CTNF 18/941,293 CTNF 101534 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Specification 06-14 AIA Applicant is reminded of the proper content of an abstract of the disclosure. A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art. If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives. Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps. Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length. See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts. 06-16 AIA Applicant is reminded of the proper language and format for an abstract of the disclosure. The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details. The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided. The abstract of the disclosure is objected to because it is written in claim language instead of in narrative form. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b). Claim Rejections - 35 USC § 112 07-30-02 AIA 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. 07-34-01 Claims 2 and 3 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Regarding Claim 2, in lines 5-7 it recites “performing a respond data identification on the device parameters to determine whether the objective vehicle device outputs real response data with respect to the operation information”. It is unclear what “respond data identification on device parameters” is, and how data is being identified to determine if it is real or not. Thus, Claim 2 is unclear and indefinite. For purposes of examination, the examiner will interpret the term to mean the existence of response data is verified. Regarding Claim 3, in lines 12-13 it recites “the objective vehicle equipment”. However, there was no previous mention of “objective vehicle equipment” prior to this in Claim 3 or any claims it is dependent from. Therefore, the term “objective vehicle equipment” lacks antecedent basis. Thus, Claim 3 is unclear and indefinite. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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 (i.e., changing from AIA to pre-AIA) 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. 07-20-aia AIA 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. 07-21-aia AIA Claim s 1, 4, 8, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Sun et al. (CN 118714287 A), in view of Majumder et al. (US 20230102762 A1), hereinafter referred to as Sun and Majumder respectively . Regarding Claim 1, Sun teaches a vehicle calibrating method comprising: displaying a predetermined driving scene to a user corresponding to an objective vehicle device in a preset virtual reality (VR) environment, wherein the predetermined driving scene is generated by a virtual simulation of a real calibration scene corresponding to the objective vehicle device; Sun [Page 3 Paragraph 7] “the video converter is used for receiving the real road spectrum and/or real video of the low-voltage differential signal format, converting the format of the real road spectrum and/or real video into the network port format, and sending the real road spectrum and/or real video after the format conversion to the external control device.” Sun [Page 3 Paragraph 8] “Further, the HIL rack for binocular vision product test further comprises a driving simulator and VR glasses, the driving simulator is connected with the vehicle model;” Sun [Page 3 Paragraph 9] “wherein the testing personnel wear the VR glasses, so as to obtain the virtual road spectrum through the VR glasses, and operate the driving simulator, so as to test whether the function of the product to be tested under human intervention satisfies the requirement.” Examiner Note: Virtual road spectrum (predetermined driving scene) is based from the real road spectrum (real calibration scene). The virtual road spectrum is presented to the testing personnel (user). This is all corresponding to a vehicle model (objective vehicle device), and is done in VR. collecting device parameters of the objective vehicle device when receiving operation information of the objective vehicle device inputted by the user ; Sun [Page 8 Paragraph 4] “ The instructions , when executed by the processor 44, receive and process signals from the sensor system 28 (e.g., sensor data), perform logic for automatically controlling components of the autonomous vehicle 10. ” Examiner Note: The instructions are the operation information of the objective vehicle device, and the logic for controlling the components are the device parameters of the objective vehicle device. However, Sun does not mention having the operation information inputted by the user. performing a simulation calculation to obtain predicted performance data of the objective vehicle device according to the operation information, the device parameters, and scene parameters of the predetermined driving scene; and displaying the predicted performance data to the user for calibrating the objective vehicle device. However, Sun doesn’t teach operation information inputted by a user; performing a simulation calculation to obtain predicted performance data of the objective vehicle device according to the operation information, the device parameters, and scene parameters of the predetermined driving scene; and displaying the predicted performance data to the user for calibrating the objective vehicle device. Majumder teaches operation information inputted by a user; Majumder [0005] “a steerable motor vehicle that is remotely controlled by a human operator at a remote control station” Examiner Note: Human operation (user) is inputting operation information. performing a simulation calculation to obtain predicted performance data of the objective vehicle device according to the operation information, the device parameters, and scene parameters of the predetermined driving scene; Majumder [0018] “Method step 204 comprises recording the behavior of the digital twin and the plurality of actuators. Not only the physical experience but also the output in terms of statistical and mathematical parameters are recorded such as the force experienced and the like. Method step 205 comprises providing a feedback to the DCTU (101) via the receiving station (102) for calibration and testing of the vehicle. The feedback is provided to the DCTU (101) by analyzing the recorded behavior of the digital twin and the plurality of actuators . ” Examiner Note: The feedback to the DCTU is the predicted performance data of the objective vehicle device, and is provided by analyzing the recorded behavior of the digital twin. In a virtual environment, simulation calculations must be done to analyze the behavior of the digital twin. Majumder [0018] “However, based on the vehicle dynamics and customer requirement, calibration/application engineer will fine tune the slip ratio to pressure rate based on the driving experience. This is an iterative method.” Examiner Note: Each iterative adjustment done will affect future runs, and therefore the predicted performance data. The adjustments involve device parameters (in this example, slip ratio, but is not limited to it, as shown in Majumder [0017] “Based on the experience, the expert can perform remote vehicle application/calibration test and shall send the feedback to the field in form of updated software/tuning parameters.”) being modified by the engineer (the user’s operation information). Majumder [0013] “The digital twin is a virtual model of the vehicle designed to accurately reflect different aspects of the vehicle's performance, such as energy output, temperature, weather conditions and more.” Examiner Note: Scene parameters are reflected in the objective vehicle device’s performance. and displaying the predicted performance data to the user for calibrating the objective vehicle device. Majumder [0018] “ However, based on the vehicle dynamics and customer requirement, calibration/application engineer will fine tune the slip ratio to pressure rate based on the driving experience. This is an iterative method. ” Examiner Note: Since the driving parameters are fine-tuned based on the driving experience (which encompasses predicted performance data), the predicted performance data would have to be displayed to the user (engineer) in order for said user to be able to calibrate the vehicle. Sun and Majumder are analogous in the art of vehicle testing and calibration using virtual environments. It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Sun’s invention of a driving simulator with operation information and device parameters of an objective vehicle device with Majumder’s invention to additionally get predicted performance data based on operation information that is inputted by a user, device parameters, and scene parameters, as it would improve the calibration process by enabling calibration decisions to be made using simulated vehicle behavior. Regarding Claim 4, Majumder further teaches the vehicle calibrating method of claim 1, further comprising: determining recommended calibration parameters corresponding to the objective vehicle device according to the predicted performance data and the device parameters; and displaying the recommended calibration parameters to the user. Majumder [0019] “This can be extended to a point where the virtual reality station (103) can suggest/predict vehicle parametrization based on deep learning algorithms using big data from digital twin model and real time data.” Majumder [0018] “For example, like, calibration of ABS maneuver for a specific vehicle required inputs like vehicle dynamics, slip ratio & pressure rate.” Examiner Note: Vehicle parametrization is suggested (recommended calibration parameters that correspond to objective vehicle device) according to big data from digital twin model (predicted performance data). In the context of Majumder, big data from digital twin model, would be all previously mentioned data relating to the digital twin model, including behavior data from digital twin model. Additionally, as shown in paragraph [0018], calibration uses device parameters like “slip ratio”. Since it is being suggested, it would follow that it is displayed to the user, as for something to be suggested it must be displayed first. Sun and Majumder are analogous in the art of vehicle testing and calibration using virtual environments. It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to additionally modify Sun’s invention of a driving simulator that uses operation information from a user, device parameters, and scene parameters to get predicted performance data with Majumder’s invention to display recommended calibration parameters to the user, as doing so would make the calibration process more efficient and improve ease of use for the user, therefore reducing calibration time. Claim 8 recites similar limitations to Claim 1, except for ( at least one processor; and a data storage storing one or more programs which when executed by the at least one processor, cause the at least one processor to: Sun [Page 8 Paragraph 3] “The controller 34 includes at least one processor 44 and a computer readable storage device or medium 46.”), and is rejected under similar rationale. Claim 11 recites similar limitations to Claim 4, and is rejected under similar rationale. Claim 15 recites similar limitations to Claim 1, except for ( a non-transitory storage medium having stored thereon instructions that, when executed by a processor of a vehicle calibrating device, causes the vehicle calibrating device to perform a vehicle calibrating method, the vehicle calibrating method comprising: Sun [Page 3 Paragraph 8] “The controller 34 includes at least one processor 44 and a computer readable storage device or medium 46.”), and is rejected under similar rationale . 07-21-aia AIA Claim s 2 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Sun et al. (CN 118714287 A), in view of Majumder et al. (US 20230102762 A1), in further view of Li et al. (CN 118312404 A) hereinafter referred to as Sun, Majumder, and Li respectively . Regarding Claim 2, Majumder further teaches the vehicle calibrating method of claim 1, wherein performing the simulation calculation to obtain the predicted performance data of the objective vehicle device according to the operation information, the device parameters, and the scene parameters of the predetermined driving scene comprises: performing a respond data identification on the device parameters to determine whether the objective vehicle device outputs real response data with respect to the operation information; Majumder [0018] “ Method step 204 comprises recording the behavior of the digital twin and the plurality of actuators. Not only the physical experience but also the output in terms of statistical and mathematical parameters are recorded such as the force experienced and the like. Method step 205 comprises providing a feedback to the DCTU (101) via the receiving station (102) for calibration and testing of the vehicle. The feedback is provided to the DCTU (101) by analyzing the recorded behavior of the digital twin and the plurality of actuators . ” Examiner Note: The recording of the behavior of the digital twin is the real response data (and behavior is a result of operation information). Since the real response data is being used in analysis, it has been verified as real response data (respond data identification on the device parameters). and converting the real response data to obtain the predicted performance data according to a mapping relationship corresponding to the scene parameters when the objective vehicle device outputs the real response data; wherein the mapping relationship is configured to indicate a mapping between the real response data and a performance of the objective vehicle device. Sun and Majumder are analogous in the art of vehicle testing and calibration using virtual environments. It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Sun’s invention of a driving simulator that uses operation information from a user, device parameters, and scene parameters to get predicted performance data with Majumder’s invention to that gets real response data according to operation information, as analyzing vehicle response information allows vehicle performance characteristics to be determined, improving calibration quality. However, Sun and Majumder don’t teach and converting the real response data to obtain the predicted performance data according to a mapping relationship corresponding to the scene parameters when the objective vehicle device outputs the real response data; wherein the mapping relationship is configured to indicate a mapping between the real response data and a performance of the objective vehicle device. Li teaches and converting the real response data to obtain the predicted performance data according to a mapping relationship corresponding to the scene parameters when the objective vehicle device outputs the real response data; wherein the mapping relationship is configured to indicate a mapping between the real response data and a performance of the objective vehicle device. Li [Page 4 Paragraph 1] “ the target data is analyzed by the driving assistance algorithm to be tested, obtaining the driving track of the vehicle in the virtual vehicle in the virtual scene and an initial vehicle control instruction for controlling the virtual vehicle to drive according to the driving track … the initial vehicle control instruction is converted into the target vehicle control instruction based on the vehicle control conversion algorithm, because the vehicle control conversion algorithm is the instruction conversion relationship between the vehicle control instruction calculated by the driving auxiliary algorithm to be tested and the execution instruction in the simulator, so as to ensure that the virtual vehicle can reach the vehicle control effect corresponding to the initial vehicle control instruction,” Examiner Note: Li shows target data (real response data) being converted into target vehicle control instruction (predicted performance data ) using a mapping relationship. The vehicle control conversion algorithm is the mapping relationship, and corresponds to a driving track (scene parameters). Sun, Majumder, and Li are analogous in the art of vehicle testing and calibration using virtual environments. It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Sun in view of Majumder’s invention of a driving simulator that uses operation information from a user, device parameters, and scene parameters to get predicted performance data using real response data according to operation information with Li’s mapping relationship, as doing so would enable a conversion from raw data to data suitable for prediction, allowing for more ways to calibrate in a simulated environment. Claim 9 recites similar limitations to Claim 2, and is rejected under similar rationale . 07-21-aia AIA Claim s 3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Sun et al. (CN 118714287 A), in view of Majumder et al. (US 20230102762 A1), in further view of Li et al. (CN 118312404 A), and Zhao et al. (TrafficNet: An Open Naturialistic Driving Scenario Library, 2017, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8317860) hereinafter referred to as Sun, Majumder, Li, and Zhao respectively . Regarding Claim 3, Majumder further teaches The vehicle calibrating method of claim 1, wherein performing the simulation calculation to obtain the predicted performance data of the objective vehicle device according to the operation information, the device parameters, and the scene parameters of the predetermined driving scene comprises: determining a device type of the objective vehicle device according to the device parameters; obtaining simulation response data of the objective vehicle device by searching a response database corresponding to the device type according to the operation information and the scene parameters, wherein the response database comprises a plurality of simulation response data of the objective vehicle device corresponding to multiple optional operations in multiple optional scenes, and the response database is established according to at least one of historical calibration data of the objective vehicle equipment, a big data technology and, an artificial intelligence (AI) inference technology; and determining the predicted performance data according to the simulation response data of the objective vehicle device. Majumder [0018] “Method step 204 comprises recording the behavior of the digital twin and the plurality of actuators. Not only the physical experience but also the output in terms of statistical and mathematical parameters are recorded such as the force experienced and the like. Method step 205 comprises providing a feedback to the DCTU (101) via the receiving station (102) for calibration and testing of the vehicle. The feedback is provided to the DCTU (101) by analyzing the recorded behavior of the digital twin and the plurality of actuators . ” Examiner Note: The recording of the behavior of the digital twin is simulation response data, and it is analyzed to get feedback (predicted performance data). Sun and Majumder are analogous in the art of vehicle testing and calibration using virtual environments. It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Sun’s invention of a driving simulator that uses operation information from a user, device parameters, and scene parameters to get predicted performance data with Majumder’s invention that uses simulation response data to get the predicted performance data, as doing so would add another avenue of achieving predicted performance data through simulated vehicle performance. However, Sun and Majumder don’t teach determining a device type of the objective vehicle device according to the device parameters; obtaining simulation response data of the objective vehicle device by searching a response database corresponding to the device type according to the operation information and the scene parameters, wherein the response database comprises a plurality of simulation response data of the objective vehicle device corresponding to multiple optional operations in multiple optional scenes, and the response database is established according to at least one of historical calibration data of the objective vehicle equipment, a big data technology and, an artificial intelligence (AI) inference technology; Li teaches determining a device type of the objective vehicle device according to the device parameters; Li [Page 6 Paragraph 2] “the electronic device can directly obtain the driving auxiliary algorithm to be tested and the specification parameter from the local database of the vehicle, also can be connected with the cloud server through the network, according to the type parameter of the vehicle and so on, obtaining the specification parameter of the vehicle from the cloud server, wherein The cloud server may pre-store specification parameters of different types of vehicles of different brands. ” Examiner Note: Type parameter is a parameter of the device. According to the type parameter (device parameter), a specification parameter is obtained (device type). obtaining simulation response data of the objective vehicle device by searching a response database corresponding to the device type according to the operation information and the scene parameters , Li [Page 6 Paragraph 2] “the electronic device can directly obtain the driving auxiliary algorithm to be tested and the specification parameter from the local database of the vehicle, also can be connected with the cloud server through the network, according to the type parameter of the vehicle and so on, obtaining the specification parameter of the vehicle from the cloud server, wherein The cloud server may pre-store specification parameters of different types of vehicles of different brands.” Examiner Note: Teaches searching a database (cloud server). wherein the response database comprises a plurality of simulation response data of the objective vehicle device corresponding to multiple optional operations in multiple optional scenes , and the response database is established according to at least one of historical calibration data of the objective vehicle equipment, a big data technology and, an artificial intelligence (AI) inference technology; and determining the predicted performance data according to the simulation response data of the objective vehicle device. Sun, Majumder, and Li are analogous in the art of vehicle testing and calibration using virtual environments. It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Sun in view of Majumder’s invention of a driving simulator that uses operation information from a user, device parameters, and scene parameters to get predicted performance data using simulation response data with Li’s determination of device type and searching of a corresponding database, as doing so would allow the simulator to better predict performance data based on a specific device type through a database, thereby improving accuracy of calibration results. However, Sun, Majumder, and Li don’t teach obtaining simulation response data of the objective vehicle device by searching a response database corresponding to the device type according to the operation information and the scene parameters, wherein the response database comprises a plurality of simulation response data of the objective vehicle device corresponding to multiple optional operations in multiple optional scenes, and the response database is established according to at least one of historical calibration data of the objective vehicle equipment, a big data technology and, an artificial intelligence (AI) inference technology; Zhao teaches obtaining simulation response data of the objective vehicle device by searching a response database corresponding to the device type according to the operation information and the scene parameters, Zhao [Page 3 Section 3] “This section describes the database we build for six scenarios, as well as the algorithms we used to query them from the SPMD database . There are two tables available for each scenario, namely the Event table and the Squence table. Here we define an event as an individual instance happens in a continuous time interval. The Event table records the primary keys of all the events such as the Device, Trip, StartTime and EndTime data, while the Sequence table records all the data for each scenario in time sequence . The tables we use here consists of DataWsu, DataFrontTargets and DataLane. DataFrontTargets and DataLane are collected by Mobil eye sensor on target type, position and speed information about the obstacle, and distance to the lanes respectively ” Zhao [Page 3 Section 3 Part A] “If the primary key of DataWsu doesn’t appear in DataFrontTargets, it indicates there is no front obstacle,” Zhao [Page 2 Section 2] “Shown in Figure 1, the tens of gigabytes of raw data collected in the chronological order are stored in the database of our back-end server, while the processing procedure extracts the data corresponding to each of the six scenarios to build the scenario database.” Examiner Note: Zhao describes a database that contains events of 6 different scenarios, each with key information in Event Table and Sequence Table. The SPMD database is the response database. The simulation response data is the Sequence table data of an event corresponding to a scenario within the database. The database contains device type (as “Device” is recorded in the Event table), as well as operating information (Sequence table that records all data for each scenario) and scene parameters (DataFrontTargets and DataLane are scene parameters that check lane distance and for obstacles. Additionally, the six different scenarios are also scene parameters.). wherein the response database comprises a plurality of simulation response data of the objective vehicle device corresponding to multiple optional operations in multiple optional scenes, Zhao [Page 2 Section 2] “TrafficNet integrates the extraction of 6 critical scenarios in the current stage, including free flow, car-following, lane change, frontal cut-in pedestrian crossing, and cyclist. Zhao [Page 5 Section 3 Part D] “The table LaneChangeEvent contains the time stamp on when the vehicle changes its lane during a trip.” Examiner Note: The plurality of simulation response data is the Sequence data of multiple different scenarios, and the six scenarios also correspond to multiple optional scenes, as well as multiple optional operations (since each scenario has different requirements to be categorized, for example the LaneChangeEvent requires a vehicle to change its lane during a trip). and the response database is established according to at least one of historical calibration data of the objective vehicle equipment, a big data technology and, an artificial intelligence (AI) inference technology; Zhao [Page 2 Section 2] “TrafficNet uses the relational database to store and organize the large volumes of traffic data from the open repository of DoT.” Examiner Note: A relational database being used to handle large amounts of data is big data technology. Sun, Majumder, Li, and Zhao are analogous in the art of vehicle testing and calibration. It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Sun in view of Majumder and Li’s invention of a driving simulator that contains operation information from a user, device parameters, scene parameters, and device type, to search a database associated to the device type as shown in Zhao to obtain simulation response data database to be used to determine predicted performance data. This combination allows the simulation response data corresponding to a particular device type to be obtained from the database taught in Zhao using operation information and scene parameters, and subsequently then using the data to determine predicted performance data. Such modification would improve simulation accuracy by using already existing data that pertains to specific scenarios and operations, allowing for a realistic and reliable approach to obtaining data to predict with. Claim 10 recites similar limitations to Claim 3, and is rejected under similar rationale . 07-21-aia AIA Claim s 5 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Sun et al. (CN 118714287 A), in view of Majumder et al. (US 20230102762 A1), in further view of Kostas (US 20230254003 A1) hereinafter referred to as Sun, Majumder, and Kostas respectively . Regarding Claim 5, Majumder further teaches The vehicle calibrating method of claim 4, wherein determining the recommended calibration parameters corresponding to the objective vehicle device according to the predicted performance data and the device parameters comprises: inputting the predicted performance data and the device parameters into a preset optimization model to obtain original calibration parameters, wherein the preset optimization model is trained according to preset multiple calibration parameter samples corresponding to the device parameters and a performance label corresponding to each of the multiple calibration parameter samples; and verifying the original calibration parameters in a vehicle, and optimizing the original calibration parameters to obtain the recommended calibration parameters according to verification results of the original calibration parameters. Majumder [0019] “This can be extended to a point where the virtual reality station (103) can suggest/predict vehicle parametrization based on deep learning algorithms using big data from digital twin model and real time data.” Examiner Note: Majumder demonstrates using big data (verification results) from the digital twin to suggest vehicle parameters (recommended calibration parameters) based off previous settings (original calibration parameters). For the recommended calibration parameters to be achieved, the original calibration parameters must first be tested. The big data from the digital twin model is the verification results of the original calibration parameters, and the suggested vehicle parametrization is the recommended calibration parameters. Sun and Majumder are analogous in the art of vehicle testing and calibration using virtual environments. It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to additionally modify Sun’s invention of a driving simulator that uses operation information from a user, device parameters, and scene parameters to get predicted performance data with Majumder’s invention to display recommended calibration parameters to the user based on previous (original) calibration parameters, as doing so would make the calibration process more efficient and improve ease of use for the user, therefore reducing calibration time. However, Sun and Majumder don’t teach wherein determining the recommended calibration parameters corresponding to the objective vehicle device according to the predicted performance data and the device parameters comprises: inputting the predicted performance data and the device parameters into a preset optimization model to obtain original calibration parameters, wherein the preset optimization model is trained according to preset multiple calibration parameter samples corresponding to the device parameters and a performance label corresponding to each of the multiple calibration parameter samples; Kostas teaches wherein determining the recommended calibration parameters corresponding to the objective vehicle device according to the predicted performance data and the device parameters comprises: inputting the predicted performance data and the device parameters into a preset optimization model to obtain original calibration parameters, wherein the preset optimization model is trained according to preset multiple calibration parameter samples corresponding to the device parameters and a performance label corresponding to each of the multiple calibration parameter samples; and Kostas [0039] “The machine learning models described herein may generally be trained to identify a subset of calibration parameters that can be predicted with yield loss below a threshold amount. That is, the calibration parameters that are predicted by the machine learning models described herein may result in calibration parameters that allow for an RF circuit to operate according to a defined set of performance parameters, without losing more than the threshold amount of circuits due to an inability to operate according to the defined set of performance parameters. Generally , the machine learning model may use, as an input, a set of exemplar parameters, corresponding to parameters calibrated by an RF parameter calibrator 110 illustrated in FIGS. 2-4, to generate a set of non-exemplar parameters.” Kostas [0041] “In another example, the machine learning model may be trained based on yield similarity clustering. In generating a training data set, a yield loss percentage may be calculated for each pair of parameters in a historical data set of RF circuit calibration parameters. ” Examiner Note: The machine learning model (preset optimization model) identifies a subset of calibration parameters (original calibration parameters). The machine learning model may use a set of parameters as input, and can be trained from a training data set consisting of a historical data set of calibration parameters (preset multiple calibration parameter samples, and device parameters as these are parameters of the vehicle device), with each pair of parameters having a yield loss percentage calculated for them (a performance label). Sun, Majumder, and Kostas are analogous in the art of calibration and optimization. It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Sun in view of Majumder’s invention of a driving simulator that uses operation information from a user, device parameters, and scene parameters to get predicted performance data with Kostas’ optimization techniques in order to optimize the calibration parameters of the vehicle, thereby reducing calibration time, minimizing manual parameter selection, and improving calibration accuracy. Claim 12 recites similar limitations to Claim 5, and is rejected under similar rationale . 07-21-aia AIA Claim s 6-7 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Sun et al. (CN 118714287 A), in view of Majumder et al. (US 20230102762 A1), in further view of TTR Studios (“iRacing in VR with Quest 3: A Step-By-Step Guide to an Easy Setup”, 01/02/2024, https://www.youtube.com/watch?v=8QvxivDwlKE) hereinafter referred to as Sun, Majumder, and TTR Studios respectively . Regarding Claim 6, Sun and Majumder fail to teach the vehicle calibrating method of claim 1, wherein displaying the predetermined driving scene to the user corresponding to the objective vehicle device in the preset VR environment comprises: displaying multiple optional driving scenes in the preset VR environment, wherein the multiple optional driving scenes are generated by a virtual simulation on multiple optional real calibration scenes; receiving selection information with respect to the multiple optional driving scenes; determining the predetermined driving scene from the multiple optional driving scenes according to the selection information. TTR Studios teaches wherein displaying the predetermined driving scene to the user corresponding to the objective vehicle device in the preset VR environment comprises: displaying multiple optional driving scenes in the preset VR environment, wherein the multiple optional driving scenes are generated by a virtual simulation on multiple optional real calibration scenes; TTR Studios [Timestamp 6:27] Figure 1: PNG media_image1.png 200 400 media_image1.png Greyscale Examiner Note: Figure 1 showcases a selection menu in a VR environment that contains various driving scenes. receiving selection information with respect to the multiple optional driving scenes; determining the predetermined driving scene from the multiple optional driving scenes according to the selection information. TTR Studios [Timestamp 8:05] Figure 2: PNG media_image2.png 200 400 media_image2.png Greyscale Examiner Note: Figure 2 shows a driving scene after being selected, with the driving scene selected being displayed. Sun, Majumder, and TTR Studios are analogous in the art of virtual vehicle simulators. It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Sun in view of Majumder’s invention of a driving simulator that uses driving scenes based off of real scenes with TTR Studios’ scene selection to allow the vehicle to be evaluated across a broad range of scenarios, and providing the user with a way of selecting them. Regarding Claim 7, Sun and Majumder fail to teach the vehicle calibrating method of claim 1, wherein after receiving the operation information of the objective vehicle device inputted by the user, the method further comprises: adjusting the predetermined driving scene in the preset VR environment according to the operation information. TTR Studios teaches the vehicle calibrating method of claim 1, wherein after receiving the operation information of the objective vehicle device inputted by the user, the method further comprises: adjusting the predetermined driving scene in the preset VR environment according to the operation information. TTR Studios [Timestamps 8:40-8:46] Figure 3: PNG media_image3.png 808 1446 media_image3.png Greyscale Examiner Note: During the time period, when the user inputs the operation instruction of gas (acceleration), the vehicle moves forward, therefore the predetermined driving scene being adjusted. Also shown in Figure 3 during this time period is the steering wheel being turned, and the car also turning accordingly, which is the driving scene responding (car turning) according to operation information (steering wheel turning). Sun, Majumder, and TTR Studios are analogous in the art of virtual vehicle simulators. It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Sun in view of Majumder’s invention of a driving simulator that has input of operation information from a user with TTR Studios’ driving scene to show a dynamically updating environment to show proper feedback to the user’s operation, improving the user experience. Claim 13 recites similar limitations to Claim 6, and is rejected under similar rationale. Claim 14 recites similar limitations to Claim 7, and is rejected under similar rationale. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID W SOON whose telephone number is (571)272-8113. The examiner can normally be reached M-F 7:30-5:00. 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, Alicia Harrington can be reached at (571) 272-2330. 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. /DAVID W SOON/Examiner, Art Unit 2615 /ALICIA M HARRINGTON/Supervisory Patent Examiner, Art Unit 2615 Application/Control Number: 18/941,293 Page 2 Art Unit: 2615 Application/Control Number: 18/941,293 Page 3 Art Unit: 2615 Application/Control Number: 18/941,293 Page 4 Art Unit: 2615 Application/Control Number: 18/941,293 Page 5 Art Unit: 2615 Application/Control Number: 18/941,293 Page 6 Art Unit: 2615 Application/Control Number: 18/941,293 Page 7 Art Unit: 2615 Application/Control Number: 18/941,293 Page 8 Art Unit: 2615 Application/Control Number: 18/941,293 Page 9 Art Unit: 2615 Application/Control Number: 18/941,293 Page 10 Art Unit: 2615 Application/Control Number: 18/941,293 Page 11 Art Unit: 2615 Application/Control Number: 18/941,293 Page 12 Art Unit: 2615 Application/Control Number: 18/941,293 Page 13 Art Unit: 2615 Application/Control Number: 18/941,293 Page 14 Art Unit: 2615 Application/Control Number: 18/941,293 Page 15 Art Unit: 2615 Application/Control Number: 18/941,293 Page 16 Art Unit: 2615 Application/Control Number: 18/941,293 Page 17 Art Unit: 2615 Application/Control Number: 18/941,293 Page 18 Art Unit: 2615 Application/Control Number: 18/941,293 Page 19 Art Unit: 2615 Application/Control Number: 18/941,293 Page 20 Art Unit: 2615 Application/Control Number: 18/941,293 Page 21 Art Unit: 2615 Application/Control Number: 18/941,293 Page 22 Art Unit: 2615 Application/Control Number: 18/941,293 Page 23 Art Unit: 2615 Application/Control Number: 18/941,293 Page 24 Art Unit: 2615 Application/Control Number: 18/941,293 Page 25 Art Unit: 2615 Application/Control Number: 18/941,293 Page 26 Art Unit: 2615
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