DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Evaluating whether a claim is eligible subject matter under 35 U.S.C. 101 adheres to the following eligibility analysis procedure:
Step 1: The examiner determines whether then claim belongs to a statutory category. See MPEP § 2016(III).
Step 2A, prong 1: The examiner evaluates whether the claim recites a judicial exception. As explained in MPEP § 2106.04(II), a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Step 2A, prong 2: The examiner evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by:
identifying whether there are any additional elements recited in the claim beyond the judicial exception, and
evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
Step 2B: The examiner evaluates whether the claim provides an inventive concept, also referred to as “significantly more”. This evaluation is performed by:
identifying whether there are any additional elements recited in the claim beyond the judicial exception, and
evaluating those additional elements individually and in combination to determine whether they amount to significantly more.
Claims 1-20 are rejected under 35 U.S.C. 101 because these claims are directed to an abstract idea without significantly more.
Regarding claim 1 (an apparatus claim):
Step 2A-I: This claim recites the abstract idea of a mathematical algorithm for estimating the wear of a tire based on a driving pattern.
Step 2A-II: The judicial exception is not integrated into a practical application because the mere performance of the algorithm offers no improvement to the tires, the vehicle, or the experience of the driver.
Step 2B: This claim does not include additional elements that are sufficient to amount to significantly more. The computer components, namely a memory and a controller, recited therein are merely general-purpose computer components configured for the implementation of the recited abstract idea and thus do not amount to significantly more (see Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (2014)).
Furthermore, the dependent claims 2-10 are also rejected because they do not recite any further additional elements that integrate the abstract idea into a practical application or amount to significantly more. The examiner notes that while claims 7-8 introduce two additional elements, they do not integrate the abstract idea into a practical application or amount to significantly more. Displaying the wear amount to the driver as recited in claim 7 is insignificant extra-solution activity for an apparatus to estimate tire wear (see Electric Power Group LLC. v. Alstom SA, 830 F.3d 1350 (2016). From Page 9 — “But merely selecting information, by content or source, for collection, analysis, and display does nothing significant to differentiate a process from ordinary mental processes, whose implicit exclusion from § 101 undergirds the information-based category of abstract ideas.”). Furthermore, warning the driver as recited in claim 8, which constitutes the triggering of an alarm, has been identified by courts as a well-understood, routine, and conventional practice (See Parker v. Flook, 437 U.S. 584 (1978). From Page 595 – “Here it is absolutely clear that respondent's application contains no claim of patentable invention. The chemical processes involved in catalytic conversion of hydrocarbons are well known, as are the practice of monitoring the chemical process variables, the use of alarm limits to trigger alarms, the notion that alarm limit values must be recomputed and readjusted, and the use of computers for ‘automatic monitoring alarming.’”).
Regarding claim 11 (a method claim):
Step 2A-I: This claim recites the abstract idea of a mathematical algorithm for estimating the wear of a tire based on a driving pattern.
Step 2A-II: The judicial exception is not integrated into a practical application because the mere performance of the algorithm offers no improvement to the tires, the vehicle, or the experience of the driver.
Step 2B: This claim does not include additional elements that are sufficient to amount to significantly more. The computer components, namely a memory and a controller, recited therein are merely general-purpose computer components configured for the implementation of the recited abstract idea and thus do not amount to significantly more (see Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (2014)).
Furthermore, the dependent claims 12-20 are also rejected because they do not recite any further additional elements that integrate the abstract idea into a practical application or amount to significantly more. The examiner notes that while claims 17-18 introduce two additional elements, they do not integrate the abstract idea into a practical application or amount to significantly more. Displaying the wear amount to the driver as recited in claim 7 is insignificant extra-solution activity for a method to estimate tire wear (see Electric Power Group LLC. v. Alstom SA, 830 F.3d 1350 (2016). From Page 9 — “But merely selecting information, by content or source, for collection, analysis, and display does nothing significant to differentiate a process from ordinary mental processes, whose implicit exclusion from § 101 undergirds the information-based category of abstract ideas.”). Furthermore, warning the driver as recited in claim 8, which constitutes the triggering of an alarm, has been identified by courts as a well-understood, routine, and conventional practice (See Parker v. Flook, 437 U.S. 584 (1978). From Page 595 – “Here it is absolutely clear that respondent's application contains no claim of patentable invention. The chemical processes involved in catalytic conversion of hydrocarbons are well known, as are the practice of monitoring the chemical process variables, the use of alarm limits to trigger alarms, the notion that alarm limit values must be recomputed and readjusted, and the use of computers for ‘automatic monitoring alarming.’”).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 6, 11-12, and 16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Herrou et al. (US 20240116515 A1, hereinafter Herrou).
Regarding claims 1, the examiner respectfully points out that Herrou teaches an apparatus ([0038] — “… e.g., an on-board module and/or a cloud-based data processing system, etc. …”; this is understood to be a processor and memory, such as an ECU, see [0053] — “Features of embodiments as described herein may be controlled by a Central Gateway Module (CGM) ECU.”) for estimating a wear amount of a tire, the apparatus comprising:
a memory storing a model ([0042] — “Model 30 may be a ML model”; it is inherent that a machine learning model is stored in memory) configured to learn a correlation between a driving pattern and the wear amount of the tire ([0043] — “Model 30 may generate correlation 32.”; [0042] — “…model 30 may compare [a] correlation between a driving pattern associated with a driver and a measure of aggregate wear associated with a vehicle operated by the driver…”; [0042] — “…model 30 may output a driver score… In some embodiments, the driver score represents a driver's contribution to vehicle wear (e.g. aggregate vehicle wear, tire wear, drive unit bearing wear, etc.)”); and
a controller ([0038] — “… e.g., an on-board module and/or a cloud-based data processing system, etc. …”) configured to estimate the wear amount of the tire corresponding to the driving pattern of a driver based on the model ([0042] — “…model 30 may output a driver score… In some embodiments, the driver score represents a driver's contribution to vehicle wear (e.g. aggregate vehicle wear, tire wear, drive unit bearing wear, etc.)”; the controller applies the model stored in memory).
Regarding claim 2, the examiner respectfully points out that Herrou further teaches wherein the controller is configured to collect ([0031] —“…the on-board module may receive a first set of signals…”) at least one of a longitudinal acceleration, a lateral acceleration, a gear ratio, a steering angle ([0031] — “The signals may be associated with… a user's operation of the vehicle [e.g.] steering angle data… and/or a measure of speed.”), a slope, a yaw rate, a vehicle speed ([0031] —“The signals may be associated with… a user's operation of the vehicle [e.g.] steering angle data… and/or a measure of speed.”), a tire air pressure, a mileage, or a combination thereof through a vehicle network ([0053] — “The CGM ECU may include a network switch that provides connectivity through Controller Area Network (CAN) ports, Local Interconnect Network (LIN) ports…”; the LIN module enables the controller to receive data from the vehicle sensors).
Regarding claim 6, the examiner respectfully points out that Herrou further teaches wherein the controller is configured to grasp the driving pattern of the driver ([0032] — “The model may… determine a correlation between the inputs and one or more outcomes such as accelerated vehicle/vehicle component wear…”; the controller applies the model which may determine a correlation with tire wear, see [0042] — “…model 30 may output a driver score… In some embodiments, the driver score represents a driver's contribution to vehicle wear (e.g. aggregate vehicle wear, tire wear, drive unit bearing wear, etc.)) based on at least one of a braking energy, a count ratio of longitudinal acceleration-lateral acceleration, an amount of work, a gear ratio, a steering angle, a slope, a yaw rate, a vehicle speed, a right front wheel tire air pressure, a left front wheel tire air pressure, a mileage, or a combination thereof ([0031] —“…the on-board module may receive a first set of signals… The signals may be associated with… a user's operation of the vehicle (e.g., steering angle data, throttle/brake pedal apply data, wheel speed data, drive unit speed/torque data, etc.), and/or an environmental context of the vehicle (e.g., ambient temperature data, data describing a following distance of the vehicle, data describing characteristics of a road surface the vehicle is traveling on, etc.). The signals may include a throttle position, a measure of stopping distance, a measure of hard cornering, a measure of rapid steering, a relative position (e.g., a position in lane, a following distance, etc.), a measure of interactions with surface characteristics (e.g., pothole strikes, etc.), a measure of torsion into a frame of the vehicle, and/or a measure of speed.”; the signals would be used by the model to grasp the driving pattern).
Regarding claim 11, the examiner respectfully points out that Herrou teaches a method of estimating a wear amount of a tire, the method comprising:
storing in a memory a model ([0042] — “Model 30 may be a ML model”; it is inherent that a machine learning model is stored in memory) that learns a correlation between a driving pattern and the wear amount of the tire ([0043] — “Model 30 may generate correlation 32.”; [0042] — “…model 30 may compare [a] correlation between a driving pattern associated with a driver and a measure of aggregate wear associated with a vehicle operated by the driver…”; [0042] — “…model 30 may output a driver score… In some embodiments, the driver score represents a driver's contribution to vehicle wear (e.g. aggregate vehicle wear, tire wear, drive unit bearing wear, etc.)”); and
estimating by a controller the wear amount of the tire corresponding to the driving pattern of a driver based on the model ([0042] — “…model 30 may output a driver score… In some embodiments, the driver score represents a driver's contribution to vehicle wear (e.g. aggregate vehicle wear, tire wear, drive unit bearing wear, etc.)”; the controller would apply the model stored in memory).
Regarding claim 12, the examiner respectfully points out that Herrou teaches wherein estimating the wear amount of the tire comprises collecting, by the controller ([0031] —“…the on-board module may receive a first set of signals…”), at least one of a longitudinal acceleration, a lateral acceleration, a gear ratio, a steering angle ([0031] — “The signals may be associated with… a user's operation of the vehicle [e.g.] steering angle data… and/or a measure of speed.”), a slope, a yaw rate, a vehicle speed ([0031] —“The signals may be associated with… a user's operation of the vehicle [e.g.] steering angle data… and/or a measure of speed.”), a tire air pressure, a mileage, or a combination thereof through a vehicle network ([0053] — “Features of embodiments as described herein may be controlled by a Central Gateway Module (CGM) ECU… The CGM ECU may include a network switch that provides connectivity through Controller Area Network (CAN) ports, Local Interconnect Network (LIN) ports…”; the method would direct the controller to collect data via the LIN module).
Regarding claim 16, the examiner respectfully points out that Herrou further teaches wherein estimating the wear amount of the tire comprises grasping, by the controller, the driving pattern of the driver ([0032] — “The model may… determine a correlation between the inputs and one or more outcomes such as accelerated vehicle/vehicle component wear…”; the controller would apply the model which may determine a correlation with tire wear, see [0042] — “…model 30 may output a driver score… In some embodiments, the driver score represents a driver's contribution to vehicle wear (e.g. aggregate vehicle wear, tire wear, drive unit bearing wear, etc.)) based on at least one of a braking energy, a count ratio of longitudinal acceleration-lateral acceleration, an amount of work, a gear ratio, a steering angle, a slope, a yaw rate, a vehicle speed, a right front wheel tire air pressure, a left front wheel tire air pressure, a mileage, or a combination thereof ([0031] —“…the on-board module may receive a first set of signals… The signals may be associated with… a user's operation of the vehicle (e.g., steering angle data, throttle/brake pedal apply data, wheel speed data, drive unit speed/torque data, etc.), and/or an environmental context of the vehicle (e.g., ambient temperature data, data describing a following distance of the vehicle, data describing characteristics of a road surface the vehicle is traveling on, etc.). The signals may include a throttle position, a measure of stopping distance, a measure of hard cornering, a measure of rapid steering, a relative position (e.g., a position in lane, a following distance, etc.), a measure of interactions with surface characteristics (e.g., pothole strikes, etc.), a measure of torsion into a frame of the vehicle, and/or a measure of speed.”; the signals would be used by the model to grasp the driving pattern).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Herrou in view of Sun et al. (US 20210018655, hereinafter Sun).
Regarding claim 3:
The examiner respectfully points out that Herrou teaches wherein the model comprises a regression ([0042] — “For example, model 30 may include a non-linear regression model. As another example, model 30 may include a linear regression model.”) but fails to teach a Bayesian Ridge regression or Huber regressor.
Sun teaches a Bayesian Ridge regression and a Huber regressor ([0040] — “The machine learning algorithms may be categorized into a number of categories… The other algorithms may include Huber Regressor… and Bayesian Ridge.”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to adjust the regression model as taught by Herrou to use a Bayesian Ridge regression as taught by Sun to better account for collinearity or to adjust the regression model as taught by Herrou to use a Huber regressor as taught by Sun to reduce sensitivity to outliers.
Regarding claim 13:
The examiner respectfully points out that Herrou teaches wherein the model comprises a regression ([0042] — “For example, model 30 may include a non-linear regression model. As another example, model 30 may include a linear regression model.”) but fails to teach a Bayesian Ridge regression or Huber regressor.
Sun teaches a Bayesian Ridge regression and a Huber regressor ([0040] — “The machine learning algorithms may be categorized into a number of categories… The other algorithms may include Huber Regressor… and Bayesian Ridge.”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to adjust the regression model as taught by Herrou to use a Bayesian Ridge regression as taught by Sun to better account for collinearity or to adjust the regression model as taught by Herrou to use a Huber regressor as taught by Sun to reduce sensitivity to outliers.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Herrou in view of Sun and Benedict et al. (US 20160009290 A1, hereinafter Benedict).
Regarding claim 4:
The examiner respectfully points out that Herrou teaches wherein the model comprises regression ([0042] — “For example, model 30 may include a non-linear regression model. As another example, model 30 may include a linear regression model.”) and the controller is configured to collect a mileage ([0075] — “At step 522, control system 230 may accrue total mileage off-roading.”; control system contains the ECU, see Fig. 2) through a vehicle network ([0053] — “The CGM ECU may include a network switch that provides connectivity through Controller Area Network (CAN) ports, Local Interconnect Network (LIN) ports…”; the LIN module enables the controller to receive data from the vehicle sensors) and collect a longitudinal acceleration, a lateral acceleration, a gear ratio, a steering angle ([0031] — “The signals may be associated with… a user's operation of the vehicle [e.g.] steering angle data… and/or a measure of speed.”), a slope, a yaw rate, a vehicle speed ([0031] —“The signals may be associated with… a user's operation of the vehicle [e.g.] steering angle data… and/or a measure of speed.”), or a tire air pressure through the vehicle network, but fails to teach the model comprising a Bayesian Ridge regression or assigning a highest weight to the mileage.
Sun teaches a Bayesian Ridge regression ([0040] — “The machine learning algorithms may be categorized into a number of categories… The other algorithms may include Huber Regressor… and Bayesian Ridge.”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to adjust the regression model as taught by Herrou to use a Bayesian Ridge regression as taught by Sun to better account for collinearity. However, Herrou and Sun do not teach assigning a highest weight to the mileage.
Benedict teaches mileage being the most important measure of tire health ([0003] — “As with many other components of a vehicle, accumulated mileage [is] often used to determine when to inspect and/or perform maintenance activities on a tire…”; that accumulated mileage is used to assess tire health means it is the most important measure). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the apparatus taught by Herrou and Sun by seeding the regression weight for mileage to be the highest, since it is the most important measure of wear as taught by Benedict, to improve wear estimation.
Regarding claim 14:
The examiner respectfully points out that Herrou teaches wherein the model comprises regression ([0042] — “For example, model 30 may include a non-linear regression model. As another example, model 30 may include a linear regression model.”) and wherein estimating the wear amount of the tire comprises collecting, by the controller, a mileage ([0075] — “At step 522, control system 230 may accrue total mileage off-roading.”; the method directs the control system, which contains an ECU, to do this) through a vehicle network ([0053] — “The CGM ECU may include a network switch that provides connectivity through Controller Area Network (CAN) ports, Local Interconnect Network (LIN) ports…”; a LIN module would enable the controller to receive data from the vehicle sensors as directed by applying the method) and collecting, by a controller, a longitudinal acceleration, a lateral acceleration, a gear ratio, a steering angle ([0031] — “The signals may be associated with… a user's operation of the vehicle [e.g.] steering angle data… and/or a measure of speed.”), a slope, a yaw rate, a vehicle speed ([0031] —“The signals may be associated with… a user's operation of the vehicle [e.g.] steering angle data… and/or a measure of speed.”), or a tire air pressure through the vehicle network, but fails to teach the model comprising a Bayesian Ridge regression or assigning, by the controller, a highest weight to the mileage.
Sun teaches a Bayesian Ridge regression ([0040] — “The machine learning algorithms may be categorized into a number of categories… The other algorithms may include Huber Regressor… and Bayesian Ridge.”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to adjust the regression model as taught by Herrou to use a Bayesian Ridge regression as taught by Sun to better account for collinearity. However, Herrou and Sun do not teach assigning, by the controller, a highest weight to the mileage.
Benedict teaches mileage being the most important measure of tire health ([0003] — “As with many other components of a vehicle, accumulated mileage [is] often used to determine when to inspect and/or perform maintenance activities on a tire…”; that accumulated mileage is used to assess tire health means it is the most important measure). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method taught by Herrou and Sun by seeding the regression weight for mileage to be the highest, since it is the most important measure of wear as taught by Benedict, to improve wear estimation.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Herrou in view of Sun and Gee et al. (US 20220194400 A1, hereinafter Gee).
Regarding claim 5:
The examiner respectfully points out that Herrou teaches wherein the model comprises regression ([0042] — “For example, model 30 may include a non-linear regression model. As another example, model 30 may include a linear regression model.”) and the controller is configured to collect a vehicle speed ([0031] —“The signals may be associated with… a user's operation of the vehicle [e.g.] steering angle data… and/or a measure of speed.”) through a vehicle network ([0053] — “The CGM ECU may include a network switch that provides connectivity through Controller Area Network (CAN) ports, Local Interconnect Network (LIN) ports…”; the LIN module enables the controller to receive data from the vehicle sensors) and collect a longitudinal acceleration, a lateral acceleration, a gear ratio, a steering angle ([0031] — “The signals may be associated with… a user's operation of the vehicle [e.g.] steering angle data… and/or a measure of speed.”), a slope, a yaw rate, a tire air pressure, or a mileage ([0075] — “At step 522, control system 230 may accrue total mileage off-roading.”; control system contains the ECU, see Fig. 2) through the vehicle network, but fails to teach the model comprising a Huber regressor or assigning a highest weight to the vehicle speed.
Sun teaches a Huber regressor ([0040] — “The machine learning algorithms may be categorized into a number of categories… The other algorithms may include Huber Regressor…”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to adjust the regression model as taught by Herrou to use a Huber regressor as taught by Sun to reduce sensitivity to outliers. However, Herrou and Sun do not teach assigning a highest weight to the vehicle speed.
Gee teaches vehicle speed being the most important weighted parameter ([0282] — “…first data from sensors of a vehicle is obtained… data describes describing conditions of components of and specifies an individual driving pattern or style of the driver. The pattern defined includes habits of the driver (e.g., driving at high rates of speed or constantly braking the vehicle)”; the first data is weighted as the most important, see [0031] — “…the weighting assigns the first data a greater importance than the second data or the third data.”; there are only three sets of data). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the apparatus taught by Herrou and Sun by seeding the regression weight for vehicle speed to be the highest, since it is the most important as taught by Gee, to improve wear estimation.
Regarding claim 15:
The examiner respectfully points out that Herrou teaches wherein the model comprises regression ([0042] — “For example, model 30 may include a non-linear regression model. As another example, model 30 may include a linear regression model.”) and estimating the wear of the tire comprises collecting, by the controller, a vehicle speed ([0031] —“The signals may be associated with… a user's operation of the vehicle [e.g.] steering angle data… and/or a measure of speed.”; the method directs the controller to collect the speed) through a vehicle network ([0053] — “The CGM ECU may include a network switch that provides connectivity through Controller Area Network (CAN) ports, Local Interconnect Network (LIN) ports…”; a LIN module would enable the controller to receive data from the vehicle sensors in applying the method) and collecting, by the controller, a longitudinal acceleration, a lateral acceleration, a gear ratio, a steering angle ([0031] — “The signals may be associated with… a user's operation of the vehicle [e.g.] steering angle data… and/or a measure of speed.”), a slope, a yaw rate, a tire air pressure, or a mileage ([0075] — “At step 522, control system 230 may accrue total mileage off-roading.”; the method directs the control system, which contains the ECU, to do this) through the vehicle network, but fails to teach the model comprising a Huber regressor or assigning a highest weight to the vehicle speed.
Sun teaches a Huber regressor ([0040] — “The machine learning algorithms may be categorized into a number of categories… The other algorithms may include Huber Regressor…”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to adjust the regression model in the method as taught by Herrou to use a Huber regressor as taught by Sun to reduce sensitivity to outliers. However, Herrou and Sun do not teach assigning a highest weight to the vehicle speed.
Gee teaches vehicle speed being the most important weighted parameter ([0282] — “…first data from sensors of a vehicle is obtained… data describes describing conditions of components of and specifies an individual driving pattern or style of the driver. The pattern defined includes habits of the driver (e.g., driving at high rates of speed or constantly braking the vehicle)”; the first data is weighted as the most important, see [0031] — “…the weighting assigns the first data a greater importance than the second data or the third data.”; there are only three sets of data). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method taught by Herrou and Sun by seeding the regression weight for vehicle speed to be the highest, since it is the most important weighted parameter as taught by Gee, to improve wear estimation.
Claims 7-9 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Herrou in view of Puranik (US 20200130420 A1).
Regarding claim 7:
The examiner respectfully points out that Herrou teaches the apparatus of claim 1 but fails to teach the controller being configured to provide the wear amount of the tire to the driver.
Puranik teaches using an imaging system to provide the wear amount of the tire to the driver ([0048] — “…an alert to the driver may be generated at step 312. The alert may be displayed, for example, on the display 126. Examples of alert conditions include, but are not limited to, wear beyond a wear threshold, the presence of one or more foreign objects in one or more tires 12, or excessive wear based on the current conditions.”; the different possible types of wear constitute an amount of wear). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to configure the controller taught by Herrou to use the imaging system taught by Puranik to provide the wear amount to the driver in order to improve vehicle safety.
Regarding claim 8:
The examiner respectfully points out that Herrou teaches the apparatus of claim 1 but fails to teach the controller being configured to warn the driver to replace the tire when the wear amount of the tire exceeds a threshold value.
Puranik teaches using an imaging system to warn the driver to replace the tire when the wear amount of the tire exceeds a threshold value ([0048] — “…an alert to the driver may be generated at step 312. The alert may be displayed, for example, on the display 126. Examples of alert conditions include, but are not limited to, wear beyond a wear threshold… In some embodiments, the alert may be a recommendation to replace one or more of the tires 12 of the vehicle 10.”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to configure the controller taught by Herrou to use the imaging system taught by Puranik to warn the driver to replace the tire in order to improve vehicle safety.
Regarding claim 9:
The examiner respectfully points out that Herrou teaches the apparatus of claim 1 but fails to teach wherein the controller is configured to provide the wear amount of the tire to a vehicle management server.
Puranik teaches using an imaging system to provide the wear amount of the tire to a vehicle management server ([0044] — “For example, the vehicle 10 may send the captured image data to the remote server 30 via an external data connection (e.g., a vehicle-to-infrastructure (“V2I”) connection). Accordingly, the remote server 30 receives the image data of one or more tires 12 of the vehicle 10 captured by the one or more image sensors 14.”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to configure the controller taught by Herrou to use the imaging system taught by Puranik to provide the wear amount of the tire to a vehicle management server to better track and understand the tire wear of many vehicles.
Regarding claim 17:
The examiner respectfully points out that Herrou teaches the method of claim 11 but fails to teach providing, by the controller, the wear amount of the tire to the driver.
Puranik teaches a method for providing, by a controller ([0018] — “The system 100 includes a communication path 101 that communicatively couples an electronic control unit (“ECU”) 102 including a processor 104 and a memory module 106…”), the wear amount of the tire to the driver ([0048] — “…an alert to the driver may be generated at step 312… Examples of alert conditions include, but are not limited to, wear beyond a wear threshold, the presence of one or more foreign objects in one or more tires 12, or excessive wear based on the current conditions.”; the method directs the controller to do this; the different possible types of wear constitute an amount of wear). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method taught by Herrou to provide the wear amount of the tire to the driver using the controller as taught by Puranik to improve vehicle safety.
Regarding claim 18:
The examiner respectfully points out that Herrou teaches the method of claim 11 but fails to teach warning, by the controller, the driver to replace the tire when the wear amount of the tire exceeds a threshold value.
Puranik teaches warning, by a controller ([0018] — “The system 100 includes a communication path 101 that communicatively couples an electronic control unit (“ECU”) 102 including a processor 104 and a memory module 106…”), the driver to replace the tire when the wear amount of the tire exceeds a threshold value ([0048] — “…an alert to the driver may be generated at step 312. The alert may be displayed, for example, on the display 126. Examples of alert conditions include, but are not limited to, wear beyond a wear threshold… In some embodiments, the alert may be a recommendation to replace one or more of the tires 12 of the vehicle 10.”; the method directs the controller to do this). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method taught by Herrou to warn, by using the controller, the driver to replace the tire when the wear amount of the tire exceeds a threshold value as taught by Puranik to improve vehicle safety.
Regarding claim 19:
The examiner respectfully points out that Herrou teaches the method of claim 11 but fails to teach providing, by the controller, the wear amount of the tire to a vehicle management server.
Puranik teaches providing, by a controller ([0018] — “The system 100 includes a communication path 101 that communicatively couples an electronic control unit (“ECU”) 102 including a processor 104 and a memory module 106…”), the wear amount of the tire to a vehicle management server ([0044] — “For example, the vehicle 10 may send the captured image data to the remote server 30 via an external data connection (e.g., a vehicle-to-infrastructure (“V2I”) connection). Accordingly, the remote server 30 receives the image data of one or more tires 12 of the vehicle 10 captured by the one or more image sensors 14.”; the method directs the controller to do this). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method taught by Herrou to provide, by the controller, the wear amount of the tire to a vehicle management server as taught by Puranik to better track and understand the tire wear of many vehicles.
Claim 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Herrou in view of Mitchell (US 20240221434 A1, hereinafter Mitchell) and Iustin (US 20230260343 A1, hereinafter Iustin).
Regarding claim 10:
The examiner respectfully points out that Herrou teaches the apparatus of claim 1 but fails to teach wherein the memory is configured to store different models corresponding to a type of vehicle and a type of tire.
Mitchell teaches training and storing models to identify tire wear ([0012] — “As discussed below, Vehicle Wear Determination System 109 may utilize artificial intelligence/machine learning (“AI/ML”) techniques or other suitable techniques in order to train models based on which Vehicle Wear Determination System 109 may identify how worn vehicle 101 is becoming, may identify specific types of wear on vehicle 101 [e.g.] engine wear, tire wear, brake wear…”; trained models are stored in memory) corresponding to different types of vehicles ([0030] — “In this manner, since different vehicle wear models 301 may be associated with different criteria [e.g.] different makes and/or models of cars… the same or similar set of inputs (e.g., vehicle wear input information 303) may yield different outputs”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to store the different models corresponding to different types of vehicles as taught by Mitchell in the memory of the apparatus as taught by Herrou in order to improve safety across different types of vehicles and applicability of the product. However, Herrou and Mitchell fail to teach wherein the memory is configured to store different models corresponding to a type of tire.
Iustin teaches storing information about the different wear rates of different tires in a look-up table ([0041] — “Thus, this takes into account that the tires of different tire manufacturers (or different tire models from the same manufacturer) may behave differently and have different wear rates. Therefore, it may be advantageous to have the tire identification data stored in a database, e.g. in a look-up table, etc.). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the models stored in memory as taught by Herrou and Mitchell to use tire information stored in memory in a look-up table as taught by Iustin to better estimate tire wear.
Regarding claim 20:
The examiner respectfully points out that Herrou teaches the apparatus of claim 11 but fails to teach storing, by the memory, different models corresponding to a type of vehicle and a type of tire.
Mitchell teaches training and storing models to identify tire wear ([0012] — “As discussed below, Vehicle Wear Determination System 109 may utilize artificial intelligence/machine learning (“AI/ML”) techniques or other suitable techniques in order to train models based on which Vehicle Wear Determination System 109 may identify how worn vehicle 101 is becoming, may identify specific types of wear on vehicle 101 [e.g.] engine wear, tire wear, brake wear…”; trained models are stored in memory) corresponding to different types of vehicles ([0030] — “In this manner, since different vehicle wear models 301 may be associated with different criteria [e.g.] different makes and/or models of cars… the same or similar set of inputs (e.g., vehicle wear input information 303) may yield different outputs”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method taught by Herrou to store, by the memory, different models corresponding to different types of vehicles as taught by Mitchell to improve safety across different types of vehicles and applicability of the product. However, Herrou and Mitchell fail to teach wherein the memory is configured to store different models corresponding to a type of tire.
Iustin teaches storing information about the different wear rates of different tires in a look-up table ([0041] — “Thus, this takes into account that the tires of different tire manufacturers (or different tire models from the same manufacturer) may behave differently and have different wear rates. Therefore, it may be advantageous to have the tire identification data stored in a database, e.g. in a look-up table, etc.). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method taught by Herrou and Mitchell to store, by the memory, tire information in a look-up table as taught by Iustin for use by the different models to be stored by the memory as taught by Herrou and Mitchell to better estimate tire wear.
Prior Art
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure:
Katsuno, Hiroyuki (US 20240257582 A1), Tire Management Apparatus, Program, and Tire Management Method
Juette, Reinhard (US 20230364948 A1), Vehicular Sensing System with Tire Profile Determination
Lo Presti, Michael Anthony (US 20180186345 A1), Tire Pressure Maintenance Apparatus and Method
Ferrone et al. (US 20060180379 A1), System and Method for Monitoring Driver Fatigue
The above prior art was used by the examiner to better contextualize the claimed invention within the art.
Conclusion
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/RYAN JAMES STEAR/Examiner, Art Unit 2857
/ARLEEN M VAZQUEZ/Supervisory Patent Examiner, Art Unit 2857