DETAILED ACTION
Response to Amendment
This office action regarding application number 18/234,668, filed August 16, 2023, is in response to the applicants arguments and amendments filed February 4, 2026. Claims 1, 3, 5-11, 13, 15-16, 18 and 20-23 have been amended. Claims 1, 3-11, 13-16, and 18-23 are currently pending and are addressed below.
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 .
Response to Arguments
The applicants arguments and amendments to the application have NOT overcome some of the objections and rejections previously set forth in the Non-Final action mailed November 5, 2025. Applicants amendments to claims 1, 11, and 16 have NOT been deemed sufficient to overcome the previous 35 USC 103 rejections through the inclusion of “determining, by the computing system, that sensors of the plurality of sensors of a sensor type on vehicles of the plurality of vehicles have moved are inoperative … determining, by the computing system, that the number of vehicles of the plurality of vehicles having the sensors of the plurality of sensors of the sensor type is greater than a selected portion of the plurality of vehicles, in response to determination that the number of the vehicles of the plurality of vehicles having the sensors of the plurality of sensors of the sensor type is greater than the selected portion causing, by the computing system, the sensors of the sensor type on the vehicles to be investigated or replaced,” the examiner finds that these additional limitations are taught by the previously cited combination of Gonzalez, Syrafril, and Ricci, therefore the rejections are maintained with changes to reflect amendments. Additionally the applicants arguments have been fully considered but are not fully persuasive for the reasons seen below.
On pages 8-10 the applicant argues “Here, Gonzalez relates to image sensors to collect image data in various geographic regions proximate to an autonomous vehicle. Importantly, the focus of Gonzalez is on a singular autonomous vehicle. The Office Action cited specific portions of Gonzalez. For example, in relation to claim features relating to sensors determined to be moved or inoperative, the following excerpt includes cited portions of Gonzalez … Gonzalez, paras. 39-40 (emphasis added). Here, Gonzalez discusses reporting of failures in sensor array(s) and determination about whether the failed sensor array(s) are essential to continued operation of a vehicle. Importantly, Gonzalez is tethered to failed sensor array(s) in relation to possible continued operation of a sole vehicle. Thus, Gonzalez fails to disclose the instant claim features. As discussed, Gonzalez is narrowly focused on sensor array failure associated with a singular vehicle. Nothing in Gonzalez discloses determination of sensor anomalies in relation to many vehicles, not merely one vehicle. Further, nothing in Gonzalez discloses determination that the number of the vehicles having affected sensors of a particular sensor type is greater than a selected portion (e.g., ratio, percentage) of a plurality of vehicles (e.g., vehicle fleet). Thus, Gonzalez does not disclose determining, by the computing system, that sensors of the plurality of sensors of a sensor type on vehicles of the plurality of vehicles have moved or are inoperative based on a calibration parameter value of the plurality of calibration parameter values being outside a predetermined acceptable range; determining, by the computing system, that the number of the vehicles of the plurality of vehicles having the sensors of the plurality of sensors of the sensor type is greater than a selected portion of the plurality of vehicles, as claimed.”, the examiner respectfully disagrees.
MPEP 2142-2144 discusses the requirements for a case of obviousness using 35 USC 103 and provides examples of such cases. MPEP 2111 discusses Broadest Reasonable Interpretation and the interpretation of claims.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
As discussed in the rejections below while Gonzalez is primarily directly to individual vehicles however Gonzalez does not exclude the monitoring of a plurality of vehicles (Paragraph [0020], “In some examples vehicle management system(s) 110 may comprise one or more processor-based devices, e.g., server(s) comprising computer-readable memory which stores software updates for one or more devices communicatively coupled to the one or more autonomous vehicles.”).
Additionally Gonzalez is not relied upon for the teachings that are being challenged here in the arguments, these limitations are taught by the combination of Syrafril and Ricci.
Syrafril teaches a vehicle information management system which includes monitoring a plurality of vehicles and associating groups of vehicles with functional configurations that correspond to each other (Abstract, “a vehicle information management system 1 for choosing a second vehicle 3B corresponding to a first vehicle 3A by selecting a vehicle having a functional configuration corresponding to that of the first vehicle as a candidate of the second vehicle”).
Ricci teaches a fleetwide vehicle telematics system including determining that sensors of a plurality of sensor of a sensor type on vehicles of the plurality of vehicles have moved or are inoperative (Paragraph [0177, “This example may be especially useful in determining whether a component recall should be issued based on the status check responses returned from a certain number of vehicle,” here the system can determine, via the computer system, that the same sensors/components on a plurality of vehicles are inoperative via a status check such as the selected portion/threshold number of vehicles as discussed above in the Syrafril reference); in response to determination that the number of the vehicles of the plurality of vehicles having the sensors of the plurality of sensors of the sensor type is greater than the selected portion causing, by the computing system, the sensors of the sensor type on the vehicles to be investigated or replaced (Paragraph [0177], “This example may be especially useful in determining whether a component recall should be issued based on the status check responses returned from a certain number of vehicles.”).
Therefore while Gonzalez is explicitly directed towards a fleet of vehicles this reference is not relied upon to teach these limitations, and these limitations are taught by the combination of Gonzalez, Syrafril and Ricci references. Therefore the rejections under 35 USC 103 are maintained.
On pages 10-12 the applicant argues “Without conceding the correctness of the rejections, the claims have been amended to advance prosecution. As amended, claim 1 recites, inter alia, "in response to determination that the number of the vehicles of the plurality of vehicles having the sensors of the plurality of sensors of the sensor type is greater than the selected portion, causing, by the computing system, the sensors of the sensor type on the vehicles to be investigated or replaced." Claims 11 and 16 recite similar claim features. The cited references fail to disclose at least these instant claim features. In relation to the instant claim features, the Office Action has newly cited Ricci. Office Action, 8. In one relevant excerpt, Ricci provides: … Ricci, para. 177 (emphasis added). Here, Ricci discusses a "diagnostic status check" but otherwise provides no details. While it does not provide a nexus or relationship, if any, with the "diagnostic status check", Ricci separately references "diagnostic signals and information", such as vehicle system warnings, sensor data, vehicle component status, service information, component health, maintenance alerts, recall notifications, and predictive analysis. Ricci, para. 176. Importantly, none of these or other references in Ricci relate to sensors of a particular sensor type. Nor does Ricci relate to a number of vehicles that is greater than a selected portion of vehicles of the plurality of vehicles constituting a vehicle fleet. That is, Ricci does not contemplate whether vehicles having sensors of the particular sensor type constitute a selected ratio or percentage of a vehicle fleet. In addition, and perhaps most fundamentally, Ricci does not determine a component recall based on a plurality of self-calibration routines. Calibration, much less self-calibration, is not mentioned once in Ricci. Thus, Ricci does not disclose in response to determination that the number of the vehicles of the plurality of vehicles having the sensors of the plurality of sensors of the sensor type is greater than the selected portion, causing, by the computing system, the sensors of the sensor type on the threshold number of vehicles to be investigated or replaced, as claimed.”, the examiner respectfully disagrees.
MPEP 2142-2144 discusses the requirements for a case of obviousness using 35 USC 103 and provides examples of such cases. MPEP 2111 discusses Broadest Reasonable Interpretation and the interpretation of claims.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
As discussed in the rejections below Ricci teaches fleetwide vehicle telematics systems and methods for managing fleetwide vehicle state data including determining, by the computing system, that sensors of the plurality of sensors of a sensor type on vehicles of the plurality of vehicles have moved are inoperative (Paragraph [0177, “This example may be especially useful in determining whether a component recall should be issued based on the status check responses returned from a certain number of vehicle,” here the system can determine, via the computer system, that the same sensors/components on a plurality of vehicles are inoperative via a status check such as the selected portion/threshold number of vehicles as discussed above in the Syrafril reference); here Ricci is monitoring a plurality of vehicles in a fleet to determine a recall if a component, such a sensor, is faulty based on status checks on a certain number of vehicles. The examiner is interpreting a component as being a sensor of a certain type. But even if Ricci does not teach this limitation, the limitation of determining that a number of vehicles having a sensor of the sensor type is greater than a selected portion/threshold is taught by the Syrafril reference (Paragraph [0113], “the cooperating vehicle choosing unit 12 extracts a vehicle having a similar configuration as that of the failed vehicle 3A as a candidate of the cooperating vehicle 3B (S502). In addition, the cooperating vehicle choosing unit 12 extracts a vehicle having a similar external environment (vehicle having similar use environment history information) as a candidate of the cooperating vehicle 3B from the candidates extracted in step S502 (S503),” here the system is choosing cooperating vehicle with a similar configuration/sensor type) (Paragraph [0114], “The cooperating vehicle choosing unit 12 checks whether or not the number of the chosen cooperating vehicles 3B is equal to or larger than the threshold set in step S501 (S504). If the number of the cooperating vehicles 3B is equal to or larger than the threshold (S504: YES), the process advances to step S505. If the number of the cooperating vehicles 3B is smaller than the threshold (S504: NO), the process advances to step S506,” here the system is determining if a number of cooperating vehicles with a similar configuration is greater than a selected portion/number has been determined).
Therefore the combination of Gonzalez, Syrafril and Ricci teaches in response to determination that the number of the vehicles of the plurality of vehicles having the sensors of the plurality of sensors of the sensor type is greater than the selected portion, causing, by the computing system, the sensors of the sensor type on the threshold number of vehicles to be investigated or replaced, and the rejections under 35 USC 103 are maintained.
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 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.
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.
Claim 1, 3, 6-7, 11, 13, 16, 18 and 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez (US-20190051015) in view of Syafril (US-20200114930) and further in view of Ricci (US-20160086391).
Regarding claim 1, Gonzalez teaches a computer implemented method comprising (Paragraph [0095], "Some of the methods described herein may be embodied as logic instructions on a computer-readable medium. When executed on a processor, the logic instructions cause a processor to be programmed as a special-purpose machine that implements the described methods.”)
monitoring, by a computing system, a plurality of calibration parameter values (Paragraph [0029], "Based on the input from self-diagnostic module, an extrinsic (i.e., 6D pose) and intrinsic (i.e., focal length, distortion, etc.) internal parameters a non-linear optimization is implemented to assert the current real state of the sensor(s). In some examples the method's plausibility relays on the narrow range for each of the involved parameters of the calibration," here the system is using extrinsic and intrinsic calibration parameters to determine/monitor the real state of the sensor) (Figure 3 shows the system collecting data from sensors and performing calibration, and then determining if sensors are in a normal state)
associated with a plurality of sensors on a plurality of vehicles (Figure 2, shows a plurality of devices including Cameras 242 and sensors 246) (Paragraph [0037], "The cloud based vehicle management system may be communicatively coupled to one or more autonomous vehicles 420.")
based on a plurality of self-calibration routines performed by the plurality of vehicles (Paragraph [0028], “Referring to FIGS. 3A-3B, at operation 310 data is collected and the sensor(s) are calibrated. For example, raw signals from sensors 330 including one or more position sensors 332, mileage sensors 334, and environment sensors such as an image acquisition device (e.g., a camera), a radar, lidar, or the like.”)
determining, by the computing system, that sensors of the plurality of sensors of a sensor type on vehicles have moved are inoperative (Paragraph [0039], "Referring to FIG. 4B, at operation 430 a failure in the sensor(s) is detected. In some examples the diagnostics module 424 may monitor the sensor arrays to detect one or more failure conditions with the sensor array(s) 422.")
based on a calibration parameter value of the plurality of calibration parameter values being outside a predetermined acceptable range (Paragraph [0029], "intrinsic (i.e., focal length, distortion, etc.) internal parameters a non-linear optimization is implemented to assert the current real state of the sensor(s). In some examples the method's plausibility relays on the narrow range for each of the involved parameters of the calibration," here the system is using a narrow range for each of the involved parameters and determining if the value is inside or outside that range when determining if the sensor is in a normal state)
and causing, by the computing system, a remedial action to be performed based on the determining that the sensors of the plurality of sensors have moved or are inoperative (Paragraph [0031-0032], "By contrast, if at operation 320 a sensor failure condition exists then control passes to operation 322 a synthetic signal generator 354 generates virtual sensor data using the output from a paired sensor and the transform(s) determined in operation 318. ... By contrast, if at operation 326 the data generated in operation 322 is not within a predetermined confidence level then the virtual sensor may be deemed insufficiently accurate to allow the vehicle to continue operations using the virtual sensor and post-incident vehicle management operations may be implemented," here when a sensor has been determined to be in a failure state/inoperative the system may generate virtual sensor data or other vehicle management operations may be implemented).
However Gonzalez does not explicitly teach determining, by the computing system, that the number of vehicles of the plurality of vehicles having the sensors of the plurality of sensors of the sensor type is greater than a selected portion of the plurality of vehicles.
Syafril teaches a vehicle information management system for choosing a second vehicle corresponding to a first vehicle including
determining, by the computing system, that the number of vehicles of the plurality of vehicles having the sensors of the plurality of sensors of the sensor type is greater than a selected portion of the plurality of vehicles (Paragraph [0113], “the cooperating vehicle choosing unit 12 extracts a vehicle having a similar configuration as that of the failed vehicle 3A as a candidate of the cooperating vehicle 3B (S502). In addition, the cooperating vehicle choosing unit 12 extracts a vehicle having a similar external environment (vehicle having similar use environment history information) as a candidate of the cooperating vehicle 3B from the candidates extracted in step S502 (S503),” here the system is choosing cooperating vehicle with a similar configuration/sensor type) (Paragraph [0114], “The cooperating vehicle choosing unit 12 checks whether or not the number of the chosen cooperating vehicles 3B is equal to or larger than the threshold set in step S501 (S504). If the number of the cooperating vehicles 3B is equal to or larger than the threshold (S504: YES), the process advances to step S505. If the number of the cooperating vehicles 3B is smaller than the threshold (S504: NO), the process advances to step S506,” here the system is determining if a number of cooperating vehicles with a similar configuration is greater than a selected portion/number has been determined).
Gonzalez and Syafril are analogous art as they are both generally related to systems and methods for monitoring status conditions of a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include determining, by the computing system, that the number of vehicles of the plurality of vehicles having the sensors of the plurality of sensors of the sensor type is greater than a selected portion of the plurality of vehicles of Syafril in the system for managing a sensor failure of Gonzalez with a reasonable expectation of success in order to improve the ability of the system to determine a root cause for a failure shorten the time necessary for specifying a failure cause (Paragraph [0139], “In addition, according to the present embodiment, when the driver feels a failure, the failure can be reported to the vehicle information management system 1. Therefore, compared to a case where the failure is reported to the vehicle information management system 1 via the vendor system 20, it is possible to shorten the time necessary for specifying the failure cause and improve usability.”).
However the combination does not explicitly teach determining, by the computing system, that sensors of the plurality of sensors of a sensor type on vehicles of the plurality of vehicles have moved are inoperative, in response to determination that the number of the vehicles of the plurality of vehicles having the sensors of the plurality of sensors of the sensor type is greater than the selected portion causing, by the computing system, the sensors of the sensor type on the vehicles to be investigated or replaced.
Ricci teaches fleetwide vehicle telematics systems and methods for managing fleetwide vehicle state data including
determining, by the computing system, that sensors of the plurality of sensors of a sensor type on vehicles of the plurality of vehicles have moved are inoperative (Paragraph [0177, “This example may be especially useful in determining whether a component recall should be issued based on the status check responses returned from a certain number of vehicle,” here the system can determine, via the computer system, that the same sensors/components on a plurality of vehicles are inoperative via a status check such as the selected portion/threshold number of vehicles as discussed above in the Syrafril reference)
in response to determination that the number of the vehicles of the plurality of vehicles having the sensors of the plurality of sensors of the sensor type is greater than the selected portion causing, by the computing system, the sensors of the sensor type on the vehicles to be investigated or replaced (Paragraph [0177], “This example may be especially useful in determining whether a component recall should be issued based on the status check responses returned from a certain number of vehicles.”) (Paragraph [0384], “One or more warnings may be stored in portion 1286. The warnings data 1286 may include warning generated by the vehicle 104, systems of the vehicle 104, manufacturer of the vehicle, federal agency, third party, and/or a user associated with the vehicle. For example, several components of the vehicle may provide health status information (e.g., stored in portion 1278) that, when considered together, may suggest that the vehicle 104 has suffered some type of damage and/or failure. Recognition of this damage and/or failure may be stored in the warnings data portion 1286. The data in portion 1286 may be communicated to one or more parties (e.g., a manufacturer, maintenance facility, user, etc.). In another example, a manufacturer may issue a recall notification for a specific vehicle 104, system of a vehicle 104, and/or a component of a vehicle 104. It is anticipated that the recall notification may be stored in the warning data field 1286. Continuing this example, the recall notification may then be communicated to the user of the vehicle 104 notifying the user of the recall issued by the manufacturer,” here the system is determining that a sensor needs to be investigated or replaced/recalled based on a determination that a status check on a certain number of vehicles/threshold, the system will issue a recall notification for a specific component/type of sensor based on the determination that a certain number of vehicles have experienced the same warning).
Gonzalez, Syafril, and Ricci are analogous art as they are both generally related to systems and methods for monitoring status conditions of a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include determining, by the computing system, that sensors of the plurality of sensors of a sensor type on vehicles of the plurality of vehicles have moved are inoperative, in response to determination that the number of the vehicles of the plurality of vehicles having the sensors of the plurality of sensors of the sensor type is greater than the selected portion causing, by the computing system, the sensors of the sensor type on the vehicles to be investigated or replaced of Ricci in the system for managing a sensor failure of Gonzalez and Syafril with a reasonable expectation of success in order to improve the improve the safety of the system by monitoring vehicles for similar failures in order to determine defective or problematic parts (Paragraph [0384], “For example, several components of the vehicle may provide health status information (e.g., stored in portion 1278) that, when considered together, may suggest that the vehicle 104 has suffered some type of damage and/or failure. Recognition of this damage and/or failure may be stored in the warnings data portion 1286. The data in portion 1286 may be communicated to one or more parties (e.g., a manufacturer, maintenance facility, user, etc.). In another example, a manufacturer may issue a recall notification for a specific vehicle 104, system of a vehicle 104, and/or a component of a vehicle 104. It is anticipated that the recall notification may be stored in the warning data field 1286. Continuing this example, the recall notification may then be communicated to the user of the vehicle 104 notifying the user of the recall issued by the manufacturer.”).
Regarding claim 3, the combination of Gonzalez, Syrafil and Ricci teaches the method as discussed above in claim 1, Gonzalez further teaches causing, by the computing system, environmental information to be collected (Paragraph [0044], “At operation 452 the cloud based vehicle management system 410 receives and integrates the sensor failure data and the environmental data. For example, the sensor failure data and the environmental data may be integrated into the database of conditions and compensations 414,” here in response to the sensor failure the system is gathering environmental condition data to be integrated into a database).
However Gonzalez does not explicitly teach causing, by the computing system, at least one of: driving patterns, driver behavior, and environmental conditions associated with the threshold number of vehicles to be analyzed in response to the threshold number of vehicles having sensors that have move or are inoperative.
Syafril teaches a vehicle information management system for choosing a second vehicle corresponding to a first vehicle including
causing, by the computing system, at least one of: driving patterns, driver behavior, and environmental conditions associated with the threshold number of vehicles to be analyzed (Paragraph [0022], “it is possible to collect information (second information) that is necessary for investigating a failure cause of the vehicle and is necessary for investigating the failure cause from another vehicle (second vehicle) having a functionality or a use environment similar to that of the failed vehicle (first vehicle). Therefore, according to the present embodiment, it is possible to effectively collect information that can be used to investigate the failure cause, effectively analyze the failure cause, and reduce the time necessary for the analysis,” here the system is collecting information for investigating a failure cause relating to an environment) (Paragraph [0076], “the cooperating vehicle choosing unit 12 investigates whether or not the failure has occurred due to an external factor of the vehicle 3A on the basis of the vehicle use environment history information collected from the failed vehicle 3A (S204).”) (Paragraph [0078], “In step S206, the cooperating vehicle choosing unit 12 collects an environmental condition that may affect failure occurrence on the basis of the use environment history information of the vehicle 3A. For example, the environmental condition may include a condition that “there are many uphill roads” in the case of a road situation, a condition of “thunderstorm” in the case of weather, a condition of “traffic jam of 30 minutes or longer” in the case of a congestion situation.”).
Gonzalez and Syafril are analogous art as they are both generally related to systems and methods for monitoring status conditions of a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include causing, by the computing system, at least one of: driving patterns, driver behavior, and environmental conditions associated with the threshold number of vehicles to be analyzed in response to the threshold number of vehicles having sensors that have move or are inoperative of Syafril in the system for managing a sensor failure of Gonzalez with a reasonable expectation of success in order to improve the ability of the system to determine a root cause for a failure shorten the time necessary for specifying a failure cause (Paragraph [0139], “In addition, according to the present embodiment, when the driver feels a failure, the failure can be reported to the vehicle information management system 1. Therefore, compared to a case where the failure is reported to the vehicle information management system 1 via the vendor system 20, it is possible to shorten the time necessary for specifying the failure cause and improve usability.”).
Regarding claim 6, the combination of Gonzalez, Syrafil and Ricci teaches the method as discussed above in claim 1, Gonzalez further teaches wherein the remedial action includes at least one of: causing sensor data collected by the sensors that have moved or are inoperative to be ignored or modified (See Figure 3A steps 322, 326 which show the system, in response to a sensor failure, generating virtual sensor data to operate the vehicle using the virtual sensor data instead of the failed sensor) (Paragraph [0031-0032], “By contrast, if at operation 320 a sensor failure condition exists then control passes to operation 322 a synthetic signal generator 354 generates virtual sensor data using the output from a paired sensor and the transform(s) determined in operation 318. By way of example, in the event that a first sensor is paired with a second sensor and the second sensor fails, a virtual second sensor may be generated by applying the transform between the first sensor data and the second sensor data to the first sensor data. … the virtual sensor may be deemed sufficiently accurate to allow the vehicle to continue operations using the virtual sensor (operation 326).”)
and causing an alert to be sent by at least one vehicle of the of vehicles (Paragraph [0039], “Referring to FIG. 4B, at operation 430 a failure in the sensor(s) is detected. In some examples the diagnostics module 424 may monitor the sensor arrays to detect one or more failure conditions with the sensor array(s) 422. At operation 432 the sensor failure(s) are reported. In some examples the diagnostics module 424 may report failure(s) in the sensor array(s) to the ADAS subsystem 426 and/or to the cloud based vehicle management system 410 via the communication interface 428,” here the system is causing the vehicle to transmit an alert/reporting a failure to the vehicle subsystem or a cloud based management system).
Regarding claim 7, the combination of Gonzalez, Syrafil and Ricci teaches the method as discussed above in claim 1, Gonzalez further teaches wherein a sensor is a camera (Paragraph [0027], “The techniques will be described in the context of sensors that are image collection devices (e.g., cameras), but such techniques apply equally to other sensors, e.g., radar, lidar, sonar, etc.”)
and the calibration parameter value is associated with at least one of: a focal length, an optical center, a scale factor, a principal point, a skew, a distortion, a rolling shutter time, and a geometric distortion associated with the camera (Paragraph [0029], “intrinsic (i.e., focal length, distortion, etc.) internal parameters”).
Regarding claim 11, Gonzalez teaches a system comprising: at least one processor, and a memory storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising (Paragraph [0017], “In yet another aspect an electronic device comprises a processor and a computer readable memory communicatively coupled to the processor and comprising logic instructions which, when executed by the processor, configure the processor to”)
monitoring a plurality of calibration parameter values (Paragraph [0029], "Based on the input from self-diagnostic module, an extrinsic (i.e., 6D pose) and intrinsic (i.e., focal length, distortion, etc.) internal parameters a non-linear optimization is implemented to assert the current real state of the sensor(s). In some examples the method's plausibility relays on the narrow range for each of the involved parameters of the calibration," here the system is using extrinsic and intrinsic calibration parameters to determine/monitor the real state of the sensor) (Figure 3 shows the system collecting data from sensors and performing calibration, and then determining if sensors are in a normal state)
associated with a plurality of sensors on a plurality of vehicles (Figure 2, shows a plurality of devices including Cameras 242 and sensors 246) (Paragraph [0037], "The cloud based vehicle management system may be communicatively coupled to one or more autonomous vehicles 420.")
based on a plurality of self-calibration routines performed on the plurality of vehicles (Paragraph [0028], “Referring to FIGS. 3A-3B, at operation 310 data is collected and the sensor(s) are calibrated. For example, raw signals from sensors 330 including one or more position sensors 332, mileage sensors 334, and environment sensors such as an image acquisition device (e.g., a camera), a radar, lidar, or the like.”)
determining that sensors of the plurality of sensors of a sensor type on vehicles have moved or are inoperative (Paragraph [0039], "Referring to FIG. 4B, at operation 430 a failure in the sensor(s) is detected. In some examples the diagnostics module 424 may monitor the sensor arrays to detect one or more failure conditions with the sensor array(s) 422.")
based on a calibration parameter value of the plurality of calibration parameter values being outside a predetermined acceptable range (Paragraph [0029], "intrinsic (i.e., focal length, distortion, etc.) internal parameters a non-linear optimization is implemented to assert the current real state of the sensor(s). In some examples the method's plausibility relays on the narrow range for each of the involved parameters of the calibration," here the system is using a narrow range for each of the involved parameters and determining if the value is inside or outside that range when determining if the sensor is in a normal state)
and causing a remedial action to be performed based on the determining that the sensors of the plurality of sensors have moved or are inoperative (Paragraph [0031-0032], "By contrast, if at operation 320 a sensor failure condition exists then control passes to operation 322 a synthetic signal generator 354 generates virtual sensor data using the output from a paired sensor and the transform(s) determined in operation 318. ... By contrast, if at operation 326 the data generated in operation 322 is not within a predetermined confidence level then the virtual sensor may be deemed insufficiently accurate to allow the vehicle to continue operations using the virtual sensor and post-incident vehicle management operations may be implemented," here when a sensor has been determined to be in a failure state/inoperative the system may generate virtual sensor data or other vehicle management operations may be implemented).
However Gonzalez does not explicitly teach determining that the number of the vehicles of the plurality of vehicles having the sensor of the plurality of sensors of the sensor type is greater than a selected portion of the plurality of vehicles.
Syafril teaches a vehicle information management system for choosing a second vehicle corresponding to a first vehicle including
determining that the number of the vehicles of the plurality of vehicles having the sensor of the plurality of sensors of the sensor type is greater than a selected portion of the plurality of vehicles (Paragraph [0113], “the cooperating vehicle choosing unit 12 extracts a vehicle having a similar configuration as that of the failed vehicle 3A as a candidate of the cooperating vehicle 3B (S502). In addition, the cooperating vehicle choosing unit 12 extracts a vehicle having a similar external environment (vehicle having similar use environment history information) as a candidate of the cooperating vehicle 3B from the candidates extracted in step S502 (S503),” here the system is choosing cooperating vehicle with a similar configuration/sensor type) (Paragraph [0114], “The cooperating vehicle choosing unit 12 checks whether or not the number of the chosen cooperating vehicles 3B is equal to or larger than the threshold set in step S501 (S504). If the number of the cooperating vehicles 3B is equal to or larger than the threshold (S504: YES), the process advances to step S505. If the number of the cooperating vehicles 3B is smaller than the threshold (S504: NO), the process advances to step S506,” here the system is determining if a number of cooperating vehicles with a similar configuration is greater than a selected portion/number has been determined).
Gonzalez and Syafril are analogous art as they are both generally related to systems and methods for monitoring status conditions of a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include determining that the number of the vehicles of the plurality of vehicles having the sensor of the plurality of sensors of the sensor type is greater than a selected portion of the plurality of vehicles of Syafril in the system for managing a sensor failure of Gonzalez with a reasonable expectation of success in order to improve the ability of the system to determine a root cause for a failure shorten the time necessary for specifying a failure cause (Paragraph [0139], “In addition, according to the present embodiment, when the driver feels a failure, the failure can be reported to the vehicle information management system 1. Therefore, compared to a case where the failure is reported to the vehicle information management system 1 via the vendor system 20, it is possible to shorten the time necessary for specifying the failure cause and improve usability.”).
However the combination does not explicitly teach determining that sensors of the plurality of sensors of a sensor type on vehicles of the plurality of vehicles have moved are inoperative, and in response to determination that the number of the vehicles of the plurality of vehicles having the sensors of the plurality of sensors of the sensor type is greater than the selected portion, causing the sensors of the sensor type on the vehicles to be investigated or replaced.
Ricci teaches fleetwide vehicle telematics systems and methods for managing fleetwide vehicle state data including
determining that sensors of the plurality of sensors of a sensor type on vehicles of the plurality of vehicles have moved are inoperative (Paragraph [0177, “This example may be especially useful in determining whether a component recall should be issued based on the status check responses returned from a certain number of vehicle,” here the system can determine, via the computer system, that the same sensors/components on a plurality of vehicles are inoperative via a status check such as the selected portion/threshold number of vehicles as discussed above in the Syrafril reference)
in response to determination that the number of the vehicles of the plurality of vehicles having the sensors of the plurality of sensors of the sensor type is greater than the selected portion, causing the sensors of the sensor type on the vehicles to be investigated or replaced (Paragraph [0177], “This example may be especially useful in determining whether a component recall should be issued based on the status check responses returned from a certain number of vehicles.”) (Paragraph [0384], “One or more warnings may be stored in portion 1286. The warnings data 1286 may include warning generated by the vehicle 104, systems of the vehicle 104, manufacturer of the vehicle, federal agency, third party, and/or a user associated with the vehicle. For example, several components of the vehicle may provide health status information (e.g., stored in portion 1278) that, when considered together, may suggest that the vehicle 104 has suffered some type of damage and/or failure. Recognition of this damage and/or failure may be stored in the warnings data portion 1286. The data in portion 1286 may be communicated to one or more parties (e.g., a manufacturer, maintenance facility, user, etc.). In another example, a manufacturer may issue a recall notification for a specific vehicle 104, system of a vehicle 104, and/or a component of a vehicle 104. It is anticipated that the recall notification may be stored in the warning data field 1286. Continuing this example, the recall notification may then be communicated to the user of the vehicle 104 notifying the user of the recall issued by the manufacturer,” here the system is determining that a sensor needs to be investigated or replaced/recalled based on a determination that a status check on a certain number of vehicles/threshold, the system will issue a recall notification for a specific component/type of sensor based on the determination that a certain number of vehicles have experienced the same warning).
Gonzalez, Syafril, and Ricci are analogous art as they are both generally related to systems and methods for monitoring status conditions of a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include in response to determining that sensors of the plurality of sensors of a sensor type on vehicles of the plurality of vehicles have moved are inoperative, and in response to determination that the number of the vehicles of the plurality of vehicles having the sensors of the plurality of sensors of the sensor type is greater than the selected portion, causing the sensors of the sensor type on the vehicles to be investigated or replaced of Ricci in the system for managing a sensor failure of Gonzalez and Syafril with a reasonable expectation of success in order to improve the improve the safety of the system by monitoring vehicles for similar failures in order to determine defective or problematic parts (Paragraph [0384], “For example, several components of the vehicle may provide health status information (e.g., stored in portion 1278) that, when considered together, may suggest that the vehicle 104 has suffered some type of damage and/or failure. Recognition of this damage and/or failure may be stored in the warnings data portion 1286. The data in portion 1286 may be communicated to one or more parties (e.g., a manufacturer, maintenance facility, user, etc.). In another example, a manufacturer may issue a recall notification for a specific vehicle 104, system of a vehicle 104, and/or a component of a vehicle 104. It is anticipated that the recall notification may be stored in the warning data field 1286. Continuing this example, the recall notification may then be communicated to the user of the vehicle 104 notifying the user of the recall issued by the manufacturer.”).
Regarding claim 13, claim 13 is similar in scope to claim 3 and therefore is rejected under similar rationale.
Regarding claim 16, Gonzalez teaches a non-transitory computer-readable storage medium including instructions that, when executed by at least on processor of a computing system, cause the computing system to perform operations comprising (Paragraph [0017], “In yet another aspect an electronic device comprises a processor and a computer readable memory communicatively coupled to the processor and comprising logic instructions which, when executed by the processor, configure the processor to”)
monitoring a plurality of calibration parameter values (Paragraph [0029], "Based on the input from self-diagnostic module, an extrinsic (i.e., 6D pose) and intrinsic (i.e., focal length, distortion, etc.) internal parameters a non-linear optimization is implemented to assert the current real state of the sensor(s). In some examples the method's plausibility relays on the narrow range for each of the involved parameters of the calibration," here the system is using extrinsic and intrinsic calibration parameters to determine/monitor the real state of the sensor) (Figure 3 shows the system collecting data from sensors and performing calibration, and then determining if sensors are in a normal state)
associated with a plurality of sensors on a plurality of vehicles (Figure 2, shows a plurality of devices including Cameras 242 and sensors 246) (Paragraph [0037], "The cloud based vehicle management system may be communicatively coupled to one or more autonomous vehicles 420.")
based on a plurality of self-calibration routines performed on the plurality of vehicles (Paragraph [0028], “Referring to FIGS. 3A-3B, at operation 310 data is collected and the sensor(s) are calibrated. For example, raw signals from sensors 330 including one or more position sensors 332, mileage sensors 334, and environment sensors such as an image acquisition device (e.g., a camera), a radar, lidar, or the like.”)
determining that sensors of the plurality of sensors of a sensor type on vehicles have moved or are inoperative (Paragraph [0039], "Referring to FIG. 4B, at operation 430 a failure in the sensor(s) is detected. In some examples the diagnostics module 424 may monitor the sensor arrays to detect one or more failure conditions with the sensor array(s) 422.")
based on a calibration parameter value of the plurality of calibration parameter values being outside a predetermined acceptable range (Paragraph [0029], "intrinsic (i.e., focal length, distortion, etc.) internal parameters a non-linear optimization is implemented to assert the current real state of the sensor(s). In some examples the method's plausibility relays on the narrow range for each of the involved parameters of the calibration," here the system is using a narrow range for each of the involved parameters and determining if the value is inside or outside that range when determining if the sensor is in a normal state)
and causing a remedial action to be performed based on the determining that the sensors of the plurality of sensors have moved or are inoperative (Paragraph [0031-0032], "By contrast, if at operation 320 a sensor failure condition exists then control passes to operation 322 a synthetic signal generator 354 generates virtual sensor data using the output from a paired sensor and the transform(s) determined in operation 318. ... By contrast, if at operation 326 the data generated in operation 322 is not within a predetermined confidence level then the virtual sensor may be deemed insufficiently accurate to allow the vehicle to continue operations using the virtual sensor and post-incident vehicle management operations may be implemented," here when a sensor has been determined to be in a failure state/inoperative the system may generate virtual sensor data or other vehicle management operations may be implemented).
However Gonzalez does not explicitly teach determining that the number of the vehicles of the plurality of vehicles having the sensor of the plurality of sensors of the sensor type is greater than a selected portion of the plurality of vehicles.
Syafril teaches a vehicle information management system for choosing a second vehicle corresponding to a first vehicle including
determining that the number of the vehicles of the plurality of vehicles having the sensor of the plurality of sensors of the sensor type is greater than a selected portion of the plurality of vehicles (Paragraph [0113], “the cooperating vehicle choosing unit 12 extracts a vehicle having a similar configuration as that of the failed vehicle 3A as a candidate of the cooperating vehicle 3B (S502). In addition, the cooperating vehicle choosing unit 12 extracts a vehicle having a similar external environment (vehicle having similar use environment history information) as a candidate of the cooperating vehicle 3B from the candidates extracted in step S502 (S503),” here the system is choosing cooperating vehicle with a similar configuration/sensor type) (Paragraph [0114], “The cooperating vehicle choosing unit 12 checks whether or not the number of the chosen cooperating vehicles 3B is equal to or larger than the threshold set in step S501 (S504). If the number of the cooperating vehicles 3B is equal to or larger than the threshold (S504: YES), the process advances to step S505. If the number of the cooperating vehicles 3B is smaller than the threshold (S504: NO), the process advances to step S506,” here the system is determining if a number of cooperating vehicles with a similar configuration is greater than a selected portion/number has been determined).
Gonzalez and Syafril are analogous art as they are both generally related to systems and methods for monitoring status conditions of a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include determining that the number of the vehicles of the plurality of vehicles having the sensor of the plurality of sensors of the sensor type is greater than a selected portion of the plurality of vehicles of Syafril in the system for managing a sensor failure of Gonzalez with a reasonable expectation of success in order to improve the ability of the system to determine a root cause for a failure shorten the time necessary for specifying a failure cause (Paragraph [0139], “In addition, according to the present embodiment, when the driver feels a failure, the failure can be reported to the vehicle information management system 1. Therefore, compared to a case where the failure is reported to the vehicle information management system 1 via the vendor system 20, it is possible to shorten the time necessary for specifying the failure cause and improve usability.”).
However the combination does not explicitly teach determining that sensors of the plurality of sensors of a sensor type on vehicles of the plurality of vehicles have moved or are inoperative, in response to determination that the number of the vehicles of the plurality of vehicles having the sensors of the plurality of sensors of the sensor type is greater than the selected portion, causing the sensors of the sensor type on the vehicles to be investigated or replaced.
Ricci teaches fleetwide vehicle telematics systems and methods for managing fleetwide vehicle state data including
determining that sensors of the plurality of sensors of a sensor type on vehicles of the plurality of vehicles have moved or are inoperative (Paragraph [0177, “This example may be especially useful in determining whether a component recall should be issued based on the status check responses returned from a certain number of vehicle,” here the system can determine, via the computer system, that the same sensors/components on a plurality of vehicles are inoperative via a status check such as the selected portion/threshold number of vehicles as discussed above in the Syrafril reference)
in response to determination that the number of the vehicles of the plurality of vehicles having the sensors of the plurality of sensors of the sensor type is greater than the selected portion, causing the sensors of the sensor type on the vehicles to be investigated or replaced (Paragraph [0177], “This example may be especially useful in determining whether a component recall should be issued based on the status check responses returned from a certain number of vehicles.”) (Paragraph [0384], “One or more warnings may be stored in portion 1286. The warnings data 1286 may include warning generated by the vehicle 104, systems of the vehicle 104, manufacturer of the vehicle, federal agency, third party, and/or a user associated with the vehicle. For example, several components of the vehicle may provide health status information (e.g., stored in portion 1278) that, when considered together, may suggest that the vehicle 104 has suffered some type of damage and/or failure. Recognition of this damage and/or failure may be stored in the warnings data portion 1286. The data in portion 1286 may be communicated to one or more parties (e.g., a manufacturer, maintenance facility, user, etc.). In another example, a manufacturer may issue a recall notification for a specific vehicle 104, system of a vehicle 104, and/or a component of a vehicle 104. It is anticipated that the recall notification may be stored in the warning data field 1286. Continuing this example, the recall notification may then be communicated to the user of the vehicle 104 notifying the user of the recall issued by the manufacturer,” here the system is determining that a sensor needs to be investigated or replaced/recalled based on a determination that a status check on a certain number of vehicles/threshold, the system will issue a recall notification for a specific component/type of sensor based on the determination that a certain number of vehicles have experienced the same warning).
Gonzalez, Syafril, and Ricci are analogous art as they are both generally related to systems and methods for monitoring status conditions of a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include in response to determining that sensors of the plurality of sensors of a sensor type on vehicles of the plurality of vehicles have moved or are inoperative, in response to determination that the number of the vehicles of the plurality of vehicles having the sensors of the plurality of sensors of the sensor type is greater than the selected portion, causing the sensors of the sensor type on the vehicles to be investigated or replaced of Ricci in the system for managing a sensor failure of Gonzalez and Syafril with a reasonable expectation of success in order to improve the improve the safety of the system by monitoring vehicles for similar failures in order to determine defective or problematic parts (Paragraph [0384], “For example, several components of the vehicle may provide health status information (e.g., stored in portion 1278) that, when considered together, may suggest that the vehicle 104 has suffered some type of damage and/or failure. Recognition of this damage and/or failure may be stored in the warnings data portion 1286. The data in portion 1286 may be communicated to one or more parties (e.g., a manufacturer, maintenance facility, user, etc.). In another example, a manufacturer may issue a recall notification for a specific vehicle 104, system of a vehicle 104, and/or a component of a vehicle 104. It is anticipated that the recall notification may be stored in the warning data field 1286. Continuing this example, the recall notification may then be communicated to the user of the vehicle 104 notifying the user of the recall issued by the manufacturer.”).
Regarding claim 18, claim 18 is similar in scope to claim 3 and therefore is rejected under similar rationale.
Regarding claim 21, claim 21 is similar in scope to claim 6 and therefore is rejected under similar rationale.
Regarding claim 22, claim 22 is similar in scope to claim 6 and therefore is rejected under similar rationale.
Claim 4, 14, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez (US-20190051015) in view of Syafril (US-20200114930) in view of Ricci (US-20160086391) and further in view of Canady (US 20210197859).
Regarding claim 4, the combination of Gonzalez, Syrafil and Ricci teaches the method as discussed above in claim 1, however Gonzalez does not explicitly teach determining, by the computing system, the predetermined acceptable range based on a machine learning model, updating, by the computing system, the predetermined acceptable range based on the plurality of calibration parameter values and the machine learning model, and providing, by the computing system, the updated predetermined acceptable range to the plurality of vehicles.
Canady teaches a sensor degradation monitor for determining a degraded state associated with a sensor including
determining, by the computing system, the predetermined acceptable range based on a machine learning model (Paragraph [0087], “In some instances, aspects of some or all of the components discussed herein can include any models, algorithms, and/or machine-learning algorithms. For example, in some instances, the components in the memory 418 and 448 can be implemented as a neural network.”) (Paragraph [0071], “Further, the calibration component can determine baseline metrics associated with the sensor, such as baseline contrast metric(s), threshold numbers of points, distances, and/or intensities, and the like. In some examples, the calibration component can determine performance metrics of a sensor as it relates to an output of subsequent processing operations, such as a localization component, a perception component, a prediction component, a planning component, and the like. In some examples,” here the system includes a calibration component which can determine performance metrics and ranges for a sensor using a machine learning algorithm)
updating, by the computing system, the predetermined acceptable range based on the plurality of calibration parameter values and the machine learning model (Paragraph [0075], “The model component 430 can include functionality to access model(s) associated with data metric(s), threshold(s), and/or time period(s), and/or to evaluate data metrics with respect to such data models. In some examples, a model may comprise one or more thresholds associating or correlating data metric(s) and action(s), as discussed herein. In some examples, the model component 430 may comprise a predictive model evaluating a data metric over time to determine a lifetime and/or maintenance interval associated with a sensor. In some examples, a model may be based at least in part on environmental characteristics to normalize a data metric based on such environmental data. For example, a contrast metric may vary based on ambient lighting,” here the system is accessing a plurality of models, thresholds, and time periods and evaluating data metrics over time to update thresholds/ranges based on environmental data such as ambient lighting)
and providing, by the computing system, the updated predetermined acceptable range to the plurality of vehicles (Paragraph [0075], “The model component 430 can include functionality to access model(s) associated with data metric(s), threshold(s), and/or time period(s), and/or to evaluate data metrics with respect to such data models.”) (Paragraph [0084], “The model component 452 can include functionality to generate models for evaluating sensor metrics, as discussed herein. For example, the model component 452 can receive sensor data and can determine data metrics associated with such sensor data. The model component 452 can aggregate data across a plurality of vehicles (e.g., a fleet of vehicles) to determine data metrics indicative of normal operations and data metrics indicative of degraded operations. Further, the model component 452 can associate data metrics with a time period of operating a sensor and a performance of components associated with such metrics to determine a predictive maintenance schedule associated with various sensors, as discussed herein. In some examples, the model component 452 can determine one or more models based real sensor data (including various levels of degradation), real sensor data with simulated degradation, and/or sensor data. Further, the model component 452 can associated models with environmental conditions to normalize models with respect to other factors (e.g., ambient light, weather, location, time of day, temperature, and the like),” here the system can access and generate models for evaluating sensors including aggregating data across a plurality of vehicles).
Gonzalez and Canady are analogous art as they are both generally related to systems and methods for monitoring status conditions of a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include determining, by the computing system, the predetermined acceptable range based on a machine learning model, updating, by the computing system, the predetermined acceptable range based on the plurality of calibration parameter values and the machine learning model, and providing, by the computing system, the updated predetermined acceptable range to the plurality of vehicles of Canady in the system for managing a sensor failure of Gonzalez with a reasonable expectation of success in order to control a vehicle according to an environment and sensor state to improve safety outcomes (Paragraph [0101], “At operation 514, the process can include controlling a vehicle based at least in part on the action(s). Accordingly, a state of a sensor and/or a state of an environment can be considered while controlling a vehicle in an environment to improve safety outcomes for passengers and other vehicles and objects in the environment.”).
Regarding claim 14, claim 14 is similar in scope to claim 4 and therefore is rejected under similar rationale.
Regarding claim 19, claim 19 is similar in scope to claim 4 and therefore is rejected under similar rationale.
Claim 5, 15, 20 and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez (US-20190051015) in view of Syafril (US-20200114930) further in view of Ricci (US-20160086391) and further in view of Schoenfield (US-20190001989).
Regarding claim 5, the combination of Gonzalez, Syrafil and Ricci teaches the method as discussed above in claim 1, Gonzalez further teaches implementing a remedial action in response to a determination that a sensor is in an inoperable state (Paragraph [0031-0032], "By contrast, if at operation 320 a sensor failure condition exists then control passes to operation 322 a synthetic signal generator 354 generates virtual sensor data using the output from a paired sensor and the transform(s) determined in operation 318. ... By contrast, if at operation 326 the data generated in operation 322 is not within a predetermined confidence level then the virtual sensor may be deemed insufficiently accurate to allow the vehicle to continue operations using the virtual sensor and post-incident vehicle management operations may be implemented," here when a sensor has been determined to be in a failure state/inoperative the system may generate virtual sensor data or other vehicle management operations may be implemented).
However Gonzalez does not explicitly teach wherein the remedial action includes changing a driving mode of at least one vehicle of the vehicles associated with a sensor that has moved or is inoperative to at least one of: fully autonomous mode, a partially autonomous mode, a manual mode, an eco mode, a sports mode, a four wheel drive mode, and a two wheel drive mode.
Schoenfield teaches a method for the self-check of at least one driving function of an autonomous or semi-autonomous vehicle in vehicle operation including
wherein the remedial action includes changing a driving mode of at least one vehicle of the vehicles associated with a sensor that has moved or is inoperative to at least one of: fully autonomous mode, a partially autonomous mode, a manual mode, an eco mode, a sports mode, a four wheel drive mode, and a two wheel drive mode (Paragraph [0023], “depending on a degree of severity of the in-vehicle error, the autonomous or semi-autonomous vehicle is switched to a manual operating state during the check of the error message. The autonomous or semi-autonomous function may thus be deactivated in the case of an error. In doing so, the vehicle may be switched to the manual mode. In the manual mode, the driver may continue his trip or head to a parking area.”).
Gonzalez and Schoenfield are analogous art as they are both generally related to systems and methods for monitoring status conditions of a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include wherein the remedial action includes changing a driving mode of at least one vehicle of the vehicles associated with a sensor that has moved or is inoperative to at least one of: fully autonomous mode, a partially autonomous mode, a manual mode, an eco mode, a sports mode, a four wheel drive mode, and a two wheel drive mode of Schoenfield in the system for managing a sensor failure of Gonzalez with a reasonable expectation of success in order to allow the vehicle to continue to be controlled despite a sensor failure (Paragraph [0010], “Otherwise, a defect must be assumed, and the autonomous or semi-autonomous vehicle is transferred into a safe state. Depending on the vehicle function affected by the error, alternatively, the autonomous or semi-autonomous functions may also be deactivated, so that the vehicle remains controllable manually. A limited operation of the vehicle may thus be allowed.”).
Regarding claim 15, claim 15 is similar in scope to claim 5 and therefore is rejected under similar rationale.
Regarding claim 20, claim 20 is similar in scope to claim 5 and therefore is rejected under similar rationale.
Regarding claim 23, claim 23 is similar in scope to claim 7 and therefore is rejected under similar rationale.
Claim 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez (US-20190051015) in view of Syafril (US-20200114930) further in view of Ricci (US-20160086391) and further in view of Navin (US-20230066919).
Regarding claim 8, the combination of Gonzalez, Syrafil and Ricci teaches the method as discussed above in claim 1, however Gonzalez does not explicitly teach wherein a sensor is an inertial measurement unit and the calibration parameter value is associated with at least one of: an accelerometer bias, a gyroscope bias, a thermal response, a sensitivity, a sample rate, a linearity, and a noise level associated with the inertial measurement unit.
Navin teaches systems and methods for determining faults and calibrating inertial measurement units including
wherein the sensor is an inertial measurement unit (Paragraph [0015], “As alluded to above, detecting IMU faults and/or calibrating IMUs according to the techniques described”)
and the calibration parameter value is associated with at least one of: an accelerometer bias, a gyroscope bias, a thermal response, a sensitivity, a sample rate, a linearity, and a noise level associated with the inertial measurement unit (Paragraph [0031], “As illustrated in FIG. 1, the IMU calibration system 128 also includes the error detection component 134. Conceptually, the error detection component 134 can determine whether an IMU is properly calibrated, based on outputs of the first calibration model 130 and/or the second calibration model 132. For instance, the error detection component 134 can determine that the first IMU 108 is properly calibrated if the results of the first calibration model 130 determine that the first IMU data 114 aligns, within a threshold, with the sensor data 106.”) (Paragraph [0055], “In some instances, IMU calibration information 332 can include mounting angles and/or positions of sensors and/or any extrinsic and/or intrinsic information associated with the one or more IMUs, including but not limited to, biases, calibration angles, mounting location, height, direction, yaw, tilt, pan, timing information, and the like.”).
Gonzalez and Navin are analogous art as they are both generally related to systems and methods for monitoring status conditions of a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include wherein the sensor is an inertial measurement unit and the calibration parameter value is associated with at least one of: an accelerometer bias, a gyroscope bias, a thermal response, a sensitivity, a sample rate, a linearity, and a noise level associated with the inertial measurement unit of Navin in the system for managing a sensor failure of Gonzalez with a reasonable expectation of success in order to ensure the a vehicle sensor including an IMU is operating properly and ensure safe travel for the vehicle (Paragraph [0008], “However, when the IMUs are improperly calibrated, combining the sensor data may result in an inaccurate, or “blurry,” representation of the vehicle. For instance, when IMUs on a vehicle are not calibrated properly, a pose of the vehicle may be incorrect, which may result in improper and potentially unsafe travel. While applicable to autonomous vehicle systems, aspects of this disclosure may be used in other systems that use sensors (e.g., robotic manipulators having one or more sensors/sensor modalities, and the like).”).
Claim 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez (US-20190051015) in view of Syafril (US-20200114930) and further in view of Batts (US-20200339151).
Regarding claim 9, the combination of Gonzalez, Syrafil and Ricci teaches the method as discussed above in claim 1, Gonzalez further teaches wherein a sensor is a radar (Paragraph [0027], “Such techniques may prove useful, e.g., in the event of an accident or other incident which causes a sensor to fail or otherwise become unreliable. The techniques will be described in the context of sensors that are image collection devices (e.g., cameras), but such techniques apply equally to other sensors, e.g., radar, lidar, sonar, etc.”).
However Gonzalez does not explicitly teach the calibration parameter value is associated with at least one of: an operating frequency, a wavelength, a beamwidth, a pulse width, an antenna radiation pattern, a peak output power, and a pulse repetition frequency associated with the radar.
Batts teaches systems and methods for implementing an autonomous vehicle response to a sensor failure including
wherein a sensor is a radar (Paragraph [0080], “In an embodiment, the sensors 121 also include sensors for sensing or measuring properties of the AV's environment. For example, monocular or stereo video cameras 122 in the visible light, infrared or thermal (or both) spectra, LiDAR 123, RADAR, ultrasonic sensors, time-of-flight (TOF) depth sensors, speed sensors, temperature sensors, humidity sensors, and precipitation sensors.”)
calibration parameter value is associated with at least one of: an operating frequency, a wavelength, a beamwidth, a pulse width, an antenna radiation pattern, a peak output power, and a pulse repetition frequency associated with the radar (Paragraph [0137], “In an embodiment, the sensor profile may include a predetermined time period of when a sensor has not returned measurement data, measurements received from a particular sensor is less than a threshold, outside a confidence interval, consistently differs from what is expected, or what is measured by other, similar sensors,” here the system is determining if a sensor is valid or inoperable by comparing the sensor parameter value, such as the repetition frequency/time period for measurement data, to a threshold associated with the sensor).
Gonzalez and Batts are analogous art as they are both generally related to systems and methods for monitoring status conditions of a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include the calibration parameter value is associated with at least one of: an operating frequency, a wavelength, a beamwidth, a pulse width, an antenna radiation pattern, a peak output power, and a pulse repetition frequency associated with the radar of Batts in the system for managing a sensor failure of Gonzalez with a reasonable expectation of success in order to determine the capability of the vehicle experiencing a sensor failure and adjusting the control of the vehicle appropriately to prevent damage or injury (Paragraph [0004], “a level of confidence of the received information from at least one sensor of a first subset of sensors of the plurality of sensors is less than a first threshold, in response to determining that the level of confidence of the received information from the at least one sensor of the first subset of sensors is less than the first threshold, comparing a number of sensors in the first subset of sensors to a second threshold, and upon determining that the number of sensors in the first subset of sensors is less than the second threshold, adjusting the driving capability of the vehicle to rely on information received from a second subset of sensors of the plurality of sensors, wherein the second subset of sensors excludes the at least one sensor of the first subset of sensors”).
Regarding claim 10, the combination of Gonzalez, Syrafil and Ricci teaches the method as discussed above in claim 1, Gonzalez further teaches wherein a sensor is a lidar (Paragraph [0027], “Such techniques may prove useful, e.g., in the event of an accident or other incident which causes a sensor to fail or otherwise become unreliable. The techniques will be described in the context of sensors that are image collection devices (e.g., cameras), but such techniques apply equally to other sensors, e.g., radar, lidar, sonar, etc.”).
However Gonzalez does not explicitly teach calibration parameter value is associated with at least one of: a beam intensity, a point density, a field-of-view, a scan pattern, a timestamp offset, and a beam angular offset associated with the lidar.
Batts teaches systems and methods for implementing an autonomous vehicle response to a sensor failure including
wherein a sensor is a lidar (Paragraph [0108], “One input 502a is a LiDAR (Light Detection and Ranging) system (e.g., LiDAR 123 shown in FIG. 1). LiDAR is a technology that uses light (e.g., bursts of light such as infrared light) to obtain data about physical objects in its line of sight. A LiDAR system produces LiDAR data as output 504a. For example, LiDAR data is collections of 3D or 2D points (also known as a point clouds) that are used to construct a representation of the environment 190.”)
calibration parameter value is associated with at least one of: a beam intensity, a point density, a field-of-view, a scan pattern, a timestamp offset, and a beam angular offset associated with the lidar (Paragraph [0139-0141], “In an embodiment, the confidence level for each sensor can be represented in a continuous manner by defining the limit of perception's field of view, accuracy of localization's positioning, reduction of slip estimation due to a wheel speed sensor giving out, and the like. In an embodiment, there can be a continuous spectrum between nominal behavior and failure. For example, a LiDAR system can have a third of its field of view occluded. Thus, some of the sensor measurements from the LiDAR system are still valid. … In an embodiment, one or more sensor profiles are stored on the AV 1304 in the data storage unit 1364. … The data storage 1364 includes multiple data fields, each describing one or more attributes of a sensor profile,” here the system is determining if a sensor is valid or inoperable by comparing the sensor parameter value such as a field of view associated with the LIDAR to a stored calibration parameter value).
Gonzalez, Syrafil, and Batts are analogous art as they are both generally related to systems and methods for monitoring status conditions of a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include calibration parameter value is associated with at least one of: a beam intensity, a point density, a field-of-view, a scan pattern, a timestamp offset, and a beam angular offset associated with the lidar of Batts in the system for managing a sensor failure of Gonzalez with a reasonable expectation of success in order to determine the capability of the vehicle experiencing a sensor failure and adjusting the control of the vehicle appropriately to prevent damage or injury (Paragraph [0004], “a level of confidence of the received information from at least one sensor of a first subset of sensors of the plurality of sensors is less than a first threshold, in response to determining that the level of confidence of the received information from the at least one sensor of the first subset of sensors is less than the first threshold, comparing a number of sensors in the first subset of sensors to a second threshold, and upon determining that the number of sensors in the first subset of sensors is less than the second threshold, adjusting the driving capability of the vehicle to rely on information received from a second subset of sensors of the plurality of sensors, wherein the second subset of sensors excludes the at least one sensor of the first subset of sensors”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Abari (US-11415683) teaches a method includes receiving sensor data from one or more sensors of an autonomous vehicle (AV); determining that a first sensor of the one or more sensors needs recalibration based on the sensor data including recalling the vehicle to a service facility based on a threshold number of sensors. Kentley (US-9958864) teaches systems, devices, and methods are configured to manage a fleet of autonomous vehicles including monitoring and handling sensor failures. Grossman (US-11594037) Systems and methods for automated vehicle sensor calibration and verification are provided including monitoring and handling errors for an entire fleet of autonomous vehicles.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER FEES whose telephone number is (303)297-4343. The examiner can normally be reached Monday-Thursday 7:30 - 5:30 MT.
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/CHRISTOPHER GEORGE FEES/Primary Examiner, Art Unit 3662