DETAILED ACTION
Response to Amendment
This office action regarding application number 18/454,964, filed August 24, 2023, is in response to the applicants arguments and amendments filed January 20, 2026. Claims 1-3, 5-6, 11, 14, 17 and 19-20 have been amended. Claims 10 and 12 have been cancelled. Claims 1-9, 11, and 13-20 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 October 20, 2025. Claims 10 and 12 have been cancelled and therefore all associated objections and rejections are withdrawn. Applicants amendments to claims 17-20 have rendered the previous interpretation under 35 USC 112(f) moot through the additional of structural language to perform the functions, therefore the interpretation is withdrawn. Applicants amendments to claims 1, 11, and 17 have been deemed sufficient to overcome the previous 35 USC 101 rejections through the inclusion of “moving the autonomous driving vehicle to a safe place and stopping the driving of the autonomous driving vehicle when it is determined that the driving of the autonomous driving vehicle needs to be stopped” therefore the rejections are withdrawn. Applicants amendments to claims 1, 11, and 17 have been deemed sufficient to overcome the previous 35 USC 103 rejections through the inclusion of “wherein the determining whether an error has occurred in the autonomous driving artificial intelligence includes determining whether the artificial intelligence has accurately recognized an object based on a comparison among recognition results of a plurality of sensors” therefore the rejections are withdrawn.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.
Applicant’s arguments with respect to claim(s) 1, 11, and 17, specifically in regards to the 102 and 103 rejections and the newly amended subject matter, have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gong (US 20210387631) in view of Frazzoli (US-20210163021).
Regarding claim 1, Gong teaches a method for changing a route of an autonomous driving vehicle when an error occurs in an autonomous driving artificial intelligence, the method comprising (Paragraph [0014], "According to various embodiment, described herein are methods and systems for reliably detecting malfunctions in a variety of software or hardware components in an autonomous driving vehicle (ADV).") (Paragraph [0074], “the processing logic issues one or more driving commands directly to a controller area network (CAN bus) in the ADV to control the ADV in response to the level of failure risk of the malfunction.”)
determining whether an error has occurred in an autonomous driving artificial intelligence (Figure 6, item 602, "Determine, by the redundant system, that a malfunction has occurred")
collecting error information of the artificial intelligence when the error of the artificial intelligence has occurred (Paragraph [0054], "In one embodiment, the malfunction evaluator 517 can use a predetermined algorithm to determine the level of failure risk 518 of a malfunction. The algorithm can examine a number of factors, which include a general category of a malfunction, a specific component where the malfunction occurs, a frequency of occurrence of the malfunction, and availability of inspection data for the malfunction," here the system is collecting information/factors and using that information to evaluate the failure)
extracting, from a storage, past error information about a same kind and version of artificial intelligence as a kind and version of the artificial intelligence based on the error information of the artificial intelligence (Paragraph [0056], "The factor of the specific component refers to a particular component where the malfunction occurs. For example, the malfunction can occur in a LiDAR sensor, or in the perception module 302. The factor provides additional details to a malfunction. The frequency of occurrence of a malfunction refers to the number of times that a particular malfunction has occurred over a past period of time. The number can be maintained in a counter in the redundant system 327," here the system is collecting information/factors including the number of times an error has occurred in the past for a particular/same kind of malfunction/error) (Paragraph [0054-0056], “The algorithm can examine a number of factors, which include a general category of a malfunction, a specific component where the malfunction occurs, a frequency of occurrence of the malfunction, and availability of inspection data for the malfunction. In one embodiment, the general category of a malfunction refers to the general area where the malfunction occurs, for example, hardware, software, or vehicle behaviors … The frequency of occurrence of a malfunction refers to the number of times that a particular malfunction has occurred over a past period of time,” here the system is determining factors for evaluating the error including a frequency/past information, this information includes the specific component and for the same type of error for the specific category of the malfunction including a software version which would be the same for the same type and category of the malfunction)
generating an error analysis result based on the past error information (Paragraph [0058], "Each of the above factors can be given a weight in the predetermined algorithm used to a level of failure risk for a malfunction. The weight of each factor can be based on user experiences. The level of failure risk for a malfunction can be high, medium, or low. Each level of risk can be associated with a range of value. Using the algorithm, the malfunction evaluator 517 can calculate a risk value for a malfunction, and then classify the risk value into one of high, medium, or low," here the system is using the collected information/factors to analyze the failure risk using information including the past error information)
generating an error analysis result message based on the error analysis result (Paragraph [0058], "Using the algorithm, the malfunction evaluator 517 can calculate a risk value for a malfunction, and then classify the risk value into one of high, medium, or low.") (Paragraph [0060], "In one embodiment, a high failure risk can cause a vehicle emergency controller 521 to perform an emergency braking on the vehicle, while a medium or a low failure risk will cause the vehicle emergency controller 521 to perform a slow braking on the braking," here they error analysis is output as a message to the emergency controller)
determining whether driving of the autonomous driving vehicle needs to be stopped based on the error analysis result message (Paragraph [0060], "In one embodiment, a high failure risk can cause a vehicle emergency controller 521 to perform an emergency braking on the vehicle, while a medium or a low failure risk will cause the vehicle emergency controller 521 to perform a slow braking on the braking," the emergency controller will use the analysis message in order to determine if the vehicle should perform emergency braking/stopping)
moving the autonomous driving vehicle to a safe place and stopping the driving of the autonomous driving vehicle when it is determined that the driving of the autonomous driving vehicle needs to be stopped (Paragraph [0061], “In the slow braking mode, the ADV 101 can slowly drive to a safer place and park there. In this mode, the vehicle emergency handler 521 can use sensors data and localization information to generate a path to a nearby safe place, e.g., the curb of the road, and send driving commands 523 directly to the CAN bus component 321 without sending the driving commands 523 to the control module 306 as the ADS 110 would normally do,” here the system has determined that it is not necessary to perform an immediate emergency stop for the autonomous vehicle, the system then resets the driving route to the safe location based on the avoidance information which indicates slow braking mode)
wherein the past error information includes number of error occurrences (Paragraph [0056], “The frequency of occurrence of a malfunction refers to the number of times that a particular malfunction has occurred over a past period of time.”).
and wherein the error analysis result includes a vehicle accident risk caused by the error of the artificial intelligence (Paragraph [0060], “In one embodiment, a high failure risk can cause a vehicle emergency controller 521 to perform an emergency braking on the vehicle, while a medium or a low failure risk will cause the vehicle emergency controller 521 to perform a slow braking on the braking.”).
However while Gong teaches performing a comparison to determine whether an error has occurred (Paragraph [0067], “The ADS checker 513 can check for any malfunctions in the ADS 110. For the perception module 302 in the ADS 110, malfunctions may include abnormal output data channel frequencies, abnormal perception processing delay, and abnormal point cloud fusion. For the localization module 301, the prediction module 303, malfunctions can include abnormal output data channel frequencies. For the control module 306, malfunctions can include abnormal output data channel frequencies and abnormal total link delay timeouts. For the prediction module 303, malfunction may include abnormal total link delay timeouts.”) (Paragraph [0047], “The redundant system 327 can compare the output parameters with expected output parameters to determine whether a malfunction has occurred.“) (Paragraph [0051], “Each checking component can compare output parameters from a software or hardware component with their respective expected parameters. Based on the comparison, the checking component can detect the presence of any malfunction in the software or hardware component.”)
Gong further teaches that evaluating a malfunction using an algorithm to determine a level of risk including using past information such as frequency of occurrence (Paragraph [0054], “the malfunction evaluator 517 can use a predetermined algorithm to determine the level of failure risk 518 of a malfunction. The algorithm can examine a number of factors, which include a general category of a malfunction, a specific component where the malfunction occurs, a frequency of occurrence of the malfunction, and availability of inspection data for the malfunction.”).
Gong does not explicitly teach wherein the determining whether an error has occurred in the autonomous driving artificial intelligence includes determining whether the artificial intelligence has accurately recognized an object based on a comparison among recognition results of a plurality of sensors, wherein the past error information includes degree of error, wherein the past error information includes number of accidents, and wherein the past error information includes a vehicle accident risk caused by errors of the same kind and version of artificial intelligences as the kind and version of the artificial intelligence in which the error has occurred.
Frazzoli teaches redundancy techniques in autonomous vehicle for safe handling of malfunctions and errors including
wherein the determining whether an error has occurred in the autonomous driving artificial intelligence includes determining whether the artificial intelligence has accurately recognized an object based on a comparison among recognition results of a plurality of sensors (Paragraph [0344], “At 3315, the autonomous vehicle determines whether there is an abnormal condition based on a difference between the first and second sensor data streams.”) (Paragraph [0526], “The perception module 402 outputs a first scene description that includes one or more classified objects (e.g., vehicles, pedestrians) detected from the LiDAR point cloud data. Concurrent (e.g., in parallel) with the LiDAR processing, the stereo camera captures stereo images which are also input into the perception module 402. The perception module 402 outputs a second scene description of one or more classified objects detected from the stereo image data.”) (Paragraph [0529], “For example, if the diagnostic modules 102a, 102b do not indicate that the LiDAR or stereo camera hardware or software has failed, the LiDAR scene description matches the simulated LiDAR scene description (e.g., all classified objects are accounted for in both scene descriptions), and the stereo camera scene description matches the simulated stereo camera scene description, then the AV continues to operate in nominal mode.”) (Paragraph [0234], “In some implementations, the first sensor signals received from the first set of the sensors 121 include one or more lists of objects detected by corresponding sensors of the first set, and the second sensor signals received from the second set of the sensors 121 include one or more lists of objects detected by corresponding sensors of the second set. In some implementations, these lists are created by the perception modules. As such, the generating of the first world view proposal by the first perception module 1710a can include creating one or more first lists of objects detected by corresponding sensors of the first set. And, the generating of the second world view proposal by the second perception module 1710b can include creating one or more second lists of objects detected by corresponding sensors of the second set,” here each of the redundant systems is using recognition results of a plurality of sensors to create object detection lists)
wherein the past error information includes degree of error (Paragraph [0525], “In an embodiment, historical data stored in data storage devices 6605a, 6605b are used to perform data analytics to analyze past failures of AV processes/systems and to predict future failures of AV processes/systems. “) (Paragraph [0293], “For instance, a machine learning algorithm operates on AV operations subsystems' historical data to determine one or more specific operational contexts for the AV in which each one of its N different AV operations subsystems performs differently, better or worse, than remaining ones of the N different AV operations subsystems. In some implementations, the historical data include data that is collected on the current trip and the determination of the mapping of operational contexts to IDs of AV operations subsystems is run in real time. In some implementations, the historical data include data that was collected on previous trips and the determination of the mapping of operational contexts to IDs of AV operations subsystems was run, e.g., overnight, before the current trip,” here the system can use historical data to evaluate previous operational contexts/errors which indicate a how the system is performing differently better or worse/degree)
wherein the past error information includes number of accidents (Paragraph [0522], “simulators 6603b, 6603a receive real-time data streams and/or historical data from storage devices 6605b, 6605a. The data streams and storage devices 105a, 105b provide external factors and/or a driver profile to simulators 6603a, 6603b which use the external factors and/or driver profile to adjust one or more models of the processes/systems being simulated … traffic conditions (e.g., traffic speed, accidents)” here the system is using simulators to evaluate real time and historical data to evaluate the vehicle, the historical data including accidents) (Paragraph [0525], “In an embodiment, historical data stored in data storage devices 6605a, 6605b are used to perform data analytics to analyze past failures of AV processes/systems and to predict future failures of AV processes/systems.“)
wherein the past error information includes a vehicle accident risk caused by errors of the same kind and version of artificial intelligences as the kind and version of the artificial intelligence in which the error has occurred (Paragraph [0525], “In an embodiment, historical data stored in data storage devices 6605a, 6605b are used to perform data analytics to analyze past failures of AV processes/systems and to predict future failures of AV processes/systems“ here the system can analyze past failures so as to improve the system) (Paragraph [0524], “Simulators 6603a, 6603b simulate the AV in the virtual world (e.g., 2D or 3D simulation) with the external factors and/or driver profile to determine how the AV will perform and whether a failure is likely to occur.“) (Paragraph [0319], “In an embodiment, failure conditions include a control system becoming nonresponsive, a potential security threat to the control system, a steering device/throttle device becoming locked/jammed, or various other failure conditions that increases the risk of the AV 100 to deviate from its desired output. “) (Paragraph [0508], “In an embodiment, other output signals or data can be provided by OBD 6306a and OBD 6306b, such as codes (e.g., binary codes) indicating a type of failure and a severity level of the failure,” here failure data, which is analyzed to improve the system includes a severity level which indicates an accident risk caused by the error).
Gong and Frazzoli are analogous art as they are both generally related to the detection and handling of malfunctions in vehicles.
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 determining whether an error has occurred in the autonomous driving artificial intelligence includes determining whether the artificial intelligence has accurately recognized an object based on a comparison among recognition results of a plurality of sensors, wherein the past error information includes degree of error, wherein the past error information includes number of accidents, and wherein the past error information includes a vehicle accident risk caused by errors of the same kind and version of artificial intelligences as the kind and version of the artificial intelligence in which the error has occurred of Frazzoli in the method for changing a route of an autonomous driving vehicle when an error occurs in an autonomous driving artificial intelligence of Gong with a reasonable expectation of success in order to improving autonomous vehicle performance by actively adapting to a driving context including detecting and responding to system malfunctions (Paragraph [0008], “Particular aspects of the foregoing techniques can provide one or more of the following advantages. For example, context selective promotion of AV operation modules that share a region of the operating envelope can lead to improved AV operation performance by active adaptation to driving context. More specifically, the foregoing disclosed technologies cause increased flexibility of operational control in AV perception stage, AV localization stage, AV planning stage, and/or AV control stage.”).
Regarding claim 2, the combination of Gong and Frazzoli teaches the method as discussed above in claim 1, Gong further teaches wherein the extracting of the past error information from the storage includes extracting, from the storage, the past error information about the same kind and version of artificial intelligence as the kind and version of the artificial intelligence in which the error has occurred (Paragraph [0054-0056], “The algorithm can examine a number of factors, which include a general category of a malfunction, a specific component where the malfunction occurs, a frequency of occurrence of the malfunction, and availability of inspection data for the malfunction. In one embodiment, the general category of a malfunction refers to the general area where the malfunction occurs, for example, hardware, software, or vehicle behaviors … The frequency of occurrence of a malfunction refers to the number of times that a particular malfunction has occurred over a past period of time,” here the system is determining factors for evaluating the error including a frequency/past information, this information includes the specific component and for the same type of error for the specific category of the malfunction including a software version which would be the same for the same type and category of the malfunction).
Regarding claim 3, the combination of Gong and Frazzoli teaches the method as discussed above in claim 1, Gong further teaches wherein the error analysis result includes the vehicle accident risk caused by the error of the artificial intelligence (Paragraph [0017], “In one embodiment, there can be three levels of failure risk: high, medium, or low. For the high level of failure risk, the redundant system can issue driving commands to an emergency parking, whereas for the medium or low level of failure risk, the redundant system can issue command to perform a slow braking to drive the ADV to a closest safe place to park.”)
and wherein the generating of the error analysis result message includes including an autonomous driving operation availability information indicating 'operation interruption' into the error analysis result message when the vehicle accident risk caused by the error of the artificial intelligence is greater than or equal to a predetermined threshold value (Paragraph [0058], “The level of failure risk for a malfunction can be high, medium, or low. Each level of risk can be associated with a range of value. Using the algorithm, the malfunction evaluator 517 can calculate a risk value for a malfunction, and then classify the risk value into one of high, medium, or low.”) (Paragraph [0060], “In one embodiment, a high failure risk can cause a vehicle emergency controller 521 to perform an emergency braking on the vehicle, while a medium or a low failure risk will cause the vehicle emergency controller 521 to perform a slow braking on the braking,” here the system is generating an error analysis result message (high, medium or low) which if the risk is greater than the threshold, the threshold being high, this high/medium/low information indicates that the vehicle will perform an emergency stop operation which is an operation interruption).
Regarding claim 4, the combination of Gong and Frazzoli teaches the method as discussed above in claim 1, Gong further teaches wherein the determining of whether the driving of the autonomous driving vehicle needs to be stopped includes determining that the driving of the autonomous driving vehicle needs to be stopped when the error analysis result message includes the autonomous driving operation availability information indicating 'operation interruption' (Paragraph [0058], “The level of failure risk for a malfunction can be high, medium, or low. Each level of risk can be associated with a range of value. Using the algorithm, the malfunction evaluator 517 can calculate a risk value for a malfunction, and then classify the risk value into one of high, medium, or low.”) (Paragraph [0060], “In one embodiment, a high failure risk can cause a vehicle emergency controller 521 to perform an emergency braking on the vehicle, while a medium or a low failure risk will cause the vehicle emergency controller 521 to perform a slow braking on the braking,” here the system is generating an error analysis result message (high, medium or low) including thresholds, if the system outputs a risk at the high threshold to the emergency response system, this indicates that the vehicle needs to perform an emergency stop operation which is an operation interruption).
Regarding claim 5, the combination of Gong and Frazzoli teaches the method as discussed above in claim 1, Gong further teaches wherein the error analysis result includes the vehicle accident risk caused by the error of the artificial intelligence (Paragraph [0017], “In one embodiment, there can be three levels of failure risk: high, medium, or low. For the high level of failure risk, the redundant system can issue driving commands to an emergency parking, whereas for the medium or low level of failure risk, the redundant system can issue command to perform a slow braking to drive the ADV to a closest safe place to park.”)
and wherein the determining of whether the driving of the autonomous vehicle needs to be stopped includes determining that the driving of the autonomous driving vehicle needs to be stopped when the vehicle accident risk caused by the error of the artificial intelligence is greater than or equal to a predetermined threshold value (Paragraph [0058], “The level of failure risk for a malfunction can be high, medium, or low. Each level of risk can be associated with a range of value. Using the algorithm, the malfunction evaluator 517 can calculate a risk value for a malfunction, and then classify the risk value into one of high, medium, or low.”) (Paragraph [0060], “In one embodiment, a high failure risk can cause a vehicle emergency controller 521 to perform an emergency braking on the vehicle, while a medium or a low failure risk will cause the vehicle emergency controller 521 to perform a slow braking on the braking,” here the system is generating an error analysis result message (high, medium or low) which if the risk is greater than the threshold, the threshold being high, the system when it receives a ‘high’ message, this indicates that the vehicle will perform an emergency stop operation which is an operation interruption).
Regarding claim 6, the combination of Gong and Frazzoli teaches the method as discussed above in claim 1, Gong further teaches wherein the error analysis result includes the vehicle accident risk caused by the error of the artificial intelligence (Paragraph [0017], “In one embodiment, there can be three levels of failure risk: high, medium, or low. For the high level of failure risk, the redundant system can issue driving commands to an emergency parking, whereas for the medium or low level of failure risk, the redundant system can issue command to perform a slow braking to drive the ADV to a closest safe place to park.”)
and error avoidance information (Paragraph [0061], “In the slow braking mode, the ADV 101 can slowly drive to a safer place and park there. In this mode, the vehicle emergency handler 521 can use sensors data and localization information to generate a path to a nearby safe place, e.g., the curb of the road, and send driving commands 523 directly to the CAN bus component 321 without sending the driving commands 523 to the control module 306 as the ADS 110 would normally do,” here the system is including avoidance information, which allows the vehicle to continue operating in a modified autonomous mode to avoid the errors and travel to a safe location)
and wherein the generating of the error analysis result message includes including an autonomous driving operation availability information indicating 'operation continuation' (Paragraph [0058], “The level of failure risk for a malfunction can be high, medium, or low. Each level of risk can be associated with a range of value. Using the algorithm, the malfunction evaluator 517 can calculate a risk value for a malfunction, and then classify the risk value into one of high, medium, or low.”) (Paragraph [0060], “In one embodiment, a high failure risk can cause a vehicle emergency controller 521 to perform an emergency braking on the vehicle, while a medium or a low failure risk will cause the vehicle emergency controller 521 to perform a slow braking on the braking,” here the system is generating an error analysis result message (high, medium or low) which if the risk is less than the threshold, the threshold being high, this indicates that the vehicle will continue autonomous operations without performing an emergency stop operation which is an operation interruption)
and the error avoidance information into the error analysis result message when the vehicle accident risk caused by the error of the artificial intelligence is less than a predetermined threshold value (Paragraph [0061], “In the slow braking mode, the ADV 101 can slowly drive to a safer place and park there. In this mode, the vehicle emergency handler 521 can use sensors data and localization information to generate a path to a nearby safe place, e.g., the curb of the road, and send driving commands 523 directly to the CAN bus component 321 without sending the driving commands 523 to the control module 306 as the ADS 110 would normally do,” here the system is including avoidance information, which allows the vehicle to continue operating in a modified autonomous mode to avoid the errors and travel to a safe location).
Regarding claim 7, the combination of Gong and Frazzoli teaches the method as discussed above in claim 1, Gong further teaches wherein the determining of whether the driving of the autonomous driving vehicle needs to be stopped includes determining that it is not necessary to stop the driving of the autonomous driving vehicle when the error analysis result message includes the autonomous driving operation availability information indicating 'operation continuation' value (Paragraph [0065], “A computer hardware checker 503 can check for malfunctions in the main computer system 401, human machine interface (HMI) hardware, and the redundant system 327 based on the presence of heartbeat signals from each type of hardware component. For example, the computer hardware checker 503 can check whether the main computer system 401 has been powered on and is working normally, whether the HMI hardware output is normal, and whether the redundant system 327 has been powered on and is working normally,” here the system can determine if software components are operating normally and therefore the system does not need to be stopped) (Paragraph [0060], “In one embodiment, a high failure risk can cause a vehicle emergency controller 521 to perform an emergency braking on the vehicle, while a medium or a low failure risk will cause the vehicle emergency controller 521 to perform a slow braking on the braking,” here the system is generating an error analysis result message (high, medium or low) which if the risk is less than the threshold, the threshold being high, this indicates that the vehicle will continue autonomous operations without performing an emergency stop operation which is an operation interruption) (Paragraph [0061], “In the slow braking mode, the ADV 101 can slowly drive to a safer place and park there. In this mode, the vehicle emergency handler 521 can use sensors data and localization information to generate a path to a nearby safe place, e.g., the curb of the road, and send driving commands 523 directly to the CAN bus component 321 without sending the driving commands 523 to the control module 306 as the ADS 110 would normally do,” here the system is including avoidance information, which allows the vehicle to continue operating in a modified autonomous mode to avoid the errors and travel to a safe location and the system does not need to perform an emergency stop).
Regarding claim 8, the combination of Gong and Frazzoli teaches the method as discussed above in claim 1, Gong further teaches further comprising resetting a driving route of the autonomous driving vehicle when it is determined that it is not necessary to stop the driving of the autonomous driving vehicle (Paragraph [0061], “In the slow braking mode, the ADV 101 can slowly drive to a safer place and park there. In this mode, the vehicle emergency handler 521 can use sensors data and localization information to generate a path to a nearby safe place, e.g., the curb of the road, and send driving commands 523 directly to the CAN bus component 321 without sending the driving commands 523 to the control module 306 as the ADS 110 would normally do,” here the system has determined that it is not necessary to perform an emergency stop for the autonomous vehicle, the system then resets the driving route to the safe location).
Regarding claim 9, the combination of Gong and Frazzoli teaches the method as discussed above in claim 1, Gong further teaches further comprising resetting a driving route of the autonomous driving vehicle based on the error avoidance information when it is determined that it is not necessary to stop the driving of the autonomous driving vehicle (Paragraph [0061], “In the slow braking mode, the ADV 101 can slowly drive to a safer place and park there. In this mode, the vehicle emergency handler 521 can use sensors data and localization information to generate a path to a nearby safe place, e.g., the curb of the road, and send driving commands 523 directly to the CAN bus component 321 without sending the driving commands 523 to the control module 306 as the ADS 110 would normally do,” here the system has determined that it is not necessary to perform an emergency stop for the autonomous vehicle, the system then resets the driving route to the safe location based on the avoidance information which indicates slow braking mode).
Claims 11 and 13-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gong (US 20210387631) in view of Wang (US 20200070821) and further in view of Frazzoli (US-20210163021).
Regarding claim 11, Gong teaches a method for changing a route in an autonomous driving system installed on an autonomous driving vehicle when an error occurs in an autonomous driving artificial intelligence, the method comprising (Paragraph [0014], "According to various embodiment, described herein are methods and systems for reliably detecting malfunctions in a variety of software or hardware components in an autonomous driving vehicle (ADV).") (Paragraph [0074], “the processing logic issues one or more driving commands directly to a controller area network (CAN bus) in the ADV to control the ADV in response to the level of failure risk of the malfunction.”)
determining whether an error has occurred in an autonomous driving artificial intelligence (Figure 6, item 602, "Determine, by the redundant system, that a malfunction has occurred")
collecting error information of the artificial intelligence when the error of the artificial intelligence has occurred (Paragraph [0054], "In one embodiment, the malfunction evaluator 517 can use a predetermined algorithm to determine the level of failure risk 518 of a malfunction. The algorithm can examine a number of factors, which include a general category of a malfunction, a specific component where the malfunction occurs, a frequency of occurrence of the malfunction, and availability of inspection data for the malfunction," here the system is collecting information/factors and using that information to evaluate the failure)
receiving an error analysis result message corresponding to the error information of the artificial intelligence (Paragraph [0058], "Each of the above factors can be given a weight in the predetermined algorithm used to a level of failure risk for a malfunction. The weight of each factor can be based on user experiences. The level of failure risk for a malfunction can be high, medium, or low. Each level of risk can be associated with a range of value. Using the algorithm, the malfunction evaluator 517 can calculate a risk value for a malfunction, and then classify the risk value into one of high, medium, or low," here the system is using the collected information/factors to analyze the failure risk using information including the past error information)
determining whether driving of the autonomous driving vehicle needs to be stopped based on the error analysis result message (Paragraph [0060], "In one embodiment, a high failure risk can cause a vehicle emergency controller 521 to perform an emergency braking on the vehicle, while a medium or a low failure risk will cause the vehicle emergency controller 521 to perform a slow braking on the braking," the emergency controller will use the analysis message in order to determine if the vehicle should perform emergency braking/stopping)
moving the autonomous driving vehicle to a safe place and stopping the driving of the autonomous driving vehicle when it is determined that the driving of the autonomous driving vehicle needs to be stopped (Paragraph [0061], “In the slow braking mode, the ADV 101 can slowly drive to a safer place and park there. In this mode, the vehicle emergency handler 521 can use sensors data and localization information to generate a path to a nearby safe place, e.g., the curb of the road, and send driving commands 523 directly to the CAN bus component 321 without sending the driving commands 523 to the control module 306 as the ADS 110 would normally do,” here the system has determined that it is not necessary to perform an immediate emergency stop for the autonomous vehicle, the system then resets the driving route to the safe location based on the avoidance information which indicates slow braking mode).
However while Gong teaches communicating with cloud servers (Paragraph [0019], “Referring to FIG. 1, network configuration 100 includes autonomous driving vehicle (ADV) 101 that may be communicatively coupled to one or more servers 103-104 over a network 102. … Server(s) 103-104 may be any kind of servers or a cluster of servers, such as Web or cloud servers”).
Gong does not explicitly teach transmitting the error information of the artificial intelligence to a cloud server and receiving an error analysis result message corresponding to the error information of the artificial intelligence from the cloud server.
Wang teaches a method and computer system for analyzing driving related data including
transmitting the error information of the artificial intelligence to a cloud server (Paragraph [0014], “Embodiments of the computing system 120 may be a computer system, a computer, a server, one or more servers, a cloud computing device “) (Paragraph [0015], “For instance, information/data related to driving patterns and the like may be transmitted to and received from the one or more vehicle systems 110, one or more sensors 111, one or more real-time data platforms 112, one or more historical data platforms 113, the one or more data repositories 114, and the computer system 120 over the network 107,” here the system is transmitting driving related information to a cloud server over a network for analysis, while the system here is not explicitly teaching that this information is error information, this methodology could reasonable be applied to the driving related information of Gong which is error information)
receiving an error analysis result message corresponding to the error information of the artificial intelligence from the cloud server (Paragraph [0014], “The cognition enabled driving pattern detection system 100 and/or computer system 120 may configured to provide information or otherwise notify drivers. Thus, the cognition enabled driving pattern detection system 100 and/or computer system 120 may be configured to detect driving patterns using learning and cognition based on information accumulated, received or otherwise acquired from various information sources, sensors, databases and the like.”) (Paragraph [0021], “The computer system 120 may utilize data collected or gathered by the one or more real-time data platforms 112 to detect patterns and make predictions and provide notifications to drivers.”) (Paragraph [0027], “The analytics module 133 may further be configured to analyze the nature and type of neighboring vehicles, along with the nature and type of the monitored vehicle (i.e. the vehicle that the computer system 120 notifies of detected patterns and provides driving suggestions to), such as whether the vehicle is an emergency vehicle, a law enforcement vehicle, a civilian passenger vehicle, a military vehicle, a commercial vehicle, a self-driving vehicle, a human driven vehicle and the like. The analytics module 133 may be configured to analyze data from regional transport system databases such as accident data, weather databases, or the like,” here the system can transmit and receive information such as analysis results from a cloud server to vehicles, while the system here is not explicitly teaching that this information is error information, this methodology could reasonable be applied to the driving related information of Gong which is error information).
Gong and Wang are analogous art as they are both generally related to systems for analyzing autonomous vehicle information.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include transmitting the error information of the artificial intelligence to a cloud server and receiving an error analysis result message corresponding to the error information of the artificial intelligence from the cloud server of Wang in the system for fail safe handling for an autonomous vehicle of Gong with a reasonable expectation of success in order to leverage the capability of cloud based computing systems for optimizing the use of computing resources (Paragraph [0065-0069], “Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service.”).
However while Gong teaches performing a comparison to determine whether an error has occurred (Paragraph [0067], “The ADS checker 513 can check for any malfunctions in the ADS 110. For the perception module 302 in the ADS 110, malfunctions may include abnormal output data channel frequencies, abnormal perception processing delay, and abnormal point cloud fusion. For the localization module 301, the prediction module 303, malfunctions can include abnormal output data channel frequencies. For the control module 306, malfunctions can include abnormal output data channel frequencies and abnormal total link delay timeouts. For the prediction module 303, malfunction may include abnormal total link delay timeouts.”) (Paragraph [0047], “The redundant system 327 can compare the output parameters with expected output parameters to determine whether a malfunction has occurred.“) (Paragraph [0051], “Each checking component can compare output parameters from a software or hardware component with their respective expected parameters. Based on the comparison, the checking component can detect the presence of any malfunction in the software or hardware component.”)
Gong does not explicitly teach wherein the determining whether an error has occurred in the autonomous driving artificial intelligence includes determining whether the artificial intelligence has accurately recognized an object based on a comparison among recognition results of a plurality of sensors.
Frazzoli teaches redundancy techniques in autonomous vehicle for safe handling of malfunctions and errors including
wherein the determining whether an error has occurred in the autonomous driving artificial intelligence includes determining whether the artificial intelligence has accurately recognized an object based on a comparison among recognition results of a plurality of sensors (Paragraph [0344], “At 3315, the autonomous vehicle determines whether there is an abnormal condition based on a difference between the first and second sensor data streams.”) (Paragraph [0526], “The perception module 402 outputs a first scene description that includes one or more classified objects (e.g., vehicles, pedestrians) detected from the LiDAR point cloud data. Concurrent (e.g., in parallel) with the LiDAR processing, the stereo camera captures stereo images which are also input into the perception module 402. The perception module 402 outputs a second scene description of one or more classified objects detected from the stereo image data.”) (Paragraph [0529], “For example, if the diagnostic modules 102a, 102b do not indicate that the LiDAR or stereo camera hardware or software has failed, the LiDAR scene description matches the simulated LiDAR scene description (e.g., all classified objects are accounted for in both scene descriptions), and the stereo camera scene description matches the simulated stereo camera scene description, then the AV continues to operate in nominal mode.”) (Paragraph [0234], “In some implementations, the first sensor signals received from the first set of the sensors 121 include one or more lists of objects detected by corresponding sensors of the first set, and the second sensor signals received from the second set of the sensors 121 include one or more lists of objects detected by corresponding sensors of the second set. In some implementations, these lists are created by the perception modules. As such, the generating of the first world view proposal by the first perception module 1710a can include creating one or more first lists of objects detected by corresponding sensors of the first set. And, the generating of the second world view proposal by the second perception module 1710b can include creating one or more second lists of objects detected by corresponding sensors of the second set,” here each of the redundant systems is using recognition results of a plurality of sensors to create object detection lists).
Gong, Wang and Frazzoli are analogous art as they are both generally related to systems for analyzing autonomous vehicle information.
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 determining whether an error has occurred in the autonomous driving artificial intelligence includes determining whether the artificial intelligence has accurately recognized an object based on a comparison among recognition results of a plurality of sensors of Frazzoli in the method for changing a route of an autonomous driving vehicle when an error occurs in an autonomous driving artificial intelligence of Gong and Wang with a reasonable expectation of success in order to improving autonomous vehicle performance by actively adapting to a driving context including detecting and responding to system malfunctions (Paragraph [0008], “Particular aspects of the foregoing techniques can provide one or more of the following advantages. For example, context selective promotion of AV operation modules that share a region of the operating envelope can lead to improved AV operation performance by active adaptation to driving context. More specifically, the foregoing disclosed technologies cause increased flexibility of operational control in AV perception stage, AV localization stage, AV planning stage, and/or AV control stage.”).
Regarding claim 13, claim 13 is similar in scope to claim 8 and is therefore rejected under similar rationale.
Regarding claim 14, claim 14 is similar in scope to claim 5 and is therefore rejected under similar rationale.
Regarding claim 15, claim 15 is similar in scope to claim 4 and is therefore rejected under similar rationale.
Regarding claim 16, claim 16 is similar in scope to claim 9 and is therefore rejected under similar rationale.
Regarding claim 17, Gong teaches an autonomous driving system installed on an autonomous driving vehicle, the system comprising a processor and memory configured to implement: (Paragraph [0014], "According to various embodiment, described herein are methods and systems for reliably detecting malfunctions in a variety of software or hardware components in an autonomous driving vehicle (ADV).") (Paragraph [0027], “Some or all of the functions of ADV 101 may be controlled or managed by ADS 110, especially when operating in an autonomous driving mode. ADS 110 includes the necessary hardware (e.g., processor(s), memory, storage) and software (e.g., operating system, planning and routing programs)”)
a recognition module configured to detect an object based on sensor data (Paragraph [0023], “Radar unit 214 may represent a system that utilizes radio signals to sense objects within the local environment of the ADV. In some embodiments, in addition to sensing objects, radar unit 214 may additionally sense the speed and/or heading of the objects. LIDAR unit 215 may sense objects in the environment in which the ADV is located using lasers. LIDAR unit 215 could include one or more laser sources, a laser scanner, and one or more detectors, among other system components.”)
determine a kind of the object using an artificial intelligence with respect to the detected object, and generate object information (Paragraph [0036], “Perception module 302 may include a computer vision system or functionalities of a computer vision system to process and analyze images captured by one or more cameras in order to identify objects and/or features in the environment of the ADV. The objects can include traffic signals, road way boundaries, other vehicles, pedestrians, and/or obstacles, etc. The computer vision system may use an object recognition algorithm, video tracking, and other computer vision techniques. In some embodiments, the computer vision system can map an environment, track objects, and estimate the speed of objects, etc. Perception module 302 can also detect objects based on other sensors data provided by other sensors such as a radar and/or LIDAR.”)
an error detection module configured to determine whether an error has occurred in the artificial intelligence based on the object information (Figure 6, item 602, "Determine, by the redundant system, that a malfunction has occurred")
an error information collection module configured to collect error information of the artificial intelligence when the error of the artificial intelligence has occurred (Paragraph [0054], "In one embodiment, the malfunction evaluator 517 can use a predetermined algorithm to determine the level of failure risk 518 of a malfunction. The algorithm can examine a number of factors, which include a general category of a malfunction, a specific component where the malfunction occurs, a frequency of occurrence of the malfunction, and availability of inspection data for the malfunction," here the system is collecting information/factors and using that information to evaluate the failure)
and receive an error analysis result message corresponding to the error information (Paragraph [0058], "Each of the above factors can be given a weight in the predetermined algorithm used to a level of failure risk for a malfunction. The weight of each factor can be based on user experiences. The level of failure risk for a malfunction can be high, medium, or low. Each level of risk can be associated with a range of value. Using the algorithm, the malfunction evaluator 517 can calculate a risk value for a malfunction, and then classify the risk value into one of high, medium, or low," here the system is using the collected information/factors to analyze the failure risk using information including the past error information)
and an error response module configured to respond to the error of the artificial intelligence based on the error analysis result message (Paragraph [0060], "In one embodiment, a high failure risk can cause a vehicle emergency controller 521 to perform an emergency braking on the vehicle, while a medium or a low failure risk will cause the vehicle emergency controller 521 to perform a slow braking on the braking," the emergency controller will use the analysis message in order to determine if the vehicle should perform emergency braking/stopping)
a vehicle control module configured to move the autonomous driving vehicle to a safe place and stope the driving of the autonomous driving vehicle when it is determined that the driving of the autonomous driving vehicle needs to be stopped (Paragraph [0061], “In the slow braking mode, the ADV 101 can slowly drive to a safer place and park there. In this mode, the vehicle emergency handler 521 can use sensors data and localization information to generate a path to a nearby safe place, e.g., the curb of the road, and send driving commands 523 directly to the CAN bus component 321 without sending the driving commands 523 to the control module 306 as the ADS 110 would normally do,” here the system has determined that it is not necessary to perform an immediate emergency stop for the autonomous vehicle, the system then resets the driving route to the safe location based on the avoidance information which indicates slow braking mode).
However while Gong teaches communicating with cloud servers (Paragraph [0019], “Referring to FIG. 1, network configuration 100 includes autonomous driving vehicle (ADV) 101 that may be communicatively coupled to one or more servers 103-104 over a network 102. … Server(s) 103-104 may be any kind of servers or a cluster of servers, such as Web or cloud servers”).
Gong does not explicitly teach a transmission and reception module configured to transmit the error information to a cloud server and receive an error analysis result message corresponding to the error information from the cloud server.
Wang teaches a method and computer system for analyzing driving related data including
a transmission and reception module configured to transmit the error information to a cloud server (Paragraph [0014], “Embodiments of the computing system 120 may be a computer system, a computer, a server, one or more servers, a cloud computing device “) (Paragraph [0015], “For instance, information/data related to driving patterns and the like may be transmitted to and received from the one or more vehicle systems 110, one or more sensors 111, one or more real-time data platforms 112, one or more historical data platforms 113, the one or more data repositories 114, and the computer system 120 over the network 107,” here the system is transmitting driving related information to a cloud server over a network for analysis, while the system here is not explicitly teaching that this information is error information, this methodology could reasonable be applied to the driving related information of Gong which is error information)
and receive an error analysis result message corresponding to the error information from the cloud server (Paragraph [0014], “The cognition enabled driving pattern detection system 100 and/or computer system 120 may configured to provide information or otherwise notify drivers. Thus, the cognition enabled driving pattern detection system 100 and/or computer system 120 may be configured to detect driving patterns using learning and cognition based on information accumulated, received or otherwise acquired from various information sources, sensors, databases and the like.”) (Paragraph [0021], “The computer system 120 may utilize data collected or gathered by the one or more real-time data platforms 112 to detect patterns and make predictions and provide notifications to drivers.”) (Paragraph [0027], “The analytics module 133 may further be configured to analyze the nature and type of neighboring vehicles, along with the nature and type of the monitored vehicle (i.e. the vehicle that the computer system 120 notifies of detected patterns and provides driving suggestions to), such as whether the vehicle is an emergency vehicle, a law enforcement vehicle, a civilian passenger vehicle, a military vehicle, a commercial vehicle, a self-driving vehicle, a human driven vehicle and the like. The analytics module 133 may be configured to analyze data from regional transport system databases such as accident data, weather databases, or the like,” here the system can transmit and receive information such as analysis results from a cloud server to vehicles, while the system here is not explicitly teaching that this information is error information, this methodology could reasonable be applied to the driving related information of Gong which is error information).
Gong and Wang are analogous art as they are both generally related to systems for analyzing autonomous vehicle information.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include a transmission and reception module configured to transmit the error information to a cloud server and receive an error analysis result message corresponding to the error information from the cloud server of Wang in the system for fail safe handling for an autonomous vehicle of Gong with a reasonable expectation of success in order to leverage the capability of cloud based computing systems for optimizing the use of computing resources (Paragraph [0065-0069], “Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service.”).
However while Gong teaches performing a comparison to determine whether an error has occurred (Paragraph [0067], “The ADS checker 513 can check for any malfunctions in the ADS 110. For the perception module 302 in the ADS 110, malfunctions may include abnormal output data channel frequencies, abnormal perception processing delay, and abnormal point cloud fusion. For the localization module 301, the prediction module 303, malfunctions can include abnormal output data channel frequencies. For the control module 306, malfunctions can include abnormal output data channel frequencies and abnormal total link delay timeouts. For the prediction module 303, malfunction may include abnormal total link delay timeouts.”) (Paragraph [0047], “The redundant system 327 can compare the output parameters with expected output parameters to determine whether a malfunction has occurred.“) (Paragraph [0051], “Each checking component can compare output parameters from a software or hardware component with their respective expected parameters. Based on the comparison, the checking component can detect the presence of any malfunction in the software or hardware component.”)
Gong does not explicitly teach wherein the error detection module is configured to determine the artificial intelligence has accurately recognized an object based on a comparison among recognition results of a plurality of sensors.
Frazzoli teaches redundancy techniques in autonomous vehicle for safe handling of malfunctions and errors including
wherein the error detection module is configured to determine the artificial intelligence has accurately recognized an object based on a comparison among recognition results of a plurality of sensors (Paragraph [0344], “At 3315, the autonomous vehicle determines whether there is an abnormal condition based on a difference between the first and second sensor data streams.”) (Paragraph [0526], “The perception module 402 outputs a first scene description that includes one or more classified objects (e.g., vehicles, pedestrians) detected from the LiDAR point cloud data. Concurrent (e.g., in parallel) with the LiDAR processing, the stereo camera captures stereo images which are also input into the perception module 402. The perception module 402 outputs a second scene description of one or more classified objects detected from the stereo image data.”) (Paragraph [0529], “For example, if the diagnostic modules 102a, 102b do not indicate that the LiDAR or stereo camera hardware or software has failed, the LiDAR scene description matches the simulated LiDAR scene description (e.g., all classified objects are accounted for in both scene descriptions), and the stereo camera scene description matches the simulated stereo camera scene description, then the AV continues to operate in nominal mode.”) (Paragraph [0234], “In some implementations, the first sensor signals received from the first set of the sensors 121 include one or more lists of objects detected by corresponding sensors of the first set, and the second sensor signals received from the second set of the sensors 121 include one or more lists of objects detected by corresponding sensors of the second set. In some implementations, these lists are created by the perception modules. As such, the generating of the first world view proposal by the first perception module 1710a can include creating one or more first lists of objects detected by corresponding sensors of the first set. And, the generating of the second world view proposal by the second perception module 1710b can include creating one or more second lists of objects detected by corresponding sensors of the second set,” here each of the redundant systems is using recognition results of a plurality of sensors to create object detection lists).
Gong, Wang and Frazzoli are analogous art as they are both generally related to systems for analyzing autonomous vehicle information.
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 error detection module is configured to determine the artificial intelligence has accurately recognized an object based on a comparison among recognition results of a plurality of sensors of Frazzoli in the method for changing a route of an autonomous driving vehicle when an error occurs in an autonomous driving artificial intelligence of Gong and Wang with a reasonable expectation of success in order to improving autonomous vehicle performance by actively adapting to a driving context including detecting and responding to system malfunctions (Paragraph [0008], “Particular aspects of the foregoing techniques can provide one or more of the following advantages. For example, context selective promotion of AV operation modules that share a region of the operating envelope can lead to improved AV operation performance by active adaptation to driving context. More specifically, the foregoing disclosed technologies cause increased flexibility of operational control in AV perception stage, AV localization stage, AV planning stage, and/or AV control stage.”).
Regarding claim 18, claim 18 is similar in scope to claims 3 and 4 and is therefore rejected under similar rationale.
Regarding claim 19, claim 19 is similar in scope to claim 5 and is therefore rejected under similar rationale.
Regarding claim 20, the combination of Gong, Wang and Frazzoli teaches the system as discussed above in claim 17, Gong further teaches further comprising a determination module configured to set a driving route of the autonomous driving vehicle (Paragraph [0029], “ADS 110 can plan an optimal route and drive vehicle 101, for example, via control system 111, according to the planned route to reach the specified destination safely and efficiently”)
wherein the error response module delivers error avoidance information included in the error analysis result message to the determination module when vehicle accident risk is included in the error analysis result message and the vehicle accident risk is less than a threshold value and wherein the determination module resets a driving route of the autonomous driving vehicle based on the error avoidance information (Paragraph [0061], “In the slow braking mode, the ADV 101 can slowly drive to a safer place and park there. In this mode, the vehicle emergency handler 521 can use sensors data and localization information to generate a path to a nearby safe place, e.g., the curb of the road, and send driving commands 523 directly to the CAN bus component 321 without sending the driving commands 523 to the control module 306 as the ADS 110 would normally do,” here the system has determined that it is not necessary to perform an emergency stop for the autonomous vehicle, the system then resets the driving route to the safe location based on the avoidance information which indicates slow braking mode).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Tariq (US-20210201464) teaches a sensor malfunction detection and remediation system. Levinson (US-20200098394) teaches determining a group of objects based at least in part on the received data, and identifying an error associated with data included in the first signal and/or the second signal. Lee (US-20220164609) teaches using a LiDAR sensor and a camera provided in an autonomous vehicle, to extract a crop image, corresponding to at least one object recognized based on the point cloud, from the image data and input the same to the multi-object classification model, and to remove a false-positive object classified into the noise class, among the at least one object, by the multi-object classification model.
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