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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/30/2025 has been entered.
Status of Claims
Claims 1-20 are pending and have been examined.
This action is in reply to the papers filed on 10/30/2025.
Information Disclosure Statement
The information disclosure statement(s) submitted: 02/27/2023, has/have been considered by the Examiner and made of record in the application file.
Amendment
The present Office Action is based upon the original patent application filed on 02/27/2023 as modified by the amendment filed on 10/30/2025.
Reasons For Allowance
Prior-Art Rejection withdrawn
Claims xxx are allowed. The closest prior art (See PTO-892, Notice of References Cited) does not teach the claimed:
The invention teaches… and the prior-art teaches…, however, the prior-art does not teach…
The closest prior-art (xxx) teach the features as disclosed in Non-final Rejection (xxxx), however, these cited references do not teach and the prior-art does not teach at least the following:
determining, at a second time after associating the information corresponding to the first loyalty card with the logged location, that a second user computing device is located within a specified distance of the logged location using a second positioning system of the second user computing device; in response to determining that the second user computing device is located within the specified distance of the logged location of the first user computing device at the first time of detecting: retrieving information corresponding to a second loyalty card, the second loyalty card being associated with the merchant and the second user computing device; and displaying, by the second user computing device, data describing the second loyalty card.
Claim Rejections - 35 USC § 103
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 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.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over: Stender et al.; in view of Gaither et al. 2018/0134161; in further view of KANE et al. 2021/0088094; in view of Esgandari (Simulation Methods for Vehicle Disc Brake Noise, Vibration and Harshness by Mohammad Esgandari, August 2014 (Esgandari)).
Stender et al. "Deep learning for brake squeal: Brake noise detection, characterization and prediction", MECHANICAL SYSTEMS AND SIGNAL PROCESSING, ELSEVIER, AMSTERDAM, NL, vol. 149, 14 August 2020 (2020-08-14), XP086336063, ISSN: 0888-3270, DOI: 10.1016/J.YMSSP.2020.107181 [retrieved on 2020-08-14].
18/043,202 – Claim 1. (Currently Amended) Stender et al. teaches A method, comprising: training a recurrent neural network using a plurality of known brake rotor statuses which respectively correspond to a plurality of training brake rotors (STENDER MERTEN ET AL [Abstract: "a recurrent neural network (RNN) is employed to learn the parametric patterns that determine the dynamic stability of an operating brake system. [ ... ] Large data sets from commercial brake system testing are used to train and validate the models."]), wherein each known brake rotor status in the plurality of known brake rotor statuses comprises: a known condition of a corresponding training brake rotor from the plurality of training brake rotors, the known condition being one of a plurality of possible brake rotor conditions (STENDER MERTEN ET AL teaches [Section 2.4: "noise classes"]); an Eigen frequency (STENDER MERTEN ET AL teaches [Section 1.1: "analysis by complex eigen-values… time-frequency analysis"]) of the corresponding training brake rotor from the plurality of training brake rotors (STENDER MERTEN ET AL teaches [section 1.2 page 4: "A microphone [ ... ] monitors the development of brake noise"]); and a temperature distribution of the corresponding training brake rotor from the plurality of training rotors (STENDER MERTEN ET AL teaches [section 1. 2 pages 3-4: "temperature ramps [ ... ] are performed. [ ... ] temperature measurements (rotor Trot, [ ... ] "]); and uploading the recurrent neural network to a non-transitory memory device in communication with a processor of a vehicle (STENDER MERTEN ET AL teaches [implied in section 2: "During vehicle operation in the field, [ ... ]" and figure 9 "During braking"]), wherein the vehicle: comprises at least one brake rotor; and has a plurality of sensors monitoring the at least one brake rotor (STENDER MERTEN ET AL teaches [implied in Section 2: "a novel noise detection algorithm implied in Section 2: "a novel noise detection algorithm [ ... ] which is applied to a large set of automotive disk brake sound measurements. [ ... ] This study uses microphone measurements as input signals." [ ... ] which is applied to a large set of automotive disk brake sound measurements. [ ... ] This study uses microphone measurements as input signals."]); and wherein the processor of the vehicle: receives sensor signals from the plurality of sensors while the vehicle is moving (STENDER MERTEN ET AL teaches [implied in section 2: "During vehicle operation in the field, [ ... ]" and figure 9 "During braking"]); evaluates the sensor signals using the recurrent neural network, resulting in an evaluation; and determines a brake rotor status for each at least one brake rotor based on the evaluation (STENDER MERTEN ET AL teaches [(e.g. section 3.5: "the model output is a boolean value (true/ false) indicating if squeal occurs during the braking.")]), the brake rotor status comprising at least one of internal cracking, surface cracking, or broken due to cracking (STENDER MERTEN ET AL teaches [(e.g. section 1.2: Figure 2 temperature measurements including rotor temperature)(section 1.1: Figure 1 disk surface temperature)(section 3.4: disk surface temperature)]).
Stender et al. may not expressly disclose the “sensor” features, however, Gaither et al. 2018/0134161 teaches (Gaither et al. 2018/0134161 [0033 - the brake sensor may indicate a brake pad thickness, a rotor condition, a brake temperature, and so on] In yet a further embodiment, the brake wear module 220 may also collect sensor data from brake wear sensors installed in the vehicle 100. The brake wear sensors may be proximity sensors, positional sensors, temperature sensors, electrical contact sensors, mechanical sensors, or another form of brake wear sensor. In either case, the brake wear module 220 is configured to receive a control signal from the brake wear sensor or some other indication that denotes a current degree of wear. Because the friction brakes 280 may be disc brakes, drum brakes, or some other form of friction brake, the brake sensor may indicate a brake pad thickness, a rotor condition, a brake temperature, and so on. Once the brake wear module 220 collects the braking data, the brake wear module 220 may store the braking data in the braking database 240, the memory 210, or another suitable memory (e.g., register). Thus, in one embodiment, the braking data may be collected into a history of braking data from previous braking events.).
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Stender et al. to include the features as taught by Gaither et al. 2018/0134161. One of ordinary skill in the art would have been motivated to do so in order to use the sensor features of Gaither in conjunction with the recurrent neural network (RNN) disclosed in Stender et al. by uploading the trained model to a device in communication with a vehicle and to provide it with input data measured while the car is moving (and in particular, while it is braking) which should prove to improve user experience, maximize profits, and optimize revenue.
Stender et al. may not expressly disclose the “status” features, however, KANE et al. 2021/0088094 teaches (KANE et al. 2021/0088094 [0049 - Brake condition monitoring program can display a simplified and/or a detailed brake rotor condition assessment to the user, to allow for the user to either quickly view an overall result (e.g., “Status: Ok”, “Status: Attention Required”) or to allow for the user to view a detailed result of the assessment with the generated image of the brake rotor. For the detailed result of the assessment, brake condition monitoring program displays the generated image of the brake rotor and can highlight one or more areas of the contact surface of the brake rotor where instances of potential scarring, cracking, warping, and excessive rusting have been detected] Brake condition monitoring program displays (412) rotor condition assessment and service projections. Brake condition monitoring program can display results for the brake rotor condition assessment and service projections to the user (i.e., vehicle operator) in a user interface of a media system. Brake condition monitoring program can display a simplified and/or a detailed brake rotor condition assessment to the user, to allow for the user to either quickly view an overall result (e.g., “Status: Ok”, “Status: Attention Required”) or to allow for the user to view a detailed result of the assessment with the generated image of the brake rotor. For the detailed result of the assessment, brake condition monitoring program displays the generated image of the brake rotor and can highlight one or more areas of the contact surface of the brake rotor where instances of potential scarring, cracking, warping, and excessive rusting have been detected. Furthermore, brake condition monitoring program can determine a severity of the instance of potential scarring, cracking, warping, and excessive rusting and display a service projection (e.g., 30,000 miles or “service now”) of how long the brake rotors can last based on the severity of the instance of potential scarring, cracking, warping, and excessive rusting.).
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Stender et al. to include the features as taught by KANE et al. 2021/0088094. One of ordinary skill in the art would have been motivated to do so in order to use the sensor features of Gaither in conjunction with the recurrent neural network (RNN) disclosed in Stender et al. by uploading the trained model to a device in communication with a vehicle and to provide it with input data measured while the car is moving (and in particular, while it is braking) which should prove to improve user experience, maximize profits, and optimize revenue.
The Office argues that Stender et al. teaches the amended “Eigen frequency” features (STENDER MERTEN ET AL teaches [Section 1.1: "analysis by complex eigen-values… time-frequency analysis"]).
In addition to what Stender et al. teaches, the following reference also teaches this feature: Simulation Methods for Vehicle Disc Brake Noise, Vibration and Harshness by Mohammad Esgandari, August 2014 (Esgandari) teaches “Eigen frequency” features. See for example the Abstract “Finite Element Analysis (FEA) method has long been used as a means of reliable simulation of brake noise, mainly using the Complex Eigenvalue Analysis (CEA) to predict the occurrence of instabilities resulting in brake noise” and page 68 “Abaqus/Standard, in general, uses the set of eigenmodes extracted in a previous eigenfrequency step to calculate the steady-state solution as a function of the frequency of the applied excitation” and page 27 “Frequency of the squeal also depends on the natural frequencies of the brake system components, and more specifically on that of the rotor”. Per Applicant’s specification the terms Eigen frequencies and Natural frequencies are equivalent. See Applicant’s specification at PGPub. 2023/0334435 [0039] In some configurations, the plurality of known brake rotor statuses can include, for the natural frequency (also known as the Eigen frequency, or vibration characteristics), and the temperature distribution, data collected over a predetermined amount of time, where the predetermined amount of time is determined before operations of the vehicle. For example, the predetermined amount of time may be thirty minutes, five minutes, thirty seconds, or any other amount of time. In other configurations, the time could be dynamic, such as one tire revolution, two tire revolutions, one braking cycle, etc.
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Stender et al. to include the features as taught by Esgandari. One of ordinary skill in the art would have been motivated to do so in order to use the eigen frequency and natural frequency features of Esgandari in conjunction with the recurrent neural network (RNN) disclosed in Stender et al. by uploading the trained model to a device in communication with a vehicle and to provide it with input data measured while the car is moving (and in particular, while it is braking) which should prove to improve user experience, maximize profits, and optimize revenue.
18/043,202 – Claim 8. A vehicle comprising: at least one brake rotor; a plurality of sensors associated with the at least one brake rotor; a processor; a non-transitory computer-readable memory device having stored therein: a recurrent neural network; and instructions which, when executed by the processor, cause the processor to perform operations comprising: receiving, from the plurality of sensors, sensor signals from the plurality of sensors while the vehicle is moving; evaluating the sensor signals using the recurrent neural network, resulting in an evaluation; and determining a brake rotor status for each of the at least one brake rotor based on the evaluation.
Claim 8, has similar limitations as of Claim(s) 1, therefore it is REJECTED under the same rationale as Claim(s) 1.
18/043,202 – Claim 16. A non-transitory computer-readable storage medium having stored therein: a recurrent neural network; and instructions which, when executed by a processor, cause the processor to perform operations comprising: receiving, from a plurality of sensors recording data associated with at least one brake rotor of a vehicle, sensor signals from the plurality of sensors while the vehicle is moving; evaluating the sensor signals using the recurrent neural network, resulting in an evaluation; and determining a brake rotor status for each at least one brake rotor based on the evaluation.
Claim 16, has similar limitations as of Claim(s) 1, therefore it is REJECTED under the same rationale as Claim(s) 1.
18/043,202 – Claim 9. The vehicle of claim 8, wherein the recurrent neural network was trained using a plurality of known brake rotor statuses which respectively correspond to a plurality of training brake rotors, wherein each known brake rotor status in the plurality of known brake rotor statuses comprises: a known condition of a corresponding training brake rotor from the plurality of training brake rotors, the known condition being one of a plurality of possible brake rotor conditions; a natural frequency of the corresponding training brake rotor from the plurality of training brake rotors; and a temperature distribution of the corresponding training brake rotor from the plurality of training brake rotors.
Claim 9, has similar limitations as of Claim(s) 1, therefore it is REJECTED under the same rationale as Claim(s) 1.
18/043,202 – Claim 17. The non-transitory computer-readable storage medium of claim 16, wherein the recurrent neural network was trained using a plurality of known brake rotor statuses which respectively correspond to a plurality of training brake rotors, wherein each known brake rotor status in the plurality of known brake rotor statuses comprises: a known condition of a corresponding training brake rotor from the plurality of training brake rotors, the known condition being one of a plurality of possible brake rotor conditions; a natural frequency of the corresponding training brake rotor from the plurality of training rotors; and a temperature distribution of the corresponding training brake rotor from the plurality of training brake rotors.
Claim 17, has similar limitations as of Claim(s) 1, therefore it is REJECTED under the same rationale as Claim(s) 1.
18/043,202 – Claim 2. (Previously Presented) Stender et al. further teaches The method of claim 1, wherein the plurality of sensors comprises: a microphone detecting a braking audio frequency of the at least one brake rotor; a thermometer detecting a temperature of the at least one brake rotor; an accelerometer located on a brake caliper associated with the at least one brake rotor; and a caliper pressure monitor of the brake caliper associated with the at least one brake rotor (Stender et al. [section 1.2: "A microphone located in proximity to the brake disc"; "temperature ramps [ ... ] are performed. [ ... ] temperature measurements (rotor Trot, [ ... ] "; "the brake pressure (p) values"; section 2: "Naturally, also vibration measurements from acceleration sensors can be used with very little changes"]).
18/043,202 – Claim 13. The vehicle of claim 8, wherein the plurality of sensors comprises: a microphone detecting a braking audio frequency of the at least one brake rotor; a thermometer detecting a temperature of the at least one brake rotor; an accelerometer located on a brake caliper associated with the at least one brake rotor; and a caliper pressure monitor of the brake caliper associated with the at least one rotor.
Claim 13, has similar limitations as of Claim(s) 2, therefore it is REJECTED under the same rationale as Claim(s) 2.
18/043,202 – Claim 3. (Currently Amended) Stender et al. further teaches The method of claim 1, wherein additional inputs to the processor of the vehicle comprise: a wheel velocity of a wheel which can be slowed by a brake associated with the at least one rotor; a normal load of the vehicle; and a brake force applied to the at least one brake rotor (STENDER MERTEN ET AL [section 1.2: "various combinations of operational parameters, such as rotational velocity, [ ... ] several pressure and velocity levels [ ... ] initial rotor velocity [ ... ] loading parameters".]); and wherein the brake rotor status consists of only the internal cracking.
Stender et al. may not expressly disclose the “brake rotor status consists of only the internal cracking” features, however, KANE et al. 2021/0088094 teaches (KANE et al. 2021/0088094 [0049 - Brake condition monitoring program can display a simplified and/or a detailed brake rotor condition assessment to the user, to allow for the user to either quickly view an overall result (e.g., “Status: Ok”, “Status: Attention Required”) or to allow for the user to view a detailed result of the assessment with the generated image of the brake rotor. For the detailed result of the assessment, brake condition monitoring program displays the generated image of the brake rotor and can highlight one or more areas of the contact surface of the brake rotor where instances of potential scarring, cracking, warping, and excessive rusting have been detected] Brake condition monitoring program displays (412) rotor condition assessment and service projections. Brake condition monitoring program can display results for the brake rotor condition assessment and service projections to the user (i.e., vehicle operator) in a user interface of a media system. Brake condition monitoring program can display a simplified and/or a detailed brake rotor condition assessment to the user, to allow for the user to either quickly view an overall result (e.g., “Status: Ok”, “Status: Attention Required”) or to allow for the user to view a detailed result of the assessment with the generated image of the brake rotor. For the detailed result of the assessment, brake condition monitoring program displays the generated image of the brake rotor and can highlight one or more areas of the contact surface of the brake rotor where instances of potential scarring, cracking, warping, and excessive rusting have been detected. Furthermore, brake condition monitoring program can determine a severity of the instance of potential scarring, cracking, warping, and excessive rusting and display a service projection (e.g., 30,000 miles or “service now”) of how long the brake rotors can last based on the severity of the instance of potential scarring, cracking, warping, and excessive rusting.).
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Stender et al. to include the features as taught by KANE et al. 2021/0088094. One of ordinary skill in the art would have been motivated to do so in order to use the sensor features of Gaither in conjunction with the recurrent neural network (RNN) disclosed in Stender et al. by uploading the trained model to a device in communication with a vehicle and to provide it with input data measured while the car is moving (and in particular, while it is braking) which should prove to improve user experience, maximize profits, and optimize revenue.
18/043,202 – Claim 14. The vehicle of claim 8, wherein additional inputs to the processor of the vehicle comprise: a wheel velocity of a wheel which can be slowed by a brake associated with the at least one brake rotor; a normal load of the vehicle; and a brake force applied to the at least one brake rotor.
Claim 14, has similar limitations as of Claim(s) 3, therefore it is REJECTED under the same rationale as Claim(s) 3.
18/043,202 – Claim 4. (Previously Presented) Stender et al. further teaches The method of claim 1, further comprising: generating, prior to the training of the recurrent neural network, a sensitivity analysis on the plurality of known brake rotor statuses, wherein the training of the recurrent neural network uses the sensitivity analysis (STENDER MERTEN ET AL [Section 2, part 3: "data pre-processing steps"]) .
18/043,202 – Claim 15. The vehicle of claim 8, wherein training of the recurrent neural network uses a sensitivity analysis, the sensitivity analysis generated prior to the training.
Claim 15, has similar limitations as of Claim(s) 4, therefore it is REJECTED under the same rationale as Claim(s) 4.
18/043,202 – Claim 5. (Currently Amended) Stender et al. further teaches The method of claim 1, wherein the plurality of known brake rotor statuses comprises, for the Eigen frequency and the temperature distribution, data collected over a predetermined amount of time (STENDER MERTEN ET AL teaches [Section 1.1: "analysis by complex eigen-values… time-frequency analysis"] [section 1.2: "The time evolution of the loading parameters is recorded in the form of time series data sampled at fs = 100 Hz". The measurements in figures 1 and 3 show results for a certain time duration. To set the time at a minimum of thirty seconds and the frequency at 1 Hz are merely obvious alternative design choices for the skilled person.]).
The Office argues that Stender et al. teaches the amended “Eigen frequency” features (STENDER MERTEN ET AL teaches [Section 1.1: "analysis by complex eigen-values… time-frequency analysis"]).
In addition to what Stender et al. teaches, the following reference also teaches this feature: Simulation Methods for Vehicle Disc Brake Noise, Vibration and Harshness by Mohammad Esgandari, August 2014 (Esgandari) teaches “Eigen frequency” features. See for example the Abstract “Finite Element Analysis (FEA) method has long been used as a means of reliable simulation of brake noise, mainly using the Complex Eigenvalue Analysis (CEA) to predict the occurrence of instabilities resulting in brake noise” and page 68 “Abaqus/Standard, in general, uses the set of eigenmodes extracted in a previous eigenfrequency step to calculate the steady-state solution as a function of the frequency of the applied excitation” and page 27 “Frequency of the squeal also depends on the natural frequencies of the brake system components, and more specifically on that of the rotor”. Per Applicant’s specification the terms Eigen frequencies and Natural frequencies are equivalent. See Applicant’s specification at PGPub. 2023/0334435 [0039] In some configurations, the plurality of known brake rotor statuses can include, for the natural frequency (also known as the Eigen frequency, or vibration characteristics), and the temperature distribution, data collected over a predetermined amount of time, where the predetermined amount of time is determined before operations of the vehicle. For example, the predetermined amount of time may be thirty minutes, five minutes, thirty seconds, or any other amount of time. In other configurations, the time could be dynamic, such as one tire revolution, two tire revolutions, one braking cycle, etc.
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Stender et al. to include the features as taught by Esgandari. One of ordinary skill in the art would have been motivated to do so in order to use the eigen frequency and natural frequency features of Esgandari in conjunction with the recurrent neural network (RNN) disclosed in Stender et al. by uploading the trained model to a device in communication with a vehicle and to provide it with input data measured while the car is moving (and in particular, while it is braking) which should prove to improve user experience, maximize profits, and optimize revenue.
18/043,202 – Claim 10. The vehicle of claim 9, wherein the plurality of known brake rotor statuses comprises, for the natural frequency and the temperature distribution, data collected at a predetermined frequency over a predetermined amount of time.
Claim 10, has similar limitations as of Claim(s) 5, therefore it is REJECTED under the same rationale as Claim(s) 5.
18/043,202 – Claim 18. The non-transitory computer-readable storage medium of claim 17, wherein the plurality of known brake rotor statuses comprises, for the natural frequency and the temperature distribution, data collected at a predetermined frequency over a predetermined amount of time.
Claim 18, has similar limitations as of Claim(s) 5, therefore it is REJECTED under the same rationale as Claim(s) 5.
18/043,202 – Claim 6. (Previously Presented) Stender et al. further teaches The method of claim 5, wherein the predetermined amount of time is a minimum of thirty minutes (STENDER MERTEN ET AL [section 1.2: "The time evolution of the loading parameters is recorded in the form of time series data sampled at fs = 100 Hz". The measurements in figures 1 and 3 show results for a certain time duration. To set the time at a minimum of thirty seconds and the frequency at 1 Hz are merely obvious alternative design choices for the skilled person.])).
18/043,202 – Claim 11. The vehicle of claim 10, wherein the predetermined frequency is one hertz.
Claim 11, has similar limitations as of Claim(s) 6, therefore it is REJECTED under the same rationale as Claim(s) 6.
18/043,202 – Claim 19. The non-transitory computer-readable storage medium of claim 18, wherein the predetermined frequency is one hertz.
Claim 19, has similar limitations as of Claim(s) 6, therefore it is REJECTED under the same rationale as Claim(s) 6.
18/043,202 – Claim 7. (Previously Presented) Stender et al. further teaches The method of claim 1, wherein the recurrent neural network defines multidimensional boundary conditions for each of the plurality of possible brake rotor conditions (STENDER MERTEN ET AL ["stability boundaries" in section 3.8 and "drawing bounding boxes and assigning labels" for "Deep learning brake noise detection" in section 2.4.]).
18/043,202 – Claim 12. The vehicle of claim 8, wherein the recurrent neural network defines multidimensional boundary conditions for each of a plurality of possible brake rotor conditions.
Claim 12, has similar limitations as of Claim(s) 7, therefore it is REJECTED under the same rationale as Claim(s) 7.
18/043,202 – Claim 20. The non-transitory computer-readable storage medium of claim 16, wherein the recurrent neural network defines multidimensional boundary conditions for each of a plurality of possible brake rotor conditions.
Claim 20, has similar limitations as of Claim(s) 7, therefore it is REJECTED under the same rationale as Claim(s) 7.
Examiner’s Response to Arguments
Per Applicants’ amendments/arguments, the rejections are withdrawn.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Examiner’s Response: Claim Rejections – 35 USC §112
Per Applicants’ amendments/arguments, the rejections are withdrawn.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Examiner’s Response: Claim Rejections – 35 USC § 103
Per Applicants’ amendments/arguments, the rejections are withdrawn. See notes above for additional reasoning and rationale for dropping prior-art rejection including Applicant’s amendments and arguments and unique combination of features and elements not taught by the prior-art without hindsight reasoning.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
THIS ACTION IS MADE FINAL
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 extension fee 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 date of this final action.
Conclusion
PERTINENT PRIOR ART – Patent Literature
The prior-art made of record and considered pertinent to applicant's disclosure.
Patel et al. 2019/0093722 Abstract: Systems and methods for detecting wear in a braking system of a vehicle. The system includes a microphone and an electronic controller configured to receive an audio signal from the microphone, determine at least one sound characteristic of the audio signal, determine, based upon the at least one sound characteristic, a condition of a braking component of the braking system, generate a notification regarding the condition, and perform at least one selected from the group of sending the notification to a display for output to a user, storing the notification in a memory, and modifying operation of the vehicle.
Medinei et al. 2019/0107163 Abstract: Technical solutions are described for determining thickness of a vehicle brake pad. An example method for estimating brake pad wear on a vehicle includes computing a corner torque for a brake based on corner brake pressure applied to the brake. The method also includes computing a corner power for the brake based on the corner torque. The method also includes computing a rotor temperature of a rotor of the brake based on the corner power. The method also includes determining a brake pad wear rate per unit of power based on the rotor temperature and the corner power. The method also includes computing a brake pad wear based on the brake pad wear rate and the corner power.
PERTINENT PRIOR ART – Non-Patent Literature (NPL)
The NPL prior-art made of record and considered pertinent to applicant's disclosure.
Ghadimi B. ET AL: "Heat flux on-line estimation in a locomotive brake disc using artificial neural networks", INTERNATIONAL JOURNAL OF THERMAL SCIENCES., vol. 90, 1 April 2015 (2015-04-01), pages 203-213, XP055813754, FR ISSN: 1290-0729, DOI: 10.1016/j.ijthermalsci.2014.12.012 Retrieved from the Internet: URL:https://www.sciencedirect.com/science/article/pii/ S1290072914003573/pdfft? md5=1 b1 f3fcbe9685d70112d4bdb3198d783&pid=1-s2.0-S1290072914003573-main.pdf
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW T. SITTNER whose telephone number is (571) 270-7137 and email: matthew.sittner@uspto.gov. The examiner can normally be reached on Monday-Friday, 8:00am - 5:00pm (Mountain Time Zone).
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah M. Monfeldt can be reached on (571) 270-1833.
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/MATTHEW T SITTNER/
Primary Examiner, Art Unit 3629b