Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments with respect to 35 U.S.C. 101 have been fully considered and are persuasive, because the “adjusting one or more vehicle systems” integrates into practical application by making meaningful adjustments to the setup of the car in the context of the vehicle systems described in paragraph [0051] of the published specification.
The eligibility rejection of record has been withdrawn.
Applicant’s arguments with respect to 35 U.S.C. 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
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.
Claims 1, 3-9, 11-16, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US20170334450 by Shiraishi et al. (hereinafter “Shiraishi”), in view of US20220194400 by Gee et al. (hereinafter “Gee”), and further in view of DE102022108677 by Markofsky (hereinafter “Markofsky”).
Regarding claim 1, Shiraishi teaches A method for optimizing a vehicle setup, the method comprising: see for example Fig. 5, where the system receives subjective user input of their preferences. See also, for example, paragraph [0134], where the system uses preference data in determining vehicle setup.
applying the see for example paragraphs [0052]-[0053], where the system uses the preference data to modify the vehicle’s features (e.g. ADAS settings).
generating, by the see for example paragraphs [0139]-[0140], where the input preference data is used by the optimization engine to generate optimization settings for the vehicle based on the user’s preferences and the vehicle data.
selecting an optimal vehicle setup for a vehicle based on the predicted subjective driver feedback; and adjusting one or more vehicle systems to configure the vehicle based on the optimal vehicle setup. See for example paragraph [0073], where the ADAS control parameters are modified and generate a graphical representation of the customization.
Shiraishi does not explicitly teach a train[ed] driver specific model, or training a driver specific model based on training data comprising a plurality of first vehicle setups. Although Shiraishi uses an “optimization engine” which uses driver preference data as input, the engine does not explicitly read on a trained model, nor does it appeared to learn from past user selections with respect to other vehicles. Shiraishi also does not explicitly teach selecting a setup based on a prediction of how the driver would evaluate the second vehicle setup based on the subjective driver feedback of each first vehicle setup.
However, Gee teaches a system including training a driver specific model based on training data comprising a plurality of first vehicle setups. See for example paragraphs [0268], where the system is trained with past user purchases and user preferences in optimizing the vehicle components, in order to subsequently output a component recommendation in paragraph [0272], where such past purchase data is analogous to training data comprising a plurality of first vehicle setups, and the preference data is analogous to subjective driver feedback of a driver on each first vehicle setup of the plurality of first vehicle setups. See also paragraphs [0116]-[0120] where the model is a machine learning model.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle customization system of Shiraishi with the trained customization model of Gee with a reasonable expectation of success. Doing so allows the optimization engine of Shiraishi to use neural networks trained on past history to evaluate a user’s preferences and optimize the setup of a new vehicle.
Neither Shiraishi nor Gee explicitly teach selecting a setup based on a prediction of how the driver would evaluate the second vehicle setup based on the subjective driver feedback of each first vehicle setup.
However, Markofsky teaches selecting a setup based on a prediction of how the driver would evaluate the second vehicle setup based on the subjective driver feedback of each first vehicle setup. See for example the second and third paragraphs on page 6: “In order to be able to objectively assess the subjective assessment of users with regard to the functionality of driver assistance systems, the invention provides a 1 system 100 shown for determining objective parameters for predicting a subjective evaluation of a driver assistance system and/or an automated driver assistance function by a user with regard to the acceptance and perception of the performance and functionality. A virtual application of a driver assistance system and/or a driver assistance function, which is also referred to as ADAS/ADS, is considered. In order to be able to assess the expected subjective perception of a user or driver, parameters are calculated according to the invention for the various traffic scenarios that are carried out or driven through by the driver assistance system and/or the driver assistance function” (emphasis added).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle customization system of Shiraishi, modified by the trained customization model of Gee, with the evaluation index of Markofsky with a reasonable expectation of success. Doing so allows the optimization engine to determine and simulate configurations based on a score given by the user.
Independent claim 9 is directed towards A system with similar limitations to claim 1 above, and is therefore rejected using a similar rationale.
Regarding claim 3, Shiraishi does not explicitly teach, but Gee does teach a system further comprising: building the training data for the driver from first vehicle setup information and subjective driver feedback information provided by the driver. See for example paragraphs [0268], where the system is trained with past user purchases and user preferences in optimizing the vehicle components.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle customization system of Shiraishi with the trained customization model of Gee with a reasonable expectation of success. Doing so allows the optimization engine of Shiraishi to use neural networks trained on past history to evaluate a user’s preferences and optimize the setup of a new vehicle.
Claim 11 has similar limitations to claim 3 above, and is therefore rejected using a similar rationale.
Regarding claim 4, Shiraishi teaches wherein each first vehicle setup of the. See for example Figure 5 or Figure 8 (or paragraphs [0206], [0210]) where the system adjusts the ADAS settings for controlling the vehicle, reading at least on electrical settings of vehicle components and also probably mechanical . . . settings (by virtue of controlling, e.g., braking force).
Shiraishi does not explicity teach a plurality of first vehicle setups.
However, Gee teaches a plurality of first vehicle setups. See for example paragraphs [0268], where the system is trained with past user purchases and user preferences in optimizing the vehicle components, in order to subsequently output a component recommendation in paragraph [0272].
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle customization system of Shiraishi with the trained customization model of Gee with a reasonable expectation of success. Doing so allows the optimization engine of Shiraishi to use neural networks trained on past history to evaluate a user’s preferences and optimize the setup of a new vehicle.
Claims 12 and 18 have similar limitations to claim 4 above, and are therefore rejected using a similar rationale.
Regarding claim 5, neither Shiraishi nor Gee explicitly teach wherein the first subjective driver feedback comprises evaluation scores assigned by the driver to vehicle states. Although Shiraishi teaches using user preferences, this does not read on an evaluation “score.” Likewise, although Gee uses past data, including historical preference and purchase data, driving styles, etc., Gee does not teach any “score” feedback.
However, Markofsky teaches wherein the first subjective driver feedback comprises evaluation scores assigned by the driver to vehicle states. See for example the paragraph spanning pages 4-5 (beginning with “According to a second aspect…”), which states in part: “Furthermore, the input module is designed to generate an evaluation index from a user via the subjective evaluation of a specific embodiment of a scenario type and to assign this evaluation index to this specific embodiment of the scenario type, so that for a large number of embodiment variants of a scenario type, a large number of score indices, and to pass the data and the score indices to the extraction module. The extraction module is designed to extract a minimum value and a maximum value for the range of values of at least one parameter for a plurality of embodiment variants of a selected scenario type evaluated with the same evaluation index when the at least one route is driven on multiple times. The analysis module is designed to create a correlation metric between the various evaluation indices and the respective extracted minimum and maximum value of a value range of a parameter for the selected scenario type and at least one parameter for predicting a subjective evaluation of the driver assistance system and/or the driver assistance function for the selected scenario type using the correlation metric” (emphasis added).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle customization system of Shiraishi, modified by the trained customization model of Gee, with the evaluation index of Markofsky with a reasonable expectation of success. Doing so allows the optimization engine to determine and simulate configurations based on a score given by the user.
Claim 13 has similar limitations to claim 5 above, and is therefore rejected using a similar rationale.
Regarding claim 6, Shiraishi does not explicitly teach, but Gee teaches wherein the training data comprises time series data obtained from sensors on the vehicle. See for example paragraph [0211], where training data includes sensor data during the driver’s operation of the vehicle, etc.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle customization system of Shiraishi with the trained customization model of Gee with a reasonable expectation of success. Doing so allows the optimization engine of Shiraishi to use neural networks trained on past history to evaluate a user’s preferences and optimize the setup of a new vehicle.
Claim 14 has similar limitations to claim 6 above, and is therefore rejected using a similar rationale.
Regarding claim 7, neither Shiraishi nor Gee explicitly teach wherein the second subjective driver feedback comprises a prediction of an evaluation scores for the second vehicle setup. Although Shiraishi teaches using user preferences, this does not read on an evaluation “score,” nor does Shiraishi predict such a score. Likewise, although Gee uses past data, including historical preference and purchase data, driving styles, etc., in recommending settings or configuring the vehicle, Gee does not teach any “score” feedback or predict a user’s score.
However, Markofsky teaches wherein the second subjective driver feedback comprises a prediction of an evaluation scores for the second vehicle setup. See again the paragraph spanning pages 4-5 (beginning with “According to a second aspect…”), which states in part: “Furthermore, the input module is designed to generate an evaluation index from a user via the subjective evaluation of a specific embodiment of a scenario type and to assign this evaluation index to this specific embodiment of the scenario type, so that for a large number of embodiment variants of a scenario type, a large number of score indices, and to pass the data and the score indices to the extraction module. The extraction module is designed to extract a minimum value and a maximum value for the range of values of at least one parameter for a plurality of embodiment variants of a selected scenario type evaluated with the same evaluation index when the at least one route is driven on multiple times. The analysis module is designed to create a correlation metric between the various evaluation indices and the respective extracted minimum and maximum value of a value range of a parameter for the selected scenario type and at least one parameter for predicting a subjective evaluation of the driver assistance system and/or the driver assistance function for the selected scenario type using the correlation metric” (emphasis added).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle customization system of Shiraishi, modified by the trained customization model of Gee, with the evaluation index of Markofsky with a reasonable expectation of success. Doing so allows the optimization engine to determine and simulate configurations based on a score given by the user.
Claim 15 has similar limitations to claim 7 above, and is therefore rejected using a similar rationale.
Regarding claim 8, Shiraishi teaches further comprising: receiving, from one or more of computer simulation and real-world driving, objective performance metrics of vehicle performance for the second vehicle setup; see for example paragraphs [0187] or [0198], where the system simulates vehicle performance with different parameters.
and providing the optimal vehicle setup based on the second subjective driver feedback and the objective performance metrics. See for example paragraph [0009], where the optimization provides the optimal setup to the user based on the simulation of vehicle characteristics and user preferences.
Claim 16 has similar limitations to claim 8 above, and is therefore rejected using a similar rationale.
Regarding claim 20, Shiraishi teaches wherein the one or more processors are further configured to execute the instructions to: obtain one or more performance metrics; and for each candidate vehicle setup, executing a optimization function on the one or more performance metrics and the subjective driver feedback corresponding to a respective candidate vehicle setup; see for example paragraphs [0187] or [0198], where the system simulates vehicle performance with different parameters.
identify a candidate vehicle setup as an optimal vehicle setup based on driving the optimization function for each candidate vehicle setup to zero; and displaying the optimal vehicle setup on the GUI. See for example paragraph [0009], where the optimization provides the optimal setup to the user based on the simulation of vehicle characteristics and user preferences.
Dependent claims 2 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Shiraishi in view of Gee and Markofsky as applied to claims 1 and 9 above, and further in view of US20140163807 by Schoggl (hereinafter “Schoggl”); independent claim 17 is likewise rejected under 35 U.S.C. 103 as being unpatentable over Shiraishi in view of Gee, Markofsky, and Schoggl.
Regarding claim 2, neither Shiraishi nor Gee explicitly teach wherein the vehicle is a race car.
However, Schoggl teaches wherein the vehicle is a race car. See for example paragraph [0020], where Schoggl teaches a method of vehicle simulation for determining race car parameters.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle customization system of Shiraishi, modified by the trained customization model of Gee and the evaluation index of Markofsky, with the race car application of Schoggl with a reasonable expectation of success. Doing so allows the optimization engine to determine and simulate configurations for a racecar to use.
Claim 10 has similar limitations to claim 2 above, and is therefore rejected using a similar rationale.
Regarding claim 17, Shiraishi teaches A vehicle setup selection system, the vehicle setup selection system comprising: at least one memory configured to store instructions; and one or more processors communicably coupled to the at least one memory and configured to execute the instruction to: see for example Fig. 2 describing computer components.
obtain candidate vehicle setup information comprising data on a plurality of candidate vehicle setups for configuring a see for example Fig. 5, where the system receives subjective user input of their preferences. See also, for example, paragraph [0134], where the system uses preference data in determining vehicle setup.
generate subjective driver feedback for each of the plurality of candidate vehicle setups by applying the candidate vehicle setup information to a see for example paragraphs [0052]-[0053], where the system uses the preference data to modify the vehicle’s features (e.g. ADAS settings). See also paragraphs [0139]-[0140], where the input preference data is used by the optimization engine to generate optimization settings for the vehicle based on the user’s preferences and the vehicle data.
provide a visualization of the plurality of candidate vehicle setups, the visualization comprising a graphical user interface (GUI) that displays each candidate vehicle setup and the subjective driver feedback for each candidate vehicle setup; and adjust one or more vehicle systems to configure the race vehicle based on the visualization of the plurality of candidate vehicle setups. See for example paragraph [0073], where the ADAS control parameters are modified and generate a graphical representation of the customization.
Shiraishi does not explicitly teach a driver specific machine learning model that is trained on subjective data of a particular driver. Although Shiraishi uses an “optimization engine” which uses driver preference data as input, the engine does not explicitly read on a trained model, nor does it appeared to learn from past user selections with respect to other vehicles. Shiraishi also does not explicitly teach wherein the generated subjective driver feedback comprises a prediction of how the driver would evaluate one or more of the plurality of candidate vehicle setups based on the subjective driver feedback for each of the plurality of candidate vehicle setups based on the subjective driver feedback for each of the plurality of candidate vehicle setups.
However, Gee teaches a driver specific machine learning model that is trained on subjective data of a particular driver. See for example paragraphs [0268], where the system is trained with past user purchases and user preferences in optimizing the vehicle components, in order to subsequently output a component recommendation in paragraph [0272]. See also paragraphs [0116]-[0120] where the model is a machine learning model.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle customization system of Shiraishi with the trained customization model of Gee with a reasonable expectation of success. Doing so allows the optimization engine of Shiraishi to use neural networks trained on past history to evaluate a user’s preferences and optimize the setup of a new vehicle.
Shiraishi does not explicitly teach wherein the generated subjective driver feedback comprises a prediction of how the driver would evaluate one or more of the plurality of candidate vehicle setups based on the subjective driver feedback for each of the plurality of candidate vehicle setups based on the subjective driver feedback for each of the plurality of candidate vehicle setups.
However, Markofsky teaches wherein the generated subjective driver feedback comprises a prediction of how the driver would evaluate one or more of the plurality of candidate vehicle setups based on the subjective driver feedback for each of the plurality of candidate vehicle setups based on the subjective driver feedback for each of the plurality of candidate vehicle setups. See for example the second and third paragraphs on page 6: “In order to be able to objectively assess the subjective assessment of users with regard to the functionality of driver assistance systems, the invention provides a 1 system 100 shown for determining objective parameters for predicting a subjective evaluation of a driver assistance system and/or an automated driver assistance function by a user with regard to the acceptance and perception of the performance and functionality. A virtual application of a driver assistance system and/or a driver assistance function, which is also referred to as ADAS/ADS, is considered. In order to be able to assess the expected subjective perception of a user or driver, parameters are calculated according to the invention for the various traffic scenarios that are carried out or driven through by the driver assistance system and/or the driver assistance function” (emphasis added).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle customization system of Shiraishi, modified by the trained customization model of Gee, with the evaluation index of Markofsky with a reasonable expectation of success. Doing so allows the optimization engine to determine and simulate configurations based on a score given by the user.
Neither Shiraishi, Gee, nor Markofsky explicitly teach a race vehicle.
However, Schoggl teaches a race vehicle. See for example paragraph [0020], where Schoggl teaches a method of vehicle simulation for determining race car parameters.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle customization system of Shiraishi, modified by the trained customization model of Gee and the evaluation index of Markofsky, with the race car application of Schoggl with a reasonable expectation of success. Doing so allows the optimization engine to determine and simulate configurations for a racecar to use.
Claim 19 is rejected under 35 U.S.C. 103 by Shiraishi in view of Gee and Markofsky as applied to claims 1 and 9 above, and further in view of US20150039278 by Hale (hereinafter “Hale”).
Regarding claim 19, Shiraishi teaches wherein the one or more processors are further configured to execute the instructions to: for each candidate vehicle setup: calculate . See again paragraph [0009] for example, where the system generates optimal vehicle settings based on input parameters. See also for example paragraph [0073], where the ADAS control parameters are modified and the system generates a graphical representation of the customization.
Shiraishi does not explicitly teach calculate[ing] a sensitivity measure.
However, Hale teaches calculate[ing] a sensitivity measure. See for example paragraph [0017], where the system analyzes the sensitivity of the output to input parameters. See also for example paragraph [0088], where sensitivity measure are calculated and the result of the sensitivity analysis is displayed.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle customization system of Shiraishi, modified by the trained customization model of Gee and the evaluation index of Markofsky, with the sensitivity measure of Hale with a reasonable expectation of success. Doing so allows the optimization engine to determine how sensitive the output of the optimization is to its input parameters.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US20170297586 by Li teaching a driver preference system that predicts a user’s habits and preferences.
US20210031655 by Tang et al. teaching predicting a user’s preferred vehicle setup.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN THOMAS SMITH whose telephone number is (571)272-0522. The examiner can normally be reached Monday - Friday, 9am - 5pm.
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, Anne Antonucci can be reached at (313) 446-6519. 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.
/JORDAN T SMITH/Examiner, Art Unit 3666
/ANNE MARIE ANTONUCCI/Supervisory Patent Examiner, Art Unit 3666