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. 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 Objections
Claim 19 is objected to because of the following informalities:
Claim 19 appears to lack a conjunction between the “calculate” step and the “display” step. That is, the claim would be clearer if it read “…calculate a sensitivity measure…, and display each sensitivity measure….”
Appropriate correction is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over US20150045988 by Gusikhin et al. (hereinafter “Gusikhin”), further in view of US20080183449 by Grichnik et al. (hereinafter “Grichnik”).
Regarding claim 1, Gusikhin teaches A method for see for example paragraphs [0032]-[0033], where the machine learning system interfaces with a driver about iteratively applied changes and receives driver feedback on those changes. See also generally paragraphs [0050]-[0056], describing the process which includes learning a user’s preferences over previous vehicles.
applying the driver specific model on a second vehicle setup; see for example paragraphs [0050]-[0056], where the system applies the preferences to a new vehicle.
generating, by the driver specific model, predicted subjective driver feedback on the second vehicle setup, wherein the predicted subjective driver feedback comprises a prediction of how the driver would evaluate the second vehicle setup based on the subjective driver feedback of each first vehicle setup as an initial vehicle setup; see for example paragraphs [0018] and [0054]-[0055], where the system predicts a driver’s preferences for a new vehicle (i.e. a vehicle that the driver has not given feedback on before, reading on an initial vehicle setup) based on previous vehicle setups and collected data, as well as paragraph [0032], where the driver gives subjective feedback to the model regarding changes made.
selecting an See again paragraphs [0050]-[0056], where the system applies the preferences to a new vehicle. See also the settings described in paragraphs [0065]-[0150], which generally read on a vehicle setup.
Gusikhin does not explicitly teach an optimal vehicle setup. Although Gusikhin does attempt to suit the setup to the user’s preferences, Gusikhin does not explicitly teach that the produced setup is in any way optimized.
However, Grichnik teaches a system that produces an optimal vehicle setup. See for example paragraph [0075], where the goal of the system is to produced an optimal setup. The model itself is optimized, see, e.g., ¶¶ [0018], [0027], and re-optimized if necessary, see ¶ [0071], in order to learn the best parameters for setup.
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 driver profile system of Gusikhin with the trained optimization model of Grichnik with a reasonable expectation of success. Doing so allows the driver settings model to generate the best parameters possible.
Independent claim 9 is directed towards A system with similar limitations to claim 1 above, and is therefore rejected using a similar rationale.
Regarding independent claim 17, Gusikhin 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 instructions to: see for example Fig. 1, discussing system layout. See also generally paragraphs [0050]-[0056], describing the process which includes learning a user’s preferences over previous vehicles.
obtain candidate vehicle setup information comprising data on a plurality of candidate vehicle setups for configuring a see for example paragraphs [0032]-[0033], where the machine learning system interfaces with a driver about iteratively applied changes and receives driver feedback on those changes. See also generally paragraphs [0050]-[0056], describing the process which includes learning a user’s preferences over previous vehicles.
generate subjective driver feedback for each of the plurality of candidate vehicle setups by applying the candidate vehicle setup information to a driver specific machine learning model trained on subjective data of a particular driver, 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 as an initial vehicle setup; see for example paragraphs [0018] and [0054]-[0055], where the system predicts a driver’s preferences for a new vehicle (i.e. a vehicle that the driver has not given feedback on before, reading on an initial vehicle setup) based on previous vehicle setups and collected data, as well as paragraph [0032], where the driver gives subjective feedback to the model regarding changes made.
and adjust one or more vehicle systems to configure the . See again paragraphs [0050]-[0056], where the system applies the preferences to a new vehicle. See also the settings described in paragraphs [0065]-[0150], which generally read on a vehicle setup.
Gusikhin does not explicitly teach a race vehicle, nor does Gusikhin teach that the system should 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.
However, Grichnik teaches a race vehicle. See for example paragraphs [0019]-[0021], describing a configuration model for a race car. See also paragraph [0069], where the system derives setup parameters for a particular race car.
Grichnik also teaches that the system should 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. See for example paragraphs [0070] and [0073], where the system displays the vehicle setup parameters via a graphical user interface.
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 driver profile system of Gusikhin with the racecar optimization display system of Grichnik with a reasonable expectation of success. Doing so allows the driver settings model to generate the best parameters possible for a racecar, and to display those settings to the driver for evaluation and feedback.
Regarding claim 2, Gusikhin does not explicitly teach, but Grichnik teaches wherein the vehicle is a race car. See for example paragraphs [0019]-[0021], describing a configuration model for a race car. See also paragraph [0069], where the system derives setup parameters for a particular race car.
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 driver profile system of Gusikhin with the racecar optimization display system of Grichnik with a reasonable expectation of success. Doing so allows the driver settings model to generate the best parameters possible for a racecar, and to display those settings to the driver for evaluation and feedback.
Claim 10 has similar limitations to claim 2 above, and is therefore rejected using a similar rationale.
Regarding claim 3, Gusikhin teaches 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 [0018], [0032]-[0033], and [0054]-[0055], where the machine learning model predicts a driver’s preferences for a new vehicle based on previous vehicle setups and collected data, as well as paragraph [0032], where the driver gives subjective feedback to the model regarding changes made.
Claim 11 has similar limitations to claim 3 above, and is therefore rejected using a similar rationale.
Regarding claim 4, Gusikhin teaches wherein each first vehicle setup of the plurality of first vehicle setups comprises one or more vehicle operating parameters defining the vehicle setup, the vehicle parameters comprising mechanical and electrical settings of vehicle components. See for example paragraphs [0018] and [0054]-[0055], where the system predicts a driver’s preferences for a new vehicle based on the driver’s preferences over a plurality of prior vehicles. See again paragraphs [0050]-[0056], where the system applies the preferences to a new vehicle. See also the settings described in paragraphs [0065]-[0150], which generally read on a vehicle setup. For example, electrical settings could be taught by paragraphs [0065]-[0069] (computer settings), [0094]-[0096] (climate control settings), etc.; likewise, mechanical settings could be taught by [0125]-[0126] (transmission shift schedule), [0147]-[0148] (tire pressure), [0086]-[0087] (power seat control), etc.
Claims 12 and 18 have similar limitations to claim 4 above, and are therefore rejected using a similar rationale.
Regarding claim 5, Gusikhin teaches wherein the first subjective driver feedback comprises evaluation scores assigned by the driver to vehicle states. See for example paragraphs [0018] and [0054]-[0055], where the system predicts a driver’s preferences for a new vehicle based on previous vehicle setups and collected data, as well as paragraph [0032], where the driver gives subjective feedback to the model regarding changes made.
Claim 13 has similar limitations to claim 5 above, and is therefore rejected using a similar rationale.
Regarding claim 6, Gusikhin does not explicitly teach, but Grichnik teaches wherein the training data comprises time series data obtained from sensors on the vehicle. See for example paragraphs [0041] and [0075], where the system acquires time data regarding setup parameters, as well as real-time data from the vehicle.
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 driver profile system of Gusikhin with the racecar optimization display system of Grichnik with a reasonable expectation of success. Doing so allows the driver settings model to generate the best parameters possible for a racecar, and to display those settings to the driver for evaluation and feedback.
Claim 14 has similar limitations to claim 6 above, and is therefore rejected using a similar rationale.
Regarding claim 7, Gusikhin teaches wherein the second subjective driver feedback comprises a prediction of an evaluation scores for the second vehicle setup. See for example paragraphs [0018] and [0054]-[0055], where the system predicts a driver’s preferences for a new vehicle based on previous vehicle setups and collected data, as well as paragraph [0032], where the driver gives subjective feedback to the model regarding changes made.
Claim 15 has similar limitations to claim 7 above, and is therefore rejected using a similar rationale.
Regarding claim 8, Gusikhin does not explicitly teach, but Grichnik 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 [0060] and [0067]-[0071], where the system predicts performance parameters based on a simulation; see also paragraph [0075], where the system can update the parameters based on real-world results.
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 driver profile system of Gusikhin with the racecar optimization display system of Grichnik with a reasonable expectation of success. Doing so allows the driver settings model to generate the best parameters possible for a racecar, and to display those settings to the driver for evaluation and feedback.
Claim 16 has similar limitations to claim 8 above, and is therefore rejected using a similar rationale.
Regarding claim 19, Gusikhin teaches wherein the one or more processors are further configured to execute the instructions to: for each candidate vehicle setup: calculate . See again for example paragraphs [0018], [0032]-[0033], and [0054]-[0055], where the machine learning model predicts a driver’s preferences for a new vehicle based on previous vehicle setups and collected data, as well as paragraph [0032], where the driver gives subjective feedback to the model regarding changes made. Further, suggested changes are displayed to the user in paragraph [0032], reading on display[ing] … with each vehicle operating parameter.
Gusikhin does not explicitly teach, but Grichnik teaches calculate[ing] a sensitivity measure. See for example paragraphs [0057]-[0062], where Grichnik uses zeta statistics to determine the sensitivity of each output parameter to each input parameter.
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 driver profile system of Gusikhin with the racecar optimization display system of Grichnik with a reasonable expectation of success. Doing so allows the driver settings model to generate the best parameters possible for a racecar, and to display those settings to the driver for evaluation and feedback.
Regarding claim 20, Gusikhin teaches wherein the one or more processors are further configured to execute the instructions to: obtain one or more see
identify a candidate vehicle setup as an . See
Gusikhin does not explicitly teach performance metrics. Although Gusikhin is attempting to generate a best-fit setup based on the driver’s feedback, Gusikhin does not explicitly teach performance metrics.
Likewise, Gusikhin does not explicitly teach an optimal vehicle setup. Although Gusikhin does attempt to suit the setup to the user’s preferences, Gusikhin does not explicitly teach that the produced setup is in any way optimized.
Finally, Gusikhin does not explicitly teach displaying the optimal vehicle setup on the GUI.
However, Grichnik teaches obtaining performance metrics. See for example paragraphs [0060] and [0067]-[0071], where the system predicts performance parameters based on a simulation; see also paragraph [0075], where the system can update the parameters based on real-world results.
Grichnik also teaches a system that produces an optimal vehicle setup. See for example paragraph [0075], where the goal of the system is to produced an optimal setup. The model itself is optimized, see, e.g., ¶¶ [0018], [0027], and re-optimized if necessary, see ¶ [0071], in order to learn the best parameters for setup.
Finally, Grichnik teaches displaying the optimal vehicle setup on the GUI. See for example paragraphs [0070] and [0073], where the system displays the vehicle setup parameters via a graphical user interface.
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 driver profile system of Gusikhin with the racecar optimization display system of Grichnik with a reasonable expectation of success. Doing so allows the driver settings model to generate the best parameters possible for a racecar, and to display those settings to the driver for evaluation and feedback.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US20170305437 by Onorato et al. teaching recommending vehicle setup settings for a user.
US20240294129 by Krishnamurthy et al. teaching identifying operator preferences based on prompts to configure the vehicle.
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/JORDAN T SMITH/Examiner, Art Unit 3666
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