collision point but restricted to a current driving lane of the autonomous vehicle (see at least, Fig 7, [100] point 706A is the shortest lateral clearance point…706B is the most constraining point of the agent with respect to the baseline, being used to generate spatial clearance constraints; *Examiner interprets baseline as the target trajectory, e.g. [0119] the control system 513 can receive commands to move the AV along a baseline path…can be a path determined based on a starting point and a destination and one or more roads, or streets connecting the starting point to the destination and shortest lateral clearance point as reading on safety point ) based on lane information included in a road map (see at least, [0066] maps include…high-precision maps of the roadway geometric properties…roadway physical properties…number of vehicular and cyclist traffic lanes, lane width, lane traffic directions) and the collision point (see at least, [0091] closest projection point defining the most constraining point of the agent with respect to the baseline projection; *Examiner interprets closest projection point as reading on collision point as shown in Fig 7, point 706B ) ; and generating, via the processor, trajectory constraints defined by the time to collision, the collision point, and the safety point in a coordinate system with a movement- distance axis and a time axis (see at least, [0131] FIG. 11 illustrates…station-lateral constraint and station time constraint analysis based on sensor data associated with an agent that is moving toward a road along which the AV is moving). PNG media_image1.png 565 666 media_image1.png Greyscale Shrivastava further does not explicitly teach receiving, via an on-board communication device, a broadcast message from an edge infrastructure; calculating, via the processor, a time to collision and a collision point based on a comparison between a target trajectory of the autonomous vehicle and the predicted future trajectories of the first object and the second objec t each object and the autonomous vehicle . However, Sharman Banjade teaches this limitation. Sharman Banjade teaches receiving, via an on-board communication device, a broadcast message from an edge infrastructure (see least, [0442] The IX 2956 couples the processor 2952 to communication circuitry 2966 for communications with other devices, such as a remote server (not shown) and/or the connected edge devices 2962. The communication circuitry 2966 is a hardware element, or collection of hardware elements, used to communicate over one or more networks (e.g., cloud 2963) and/or with other devices (e.g., edge devices 2962); calculating, via the processor, a time to collision and a collision point based on a comparison between a target trajectory of the autonomous vehicle and the predicted future trajectories of the first object and the second objec t each object and the autonomous vehicle (see at least, Fig 1, [0357] VRUs 116/117 and vehicles which are in a conflict situation need to detect it at least 5 to 6 seconds before reaching the conflict point to be sure to have the capability to act on time to avoid a collision…collision risk indicators…TTC…are used to predict the instant of the conflict…the time required by the subject VRU and the subject vehicle to reach together the conflict point). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shrivastava to include receiving, via an on-board communication device, a broadcast message from an edge infrastructure; calculating, via the processor, a time to collision and a collision point based on a comparison between a target trajectory of the autonomous vehicle and the predicted future trajectories of the first object and the second object each object and the autonomous vehicle as taught by Sharman Banjade so that the edge computing node may communicate with close devices, e.g., within about 10 meters in order to avoid collision (Sharman Banjade, [0444]). Shrivastava further teaches a broadcasting device (see at least, [0031] Vehicle-to-Infrastructure (V2I) device 110 (sometimes referred to as a Vehicle-to-Infrastructure or Vehicle-to-Everything (V2X) device) includes at least one device configured to be in communication with vehicles 102 and/or V2I infrastructure system 11) ; but does not explicitly teach the broadcast message comprising a traveler information message (TIM) having an extended field containing a predicted future trajectory of a first object as taught by Kim. Kim teaches the broadcast message comprising a traveler information message (TIM) (see at least, [0076] a roadside alert (RSA) message for supporting a traveler information application, a traveler information message (TIM)) having an extended field containing a predicted future trajectory of a first object (see at least, [0048] As illustrated in FIG. 1, in the C-ITS, a pedestrian device 1010, an RSU 1020, and vehicles 1030, 1040, and 1050, each of which includes the V2X communication device, perform communication with one another; [0116] A moving ITS station, such as all ITS-vehicles or a pedestrian station, has its own past path history and predicted path, and its own current location information). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shrivastava to include the broadcast message comprising a traveler information message (TIM) having an extended field containing a predicted future trajectory of a first object as taught by Kim in order to have secure performance of safe the V2X communication and achieve a reduction of a channel load (Kim, [0146]). Regarding claim 3 , the combination of Shrivastava, Sharman Banjade and Kim teaches the method of claim 1 . Kim further teaches a format of the traveler information message comprises a basic field defined in V2X communication standard (see at least, [0048] As illustrated in FIG. 1, in the C-ITS, a pedestrian device 1010, an RSU 1020, and vehicles 1030, 1040, and 1050, each of which includes the V2X communication device, perform communication with one another) and the extended field where the future trajectory of the first object is recorded (see at least, [0048] As illustrated in FIG. 1, in the C-ITS, a pedestrian device 1010, an RSU 1020, and vehicles 1030, 1040, and 1050, each of which includes the V2X communication device, perform communication with one another). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Shrivastava to include a format of the traveler information message comprises a basic field defined in V2X communication standard and the extended field where the future trajectory of the first object is recorded as taught by as taught by Kim in order to have secure performance of safe the V2X communication and achieve a reduction of a channel load (Kim, [0146]). Regarding claim 4 , the combination of Shrivastava, Sharman Banjade and Kim teaches the method of claim 1. Shrivastava further teaches comprising, before calculating the time to collision and the collision point, sensing the second object to generate sensor data by using a sensor device equipped in the autonomous vehicle (see at least, [0103] FIG. 9 illustrates an autonomous vehicle 906 moving across a lane, which obtains sensor data….indicative of….a second agent (e.g., a vehicle and/or a pedestrian and/or a bicyclist)….For example, 904A, 904B, 904C is a second agent (e.g., an obstacle) at T seconds, T+1 seconds and T+2 seconds, respectively) ; and predicting the future trajectory of the second object from the sensor data of the second object based on a deep learning model (see at least, [0069] perception system 402, planning system 404, localization system 406, and/or control system 408 implement at least one machine learning model as part of a pipeline…e.g., a pipeline for identifying one or more objects located in an environment). Regarding claim 5 , the combination of Shrivastava, Sharman Banjade and Kim teaches the method of claim 1. Shrivastava further teaches wherein the trajectory constraints are defined as a rectangular area (see at least, Fig 8B, 804) expressed by the time to collision (see at least, Fig 8B, T2, T3) , the collision point (see at least, Fig 8B, S1, S2) , and the safety point (see at least, Fig 8B, S0) , in a coordinate system and wherein the rectangular area is defined by a diagonal line connecting first coordinates (SP - Ad, TTC - At) to second coordinates (CP + Ad, TTC + At), where TTC denotes the time to collision, SP denotes the safety point, Ad denotes a predetermined distance buffer, and At denotes a predetermined time buffer (see at least, Fig 8B) . PNG media_image3.png 491 360 media_image3.png Greyscale Regarding claim 6 , the combination of Shrivastava, Sharman Banjade and Kim teaches the method of claim 1. Shrivastava further teaches wherein the trajectory constraints are defined as a rectangular area where a line connecting first coordinates, consisting of an x-axis value obtained by subtracting a certain distance Δd from the safety point and a y-axis value obtained by subtracting a certain time Δt from the time to collision (see at least, Fig 11; [0133] constraining agent region 1124) , to second coordinates consisting of an x-axis value obtained by adding the certain distance Δd to the collision point and a y-axis value obtained by adding the certain time Δt to the time to collision is a diagonal line (see at least Fig 11; [0131] The longitudinal positions of the projected vertices of the agent polygon 1101 can be represented as the traces 1114, 1116, and 1118). Regarding claim 7 , Shrivastava teaches an infrastructure cooperative autonomous driving system equipped in autonomous vehicles (see at least Fig 1 below) , PNG media_image2.png 515 737 media_image2.png Greyscale the infrastructure cooperative autonomous driving system comprising: a collision point calculator configured compare target trajectory information about the autonomous vehicle with future trajectory information including the first future trajectory of the first object (see at least, [0103] an autonomous vehicle 906 moving across a lane, which obtains sensor data…indicative of a first...agent (e.g., a vehicle and/or a pedestrian and/or a bicyclist)…at T seconds, T+1 seconds and T+2 seconds, respectively) ; a safety point calculator configured to calculate a point (see at least, [0108] at least one processor…to generate safe trajectories that do not intercept with the dynamic trajectory of the agent) , which is closest to the collision point and is not included in another lane area where the autonomous vehicle does not drive, of a trajectory of the autonomous vehicle as a safety point (see at least, [0112] A distance associated with the closest projection point with respect to the baseline …defines the most constraining point of the agent and is….used for lateral clearance…the distance associated with the closest projection point with respect to the baseline is the projected distance) , based on lane information included in a road map and the collision point (see at least, [0070] database 410 stores data associated with 2D and/or 3D maps…a vehicle…can drive along one or more drivable regions…e.g., single-lane roads, multi-lane roads…to generate data associated with an image representing the objects included in a field of view of the at least one LiDAR sensor) ; and a constraint generator (see at least, Fig. 5 Box 508- Constraint Generation System) configured to generate constraints defined by the time to collision, the collision point, and the safety point (see at least, [0130] generating the constraints…may include an analysis of the projection of an agent station-lateral-time (S-L-T) domain…the outcome of projecting the agent in the (S-L-T) domain can be an S-T map including an agent S-T region corresponding to the constraining boundary and a safe S-T region available for the AV to navigate). Shrivastava does not explicitly teach the infrastructure cooperative autonomous driving system comprising: an on-board unit configured to receive a first future trajectory of a first object from an edge infrastructure; and a future trajectory of the second object predicted from the autonomous vehicle to calculate a time to collision and a collision point between each object and the autonomous vehicle. However, Sharman Banjade teaches these limitations. Sharman Banjade teaches the infrastructure cooperative autonomous driving system comprising: an on-board unit (see at least, Fig 23, [0382] the vehicle computing system 2300 includes a V-ITS-S 2301) configured to receive a first future trajectory of a first object from an edge infrastructure (see at least, [0066] VRU awareness services …can be extended or enhanced; claim 42, the processor circuitry is to operate the VBS to: generate a VRU Awareness Message (VAM) to include a motion prediction container) ; and a future trajectory of the second object predicted from the autonomous vehicle to calculate a time to collision and a collision point between each object and the autonomous vehicle (see at least, Fig 20-Box 2016, Collision Risk Analysis, Fig 1, [0357] VRUs 116/117 and vehicles which are in a conflict situation need to detect it at least 5 to 6 seconds before reaching the conflict point to be sure to have the capability to act on time to avoid a collision…TTC…used to predict the instant of the conflict). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shrivastava to include an on-board unit configured to receive a first future trajectory of a first object from an edge infrastructure; and a future trajectory of the second object predicted from the autonomous vehicle to calculate a time to collision and a collision point between each object and the autonomous vehicle as taught by Sharman Banjade so that the edge computing node may communicate with close devices, e.g., within about 10 meters in order to avoid collision (Sharman Banjade, [0444]). Regarding claim 8 , the combination of Shrivastava, Sharman Banjade and Kim teaches the infrastructure cooperative autonomous driving system of claim 7. Kim further teaches a format of the traveler information message comprises a basic field defined in V2X communication standard (see at least, [0048] As illustrated in FIG. 1, in the C-ITS, a pedestrian device 1010, an RSU 1020, and vehicles 1030, 1040, and 1050, each of which includes the V2X communication device, perform communication with one another) and the extended field where the future trajectory of the first object is recorded (see at least, [0048] As illustrated in FIG. 1, in the C-ITS, a pedestrian device 1010, an RSU 1020, and vehicles 1030, 1040, and 1050, each of which includes the V2X communication device, perform communication with one another). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Shrivastava to include a format of the traveler information message comprises a basic field defined in V2X communication standard and the extended field where the future trajectory of the first object is recorded as taught by as taught by Kim in order to have secure performance of safe the V2X communication and achieve a reduction of a channel load (Kim, [0146]). Regarding claim 9 , the combination of Shrivastava, Sharman Banjade and Kim teaches the infrastructure cooperative autonomous driving system of claim 8. Kim wherein the processor is furthe r configured to parse the traveler information message to extract the future trajectory of the first object (see at least, [0145] a system that controls an RSU may be referred to as one V2X communication device including a plurality of RSUs. In such a case, a V2X communication device corresponding to an RSU control system may continuously perform the operations of FIGS. 9 and 10). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Shrivastava to include wherein the processor is further configured to parse the traveler information message to extract the future trajectory of the first object as taught by Kim in order to have secure performance of safe the V2X communication and achieve a reduction of a channel load (Kim, [0146]). Regarding claim 10 , the combination of Shrivastava, Sharman Banjade and Kim teaches the infrastructure cooperative autonomous driving system of claim 7. Shrivastava further teaches comprising: wherein the processor is further configured to predict the future trajectory of the second object from the sensor data by using a deep learning model (see at least, [0069] perception system 402, planning system 404, localization system 406, and/or control system 408 implement at least one machine learning model as part of a pipeline…e.g., a pipeline for identifying one or more objects located in an environment). Regarding claim 11 , the combination of Shrivastava, Sharman Banjade and Kim teaches the infrastructure cooperative autonomous driving system of claim 7. Shrivastava further teaches wherein the trajectory constraints are defined as a rectangular area (see at least, Fig 8B, 804) expressed by the time to collision (see at least, Fig 8B, T2, T3) , the collision point (see at least, Fig 8B, S1, S2) , and the safety point (see at least, Fig 8B, S0) , in a coordinate system where the movement-distance axis is an x axis (see at least, Fig 8B, S axis) and the time axis is a y axis in the target trajectory of the autonomous vehicle (see at least, Fig 8B, T axis) . PNG media_image3.png 491 360 media_image3.png Greyscale Regarding claim 12 , the combination of Shrivastava, Sharman Banjade and Kim teaches the infrastructure cooperative autonomous driving system of claim 11. Shrivastava further teaches wherein the processor is further is configured to calculate the target trajectory of the autonomous vehicle based on an increase and a reduction in the rectangular area (see at least, [0096] the system 500 is configured to generate a trajectory from each homotopy via a control optimization method (e.g., model predictive control)…uses the constraints (e.g., station constraint 508a and/or lateral constraint 508a) contained in a given homotopy to determine an optimized trajectory based on specific objective parameters…e.g., dynamically feasible trajectory). Regarding claim 13 , Shrivastava teaches a method of generating trajectory constraints for collision avoidance in autonomous vehicles (see at least, [0073] determining…constraints for autonomous vehicle route planning) in an infrastructure cooperative autonomous driving system (see at least Fig 1 below), PNG media_image2.png 515 737 media_image2.png Greyscale the method comprising: a step of calculating a safety point by using a safety point calculator of the autonomous vehicle (see at least, [0108] at least one processor…to generate safe trajectories that do not intercept with the dynamic trajectory of the agent) , based on lane information about a road map and the collision point (see at least, [0070] database 410 stores data associated with 2D and/or 3D maps…a vehicle…can drive along one or more drivable regions…e.g., single-lane roads, multi-lane roads…to generate data associated with an image representing the objects included in a field of view of the at least one LiDAR sensor) ; and a step of generating constraints defined by the time to collision, the collision point, and the safety point by using a constraint generator (see at least, Fig. 5 Box 508- Constraint Generation System) of the autonomous vehicle (see at least, [0130] generating the constraints…may include an analysis of the projection of an agent station-lateral-time (S-L-T) domain…the outcome of projecting the agent in the (S-L-T) domain can be an S-T map including an agent S-T region corresponding to the constraining boundary and a safe S-T region available for the AV to navigate). Shrivastava does not explicitly teach the method comprising: a step of generating a first future trajectory of a first object and broadcasting a broadcast message including the first future trajectory by using an edge infrastructure; a step of receiving the broadcast message by using an on-board unit of an autonomous vehicle; a step of calculating a time to collision and a collision point between each object and the autonomous vehicle by using a collision point calculator of the autonomous vehicle, based on future trajectory information including the first future trajectory of the first object included in the broadcast message and a future trajectory of a second object predicted from the autonomous vehicle. However, Sharman Banjade teaches these limitations. Sharman Banjade teaches a step of generating a first future trajectory of a first object and broadcasting a broadcast message including the first future trajectory by using an edge infrastructure (see at least, [0321] activate or de-activate the broadcasting of the VAMs or other standard message (e.g., DENMs) according to the state and types of associated VRU) ; a step of receiving the broadcast message by using an on-board unit (see at least, Fig 23, [0382] the vehicle computing system 2300 includes a V-ITS-S 2301) of an autonomous vehicle (see at least, [0066] VRU awareness services …can be extended or enhanced; claim 42, the processor circuitry is to operate the VBS to: generate a VRU Awareness Message (VAM) to include a motion prediction container) ; a step of calculating a time to collision and a collision point between each object and the autonomous vehicle by using a collision point calculator (see at least, Fig 20-Box 2016, Collision Risk Analysis) of the autonomous vehicle, based on future trajectory information including the first future trajectory of the first object included in the broadcast message (see at least, Fig 6, Step 2; Claim 42, the processor circuitry is to operate the VBS to: generate a VRU Awareness Message (VAM) to include a motion prediction container) and a future trajectory of a second object predicted from the autonomous vehicle (see at least, Fig 1, [0357] VRUs 116/117 and vehicles which are in a conflict situation need to detect it at least 5 to 6 seconds before reaching the conflict point to be sure to have the capability to act on time to avoid a collision…TTC…used to predict the instant of the conflict). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shrivastava to include a step of generating a first future trajectory of a first object and broadcasting a broadcast message including the first future trajectory by using an edge infrastructure; a step of receiving the broadcast message by using an on-board unit of an autonomous vehicle; a step of calculating a time to collision and a collision point between each object and the autonomous vehicle by using a collision point calculator of the autonomous vehicle, based on future trajectory information including the first future trajectory of the first object included in the broadcast message and a future trajectory of a second object predicted from the autonomous vehicle as taught by Sharman Banjade so that the edge computing node may communicate with close devices, e.g., within about 10 meters in order to avoid collision (Sharman Banjade, [0444]). Regarding claim 14 , the combination of Shrivastava, Sharman Banjade and Kim teaches the method of claim 13. Shrivastava further teaches wherein the safety point is closest to the collision point and is not included in another lane area where the autonomous vehicle does not drive (see at least, [0112] A distance associated with the closest projection point with respect to the baseline…defines the most constraining point of the agent and is….used for lateral clearance…the distance associated with the closest projection point with respect to the baseline is the projected distance). Regarding claim 15 , the combination of Shrivastava, Sharman Banjade and Kim teaches the method of claim 13. Shrivastava further teaches wherein the trajectory constraints are defined as a rectangular area (see at least, Fig 8B, 804) expressed by the time to collision (see at least, Fig 8B, T2, T3) , the collision point (see at least, Fig 8B, S1, S2) , and the safety point (see at least, Fig 8B, S0) , in a coordinate system where the movement-distance axis is an x axis (see at least, Fig 8B, S axis) a y axis in the target trajectory of the autonomous vehicle (see at least, Fig 8B, T axis) . PNG media_image3.png 491 360 media_image3.png Greyscale Regarding claim 16 , the combination of Shrivastava, Sharman Banjade and Kim teaches the method of claim 13. Sharman Banjade further teaches wherein the broadcasting of the broadcast message comprises: sensing the first object to generate sensor data a sensor device of the edge infrastructure; generating the future trajectory of the first object from the sensor data by using an edge computer of the edge infrastructure (see at least, [0030] Vehicle-to-Everything (V2X) applications …include…roadside infrastructure or roadside units (RSUs)….collect knowledge of their local environment (e.g., information received from other vehicles or sensor equipment in proximity) to process and share that knowledge in order to provide more intelligent services) , based on a deep learning model (see at least, [0422] The edge computing node 2950 includes processing circuitry in the form of one or more processors 2952…The one or more accelerators may include, for example, computer vision and/or deep learning accelerators) ; generating a broadcast message including the future trajectory via the edge computer of the edge infrastructure (see at least, Fig 6, Step 2; Claim 42, the processor circuitry is to operate the VBS to: generate a VRU Awareness Message (VAM) to include a motion prediction container) ; and broadcasting the broadcast message via a road side unit of the edge infrastructure (see at least, Fig 1; [0061] the RSU 130…may provide VRU services among the one or more services/capabilities 180… The V-ITS-Ss 110 may also share such messages with each other, with RSU 130, and/or with VRUs. These messages may include the various data elements and/or data fields as discussed herein). Regarding claim 17 , the combination of Shrivastava, Sharman Banjade and Kim teaches the method of claim 13. Shrivastava further teaches wherein one of the first object and the second object is a vehicle, and the other object is a non-vehicle object other than a vehicle (see at least, Fig 1, [0028] Objects 104a-104n… include….at least one vehicle, at least one pedestrian….Each object 104 is…mobile…e.g., having a velocity and associated with at least one trajectory; *Examiner interprets pedestrian as non-vehicle object ) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Quirynen et al. (US 20240331535 A1) discloses an infrastructure cooperative autonomous driving system equipped in autonomous vehicles, the infrastructure cooperative autonomous driving system comprising: an on-board unit configured to receive a first future trajectory of a first object from an edge infrastructure (e.g. [0085] A communication between the cloud network 118 and a vehicle, such as the vehicle 126 on the road segment 105, needs to propagate through the RSU 122 or the RSU 124 and the core network 120. In some embodiments, safe mobility of the vehicles 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 146 is controlled by a hierarchical optimization-based traffic control system using a cloud-based or edge-based network). Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). 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 TOYA PETTIEGREW whose telephone number is (313)446-6636. The examiner can normally be reached 8:30pm - 5:00pm M-F. 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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. /TOYA PETTIEGREW/Primary Examiner, Art Unit 3662 Application/Control Number: 18/678,658 Page 2 Art Unit: 3662 Application/Control Number: 18/678,658 Page 3 Art Unit: 3662 Application/Control Number: 18/678,658 Page 4 Art Unit: 3662 Application/Control Number: 18/678,658 Page 5 Art Unit: 3662 Application/Control Number: 18/678,658 Page 6 Art Unit: 3662 Application/Control Number: 18/678,658 Page 7 Art Unit: 3662 Application/Control Number: 18/678,658 Page 8 Art Unit: 3662 Application/Control Number: 18/678,658 Page 9 Art Unit: 3662 Application/Control Number: 18/678,658 Page 10 Art Unit: 3662 Application/Control Number: 18/678,658 Page 11 Art Unit: 3662 Application/Control Number: 18/678,658 Page 12 Art Unit: 3662 Application/Control Number: 18/678,658 Page 13 Art Unit: 3662 Application/Control Number: 18/678,658 Page 14 Art Unit: 3662 Application/Control Number: 18/678,658 Page 15 Art Unit: 3662 Application/Control Number: 18/678,658 Page 16 Art Unit: 3662 Application/Control Number: 18/678,658 Page 17 Art Unit: 3662 Application/Control Number: 18/678,658 Page 18 Art Unit: 3662 Application/Control Number: 18/678,658 Page 19 Art Unit: 3662 Application/Control Number: 18/678,658 Page 20 Art Unit: 3662 Application/Control Number: 18/678,658 Page 21 Art Unit: 3662 Application/Control Number: 18/678,658 Page 22 Art Unit: 3662
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