DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/23/2025 has been entered.
Response to Arguments
Applicant's arguments filed 12/23/2025 have been fully considered but are considered moot because the arguments are directed toward elements that have not been previously considered and have necessitated a new grounds of rejection.
Response to Amendment
Regarding the rejections under 35 USC §103, amendments made to the claims have necessitated a new grounds of rejection as outlined below.
Claim Interpretation
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.
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(s) is/are: “perception system” in claims 1, 10, and 19. Applicant’s specification recites “a perception system including a camera system 104, a LiDAR system 106, a GNSS receiver 108, an inertial measurement unit (IMU) 110, and/or a perception module 202” in paragraph [0021].
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) 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 § 103
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.
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.
Claims 1-4, 6, 8-13, 15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Duan et al. (U.S. Patent Application Publication No. 2021/0331703 A1; hereinafter Duan) in view of Lin et al. (U.S. Patent Application Publication No. 2020/0174493 A1; hereinafter Lin) and Caveney (U.S. Patent Application Publication No. 2020/0241530 A1).
Regarding claim 1, Duan discloses:
A method of controlling an autonomous vehicle (computing devices for controller the vehicle such as an autonomous vehicle, see at least [0012]), comprising:
collecting, by at least one processor (perception component 322 and prediction component 324, see at least [0035]; software executed by processors, see at least [0063]), perception data representing a perceived environment of the autonomous vehicle using a perception system on board the autonomous vehicle while the autonomous vehicle is traveling along a roadway, the perception data includes at least one of perceived objects or perceived features of the roadway (cameras onboard vehicle capture environment, see at least [0014]; estimation component can output an estimated map 112 based on lidar and camera data, see at least [0009] and [0024]; perception component 322 performs object detection based at least in part on sensor data received from sensor component, see at least [0037]; prediction component 324 can receive sensor data and map data and perception data output to output predictions associated with one or more objects within the environment, see at least [0038]);
comparing, by the at least one processor, the perception data collected with digital map data (comparing the estimated map data 112 with the stored map data 120 to output a confidence score or consistency output 124, see at least [0028]), a fleet of vehicles, the fleet of vehicles including the autonomous vehicle, (the system 300 can include a plurality of vehicles such as a fleet, see at least [0034])
generating, by the at least one processor, a confidence level based on the comparing of the perception data and the digital map data (consistency checking component can output confidence scores based on comparing estimated data with the stored data, see at least [0028]); and
modifying, by the at least one processor, operation of the autonomous vehicle based on the generated confidence level being below the threshold confidence level (if an inconsistency is detected, the vehicle can be caused to decelerate or stop until confidence scores exceed a threshold or a trajectory can be altered [0096]-[0097]).
Duan does not explicitly disclose:
the digital map data including at least one of the perceived objects or the perceived features perceived by a fleet of vehicles
determining a threshold confidence level associated with at least one of the perceived objects or the perceived features, based on a number of times that the perceived objects or the perceived features have been perceived by the fleet;
However, Lin teaches:
the digital map data including at least one of the perceived objects or the perceived features perceived by a fleet of vehicles (fleet, see at least [0030]; data may be used to update a central map, see at least [0062]),
determining a threshold confidence level associated with at least one of the perceived objects or the perceived features, based on a number of times that the perceived objects or the perceived features have been perceived by the fleet (the relevancy or confidence level for the data may be based on a number of transmissions such that additional vehicles providing indications of the obstacle may increase the relevancy, confidence level, or trustworthiness of the data associated with the obstacle 204, see at least [0033])
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the map consistency checker disclosed by Duan by adding the object confidence from a fleet taught by Lin with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification in order to determine a validity of data for incorporating the data into a perception system of the vehicle based on the confidence level (see [0011]).
Furthermore, Caveney teaches:
determining a threshold confidence level associated with at least one of the perceived objects or the perceived features (trust level can be determined for a vehicle or data reliability, see at least [0035]-[0036]; integrity model can map the object in a vehicular environment based on trust level, see at least [0043])
modifying, by the at least one processor, operation of the autonomous vehicle based on the generated confidence level being below the threshold confidence level (guide host vehicle using the obscured obstacle and trust level, see at least [0045]-[0046])
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the map consistency checker disclosed by Duan and the object confidence from a fleet taught by Lin by adding the guidance of a vehicle using additional data taught by Caveney with a reasonable expectation of success. By modifying the map consistency determination disclosed by Duan by adding the confidence level or trust or validity of data about an object increasing based on number of times the object is detected by a vehicle as taught by Lin and further adding the guidance of a host vehicle based on a trust level regardless of whether the object is currently detected by the host vehicle or not, see the “obscured obstacle” of Caveney, a vehicle is able to operate using reliable information about an obstacle even if the on board sensors of the vehicle is unable to detect the obstacle. One of ordinary skill in the art would have been motivated to make this modification in order “to navigate the host vehicle using a guidance input for avoiding the obscured obstacle” (see [0003]).
Regarding claim 2, the combination of Duan, Lin, and Caveney teaches the elements above and Duan further discloses:
the modifying of the operation of the autonomous vehicle includes implementing more conservative driving of the autonomous vehicle as a function of the amount of difference between the perception data and the digital map data (decelerate a vehicle to below a threshold until the confidence score meets or exceeds a threshold, see at least [0029]).
Regarding claim 3, the combination of Duan, Lin, and Caveney teaches the elements above and Duan further discloses:
the more conservative driving includes at least one of slowing a speed of the autonomous vehicle or prohibiting lane changes (decelerate a vehicle to below a threshold until the confidence score meets or exceeds a threshold, see at least [0029]).
Regarding claim 4, the combination of Duan, Lin, and Caveney teaches the elements above and Duan further discloses:
the modifying of the operation of the autonomous vehicle is also based on a type of difference between the perception data and the digital map data (confidence scores can be based at least in part on a weight associated with a map element, see at least [0091])
Regarding claim 6, the combination of Duan, Lin, and Caveney teaches the elements above and Duan further discloses:
modifying the autonomous vehicle operation includes stopping the autonomous vehicle (if not consistent, computing device can cause vehicle to decelerate and stop, see at least [0029]).
Regarding claim 8, the combination of Duan, Lin, and Caveney teaches the elements above and Duan further discloses:
further including modifying the confidence level associated with the digital map data based on the amount of difference between the perception data and the digital map data (consistency checking component can output confidence scores indicating information associated with stored map is reliable, see at least [0028]).
Regarding claim 9, the combination of Duan, Lin, and Caveney teaches the elements above and Duan further discloses:
the digital map is stored on the autonomous vehicle (local and or global map can be stored bap that has been generated by previous data collection efforts, see at least [0025], stored map data, see at least [0026]; storage 330 on vehicle can store maps, see at least [0035]).
Regarding claim 10, Duan discloses:
A system for controlling an autonomous vehicle (computing devices for controller the vehicle such as an autonomous vehicle, see at least [0012]), comprising:
a perception system (perception component 322 and prediction component 324, see at least [0035]; sensor components include lidar, radar, sonar, ultrasonic, camera, etc., see at least [0013]);
a processing device; (software executed by processors, see at least [0063]); and
a memory storing digital map data and one or more processor-readable instructions (memory 318 stores maps and models, see at least [0035]; memory stores components to perform various functionalities, see at least [0050]), that when executed by the processing device, cause the system to:
collect perception data representing a perceived environment of the autonomous vehicle using the perception system on board the autonomous vehicle while the autonomous vehicle is traveling along a roadway, the perception data includes at least one of perceived objects or perceived features of the roadway (cameras onboard vehicle capture environment, see at least [0014]; estimation component can output an estimated map 112 based on lidar and camera data, see at least [0009] and [0024]; perception component 322 performs object detection based at least in part on sensor data received from sensor component, see at least [0037]; prediction component 324 can receive sensor data and map data and perception data output to output predictions associated with one or more objects within the environment, see at least [0038]);
compare the perception data collected with the digital map data (comparing the estimated map data 112 with the stored map data 120 to output a confidence score or consistency output 124, see at least [0028])
a fleet of vehicles, the fleet of vehicles including the autonomous vehicle (the system 300 can include a plurality of vehicles such as a fleet, see at least [0034])
generate a confidence level based on the comparing of the perception data and the digital map data(consistency checking component can output confidence scores based on comparing estimated data with the stored data, see at least [0028]); and
modify operation of the autonomous vehicle based on the generated confidence level being below the threshold confidence level (if an inconsistency is detected, the vehicle can be caused to decelerate or stop until confidence scores exceed a threshold or a trajectory can be altered [0096]-[0097])
Duan does not explicitly disclose:
the digital map data including at least one of the perceived objects or the perceived features perceived by a fleet of vehicles
determining a threshold confidence level associated with at least one of the perceived objects or the perceived features, based on a number of times that the perceived objects or the perceived features have been perceived by the fleet;
However, Lin teaches:
the digital map data including at least one of the perceived objects or the perceived features perceived by a fleet of vehicles (fleet, see at least [0030]; data may be used to update a central map, see at least [0062]),
determining a threshold confidence level associated with at least one of the perceived objects or the perceived features, based on a number of times that the perceived objects or the perceived features have been perceived by the fleet (the relevancy or confidence level for the data may be based on a number of transmissions such that additional vehicles providing indications of the obstacle may increase the relevancy, confidence level, or trustworthiness of the data associated with the obstacle 204, see at least [0033])
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the map consistency checker disclosed by Duan by adding the object confidence from a fleet taught by Lin with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification in order to determine a validity of data for incorporating the data into a perception system of the vehicle based on the confidence level (see [0011]).
Furthermore, Caveney teaches:
determining a threshold confidence level associated with at least one of the perceived objects or the perceived features (trust level can be determined for a vehicle or data reliability, see at least [0035]-[0036]; integrity model can map the object in a vehicular environment based on trust level, see at least [0043])
modifying, by the at least one processor, operation of the autonomous vehicle based on the generated confidence level being below the threshold confidence level (guide host vehicle using the obscured obstacle and trust level, see at least [0045]-[0046])
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the map consistency checker disclosed by Duan and the object confidence from a fleet taught by Lin by adding the guidance of a vehicle using additional data taught by Caveney with a reasonable expectation of success. By modifying the map consistency determination disclosed by Duan by adding the confidence level or trust or validity of data about an object increasing based on number of times the object is detected by a vehicle as taught by Lin and further adding the guidance of a host vehicle based on a trust level regardless of whether the object is currently detected by the host vehicle or not, see the “obscured obstacle” of Caveney, a vehicle is able to operate using reliable information about an obstacle even if the on board sensors of the vehicle is unable to detect the obstacle. One of ordinary skill in the art would have been motivated to make this modification in order “to navigate the host vehicle using a guidance input for avoiding the obscured obstacle” (see [0003]).
Regarding claim 11, the combination of Duan, Lin, and Caveney teaches the elements above and Duan further discloses:
modifying operation of the autonomous vehicle includes implementing more conservative driving of the autonomous vehicle as a function of the amount of difference between the perception data and the digital map data (decelerate a vehicle to below a threshold until the confidence score meets or exceeds a threshold, see at least [0029]).
Regarding claim 12, the combination of Duan, Lin, and Caveney teaches the elements above and Duan further discloses:
the more conservative driving includes at least one of slowing a speed of the autonomous vehicle or prohibiting lane changes (decelerate a vehicle to below a threshold until the confidence score meets or exceeds a threshold, see at least [0029]).
Regarding claim 13, the combination of Duan, Lin, and Caveney teaches the elements above and Duan further discloses:
modifying the operation of the autonomous vehicle is also based on a type of difference between the perception data and the digital map data (confidence scores can be based at least in part on a weight associated with a map element, see at least [0091]).
Regarding claim 15, the combination of Duan, Lin, and Caveney teaches the elements above and Duan further discloses:
modifying the autonomous vehicle operation includes stopping the autonomous vehicle (if not consistent, computing device can cause vehicle to decelerate and stop, see at least [0029]).
Regarding claim 17, the combination of Duan, Lin, and Caveney teaches the elements above and Duan further discloses:
further including modifying the confidence level associated with the digital map based on the amount of difference between the perception data and the digital map data (consistency checking component can output confidence scores indicating information associated with stored map is reliable, see at least [0028]).
Regarding claim 18, the combination of Duan, Lin, and Caveney teaches the elements above and Duan further discloses:
the digital map is stored on the autonomous vehicle (local and or global map can be stored bap that has been generated by previous data collection efforts, see at least [0025], stored map data, see at least [0026]; storage 330 on vehicle can store maps, see at least [0035]).
Regarding claim 19, Duan discloses:
A method of controlling an autonomous vehicle (computing devices for controller the vehicle such as an autonomous vehicle, see at least [0012]), comprising:
collecting, by at least one processor (perception component 322 and prediction component 324, see at least [0035]; software executed by processors, see at least [0063]), perception data representing a perceived environment of the autonomous vehicle using a perception system on board the autonomous vehicle while the autonomous vehicle is traveling along a roadway, the perception data includes at least one of perceived objects or perceived features of the roadway (cameras onboard vehicle capture environment, see at least [0014]; estimation component can output an estimated map 112 based on lidar and camera data, see at least [0009] and [0024]; perception component 322 performs object detection based at least in part on sensor data received from sensor component, see at least [0037]; prediction component 324 can receive sensor data and map data and perception data output to output predictions associated with one or more objects within the environment, see at least [0038]);
comparing, by the at least one processor, the perception data collected with digital map data stored on the autonomous vehicle (comparing the estimated map data 112 with the stored map data 120 to output a confidence score or consistency output 124, see at least [0028]);
a fleet of vehicles, the fleet of vehicles including the autonomous vehicle (the system 300 can include a plurality of vehicles such as a fleet, see at least [0034])
providing, by the at least one processor, comparison data associated with the comparison between the perception data collected and the digital map data stored on the autonomous vehicle to a server remote from the autonomous vehicle (monitoring component 327, perception component 322, and prediction component 324 can send outputs over networks 332 to computing devices 334, see at least [0042] and Fig. 3); and
modifying, by the at least one processor, operation of the autonomous vehicle based on the comparison data provided to the server (if an inconsistency is detected, the vehicle can be caused to decelerate or stop until confidence scores exceed a threshold or a trajectory can be altered [0096]-[0097]).
Duan does not explicitly disclose:
the digital map data including at least one of the perceived objects or the perceived features perceived by a fleet of vehicles
determining a threshold confidence level associated with at least one of the perceived objects or the perceived features, based on a number of times that the perceived objects or the perceived features have been perceived by the fleet;
However, Lin teaches:
the digital map data including at least one of the perceived objects or the perceived features perceived by a fleet of vehicles (fleet, see at least [0030]; data may be used to update a central map, see at least [0062]),
determining a threshold confidence level associated with at least one of the perceived objects or the perceived features, based on a number of times that the perceived objects or the perceived features have been perceived by the fleet (the relevancy or confidence level for the data may be based on a number of transmissions such that additional vehicles providing indications of the obstacle may increase the relevancy, confidence level, or trustworthiness of the data associated with the obstacle 204, see at least [0033])
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the map consistency checker disclosed by Duan by adding the object confidence from a fleet taught by Lin with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification in order to determine a validity of data for incorporating the data into a perception system of the vehicle based on the confidence level (see [0011]).
Furthermore, Caveney teaches:
determining a threshold confidence level associated with at least one of the perceived objects or the perceived features (trust level can be determined for a vehicle or data reliability, see at least [0035]-[0036]; integrity model can map the object in a vehicular environment based on trust level, see at least [0043])
modifying, by the at least one processor, operation of the autonomous vehicle based on the generated confidence level being below the threshold confidence level (guide host vehicle using the obscured obstacle and trust level, see at least [0045]-[0046])
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the map consistency checker disclosed by Duan and the object confidence from a fleet taught by Lin by adding the guidance of a vehicle using additional data taught by Caveney with a reasonable expectation of success. By modifying the map consistency determination disclosed by Duan by adding the confidence level or trust or validity of data about an object increasing based on number of times the object is detected by a vehicle as taught by Lin and further adding the guidance of a host vehicle based on a trust level regardless of whether the object is currently detected by the host vehicle or not, see the “obscured obstacle” of Caveney, a vehicle is able to operate using reliable information about an obstacle even if the on board sensors of the vehicle is unable to detect the obstacle. One of ordinary skill in the art would have been motivated to make this modification in order “to navigate the host vehicle using a guidance input for avoiding the obscured obstacle” (see [0003]).
Regarding claim 20, the combination of Duan, Lin, and Caveney teaches the elements above and Duan further discloses:
the modifying of the operation of the autonomous vehicle includes implementing more conservative driving of the autonomous vehicle as a function of an amount of difference between the perception data and the digital map data. (decelerate a vehicle to below a threshold until the confidence score meets or exceeds a threshold, see at least [0029])
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Duan in view of Lin and Caveney as applied to claims 1 and 10 above and further in view of Zeng et al. (CN 111935642 A; see reference N on previous PTO-892; hereinafter Zeng).
Regarding claim 5, the combination of Duan, Lin, and Caveney teaches the elements above and Duan further discloses:
the type of difference includes differences between the perceived features of the roadway (determine whether lane lines in the estimated and stored map are different, see at least [0030]), and the modifying of the operation of the autonomous vehicle when the type of difference is based on perceived features of the roadway (confidence scores can be based at least in part on a weight associated with a map element and in some examples, if the confidence score meets or exceeds a threshold, the monitoring component can determine an inconsistency between the stored map and estimated map, see at least [0091]; map elements can include lane lines, see at least [0089]; if stored map is not consistent with estimated map, autonomous vehicle can decelerate or stop, see at least [0008])
Duan does not explicitly disclose:
the type of difference includes differences between the perceived objects
the modifying of the operation of the autonomous vehicle is more conservative when the type of difference is based on perceived features of the roadway
However, Zeng teaches:
the type of difference includes differences between the perceived objects (tree and structural high buildings) and perceived features of the roadway (actual intersection and lane line), and the modifying of the operation of the autonomous vehicle is more conservative when the type of difference is based on perceived features of the roadway (corresponding weight for lane lines and intersections are higher than for trees and high buildings, see at least [0101])
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the confidence level determined by weights of elements as disclosed by Duan, the object confidence from a fleet taught by Lin, and the guidance of a vehicle using additional data taught by Caveney by adding the higher weights for lane lines than for trees and buildings as taught by Zeng with a reasonable expectation of success. If the weights of Zeng are combined with the confidence scores of Duan, the confidence score of roadway features would trigger the threshold indicating inconsistencies between the map and cause the vehicle to decelerate. One of ordinary skill in the art would have been motivated to make this modification because “actual intersections, lane lines, signs and other artificial signs are more referential” and are more important whereas “trees and structured tall buildings are less referential” and are less critical in controlling a vehicle in an environment (see [0101]).
Regarding claim 14, the combination of Duan, Lin, and Caveney teaches the elements above and Duan further discloses:
the type of difference includes differences between the perceived features of the roadway (determine whether lane lines in the estimated and stored map are different, see at least [0030]), and the modifying of operation of the autonomous vehicle when the type of difference is based on perceived features of the roadway (confidence scores can be based at least in part on a weight associated with a map element and in some examples, if the confidence score meets or exceeds a threshold, the monitoring component can determine an inconsistency between the stored map and estimated map, see at least [0091]; map elements can include lane lines, see at least [0089]; if stored map is not consistent with estimated map, autonomous vehicle can decelerate or stop, see at least [0008])
Duan does not explicitly disclose:
the type of difference includes differences between the perceived objects and the perceived features of the roadway
the modifying of operation of the autonomous vehicle is more conservative when the type of difference is based on perceived features of the roadway
However, Zeng teaches:
the type of difference includes differences between the perceived objects (tree and structural high buildings) and the perceived features of the roadway (actual intersection and lane line)
the modifying of operation of the autonomous vehicle is more conservative when the type of difference is based on perceived features of the roadway (corresponding weight for lane lines and intersections are higher than for trees and high buildings, see at least [0101])
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the confidence level determined by weights of elements as disclosed by Duan, the object confidence from a fleet taught by Lin, and the guidance of a vehicle using additional data taught by Caveney by adding the higher weights for lane lines than for trees and buildings as taught by Zeng with a reasonable expectation of success. If the weights of Zeng are combined with the confidence scores of Duan, the confidence score of roadway features would trigger the threshold indicating inconsistencies between the map and cause the vehicle to decelerate. One of ordinary skill in the art would have been motivated to make this modification because “actual intersections, lane lines, signs and other artificial signs are more referential” and are more important whereas “trees and structured tall buildings are less referential” and are less critical in controlling a vehicle in an environment (see [0101]).
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Duan in view of Lin and Caveney as applied to claims 1 and 10 above and further in view of Arnicar et al. (U.S. Patent No. 12,235,112 B1; hereinafter Arnicar).
Regarding claim 7, the combination of Duan, Lin, and Caveney teaches the elements above but does not teach:
further including collecting more perception data as the autonomous vehicle is stopped
However, Arnicar teaches:
further including collecting more perception data as the autonomous vehicle is stopped (remedial action when an error is present can include to cause a vehicle to perform a safe stopping maneuver and initiating a remapping of the environment by using different sensors and perception approaches, see at least col. 11 lines 21-45)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the map consistency checker disclosed by Duan, the object confidence from a fleet taught by Lin, and the guidance of a vehicle using additional data taught by Caveney by adding the remapping taught by Arnicar with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification so that “reliability of vehicle systems and stored maps can be improved” (see col. 4 lines 34-35).
Regarding claim 16, the combination of Duan, Lin, and Caveney teaches the elements above but does not teach:
further including collecting more perception data as the autonomous vehicle is stopped
However, Arnicar teaches:
further including collecting more perception data as the autonomous vehicle is stopped (remedial action when an error is present can include to cause a vehicle to perform a safe stopping maneuver and initiating a remapping of the environment by using different sensors and perception approaches, see at least col. 11 lines 21-45)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the map consistency checker disclosed by Duan, the object confidence from a fleet taught by Lin, and the guidance of a vehicle using additional data taught by Caveney by adding the remapping taught by Arnicar with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification so that “reliability of vehicle systems and stored maps can be improved” (see col. 4 lines 34-35).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yu et al. (U.S. Patent Application Publication No. 2023/0038372 A1) teaches vehicle communications in a platoon of sharing sensor data.
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/H.L./Examiner, Art Unit 3662
/DALE W HILGENDORF/Primary Examiner, Art Unit 3662