Detailed Office Action
Status of Claims
This Office Action is in response to the Applicant’s amendments and remarks filed 04/14/2026. The applicant has amended claims 1, 8, 9, 17, and 18. Applicant has cancelled Claims 15 and 16. Claims 1-14 and 17-20 are presently pending and are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 04/22/2026 is being considered by the examiner.
Response to Amendment
The amendment filed 04/14/2026 has been entered. Claims 1-14 and 17-20 remain pending in the application.
Reply to Applicant’s Remarks
Applicant’s remarks filed 04/14/2026 have been fully considered and are addressed as follows:
Claim Rejections Under 35 U.S.C. 102/103:
Applicant’s arguments, see Arguments/Remarks, filed 04/14/2026, with regard to the rejections of Claims 1, 8, and 17 under 35 U.S.C. 102/103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of newly found prior art reference(s).
Claim Rejections - 35 USC § 112
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.
Claims 1, 8 and 17 are 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.
The term “high load” in claims 1, 8, and 17 is a relative term which renders the claim indefinite. The term “high load” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 8-9, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kentley-Klay et al (US 11067983 B2) in view of Leone et al (US 20190071067 A1) and De Smet et al (US 20170051652 A1). Hereafter referred to as Kentley, Leone 067, and De Smet respectively.
Independent Claims
Regarding Claim 1, Kentley teaches a remote computing system (see at least Kentley [C4 L53 to C5 L22] FIG. 1 is a diagram depicting an implementation of a fleet of autonomous vehicles that are communicatively networked to an autonomous vehicle service platform, according to some embodiments. Diagram 100 depicts a fleet of autonomous vehicles 109 (e.g., one or more of autonomous vehicles 109a to 109e) operating as a service, each autonomous vehicle 109 being configured to self-drive a road network 110 and establish a communication link 192 with an autonomous vehicle service platform 101)
comprising:
a communication interface structured to couple to a network (see at least Kentley [Fig. 1, Fig. 4, C13 L47-50, C4 L63 to C5 L2] element of autonomous vehicle service platform 401 may independently communicate with the autonomous vehicle 430 via the communication layer 402… user 102 may transmit a request 103 for autonomous transportation via one or more networks 106 to autonomous vehicle service platform 101. In response, autonomous vehicle service platform 101 may dispatch one of autonomous vehicles 109 to transport user 102 autonomously from geographic location 119 to geographic location 111)
a map database storing centralized map data (see at least Kentley [C11 L65 to C12 L25, C15 L45-48] According to some examples, localizer 468 retrieves reference data originating from a reference data repository 405, which includes a map data repository 405a for storing 2O map data, 3O map data, 4O map data, and the like…reference data generator 705 may be configured to access 2D maps in 2D map data repository 720, access 3D maps in 3D map data repository 722, and access route data in route data repository 724)
one or more processors; and one or more memory devices storing instructions (see at least Kentley [C15 L 50-52, C36 L17-25] Vehicle data controller 702 may be configured to perform a variety of operations... module 3350 of FIG. 33, module 3450 of FIG. 34, and module 3550 of FIG. 35, or one or more of its components, or any process or device described herein, can be implemented in one or more computing devices (i.e., any mobile computing device, such as a wearable device, an audio device (such as headphones or a headset) or mobile phone, whether worn or carried) that include one or more processors configured to execute one or more algorithms in memory)
that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving a transport request, the transport request comprising a first location and a second location (see at least Kentley [C24 L11-32] FIG. 17 is an example of a flow diagram to manage a fleet of autonomous vehicles, according to some embodiments. At 1702, flow 1700 begins. At 1702, policy data is received. The policy data may include parameters that define how best apply to select an autonomous vehicle for servicing a transit request....At 1706, data representing a transit request is received. For exemplary purposes, the transit request could be for transportation from a first geographic location to a second geographic location. At 1708, attributes based on the policy data are calculated to determine a subset of autonomous vehicles that are available to service the request. For example, attributes may include a battery charge level and time until next scheduled maintenance. At 1710, an autonomous vehicle is selected as transportation from the first geographic location to the second geographic location, and data is generated to dispatch the autonomous vehicle to a third geographic location associated with the origination of the transit request)
selectively transmitting the centralized map data to the first vehicle (see at least Kentley [Fig.4, C9 L12-19, C11 L66 to C12 L2] autonomous vehicle controller 347a may receive any other sensor data 356, as well as reference data 339. In some cases, reference data 339 includes map data (e.g., 3D map data, 2D map data, 4D map data (e.g., including Epoch Determination)) and route data (e.g., road network data, including, but not limited to, RNDF data (or similar data), MDF data (or similar data), etc....localizer 468 retrieves reference data originating from a reference data repository 405, which includes a map data repository 405a for storing 2O map data, 3O map data, 4O map data, and the like)
causing the first vehicle to complete the transport request (see at least Kentley [C4 L63 to C5 L6] a user 102 may transmit a request 103 for autonomous transportation via one or more networks 106 to autonomous vehicle service platform 101. In response, autonomous vehicle service platform 101 may dispatch one of autonomous vehicles 109 to transport user 102 autonomously from geographic location 119 to geographic location 111. Autonomous vehicle service platform 101 may dispatch an autonomous vehicle from a station 190 to geographic location 119, or may divert an autonomous vehicle 109c, already in transit (e.g., without occupants), to service the transportation request for user 102)
wherein completing the transport request comprises:
causing the first vehicle to collect first sensor data while autonomously transporting from the first location to the second location (see at least Kentley [C6 L3-24, C9 L20-29] the localizer may use acquired sensor data, such as sensor data associated with surfaces of buildings 115 and 117, which can be compared against reference data, such as map data (e.g., 3D map data, including reflectance data) to determine a local pose. Further, a perception engine (not shown) of autonomous vehicle controller 147 may be configured to detect, classify, and predict the behavior of external objects, such as external object 112 (a “tree” and external object 114 (a “pedestrian”). Classification of such external objects may broadly classify objects as static objects, such as external object 112, and dynamic objects, such as external object 114. The localizer and the perception engine, as well as other components of the AV controller 147, collaborate to cause autonomous vehicles 109 to drive autonomously.... Localizer 368 is configured to receive sensor data from one or more sources, such as GPS data 352, wheel data, MU data 354, Lidar data 346a, camera data 340a, radar data 348a, and the like, as well as reference data 339 (e.g., 3D map data and route data). Localizer 368 integrates (e.g., fuses the sensor data) and analyzes the data by comparing sensor data to map data to determine a local pose (or position) of bidirectional autonomous vehicle 330. According to some examples, localizer 368 may generate or update the pose or position of any autonomous vehicle in real-time or near real-time)
receiving the first sensor data; and selectively updating the centralized map data with the first sensor data (see at least Kentley [C13 L50-62] Map updater 406 is configured to receive map data (e.g., from local map generator 440, sensors 460, or any other component of autonomous vehicle controller 447), and is further configured to detect deviations, for example, of map data in map data repository 405a from a locally-generated map. Vehicle data controller 408 can cause map updater 406 to update reference data within repository 405 and facilitate updates to 20, 30, and/or 40 map data. In some cases, vehicle data controller 408 can control the rate at which local map data is received into autonomous vehicle service platform 408 as well as the frequency at which map updater 406 performs updating of the map data).
However, while Kentely teaches selecting a vehicle among a plurality of vehicles based on the multiple parameters defining the state of the vehicle, it does not explicitly teach selecting a first vehicle of a plurality of vehicles for the transport request, the first vehicle selected based on the transport request being a high load request, and vehicle data regarding the first vehicle indicating a request for an aftertreatment system component regeneration.
Leone 067, in the same field as the endeavor, teaches selecting a first vehicle of a plurality of vehicles for the transport request, the first vehicle selected based on the transport request being a high load request, and vehicle data regarding the first vehicle indicating a request for an aftertreatment system component regeneration (see at least Leone 067 [¶ 16, 6, 31, 34, 67-68] The remote sources may include a line of vehicles (also referred herein as fleet)…The fleet 120 may comprise multiple vehicles 122, 124, 126, and 127. Each vehicle of the fleet 120 may include a control system 112 similar to the control system 112 of vehicle 110. A navigation system 154 and a wireless communication device 152 may be coupled to the control system 112 of each vehicle in the fleet 120. The on-board controllers in the vehicles in the fleet may communicate with each other…Particulate matter load on the PF may be estimated based on the exhaust backpressure as estimated via a pressure sensor 76 coupled across the PF. The pressure sensor 76 may be a differential (delta) pressure sensor that measures the change in exhaust pressure as exhaust flows through the PF, such as a Delta Pressure Feedback Exhaust (DPFE) sensor. In other examples, the pressure sensor may be an absolute pressure sensor and the controller may measure the pressure change across the PF based on outputs from pressure sensors coupled upstream and downstream of the filter. Once the PF reaches a threshold load, the PF 72 may be periodically or opportunistically regenerated…Upcoming periods of increased engine load may be estimated based on the retrieved traffic information and road segment characteristics. Regeneration of an exhaust particulate filter may be opportunistically scheduled during such periods of increased engine load…based on the traffic and road conditions data, the controller may determine an upcoming period of higher than threshold torque demand and during this period, the controller may send a signal to the fuel injector 66 to adjust the engine air fuel ratio to be leaner than stoichiometric, thereby facilitating PF 72 regeneration) The disclosure in Leone 067 teaches a set of vehicles travelling in a fleet that use traffic information and received road segment characteristics to complete the said road segments, the fleet further is determining if the vehicles need PF regeneration and may schedule it when there is in upcoming road segment that will use an increased engine load. Therefore, Leone 067 teaches when if the vehicle among the fleet of vehicles is to complete a road segment (a transport request) and the engine load is high for such a request and the vehicle needs filter regeneration, that vehicle will be selected to complete the road segment while performing particle filter regeneration.
Therefore, 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 the system set forth in Kentely to contain a system for selecting a first vehicle of a plurality of vehicles for the transport request, the first vehicle selected based on the transport request being a high load request, and vehicle data regarding the first vehicle indicating a request for an aftertreatment system component regeneration with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of only performing filter regeneration during times of high engine load as to not waste resources as discussed in Leone 067 (see at least Leone 067 [¶ 31] the PF 72 may be periodically or opportunistically regenerated to reduce the particulate matter load and the corresponding exhaust back pressure. In order to regenerate the PF, a higher than threshold exhaust temperature and a leaner than stoichiometric is desired at the PF. An increase in exhaust temperature may correspond to increased torque demand).
Further, Kentely does not explicitly teach wherein completing the transport request comprises: causing the first vehicle to autonomously transport from the first location to the second location, such that aftertreatment system component regeneration occurs while completing the transport request.
De Smet, in the same field as the endeavor, teaches wherein completing the transport request comprises: causing the first vehicle to autonomously transport from the first location to the second location, such that aftertreatment system component regeneration occurs while completing the transport request (see at least De Smet [Abstract, ¶ 24, 76-78] Embodiments for regeneration a particulate filter in a motor vehicle having a self-control mode for the autonomous control of a drive mode…a route to be traveled can be specified at the discretion of the driver…If it is determined that the vehicle is operating in the self-control mode, method 200 proceeds to 208 to determine if particulate filter regeneration is indicated. Particulate filter regeneration may be indicated when the particulate/soot load on the particulate filter exceeds a threshold load. The particulate filter load may be determined based on output from a sensor (such as a particulate matter sensor or a pressure sensor positioned in the exhaust) or based on an estimated particulate load (e.g., based on a duration since a previous regeneration…If it is determined at 208 that a particulate filter regeneration is indicated, method 200 proceeds to 212 to deviate from the velocity profile to perform the regeneration…If the regeneration is complete, method 200 proceeds to 224 to resume operation the velocity profile in its original, undeviated form. Method 200 then returns)
Therefore, 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 the system set forth in Kentely to contain a system for wherein completing the transport request comprises: causing the first vehicle to autonomously transport from the first location to the second location, such that aftertreatment system component regeneration occurs while completing the transport request with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of protecting the particulate filter by monitoring for when to perform the regeneration during driving trips, among other benefits, as discussed in De Smet (see at least De Smet [¶ 65] the self-control mode of the motor vehicle 1 can be utilized in order to carry out the regularly necessary regeneration phases of the diesel particulate filter in a controlled manner. In this case, the regeneration process can be thermally controlled by influencing the driving profile in the self-control. Overheating and premature aging can be avoided as a result. Therefore, the service life of the diesel particulate filter can be extended. Furthermore, fault memory entries resulting from overheatings of the diesel particulate filter can be avoided and service intervals can be extended. Overall, improved compliance with emission standards, which may even be stringent, can be achieved over the entire service life of the motor vehicle 1, while maintenance costs are reduced).
Regarding Claim 8, Kentley teaches a method comprising:
receiving, by a computing system, a transport request, the transport request comprising a first location and a second location (see at least Kentley [C24 L11-32] FIG. 17 is an example of a flow diagram to manage a fleet of autonomous vehicles, according to some embodiments. At 1702, flow 1700 begins. At 1702, policy data is received. The policy data may include parameters that define how best apply to select an autonomous vehicle for servicing a transit request....At 1706, data representing a transit request is received. For exemplary purposes, the transit request could be for transportation from a first geographic location to a second geographic location. At 1708, attributes based on the policy data are calculated to determine a subset of autonomous vehicles that are available to service the request. For example, attributes may include a battery charge level and time until next scheduled maintenance. At 1710, an autonomous vehicle is selected as transportation from the first geographic location to the second geographic location, and data is generated to dispatch the autonomous vehicle to a third geographic location associated with the origination of the transit request)
selectively transmitting, by the computing system, centralized map data to the first vehicle (see at least Kentley [Fig.4, C9 L12-19, C11 L66 to C12 L2] autonomous vehicle controller 347a may receive any other sensor data 356, as well as reference data 339. In some cases, reference data 339 includes map data (e.g., 3D map data, 2D map data, 4D map data (e.g., including Epoch Determination)) and route data (e.g., road network data, including, but not limited to, RNDF data (or similar data), MDF data (or similar data), etc....localizer 468 retrieves reference data originating from a reference data repository 405, which includes a map data repository 405a for storing 2O map data, 3O map data, 4O map data, and the like)
causing, by the computing system, the first vehicle to complete the transport request (see at least Kentley [C4 L63 to C5 L6] a user 102 may transmit a request 103 for autonomous transportation via one or more networks 106 to autonomous vehicle service platform 101. In response, autonomous vehicle service platform 101 may dispatch one of autonomous vehicles 109 to transport user 102 autonomously from geographic location 119 to geographic location 111. Autonomous vehicle service platform 101 may dispatch an autonomous vehicle from a station 190 to geographic location 119, or may divert an autonomous vehicle 109c, already in transit (e.g., without occupants), to service the transportation request for user 102)
wherein completing the transport request comprises:
causing the first vehicle to collect first sensor data while autonomously transporting from the first location to the second location (see at least Kentley [C6 L3-24, C9 L20-29] the localizer may use acquired sensor data, such as sensor data associated with surfaces of buildings 115 and 117, which can be compared against reference data, such as map data (e.g., 3D map data, including reflectance data) to determine a local pose. Further, a perception engine (not shown) of autonomous vehicle controller 147 may be configured to detect, classify, and predict the behavior of external objects, such as external object 112 (a “tree” and external object 114 (a “pedestrian”). Classification of such external objects may broadly classify objects as static objects, such as external object 112, and dynamic objects, such as external object 114. The localizer and the perception engine, as well as other components of the AV controller 147, collaborate to cause autonomous vehicles 109 to drive autonomously.... Localizer 368 is configured to receive sensor data from one or more sources, such as GPS data 352, wheel data, MU data 354, Lidar data 346a, camera data 340a, radar data 348a, and the like, as well as reference data 339 (e.g., 3D map data and route data). Localizer 368 integrates (e.g., fuses the sensor data) and analyzes the data by comparing sensor data to map data to determine a local pose (or position) of bidirectional autonomous vehicle 330. According to some examples, localizer 368 may generate or update the pose or position of any autonomous vehicle in real-time or near real-time)
receiving, by the computing system, the first sensor data and selectively updating, by the computing system, the centralized map data with the first sensor data. (see at least Kentley [C13 L50-62] Map updater 406 is configured to receive map data (e.g., from local map generator 440, sensors 460, or any other component of autonomous vehicle controller 447), and is further configured to detect deviations, for example, of map data in map data repository 405a from a locally-generated map. Vehicle data controller 408 can cause map updater 406 to update reference data within repository 405 and facilitate updates to 20, 30, and/or 40 map data. In some cases, vehicle data controller 408 can control the rate at which local map data is received into autonomous vehicle service platform 408 as well as the frequency at which map updater 406 performs updating of the map data).
However, while Kentely teaches selecting a vehicle among a plurality of vehicles based on the multiple parameters defining the state of the vehicle, it does not explicitly teach selecting, by the computing system, a first vehicle of a plurality of vehicles for the transport request, the first vehicle selected based on: the transport request being a high load request, and vehicle data regarding the first vehicle indicating a request for an aftertreatment system component regeneration.
Leone 067, in the same field as the endeavor, teaches selecting, by the computing system, a first vehicle of a plurality of vehicles for the transport request, the first vehicle selected based on: the transport request being a high load request, and vehicle data regarding the first vehicle indicating a request for an aftertreatment system component regeneration. (see at least Leone 067 [¶ 16, 6, 31, 34, 67-68] The remote sources may include a line of vehicles (also referred herein as fleet)…The fleet 120 may comprise multiple vehicles 122, 124, 126, and 127. Each vehicle of the fleet 120 may include a control system 112 similar to the control system 112 of vehicle 110. A navigation system 154 and a wireless communication device 152 may be coupled to the control system 112 of each vehicle in the fleet 120. The on-board controllers in the vehicles in the fleet may communicate with each other…Particulate matter load on the PF may be estimated based on the exhaust backpressure as estimated via a pressure sensor 76 coupled across the PF. The pressure sensor 76 may be a differential (delta) pressure sensor that measures the change in exhaust pressure as exhaust flows through the PF, such as a Delta Pressure Feedback Exhaust (DPFE) sensor. In other examples, the pressure sensor may be an absolute pressure sensor and the controller may measure the pressure change across the PF based on outputs from pressure sensors coupled upstream and downstream of the filter. Once the PF reaches a threshold load, the PF 72 may be periodically or opportunistically regenerated…Upcoming periods of increased engine load may be estimated based on the retrieved traffic information and road segment characteristics. Regeneration of an exhaust particulate filter may be opportunistically scheduled during such periods of increased engine load…based on the traffic and road conditions data, the controller may determine an upcoming period of higher than threshold torque demand and during this period, the controller may send a signal to the fuel injector 66 to adjust the engine air fuel ratio to be leaner than stoichiometric, thereby facilitating PF 72 regeneration) The disclosure in Leone 067 teaches a set of vehicles travelling in a fleet that use traffic information and received road segment characteristics to complete the said road segments, the fleet further is determining if the vehicles need PF regeneration and may schedule it when there is in upcoming road segment that will use an increased engine load. Therefore, Leone 067 teaches when if the vehicle among the fleet of vehicles is to complete a road segment (a transport request) and the engine load is high for such a request and the vehicle needs filter regeneration, that vehicle will be selected to complete the road segment while performing particle filter regeneration.
Therefore, 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 the system set forth in Kentely to contain a system for selecting a first vehicle of a plurality of vehicles for the transport request, the first vehicle selected based on the transport request being a high load request, and vehicle data regarding the first vehicle indicating a request for an aftertreatment system component regeneration with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of only performing filter regeneration during times of high engine load as to not waste resources as discussed in Leone 067 (see at least Leone 067 [¶ 31] the PF 72 may be periodically or opportunistically regenerated to reduce the particulate matter load and the corresponding exhaust back pressure. In order to regenerate the PF, a higher than threshold exhaust temperature and a leaner than stoichiometric is desired at the PF. An increase in exhaust temperature may correspond to increased torque demand).
Further, Kentely does not explicitly teach wherein completing the transport request comprises: causing the first vehicle to autonomously transport from the first location to the second location, such that aftertreatment system component regeneration occurs while completing the transport request.
De Smet, in the same field as the endeavor, teaches wherein completing the transport request comprises: causing the first vehicle to autonomously transport from the first location to the second location, such that aftertreatment system component regeneration occurs while completing the transport request (see at least De Smet [Abstract, ¶ 24, 76-78] Embodiments for regeneration a particulate filter in a motor vehicle having a self-control mode for the autonomous control of a drive mode…a route to be traveled can be specified at the discretion of the driver…If it is determined that the vehicle is operating in the self-control mode, method 200 proceeds to 208 to determine if particulate filter regeneration is indicated. Particulate filter regeneration may be indicated when the particulate/soot load on the particulate filter exceeds a threshold load. The particulate filter load may be determined based on output from a sensor (such as a particulate matter sensor or a pressure sensor positioned in the exhaust) or based on an estimated particulate load (e.g., based on a duration since a previous regeneration…If it is determined at 208 that a particulate filter regeneration is indicated, method 200 proceeds to 212 to deviate from the velocity profile to perform the regeneration…If the regeneration is complete, method 200 proceeds to 224 to resume operation the velocity profile in its original, undeviated form. Method 200 then returns)
Therefore, 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 the system set forth in Kentely to contain a system for wherein completing the transport request comprises: causing the first vehicle to autonomously transport from the first location to the second location, such that aftertreatment system component regeneration occurs while completing the transport request with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of protecting the particulate filter by monitoring for when to perform the regeneration during driving trips, among other benefits, as discussed in De Smet (see at least De Smet [¶ 65] the self-control mode of the motor vehicle 1 can be utilized in order to carry out the regularly necessary regeneration phases of the diesel particulate filter in a controlled manner. In this case, the regeneration process can be thermally controlled by influencing the driving profile in the self-control. Overheating and premature aging can be avoided as a result. Therefore, the service life of the diesel particulate filter can be extended. Furthermore, fault memory entries resulting from overheatings of the diesel particulate filter can be avoided and service intervals can be extended. Overall, improved compliance with emission standards, which may even be stringent, can be achieved over the entire service life of the motor vehicle 1, while maintenance costs are reduced).
Regarding Claim 17, Kentley teaches a system comprising:
one or more sensors; and a controller coupled to the one or more sensors (see at least Kentley [C5 L60-67] bidirectional autonomous vehicle 130 may include an autonomous vehicle controller 147 that includes logic (e.g., hardware or software, or as combination thereof) that is configured to control a predominate number of vehicle functions, including driving control (e.g., propulsion, steering, etc.) and active sources 136 of light, among other functions. Bidirectional autonomous vehicle 130 also includes a number of sensors 139 disposed at various locations on the vehicle (other sensors are not shown)
the controller configured to:
receive, from a remote computing system, map data (see at least Kentley [Fig.4, C9 L12-19, C11 L66 to C12 L2] autonomous vehicle controller 347a may receive any other sensor data 356, as well as reference data 339. In some cases, reference data 339 includes map data (e.g., 3D map data, 2D map data, 4D map data (e.g., including Epoch Determination)) and route data (e.g., road network data, including, but not limited to, RNDF data (or similar data), MDF data (or similar data), etc....localizer 468 retrieves reference data originating from a reference data repository 405, which includes a map data repository 405a for storing 2O map data, 3O map data, 4O map data, and the like)
receive, from the one or more sensors, sensor data while autonomously transporting from the first location to the second location (see at least Kentley [C6 L3-24, C9 L20-29] the localizer may use acquired sensor data, such as sensor data associated with surfaces of buildings 115 and 117, which can be compared against reference data, such as map data (e.g., 3D map data, including reflectance data) to determine a local pose. Further, a perception engine (not shown) of autonomous vehicle controller 147 may be configured to detect, classify, and predict the behavior of external objects, such as external object 112 (a “tree” and external object 114 (a “pedestrian”). Classification of such external objects may broadly classify objects as static objects, such as external object 112, and dynamic objects, such as external object 114. The localizer and the perception engine, as well as other components of the AV controller 147, collaborate to cause autonomous vehicles 109 to drive autonomously.... Localizer 368 is configured to receive sensor data from one or more sources, such as GPS data 352, wheel data, MU data 354, Lidar data 346a, camera data 340a, radar data 348a, and the like, as well as reference data 339 (e.g., 3D map data and route data). Localizer 368 integrates (e.g., fuses the sensor data) and analyzes the data by comparing sensor data to map data to determine a local pose (or position) of bidirectional autonomous vehicle 330. According to some examples, localizer 368 may generate or update the pose or position of any autonomous vehicle in real-time or near real-time)
selectively updating the received map data with the sensor data (see at least Kentley [C13 L50-62] Map updater 406 is configured to receive map data (e.g., from local map generator 440, sensors 460, or any other component of autonomous vehicle controller 447), and is further configured to detect deviations, for example, of map data in map data repository 405a from a locally-generated map. Vehicle data controller 408 can cause map updater 406 to update reference data within repository 405 and facilitate updates to 20, 30, and/or 40 map data. In some cases, vehicle data controller 408 can control the rate at which local map data is received into autonomous vehicle service platform 408 as well as the frequency at which map updater 406 performs updating of the map data).
However, Kentely does not explicitly teach a controller configured to:
provide, to the remote computing system, vehicle data indicating a request for an aftertreatment system component regeneration;
receive, from the remote computing system, transportation instructions corresponding to a high load request based on the request for the aftertreatment system component regeneration, the transportation instructions comprising a first location and a second location;
Leone 067, in the same field as the endeavor, teaches a controller configured to: provide, to the remote computing system, vehicle data indicating a request for an aftertreatment system component regeneration; (see at least Leone 067 [¶ 16, 6, 31, 34, 67-68] Particulate matter load on the PF may be estimated based on the exhaust backpressure as estimated via a pressure sensor 76 coupled across the PF. The pressure sensor 76 may be a differential (delta) pressure sensor that measures the change in exhaust pressure as exhaust flows through the PF, such as a Delta Pressure Feedback Exhaust (DPFE) sensor. In other examples, the pressure sensor may be an absolute pressure sensor and the controller may measure the pressure change across the PF based on outputs from pressure sensors coupled upstream and downstream of the filter. Once the PF reaches a threshold load, the PF 72 may be periodically or opportunistically regenerated)
receive, from the remote computing system, transportation instructions corresponding to a high load request based on the request for the aftertreatment system component regeneration, the transportation instructions comprising a first location and a second location (see at least Leone 067 [¶ 16, 6, 31, 34, 67-68] Upcoming periods of increased engine load may be estimated based on the retrieved traffic information and road segment characteristics. Regeneration of an exhaust particulate filter may be opportunistically scheduled during such periods of increased engine load…based on the traffic and road conditions data, the controller may determine an upcoming period of higher than threshold torque demand and during this period, the controller may send a signal to the fuel injector 66 to adjust the engine air fuel ratio to be leaner than stoichiometric, thereby facilitating PF 72 regeneration)
Therefore, 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 the system set forth in Kentely to contain a controller configured to: provide, to the remote computing system, vehicle data indicating a request for an aftertreatment system component regeneration and receive, from the remote computing system, transportation instructions corresponding to a high load request based on the request for the aftertreatment system component regeneration, the transportation instructions comprising a first location and a second location with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of only performing filter regeneration during times of high engine load as to not waste resources as discussed in Leone 067 (see at least Leone 067 [¶ 31] the PF 72 may be periodically or opportunistically regenerated to reduce the particulate matter load and the corresponding exhaust back pressure. In order to regenerate the PF, a higher than threshold exhaust temperature and a leaner than stoichiometric is desired at the PF. An increase in exhaust temperature may correspond to increased torque demand).
Further, Kentely does not explicitly teach a controller configured to: generate autonomous vehicle control signals, the autonomous vehicle control signals causing a vehicle including the system to autonomously transport from the first location to the second location such that the aftertreatment system component regeneration occurs while executing the transportation instructions.
De Smet, in the same field as the endeavor, teaches a controller configured to: generate autonomous vehicle control signals, the autonomous vehicle control signals causing a vehicle including the system to autonomously transport from the first location to the second location such that the aftertreatment system component regeneration occurs while executing the transportation instructions (see at least De Smet [Abstract, ¶ 24, 76-78] Embodiments for regeneration a particulate filter in a motor vehicle having a self-control mode for the autonomous control of a drive mode…a route to be traveled can be specified at the discretion of the driver…If it is determined that the vehicle is operating in the self-control mode, method 200 proceeds to 208 to determine if particulate filter regeneration is indicated. Particulate filter regeneration may be indicated when the particulate/soot load on the particulate filter exceeds a threshold load. The particulate filter load may be determined based on output from a sensor (such as a particulate matter sensor or a pressure sensor positioned in the exhaust) or based on an estimated particulate load (e.g., based on a duration since a previous regeneration…If it is determined at 208 that a particulate filter regeneration is indicated, method 200 proceeds to 212 to deviate from the velocity profile to perform the regeneration…If the regeneration is complete, method 200 proceeds to 224 to resume operation the velocity profile in its original, undeviated form. Method 200 then returns)
Therefore, 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 the system set forth in Kentely to contain a controller configured to: generate autonomous vehicle control signals, the autonomous vehicle control signals causing a vehicle including the system to autonomously transport from the first location to the second location such that the aftertreatment system component regeneration occurs while executing the transportation instructions with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of protecting the particulate filter by monitoring for when to perform the regeneration during driving trips, among other benefits, as discussed in De Smet (see at least De Smet [¶ 65] the self-control mode of the motor vehicle 1 can be utilized in order to carry out the regularly necessary regeneration phases of the diesel particulate filter in a controlled manner. In this case, the regeneration process can be thermally controlled by influencing the driving profile in the self-control. Overheating and premature aging can be avoided as a result. Therefore, the service life of the diesel particulate filter can be extended. Furthermore, fault memory entries resulting from overheatings of the diesel particulate filter can be avoided and service intervals can be extended. Overall, improved compliance with emission standards, which may even be stringent, can be achieved over the entire service life of the motor vehicle 1, while maintenance costs are reduced).
Dependent Claims
Regarding Claim 2 and Claim 9, Kentley in view of Leone 067 and De Smet teaches all limitations of the system of Claim 1 and the method of Claim 8 as set forth above. Kentley further teaches wherein the first vehicle is positioned at a third location, and wherein the operations further comprise:
causing the first vehicle to autonomously transport from the third location to the first location (see at least Kentley [C24 L28-33] At 1710, an autonomous vehicle is selected as transportation from the first geographic location to the second geographic location, and data is generated to dispatch the autonomous vehicle to a third geographic location associated with the origination of the transit request)
causing the first vehicle to collect second sensor data while autonomously transporting from the third location to the first location (see at least Kentley [C9 L20-29] Localizer 368 is configured to receive sensor data from one or more sources, such as GPS data 352, wheel data, MU data 354, Lidar data 346a, camera data 340a, radar data 348a, and the like, as well as reference data 339 (e.g., 3D map data and route data). Localizer 368 integrates (e.g., fuses the sensor data) and analyzes the data by comparing sensor data to map data to determine a local pose (or position) of bidirectional autonomous vehicle 330. According to some examples, localizer 368 may generate or update the pose or position of any autonomous vehicle in real-time or near real-time).
Regarding Claim 20, Kentley in view of Leone 067 and De Smet teaches all limitations of Claim 17 as set forth above. Kentley further teaches wherein the autonomous vehicle control signals comprise at least one of: an acceleration control that causes the vehicle to accelerate by increasing a fueling rate of an engine or increasing a power provided to an electric motor; a steer control that causes the vehicle to change direction by actuating a vehicle steering assembly; or a brake control that causes the vehicle to brake by actuating a brake system of the vehicle (see at least Kentley [C13 L18-26, C15 L31-35, C10 L17-39] User experience may be optimized by moderating accelerations in various linear and angular directions (e.g., to reduce jerking-like travel or other unpleasant motion)…Motion controller process uses trajectories data 665 to generate low-level commands or control signals for application to actuators 650 to cause changes in steering angles and/or velocity…planner 364 may transmit steeling and propulsion commands (as well as decelerating or braking commands) to motion controller 362. Motion controller 362 subsequently may convert any of the commands, such as a steering command, a throttle or propulsion command, and a braking command, into control signals (e.g., for application to actuators or other mechanical interfaces) to implement changes in steering or wheel angles 351 and/or velocity 353).
Claims 3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Kentley-Klay et al (US 11067983 B2) in view of Leone et al (US 20190071067 A1), De Smet et al (US 20170051652 A1), Kislovskiy et al (US 20180341880 A1) and Willis et al (EP 3640102 A1). Hereafter referred to as Kentley, Leone 067, De Smet, Kislovskiy and Willis respectively.
Regarding Claim 3 and Claim 10, Kentley in view of Leone 067 and De Smet teaches all limitations of the system of Claim 1 and the method of Claim 8 as set forth above. However, Kentley does not explicitly teach comparing a load value of the transport request to a load threshold of the first vehicle; and selecting the first vehicle responsive to determining that the load value is at or below the load threshold.
Kislovskiy, in the same field as the endeavor, teaches comparing a load value of the transport request to a load threshold of the first vehicle; and selecting the first vehicle responsive to determining that the load value is at or below the load threshold (see at least Kislovskiy [¶ 78] if the trip risk value 332 is below all thresholds for a verified software version 346, then the trip classifier 350 can enable all vehicles types (HDVs 387, SDAVs 381, and FAVs 389) to service the transport request 371....the matching engine 320 will ultimately select an optimal vehicle to service the transport request 371 across those vehicles authorized by the trip classifier(s) 350, and other factors such as estimated trip cost or revenue 348, and estimated distance or time of the vehicle from the pick-up location)
Willis, in the same field as the endeavor, teaches wherein load is specifically a value regarding the weight of the vehicle and its cargo (see at least Willis [English Translation pg.5 para.3, pg.3 para.6] a route (or route portion) is divided into the first route segment and the second route segment in response to the controller determining or predicting that the load condition of the vehicle in the first route segment differs from the load condition of the vehicle in the second route segment by greater than a specified load threshold (e.g., a weight of the load)...a route can be a predetermined route or a route that can change on the fly as the vehicle proceeds on its trip).
Therefore, 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 the system set forth in Kentley to contain a system for comparing a load value of the transport request to a load threshold of the first vehicle; and selecting the first vehicle responsive to determining that the load value is at or below the load threshold with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of reducing the risk of the vehicle selection by only choosing vehicles that can handle the requirements of the request as discussed in Kislovskiy.
Claims 4, 5, 7 11, 12, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kentley-Klay et al (US 11067983 B2) in view of Leone et al (US 20190071067 A1), De Smet et al (US 20170051652 A1), and Arditi (US 20190147331 A1). Hereafter referred to as Kentley, Leone 067, De Smet, and Arditi respectively.
Regarding Claim 4 and Claim 11, Kentley in view of Leone 067 and De Smet teaches all limitations of the system of Claim 1 and the method of Claim 8 as set forth above. Kentley further teaches wherein the first sensor data comprises map data and the operations further comprise:
comparing the map data to the centralized map data (see at least Kentley [C13 L50-62] Map updater 406 is configured to receive map data (e.g., from local map generator 440, sensors 460, or any other component of autonomous vehicle controller 447), and is further configured to detect deviations, for example, of map data in map data repository 405a from a locally-generated map. Vehicle data controller 408 can cause map updater 406 to update reference data within repository 405 and facilitate updates to 20, 30, and/or 40 map data. In some cases, vehicle data controller 408 can control the rate at which local map data is received into autonomous vehicle service platform 408 as well as the frequency at which map updater 406 performs updating of the map data.
However, Kentley does not explicitly teach responsive to determining that a difference between the map data and the centralized map data is less than a predetermined threshold, discarding the map data; and responsive to determining that the difference between the map data and the centralized map data is equal to or greater than the predetermined threshold, updating the centralized map data.
Arditi, in the same field as the endeavor, teaches responsive to determining that a difference between the map data and the centralized map data is less than a predetermined threshold, discarding the map data; and responsive to determining that the difference between the map data and the centralized map data is equal to or greater than the predetermined threshold, updating the centralized map data (see at least Arditi [¶ 47] at step 730, the computing system may access the HD map stored on the autonomous vehicle for map data associated with the particular location (e.g., x, y coordinates). Then at step 740, the computing system may compare the map data associated with the location (e.g., x, y coordinates) with the object detected in step 720 to determine whether the detected objects exist in the map data. For example, for each detected object, the system may check whether that object exists in the map data. In particular embodiments, the system may generate a confidence score representing the likelihood of the detected object being accounted for in the map data. The confidence score may be based on, for example, a similarity comparison of the measured size, dimensions, classification, and/or location of the detected object with known objects in the map data. At step 745, if the comparison results in a determination that the detected object(s) exists or is known in the HD map (e.g., the confidence score in the object existing in the map is higher than a threshold), then the system may not perform any map-updating operation and return to obtaining sensor data (e.g., step 710). On the other hand, if the comparison results in a determination that at least one detected object does not exist or is not known in the HD map (e.g., the confidence score in the object existing in the map is lower than a threshold), then the system may proceed with a map-updating operation).
Therefore, 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 the system set forth in Kentley to contain a system for determining that a difference between the map data and the centralized map data is less than a predetermined threshold, discarding the map data; and responsive to determining that the difference between the map data and the centralized map data is equal to or greater than the predetermined threshold, updating the centralized map data with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of improving the safety of the operation of vehicles by keeping their maps up to date via updating as discussed by Arditi (see at least Arditi [¶ 14] HD maps need to include highly accurate and detailed information to ensure the safe operation of autonomous vehicles).
Regarding Claim 5 and Claim 12, Kentley in view of Leone 067, De Smet, and Arditi teaches all limitations of the system of Claim 4 and the method of Claim 11 as set forth above. However, Kentley does not explicitly teach wherein updating the centralized map data further comprises updating a first portion of the centralized map data, and wherein a difference between a portion the centralized map data and a corresponding portion of the map data is greater than or equal to the predetermined threshold.
Arditi, in the same field as the endeavor, teaches wherein updating the centralized map data further comprises updating a first portion of the centralized map data, and wherein a difference between a portion the centralized map data and a corresponding portion of the map data is greater than or equal to the predetermined threshold (see at least Arditi [¶ 41, 47] the computing system may use the generated map data, which may be associated with a particular location, to generate or update a portion of an HD map of a region…At step 730, the computing system may access the HD map stored on the autonomous vehicle for map data associated with the particular location (e.g., x, y coordinates). Then at step 740, the computing system may compare the map data associated with the location (e.g., x, y coordinates) with the object detected in step 720 to determine whether the detected objects exist in the map data. For example, for each detected object, the system may check whether that object exists in the map data. In particular embodiments, the system may generate a confidence score representing the likelihood of the detected object being accounted for in the map data. The confidence score may be based on, for example, a similarity comparison of the measured size, dimensions, classification, and/or location of the detected object with known objects in the map data. At step 745, if the comparison results in a determination that the detected object(s) exists or is known in the HD map (e.g., the confidence score in the object existing in the map is higher than a threshold), then the system may not perform any map-updating operation and return to obtaining sensor data (e.g., step 710). On the other hand, if the comparison results in a determination that at least one detected object does not exist or is not known in the HD map (e.g., the confidence score in the object existing in the map is lower than a threshold), then the system may proceed with a map-updating operation).
Therefore, 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 the system set forth in Kentley to contain a system for wherein updating the centralized map data further comprises updating a first portion of the centralized map data, and wherein a difference between a portion the centralized map data and a corresponding portion of the map data is greater than or equal to the predetermined threshold with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of improving the safety of the operation of vehicles by keeping their maps up to date via updating as discussed by Arditi (see at least Arditi [¶ 14] HD maps need to include highly accurate and detailed information to ensure the safe operation of autonomous vehicles).
Regarding Claim 7 and Claim 14, Kentley in view of Leone 067 and De Smet teaches all limitations of the system of Claim 1 and the method of Claim 8 as set forth above. Kentley further teaches wherein the operations further comprise:
communicably coupling to one or more sensors (see at least Kentley [C11 L19-50] sensors 470 may be configured to provide sensor data to components of autonomous vehicle controller 447 and to elements of autonomous vehicle service platform 401)
receiving, from the one or more sensors, map data (see at least Kentley [C6 L3-24, C9 L20-29] the localizer may use acquired sensor data, such as sensor data associated with surfaces of buildings 115 and 117, which can be compared against reference data, such as map data (e.g., 3D map data, including reflectance data) to determine a local pose. Further, a perception engine (not shown) of autonomous vehicle controller 147 may be configured to detect, classify, and predict the behavior of external objects, such as external object 112 (a “tree” and external object 114 (a “pedestrian”). Classification of such external objects may broadly classify objects as static objects, such as external object 112, and dynamic objects, such as external object 114. The localizer and the perception engine, as well as other components of the AV controller 147, collaborate to cause autonomous vehicles 109 to drive autonomously.... Localizer 368 is configured to receive sensor data from one or more sources, such as GPS data 352, wheel data, MU data 354, Lidar data 346a, camera data 340a, radar data 348a, and the like, as well as reference data 339 (e.g., 3D map data and route data). Localizer 368 integrates (e.g., fuses the sensor data) and analyzes the data by comparing sensor data to map data to determine a local pose (or position) of bidirectional autonomous vehicle 330. According to some examples, localizer 368 may generate or update the pose or position of any autonomous vehicle in real-time or near real-time)
comparing the map data to the centralized map data (see at least Kentley [C13 L50-62] Map updater 406 is configured to receive map data (e.g., from local map generator 440, sensors 460, or any other component of autonomous vehicle controller 447), and is further configured to detect deviations, for example, of map data in map data repository 405a from a locally-generated map. Vehicle data controller 408 can cause map updater 406 to update reference data within repository 405 and facilitate updates to 20, 30, and/or 40 map data. In some cases, vehicle data controller 408 can control the rate at which local map data is received into autonomous vehicle service platform 408 as well as the frequency at which map updater 406 performs updating of the map data).
However, Kentley does not explicitly teach responsive to determining that a difference between the map data and the centralized map data is less than a predetermined threshold, discarding the map data; and responsive to determining that the difference between the map data and the centralized map data is equal to or greater than the predetermined threshold, updating the centralized map data.
Arditi, in the same field as the endeavor, teaches responsive to determining that a difference between the map data and the centralized map data is less than a predetermined threshold, discarding the map data; and responsive to determining that the difference between the map data and the centralized map data is equal to or greater than the predetermined threshold, updating the centralized map data (see at least Arditi [¶ 47 and FIG. 8] At step 730, the computing system may access the HD map stored on the autonomous vehicle for map data associated with the particular location (e.g., x, y coordinates). Then at step 740, the computing system may compare the map data associated with the location (e.g., x, y coordinates) with the object detected in step 720 to determine whether the detected objects exist in the map data. For example, for each detected object, the system may check whether that object exists in the map data. In particular embodiments, the system may generate a confidence score representing the likelihood of the detected object being accounted for in the map data. The confidence score may be based on, for example, a similarity comparison of the measured size, dimensions, classification, and/or location of the detected object with known objects in the map data. At step 745, if the comparison results in a determination that the detected object(s) exists or is known in the HD map (e.g., the confidence score in the object existing in the map is higher than a threshold), then the system may not perform any map-updating operation and return to obtaining sensor data (e.g., step 710). On the other hand, if the comparison results in a determination that at least one detected object does not exist or is not known in the HD map (e.g., the confidence score in the object existing in the map is lower than a threshold), then the system may proceed with a map-updating operation).
Therefore, 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 the system set forth in Kentley to contain a system for responsive to determining that a difference between the map data and the centralized map data is less than a predetermined threshold, discarding the map data; and responsive to determining that the difference between the map data and the centralized map data is equal to or greater than the predetermined threshold, updating the centralized map data with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of improving the safety of the operation of vehicles by keeping their maps up to date via updating as discussed by Arditi (see at least Arditi [¶ 14] HD maps need to include highly accurate and detailed information to ensure the safe operation of autonomous vehicles).
Claims 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Kentley-Klay et al (US 11067983 B2) in view of Leone et al (US 20190071067 A1), De Smet et al (US 20170051652 A1), and Sergeev et al (US 20210199445 A1). Hereafter referred to as Kentley, Leone 067, De Smet, and Sergeev respectively.
Regarding Claim 6 and Claim 13, Kentley in view of Leone 067 and De Smet teaches all limitations of the system of Claim 1 and the method of Claim 8 as set forth above. However, Kentley does not explicitly teach wherein selectively transmitting the centralized map data to the first vehicle comprises providing a first portion of the centralized map data to the first vehicle, wherein the first portion of the centralized map data comprises information regarding a route between the first location and the second location.
Sergeev, in the same field as the endeavor, teaches wherein selectively transmitting the centralized map data to the first vehicle comprises providing a first portion of the centralized map data to the first vehicle, wherein the first portion of the centralized map data comprises information regarding a route between the first location and the second location (see at least Sergeev [¶ 32, 15, 39] The rideshare service 158 can receive requests to be picked up or dropped off from passenger ridesharing application 170 and can dispatch autonomous vehicle 102 for the trip....maps, defined by a geographical area around an autonomous vehicle navigating a route, can be received and stored in shared memory, where the shared memory can be a specific physical block of RAM. Concurrent access to the map within the shared memory can be granted to downstream nodes on the autonomous vehicle as the autonomous vehicle navigates the route....In FIG. 3A, method 300 starts by receiving (302) a map defined by a geographical area around an autonomous vehicle navigating a route, which will become a map shared by multiple nodes at the same time. In some embodiments, the entire map may be too large for efficient map processing in a micro-service architecture, which relies on multiple nodes to handle different services (e.g., lidar service, camera service, etc.), and so a subset of the entire map can be received by and stored (304) in shared memory).
Therefore, 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 the system set forth in Kentley to contain a system for selectively transmitting the centralized map data to the first vehicle comprises providing a first portion of the centralized map data to the first vehicle, wherein the first portion of the centralized map data comprises information regarding a route between the first location and the second location. with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of improving the efficient processing of the system by only providing the section of map that is necessary for the route as discussed in Sergeev (see at least Sergeev [¶ 39] the entire map may be too large for efficient map processing in a micro-service architecture, which relies on multiple nodes to handle different services (e.g., lidar service, camera service, etc.), and so a subset of the entire map can be received by and stored (304) in shared memory).
Claims 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kentley-Klay et al (US 11067983 B2) in view of Leone et al (US 20190071067 A1), De Smet et al (US 20170051652 A1), and Abramson et al (US 20180356237 A1). Hereafter referred to as Kentley, Leone 067, De Smet, and Abramson respectively.
Regarding Claim 18, Kentley in view of Leone 067 and De Smet teaches all limitations of Claim 17 as set forth above. Kentley further teaches wherein generating the autonomous vehicle control signals comprises:
determining a location of the vehicle based on the map data (see at least Kentley [C6 L3-24] A localizer (not shown) of autonomous vehicle controller 147 can determine a local pose at the geographic location 111. As such, the localizer may use acquired sensor data, such as sensor data associated with surfaces of buildings 115 and 117, which can be compared against reference data, such as map data (e.g., 3D map data, including reflectance data) to determine a local pose).
However, Kentley does not explicitly teach determining a route for the vehicle based on the location of the vehicle, the first location, and the second location, wherein the route is selected based on at least one of: a distance of the route is less than a predefined distance threshold, a number of turns of the route between the first location and the second location is less than a predefined turning threshold, a load of the route experienced between the first location and the second location is at or below a predefined maximum load threshold, or the load of the route experience between the first location and the second location is at or above a predefined minimum load threshold.
Abramson, in the same field as the endeavor teaches determining a route for the vehicle based on the location of the vehicle, the first location, and the second location, wherein the route is selected based on at least one of: a distance of the route is less than a predefined distance threshold, a number of turns of the route between the first location and the second location is less than a predefined turning threshold, a load of the route experienced between the first location and the second location is at or below a predefined maximum load threshold, or the load of the route experience between the first location and the second location is at or above a predefined minimum load threshold (see at least Abramson [¶ 121] various metrics such as the determined distance and/or complexity (e.g., number of turns/instructions) and/or a lowest economic cost of the trip and/or an ecological cost of a trip can be used in lieu of or in addition to the referenced ‘shortest time’ metric in order to determine whether the increased utility is sufficiently high as compared to the decrease in familiarity (to the user) and/or increase in complexity to justify such a route choice (or re-routing choice), based upon default trade-off threshold or user threshold values provided by the user).
Therefore, 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 the system set forth in Kentley to contain a system for determining a route for the vehicle based on the location of the vehicle, the first location, and the second location, wherein the route is selected based on at least one of: a distance of the route is less than a predefined distance threshold, a number of turns of the route between the first location and the second location is less than a predefined turning threshold, a load of the first route experienced between the first location and the second location is at or below a predefined maximum load threshold, or a load of the first route experience between the first location and the second location is at or above a predefined minimum load threshold with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of selecting a route that contains common characteristics of what is considered an optimal route in the art like distance and number of turns.
Regarding Claim 19, Kentley in view of Leone 067, De Smet, and Abramson teaches all limitations of Claim 18 as set forth above. Kentley further teaches wherein herein generating the autonomous vehicle control signals further comprises:
analyzing the sensor data by:
identifying one or more objects within a predetermined distance of the vehicle and performing an object recognition on the sensor data to determine one or more characteristics of the one or more objects (see at least Kentley [C6 L1-L24] As such, the localizer may use acquired sensor data, such as sensor data associated with surfaces of buildings 115 and 117, which can be compared against reference data, such as map data (e.g., 3D map data, including reflectance data) to determine a local pose. Further, a perception engine (not shown) of autonomous vehicle controller 147 may be configured to detect, classify, and predict the behavior of external objects, such as external object 112 (a “tree” and external object 114 (a “pedestrian”))
determining whether each of the one or more objects is moving and a trajectory of each of the one or more objects, and responsive to analyzing the sensor data, determining at least one correction to the route (see at least Kentley [C14 L24-65] data representing objects based on the least two subsets of sensor data may be derived at a processor. For example, data identifying static objects or dynamic objects may be derived (e.g., at a perception engine) from at least Lidar and camera data. At 508, a detected object is determined to affect a planned path, and a subset of trajectories are evaluated (e.g., at a planner) responsive to the detected object at 510…As such, a request for an alternate path may be transmitted to a teleoperator computing device at 514. Thereafter, the teleoperator computing device may provide a planner with an optimal trajectory over which an autonomous vehicle made travel. In situations, the vehicle may also determine that executing a safe-stop maneuver is the best course of action (e.g., safely and automatically causing an autonomous vehicle to a stop at a location of relatively low probabilities of danger))
the at least one correction causing the vehicle to:
change a speed of the vehicle or change a direction of the vehicle to avoid the one or more objects within the predetermined distance of the vehicle, change the speed of the vehicle or change the direction of the vehicle based on the one or more characteristics of the one or more objects, or change the speed of the vehicle or change the direction of the vehicle to avoid the trajectory of each of the one or more objects (see at least Kentley [C14 L66 to C15 L34] Perception process 666 is configured to generate static and dynamic object map data 667, which, in turn, may be transmitted to planner process 664. In some examples, static and dynamic object map data 667 may be transmitted with other data, such as semantic classification information and predicted object behavior. Planner process 664 is configured to generate trajectories data 665, which describes a number of trajectories generated by planner 664. Motion controller process uses trajectories data 665 to generate low-level commands or control signals for application to actuators 650 to cause changes in steering angles and/or velocity).
Conclusion
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.
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/JOSEPH ANDERSON YANOSKA/Examiner, Art Unit 3664
/RACHID BENDIDI/Supervisory Patent Examiner, Art Unit 3664
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