DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Examiner notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1 recites the limitation "for the at least partial autonomous guidance". There is insufficient antecedent basis for this limitation in the claim, as it has not been introduced before. Suggested correction, "for an autonomous guidance". Correction is therefore required.
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 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 1-6, 8-10, and 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Modalavalasa et al., US 2021/0096571 A1 (Modalavalasa) in view of O’Malley et al,. US 2020/0410063 A1 (O’Malley).
Claim 1.
Modalavalasa teaches A computer-implemented method for performing a virtual test of a device for the at least partial autonomous guidance of a motor vehicle, the method comprising: (Modalavalasa 0016) “Simulations can be used to validate software (e.g., a vehicle controller) executed on vehicles (e.g., autonomous vehicles) and gather safety metrics to ensure that the software is able to safely control such vehicles in various scenarios.”
providing at least one parameter set of driving situation parameters, the driving situation parameters comprising at least one first parameter detected by at least one vehicle sensor, (Modalavalasa 0117) “At operation 808 of example process 800, the process 800 can include determining, based on the scenario parameter and the error model, a parameterized scenario. These can be combined to create a parameterized scenario that can cover the variations provided by the scenario parameter(s) and/or the error model. In some instances, the scenario parameter(s) can be randomly selected and combined to create the parameterized scenario. In some instances, the scenario parameter(s) can be combined based on a probability of occurring concurrently. By way of example and without limitation, log data can indicate that 5% of driving experiences include a pedestrian encounter and the parameterized scenario component can include a pedestrian as a scenario parameter in 5% of the parameterized scenarios generated by the parameterized scenario component.” {EXAMINERS NOTE: Scenario parameters are parameters that are detected by the vehicle sensor, such as a pedestrian.}
and comprising at least one second parameter representing at least one further scenario object; (Modalavalasa 0039) “The parameter component 112 can determine scenario parameter(s) associated with the initial scenario(s) 110 identified by the scenario editor component 108.”
performing the virtual test by an algorithm using the at least one parameter set of driving situation parameters, the virtual test performed by the algorithm simulating the at least one parameter set of driving situation parameters; (Modalavalasa 0055) “The simulation component 124 can execute the parameterized scenario 122 as a set of simulation instructions and generate simulation data 126. For example, the simulation component 124 can instantiate a vehicle controller in the simulated scenario. In some instances, the simulation component 124 can execute multiple simulated scenarios simultaneously and/or in parallel.” {EXAMINERS NOTE: Driving situation parameters are the ‘parameters’ of the parameterized vehicle scenario.}
changing the at least one first parameter detected by the at least one vehicle sensor and/or a third parameter relating to a vehicle actuator during a runtime of the virtual test. (Modalavalasa 0076) “the simulation component 124 can perturb the simulation by continuously injecting the error into the simulation.” (0118) “the process 800 can include perturbing the parameterized scenario by modifying, based at least in part on the error, at least one of the parameterized scenario, the scenario parameter, or a component of a simulated vehicle.” {EXAMINERS NOTE: The simulation being done, is the running of the virtual test. Therefore, it is changing during a runtime of the virtual test}
Modalavalasa does not explicitly teach, but O’Malley teaches monitoring at least one driving situation parameter of the parameter set of driving situation parameters; (O’Malley 0082) “simulation component that is executing on the computing device(s) 420 can monitor a location of the simulated vehicle 434 to determine when the simulated vehicle 434 will activate/trigger a trigger region.”
and if a predetermined condition and/or a condition determined by the algorithm is fulfilled, (O’Malley 0026) “The scenario generator can associate the instantiation attribute and/or the termination attribute with a trigger region in the simulated scenario.” (0027) “During an execution of the simulated scenario, the scenario generator can determine a simulation parameter associated with the simulated object such as the amount of time that the object has been simulated in the scenario and/or a distance from the simulated vehicle.”
are analogous to the claimed invention because they are from the same field of endeavor of
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Modalavalasa and O’Malley before him or her, to modify the driving parameters of Modalavalasa with the monitoring system of O’Malley in order to “determine an amount of redundancy that is required in an autonomous controller, or how to modify a behavior of the autonomous controller based on what is learned through simulations” (O’Malley 0011).
Claim 2.
Modified Modalavalasa with O’Malley teaches The computer-implemented method according to claim 1, wherein the predetermined condition and/or the condition determined by the algorithm is fulfilled if at least one of the provided driving situation parameters and/or an indicator representing a plurality of driving situation parameters lies outside a predetermined value range and/or a value range determined by the algorithm. From the above list of alternatives, the Examiner is selecting "the condition determined by the algorithm is fulfilled if at least one of the provided driving situation parameters. . . lies outside a predetermined value range ". (O’Malley 0027) “During an execution of the simulated scenario, the scenario generator can determine a simulation parameter associated with the simulated object such as the amount of time that the object has been simulated in the scenario and/or a distance from the simulated vehicle.”
Claim 3.
Modified Modalavalasa teaches The computer-implemented method according to claim 2, wherein values lying outside the predetermined value range and/or the value range determined by the algorithm represent a safety-critical driving situation or that a longitudinal and/or lateral distance of an ego vehicle from a fellow vehicle is less than or equal to a predetermined threshold value. From the above list of alternatives, the Examiner is selecting "that a longitudinal . . . of an ego vehicle from a fellow vehicle is less than or equal to a predetermined threshold value ". (Modalavalasa 0069) “The scenario data 208 can indicate a base scenario that includes a vehicle 210 traversing along a driving surface and an object 212 (which can be a different vehicle) traversing near the vehicle 210 in a same direction as the vehicle 210.” (0025) “the parameterized scenario can represent a simulation environment including a vehicle controlled by an autonomous vehicle controller traveling at a speed and performing a stop action before an object that is in front of the vehicle.”
Claim 4.
Modified Modalavalasa teaches The computer-implemented method according to claim 3, wherein the driving situation parameter values which lie outside the predetermined value range and/or a value range determined by the algorithm (Modalavalasa 0063) “The analysis component 128 can adjust a magnitude of the scenario parameter and perform the sensitivity analysis based on the magnitude of the scenario parameter to determine a threshold associated with the scenario parameter that can result in a successful or an unsuccessful outcome of the simulation. In some instances, a binary search algorithm, a particle filter algorithm, and/or a Monte Carlo method can be used to determine the threshold, although other suitable algorithms are contemplated.”
and which represent the safety-critical driving situation lie within a parameter space. (Modalavalasa 0060) “the simulation data 126 output by the simulation component 124 can indicate variations of the parameterized scenario 122 associated with a success or a failure. . . By way of example and without limitation, a variation of the parameterized scenario 122 associated with a failure can represent a vehicle traveling on a driving surface at a speed of 15 meters per second and an animal crossing the driving surface at a distance of20 meters in front of the vehicle.” (0064) “the vehicle performance data can indicate safety metrics. By way of example and without limitation, the vehicle performance data 132 can indicate an event (e.g., a failure) and a cause of the event.”
Claim 5.
Modified Modalavalasa teaches The computer-implemented method according to claim 4, wherein the algorithm determines test cases representing the safety-critical driving situation within the parameter space. (Modalavalasa 0085) “the result of a simulation can be a failure where a scenario parameter was associated with a vehicle velocity of 15 meters per second. The simulation component 124 can identify velocities that are near the 15 meters per second velocity to determine a threshold at which the simulation will pass which can further assist the development of safer vehicle controllers.” (0060) “a variation of the parameterized scenario 122 associated with a failure can represent a vehicle traveling on a driving surface at a speed of 15 meters per second and an animal crossing the driving surface at a distance of20 meters in front of the vehicle.” (0054) “the parameterized scenario 122 can allow for a total of 9 various scenarios by varying the scenario parameters and the regions (e.g., 3 velocities*3 regions=9 scenarios/permutations).”
Claim 6.
Modified Modalavalasa teaches The computer-implemented method according to claim 1, wherein changing the at least one first parameter, detected by the at least one vehicle sensor, and/or the third parameter relating to the vehicle actuator during the runtime of the virtual test represents generating a failure and/or a malfunction of the at least one vehicle sensor and/or the at least one vehicle actuator. From the above list of alternatives, the Examiner is selecting "detected by at least one vehicle sensor ". (Modalavalasa 0076) “the simulation component 124 can perturb the simulation by continuously injecting the error into the simulation . . . The bounding box 404 can represent a detection of the object by a vehicle that includes an error such as an error in a size of the bounding box and/ or a position of the bounding box.” (0118) “the process 800 can include perturbing the parameterized scenario by modifying, based at least in part on the error, at least one of the parameterized scenario, the scenario parameter, or a component of a simulated vehicle.”
Claim 8.
Modified Modalavalasa teaches The computer-implemented method according to claim 6, wherein the algorithm determines variable values for generating the failure and/or a malfunction of the at least one vehicle sensor and/or the at least one vehicle actuator. From the above list of alternatives, the Examiner is selecting "wherein the algorithm determines variable values for generating failure". (Modalavalasa 0045) “By comparing the input data 102 with the ground truth data, the error model component 116 can determine a perception error.” (0046) “Then the error model component 116 can determine a probability distribution (also referred to as an error distribution) associated with a range of errors of the object and associate the probability distribution with the object within the initial scenario(s) 110.”
Claim 9.
Modified Modalavalasa teaches The computer-implemented method according to claim 1, wherein the algorithm generates contradictory data or mutually inconsistent data for redundant and/or different vehicle sensors. From the above list of alternatives, the Examiner selects "mutually inconsistent data". (Modalavalasa 0021) “the computing device can be configured to introduce inconsistencies in a scenario parameter of an object. For example, an error model can indicate an error and/or an error percentage associated with a scenario parameter. The scenario can incorporate the error and/or the error percentage into a simulated scenario and simulate the response of the autonomous vehicle controller.”
Claim 10.
Modified Modalavalasa with O’Malley The computer-implemented method according to claim 1, wherein changing the at least one first parameter, detected by the at least one vehicle sensor, and/or the third parameter relating to the vehicle actuator during the runtime of the virtual test comprises writing a variable representing the at least one driving situation parameter. From the above list of alternatives, the Examiner is selecting "wherein changing the at least one first parameter". (O’Malley 0066) “A scenario generator, based on the log data, can determine the log data attributes table 202 that identifies attributes associated with one or more objects of an environment at various times of log data.” (0069) “the attributes component 206 can modify the log data attributes table 202 and replace inconsistent attributes with, for example, the estimated attribute 210. In some instances, the attributes component 206 can generate the estimated attributes table 204.” (0156) “the simulation component 648 can update a position and velocity of the simulated object.”
Claim 12.
Modified Modalavalasa teaches The computer-implemented method according claim 1, wherein the first parameter detected by the vehicle sensor is a vehicle speed of an ego vehicle, a distance of the ego vehicle to a fellow vehicle driving ahead or behind, and/or an acceleration or deceleration of the ego vehicle and/or a fellow vehicle. From the above list of alternatives, the Examiner is selecting "acceleration or deceleration of the . . . a fellow vehicle ". (Modalavalasa 0069) “an object 212 (which can be a different vehicle) traversing near the vehicle 210 in a same direction as the vehicle 210. The vehicle 210 and the object 212 can be approaching a junction with a crosswalk. The parameter component 112 can determine a scenario parameter that indicates a distance between the vehicle 210 and the object 212 as having a range of distances.”
Claim 13.
Modified Modalavalasa teaches The computer-implemented method according to claim 1, wherein the at least one second parameter representing at least one further scenario object is a lane width, a road course, traffic signs, road users, buildings, and/or vegetation. From the above list of alternatives, the Examiner is selecting "buildings". (Modalavalasa 0030) “For example, some of the sensor data can be associated with objects (e.g., vehicles, cyclists, and/or pedestrians). In some instances, the sensor data can be associated with other objects including, but not limited to, buildings, road surfaces, signage, barriers, etc.”
Claim 14.
Modalavalasa teaches A system for performing a virtual test of a device for the at least partial autonomous guidance of a motor vehicle, the system comprising: a data memory, which is set up to provide at least one parameter set of driving situation parameters, the driving situation parameters comprising at least one first parameter detected by at least one vehicle sensor and comprising at least one second parameter representing at least one further scenario object; (Modalavalasa 0037) “In some instances, the scenario editor component 108 can generate a library of scenarios and store the library of scenarios in a database within the scenario editor component 108. By way of example and without limitation, the library of scenarios can include crosswalk scenarios, merging scenarios, lane change scenarios, and the like.” (0039) “the parameter component 112 can analyze the jaywalking scenario and determine scenario parameters associated with the jaywalking scenario that include a position of the pedestrian, a pose of the pedestrian, a size of the pedestrian, a velocity of the pedestrian, a track of the pedestrian, a distance between a vehicle and the pedestrian, a velocity of the vehicle, a width of a road, and the like.” {EXAMINERS NOTE: Parameter component 112 is a vehicle sensor detecting scenario objects, in this case pedestrians.}
and a computing device that is set up to perform the virtual test by an algorithm using the at least one parameter set of driving situation parameters, the virtual test performed by the algorithm simulating the at least one parameter set of driving situation parameters, (Modalavalasa 0055) “The simulation component 124 can execute the parameterized scenario 122 as a set of simulation instructions and generate simulation data 126. For example, the simulation component 124 can instantiate a vehicle controller in the simulated scenario.”
and further set up to change the at least one first parameter detected by the at least one vehicle sensor and/or a third parameter relating to a vehicle actuator during a runtime of the virtual test if a predetermined condition and/or a condition determined by the algorithm is fulfilled. (Modalavalasa 0075) “at each simulation time (e.g., t0 , t1 , or t2 ), the simulation component 124 can use a different error 306 based on the probability 308 associated with the error 306. At a time t2 , the simulation component 124 can use the bounding box 408 that represents the object and that includes a different error.” {EXAMINERS NOTE: The simulation being done, is the running of the virtual test.}
Modalavalasa does not explicitly teach, but O’Malley teaches the computing device being further set up to monitor at least one driving situation parameter of the parameter set of driving situation parameters (O’Malley 0082) “A simulation component that is executing on the computing device(s) 420 can monitor a location of the simulated vehicle 434 to determine when the simulated vehicle 434 will activate/trigger a trigger region.”
are analogous to the claimed invention because they are from the same field of endeavor of
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Modalavalasa and O’Malley before him or her, to modify the driving parameters of Modalavalasa with the monitoring system of O’Malley in order to “determine an amount of redundancy that is required in an autonomous controller, or how to modify a behavior of the autonomous controller based on what is learned through simulations” (O’Malley 0011).
Claim 15.
Modified Modalavalasa with O’Malley teaches A computer program with a program code to carry out the method of claim 1, when the computer program is executed on a computer. (0111) “The memory 718 and 732 can store an operating system and one or more software applications, instructions, programs, and/or data to implement the methods described herein and the functions attributed to the various systems.”
Claims 7 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Modalavalasa et al., US 2021/0096571A1 (Modalavalasa) in view of O’Malley et al,. US 2020/0410063 A1 (O’Malley) in further view of Mastromatto et al., “A Framework for Automated Driving System Testable Cases and Scenarios.” (Mastromatto).
Claim 7.
Modified Modalavalasa with O’Malley does not explicitly teach, but Mastromatto teaches The computer-implemented method according to claim 6, wherein generating the failure and/or the malfunction of the at least one vehicle sensor and/or of the at least one vehicle actuator comprises an interruption of a communication link between a control unit and the vehicle sensor and/or vehicle actuator, an interruption of a power supply of the vehicle sensor and/or vehicle actuator, and a limitation of a performance of the vehicle sensor due to a contamination of the vehicle sensor. From the above list of alternatives, the Examiner is selecting "an interruption of a power supply". (Mastromatto Pg 85 Paragraph 3) “Failure modes associated with exteroceptive sensors include loss of power, loss of data connection, internal hardware failures, and emitter/receiver fouling (e.g., mud, dirt).”
are analogous to the claimed invention because they are from the same field of endeavor of
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Modalavalasa, O’Malley, and Mastromatto before him or her, to modify the driving parameters of Modalavalasa with the loss of power test of Mastromatto and the monitoring system of O’Malley in order to “determine an amount of redundancy that is required in an autonomous controller, or how to modify a behavior of the autonomous controller based on what is learned through simulations” (O’Malley 0011).
Claim 11.
Modified Modalavalasa with O’Malley and Mastromatto teaches The computer-implemented method according to claim 1, wherein the vehicle sensor is a camera sensor, a radar sensor, a LiDAR sensor, an ultrasonic sensor, an infrared sensor, a tire pressure sensor, a wheel speed sensor, and/or a GNSS sensor or a GPS sensor, From the above list of alternatives the Examiner is selecting "a GPS sensor". (Modalavalasa 0032) “In some instances, the sensor data can include data captured by sensors such as time-of-flight sensors, location sensors (e.g., GPS, compass, etc.), inertial sensors (e.g., inertial measurement units (IMUs), accelerometers, magnetometers, gyroscopes, etc.), lidar sensors, radar sensors, sonar sensors, infrared sensors, cameras (e.g., RGB, IR, intensity, depth, etc.), microphone sensors, environmental sensors (e.g., temperature sensors, humidity sensors, light sensors, pressure sensors, etc.), ultrasonic transducers, wheel encoders, etc.”
and wherein the vehicle actuator is an adaptive cruise control, a lane departure warning system, an active brake assist, and/or a parking assist. (Mastromatto pg.33 Section Category 1, L3 Conditional Automated Traffic Jam Drive Feature) “The Audi Traffic Jam Pilot (Audi, 2015) uses adaptive cruise control and LKA to allow slow driving in traffic jams.”
Conclusion
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/JOHN DAVID HAGLER/ Examiner, Art Unit 2189
/REHANA PERVEEN/ Supervisory Patent Examiner, Art Unit 2189
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