DETAILED ACTION
1. Claims 1-3, 5-10, and 12-20 have been presented for examination.
Claims 4 and 11 have been cancelled.
Notice of Pre-AIA or AIA Status
2. 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
3. Applicant's arguments filed 2/5/26 have been fully considered but they are not persuasive.
i) Following Applicants arguments and amendments and additional prior art rejection has been presented below.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) 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.
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.
4. Claim(s) 1-3, 6, 9-10, 13, 16-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen, Yu, et al. "Mixed test environment-based vehicle-in-the-loop validation-a new testing approach for autonomous vehicles." 2020 IEEE intelligent vehicles symposium (IV). IEEE, 2020 hereafter Chen, in view of U.S. Patent Publication No. 20210398441, hereafter Fok.
Regarding Claim 1: The reference discloses A system comprising: a memory device; and a processing device, operatively coupled to the memory device, to perform operations comprising:
receiving a set of real-world log data defining a real-world driving environment and generated by an autonomous vehicle (AV), (Chen. Figure 1, Real vehicle top right with sensors bottom right) wherein the set of real-world log data comprises at least one set of real-world parameters defining at least one real-world object observed by the AV operating within the real-world driving environment; (Chen. Page 1283, bottom right, “Our method enables the reconstruction of any real scene based on the corresponding HD map to create a precise semantic map and a realistic rendered visualization for the virtual world. Plus, when merging the real world with virtual elements, the real vehicle states are synchronized to a virtual equivalent in simulation using the internal communication mechanism of our software stack, data of the “perceived” virtual elements as well as the real environment are then transmitted to the computing unit on vehicle for evaluation.”)
generating, based on the set of real-world log data, a set of simulated log data defining a simulated driving environment, wherein the set of simulated log data comprises at least one set of simulated parameters defining at least one simulated object within the simulated driving environment, and wherein generating the set of simulated log data comprises perturbing at least one real-world parameter of the at least one set of real-world parameters to obtain the at least one set of simulated parameters; and (Chen. Page 1284, “Fig. 1 is a diagram that illustrates the framework of the proposed method, with a closed loop consisting of a real autonomous vehicle and a mixed test environment. On the one hand, the vehicle is equipped with a computing unit and is loaded with necessary sensors, such as GPS, Lidar, camera, etc., for positioning and perception. When the vehicle receives a motion command, its status is constantly updated in the environment. On the other hand, the mixed environment consists of two parts. One is the real-world scenario in free space or on structured roads, and it’s free to add a certain number of normal vehicles, pedestrians or dummy obstacles to improve the randomness of the test within a safe range; the other part is the computer generated elements with rendered visualization in simulation. At the same time, the simulation visualization interface also contains structured roads reconstructed from HD maps. The raw data perceived by real sensors will then be transmitted to the core algorithm module for fusion processing, The virtual elements, excluding virtual roads, are directly processed as fusioned results to be combined with the real fusion results and fed into subsequent algorithmic processing module. Finally, the generated vehicle driving commands including throttle, brake, steering, etc., will be sent to the vehicle to complete a whole cycle of closed-loop validation.”)
causing a simulation to be performed using the set of simulated log data; (Chen. Page 1289, top left column, “Subsequently, when the vehicle approaches the bus, the pedestrian model generated by the computer will suddenly appear from the front of the bus and cross to the opposite side of the road. At this time, the vehicle should make a decision according to the perceived results quickly, deciding whether to make a second stage of deceleration or directly stop considering the distance to obstacle. Finally, its also required that the vehicle can plan out a new driving trajectory regarding its current state as well as the road constraints. Not only the logic of the decision-making module is verified in this way, but the experimental limit safe distance and the vehicles control performance can also be verified in the testing process. Fig. 7 shows the vehicle speed change in a safe collision avoidance action. Experiments integrated with virtual environment have effectively improved the robustness of the control algorithms.”)
Chen does not explicitly recite obtaining a simulation output reflecting whether performance of the AV within the simulated driving environment satisfies a condition for an update to control software of the AV.
However Fok recites obtaining a simulation output reflecting whether performance of the AV within the simulated driving environment satisfies a condition for an update to control software of the AV. (Fok. “[0027] Turning now to FIG. 3, an autonomous vehicle simulation system 300 is shown. The autonomous vehicle simulation system 300 is configured to perform a simulation of the autonomous vehicle 102 of FIG. 1. The autonomous vehicle simulation system 300 may simulate the performance of the autonomous vehicle 102 in varying driving scenarios. One or more evaluators may then evaluate the performance of the autonomous vehicle 102 during a simulation. The autonomous driving software of the autonomous vehicle 102 may then be updated to improve the driving performance of the autonomous vehicle 102. As such, the autonomous vehicle simulation system 300 allows the autonomous vehicle 102 to be tested in a simulated environment before deploying the autonomous vehicle 102 onto real world streets in real driving scenarios.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the control software update of the AV of Fok with the simulation system of Chen since it would allow for testing “in a simulated environment before deploying the autonomous vehicle 102 onto real world streets in real driving scenarios.” (Fok. [0027])
Regarding Claim 2: The reference discloses The system of claim 1, wherein the at least one real-world object comprises at least one agent observed by the AV. (Chen. Figure 1, Pedestrians/Obstacles/Vehicles)
Regarding Claim 3: The reference discloses The system of claim 1, wherein perturbing the at least one parameter of the at least one set of real-world parameters comprises at least one of: physically shifting an AV trajectory, (Chen. Figure 1, Steering Wheel Angle), physically shifting an object trajectory (Chen. Figure 1, Pedestrians/Obstacles/Vehicles), temporally shifting the AV trajectory (Chen. Figure 1, Trajectory Planning), temporally shifting the object trajectory (Chen. Figure 1, Vehicles), changing AV speed (Chen. Figure 1, Throttle), changing object speed (Chen. Figure 1, Vehicles), changing one or more object dimensions, (Chen. Page 1287, left column, “The UFDF is a specially designed data format for precise information description of fusioned objects, including necessary parameters such as position, orientation, 3D bounding box, shape polygon, speed, acceleration, etc.”) or changing an object type. (Chen. Figure 1, Steering Wheel Angle)
Regarding Claim 6: The reference discloses The system of claim 1, wherein the operations further comprise: in response to determining that the simulation output reflects that the AV performance satisfies the condition, identifying an update to a component of the control software of the AV that was used during the simulation as a validated update; and integrating the validated update within the AV. (Chen. Page 1289, top left column, “Subsequently, when the vehicle approaches the bus, the pedestrian model generated by the computer will suddenly appear from the front of the bus and cross to the opposite side of the road. At this time, the vehicle should make a decision according to the perceived results quickly, deciding whether to make a second stage of deceleration or directly stop considering the distance to obstacle. Finally, its also required that the vehicle can plan out a new driving trajectory regarding its current state as well as the road constraints. Not only the logic of the decision-making module is verified in this way, but the experimental limit safe distance and the vehicles control performance can also be verified in the testing process. Fig. 7 shows the vehicle speed change in a safe collision avoidance action. Experiments integrated with virtual environment have effectively improved the robustness of the control algorithms.”)
Regarding Claim 9: See analogous rejection for claim 1.
Regarding Claim 10: See analogous rejection for claim 3.
Regarding Claim 13: See analogous rejection for claim 6.
Regarding Claim 16: See analogous rejection for claim 1.
Regarding Claim 17: The reference discloses The non-transitory computer-readable storage medium of claim 16, wherein perturbing the at least one parameter of the at least one set of real-world parameters comprises at least one of: physically shifting an AV trajectory (Chen. Figure 1, Steering Wheel Angle), physically shifting an object trajectory (Chen. Figure 1, Pedestrians/Obstacles/Vehicles), temporally shifting the AV trajectory (Chen. Figure 1, Trajectory Planning), temporally shifting the object trajectory (Chen. Figure 1, Vehicles), changing AV speed (Figure 1, Throttle), changing object speed (Chen. Figure 1, Vehicles), changing one or more object dimensions, (Chen. Page 1287, left column, “The UFDF is a specially designed data format for precise information description of fusioned objects, including necessary parameters such as position, orientation, 3D bounding box, shape polygon, speed, acceleration, etc.”) or changing an object type. (Chen. Figure 1, Steering Wheel Angle)
Regarding Claim 19: The reference discloses The non-transitory computer-readable storage medium of claim 16, wherein addressing the failed update comprises performing at least one of: flagging the failed update for review, analyzing the failed update to generate simulation metrics for review, or obtaining a modified update to improve operation of the simulated AV within the simulated driving environment. (Chen. Page 1289, top left column, “Subsequently, when the vehicle approaches the bus, the pedestrian model generated by the computer will suddenly appear from the front of the bus and cross to the opposite side of the road. At this time, the vehicle should make a decision according to the perceived results quickly, deciding whether to make a second stage of deceleration or directly stop considering the distance to obstacle. Finally, its also required that the vehicle can plan out a new driving trajectory regarding its current state as well as the road constraints. Not only the logic of the decision-making module is verified in this way, but the experimental limit safe distance and the vehicles control performance can also be verified in the testing process. Fig. 7 shows the vehicle speed change in a safe collision avoidance action. Experiments integrated with virtual environment have effectively improved the robustness of the control algorithms.”)
Regarding Claim 20: The reference discloses The non-transitory computer-readable storage medium of claim 16, wherein the operations further comprise: in response to determining that the simulation output indicates that the AV performance satisfies the condition, identifying the update to the component of the control software of the AV that was used during the simulation as a validated update; and integrating the validated update within the AV. (Chen. Page 1289, top left column, “Subsequently, when the vehicle approaches the bus, the pedestrian model generated by the computer will suddenly appear from the front of the bus and cross to the opposite side of the road. At this time, the vehicle should make a decision according to the perceived results quickly, deciding whether to make a second stage of deceleration or directly stop considering the distance to obstacle. Finally, its also required that the vehicle can plan out a new driving trajectory regarding its current state as well as the road constraints. Not only the logic of the decision-making module is verified in this way, but the experimental limit safe distance and the vehicles control performance can also be verified in the testing process. Fig. 7 shows the vehicle speed change in a safe collision avoidance action. Experiments integrated with virtual environment have effectively improved the robustness of the control algorithms.”)
5. Claim(s) 5, 12, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Fok, further in view of U.S. Patent No. 10019011, hereafter Green.
Regarding Claim 5: Chen and Fok do not explicitly recite The system of claim 1, wherein the simulation output indicates a number of negative events that occurred during the simulation, and wherein the simulation output reflects that the performance of the AV satisfies the condition when the number of negative events is less than or equal to a threshold number of negative events.
However Green recites The system of claim 1, wherein the simulation output indicates a number of negative events that occurred during the simulation, and wherein the simulation output reflects that the performance of the AV satisfies the condition when the number of negative events is less than or equal to a threshold number of negative events. (Green. Column 10, Lines 21-44, “(52) As one example, the yield behaviors (e.g., either real-world or simulated) can be hand-labelled as positive training examples or negative training examples by a human reviewer. As another example, the yield behaviors can be automatically scored using scoring functions. For example, the scoring functions or other labelling rules can be derived from a data analysis of human driving behaviors and/or human passenger feedback. In some implementations, if the yield behavior receives a score that is greater than a first threshold (or less than depending on scoring style) the yield behavior can be labelled as a positive training example; while if the yield behavior receives a score that is less than a second threshold (or greater than depending on scoring style) the yield behavior can be labelled as a negative training example. In some implementations, the first threshold and the second threshold can be the same value. In some implementations, the first threshold and the second threshold can be different values and yield behaviors that receive scores between the first threshold and the second threshold can simply be discarded. In some implementations, the scores provided for the yield behaviors are included as labels to train the yield model, in addition or alternatively to a simple positive or negative label.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the threshold calculation of Green for the simulation in Chen in order to accurately “train the yield model” and to provide “in addition or alternatively to a simple positive or negative label.” (Green, Column 10, Lines 21-44)
Regarding Claim 12: The reference discloses The method of claim 11, wherein the simulation output indicates a number of negative events that occurred during the simulation, and wherein determining whether the simulation output indicates that the performance satisfies the threshold condition comprises determining whether the number of negative events is less than or equal to a threshold number of negative events. (See rejection for claim 5)
Regarding Claim 18: The reference discloses The non-transitory computer-readable storage medium of claim 16, wherein the simulation output indicates a number of negative events that occurred during the simulation, and wherein determining whether the simulation output indicates that the performance satisfies the threshold condition comprises determining whether the number of negative events is less than or equal to a threshold number of negative events. (See rejection for claim 5)
6. Claim(s) 7-8 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Fok, further in view of U.S. Patent Publication No. 20190351914, hereafter Yu.
Regarding Claim 7: Chen and Fok do not explicitly recite The system of claim 1, wherein the operations further comprise: in response to determining that the simulation output reflects that the AV performance does not satisfy the condition, identifying an update to a component of the control software of the AV that was used during the simulation as a failed update; and addressing the failed update.
However Yu discloses The system of claim 1, wherein the operations further comprise: in response to determining that the simulation output reflects that the AV performance does not satisfy the condition, identifying an update to a component of the control software of the AV that was used during the simulation as a failed update; and addressing the failed update. (Yu. [0059] “In this simulation, the personnel can make evaluations on the effectiveness of the updates made to the machine-readable instructions. As such, the effectiveness of the updates (e.g., firmware and/or software updates) in addressing the issues or bugs uncovered by the pattern determination engine 306 can be evaluated under a safe, simulated environment, rather than having to test the updated machine-readable instructions in an autonomous vehicle on road. Once machine-readable instructions are verified and tested (e.g., simulated), in some embodiments, the machine-readable instructions can be deployed (or redeployed) to the instruction engine 210 of FIG. 2 for further testing.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the update software analysis of Yu for the AV system of Chen and Fok in order to allow for the software update to be “evaluated under a safe, simulated environment, rather than having to test the updated machine-readable instructions in an autonomous vehicle on road.” (Yu. [0059])
Regarding Claim 8: The reference discloses The system of claim 7, wherein addressing the failed update comprises performing at least one of: flagging the failed update for review, analyzing the failed update to generate simulation metrics for review, or obtaining a modified update to improve operation of the simulated AV within the simulated driving environment. (Chen. Page 1289, top left column, “Subsequently, when the vehicle approaches the bus, the pedestrian model generated by the computer will suddenly appear from the front of the bus and cross to the opposite side of the road. At this time, the vehicle should make a decision according to the perceived results quickly, deciding whether to make a second stage of deceleration or directly stop considering the distance to obstacle. Finally, its also required that the vehicle can plan out a new driving trajectory regarding its current state as well as the road constraints. Not only the logic of the decision-making module is verified in this way, but the experimental limit safe distance and the vehicles control performance can also be verified in the testing process. Fig. 7 shows the vehicle speed change in a safe collision avoidance action. Experiments integrated with virtual environment have effectively improved the robustness of the control algorithms.” See also (Yu. [0059] “In this simulation, the personnel can make evaluations on the effectiveness of the updates made to the machine-readable instructions. As such, the effectiveness of the updates (e.g., firmware and/or software updates) in addressing the issues or bugs uncovered by the pattern determination engine 306 can be evaluated under a safe, simulated environment, rather than having to test the updated machine-readable instructions in an autonomous vehicle on road. Once machine-readable instructions are verified and tested (e.g., simulated), in some embodiments, the machine-readable instructions can be deployed (or redeployed) to the instruction engine 210 of FIG. 2 for further testing.”)
Regarding Claim 14: See analogous rejection for claim 7.
Regarding Claim 15: See analogous rejection for claim 8.
Conclusion
7. 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.
8. All Claims are rejected.
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
i) U.S. Patent Publication No. 20190316913 which teaches the monitoring of autonomous vehicle status using crowdsourcing data.
ii) U.S. Patent Publication No. 20180143644 which teaches prediction of vehicle traffic behavior with autonomous vehicles in order to determine driving decisions.
iii) Rosique, Francisca, et al. "A systematic review of perception system and simulators for autonomous vehicles research." Sensors 19.3 (2019): 648.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Saif A. Alhija whose telephone number is (571) 272-8635. The examiner can normally be reached on M-F, 10:00-6:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Renee Chavez, can be reached at (571) 270-1104. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Informal or draft communication, please label PROPOSED or DRAFT, can be additionally sent to the Examiners fax phone number, (571) 273-8635.
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SAA
/SAIF A ALHIJA/Primary Examiner, Art Unit 2186
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