CTNF 19/184,072 CTNF 100003 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. DETAILED ACTION Priority Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55 with regards to KR10-2025-0047470 with the effective date of 04/11/2025. However, please note the following. 02-25 AIA Acknowledgment is made of applicant's claim for foreign priority based on an application filed in Korea on 05/14/2024 . It is noted, however, that applicant has not filed a certified copy of the KR10-2024-0063158 application as required by 37 CFR 1.55. Information Disclosure 06-52 The information disclosure statement (IDS) submitted on 04/21/2025 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. 12-151 AIA 26-51 12-51 Status of Claims The following is a non-final office action in response to the communication filed on 04/21/2025. Claims 1-19 are pending and have been examined. Claims 1-19 are rejected. Objections to the Specification 07-29 AIA The disclosure is objected to because of the following informalities: Paragraph [0045], Line 1 reads, “The green vehicle,” when upon publication, the drawings are rendered in black and white. An alternate adjective and/or description of the vehicle is respectfully requested . Appropriate correction is required. Claim Objections 07-29-01 AIA Claim s 9 and 18-19 objected to because of the following informalities: Claim 9, Line 3 reads, “predicts an occupancy grid map,” when it should read, “predicts the an occupancy grid map,” in order to properly refer back to the limitation’s introduction in Claim 1, Lines 10-11. Claim 18, Line 3 reads, “by a camera,” when it should read, “by the a camera,” in order to properly refer back to the limitation’s introduction in Claim 13, Line 12. Claim 19, Line 3 reads, “by a camera,” when it should read, “by the a camera,” in order to properly refer back to the limitation’s introduction in Claim 13, Line 12. Claim 19, Line 9 reads, “a query map,” when it should read, “ the a query map,” in order to properly refer back to the limitation’s introduction in Claim 18, Line 7 . Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claim 10 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 10 recites the limitation "the query map" in Line 4. There is insufficient antecedent basis for this limitation in the claim. “A query” map is introduced in Claim 9, however, claim 10 does not depend from claim 9. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 07-30-05 The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation is: “a static object information preprocessing unit configured to perform preprocessing on the static object information,” in claim 1, Lines 8-9. Regarding Prong 1: A static object information preprocessing unit is a nonce term/substitute for means. Regarding Prong 2: The unit is modified by the functional language, “configured to.” Regarding Prong 3: The static object information preprocessing unit is not modified by sufficient structure, material, or acts for performing the claimed function of preprocessing. Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. The portion of the specification used to interpret the preceding limitation for the purposes of examination will be, (Applicant’s Specification, (Paragraph [0009]) “The static object information preprocessing unit converts global coordinates of nodes and links included in the static object information into a coordinate system defined based on the current position and heading direction of the autonomous vehicle.” If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Rejections - 35 USC § 101 Examiner Note: Due to the 35 U.S.C. 112(f) interpretation of the claim language including global coordinate conversion into a separate coordinate system, the examiner finds the claims do not recite a mental process of predicting an occupancy grid map, as the human mind cannot reasonably do the coordinate conversion. Claim Rejections - 35 USC § 102 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. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim s 1-2, 4, and 12-13 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Chen et al. (US 2025/0171017 A1, hereinafter Chen) Claim 1 Discloses: “A driving environment map generation apparatus for autonomous driving,” Chen teaches, (Paragraph [0003], Lines 1-8) “Embodiments of the present disclosure relate to systems and methods for implementing a traffic model configured to continuously generate outputs that are usable for, among other things, interfacing with LLMs. More specifically, embodiments disclosed herein relate to systems that can encode traffic scene data (e.g., generated by a perception system) observed over a period of time into an observable latent space .” “comprising: a vehicle position and orientation prediction unit configured to predict current position information and heading direction of an autonomous vehicle using sensors mounted on the autonomous vehicle; an autonomous vehicle surrounding static object information return unit configured to receive static object information from a commercial navigation system; a static object information preprocessing unit configured to perform preprocessing on the static object information;” Chen teaches, (Paragraph [0202], Lines 1-2) “The vehicle can further include IMU sensor(s) 866,” wherein, (Paragraph [0203], Lines 1-11) “the IMU sensor(s) 866 can be implemented as a miniature, high performance GPS -Aided Inertial Navigation System (GPS/INS) that combines micro-electro-mechanical systems (MEMS) inertial sensors, a high-sensitivity GPS receiver , and advanced Kalman filtering algorithms to provide estimates of position, velocity, and attitude . As such, in some examples, the IMU sensor(s) 866 can enable the vehicle 800 to estimate heading without requiring input from a magnetic sensor by directly observing and correlating the changes in velocity from GPS to the IMU sensor(s) 866.” Chen additionally teaches, (Paragraph [0058], Lines 9-13) “the vehicle processor can implement the second edge function to output a projection of the agent's position on the lane polyline as well as the starting and ending points of the lane segment in a coordinate frame established for the agent ,” wherein, (Paragraph [006], Lines 5-10) “For example, the a2a edge function can calculate the relative position in the local coordinate frame , the a2l edge function output can contain the project of the agent's position on the lane polyline as well as the starting and ending points of the lane segment in the agent coordinate frame.” “and an occupancy grid map prediction unit configured to predict an occupancy grid map using the preprocessed static object information and information acquired by a camera mounted on the autonomous vehicle.” Chen teaches, (Paragraph [0129], Lines 7-13) “The outputs can include information such as vehicle velocity, speed, time, map data (e.g., the High Definition (“HD”) map 822 of FIG. 8C), location data (e.g., the vehicle's 800 location, such as on a map), direction, location of other vehicles ( e.g., an occupancy grid ), information about objects and status of objects as perceived by the controller(s) 836, etc,” and that, (Paragraph [0135], Lines 1-7) “ Cameras with a field of view that include portions of the environment in front of the vehicle 800 (e.g., front-facing cameras) can be used for surround view, to help identify forward facing paths and obstacles, as well aid in, with the help of one or more controllers 836 and/or control SoCs, providing information critical to generating an occupancy grid and/or determining the preferred vehicle paths,” and further that, (Paragraph [0193], Lines 1-5) “The vehicle 800 can further include GNSS sensor(s) 858. The GNSS sensor(s) 858 (e.g., GPS, assisted GPS sensors, differential GPS (DGPS) sensors, etc.), to assist in mapping, perception, occupancy grid generation, and/or path planning functions .” Chen additionally teaches, (Paragraph [0063], Lines 1-5) “The method 200, at block 208 includes decoding the joint scene mode distribution. For example, the vehicle processor can decode the joint scene mode distribution into one or more trajectory predictions and/or one or more categorical predictions for the one or more agents ,” wherein, (Paragraph [0097], Lines 1-6) “Referring now to FIG. 6, is a diagram 600 of scene mode conditioned prediction trajectories (e.g., trajectory predictions that are the same as, or similar to, the trajectory predictions 324 of FIG. 3) generated by a model is shown, in accordance with some embodiments of the present disclosure.” Claim 2 Discloses: “The driving environment map generation apparatus for autonomous driving according to claim 1, wherein the vehicle position and orientation prediction unit predicts the current position information and heading information using a GPS and an IMU mounted on the autonomous vehicle.” Chen teaches, (Paragraph [0202], Lines 1-2) “The vehicle can further include IMU sensor(s) 866,” wherein, (Paragraph [0203], Lines 1-11) “the IMU sensor(s) 866 can be implemented as a miniature, high performance GPS -Aided Inertial Navigation System ( GPS /INS) that combines micro-electro-mechanical systems (MEMS) inertial sensors, a high-sensitivity GPS receiver , and advanced Kalman filtering algorithms to provide estimates of position, velocity, and attitude . As such, in some examples, the IMU sensor(s) 866 can enable the vehicle 800 to estimate heading without requiring input from a magnetic sensor by directly observing and correlating the changes in velocity from GPS to the IMU sensor(s) 866.” Claim 4 Discloses: “The driving environment map generation apparatus for autonomous driving according to claim 1, wherein the static object information preprocessing unit converts global coordinates of nodes and links included in the static object information into a coordinate system defined based on the current position and heading direction of the autonomous vehicle.” Chen teaches, (Paragraph [0203], Lines 1-11) “the IMU sensor(s) 866 can be implemented as a miniature, high performance GPS -Aided Inertial Navigation System (GPS/INS) that combines micro-electro-mechanical systems (MEMS) inertial sensors, a high-sensitivity GPS receiver , and advanced Kalman filtering algorithms to provide estimates of position, velocity, and attitude . As such, in some examples, the IMU sensor(s) 866 can enable the vehicle 800 to estimate heading without requiring input from a magnetic sensor by directly observing and correlating the changes in velocity from GPS to the IMU sensor(s) 866.” Chen additionally teaches, (Paragraph [0058], Lines 9-13) “the vehicle processor can implement the second edge function to output a projection of the agent's position on the lane polyline as well as the starting and ending points of the lane segment in a coordinate frame established for the agent ,” wherein, (Paragraph [006], Lines 5-10) “For example, the a2a edge function can calculate the relative position in the local coordinate frame , the a2l edge function output can contain the project of the agent's position on the lane polyline as well as the starting and ending points of the lane segment in the agent coordinate frame.” Claim 12 Discloses: “The driving environment map generation apparatus for autonomous driving according to claim 1, further comprising: an autonomous vehicle local path generation unit configured to receive an output from the occupancy grid map prediction unit and generate a local path for the autonomous vehicle, the local path being output in the form of waypoints indicating the route the autonomous vehicle should follow.” Chen teaches, (Paragraph [0035], Lines 7-13) “the system can encode the scene data associated with a traffic scenario by providing the scene data to an encoder that is configured to communicate with a graph neural network (GNN) to generate an output. The output can include data associated with the latent representations of the agents and/or lane segments represented by the scene data,” (Paragraph [0129]) wherein, “One or more of the controller(s) 836 (e.g., vehicle processor 110) can obtain inputs … The outputs can include information such as vehicle velocity, … location data (e.g., the vehicle's 800 location, such as on a map), direction, location of other vehicles (e.g., an occupancy grid ).” Chen additionally teaches, (Paragraph [0055], Lines 18-20) “the lane segments can be represented as polylines consisting of multiple waypoints that are based at least on lane markings in the environment,” wherein, (Paragraph [0064], Lines 27-32) “the vehicle processor can then generate control data to provide to a planning system and/or a control system to update one or more trajectories to be selected and/or control signals to be transmitted to cause operation of the vehicle in accordance with the output(s) of the LLM,” and further wherein, (Paragraph [0097], Lines 1-6) “Referring now to FIG. 6, is a diagram 600 of scene mode conditioned prediction trajectories (e.g., trajectory predictions that are the same as, or similar to, the trajectory predictions 324 of FIG. 3) generated by a model is shown, in accordance with some embodiments of the present disclosure.” Claim 13 Discloses: “A method for generating a driving environment map for autonomous driving,” Chen teaches, (Paragraph [0003], Lines 1-8) “Embodiments of the present disclosure relate to systems and methods for implementing a traffic model configured to continuously generate outputs that are usable for, among other things, interfacing with LLMs. More specifically, embodiments disclosed herein relate to systems that can encode traffic scene data (e.g., generated by a perception system) observed over a period of time into an observable latent space.” “performed by a driving environment map generation apparatus for autonomous driving, the method comprising: predicting current position information and heading direction of an autonomous vehicle using sensors mounted on the autonomous vehicle; receiving static object information from a commercial navigation system based on a prediction result of the current position information and heading direction of the autonomous vehicle; performing preprocessing on the static object information received from the commercial navigation system;” Chen teaches, (Paragraph [0202], Lines 1-2) “The vehicle can further include IMU sensor(s) 866,” wherein, (Paragraph [0203], Lines 1-11) “the IMU sensor(s) 866 can be implemented as a miniature, high performance GPS -Aided Inertial Navigation System (GPS/INS) that combines micro-electro-mechanical systems (MEMS) inertial sensors, a high-sensitivity GPS receiver , and advanced Kalman filtering algorithms to provide estimates of position, velocity, and attitude . As such, in some examples, the IMU sensor(s) 866 can enable the vehicle 800 to estimate heading without requiring input from a magnetic sensor by directly observing and correlating the changes in velocity from GPS to the IMU sensor(s) 866.” Chen additionally teaches, (Paragraph [0058], Lines 9-13) “the vehicle processor can implement the second edge function to output a projection of the agent's position on the lane polyline as well as the starting and ending points of the lane segment in a coordinate frame established for the agent ,” wherein, (Paragraph [006], Lines 5-10) “For example, the a2a edge function can calculate the relative position in the local coordinate frame , the a2l edge function output can contain the project of the agent's position on the lane polyline as well as the starting and ending points of the lane segment in the agent coordinate frame.” “predicting an occupancy grid map using the preprocessed static object information and information acquired by a camera mounted on the autonomous vehicle;” Chen teaches, (Paragraph [0129], Lines 7-13) “The outputs can include information such as vehicle velocity, speed, time, map data (e.g., the High Definition (“HD”) map 822 of FIG. 8C), location data (e.g., the vehicle's 800 location, such as on a map), direction, location of other vehicles ( e.g., an occupancy grid ), information about objects and status of objects as perceived by the controller(s) 836, etc,” and that, (Paragraph [0135], Lines 1-7) “ Cameras with a field of view that include portions of the environment in front of the vehicle 800 (e.g., front-facing cameras) can be used for surround view, to help identify forward facing paths and obstacles, as well aid in, with the help of one or more controllers 836 and/or control SoCs, providing information critical to generating an occupancy grid and/or determining the preferred vehicle paths,” and further that, (Paragraph [0193], Lines 1-5) “The vehicle 800 can further include GNSS sensor(s) 858. The GNSS sensor(s) 858 (e.g., GPS, assisted GPS sensors, differential GPS (DGPS) sensors, etc.), to assist in mapping, perception, occupancy grid generation, and/or path planning functions.” “and generating a local path using a prediction output of the occupancy grid map.” Chen teaches, (Paragraph [0035], Lines 7-13) “the system can encode the scene data associated with a traffic scenario by providing the scene data to an encoder that is configured to communicate with a graph neural network (GNN) to generate an output. The output can include data associated with the latent representations of the agents and/or lane segments represented by the scene data,” (Paragraph [0129]) wherein, “One or more of the controller(s) 836 (e.g., vehicle processor 110) can obtain inputs … The outputs can include information such as vehicle velocity, … location data (e.g., the vehicle's 800 location, such as on a map), direction, location of other vehicles (e.g., an occupancy grid ).” Chen additionally teaches, (Paragraph [0055], Lines 18-20) “the lane segments can be represented as polylines consisting of multiple waypoints that are based at least on lane markings in the environment,” wherein, (Paragraph [0064], Lines 27-32) “the vehicle processor can then generate control data to provide to a planning system and/or a control system to update one or more trajectories to be selected and/or control signals to be transmitted to cause operation of the vehicle in accordance with the output(s) of the LLM,” wherein, (Paragraph [0097], Lines 1-6) “Referring now to FIG. 6, is a diagram 600 of scene mode conditioned prediction trajectories (e.g., trajectory predictions that are the same as, or similar to, the trajectory predictions 324 of FIG. 3) generated by a model is shown, in accordance with some embodiments of the present disclosure.” 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 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Marchetti-Bowick et al. (US 2021/0004012 A1, hereinafter Marchetti-Bowick) Claim 3 Discloses: “The driving environment map generation apparatus for autonomous driving according to claim 1, wherein the autonomous vehicle surrounding object information return unit receives the static object information located within a predetermined distance from the autonomous vehicle.” Chen does not explicitly define a predetermined distance from the autonomous vehicle. However, Chen does teach applicable ranges its sensors are capable of detecting. Chen teaches, (Paragraph [0200], Lines 1-5) “In some examples, the LIDAR sensor(s) 864 can be capable of providing a list of objects and their distance for a 360-degree field of view. Commercially available LIDAR sensor(s) 864 can have an advertised range of approximately 700 m ,” and that, (Paragraph [0198], Lines 5-7) “A wide variety of ultrasonic sensor(s) 862 can be used, and different ultrasonic sensor(s) 862 can be used for different ranges of detection (e.g., 2.5 m, 4 m ),” and further that, (Paragraph [0196], Lines 1-4) “Mid-range RADAR systems can include, as an example , a range of up to 760 m (front) or 80 m (rear) , and a field of view of up to 42 degrees (front) or 750 degrees (rear).” Marchetti-Bowick does teach the preceding limitations. Marchetti-Bowick teaches, (Abstract, Lines 1-7) “An autonomous vehicle [which] can obtain state data associated with an object in an environment, obtain map data including information associated with spatial relationships between at least a subset of lanes of a road network, and determine a set of candidate paths that the object may follow in the environment based at least in part on the spatial relationships between at least two lanes of the road network,” wherein, (Paragraph [0028], Lines 1-4) “A set of paths for an object can be generated for an object of interest at a particular time by querying the map to identify lanes that fall within a predetermined distance (e.g., 2 meters) of the object's location .” Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the system of Chen with the explicit identification of a predetermined identification distance as taught by Marchetti-Bowick, in order to yield predictable results. Combining the references would yield the benefits of determining whether objects/actors exist within the vicinity of an autonomous vehicle to ensure the path it travels avoids conflict. As Marchetti-Bowick describes, (Paragraph [0028], Lines 4-13) “Starting from the object's current position, a path can be generated by following the lane successor relationships, up to a fixed distance , for example. This process can yield a set of candidate paths for the object. The spatial area covered by the union of all the paths can determine the region over which the occupancy of the object can be predicted . In this manner, the map topology can be utilized by the system to predict the occupancy of other actors in these specific regions which are typically of much higher importance.” Claim 14 Discloses: “The method according to claim 13, wherein the step of receiving static object information from the commercial navigation system based on the prediction result of the current position information and heading direction of the autonomous vehicle includes receiving the static object information including road network information located within a predetermined distance from the autonomous vehicle.” Chen does not explicitly road network information located a predetermined distance from the autonomous vehicle. However, Chen does teach applicable ranges its sensors are capable of detecting. Chen teaches, (Paragraph [0200], Lines 1-5) “In some examples, the LIDAR sensor(s) 864 can be capable of providing a list of objects and their distance for a 360-degree field of view. Commercially available LIDAR sensor(s) 864 can have an advertised range of approximately 700 m ,” and that, (Paragraph [0198], Lines 5-7) “A wide variety of ultrasonic sensor(s) 862 can be used, and different ultrasonic sensor(s) 862 can be used for different ranges of detection (e.g., 2.5 m, 4 m ),” and further that, (Paragraph [0196], Lines 1-4) “Mid-range RADAR systems can include, as an example , a range of up to 760 m (front) or 80 m (rear) , and a field of view of up to 42 degrees (front) or 750 degrees (rear).” Marchetti-Bowick does teach the preceding limitations. Marchetti-Bowick teaches, (Abstract, Lines 1-7) “An autonomous vehicle [which] can obtain state data associated with an object in an environment, obtain map data including information associated with spatial relationships between at least a subset of lanes of a road network , and determine a set of candidate paths that the object may follow in the environment based at least in part on the spatial relationships between at least two lanes of the road network,” wherein, (Paragraph [0028], Lines 1-4) “A set of paths for an object can be generated for an object of interest at a particular time by querying the map to identify lanes that fall within a predetermined distance (e.g., 2 meters) of the object's location .” Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the system of Chen with the explicit identification of a predetermined road network information identification distance as taught by Marchetti-Bowick, in order to yield predictable results. Combining the references would yield the benefits of determining whether objects/actors exist within the vicinity of an autonomous vehicle to ensure the path it travels avoids conflict. As Marchetti-Bowick describes, (Paragraph [0028], Lines 4-13) “Starting from the object's current position, a path can be generated by following the lane successor relationships, up to a fixed distance , for example. This process can yield a set of candidate paths for the object. The spatial area covered by the union of all the paths can determine the region over which the occupancy of the object can be predicted . In this manner, the map topology can be utilized by the system to predict the occupancy of other actors in these specific regions which are typically of much higher importance.” 07-21-aia AIA Claim s 5-6, 9-11, 15, and 17-19 rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Agro et al. (US 2026/0110799 A1, hereinafter Agro) Claim 5 Discloses: “The driving environment map generation apparatus for autonomous driving according to claim 4, wherein the static object information preprocessing unit represents the information of the nodes and links as vectors of a predetermined dimension, and generates a separate vector for each position point when the link is composed of a plurality of position points.” Chen teaches, (Paragraph [0006], Lines 3-13) “a graph neural network (GNN) including a plurality of node embeddings and a plurality of edge embeddings based at least on the first pairwise relationships and the second pairwise relationships. The plurality of node embeddings can include a first subset of node embeddings associated with one or more lane segments of the environment , a second subset of node embeddings associated with the movement of one or more agents during a first period of time, and a third subset of node embeddings associated with future movement of the one or more agents during a second period of time,” wherein, (Paragraph [0035], Lines 7-13) “the system can encode the scene data associated with a traffic scenario by providing the scene data to an encoder that is configured to communicate with a graph neural network (GNN) to generate an output. The output can include data associated with the latent representations of the agents and/or lane segments represented by the scene data.” Chen additionally teaches, (Paragraph [0058], Lines 6-13) “The vehicle processor can implement the first edge function to calculate the relative position between one or more agents in the environment in a local coordinate frame. Similarly, the vehicle processor can implement the second edge function to output a projection of the agent's position on the lane polyline as well as the starting and ending points of the lane segment in a coordinate frame established for the agent,” wherein, (Paragraph [0055], Lines 18-20) “the lane segments can be represented as polylines consisting of multiple waypoints that are based at least on lane markings in the environment.” Chen additionally teaches that, (Paragraph [0119], Lines 5-15) “each encoder can accept a sequence of vectors , passing each vector through the self-attention layer, then the feedforward network, and then upwards to the next encoder in the stack. Any known self-attention technique can be used. For example, to calculate a self-attention score for each token (word), a query vector, a key vector, and a value vector can be created for each token, a self-attention score can be calculated for pairs of tokens by taking the dot product of the query vector with the corresponding key vectors, normalizing the resulting scores.” However, Chen does not explicitly teach generating a separate vector for each position point. Agro does explicitly teach generating a separate vector for each position point. Agro teaches a system wherein, (Abstract, Lines 1-4) “Implicit occupancy for autonomous systems include receiving a request for a point attribute at a query point matching a geographic location, obtaining a query point feature vector from a feature map,” further wherein, (Paragraph [0051], Lines 5-8) “The map encoder model (502) is a machine learning model that is configured to transform map data into map feature vectors for each sub-region of the geographic region .” Agro additionally teaches, (Paragraph [0042], Lines 1-5) “Continuing with FIG. 3, an implicit decoder model (310) is a machine learning model configured to obtain a set of one or more query points (318) and output a set of one or more point attributes (320) for each of the query points (318),” and that, (Paragraph [0062], Lines 1-3) “A cross attention layer (614) obtains the offset feature vectors (612) and the point feature vector (604) and generates a combined feature vector (616).” Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the disclosure of Chen with an explicit vector generated for each position point as taught by Agro, in order to yield predictable results. Combining the references would yield the benefits of using vectors along points within an environment to determine occupancy. As Agro describes, (Paragraph [0021], Lines 17-20) “The determination of occupancy is important for an autonomous system because if an autonomous system moves to an occupied geographic location, then a collision occurs,” and further describes, (Paragraph [0023], Lines 2-4) “The query point is used as an input to the various machine learning models that determine the implicit occupancy .” Claim 6 Discloses: “The driving environment map generation apparatus for autonomous driving according to claim 5, wherein the static object information preprocessing unit determines attribute information that is considered helpful for occupancy grid prediction when constructing the vector, and constructs the vector based on the determination regarding the attribute information.” Chen teaches, (Paragraph [0119], Lines 19-22) “Any number of encoders can be cascaded to generate a context vector encoding the input . An attention projection layer 740 can convert the context vector into attention vectors (keys and values) for the decoder(s) 745.” Applicant’s specification provide non-limiting example of attribute information that is considered help helpful for occupancy grid prediction when constructing the vector, which reads, (Applicant’s Specification, Paragraph [0071]) “The attribute information includes various data such as the type of the node or link, the number of lanes, and the maximum speed limit.” Chen teaches. (Paragraph [0006], Lines 3-13) “a graph neural network (GNN) including a plurality of node embeddings and a plurality of edge embeddings based at least on the first pairwise relationships and the second pairwise relationships. The plurality of node embeddings can include a first subset of node embeddings associated with one or more lane segments of the environment , a second subset of node embeddings associated with the movement of one or more agents during a first period of time, and a third subset of node embeddings associated with future movement of the one or more agents during a second period of time.” Therefore, the type of node is used as an example of attribute information under broadest reasonable interpretation. Chen additionally teaches that, (Paragraph [0078], Lines 1-4) “The architecture of the encoder 308 can be based at least on attention-based models (e.g., can include some or all of the components of a transformer) and/or graphical neural networks (GNNs),” and therefore, (Paragraph [0119], Lines 5-15) “each encoder can accept a sequence of vectors , passing each vector through the self-attention layer, then the feedforward network, and then upwards to the next encoder in the stack. Any known self-attention technique can be used. For example, to calculate a self-attention score for each token (word), a query vector, a key vector, and a value vector can be created for each token, a self-attention score can be calculated for pairs of tokens by taking the dot product of the query vector with the corresponding key vectors, normalizing the resulting scores.” Claim 9 Discloses: “The driving environment map generation apparatus for autonomous driving according to claim 5, wherein the occupancy grid map prediction unit predicts an occupancy grid map by simultaneously using a query map and the node and link vectors as inputs.” Chen teaches. (Paragraph [0006], Lines 3-13) “a graph neural network (GNN) including a plurality of node embeddings and a plurality of edge embeddings based at least on the first pairwise relationships and the second pairwise relationships. The plurality of node embeddings can include a first subset of node embeddings associated with one or more lane segments of the environment , a second subset of node embeddings associated with the movement of one or more agents during a first period of time, and a third subset of node embeddings associated with future movement of the one or more agents during a second period of time,” wherein, (Paragraph [0035], Lines 7-13) “the system can encode the scene data associated with a traffic scenario by providing the scene data to an encoder that is configured to communicate with a graph neural network (GNN) to generate an output. The output can include data associated with the latent representations of the agents and/or lane segments represented by the scene data.” Chen additionally teaches, (Paragraph [0059], Lines 13-16) “In some embodiments, cross-attention can include determining a query for one subset of nodes that attends to the elements of another subset of nodes ,” wherein, (Paragraph [0119]) “each encoder can accept a sequence of vectors, passing each vector through the self-attention layer … The encoder can apply multi-headed attention in which the attention mechanism is applied multiple times in parallel with different learned weight matrices ,” and that, (Paragraph [0129]) “One or more of the controller(s) 836 (e.g., vehicle processor 110) can obtain inputs … The outputs can include information such as vehicle velocity, … location data (e.g., the vehicle's 800 location, such as on a map), direction, location of other vehicles (e.g., an occupancy grid ).” Claim 10 Discloses: “The driving environment map generation apparatus according to claim 5, wherein the occupancy gird map prediction unit comprises a layer into which the node and link vectors are input, and the query map, after passing through a self-attention layer, interacts with the node and link vectors through the layer, thereby acquiring static object information surrounding the autonomous vehicle from the nodes and links. Chen teaches. (Paragraph [0006], Lines 3-13) “a graph neural network (GNN) including a plurality of node embeddings and a plurality of edge embeddings based at least on the first pairwise relationships and the second pairwise relationships. The plurality of node embeddings can include a first subset of node embeddings associated with one or more lane segments of the environment , a second subset of node embeddings associated with the movement of one or more agents during a first period of time, and a third subset of node embeddings associated with future movement of the one or more agents during a second period of time,” wherein, (Paragraph [0035], Lines 7-13) “the system can encode the scene data associated with a traffic scenario by providing the scene data to an encoder that is configured to communicate with a graph neural network (GNN) to generate an output. The output can include data associated with the latent representations of the agents and/or lane segments represented by the scene data.” Chen additionally teaches, (Paragraph [0059], Lines 13-16) “In some embodiments, cross-attention can include determining a query for one subset of nodes that attends to the elements of another subset of nodes ,” wherein, (Paragraph [0119]) “each encoder can accept a sequence of vectors, passing each vector through the self-attention layer … The encoder can apply multi-headed attention in which the attention mechanism is applied multiple times in parallel with different learned weight matrices ,” and that, (Paragraph [0129]) “One or more of the controller(s) 836 (e.g., vehicle processor 110) can obtain inputs … The outputs can include information such as vehicle velocity, … location data (e.g., the vehicle's 800 location, such as on a map), direction, location of other vehicles (e.g., an occupancy grid ).” Claim 11 Discloses: “The driving environment map generation apparatus for autonomous driving according to claim 10, wherein the occupancy grid map prediction unit comprises a node/link update transformer that uses the node and link vectors as queries, keys, and values, and performs updates on the node and link vectors based on relationships among the nodes and links.” Chen teaches, (Paragraph [0119]) “In an example implementation, the encoder(s) 735 forms an encoder stack, where each encoder includes a self-attention layer and a feedforward network. In an example transformer architecture , each token (e.g., word) flows through a separate path. As such, each encoder can accept a sequence of vectors, passing each vector through the self-attention layer, then the feedforward network, and then upwards to the next encoder in the stack. Any known self-attention technique can be used. For example, to calculate a self-attention score for each token (word), a query vector, a key vector, and a value vector can be created for each token, a self-attention score can be calculated for pairs of tokens by taking the dot product of the query vector with the corresponding key vectors, normalizing the resulting scores, multiplying by corresponding value vectors, and summing weighted value vectors. The encoder can apply multi-headed attention in which the attention mechanism is applied multiple times in parallel with different learned weight matrices. Any number of encoders can be cascaded to generate a context vector encoding the input. An attention projection layer 740 can convert the context vector into attention vectors (keys and values) for the decoder(s) 745 .” Claim 15 Discloses: “The method according to claim 13, wherein the step of performing preprocessing on the static object information received from the commercial navigation system comprises: converting global coordinates of nodes and links included in the static object information into a coordinate system defined based on the current position and heading direction of the autonomous vehicle; and representing the information of the nodes and links as vectors of a predetermined dimension, wherein, when a link is composed of a plurality of position points, a separate vector is generated for each position point.” Chen teaches, (Paragraph [0006], Lines 3-13) “a graph neural network (GNN) including a plurality of node embeddings and a plurality of edge embeddings based at least on the first pairwise relationships and the second pairwise relationships. The plurality of node embeddings can include a first subset of node embeddings associated with one or more lane segments of the environment , a second subset of node embeddings associated with the movement of one or more agents during a first period of time, and a third subset of node embeddings associated with future movement of the one or more agents during a second period of time,” wherein, (Paragraph [0035], Lines 7-13) “the system can encode the scene data associated with a traffic scenario by providing the scene data to an encoder that is configured to communicate with a graph neural network (GNN) to generate an output. The output can include data associated with the latent representations of the agents and/or lane segments represented by the scene data.” Chen additionally teaches, (Paragraph [0058], Lines 6-13) “The vehicle processor can implement the first edge function to calculate the relative position between one or more agents in the environment in a local coordinate frame. Similarly, the vehicle processor can implement the second edge function to output a projection of the agent's position on the lane polyline as well as the starting and ending points of the lane segment in a coordinate frame established for the agent,” wherein, (Paragraph [0055], Lines 18-20) “the lane segments can be represented as polylines consisting of multiple waypoints that are based at least on lane markings in the environment.” Chen additionally teaches that, (Paragraph [0119], Lines 5-15) “each encoder can accept a sequence of vectors , passing each vector through the self-attention layer, then the feedforward network, and then upwards to the next encoder in the stack. Any known self-attention technique can be used. For example, to calculate a self-attention score for each token (word), a query vector, a key vector, and a value vector can be created for each token, a self-attention score can be calculated for pairs of tokens by taking the dot product of the query vector with the corresponding key vectors, normalizing the resulting scores.” However, Chen does not explicitly teach generating a separate vector for each position point. Agro does explicitly teach generating a separate vector for each position point. Agro teaches a system wherein, (Abstract, Lines 1-4) “Implicit occupancy for autonomous systems include receiving a request for a point attribute at a query point matching a geographic location, obtaining a query point feature vector from a feature map,” further wherein, (Paragraph [0051], Lines 5-8) “The map encoder model (502) is a machine learning model that is configured to transform map data into map feature vectors for each sub-region of the geographic region .” Agro additionally teaches, (Paragraph [0042], Lines 1-5) “Continuing with FIG. 3, an implicit decoder model (310) is a machine learning model configured to obtain a set of one or more query points (318) and output a set of one or more point attributes (320) for each of the query points (318),” and that, (Paragraph [0062], Lines 1-3) “A cross attention layer (614) obtains the offset feature vectors (612) and the point feature vector (604) and generates a combined feature vector (616).” Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the disclosure of Chen with an explicit vector generated for each position point as taught by Agro, in order to yield predictable results. Combining the references would yield the benefits of using vectors along points within an environment to determine occupancy. As Agro describes, (Paragraph [0021], Lines 17-20) “The determination of occupancy is important for an autonomous system because if an autonomous system moves to an occupied geographic location, then a collision occurs,” and further describes, (Paragraph [0023], Lines 2-4) “The query point is used as an input to the various machine learning models that determine the implicit occupancy.” Claim 17 Discloses: “The method according to claim 15, wherein the step of predicting the occupancy grid map using the preprocessed static object information and the information acquired by a camera mounted on the autonomous vehicle comprises predicting the occupancy grid map by simultaneously using a query map and the node and link vectors as inputs.” Chen teaches, (Paragraph [0006], Lines 3-13) “a graph neural network (GNN) including a plurality of node embeddings and a plurality of edge embeddings based at least on the first pairwise relationships and the second pairwise relationships. The plurality of node embeddings can include a first subset of node embeddings associated with one or more lane segments of the environment , a second subset of node embeddings associated with the movement of one or more agents during a first period of time, and a third subset of node embeddings associated with future movement of the one or more agents during a second period of time,” wherein, (Paragraph [0035], Lines 7-13) “the system can encode the scene data associated with a traffic scenario by providing the scene data to an encoder that is configured to communicate with a graph neural network (GNN) to generate an output. The output can include data associated with the latent representations of the agents and/or lane segments represented by the scene data.” Chen additionally teaches, (Paragraph [0059], Lines 13-16) “In some embodiments, cross-attention can include determining a query for one subset of nodes that attends to the elements of another subset of nodes ,” wherein, (Paragraph [0119]) “each encoder can accept a sequence of vectors, passing each vector through the self-attention layer … The encoder can apply multi-headed attention in which the attention mechanism is applied multiple times in parallel with different learned weight matrices ,” and that, (Paragraph [0129]) “One or more of the controller(s) 836 (e.g., vehicle processor 110) can obtain inputs … The outputs can include information such as vehicle velocity, … location data (e.g., the vehicle's 800 location, such as on a map), direction, location of other vehicles (e.g., an occupancy grid ).” Claim 18 Discloses: “The method according to claim 18, wherein the step of predicting the occupancy grid map using the preprocessed static object information and the information acquired by a camera mounted on the autonomous vehicle comprises: utilizing a layer into which the node and link vectors are input; and acquiring static object information surrounding the autonomous vehicle and predicting the occupancy grid map as a query map that has passed through a self-attention layer interacts with the node and link vectors through the layer.” Chen teaches. (Paragraph [0006], Lines 3-13) “a graph neural network (GNN) including a plurality of node embeddings and a plurality of edge embeddings based at least on the first pairwise relationships and the second pairwise relationships. The plurality of node embeddings can include a first subset of node embeddings associated with one or more lane segments of the environment , a second subset of node embeddings associated with the movement of one or more agents during a first period of time, and a third subset of node embeddings associated with future movement of the one or more agents during a second period of time,” wherein, (Paragraph [0035], Lines 7-13) “the system can encode the scene data associated with a traffic scenario by providing the scene data to an encoder that is configured to communicate with a graph neural network (GNN) to generate an output. The output can include data associated with the latent representations of the agents and/or lane segments represented by the scene data.” Chen additionally teaches, (Paragraph [0059], Lines 13-16) “In some embodiments, cross-attention can include determining a query for one subset of nodes that attends to the elements of another subset of nodes ,” wherein, (Paragraph [0119]) “each encoder can accept a sequence of vectors, passing each vector through the self-attention layer … The encoder can apply multi-headed attention in which the attention mechanism is applied multiple times in parallel with different learned weight matrices ,” and that, (Paragraph [0129]) “One or more of the controller(s) 836 (e.g., vehicle processor 110) can obtain inputs … The outputs can include information such as vehicle velocity, … location data (e.g., the vehicle's 800 location, such as on a map), direction, location of other vehicles (e.g., an occupancy grid ).” Claim 19 Discloses: “The method according to claim 18, wherein the step of predicting the occupancy grid map using the preprocessed static object information and the information acquired by a camera mounted on the autonomous vehicle comprises: using a node/link update transformer that uses the node and link vectors as a query, key, and value, and performs an update on the node and link vectors by utilizing relationships among them;” Chen teaches, (Paragraph [0119]) “In an example implementation, the encoder(s) 735 forms an encoder stack, where each encoder includes a self-attention layer and a feedforward network. In an example transformer architecture , each token (e.g., word) flows through a separate path. As such, each encoder can accept a sequence of vectors, passing each vector through the self-attention layer, then the feedforward network, and then upwards to the next encoder in the stack. Any known self-attention technique can be used. For example, to calculate a self-attention score for each token (word), a query vector, a key vector, and a value vector can be created for each token, a self-attention score can be calculated for pairs of tokens by taking the dot product of the query vector with the corresponding key vectors, normalizing the resulting scores, multiplying by corresponding value vectors, and summing weighted value vectors. The encoder can apply multi-headed attention in which the attention mechanism is applied multiple times in parallel with different learned weight matrices. Any number of encoders can be cascaded to generate a context vector encoding the input. An attention projection layer 740 can convert the context vector into attention vectors (keys and values) for the decoder(s) 745 .” “and predicting the occupancy grid map using an occupancy grid map prediction transformer that receives, as inputs, the updated node and link vectors, a query map, and an image feature map, and generates a final predicted occupancy grid map.” Chen teaches, (Paragraph [0006], Lines 3-13) “a graph neural network (GNN) including a plurality of node embeddings and a plurality of edge embeddings based at least on the first pairwise relationships and the second pairwise relationships. The plurality of node embeddings can include a first subset of node embeddings associated with one or more lane segments of the environment , a second subset of node embeddings associated with the movement of one or more agents during a first period of time, and a third subset of node embeddings associated with future movement of the one or more agents during a second period of time,” wherein, (Paragraph [0035], Lines 7-13) “the system can encode the scene data associated with a traffic scenario by providing the scene data to an encoder that is configured to communicate with a graph neural network (GNN) to generate an output. The output can include data associated with the latent representations of the agents and/or lane segments represented by the scene data.” Chen additionally teaches, (Paragraph [0059], Lines 13-16) “In some embodiments, cross-attention can include determining a query for one subset of nodes that attends to the elements of another subset of nodes ,” wherein, (Paragraph [0119]) “each encoder can accept a sequence of vectors, passing each vector through the self-attention layer … The encoder can apply multi-headed attention in which the attention mechanism is applied multiple times in parallel with different learned weight matrices ,” and that, (Paragraph [0129]) “One or more of the controller(s) 836 (e.g., vehicle processor 110) can obtain inputs … The outputs can include information such as vehicle velocity, … location data (e.g., the vehicle's 800 location, such as on a map), direction, location of other vehicles (e.g., an occupancy grid ).” 07-21-aia AIA Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Agro, further in view of Chai et al., (US 2019/0049255 A1) further in view of Kim. (KR102022776B1, hereinafter Kim) Claim 7 Discloses: “The driving environment map generation apparatus for autonomous driving according to claim 5, wherein the static object information preprocessing unit performs normalization on the vector using a predetermined constant.” Chen and Agro do not teach the preceding limitations; however it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to apply data normalization towards a vector is order to prevent vector elements from being too large to properly train a network. Chai is relevant to the Applicant’s disclosure due to its disclosure establishing the level of ordinary skill in the art with regards to vector normalization being conducted by dividing by a constant value. Chai teaches, (Paragraph [0060], Lines 3-7) “A normalized derived speed vector 330 can be generated by dividing the observed speed vector 320 by a predetermined number , such as a maximum reasonable speed above which any observations are assumed to be faulty and discarded,” therefore, a well-known methodology to normalize a vector is to divide by a constant value representative of the maximum reasonable measurement. Kim is relevant to the applicant’s disclosure due to its teaching’s towards that rationale of normalizing a vector which is inputted into a machine learning model, in the context of an, (Description KR102022776B1) “Artificial neural network model learning method and deep learning system,” wherein, (Paragraph [0052], Lines 3-4) “a vector space is a space where training data is located in a Cartesian coordinate system, and the factor determining the dimension is the features of the data.” Kim teaches, (Paragraph [0206]) “Unlike data used for signal processing, the financial statement data primarily dealt with in this study does not have fixed minimum and maximum values; therefore, applying existing machine learning techniques directly results in values that are too large , inevitably leading to a large loss. Therefore, learning does not occur at all, or even if it does, it takes a very long time. Therefore, it is absolutely necessary to apply data normalization .” Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to apply to Chen a known technique of applying data normalization to improve a similar device in the same way. As Kim describes, (Paragraph [0206]) applying existing machine learning techniques directly results in values that are too large , inevitably leading to a large loss. Therefore, learning does not occur at all, or even if it does, it takes a very long time. Therefore, it is absolutely necessary to apply data normalization ,” therefore, a similar machine learning technique is improved by ensuring data isn’t too large . 07-21-aia AIA Claim s 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Chen, in view of Agro, further in view of Raj et al. (US 2024/0311653 A1, hereinafter Raj) Claim 8 Discloses: “The driving environment map generation apparatus for autonomous driving according to claim 5, wherein the static object information preprocessing unit, when a length mismatch exists between the vectors of the nodes and links, adds elements to the relatively shorter vector to equalize the lengths of the vectors.” Chen and Agro do not explicitly teach the preceding limitations; however it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to add elements to the relatively shorter vector to equalize the lengths of the vectors by, for example, adding padding. Raj is relevant to the Applicant’s disclosure due to its disclosure establishing the level of ordinary skill in the art with regards to resolving a vector mismatch by adding “padding.” Raj teaches, (Paragraph [0082], Lines 22-29) “The zero padding may take the form of adding zeros before and/or after the non-zero input sequence to ensure the vector is ‘filled’ to its standardized length . For example, where the standardized length is 10 and the encoded input sequence contains three encoded inputs, then seven zeros are added to the vector prior to the three encoded inputs to ensure the overall vector length is equal to the standardized length.” Raj additionally teaches, (Paragraph [0083]) “a representative structure for a machine-learning model 700 … It will be appreciated by one of ordinary skill in the art that one or more transformers may be utilized as part of or in replacement of the memory layer, depending on the specific needs and requirements of the task at hand. Transformers may use self-attention mechanisms to compute contextual embeddings of the input sequence.” Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to apply to Chen a known technique of adding “padding” to resolve a vector mismatch to improve a similar device in the same way, depending on the particular structure of the model. As Raj describes, (Paragraph [0082], Lines 15-22) “ a standardized vector length, may be determined by the structure of the model (i.e. only a certain number of inputs are accepted by the model), or it may be the length of an input which produces the highest quality results. In such cases where a standardized length is present, when an encoded input sequence includes fewer entries than the standardized length, the vector is ‘zero-padded’ prior to being passed to one or more machine-learning model .” Claim 16 Discloses: “The method according to claim 15, wherein the step of performing preprocessing on the static object information received from the commercial navigation system comprises: adding elements to the relatively shorter vector to equalize the lengths of the vectors when a length mismatch exists between the vectors of the nodes and links.” Chen and Agro do not explicitly teach the preceding limitations; however it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to add elements to the relatively shorter vector to equalize the lengths of the vectors by, for example, adding padding. Raj is relevant to the Applicant’s disclosure due to its disclosure establishing the level of ordinary skill in the art with regards to resolving a vector mismatch by adding “padding.” Raj teaches, (Paragraph [0082], Lines 22-29) “The zero padding may take the form of adding zeros before and/or after the non-zero input sequence to ensure the vector is ‘filled’ to its standardized length . For example, where the standardized length is 10 and the encoded input sequence contains three encoded inputs, then seven zeros are added to the vector prior to the three encoded inputs to ensure the overall vector length is equal to the standardized length.” Raj additionally teaches, (Paragraph [0083]) “a representative structure for a machine-learning model 700 … It will be appreciated by one of ordinary skill in the art that one or more transformers may be utilized as part of or in replacement of the memory layer, depending on the specific needs and requirements of the task at hand. Transformers may use self-attention mechanisms to compute contextual embeddings of the input sequence.” Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to apply to Chen a known technique of adding “padding” to resolve a vector mismatch to improve a similar device in the same way, depending on the particular structure of the model. As Raj describes, (Paragraph [0082], Lines 15-22) “ a standardized vector length, may be determined by the structure of the model (i.e. only a certain number of inputs are accepted by the model), or it may be the length of an input which produces the highest quality results. In such cases where a standardized length is present, when an encoded input sequence includes fewer entries than the standardized length, the vector is ‘zero-padded’ prior to being passed to one or more machine-learning model .” RELEVANT, BUT NOT CITED PRIOR ART 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Al Rfou et al., (US 2023/0406360 A1) discloses, (Abstract) “Methods, systems, and apparatus for generating trajectory predictions for one or more target agents . In one aspect, a system comprises one or more computers configured to obtain scene context data characterizing a scene in an environment at a current time point, where the scene includes multiple agents that include a target agent and one or more context agents, and the scene context data includes respective context data for each of multiple different modalities of context data. The one or more computers then generate an encoded representation of the scene in the environment that includes one or more embeddings and process the encoded representation of the scene context data using a decoder neural network to generate a trajectory prediction output for the target agent that predicts a future trajectory of the target after the current time point.” Ng et al., (US 2024/0059285 A1) teaches, (Paragraph [0064]) “with respect to FIG. 5C , a first time slice, T.sub.N, may include a number of different groups of points 508 (e.g., 508A). The groups of points (or clusters) 508 may be identified using clustering, weighted averaging, and/or other techniques, such as those described herein. For example, at time slice, T.sub.N, the group of points 508A-1 may be determined (other groups of points 508 may also be determined, but may be occluded in the visualization 510 by other time slices (e.g., T.sub.N−1 and T.sub.N−2)), and a set of vectors 512A-1 from the vector field 328 corresponding to the time slice, T.sub.N, may be determined as a result (e.g., the group of vectors from the vector field 328 corresponding to the same (x, y) coordinates as the group of points 508 in the confidence field 326). “ PNG media_image1.png 467 499 media_image1.png Greyscale Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER V. GENTILE whose telephone number is (703)756-1501. The examiner can normally be reached Monday - Friday 9-5. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kito R. Robinson can be reached at (571)270-3921. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ALEXANDER V GENTILE/Examiner, Art Unit 3664 /KITO R ROBINSON/Supervisory Patent Examiner, Art Unit 3664 Application/Control Number: 19/184,072 Page 2 Art Unit: 3664 Application/Control Number: 19/184,072 Page 3 Art Unit: 3664 Application/Control Number: 19/184,072 Page 4 Art Unit: 3664 Application/Control Number: 19/184,072 Page 5 Art Unit: 3664 Application/Control Number: 19/184,072 Page 6 Art Unit: 3664 Application/Control Number: 19/184,072 Page 7 Art Unit: 3664 Application/Control Number: 19/184,072 Page 8 Art Unit: 3664 Application/Control Number: 19/184,072 Page 9 Art Unit: 3664 Application/Control Number: 19/184,072 Page 10 Art Unit: 3664 Application/Control Number: 19/184,072 Page 11 Art Unit: 3664 Application/Control Number: 19/184,072 Page 12 Art Unit: 3664 Application/Control Number: 19/184,072 Page 13 Art Unit: 3664 Application/Control Number: 19/184,072 Page 14 Art Unit: 3664 Application/Control Number: 19/184,072 Page 15 Art Unit: 3664 Application/Control Number: 19/184,072 Page 16 Art Unit: 3664 Application/Control Number: 19/184,072 Page 18 Art Unit: 3664 Application/Control Number: 19/184,072 Page 19 Art Unit: 3664 Application/Control Number: 19/184,072 Page 20 Art Unit: 3664 Application/Control Number: 19/184,072 Page 21 Art Unit: 3664 Application/Control Number: 19/184,072 Page 22 Art Unit: 3664 Application/Control Number: 19/184,072 Page 23 Art Unit: 3664 Application/Control Number: 19/184,072 Page 24 Art Unit: 3664 Application/Control Number: 19/184,072 Page 25 Art Unit: 3664 Application/Control Number: 19/184,072 Page 26 Art Unit: 3664 Application/Control Number: 19/184,072 Page 27 Art Unit: 3664 Application/Control Number: 19/184,072 Page 28 Art Unit: 3664 Application/Control Number: 19/184,072 Page 29 Art Unit: 3664 Application/Control Number: 19/184,072 Page 30 Art Unit: 3664 Application/Control Number: 19/184,072 Page 31 Art Unit: 3664 Application/Control Number: 19/184,072 Page 32 Art Unit: 3664 Application/Control Number: 19/184,072 Page 33 Art Unit: 3664 Application/Control Number: 19/184,072 Page 34 Art Unit: 3664 Application/Control Number: 19/184,072 Page 35 Art Unit: 3664
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