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 .
Examiner notes that the fundamentals of the rejections are based on the broadest reasonable interpretation of the claim language. Any reference to specific figures, columns, lines and paragraphs should not be considered limiting in any way, the entire cited reference, as well as any secondary teaching reference(s), are considered to provide relevant disclosure relating to the claimed invention. Applicant is kindly invited to consider the reference as a whole. References are to be interpreted as by one of ordinary skill in the art rather than as by a novice. See MPEP 2141. Therefore, the relevant inquiry when interpreting a reference is not what the reference expressly discloses on its face but what the reference would teach or suggest to one of ordinary skill in the art.
Status of Claims
This is an Office Action on the merits of Application No. 18/568,423, in response to Applicant’s amendments and remarks filed on 01/14/2026. The Applicant has amended claims 15, 27, and 28 without prejudice, and cancelled claim 26. No new claims have been added and no new matter has been introduced. Claims 15 – 25, and 27 – 28 are currently pending in the application and are addressed below.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 01/27/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Reply to Applicant’s Remarks
Applicant amendments have fully overcome 35 U.S.C. 112(f)(b) and 101 Claim Rejections.
Claim Rejections Under 35 U.S.C. 103:
Regarding Applicant’s arguments that “neither Steer nor Yen discloses "wherein the classifying is performed by a machine learning model that receives for every temporal time window the temperature data and the determined features from the braking event data for the corresponding temporal data window, the machine learning model applying to the determined features and the temperature data a logistic regression according to which a discriminant function maps the determined features and the temperature data to a class.", and, “Steer discloses the use of a linear regression model that loops through braking events in order to monitor a braking system. Steer at [0231], [0233]. Yen discloses a brake-monitoring module that determines whether a braking event is within an acceptable pre-established limit. However, neither of these references uses a machine learning model in the manner recited in the amended claims.”, the Applicant’s arguments are moot in view of the art as applied to the amended claims.
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 15 – 28 are rejected under 35 U.S.C. 103 as being unpatentable over US 20170291591 Steer et al. (Steer hereafter) in view of US 20170369069 Yen et al. (Yen hereafter) and further in view of US 20180134161 Gaither et al. (Gaither hereafter).
Regarding Claim 15, Steer discloses a method for determining a state of wear of a brake pad of a vehicle (see at least Steer [¶0054], “The method includes, for at least some of the braking events, determining the time the braking event occurred. This enables the operator to monitor changes in braking performance over time, and assists in scheduling braking maintenance.”), comprising the following steps: receiving time series data, the time series data including a time series of brake system-related data of the vehicle (see at least Steer [¶0031, 0036, 0089], “the data set comprises the n most recent braking events. The data set can comprise braking events from the N most recent journeys. The data set can comprise braking events between two time values, for example between two dates.”, “This can include plotting a plurality of data trends on a vehicle deceleration vs braking demand graph. By comparing the braking performance for different time periods, that is different data sets of braking events, it is possible to determine changes in braking performance over time.”, “The microprocessor is arranged to obtain, for at least some braking events: time data for the braking event. The brake monitoring system includes a clock arranged to provide time data for a braking event. The clock can be integrated within the microprocessor package.”);
identifying at least one braking event in the time series data, each braking event identified in the time series data corresponding to a temporal data window of braking event data of the time series data, the data window correlating with a real braking event of the vehicle (see at least Steer [¶0117, 0182], “The apparatus includes a brake monitoring system 25, which is used to monitor the performance of the braking system 7. The brake monitoring system 25 is arranged to determine if the braking system 7 is operating in a satisfactory manner, or is used to at least record the appropriate data required to determine if the braking system 7 is operating in a satisfactory manner. The brake monitoring system 25 obtains braking event data for at least some braking events.”, “Throughout the braking event, the brake monitoring system 25 monitors, amongst other things, the current delivery pressure, current demand pressure, the current measurement time stamp, current high-resolution timer value and current vehicle speed, and determines from those inputs when certain braking event conditions have been met.”);
determining features from the braking event data by using predetermined operators for every identified braking event (see at least Steer [¶0049, 0050], “The brake monitoring system applies at least one braking event qualifying test to at least some of the determined data, such as the braking event duration, vehicle deceleration and braking demand, and stores the braking event data collected for the braking event only if the determined data passes the or each qualifying test. [] The method includes determining a braking event is non-qualifying, at least in part, in response to determining that the duration of the braking event is less than or equal to a threshold value.”);
Steer does not explicitly disclose receiving temperature data, wherein the temperature data is generated by a sensor that measures a temperature of the brake pad and/or a temperature of a brake disk of the vehicle and/or an ambient temperature of the vehicle; and
wherein the classifying is performed by a machine learning model that receives for every temporal time window the temperature data and the determined features from the braking event data for the corresponding temporal data window, the machine learning model applying to the determined features and the temperature data a logistic regression according to which a discriminant function maps the determined features and the temperature data to a class.
However, Gaither is directed towards systems and methods for adaptive braking using brake wear data and discloses receiving temperature data, wherein the temperature data is generated by a sensor that measures a temperature of the brake pad and/or a temperature of a brake disk of the vehicle and/or an ambient temperature of the vehicle (See at least Gaither [¶0033-0034], “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. [] the brake sensor may indicate a brake pad thickness, a rotor condition, a brake temperature, and so on.
the brake wear module 220 uses the braking data to select a particular lookup table that correlates with the characteristics of the braking event, e.g., ambient air temperature, brake age, brake type and size, current speed and so on.”) and
wherein the classifying is performed by a machine learning model that receives for every temporal time window the temperature data and the determined features from the braking event data for the corresponding temporal data window, the machine learning model applying to the determined features and the temperature data a logistic regression according to which a discriminant function maps the determined features and the temperature data to a class. (See at least Gaither [¶0084], “one or more of the modules described herein can include artificial or computational intelligence elements, e.g., neural network, fuzzy logic or other machine learning algorithms. Further, in one or more arrangements, one or more of the modules can be distributed among a plurality of the modules described herein. In one or more arrangements, two or more of the modules described herein can be combined into a single module.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of Gaither to modify Steer, with a reasonable expectation of success, to use the techniques of receiving temperature data from sensors on the brake pads and/or disks and/or ambient, and utilizing a machine learning model to analyze and process the data, for the purpose of modeling the brake wear, performance and other parameters and control variables of the vehicle braking system.
Steer does not explicitly disclose classifying the at least one braking event by using the determined features, the classification being associated with a state of wear of the brake pad of the vehicle.
However, Yen, directed towards driving behavior analysis based on vehicle braking, discloses classifying the at least one braking event by using the determined features (see at least Yen [¶0162], “the activity module 320 includes an event-classifier sub-module 322, or braking-event classifier. The sub-module 322 determines which of multiple categories a braking event falls into, such as normal braking, brake dragging, or hard braking.”), the classification being associated with a state of wear of the brake pad of the vehicle (see at least Yen [¶0046, 0190], “an on-board device (OBD) (not shown in detail), such as a wheel sensor, a brake sensor, an accelerometer, a rotor-wear sensor, a brake lining wear sensor, [] Support can be provided, such as that hard braking, and to a lesser extent, brake dragging, generates excessive heat, creates high thermal stress, and wears brake pads faster.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of Yen to modify Steer, with a reasonable expectation of success, to use the technique of classifying the at least one braking event by using the determined features, the classification being associated with a state of wear of the brake pad of the vehicle for the purpose of enabling the operator to monitor changes in braking performance over time, determine brake system wear, and allow more efficient scheduling of braking maintenance, improving safety and reducing costs.
Regarding Claim 27, Steer discloses A device configured to determine a state of wear of a brake pad of a vehicle (see at least Steer [¶0054, 0117] “The apparatus includes a brake monitoring system 25, which is used to monitor the performance of the braking system 7. The brake monitoring system 25 is arranged to determine if the braking system 7 is operating in a satisfactory manner, or is used to at least record the appropriate data required to determine if the braking system 7 is operating in a satisfactory manner.”), the device comprising a processor configured to: receive time series data, the time series data including a time series of brake system-related data of the vehicle (see at least Steer [¶0031, 0036, 0089], “the data set comprises the n most recent braking events. []. The data set can comprise braking events from the N most recent journeys. The data set can comprise braking events between two time values, for example between two dates.”, “This can include plotting a plurality of data trends on a vehicle deceleration vs braking demand graph. By comparing the braking performance for different time periods, that is different data sets of braking events, it is possible to determine changes in braking performance over time.”, “The microprocessor is arranged to obtain, for at least some braking events: time data for the braking event. The brake monitoring system includes a clock arranged to provide time data for a braking event. The clock can be integrated within the microprocessor package.”);
identify at least one braking event in the time series data, each braking event identified in the time series data corresponding to a temporal data window of braking event data of the time series data, the data window correlating with a real braking event of the vehicle (see at least Steer [¶0117, 0182], “The apparatus includes a brake monitoring system 25, which is used to monitor the performance of the braking system 7. The brake monitoring system 25 is arranged to determine if the braking system 7 is operating in a satisfactory manner, or is used to at least record the appropriate data required to determine if the braking system 7 is operating in a satisfactory manner. The brake monitoring system 25 obtains braking event data for at least some braking events.”, “Throughout the braking event, the brake monitoring system 25 monitors, amongst other things, the current delivery pressure, current demand pressure, the current measurement time stamp, current high-resolution timer value and current vehicle speed, and determines from those inputs when certain braking event conditions have been met.”);
determine features from the braking event data by using predetermined operators for every identified braking event (see at least Steer [¶0049, 0050], “The brake monitoring system applies at least one braking event qualifying test to at least some of the determined data, such as the braking event duration, vehicle deceleration and braking demand, and stores the braking event data collected for the braking event only if the determined data passes the or each qualifying test. [] The method includes determining a braking event is non-qualifying, at least in part, in response to determining that the duration of the braking event is less than or equal to a threshold value.”);
Steer does not explicitly disclose receive temperature data, wherein the temperature data is generated by a sensor that measures a temperature of the brake pad and/or a temperature of a brake disk of the vehicle and/or an ambient temperature of the vehicle; and
wherein the classifying is performed by a machine learning model that receives for every temporal time window the temperature data and the determined features from the braking event data for the corresponding temporal data window, the machine learning model applying to the determined features and the temperature data a logistic regression according to which a discriminant function maps the determined features and the temperature data to a class.
However, Gaither is directed towards systems and methods for adaptive braking using brake wear data and discloses receive temperature data, wherein the temperature data is generated by a sensor that measures a temperature of the brake pad and/or a temperature of a brake disk of the vehicle and/or an ambient temperature of the vehicle (See at least Gaither [¶0033-0034], “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. [] the brake sensor may indicate a brake pad thickness, a rotor condition, a brake temperature, and so on.
the brake wear module 220 uses the braking data to select a particular lookup table that correlates with the characteristics of the braking event, e.g., ambient air temperature, brake age, brake type and size, current speed and so on.”) and
wherein the classifying is performed by a machine learning model that receives for every temporal time window the temperature data and the determined features from the braking event data for the corresponding temporal data window, the machine learning model applying to the determined features and the temperature data a logistic regression according to which a discriminant function maps the determined features and the temperature data to a class. (See at least Gaither [¶0084], “one or more of the modules described herein can include artificial or computational intelligence elements, e.g., neural network, fuzzy logic or other machine learning algorithms. Further, in one or more arrangements, one or more of the modules can be distributed among a plurality of the modules described herein. In one or more arrangements, two or more of the modules described herein can be combined into a single module.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of Gaither to modify Steer, with a reasonable expectation of success, to use the techniques of receiving temperature data from sensors on the brake pads and/or disks and/or ambient, and utilizing a machine learning model to analyze and process the data, for the purpose of modeling the brake wear, performance and other parameters and control variables of the vehicle braking system.
Steer does not explicitly disclose classify the at least one braking event by using the determined features, the classification being associated with a state of wear of the brake pad of the vehicle.
However, Yen, directed towards driving behavior analysis based on vehicle braking, discloses classify the at least one braking event by using the determined features (see at least Yen [¶0162], “the activity module 320 includes an event-classifier sub-module 322, or braking-event classifier. The sub-module 322 determines which of multiple categories a braking event falls into, such as normal braking, brake dragging, or hard braking.”), the classification being associated with a state of wear of the brake pad of the vehicle (see at least Yen [¶0046, 0190], “an on-board device (OBD) (not shown in detail), such as a wheel sensor, a brake sensor, an accelerometer, a rotor-wear sensor, a brake lining wear sensor, [] Support can be provided, such as that hard braking, and to a lesser extent, brake dragging, generates excessive heat, creates high thermal stress, and wears brake pads faster.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of Yen to modify Steer, with a reasonable expectation of success, to use the technique of classifying the at least one braking event by using the determined features, the classification being associated with a state of wear of the brake pad of the vehicle for the purpose of enabling the operator to monitor changes in braking performance over time, determine brake system wear, and allow more efficient scheduling of braking maintenance, improving safety and reducing costs.
Regarding Claim 28, Steer discloses a non-transitory computer-readable medium on which is stored a computer program for determining a state of wear of a brake pad of a vehicle (see at least Steer [¶0118], “The brake monitoring system 25 includes at least one microprocessor device 27, non-volatile memory 29”), the computer program, when executed by a computer, causing the computer to perform the following steps:
receiving time series data, the time series data including a time series of brake system-related data of the vehicle (see at least Steer [¶0031, 0036, 0089], “the data set comprises the n most recent braking events. []. The data set can comprise braking events from the N most recent journeys. The data set can comprise braking events between two time values, for example between two dates.”, “This can include plotting a plurality of data trends on a vehicle deceleration vs braking demand graph. By comparing the braking performance for different time periods, that is different data sets of braking events, it is possible to determine changes in braking performance over time.”, “The microprocessor is arranged to obtain, for at least some braking events: time data for the braking event. The brake monitoring system includes a clock arranged to provide time data for a braking event. The clock can be integrated within the microprocessor package.”);
identifying at least one braking event in the time series data, each braking event identified in the time series data corresponding to a temporal data window of braking event data of the time series data, the data window correlating with a real braking event of the vehicle (see at least Steer [¶0117, 0182], “The apparatus includes a brake monitoring system 25, which is used to monitor the performance of the braking system 7. The brake monitoring system 25 is arranged to determine if the braking system 7 is operating in a satisfactory manner, or is used to at least record the appropriate data required to determine if the braking system 7 is operating in a satisfactory manner. The brake monitoring system 25 obtains braking event data for at least some braking events.”, “Throughout the braking event, the brake monitoring system 25 monitors, amongst other things, the current delivery pressure, current demand pressure, the current measurement time stamp, current high-resolution timer value and current vehicle speed, and determines from those inputs when certain braking event conditions have been met.”);
determining features from the braking event data by using predetermined operators for every identified braking event (see at least Steer [¶0049, 0050], “The brake monitoring system applies at least one braking event qualifying test to at least some of the determined data, such as the braking event duration, vehicle deceleration and braking demand, and stores the braking event data collected for the braking event only if the determined data passes the or each qualifying test. [] The method includes determining a braking event is non-qualifying, at least in part, in response to determining that the duration of the braking event is less than or equal to a threshold value.”);
Steer does not explicitly disclose receiving temperature data, wherein the temperature data is generated by a sensor that measures a temperature of the brake pad and/or a temperature of a brake disk of the vehicle and/or an ambient temperature of the vehicle; and
wherein the classifying is performed by a machine learning model that receives for every temporal time window the temperature data and the determined features from the braking event data for the corresponding temporal data window, the machine learning model applying to the determined features and the temperature data a logistic regression according to which a discriminant function maps the determined features and the temperature data to a class.
However, Gaither is directed towards systems and methods for adaptive braking using brake wear data and discloses receiving temperature data, wherein the temperature data is generated by a sensor that measures a temperature of the brake pad and/or a temperature of a brake disk of the vehicle and/or an ambient temperature of the vehicle (See at least Gaither [¶0033-0034], “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. [] the brake sensor may indicate a brake pad thickness, a rotor condition, a brake temperature, and so on.
the brake wear module 220 uses the braking data to select a particular lookup table that correlates with the characteristics of the braking event, e.g., ambient air temperature, brake age, brake type and size, current speed and so on.”) and
wherein the classifying is performed by a machine learning model that receives for every temporal time window the temperature data and the determined features from the braking event data for the corresponding temporal data window, the machine learning model applying to the determined features and the temperature data a logistic regression according to which a discriminant function maps the determined features and the temperature data to a class. (See at least Gaither [¶0084], “one or more of the modules described herein can include artificial or computational intelligence elements, e.g., neural network, fuzzy logic or other machine learning algorithms. Further, in one or more arrangements, one or more of the modules can be distributed among a plurality of the modules described herein. In one or more arrangements, two or more of the modules described herein can be combined into a single module.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of Gaither to modify Steer, with a reasonable expectation of success, to use the techniques of receiving temperature data from sensors on the brake pads and/or disks and/or ambient, and utilizing a machine learning model to analyze and process the data, for the purpose of modeling the brake wear, performance and other parameters and control variables of the vehicle braking system.
Steer does not explicitly disclose classifying the at least one braking event by using the determined features, the classification being associated with a state of wear of the brake pad of the vehicle.
However, Yen, directed towards driving behavior analysis based on vehicle braking, discloses classifying the at least one braking event by using the determined features (see at least Yen [¶0162], “the activity module 320 includes an event-classifier sub-module 322, or braking-event classifier. The sub-module 322 determines which of multiple categories a braking event falls into, such as normal braking, brake dragging, or hard braking.”), the classification being associated with a state of wear of the brake pad of the vehicle (see at least Yen [¶0046, 0190], “an on-board device (OBD) (not shown in detail), such as a wheel sensor, a brake sensor, an accelerometer, a rotor-wear sensor, a brake lining wear sensor, [] Support can be provided, such as that hard braking, and to a lesser extent, brake dragging, generates excessive heat, creates high thermal stress, and wears brake pads faster.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of Yen to modify Steer, with a reasonable expectation of success, to use the technique of classifying the at least one braking event by using the determined features, the classification being associated with a state of wear of the brake pad of the vehicle for the purpose of enabling the operator to monitor changes in braking performance over time, determine brake system wear, and allow more efficient scheduling of braking maintenance, improving safety and reducing costs.
Regarding Claim 16, Steer, Yen, and Gaither in combination disclose The method as recited in claim 15, Steer further discloses wherein the braking-related data include sensor data and/or control device data and/or brake system data of the vehicle (see at least Steer, [¶0123], “The brake monitoring system 25 is connected to the braking system 7 via the CAN bus 17. This enables the brake monitoring system 25 to monitor signals available to the braking system 7, which may include, for example, outputs from sensors that monitor: wheel speed; vehicle speed; braking demand (pneumatic, hydraulic, and/or electrically signalled); suspension pressure; reservoir pressure; delivery pressure (pneumatic, hydraulic, and/or electrically signalled); tyre pressure; odometer; and lateral acceleration. Other data includes DTCs (Diagnostic Trouble Codes) and braking system status data.”).
Regarding Claim 17 , Steer, Yen, and Gaither in combination disclose The method as recited in claim 16, Steer further discloses wherein the sensor data are provided by a master brake cylinder pressure sensor and/or a tire rotational speed sensor and/or a vehicle inertial sensor and/or a brake system sensor (see at least Steer, [¶0123], “The brake monitoring system 25 is connected to the braking system 7 via the CAN bus 17. This enables the brake monitoring system 25 to monitor signals available to the braking system 7, which may include, for example, outputs from sensors that monitor: wheel speed; vehicle speed; braking demand (pneumatic, hydraulic, and/or electrically signalled); suspension pressure; reservoir pressure; delivery pressure (pneumatic, hydraulic, and/or electrically signalled); tyre pressure; odometer; and lateral acceleration. Other data includes DTCs (Diagnostic Trouble Codes) and braking system status data.”).
Regarding Claim 18, Steer, Yen, and Gaither in combination disclose The method as recited in claim 16, Steer further discloses wherein the brake system data include a brake system status and/or a brake system flag (see at least Steer, [¶0123], “Other data includes DTCs (Diagnostic Trouble Codes) and braking system status data.”).
Regarding Claim 19, Steer, Yen, and Gaither in combination disclose The method as recited in claim 15, Steer further discloses wherein the identification of the at least one braking event includes:
receiving at least one brake trigger, the brake trigger correlating with a real braking event of the vehicle (see at least Steer [¶0163], “The brake monitoring system 25 determines if the current delivery pressure (del.sub.C) is greater than a predetermined delivery pressure value, such as greater than 0 bar, for the set period of time. For any braking actuation where the current delivery pressure is greater than the set value, for a period of time which is greater than or equal to the set time value, passes the test. For any braking actuation where the current delivery pressure is greater than the set value, for a period of time which is less than or equal the set time value, the braking event is determined as non-qualifying, and therefore the braking event is determined as not having started. The time set value is typically in the range 0.1 seconds to 1.5 seconds.”);
identifying the at least one braking event by using the at least one received brake trigger (see at least Steer [¶0162], “The brake monitoring system 25 applies three data filters each time the driver actuates the braking system 7: a brake delivery time filter 100; a vehicle speed filter 102; and a brake demand filter 104. These data filters distinguish between qualifying braking events, which are considered to be useful for data processing purposes, and non-qualifying braking events which are considered not to be useful for data processing purposes. In some applications a road gradient filter can be applied to filter out some braking events.”).
Regarding Claim 20, Steer, Yen, and Gaither in combination disclose The method as recited in claim 15, Steer further discloses wherein the at least one brake trigger includes a state of the brake light switch and/or a longitudinal acceleration of the vehicle and/or a motor state (see at least Steer [¶0124] “The brake monitoring system 25 can include, or can be arranged to receive signals from, at least one sensor which is sensitive to changes in acceleration. For example, the braking monitoring system 25 can include, or can be arranged to receive signals from, at least one accelerometer 42, such as a 3D accelerometer. Additionally, or alternatively, the braking monitoring system 25 can include, or can be arranged to receive signals from, at least one gyroscope 43. The microprocessor 27 is arranged to receive data from the accelerometer 42 and/or gyroscope 43 for a braking event.”).
Regarding Claim 21, Steer, Yen, and Gaither in combination disclose The method as recited in claim 15, Steer further discloses further comprising: discarding superfluous time series data which cannot be assigned to a braking event (see at least Steer [¶0162], “The brake monitoring system 25 applies three data filters each time the driver actuates the braking system 7: a brake delivery time filter 100; a vehicle speed filter 102; and a brake demand filter 104. These data filters distinguish between qualifying braking events, which are considered to be useful for data processing purposes, and non-qualifying braking events which are considered not to be useful for data processing purposes. In some applications a road gradient filter can be applied to filter out some braking events.”).
Regarding Claim 22, Steer, Yen, and Gaither in combination disclose The method as recited in claim 15, Steer further discloses further comprising: discarding time series data which are not suitable for determining features (see at least Steer [¶0162], “The brake monitoring system 25 applies three data filters each time the driver actuates the braking system 7: a brake delivery time filter 100; a vehicle speed filter 102; and a brake demand filter 104. These data filters distinguish between qualifying braking events, which are considered to be useful for data processing purposes, and non-qualifying braking events which are considered not to be useful for data processing purposes. In some applications a road gradient filter can be applied to filter out some braking events.”).
Regarding Claim 23, Steer, Yen, and Gaither in combination disclose The method as recited in claim 15, Steer further discloses further comprising: assigning a relevance to each of the determined features;
using a previously defined number of features having a highest relevance for classifying the at least one braking event (see at least Steer [¶0282], “A weighted trend analysis technique gives greater weighting to some braking events than other braking events when generating the trend. Using a weighted technique can be useful since many of the braking events during normal driving take place under conditions which are quite different from “in service” testing requirements. A weighting technique may, for example give greater weighting to braking events which are considered to be more useful for predicting the performance of the braking system 7 than those that are considered to be less important.”).
Regarding Claim 24, Steer, Yen, and Gaither in combination disclose The method as recited in claim 15, Steer further discloses wherein the receiving of the time series data includes: storing the received time series data in a memory (see at least Steer [¶0088, 0089] The apparatus includes data storage means located on the vehicle, wherein the microprocessor is arranged to store at least some braking event data in the data storage means. Preferably the monitoring system is arranged to store braking event data for qualifying braking events only. [] The microprocessor is arranged to obtain, for at least some braking events: time data for the braking event. The brake monitoring system includes a clock arranged to provide time data for a braking event. The clock can be integrated within the microprocessor package.);
wherein the time series data are retained in the memory for as long as the memory is not exhausted or as long as the features of the time series data have not been determined (see at least Steer [¶0132], “The data aggregation and storage module 42 is arranged to receive data from the vehicle via the data connection 44, and record the data received in the consolidated data store 46. The data aggregation and storage module 42 can be arranged to communicate with at least one database 43 to obtain data for correcting at least one parameter. For example, the module 42 can be arranged to access at least one database 43 which stores environmental data, such as road conditions and/or weather data, for use in an environmental data correction process. Typically, the module 42 determines whether it is necessary to adjust the deceleration data received from the vehicle for environmental conditions, and if so, applies the correction.”).
Regarding Claim 25, Steer, Yen, and Gaither in combination disclose The method as recited in claim 15, Yen further discloses wherein the at least one braking event is classified by taking into account a braking history of the vehicle (Yen [¶0013] The braking-monitoring module, in determining whether the braking event is within the acceptable pre-established limit, when executed by the hardware-based processing unit, in some cases determines whether the braking event is within the acceptable pre-established limit based on the braking data and context data. The braking context data can include any of context data indicating regional braking trends; context data indicating characteristics of historic braking events for an operator of the vehicle initiating the present braking event; context data indicating date of braking event; and context data indicating time of day of braking event, as a few examples.).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have considered the teachings of Yen to modify Steer, with a reasonable expectation of success, to use the technique of the at least one braking event is classified by taking into account a braking history of the vehicle, for the purpose of enabling the operator to monitor changes in braking performance over time, determine brake system wear, and allow more efficient scheduling of braking maintenance, improving safety and reducing costs.
Conclusion
Examiner encourages Applicant to fill out and submit form PTO-SB-439 to allow internet communications in accordance with 37 CFR 1.33 (MPEP 502.03). Should the need arise to perfect applicant-proposed or examiner’s amendments, authorization for e-mail correspondence would have already been authorized and would save time.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Neit J. Nieves Flores whose telephone number is (703)756-5864. The examiner can normally be reached M-F 0930-1800 AST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rachid Bendidi can be reached at (571) 272-4896. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Neit J. Nieves Flores/
Patent Examiner, Art Unit 3664
/RACHID BENDIDI/Supervisory Patent Examiner, Art Unit 3664
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