DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 103
3. 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.
4. Claim(s) 1-6, 8-10, 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mehdi et al. (USPG_PUB No. 2023/0339517), in view of Konrardy et al. (U.S. Patent no. 11,242,051).
4.1 In considering claims 1 and 13, Mehdi et al. teaches a system, comprising:
a driving simulator (see para [0003]-A system comprises a computer including a processor and a memory having instructions such that the processor is programmed to: execute an autonomous vehicle algorithm simulating vehicle operations within a simulated environment), the driving simulator comprising: one or more input devices corresponding to controls of a vehicle (see para[0036-0037], in a semi-autonomous mode the computer controls one or two of vehicles 105 propulsion, braking, and steering; in a non-autonomous mode a human operator controls each of vehicle 105 propulsion, braking, and steering. [0037] The computer may include programming to operate one or more of vehicle 105 brakes, propulsion (e.g., control of acceleration in the vehicle by controlling one or more of an internal combustion engine, electric motor, hybrid engine, etc.), steering, climate control, interior and/or exterior lights, etc., as well as to determine whether and when the computer, as opposed to a human operator, is to control such operations. [0064], Furthermore, the computing device 300 can include an input device such as a touchscreen, mouse, keyboard, etc. In certain implementations, the computing device 300 can include fewer or more components than those shown in FIG. 3. [0068]); a display (see para [0069] The I/O devices/interfaces 320 may include one or more devices for presenting output to a user, including, but not limited to, a graphics engine, a display (e.g., a display screen), one or more output drivers (e.g., display drivers), one or more audio speakers, and one or more audio drivers. In certain implementations, devices/interfaces 320 is configured to provide graphical data to a display for presentation to a user. The graphical data may be representative of one or more graphical user interfaces and/or any other graphical content as may serve a particular implementation.); and one or more processors (see fig.3 (305), the computing device can comprise a processor 305, memory 310, a storage device 315, an I/O interface 320, and a communication interface 325.) communicatively coupled with the one or more input devices and the display, (see para [0064] FIG. 3 illustrates an example computing device 300 i.e., computer 110 and/or server(s)145 that may be configured to perform one or more of the processes described herein. As shown, the computing device can comprise a processor 305, memory 310, a storage device 315, an I/O interface 320, and a communication interface 325. Furthermore, the computing device 300 can include an input device such as a touchscreen, mouse, keyboard, etc. In certain implementations, the computing device 300 can include fewer or more components than those shown in FIG. 3. [0068] The computing device 300 also includes one or more input or output (“I/O”) devices/interfaces 320, which are provided to allow a user to provide input to (such as user strokes), receive output from, and otherwise transfer data to and from the computing device 300.) the one or more processors configured to simulate, on the display, an autonomous vehicle driving through a simulated environment in a manual mode based on inputs from the one or more input devices (see [0053] The challenge generation module 215 can receive the measure M and generate a simulated driving situation based on the measure M. For example, the challenge generation module 215 generates a scenario file, i.e., JSON file, text file, etc. that includes variables for defining a simulated scenario. The scenario file can be stored in the storage module 235. For instance, the challenge generation module 215 can generate a driving challenge based on simulated weather conditions, i.e., icy road conditions, objects obscuring lane markings, and/or windy conditions, based on ethical complexities, i.e., whether the ego-vehicle yields to another vehicle that does not have the right-of-way and/or perform a vehicle maneuver. [0071] FIG. 4 is a flowchart of an example process 400 for benchmarking driving operations within a simulated driving environment during a prescriptive mode of operation according to the techniques described herein. Blocks of the process 400 can be executed by the server 145. The process 400 begins at block 405 in which one or more simulated driving conditions are generated. The simulated driving conditions can be based on the measure M. As discussed above, the measure M can be used to characterize a given driving condition, e.g., situation.), automatically in an autonomous mode (see para 0036] The computer 110 may operate a vehicle 105 in an autonomous, a semi-autonomous mode, or a non-autonomous (manual) mode. For purposes of this disclosure, an autonomous mode is defined as one in which each of vehicle 105 propulsion, braking, and steering are controlled by the computer 110; in a semi-autonomous mode the computer 110 controls one or two of vehicles 105 propulsion, braking, and steering; in a non-autonomous mode a human operator controls each of vehicle 105 propulsion, braking, and steering.), and a remote computing device (see para [0071] FIG. 4 is a flowchart of an example process 400 for benchmarking driving operations within a simulated driving environment during a prescriptive mode of operation according to the techniques described herein. Blocks of the process 400 can be executed by the remote server 145 of fig.1.), the remote computing device configured to: receive an input from a user interface displayed at the remote computing device during a simulation of the autonomous vehicle in the autonomous mode (see para [0044], In addition, the computer 110 may be configured for communicating via a vehicle-to-vehicle communication module or interface 130 with devices outside of the vehicle 105, e.g., through a vehicle to vehicle (V2V) or vehicle-to-infrastructure (V2X) wireless communications to another vehicle, to (typically via the network 135) a remote server 145. [0071] FIG. 4 is a flowchart of an example process 400 for benchmarking driving operations within a simulated driving environment during a prescriptive mode of operation according to the techniques described herein. Blocks of the process 400 can be executed by the server 145. [0088] The module may include one or more interface circuits. In some examples, the interface circuits may include wired or wireless interfaces that are connected to a local area network (LAN), the Internet, a wide area network (WAN), or combinations thereof. The functionality of any given module of the present disclosure may be distributed among multiple modules that are connected via interface circuits. For example, multiple modules may allow load balancing. In a further example, a server (also known as remote, or cloud) module may accomplish some functionality on behalf of a client module. [0036] The computer 110 may operate a vehicle 105 in an autonomous, a semi-autonomous mode, or a non-autonomous (manual) mode. For purposes of this disclosure, an autonomous mode is defined as one in which each of vehicle 105 propulsion, braking, and steering are controlled by the computer 110; in a semi-autonomous mode the computer 110 controls one or two of vehicles 105 propulsion, braking, and steering; in a non-autonomous mode a human operator controls each of vehicle 105 propulsion, braking, and steering.), the input causing a fault in the operation of the autonomous vehicle in the autonomous mode (see para [0021], the processor is programmed to: determine a challenge rating based on a defined measure M using sensor data from one or more sensors and generate at least one of a driver takeover recommendation or an alert indicating a presence of a fault based on a comparison of vehicle performance with the challenge rating. [0036] The computer 110 may operate a vehicle 105 in an autonomous, a semi-autonomous mode, or a non-autonomous (manual) mode. For purposes of this disclosure, an autonomous mode is defined as one in which each of vehicle 105 propulsion, braking, and steering are controlled by the computer 110; 0063] the computer 110 can detect and report possible faults within the vehicle 105). While Mehdi et al. does not specifically state that a mode switch is performed, he provide for benchmarking driving operation within a simulated driving environment during a prescribed mode of operation and that multiple modes of operations could be prescribed and switch between them as would be understood by a person of skilled in the art.
Nonetheless, Konrardy et al. provides an autonomous vehicle action communications (see title, abstract) that includes a step of which a transition between manual mode and autonomous mode is accomplished (see col.44 lines 4-10, In some embodiments, the autonomous vehicles 182.1-182.N may include functionality to switch into a manual mode by for example, disabling the autonomous operation features. The autonomous vehicles 182.1-182.N may also include functionality to switch back into the autonomous mode by automatically enabling the autonomous operation features and/or enabling the autonomous operation features in response to a request by a vehicle operator. Col.46 lines 37-60, For example, when the overall condition of the road segment is very poor due to potholes, ice patches, unexpected debris on the road segment, cracks, etc., the server 140 may determine that autonomous vehicles 182.1-182.N in a manual mode should switch to an autonomous mode. This may be because the autonomous operation features have faster reaction times than vehicle operators for dealing with dangerous conditions, such as icy patches or big potholes. Accordingly, a vehicle operator may select a control within the autonomous vehicle 182.1-182.N to switch into the autonomous mode, enabling autonomous operation features. In other embodiments, the autonomous vehicle 182.1-182.N may automatically enable the autonomous operation features. (174) The recommendation may also be to switch into an autonomous mode when several autonomous vehicles 182.1-182.N on the same road and/or within a predetermined threshold distance of each other are travelling to the same destination. When the autonomous vehicles 182.1-182.N switch into the autonomous mode, they may form a platoon so that the autonomous vehicles 182.1-182.N may follow each other to the destination. In another example, the recommendation may be to switch from the autonomous mode to the manual mode.).
Mehdi et al. and Konrardy et al. are analogous art because they are from the same field of endeavor and that the model analyzes by Konrardy et al. is similar to that of Mehdi et al. Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Konrardy et al. with that of Mehdi et al. because Konrardy et al. teaches the improvement of the effectiveness of the autonomous operation features (see col.18 lines 58-59).
4.2 As per claims 2, 14, the combined teachings of Mehdi et al. and Konrardy et al. teaches that wherein the input comprises an identification of a type of fault, and wherein the fault in the operation of the autonomous vehicle corresponds to the type of fault (see Konrardy et al. col.28 lines 2-8, In some embodiments, a preliminary determination of fault may also be produced and stored. The information may further include a determination of whether the vehicle 108 has continued operating (either autonomously or manually) or whether the vehicle 108 is capable of continuing to operate in compliance with applicable safety and legal requirements. Further see Mehdi et al. para [0063] In some implementations, the computer 110 can detect and report possible faults within the vehicle 105. More specifically, the computer 110 compares the measure M to the one or more challenge ratings stored within the profile. The computer 110 also analyzes a performance of the selected autonomous vehicle driving algorithm within the driving environment, i.e., environment corresponding to measure M. The computer 110 can then determine whether the performance is lower than expected for a given challenge, the computer 110 can log the data, transmit the data with a vehicle manufacturer, and/or generate an alert to notify the vehicle 105 operator. For instance, the alert may indicate that the vehicle operator should schedule a dealership visit.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Konrardy et al. with that of Mehdi et al. because Konrardy et al. teaches the improvement of the effectiveness of the autonomous operation features (see col.18 lines 58-59).
4.3 Regarding claims 3, 15, the combined teachings of Mehdi et al. and Konrardy et al. teaches that wherein the type of fault is selected from a plurality of types of faults comprising one or more of the autonomous vehicle exceeding a speed limit applicable to the autonomous vehicle within the simulated environment, veering the autonomous vehicle off of a road within the simulated environment, or the autonomous vehicle suddenly breaking in on the road within the simulated environment (see Mehdi et al. para [0052] Examples of chaos can comprise frequent lane changes by other vehicles proximate to the ego-vehicle, relatively high-speed variation of vehicles proximate to the ego-vehicle, other vehicles not following the center lane, other vehicles not obeying lane markers, other vehicles involved in double parking, and/or pedestrians and/or animals crossing the street. [0053] The challenge generation module 215 can receive the measure M and generate a simulated driving situation based on the measure M. For example, the challenge generation module 215 generates a scenario file, i.e., JSON file, text file, etc. that includes variables for defining a simulated scenario. The scenario file can be stored in the storage module 235. For instance, the challenge generation module 215 can generate a driving challenge based on simulated weather conditions, i.e., icy road conditions, objects obscuring lane markings, and/or windy conditions, based on ethical complexities, i.e., whether the ego-vehicle yields to another vehicle that does not have the right-of-way and/or perform a vehicle maneuver due to detected pedestrian, based on mapping and localization complexities, i.e., inaccurate or sparsely detailed maps and/or GPS unavailability. [0063] In implementations, the computer 110 can detect and report possible faults within the vehicle 105. More specifically, the computer 110 compares the measure M to the one or more challenge ratings stored within the profile. The computer 110 also analyzes a performance of the selected autonomous vehicle driving algorithm within the driving environment, i.e., environment corresponding to measure M. The computer 110 can then determine whether the performance is lower than expected e.g. sudden breaking for a given challenge, the computer 110 can log the data, transmit the data with a vehicle manufacturer, and/or generate an alert to notify the vehicle 105 operator. For instance, the alert may indicate that the vehicle operator should schedule a dealership visit.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Konrardy et al. with that of Mehdi et al. because Konrardy et al. teaches the improvement of the effectiveness of the autonomous operation features (see col.18 lines 58-59).
4.4 As per claims 4, 16, the combined teachings of Mehdi et al. and Konrardy et al. teaches that wherein the one or more input devices comprise a steering wheel (see Mehdi et al. para [0043], Non-limiting examples of components 125 include a propulsion component (that includes, e.g., an internal combustion engine and/or an electric motor, etc.), a steering component (e.g., that may include one or more of a steering wheel, a steering rack, etc.), a brake component (as described below), a park assist component, an adaptive steering component, a movable seat, etc. Konrardy et al. col.11 lines 5-9, In other embodiments, the control components may be disposed within or supplement other vehicle operator control components (not shown), such as steering wheels, accelerator or brake pedals, or ignition switches.), and wherein the fault comprises a change in orientation of the steering wheel (see Konrardy et al. col.23 lines 41-53, When the controller 204 determines an autonomous control action is required (block 308), the controller 204 may cause the control components of the vehicle 108 to adjust the operating controls of the vehicle to achieve desired operation (block 310). For example, the controller 204 may send a signal to open or close the throttle of the vehicle 108 to achieve a desired speed. Alternatively, the controller 204 may control the steering of the vehicle 108 to adjust the direction of movement. In some embodiments, the vehicle 108 may transmit a message or indication of a change in velocity or position using the communication component 122 or the communication module 220, which signal may be used by other autonomous vehicles to adjust their controls). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Konrardy et al. with that of Mehdi et al. because Konrardy et al. teaches the improvement of the effectiveness of the autonomous operation features (see col.18 lines 58-59).
4.5 With regards to claims 5, 17, the combined teachings of Mehdi et al. and Konrardy et al. teaches that wherein the fault comprises an adjustment to a predetermined path based on which the autonomous vehicle is driving within the simulated environment (see Konrardy et al. col.23 lines 19-25 and 41-53, If the vehicle 108 is beginning to drift or slide (e.g., as on ice or water), the controller 204 may determine appropriate adjustments to the controls of the vehicle to maintain the desired bearing. If the vehicle 108 is moving within the desired path, the controller 204 may nonetheless determine whether adjustments are required to continue following the desired route (e.g., following a winding road). Under some conditions, the controller 204 may determine to maintain the controls based upon the sensor data (e.g., when holding a steady speed on a straight road). When the controller 204 determines an autonomous control action is required (block 308), the controller 204 may cause the control components of the vehicle 108 to adjust the operating controls of the vehicle to achieve desired operation (block 310). For example, the controller 204 may send a signal to open or close the throttle of the vehicle 108 to achieve a desired speed. Alternatively, the controller 204 may control the steering of the vehicle 108 to adjust the direction of movement. In some embodiments, the vehicle 108 may transmit a message or indication of a change in velocity or position using the communication component 122 or the communication module 220, which signal may be used by other autonomous vehicles to adjust their controls.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Konrardy et al. with that of Mehdi et al. because Konrardy et al. teaches the improvement of the effectiveness of the autonomous operation features (see col.18 lines 58-59).
4.6 As per claims 6, 18, the combined teachings of Mehdi et al. and Konrardy et al. teaches that wherein the remote computing device is configured to transmit an identification of the fault to the one or more processors, receipt of the identification by the one or more processors causing the fault in the operation of the autonomous vehicle (see Mehdi et al. para [0063] In some implementations, the computer 110 can detect and report possible faults within the vehicle 105. More specifically, the computer 110 compares the measure M to the one or more challenge ratings stored within the profile. The computer 110 also analyzes a performance of the selected autonomous vehicle driving algorithm within the driving environment, i.e., environment corresponding to measure M. The computer 110 can then determine whether the performance is lower than expected for a given challenge, the computer 110 can log the data, transmit the data with a vehicle manufacturer, and/or generate an alert to notify the vehicle 105 operator. For instance, the alert may indicate that the vehicle operator should schedule a dealership visit. [0080], In some implementations, the data is transmitted to the vehicle 105 manufacturer. In some implementations, the computer 110 generates an alert to indicate that a dealership visit is recommended due to the presence of a fault.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Konrardy et al. with that of Mehdi et al. because Konrardy et al. teaches the improvement of the effectiveness of the autonomous operation features (see col.18 lines 58-59).
4.7 With regards to claims 8, 19, the combined teachings of Mehdi et al. and Konrardy et al. teaches that wherein the remote device: receives a second input indicating a training scenario (see Konrardy et al. col.15 line 53-col.16 line 15, For example, mobile computing device 184.1 and/or 184.2 may collect data (e.g., geographic location data and/or telematics data) as described herein, but may send the data to external computing device 186 for remote processing instead of processing the data locally. In such embodiments, external computing device 186 may receive and process the data to determine whether an anomalous condition exists and, if so, whether to send an alert notification to one or more mobile computing devices 184.1 and 184.2 or take other actions. For example, external computing device 186 may facilitate the receipt of autonomous operation or other data from one or more mobile computing devices 184.1-184.N, which may each be running a Data Application to obtain such data from autonomous operation features or sensors 120 associated therewith.); and adjusts the simulated environment based on the training scenario (see Konrardy et al. col.23 lines 12-22, As another example, the controller 204 may process the sensor data to determine whether the vehicle 108 is remaining with its intended path (e.g., within lanes on a roadway). If the vehicle 108 is beginning to drift or slide (e.g., as on ice or water), the controller 204 may determine appropriate adjustments to the controls of the vehicle to maintain the desired bearing. If the vehicle 108 is moving within the desired path, the controller 204 may nonetheless determine whether adjustments are required to continue following the desired route (e.g., following a winding road); further see Mehdi et al. para [0053], For example, the challenge generation module 215 generates a scenario file, i.e., JSON file, text file, etc. that includes variables for defining a simulated scenario. The scenario file can be stored in the storage module 235. For instance, the challenge generation module 215 can generate a driving challenge based on simulated weather conditions, i.e., icy road conditions, objects obscuring lane markings, and/or windy conditions, based on ethical complexities, i.e., whether the ego-vehicle yields to another vehicle that does not have the right-of-way and/or perform a vehicle maneuver due to detected pedestrian, based on mapping and localization complexities, i.e., inaccurate or sparsely detailed maps and/or GPS unavailability, based on a complete or partial failure of one or more vehicle 105 systems, based on low visibility due to weather conditions, based on traffic behavior variations due to weather and/or road conditions, and/or modified traffic patterns due to accidents.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Konrardy et al. with that of Mehdi et al. because Konrardy et al. teaches the improvement of the effectiveness of the autonomous operation features (see col.18 lines 58-59).
4.8 As per claims 9, 20, the combined teachings of Mehdi et al. and Konrardy et al. teaches that wherein the remote device: transmits an identification of the training scenario (see Konrardy et al. col.23 lines 41-62, In some embodiments, the vehicle 108 may transmit a message or indication of a change in velocity or position using the communication component 122 or the communication module 220, which signal may be used by other autonomous vehicles to adjust their controls. As discussed elsewhere herein, the controller 204 may also log or transmit the autonomous control actions to the server 140 via the network 130 for analysis. In some embodiments, an application (which may be a Data Application) executed by the controller 204 may communicate data to the server 140 via the network 130 or may communicate such data to the mobile device 110 for further processing, storage, transmission to nearby vehicles or infrastructure, and/or communication to the server 140 via network 130.); and wherein the driving simulator executes a set of code corresponding to the training scenario based on the identification of the training scenario (see Mehdi et al. para [0056] The performance assessment module 225 monitors the outcome, i.e., assess, of executing autonomous vehicle algorithm for each driving situation and outputs data including each driving situation and an indication if driving operations selected the autonomous vehicle algorithm based on the driving situation passed or failed. [0057] Further, the performance assessment module 225 can generate autonomous vehicle metrics, e.g., statistics, about the autonomous vehicle algorithm, such as an indication of how many times each simulated driving condition was associated with a failure of the autonomous vehicle algorithm, how many times each simulated driving condition was associated with a success of the autonomous vehicle algorithm, and the like. Based on the data, the performance assessment module 225 can generate a single scalar score that represents an aggregation of the assessment factors.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Konrardy et al. with that of Mehdi et al. because Konrardy et al. teaches the improvement of the effectiveness of the autonomous operation features (see col.18 lines 58-59).
4.9 Regarding claim 10, the combined teachings of Mehdi et al. and Konrardy et al. teaches that wherein the training scenario comprises a second simulated vehicle driving in front of the autonomous vehicle within the simulated environment (see Mehdi et al. para [0036] The computer 110 may operate a vehicle 105 in an autonomous, a semi-autonomous mode, or a non-autonomous (manual) mode. For purposes of this disclosure, an autonomous mode is defined as one in which each of vehicle 105 propulsion, braking, and steering are controlled by the computer 110; in a semi-autonomous mode the computer 110 controls one or two of vehicles 105 propulsion, braking, and steering; in a non-autonomous mode a human operator controls each of vehicle 105 propulsion, braking, and steering. [0041] As another example, one or more radar sensors 115 fixed to vehicle 105 bumpers may provide data to provide and range velocity of objects (possibly including second vehicles 106), etc., relative to the location of the vehicle 105.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Konrardy et al. with that of Mehdi et al. because Konrardy et al. teaches the improvement of the effectiveness of the autonomous operation features (see col.18 lines 58-59).
5. Claim(s) 7, 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Mehdi et al. (USPG_PUB No. 2023/0339517), in view of Konrardy et al. (U.S. Patent no. 11,242,051), further in view of Yang (USPG_PUB No. 2020/0377109).
5.1 Regarding claims 7, 11, the combined teachings of Mehdi et al., as modified by Konrardy et al. teaches most of the instant invention; however, he does not expressly teach that wherein the driving simulator comprises a housing in the shape of a semi-trailer truck cabin, wherein the one or more processors are stored within the housing, and wherein the display is a display external to the housing. Yang teaches that wherein the driving simulator comprises a housing in the shape of a semi-trailer truck cabin (see fig.1, para [0043] For cases where the autonomous vehicle is a semi-trailer truck, the simulation parameters may be designed to test the cab portion separately from the trailer portion. For example, a same cab may be paired with different trailers and therefore simulations may be performed on using different combinations of semi (cabs) and trailers. Thus, for example, simulation parameters that indicate a presence of an object designed to veer into a lane occupied by and in front of the semi-trailer truck can be used to test the response of the cab portion separately from the trailer portion at least because different trailer lengths can have a different response to evasive maneuvering by a cab portion. The response of the cab portion and the trailer portion can be obtained from their respective sensors (e.g., accelerometers), wherein the one or more processors are stored within the housing (see fig.1, para [0009], The system includes an autonomous vehicle and a computer that includes a processor and a memory that stores instructions.), and wherein the display is a display external to the housing (see para [0040], vehicle simulation computer (e.g., simulation module) can send a message to be displayed on the vehicle simulation computer external of the cabin). Mehdi et al., Konrardy et al., and Yang are analogous art because they are from the same field of endeavor and that the model analyzes by Yang is similar to that of Mehdi et al and Konrardy et al. Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Yang with that of Mehdi et al. and Konrardy et al. because Yang teaches the benefit of safely simulate scenarios to test the operations and/or performance of the autonomous vehicle (see para [0034]).
5.2 With regards to claim 12, the combined teachings of Mehdi et al., Konrardy et al., and Yang teaches the display device, wherein the display device surrounds the housing (see Yang para [0040], vehicle simulation computer (e.g., simulation module) can send a message to be displayed on the vehicle simulation computer external of the cabin), and wherein the one or more processors transmit a view of the simulation to the display device (see Yang para [0040], vehicle simulation computer (e.g., simulation module) can send a message to be displayed on the vehicle simulation computer external of the cabin). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Yang with that of Mehdi et al. and Konrardy et al. because Yang teaches the benefit of safely simulate scenarios to test the operations and/or performance of the autonomous vehicle (see para [0034]).
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
6.1 Yang et al. (USPG_PUB No. 2020/0384998) teaches techniques for analysis of autonomous vehicle operations using an autonomous vehicle simulation system.
6.2 Nielsen et al. (USPG_PUB No. 2010/0330542) teaches systems for and methods of simulating facilities for use in locate operations training exercises.
7. Claims 1-20 are rejected and this action is non-final. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRE PIERRE-LOUIS whose telephone number is (571)272-8636. The examiner can normally be reached M-F 9:00 AM-5:00 PM.
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/ANDRE PIERRE LOUIS/Primary Patent Examiner, Art Unit 2187 September 2, 2026
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