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 .
Applicant cancelled claims 1-12 and added claims 13-27.
Claim Objections
Claim 18 is objected to because of the following informalities:
In claim 18, line 7, change “first vertical acceleration values wherein” to -first
vertical acceleration values measured on each one or more of the motor vehicles, wherein-. Appropriate correction is required.
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.
Claims 13-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
Under Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed to a process (claim 13, a method) or a machine (claims 18 and 23, a system), which are statutory categories.
However, evaluating claim 13, under Step 2A, Prong One, the claim is
directed to the judicial exception of an abstract idea using the grouping of a mathematical relationship/mental process. The limitations include:
computing first root mean square values of the first vertical acceleration values; determining one or more vehicle transfer functions mathematically relating vehicle vertical acceleration root mean square values and international roughness index values at the one or more given constant speeds, based on the known international roughness index values or road profiles, the first vehicle geo-referencing data, the first vehicle speed data, and the first root mean square values; and in an international roughness index estimation stage: computing second root mean square values of the second vertical acceleration values; and estimating an international roughness index value for the given road or road segment based on one or more vehicle transfer functions determined in the preliminary stage and on the second root mean square values and the driving speed of the given motor vehicle.
Next, Step 2A, Prong Two evaluates whether additional elements of the claim “integrate the abstract idea into a practical application” in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The claim does not recite additional elements that integrate the judicial exception into a practical application.
Therefore, the claims are directed to an abstract idea.
At Step 2B, consideration is given to additional elements that may make the abstract idea significantly more. Under Step 2B, there are no additional elements that make the claim significantly more than the abstract idea.
The additional elements of “collecting: first vertical acceleration values measured on one or more motor vehicles driven at one or more given constant speeds on one or more roads or road segments associated with known international roughness index values or known road profiles; first vehicle geo-referencing data of the measured first vertical acceleration values; and first vehicle speed data indicative of the one or more given constant speeds associated with the measured first vertical acceleration values” and “acquiring second vertical acceleration values measured on a given motor vehicle driven at a driving speed on a given road or road segment” are considered insignificant extra-solution activity of collecting data that is not sufficient to integrate the claim into a particular practical application. The act of data gathering by the sensors is considered insufficient to elevate the claim to a practical application.
The examiner notes that applying conventional mathematical analysis to conventional data using conventional vehicle hardware does not amount to significantly more that the abstract idea.
The limitations have been considered individually and as a whole and do not amount to significantly more than the abstract idea itself.
Dependent claims 14-17 do not add anything which would render the claimed invention a patent eligible application of the abstract idea. The claim merely extends (or narrow) the abstract idea which do not amount for "significant more" because it merely adds details to the algorithm which forms the abstract idea as discussed above.
The recited step of “driving one or more motor vehicles of one and the same given vehicle type and/or of one and the same given vehicle model at one or more given constant speeds on one or more roads or road segments associated with known international roughness index values or known road profiles” is an insignificant extra-solution activity that merely supports collection of reference data. The step does not improve the functioning of the vehicle or any vehicle subsystem, does not integrate the abstract idea into a practical application, does not effect a transformation of an article to a different state or thing and therefore does not amount to significantly more than the abstract idea.
4.2. Claims 18 and 23 are rejected 35 USC § 101 for the same rationale as in claim 13.
Regarding claim 18, This judicial exception is not integrated into a practical application because the remaining elements (i.e., cloud computing system) amount to no more than general purpose computer components programmed to perform the abstract ideas. As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ing] instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Regarding claim 23, This judicial exception is not integrated into a practical application because the remaining elements (i.e., cloud computing system) amount to no more than general purpose computer components programmed to perform the abstract ideas. As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ing] instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Dependent claims 19-22 and 24-27, either depending of claim 18 or claim 13, do not add anything which would render the claimed invention a patent eligible application of the abstract idea. The claim merely extends (or narrow) the abstract idea which do not amount for "significant more" because it merely adds details to the algorithm which forms the abstract idea as discussed above.
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 13-17 are rejected under 35 U.S.C. 103 as being unpatentable over M
u
´
c
˘
ka
(NPL “Rod Roughness Limit Values Based on Measured Vehicle Vibration” DOI: 10.1061/
(ASCE)IS.1943-555X.0000325. © 2016 American Society of Civil Engineers) in view Du et al. (Pub. No. US 2020/0406925) (hereinafter Du).
As per claims 13, 14, 17, M
u
´
c
˘
ka teaches in a preliminary stage, collecting: first vertical acceleration values measured on one or more motor vehicles driven at one or more given constant speeds on one or more roads or road segments associated with known international roughness index values or known road profiles and first vehicle speed data indicative of the one or more given constant speeds associated with the measured first vertical acceleration values and in an international roughness index estimation stage: acquiring second vertical acceleration values measured on a given motor vehicle driven at a driving speed on a given road or road segment (see page 1, section Vehicle Vibration Response “Ride comfort quantity—The RMS value of the frequency-weighted vertical acceleration (awZ) or total acceleration (awXYZ) on the seat measured in three orthogonal directions: x (fore-and-aft), y (lateral), and z (vertical), and in section International Roughness Index and Ride comfort Response M
u
´
c
˘
ka further teaches “The results of processing the data taken from nine references are summarized in Table 2. An overview of the relations in Table 2 was supplemented by the following information: (1) reference; (2) vehicle type; (3) measuring point on the vehicle; (4) total length and number of processed road sections, evaluation interval, and range of IRI; (5) vehicle velocity; and (6) regression relationship and coefficient of determination (R2). Vibration response was measured in a passenger car, multifunction vehicle, van, ambulance, bus, or truck with trailer. Vehicle velocity ranged from 30 to 120 km=h. A velocity of 60 km=h was assumed. Ihs et al. (2004) used a test dummy instead of a human body test subject. Ahlin et al. (2000) provided measurement on the longest part of a road network with a total length of approximately 58 km”, and Tables 2 and 3 list fixed velocities (e.g. 30, 50, 70, 100 km/h)); further in the preliminary stage: computing first root mean square values of the first vertical acceleration values, computing second root mean square values of the second vertical acceleration values (see page 2, International Roughness Index and Ride comfort Response, M
u
´
c
˘
ka further establishes an explicit quantitative relationship between RMS vehicle vibration and international roughness index, teaching that a linear regression relationship between RMS acceleration (awZ or awXYZ) and IRI is appropriate and providing the relationship awXYZ=b1
∙
IRI + b2 (Eq. (3), M
u
´
c
˘
ka also teaches estimating or determining IRI values from measured RMS vertical acceleration by mathematically inverting this relationship, explicitly stating that IRI limit values are explicitly dependent on vehicle speed, explaining that the regression parameters vary with velocity, that IRI threshold values should be a function of speed, and deriving velocity-related IRI limit curves: Eq. (7)); determining one or more vehicle transfer functions mathematically relating vehicle vertical acceleration root mean square values and international roughness index values at the one or more given constant speeds, based on the known international roughness index values or road profiles, the first vehicle speed data, and the first root mean square and estimating an international roughness index value for the given road or road segment based on one or more vehicle transfer functions determined in the preliminary stage and on the second root mean square values and the driving speed of the given motor vehicle (see pages 5-6, section Discussion Eq. (5)).
M
u
´
c
˘
ka does not teach associating the measured vertical acceleration values or derived IRI values with first vehicle geo-referencing data, nor does M
u
´
c
˘
ka teach associating the measured vertical acceleration values, or the IRI values, with vehicle geo-referencing data to form a geo-referenced road profile.
However, Du teaches a vehicle control and planning system that associates road roughness information, including IRI, with specific roads or road segments using map data or infrastructure-provided information, and that uses such geo-referenced roughness information to plan vehicle speed and driving strategy for improving ride comfort (see ¶¶ [0028]-[0030], [0034],[0046]-[0053], [0119] and [0122]). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to incorporate Du’s teaching into M
u
´
c
˘
ka’s teaching by associating the vibration-derived, speed-dependent IRI values taught by M
u
´
c
˘
ka with the geo-referenced road segment information as taught by Du because it would improve the ride comfort based on road roughness across different speeds, thereby improving robustness and accuracy of comfort-based vehicle operation.
As per claims 15 and 16, the combination of M
u
´
c
˘
ka and Du teach the system as stated above. M
u
´
c
˘
ka further teaches that the preliminary stage comprises: driving one or more motor vehicles of one and the same given vehicle type and/or of one and the same given vehicle model at one or more given constant speeds on one or more roads or road segments associated with known international roughness index values or known road profiles (see section International Roughness Index and Ride Comfort Response “Vibration response was measured in a passenger car, multifunction vehicle, van, ambulance, bus, or truck with trailer. Vehicle velocity ranged from 30 to 120 km=h. Fichera et al. (2007) did not report a bus velocity. A velocity of 60 km=h was assumed”), “IRI is proportional to the RMS value of suspension relative velocity (Sun et al. 2001). Thus, a linear relationship with the RMS value of vibration response can be expected. Based on processing of reported data, a linear relationship between awXYZ (or awZ) and IRI was identified as most appropriate, which is expressed as follows: awXYZ = b1
∙
IRI + b2 (3) where b1 = slope of a straight line, and b2 = an intercept. A constant term, b2, should be approximately zero, b2 ∼ 0, theoretically, because no response may be expected on an even road characterized by IRI = 0. The positive intercept may imply the presence of vibration sources other than road roughness, such as tire imbalance, engine vibration, or aerodynamics (Perera et al. 2009). Granlund (2012) reported further possible reasons for b2 > 0, for example: (1) variance in pavement deflection at soft spots in the road; (2) IRI
does not reflect megatexture waves <0.5 m; (3) wheel geometric and stiffness eccentricity; and (4) truck frame beaming at long wave unevenness” and Tables 2 and 3).
Claims 18-27 are rejected under 35 U.S.C. 103 as being unpatentable over M
u
´
c
˘
ka
in view Du and further in view of Singh et al. (Pub. No. US 2021/0229670) (hereinafter Singh).
As per claims 18, 19, 22-24 and 27, M
u
´
c
˘
ka teaches in a preliminary stage, to receive: first vertical acceleration measured on each one or more of the motor vehicles, wherein the respective motor vehicle is driven at one or more given constant speeds on one or more road segments associated with known international roughness index values or known road profiles and first vehicle speed data indicative of the one or more given constant speeds associated with the measured first vertical acceleration values and in an international roughness index estimation stage: acquire second vertical acceleration values measured on a given motor vehicle driven at a driving speed on a given road or road segment (see page 1, section Vehicle Vibration Response “Ride comfort quantity—The RMS value of the frequency-weighted vertical acceleration (awZ) or total acceleration (awXYZ) on the seat measured in three orthogonal directions: x (fore-and-aft), y (lateral), and z (vertical), and in section International Roughness Index and Ride comfort Response M
u
´
c
˘
ka further teaches “The results of processing the data taken from nine references are summarized in Table 2. An overview of the relations in Table 2 was supplemented by the following information: (1) reference; (2) vehicle type; (3) measuring point on the vehicle; (4) total length and number of processed road sections, evaluation interval, and range of IRI; (5) vehicle velocity; and (6) regression relationship and coefficient of determination (R2). Vibration response was measured in a passenger car, multifunction vehicle, van, ambulance, bus, or truck with trailer. Vehicle velocity ranged from 30 to 120 km=h. A velocity of 60 km=h was assumed. Ihs et al. (2004) used a test dummy instead of a human body test subject. Ahlin et al. (2000) provided measurement on the longest part of a road network with a total length of approximately 58 km”, and Tables 2 and 3 list fixed velocities (e.g. 30, 50, 70, 100 km/h)); compute first root mean square values of the first vertical acceleration values, computing second root mean square values of the second vertical acceleration values (see page 2, International Roughness Index and Ride comfort Response, M
u
´
c
˘
ka further establishes an explicit quantitative relationship between RMS vehicle vibration and international roughness index, teaching that a linear regression relationship between RMS acceleration (awZ or awXYZ) and IRI is appropriate and providing the relationship awXYZ=b1
∙
IRI + b2 (Eq. (3), M
u
´
c
˘
ka also teaches estimating or determining IRI values from measured RMS vertical acceleration by mathematically inverting this relationship, explicitly stating that IRI limit values are explicitly dependent on vehicle speed, explaining that the regression parameters vary with velocity, that IRI threshold values should be a function of speed, and deriving velocity-related IRI limit curves: Eq. (7)); determine one or more vehicle transfer functions mathematically relating vehicle vertical acceleration root mean square values and international roughness index values at the one or more given constant speeds, based on the known international roughness index values or road profiles, the first vehicle speed data, and the first root mean squire values and estimating an international roughness index value for the given road or road segment based on one or more vehicle transfer functions determined in the preliminary stage and on the second root mean square values and the driving speed of the given motor vehicle (see pages 5-6, section Discussion Eq. (5)).
M
u
´
c
˘
ka does not teach associating the measured vertical acceleration values or derived IRI values with first vehicle geo-referencing data, nor does M
u
´
c
˘
ka teach associating the measured vertical acceleration values, or the IRI values, with vehicle geo-referencing data to form a geo-referenced road profile.
However, Du teaches a vehicle control and planning system that associates road roughness information, including IRI, with specific roads or road segments using map data or infrastructure-provided information, and that uses such geo-referenced roughness information to plan vehicle speed and driving strategy for improving ride comfort (see ¶¶ [0028]-[0030], [0034],[0046]-[0053], [0119] and [0122]). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to incorporate Du’s teaching into M
u
´
c
˘
ka’s teaching by associating the vibration-derived, speed-dependent IRI values taught by M
u
´
c
˘
ka with the geo-referenced road segment information as taught by Du because it would improve the ride comfort based on road roughness across different speeds, thereby improving robustness and accuracy of comfort-based vehicle operation.
M
u
´
c
˘
ka does not teach (i) associating the measured vertical acceleration values or derived IRI values with first vehicle geo-referencing data, nor does M
u
´
c
˘
ka teach associating the measured vertical acceleration values, or the IRI values, with vehicle geo-referencing data to form a geo-referenced road profile, (ii) implementing the processing within a cloud computing system, or (iii) using acquisition devices coupled to a respective vehicle bus.
However, Du teaches a vehicle control and planning system that associates road roughness information, including IRI, with specific roads or road segments using map data or infrastructure-provided information, and that uses such geo-referenced roughness information to plan vehicle speed and driving strategy for improving ride comfort (see ¶¶ [0028]-[0030], [0034],[0046]-[0053], [0119] and [0122]) and Du further teaches transmitting to a central database or GIS system, such that measured vehicle vibration data is georeferenced to road segments and aggregated across vehicles (see ¶¶ [0133]-[0134]). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to incorporate Du’s teaching into M
u
´
c
˘
ka’s teaching by associating the vibration-derived, speed-dependent IRI values taught by M
u
´
c
˘
ka with the geo-referenced road segment information as taught by Du because it would improve the ride comfort based on road roughness across different speeds, thereby improving robustness and accuracy of comfort-based vehicle operation.
Du also fails to teach (ii) implementing the processing within a cloud computing system, or (iii) using acquisition devices coupled to a respective vehicle bus.
However, Singh teaches first and second acquisition devices coupled to a respective vehicle bus of the motor vehicle, specifically a vehicle CAN bus, and communication between vehicle-mounted acquisition/processing devices and a remote internet or cloud-based computing system (see ¶¶ [0017] and [0037]-[0044]) and transmit such data to remote or cloud-based processors configured to receive vehicle sensor measurements and perform higher-level analysis (see ¶¶ 0035], [0041] and [0046]). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to incorporate Singh’s teaching into the combination of M
u
´
c
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ka and Du’s teaching as it would provide a vehicle bus, cloud-based architecture for collecting vehicle sensor data from multiple vehicles for processing vehicle sensor data, thereby it would improve the ride comfort based on road roughness across different speeds and robustness and accuracy of comfort-based vehicle operation would be enhanced.
Regarding the limitation “for each of the one or more motor vehicles, a respective electronic control unit installed onboard the motor vehicle and coupled to the respective second acquisition device” (claim 23), Singh further teaches that a vehicle-mounted processor (i.e., processor 38) coupled to vehicle-mounted sensing devices and to the vehicle CAN bus for receiving and processing sensor data (see ¶¶ [0036]-[0038]), which constitutes an onboard “electronic control unit” coupled to the acquisition devices).
As per claims 20, 21, 25 and 26, the combination of M
u
´
c
˘
ka and Du teach the system as stated above. M
u
´
c
˘
ka further teaches that the preliminary stage comprises: driving one or more motor vehicles of one and the same given vehicle type and/or of one and the same given vehicle model at one or more given constant speeds on one or more roads or road segments associated with known international roughness index values or known road profiles (see section International Roughness Index and Ride Comfort Response “Vibration response was measured in a passenger car, multifunction vehicle, van, ambulance, bus, or truck with trailer. Vehicle velocity ranged from 30 to 120 km=h. Fichera et al. (2007) did not report a bus velocity. A velocity of 60 km=h was assumed”), “IRI is proportional to the RMS value of suspension relative velocity (Sun et al. 2001). Thus, a linear relationship with the RMS value of vibration response can be expected. Based on processing of reported data, a linear relationship between awXYZ (or awZ) and IRI was identified as most appropriate, which is expressed as follows: awXYZ = b1
∙
IRI + b2 (3) where b1 = slope of a straight line, and b2 = an intercept. A constant term, b2, should be approximately zero, b2 ∼ 0, theoretically, because no response may be expected on an even road characterized by IRI = 0. The positive intercept may imply the presence of vibration sources other than road roughness, such as tire imbalance, engine vibration, or aerodynamics (Perera et al. 2009). Granlund (2012) reported further possible reasons for b2 > 0, for example: (1) variance in pavement deflection at soft spots in the road; (2) IRI
does not reflect megatexture waves <0.5 m; (3) wheel geometric and stiffness eccentricity; and (4) truck frame beaming at long wave unevenness” and Tables 2 and 3).
Prior art
The prior art made record and not relied upon is considered pertinent to applicant’s
disclosure:
Louhghalam et al. [‘053] discloses a method of monitoring quality of a road segment from driver data is provided. The method includes receiving, by a server over a network, the driver data for a road segment from one or more sensing units in one or more vehicles. The method also includes calculating, in one or more computing devices, one or more quantitative pavement surface characteristics of the road segment from the driver data using a probabilistic inverse analysis framework. The method also includes identifying, in the one or more computing devices, one or more quantitative vehicle properties of the one or more vehicles from the driver data using the probabilistic inverse analysis framework. The method then includes estimating, in the one or more computing devices, one or more road quality characteristics of the road segment based on at least one of the quantitative pavement surface characteristics of the road segment and the quantitative vehicle properties of the one or more vehicles.
Nagayama e al. [‘824] discloses a road surface profile estimation device and so on with which any road surface profile, including that of a general road, can be estimated with a high degree of precision using a general-purpose vehicle. The road surface profile estimation device includes an acquisition unit that acquires a vertical acceleration and angular velocity about a pitch axis, a first calculation unit that calculates a vertical displacement and an angular displacement about the pitch axis, a prediction unit that predicts the time evolution of state variables of the vehicle on the basis of a simulation model, a second calculation unit that calculates the acceleration, the angular velocity, the displacement, and the angular displacement from the state variables on the basis of an observation model, an updating unit that updates the state variables by data-assimilating the acceleration and angular velocity acquired by the acquisition unit and the displacement and angular displacement calculated by the first calculation unit with the acceleration, angular velocity, displacement, and angular displacement calculated by the second calculation unit, and an estimation unit that estimates the road surface profile on the basis of the state variables.
Magnusson et al. [‘380] discloses a system for monitoring the condition of a road surface travelled by a plurality of vehicles, each including at least one sensor, is provided. The system includes a central processing arrangement arranged to: map at least a part of the road surface with a number of cells; receive road surface data for the cells, which road surface data is based on measurements made by the sensors as the plurality of vehicles travel on the road surface; and calculate a probability for at least one road surface parameter for each cell travelled by the plurality of vehicles based at least on road surface data received from the plurality of vehicles.
Contact information
Any inquiry concerning this communication or earlier communications from the
examiner should be directed to MOHAMED CHARIOUI whose telephone number is (571)272-2213. The examiner can normally be reached Monday through Friday, from 9 am to 6 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached on (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Mohamed Charioui
/MOHAMED CHARIOUI/Primary Examiner, Art Unit 2857