DETAILED ACTION
Response to Amendments
This office action regarding application number 18/327,704, filed June 1, 2023, is in response to the applicants arguments and amendments filed November 3, 2025. This is a Non-Final Office Action on the merits, Claims 1-20 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 overcome some of the objections and rejections previously set forth in the Non-Final action mailed August 6, 2025. Applicants amendments to the drawings have been deemed sufficient to overcome the previous drawing objections through the inclusion of descriptive text labels and correcting minor typographical errors, therefore the objections are withdrawn. Applicants amendments to claims 1, 8, and 15 have been deemed sufficient to overcome the previous 35 USC 101 rejections through the inclusion of “controlling a navigation of the vehicle using the changed program” therefore the rejections are withdrawn. Applicants amendments to claims 1, 8, and 15 have been deemed sufficient to overcome the previous 35 USC 102 rejections through the inclusion of “creating a new failure domain at the remote processor when the internal parameter and the external parameter do not identify the failure domain” therefore the rejections are withdrawn. However as this changes the scope of the claims, new art rejections have been made based on the changes in scope.
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 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Giovanardi (US-20220281456) in view of [ 3 ].
Regarding claim 1, Giovanardi teaches a method of operating a driving system for a vehicle comprising (Abstract, "Systems and methods described herein include implementation of road surface-based localization techniques for advanced vehicle features and control methods including advanced driver assistance systems (ADAS)")
obtaining an external parameter indicative of a lane marking using a first sensor (Paragraph [0068], "This safety feature commonly relies on vision-based sensor systems like forward and sideways-facing cameras, or distance- or range-based sensor systems like LiDAR or Radar, to identify lane markers and determine an appropriate path to take in order to remain within the travel lane,” here the system uses sensors such as cameras or lidar to identify lane markers)
during a time period in which an automated driving program is being operated at the vehicle (Paragraph [0405], "For example, a deviation from the average travel lane characterized by long periods of drift, for example 5 sec long, or 1 sec long, with abrupt corrections, may be an indication of the driver not being fully alert, distracted, in an impaired state due to drugs or alcohol, or falling asleep. If the operator is a machine (e.g., an autonomous or semi-autonomous driving system), then a deviation from the path may be used to diagnose sensor and/or actuator functions, calibrations, and offsets,” here the system is monitoring deviation from a travel lane during operation of the vehicle including autonomous or semi autonomous operation)
obtaining an internal parameter of the vehicle using a second sensor during the time period (Paragraph [0084], "obtaining, from one or more sensors corresponding to a left wheel of a vehicle, left wheel data as the vehicle traverses a road segment. The method also includes obtaining, from one or more sensors corresponding to a+D11 right wheel of a vehicle, right wheel data as the vehicle traverses the road segment,” here the system also obtains internal data such as wheel data from wheel sensors)
detecting a lane touch event occurring during the time period (Paragraph [0084], "The method also includes determining, based on a difference between the first location and the second location, that the vehicle has completed a lane drift behavior.")
determining at a remote processor an error occurring at the driving system resulting in the lane touch event based on the internal parameter and the external parameter (Paragraph [0405], "If the operator is a machine (e.g., an autonomous or semi-autonomous driving system), then a deviation from the path may be used to diagnose sensor and/or actuator functions, calibrations, and offsets. For example, a constant offset to one side may be an indication of a camera malfunction or calibration error in systems using a camera as the primary feedback sensor for lane keeping," here if the system detects deviation from the path, such as lane drift/touch, during machine operation, then that deviation can be used to diagnose sensor malfunctions or calibrations errors)
identify, at the remote processor, a failure domain of the driving system in which the error occurs based on the internal parameter and the external parameter (Paragraph [0405], "If the operator is a machine (e.g., an autonomous or semi-autonomous driving system), then a deviation from the path may be used to diagnose sensor and/or actuator functions, calibrations, and offsets. For example, a constant offset to one side may be an indication of a camera malfunction or calibration error in systems using a camera as the primary feedback sensor for lane keeping.") (Paragraph [0488], "In some implementations, the performance supervisor 1400 may communicate a relative improvement score to a cloud database. In some instances, the relative improvements scores stored in the cloud database may be used for vehicle diagnostics. In some implementations, relative improvement scores sent to the cloud database may be used to determine particular models or types of vehicles where performance may be improved. For example, consistently lower relative improvement scores for one model of vehicle may indicate that the actuator commands are mismatched to vehicle performance for that model, or that that model's sensors are faulty, etc," here the system can use a lane touch/drift event in order to diagnose sensor malfunctions or errors, further the system can use vehicle scores to determine failure domains such as actuator issues or faulty sensors on a particular model)
and changing, at the remote processor, a design of a program of the driving system related to the failure domain (Paragraph [0170], “According to another aspect, a method of adaptively tuning a vehicle is disclosed. The method includes (a) identifying a tuning parameter relating to one or more modifiable settings for vehicle performance, (b) obtaining, from a terrain-based localization system, road event information for a road segment on which the vehicle is traveling, (c) localizing the vehicle on the road segment, (d) determining that the vehicle has interacted with the road event, (e) determining a performance metric for the tuning parameter during the interaction of the vehicle with the road event, (f) comparing the performance metric with one or more stored performance metrics, (g) determining a new value for the tuning parameter based on the comparison in (f), and (h) updating a value of the tuning parameter.”)(Paragraph [0629], "Adaptive improvements may be used during the vehicle development phase to more rapidly converge on an optimal performance of a system. ... Adaptive improvements may also be used after launch of a vehicle to adapt to changing behavior of the vehicle or its components. Adapting to changing behavior of the vehicle or its components may be, for example, due to aging or environmental factors, due to different loading of the vehicle, etc," here the system can make changes to the vehicle program/system in response to monitored conditions)
transmitting the changed program from the remote processor to the vehicle (Paragraph [0005], “In some implementations, the method also includes transmitting the one or more operating parameters to the vehicle.”)
and controlling a navigation of the vehicle using the changed program (Paragraph [0005], “In some instances, the method further includes operating the one or more vehicle systems based at least partly on the one or more operating parameters”).
However Giovanardi does not explicitly teach creating a new failure domain at the remote processor when the internal parameter and the external parameter do not identify the failure domain.
Moustafa teaches an autonomous vehicle system which uses sensor data to monitor and modify the control programs of the vehicle including
creating a new failure domain at the remote processor (Paragraph [0603], “In various embodiments, the addition of the SRU model may enhance the system's intelligence to report unknown situations (time-based events) that were not been seen by the system previously (either at training or test phases). The system may be able to encode a new sequence of anomaly data and assign a label to it to create a new class. When the label is generated, any given data representation to this type of anomaly may be decoded,” here when the system determines an unknown anomaly/failure domain, the system will encode a new data sequence and assign a new label to create a new class, this system of Moustafa can reasonably be combined with the system of Giovanardi as discussed above)
when the internal parameter and the external parameter do not identify the failure domain (Paragraph [0603], “FIG. 85 depicts a system 8500 for anomaly detection in accordance with certain embodiments. The addition of an anomaly detector may enhance the intelligence of a system to enable reporting of unknown situations (e.g., time-based events)”).
Giovanardi and Moustafa are analogous art as they are both generally related to systems for improving the control of vehicles by analyzing sensor data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include creating a new failure domain at the remote processor when the internal parameter and the external parameter do not identify the failure domain of Moustafa in the system for analyzing sensor data to detect lane touch events of Giovanardi with a reasonable expectation of success in order to enhances the systems ability to detect and respond to situations or failure domains that is has not previously encountered (Paragraph [0603], “For example, an anomaly may be an unknown detected object or an unknown detected event sequence. In various embodiments, the addition of the SRU model may enhance the system's intelligence to report unknown situations (time-based events) that were not been seen by the system previously”).
Regarding claim 2, the combination of Giovanardi and Moustafa teaches the method as discussed above in claim 1, Giovanardi further teaches wherein each of the internal parameter and the external parameter includes a time series of data obtained over a time interval (Paragraph [0175], “According to one aspect, the disclosure provides a method of tracking a rate of change, over time, of at least one parameter associated with a segment of a road surface.”) (Paragraph [0517], “In some implementations, microprocessor 3915, shown in FIG. 47, may track the history of performance metrics of various vehicle systems over time as a function of, for example, vehicle age or miles driven since new. Systems that may be tracked may include, for example, braking systems, active suspension systems, semi-active suspension systems, anti-lock braking systems (ABS), stability control systems, electric power steering systems (EPS), propulsion systems, etc. Such systems may be tracked on an aggregate basis by, for example, averaging all or most of the data received by database 3914.”).
Regarding claim 3, the combination of Giovanardi and Moustafa teaches the method as discussed above in claim 1, Giovanardi further teaches wherein the failure domain is at least one of: (i) a vehicle sensing domain; (ii) a path planning domain; (iii) a vehicle control domain; (iv) a reporting domain (Paragraph [0405], "If the operator is a machine (e.g., an autonomous or semi-autonomous driving system), then a deviation from the path may be used to diagnose sensor and/or actuator functions, calibrations, and offsets. For example, a constant offset to one side may be an indication of a camera malfunction or calibration error in systems using a camera as the primary feedback sensor for lane keeping.") (Paragraph [0488], "In some implementations, the performance supervisor 1400 may communicate a relative improvement score to a cloud database. In some instances, the relative improvements scores stored in the cloud database may be used for vehicle diagnostics. In some implementations, relative improvement scores sent to the cloud database may be used to determine particular models or types of vehicles where performance may be improved. For example, consistently lower relative improvement scores for one model of vehicle may indicate that the actuator commands are mismatched to vehicle performance for that model, or that that model's sensors are faulty, etc," here the system can use a lane touch/drift event in order to diagnose sensor malfunctions or errors, further the system can use vehicle scores to determine failure domains such as actuator issues/control domain or faulty sensors on a particular model/sensing domain).
Regarding claim 4, the combination of Giovanardi and Moustafa teaches the method as discussed above in claim 1, Giovanardi further teaches wherein an error in the vehicle sensing domain includes an error in detecting a lane marking (Paragraph [0405], "If the operator is a machine (e.g., an autonomous or semi-autonomous driving system), then a deviation from the path may be used to diagnose sensor and/or actuator functions, calibrations, and offsets. For example, a constant offset to one side may be an indication of a camera malfunction or calibration error in systems using a camera as the primary feedback sensor for lane keeping," here lane drift/touch may be caused by a sensor error/camera malfunction being used for lane keeping/detecting a lane marking).
Regarding claim 5, the combination of Giovanardi and Moustafa teaches the method as discussed above in claim 1, Giovanardi further teaches further comprising determining an error in the path planning domain when a lane touch occurs (Paragraph [0073], “Comparing this reference path to the trajectory determined by the vision-based system allows for fault detection and for a correction or a disengagement of the system if the trajectory is determined to be incorrect or not trustworthy”)
by comparing a difference between a planned path of the vehicle to an actual path of the vehicle to a threshold value (Paragraph [0038], “In some implementations, the method also includes comparing the error to a threshold and determining that a current path of the first vehicle is inappropriate for traversing the road segment. In some instances, the method also includes calculating, based on the error, a corrective action to bring the current trajectory to match the target travel path.”).
Regarding claim 6, the combination of Giovanardi and Moustafa teaches the method as discussed above in claim 1, Giovanardi further teaches further comprising determining a lane change issue to due to one of (i) a late abort of a lane change; (ii) oversteering during a lane change; (iii) an over adjustment for the lane change (Paragraph [0038], “In some implementations, the method also includes comparing the error to a threshold and determining that a current path of the first vehicle is inappropriate for traversing the road segment. In some instances, the method also includes calculating, based on the error, a corrective action to bring the current trajectory to match the target travel path. In some instances, the method also includes initiating the corrective action with an advanced driver assistance system of the first vehicle that at least partially influences the steering of the first vehicle.”) (Paragraph [0418], “In some instances, a lane drift may be treated as an intermediary step in a lane change maneuver. For example, on a multi-lane road, if a driver initiates a left turn signal, the terrain-based localization system determines that there is a same-travel direction lane to the left, and the terrain-based lane drift detection system determines a lane drift to the left, the other vehicle system may not display a warning message as the lane drift is determined to be an intermediary step of a desired lane change maneuver. In some implementations, the controller 2118 may compare a previous position of the vehicle 2102 to a current position of the vehicle 2102 to determine if the maneuver has completed, is completing, or is ongoing,” here the system can determine if a lane change maneuver is occurring and if it has been completed or aborted, the system can also compare a current path of the vehicle, such as the lane change, and determine that it is inappropriate and initiate a corrective action due to the issue such as correcting oversteering, or aborting the lane change).
Regarding claim 7, the combination of Giovanardi and Moustafa teaches the method as discussed above in claim 1, Giovanardi further teaches further comprising determining a responsiveness of the driving system to the lane touch event (Paragraph [0488], "In some implementations, the performance supervisor 1400 may communicate a relative improvement score to a cloud database. In some instances, the relative improvements scores stored in the cloud database may be used for vehicle diagnostics. In some implementations, relative improvement scores sent to the cloud database may be used to determine particular models or types of vehicles where performance may be improved. For example, consistently lower relative improvement scores for one model of vehicle may indicate that the actuator commands are mismatched to vehicle performance for that model, or that that model's sensors are faulty, etc," here the system can use a lane touch/drift event in order to diagnose sensor malfunctions or errors, further the system can use vehicle scores to determine failure domains such as actuator issues or faulty sensors on a particular model and the responsiveness of the vehicle corrections).
Regarding claim 8, Giovanardi teaches a driving system for a vehicle comprising (Abstract, "Systems and methods described herein include implementation of road surface-based localization techniques for advanced vehicle features and control methods including advanced driver assistance systems (ADAS)")
a first sensor configured to obtain an external parameter indicative of a lane marking (Paragraph [0068], "This safety feature commonly relies on vision-based sensor systems like forward and sideways-facing cameras, or distance- or range-based sensor systems like LiDAR or Radar, to identify lane markers and determine an appropriate path to take in order to remain within the travel lane,” here the system uses sensors such as cameras or lidar to identify lane markers)
during a time period in which an automated driving program is being operated at the vehicle (Paragraph [0405], "For example, a deviation from the average travel lane characterized by long periods of drift, for example 5 sec long, or 1 sec long, with abrupt corrections, may be an indication of the driver not being fully alert, distracted, in an impaired state due to drugs or alcohol, or falling asleep. If the operator is a machine (e.g., an autonomous or semi-autonomous driving system), then a deviation from the path may be used to diagnose sensor and/or actuator functions, calibrations, and offsets,” here the system is monitoring deviation from a travel lane during operation of the vehicle including autonomous or semi autonomous operation)
a second sensor configured to obtain an internal parameter of the vehicle during the time period (Paragraph [0084], "obtaining, from one or more sensors corresponding to a left wheel of a vehicle, left wheel data as the vehicle traverses a road segment. The method also includes obtaining, from one or more sensors corresponding to a+D11 right wheel of a vehicle, right wheel data as the vehicle traverses the road segment,” here the system also obtains internal data such as wheel data from wheel sensors)
a processor configured to (Paragraph [0281], “A vehicle control system may be operated by one or more processors. The one or more processors may be configured to execute computer readable instructions stored in volatile or non-volatile computer readable memory that when executed perform any of the methods disclosed herein.”)
detect a lane touch event during the time period (Paragraph [0084], "The method also includes determining, based on a difference between the first location and the second location, that the vehicle has completed a lane drift behavior.")
determine an error occurring in the driving system resulting in the lane touch event based on the internal parameter and the external parameter (Paragraph [0405], "If the operator is a machine (e.g., an autonomous or semi-autonomous driving system), then a deviation from the path may be used to diagnose sensor and/or actuator functions, calibrations, and offsets. For example, a constant offset to one side may be an indication of a camera malfunction or calibration error in systems using a camera as the primary feedback sensor for lane keeping," here if the system detects deviation from the path, such as lane drift/touch, during machine operation, then that deviation can be used to diagnose sensor malfunctions or calibrations errors)
identify a failure domain of the driving system in which the error occurs based on the internal parameter and the external parameter (Paragraph [0405], "If the operator is a machine (e.g., an autonomous or semi-autonomous driving system), then a deviation from the path may be used to diagnose sensor and/or actuator functions, calibrations, and offsets. For example, a constant offset to one side may be an indication of a camera malfunction or calibration error in systems using a camera as the primary feedback sensor for lane keeping.") (Paragraph [0488], "In some implementations, the performance supervisor 1400 may communicate a relative improvement score to a cloud database. In some instances, the relative improvements scores stored in the cloud database may be used for vehicle diagnostics. In some implementations, relative improvement scores sent to the cloud database may be used to determine particular models or types of vehicles where performance may be improved. For example, consistently lower relative improvement scores for one model of vehicle may indicate that the actuator commands are mismatched to vehicle performance for that model, or that that model's sensors are faulty, etc," here the system can use a lane touch/drift event in order to diagnose sensor malfunctions or errors, further the system can use vehicle scores to determine failure domains such as actuator issues or faulty sensors on a particular model)
change a design of a program of the driving system related to the failure domain (Paragraph [0170], “According to another aspect, a method of adaptively tuning a vehicle is disclosed. The method includes (a) identifying a tuning parameter relating to one or more modifiable settings for vehicle performance, (b) obtaining, from a terrain-based localization system, road event information for a road segment on which the vehicle is traveling, (c) localizing the vehicle on the road segment, (d) determining that the vehicle has interacted with the road event, (e) determining a performance metric for the tuning parameter during the interaction of the vehicle with the road event, (f) comparing the performance metric with one or more stored performance metrics, (g) determining a new value for the tuning parameter based on the comparison in (f), and (h) updating a value of the tuning parameter.”)(Paragraph [0629], "Adaptive improvements may be used during the vehicle development phase to more rapidly converge on an optimal performance of a system. ... Adaptive improvements may also be used after launch of a vehicle to adapt to changing behavior of the vehicle or its components. Adapting to changing behavior of the vehicle or its components may be, for example, due to aging or environmental factors, due to different loading of the vehicle, etc," here the system can make changes to the vehicle program/system in response to monitored conditions)
and control a navigation of the vehicle using the changed program (Paragraph [0005], “In some instances, the method further includes operating the one or more vehicle systems based at least partly on the one or more operating parameters”).
However Giovanardi does not explicitly teach create a new failure domain at the remote processor when the internal parameter and the external parameter do not identify the failure domain.
Moustafa teaches an autonomous vehicle system which uses sensor data to monitor and modify the control programs of the vehicle including
create a new failure domain at the remote processor (Paragraph [0603], “In various embodiments, the addition of the SRU model may enhance the system's intelligence to report unknown situations (time-based events) that were not been seen by the system previously (either at training or test phases). The system may be able to encode a new sequence of anomaly data and assign a label to it to create a new class. When the label is generated, any given data representation to this type of anomaly may be decoded,” here when the system determines an unknown anomaly/failure domain, the system will encode a new data sequence and assign a new label to create a new class, this system of Moustafa can reasonably be combined with the system of Giovanardi as discussed above)
when the internal parameter and the external parameter do not identify the failure domain (Paragraph [0603], “FIG. 85 depicts a system 8500 for anomaly detection in accordance with certain embodiments. The addition of an anomaly detector may enhance the intelligence of a system to enable reporting of unknown situations (e.g., time-based events)”).
Giovanardi and Moustafa are analogous art as they are both generally related to systems for improving the control of vehicles by analyzing sensor data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include create a new failure domain at the remote processor when the internal parameter and the external parameter do not identify the failure domain of Moustafa in the system for analyzing sensor data to detect lane touch events of Giovanardi with a reasonable expectation of success in order to enhances the systems ability to detect and respond to situations or failure domains that is has not previously encountered (Paragraph [0603], “For example, an anomaly may be an unknown detected object or an unknown detected event sequence. In various embodiments, the addition of the SRU model may enhance the system's intelligence to report unknown situations (time-based events) that were not been seen by the system previously”).
Regarding claim 9, claim 9 is similar in scope to claim 2, and therefore is rejected under similar rationale.
Regarding claim 10, claim 10 is similar in scope to claim 3, and therefore is rejected under similar rationale.
Regarding claim 11, claim 11 is similar in scope to claim 4, and therefore is rejected under similar rationale.
Regarding claim 12, claim 12 is similar in scope to claim 5, and therefore is rejected under similar rationale.
Regarding claim 13, claim 13 is similar in scope to claim 6, and therefore is rejected under similar rationale.
Regarding claim 14, claim 14 is similar in scope to claim 7, and therefore is rejected under similar rationale.
Regarding claim 15, Giovanardi teaches a system for designing a driving system for a vehicle, comprising: (Abstract, "Systems and methods described herein include implementation of road surface-based localization techniques for advanced vehicle features and control methods including advanced driver assistance systems (ADAS)")
a first sensor configured to obtain an external parameter indicative of a lane marking (Paragraph [0068], "This safety feature commonly relies on vision-based sensor systems like forward and sideways-facing cameras, or distance- or range-based sensor systems like LiDAR or Radar, to identify lane markers and determine an appropriate path to take in order to remain within the travel lane,” here the system uses sensors such as cameras or lidar to identify lane markers)
during a time period in which an automated driving program is being operated at the vehicle (Paragraph [0405], "For example, a deviation from the average travel lane characterized by long periods of drift, for example 5 sec long, or 1 sec long, with abrupt corrections, may be an indication of the driver not being fully alert, distracted, in an impaired state due to drugs or alcohol, or falling asleep. If the operator is a machine (e.g., an autonomous or semi-autonomous driving system), then a deviation from the path may be used to diagnose sensor and/or actuator functions, calibrations, and offsets,” here the system is monitoring deviation from a travel lane during operation of the vehicle including autonomous or semi autonomous operation)
a second sensor configured to obtain an internal parameter of the vehicle during the time period (Paragraph [0084], "obtaining, from one or more sensors corresponding to a left wheel of a vehicle, left wheel data as the vehicle traverses a road segment. The method also includes obtaining, from one or more sensors corresponding to a+D11 right wheel of a vehicle, right wheel data as the vehicle traverses the road segment,” here the system also obtains internal data such as wheel data from wheel sensors)
a processor configured to (Paragraph [0281], “A vehicle control system may be operated by one or more processors. The one or more processors may be configured to execute computer readable instructions stored in volatile or non-volatile computer readable memory that when executed perform any of the methods disclosed herein.”)
detect a lane touch event during the time period (Paragraph [0084], "The method also includes determining, based on a difference between the first location and the second location, that the vehicle has completed a lane drift behavior.")
determine an error occurring in the driving system resulting in the lane touch event based on the internal parameter and the external parameter (Paragraph [0405], "If the operator is a machine (e.g., an autonomous or semi-autonomous driving system), then a deviation from the path may be used to diagnose sensor and/or actuator functions, calibrations, and offsets. For example, a constant offset to one side may be an indication of a camera malfunction or calibration error in systems using a camera as the primary feedback sensor for lane keeping," here if the system detects deviation from the path, such as lane drift/touch, during machine operation, then that deviation can be used to diagnose sensor malfunctions or calibrations errors)
identify a failure domain of the driving system in which the error occurs based on the internal parameter and the external parameter (Paragraph [0405], "If the operator is a machine (e.g., an autonomous or semi-autonomous driving system), then a deviation from the path may be used to diagnose sensor and/or actuator functions, calibrations, and offsets. For example, a constant offset to one side may be an indication of a camera malfunction or calibration error in systems using a camera as the primary feedback sensor for lane keeping.") (Paragraph [0488], "In some implementations, the performance supervisor 1400 may communicate a relative improvement score to a cloud database. In some instances, the relative improvements scores stored in the cloud database may be used for vehicle diagnostics. In some implementations, relative improvement scores sent to the cloud database may be used to determine particular models or types of vehicles where performance may be improved. For example, consistently lower relative improvement scores for one model of vehicle may indicate that the actuator commands are mismatched to vehicle performance for that model, or that that model's sensors are faulty, etc," here the system can use a lane touch/drift event in order to diagnose sensor malfunctions or errors, further the system can use vehicle scores to determine failure domains such as actuator issues or faulty sensors on a particular model)
change a design of a program of the driving system related to the failure domain (Paragraph [0170], “According to another aspect, a method of adaptively tuning a vehicle is disclosed. The method includes (a) identifying a tuning parameter relating to one or more modifiable settings for vehicle performance, (b) obtaining, from a terrain-based localization system, road event information for a road segment on which the vehicle is traveling, (c) localizing the vehicle on the road segment, (d) determining that the vehicle has interacted with the road event, (e) determining a performance metric for the tuning parameter during the interaction of the vehicle with the road event, (f) comparing the performance metric with one or more stored performance metrics, (g) determining a new value for the tuning parameter based on the comparison in (f), and (h) updating a value of the tuning parameter.”)(Paragraph [0629], "Adaptive improvements may be used during the vehicle development phase to more rapidly converge on an optimal performance of a system. ... Adaptive improvements may also be used after launch of a vehicle to adapt to changing behavior of the vehicle or its components. Adapting to changing behavior of the vehicle or its components may be, for example, due to aging or environmental factors, due to different loading of the vehicle, etc," here the system can make changes to the vehicle program/system in response to monitored conditions)
and control a navigation of the vehicle using the changed program (Paragraph [0005], “In some instances, the method further includes operating the one or more vehicle systems based at least partly on the one or more operating parameters”).
However Giovanardi does not explicitly teach create a new failure domain at the remote processor when the internal parameter and the external parameter do not identify the failure domain.
Moustafa teaches an autonomous vehicle system which uses sensor data to monitor and modify the control programs of the vehicle including
create a new failure domain at the remote processor (Paragraph [0603], “In various embodiments, the addition of the SRU model may enhance the system's intelligence to report unknown situations (time-based events) that were not been seen by the system previously (either at training or test phases). The system may be able to encode a new sequence of anomaly data and assign a label to it to create a new class. When the label is generated, any given data representation to this type of anomaly may be decoded,” here when the system determines an unknown anomaly/failure domain, the system will encode a new data sequence and assign a new label to create a new class, this system of Moustafa can reasonably be combined with the system of Giovanardi as discussed above)
when the internal parameter and the external parameter do not identify the failure domain (Paragraph [0603], “FIG. 85 depicts a system 8500 for anomaly detection in accordance with certain embodiments. The addition of an anomaly detector may enhance the intelligence of a system to enable reporting of unknown situations (e.g., time-based events)”).
Giovanardi and Moustafa are analogous art as they are both generally related to systems for improving the control of vehicles by analyzing sensor data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include create a new failure domain at the remote processor when the internal parameter and the external parameter do not identify the failure domain of Moustafa in the system for analyzing sensor data to detect lane touch events of Giovanardi with a reasonable expectation of success in order to enhances the systems ability to detect and respond to situations or failure domains that is has not previously encountered (Paragraph [0603], “For example, an anomaly may be an unknown detected object or an unknown detected event sequence. In various embodiments, the addition of the SRU model may enhance the system's intelligence to report unknown situations (time-based events) that were not been seen by the system previously”).
Regarding claim 16, claim 16 is similar in scope to claim 3, and therefore is rejected under similar rationale.
Regarding claim 17, claim 17 is similar in scope to claim 4, and therefore is rejected under similar rationale.
Regarding claim 18, claim 18 is similar in scope to claim 5, and therefore is rejected under similar rationale.
Regarding claim 19, claim 19 is similar in scope to claim 6, and therefore is rejected under similar rationale.
Regarding claim 20, claim 20 is similar in scope to claim 7, and therefore is rejected under similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Lin (US-20230192103) teaches fault isolation and mitigation for vehicles including receiving an indication indicating a misdetection of lane markings on the roadway based on data received from the first sensor, the controller is configured to execute in parallel a plurality of procedures configured to detect a plurality of causes for the misdetection of lane markings. Zhou (US-20180276912) teaches machine learning for triaging failures in autonomous vehicle including generating failure type labels in the form of training data to detect and classify vehicle faults. Attard (US-20150178998) teaches detecting faults in the operation of a vehicle and determining causes for the degradation of data collection devices.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER FEES whose telephone number is (303)297-4343. The examiner can normally be reached Monday-Thursday 7:30 - 5:30 MT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aniss Chad can be reached at (571) 270-3832. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CHRISTOPHER GEORGE FEES/Examiner, Art Unit 3662
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