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 .
Notice to Applicant
Claims 1- 15 have been examined in this application. This communication is the first action on the merits. The Information Disclosure Statement (IDS) filed 6/28/2024 is acknowledged.
Claim Objections
A series of singular dependent claims is permissible in which a dependent claim refers to a preceding claim which, in turn, refers to another preceding claim.
A claim which depends from a dependent claim should not be separated by any claim which does not also depend from said dependent claim. It should be kept in mind that a dependent claim may refer to any preceding independent claim. In general, applicant's sequence will not be changed. See MPEP § 608.01(n).
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 therefore, subject to the conditions and requirements of this title.
Claims 1- 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-15 are directed to identifying and road conditions.
Claim 1 recites a method for identifying road conditions, Claim 13 recites a system for identifying road conditions and Claim 14 recites an article of manufacture for identifying road conditions, which include identifying a road condition on the basis of measured data (9) from inertial sensors (5) of a vehicle (1), the method comprising: receiving the measured data (9), wherein the measured data (9) indicates an acceleration and/or yaw rate of the vehicle (1) measured by the inertial sensors (5); determining noise values (13) indicating an intensity of a noise in the measured data (9); and identifying the road condition according to the noise values (13).
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Mental Processes” – evaluation. The recitation of “controller”, “processor” and “computer readable medium”, provide nothing in the claim elements to preclude the step from being “Mental Processes”- evaluation. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “controller”, “processor” and “computer readable medium” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Regarding the additional element of “sensor”- it is MPEP 2106.05(h) – field of use.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in data analysis.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “controller”, “processor” and “computer readable medium” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Regarding Step 2B and the additional element of “sensor”- it is MPEP 2106.05(h) – field of use.
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-11, and 15 recite wherein the measured data (9) comprises measured values for at least two different measurement dimensions (ax, ay, az,ox,coy,oz); wherein noise values (13) are determined from the measured values of each measurement dimension (ax, ay, az,wx,coy,oz), which indicate an intensity of a noise associated with the measurement dimension (ax, ay, az,x,wy,(OZ); wherein the road condition is identified according to the noise values (13) of different measurement dimensions (ax, ay, az,ox,y,(oZ); wherein (13) the noise values are determined at different predetermined frequency ranges, particularly in three to eight different predetermined frequency ranges; wherein the measured data (9) and/or measured data (25) on the basis of the measured data (9) are input as input data (19) into a smoothing filter (18) to obtain output data (21) smoothed against the input data (19); wherein a difference is formed between the input data (19) and the output data (21); wherein the noise values (13) are determined from the difference; wherein the noise values (13) are determined by squaring the difference; wherein the measured data (9) are received in a plurality of successive time steps and the noise values (13) are determined in a current time step from the measured data (9) of different time steps; wherein the measured data (9) of different time steps are input into an edge filter (23) to obtain filter data (25) in which the noise is amplified against the measured data (9); wherein the noise values (13) are be determined from the filter data (25); wherein the noise values (13) are determined by squaring the filter data (25); wherein the filter data (25) is input into the smoothing filter (18) as the input data (19); wherein at least one detection one detection value (15) indicating a degree of wetness of a road (3) of the vehicle and/or a risk of aquaplaning for the vehicle (1) is determined to identify the road condition; (vx) wherein the road condition is additionally identified according to a current speed of the vehicle (1); wherein additional statistical values are determined indicating a variance with respect to the measured data (9) and/or the noise values (13); wherein the road condition is additionally identified according to the statistical values; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 12 and 20. Regarding Claim, 14, and the additional elements of “computer readable medium” it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
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, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3 and 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Hanatsuka et al., US Publication No. 20170241778A1, [hereinafter Hanatsuka], in view of Hanatsuka et al., US Publication No. 20120330493A1, [hereinafter Hanatsuka2].
Regarding Claim 1,
Hanatsuka teaches
A method for identifying a road condition on the basis of measured data (9) from inertial sensors (5) of a vehicle (1), the method comprising: receiving the measured data (9), wherein the measured data (9) indicates an acceleration and/or yaw rate of the vehicle (1) measured by the inertial sensors (5); (Hanatsuka Par. 11-15-“The present invention has been made to solve the foregoing problems, and an object of the invention is to provide a method for accurately predicting a road surface condition at a location within a predetermined range. A method for predicting a road surface condition according to an embodiment of the present invention provides a method including the steps of obtaining vehicular information, which is information on the behavior of a vehicle during travel by an on-board sensor mounted on the vehicle and predicting a road surface condition at a location within a predetermined range, using road surface estimation decision values to be used in the estimation of road surface conditions, which are calculated using the vehicular information or estimated road surface conditions estimated using the road surface estimation decision values. In the step of predicting a road surface condition, predicted incidence rates SRp for respective road surface conditions, which are the incidence rates of road surface conditions at the location within the predetermined range, are calculated from time-dependent changes in the road surface estimation decision values calculated using the vehicle information obtained by the vehicles having passed the location within the predetermined range or time-dependent changes in the estimated road surface conditions, and then the road surface condition at the location within the predetermined range is predicted from the calculated predicted incidence rates SRp.; Fig. 2; Fig. 3)
determining noise values (13)… in the measured data (9) (Hanatsuka Par. 5-As methods having been proposed for estimating the condition of a road surface under a traveling vehicle, there are methods for estimating the condition of a road surface by detecting the vibration of the tire of the traveling vehicle and estimating the road surface condition from the time-series waveform of the detected tire vibration (see Patent Documents 1 to 3, for instance) and methods for estimating a road surface condition from a detected sound pressure level of tire noise ; Par. 165-Or such a road surface estimating means may detect the tire noise arising from the running tire. And by comparing the mean value of sound pressure levels within a set frequency range of the detected tire noise against the reference sound pressure levels, it may estimate whether the road surface is an asphalt road amply wet with water, a slightly wet asphalt road, a dry asphalt road, or an ice-covered road.)
and identifying the road condition according to the noise values (13) (Hanatsuka Par. 5-and methods for estimating a road surface condition from a detected sound pressure level of tire noise by detecting tire noise arising from a tire; Par. 165)
Hanatsuka teaches road condition analysis, and the feature is expounded upon Hanatsuka2:
…indicating an intensity of a noise… (Hanatsuka2 Par. 9-“ The present invention provides a method for determining a road surface condition, which includes the steps of detecting vibrations of a moving tire, extracting time-series waveforms of tire vibrations in predetermined time widths from the tire vibrations detected, calculating feature vectors from the time-series waveforms, calculating likelihoods of the feature vectors respectively for a plurality of hidden Markov models (HMMS) structured to represent predetermined road surface conditions, and comparing the likelihoods calculated respectively for the plurality of hidden Markov models with one another and determining a road surface condition corresponding to the hidden Markov model showing the highest likelihood to be the condition of the road surface on which the tire is running, in which each of the feature vectors is vibration levels in specific frequency bands or a function of the vibration levels and in which each of the hidden Markov models has at least four different states. ; Claim 4)
Hanatsuka and Hanatsuka2 are directed to road condition analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Hanatsuka, as taught by Hanatsuka2, by utilizing vibration analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Hanatsuka with the motivation of improving the accuracy in determining a road surface condition (Hanatsuka2 Par. 11).
Regarding Claim 2 – No Prior art applied
Regarding Claim 3,
The method according to one of the preceding claims, wherein (13) the noise values are determined at different predetermined frequency ranges, particularly in three to eight different predetermined frequency ranges (Hanatsuka Par. 36- Such a road surface condition estimating unit 13K may calculate respective feature vectors Xk (ak1, ak2, . . . , akm) having vibration levels (ak1 to akm) in a plurality of specific frequency bands as the components from the time-series waveforms of the respective time windows extracted by windowing the time-series waveform of tire vibration at predetermined time width T as shown in FIG. 3B, calculates the kernel functions from these feature vectors and the feature vectors for the respective road surface conditions having been determined in advance, and estimates a road surface condition to be any one of DRY road surface, WET road surface, SNOW road surface, and ICE road surface from the values of identification (discriminant) functions using the kernel functions. It is to be noted that the feature vectors for the respective road surface conditions are the feature vectors having a plurality of specific frequency bands as the components which were determined by a test vehicle traveling on DRY road surface, WET road surface, SNOW road surface, and ICE road surface, respectively.”).
Regarding Claim 6,
The method according to one of the preceding claims, wherein the measured data (9) are received in a plurality of successive time steps and the noise values (13) are determined in a current time step from the measured data (9) of different time steps. (Hanatsuka Par. 68-73- Then, at the statistical data generating means 31, the actual statistical data Mk, which are the statistical data of the counts of vehicles having estimated a DRY road surface, vehicles having estimated a WET road surface, vehicles having estimated a SNOW road surface, and vehicles having estimated an ICE road surface at the predetermined location Lr, are generated for each of times tk (k=−n to −1), and they are arranged in a time series (step S13). Next, the time-dependent changes in the incidence rates SRk of estimated road surface conditions being R at times t−n t−1 are respectively approximated by the n-th functions Gr(t) (step S14), and the function values GR(tp), which are the values of n-th functions GR(t)) at a future time tp, are derived, respectively. And the predicted incidence rates SRp of road surface conditions R at time tp, are derived using these four function values GR(tp), respectively (step S15). And the estimated road surface condition R indicating the highest value of predicted incidence rates SRp is predicted to be the road surface condition R(tp) at time tp (step P16).”).
Regarding Claim 7, Hanatsuka in view Hanatsuka2 teach The method according to claim 6…
Hanatsuka teaches road condition analysis and the feature is expounded upon Hanatsuka2:
wherein the measured data (9) of different time steps are input into an edge filter (23) to obtain filter data (25) in which the noise is amplified against the measured data (9); wherein the noise values (13) are be determined from the filter data (25) (Hanatsuka2 Par. 31-33-“ The signal input-output unit 12 includes an amplifier 12 a that amplifies the output of the acceleration sensor 11 and an A/D converter 12 b that converts an amplified signal into a digital signal. The signal input-output unit 12 is disposed integrally with the acceleration sensor 11. The transmitter 13, which is disposed near a valve 20 v of the tire 20, is provided with an antenna 13 a for transmitting the A/D-converted data of tire vibrations to the arithmetic processing section 10B installed on the vehicle body.; Par. 40; Par. 71-72)
Hanatsuka and Hanatsuka2 are directed to road condition analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Hanatsuka, as taught by Hanatsuka2, by utilizing vibration analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Hanatsuka with the motivation of improving the accuracy in determining a road surface condition (Hanatsuka2 Par. 11).
Regarding Claim 8 – Hanatsuka in view of Hanatsuka2 teach The method according to claim 7…
wherein the noise values (13) are determined by squaring the filter data (25) (Hanatsuka2 Par. 79- Also, in the foregoing embodiment, the power values xk(t) of filtered waves are used for the feature vector X, but the mean value μk and standard deviation σk of the time-varying dispersion of the power values xk(t) of filtered waves may be used instead. The time-varying dispersion can be expressed as log[xk 2(t)+xk-1 2(t)]. In this case, the dimensions of the feature vector x are: “number of frequency bands (6)×number of parameters (2)=12”.”)
Hanatsuka and Hanatsuka2 are directed to road condition analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Hanatsuka, as taught by Hanatsuka2, by utilizing vibration analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Hanatsuka with the motivation of improving the accuracy in determining a road surface condition (Hanatsuka2 Par. 11).
Regarding Claim 10,
The method according to one of the preceding claims, wherein at least one detection one detection value (15) indicating a degree of wetness of a road (3) of the vehicle and/or a risk of aquaplaning for the vehicle (1) is determined to identify the road condition. (Hanatsuka Par. 54- The actual statistical data Mk are the incidence rates SRk of estimated road surface conditions R (R: DRY, WET, SNOW, ICE) at time tk (k=−n1 to −1) calculated for each of the road surface conditions R. The predicted statistical data MpC are the predicted values SRp of incidence rates at a future time tp (p>0) (hereinafter referred to as predicted incidence rates) calculated for each of the road surface conditions R.; Par. 61; Par. 82”).
Regarding Claim 11 – Hanatsuka in view of Hanatsuka2 teach The method according to claim 7…
The method according to one of the preceding claims, wherein the road condition is additionally identified according to a current speed of the vehicle (1). (Hanatsuka2 Par. 14, Par. 85- Four test vehicles A to D each fitted with a tire which had an acceleration sensor installed thereon were operated at speeds of 30 to 90 km/h on each of dry, wet, snowy, and icy roads. The time-series waveforms of tire vibrations were thus obtained, and the road surface conditions were determined, using the road-surface HMMs.”)
Hanatsuka and Hanatsuka2 are directed to road condition analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Hanatsuka, as taught by Hanatsuka2, by utilizing vibration analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Hanatsuka with the motivation of improving the accuracy in determining a road surface condition (Hanatsuka2 Par. 11).
Regarding Claim 12,
The method according to one of the preceding claims, wherein additional statistical values are determined indicating a variance with respect to the measured data (9) and/or the noise values (13); wherein the road condition is additionally identified according to the statistical values. (Hanatsuka Par. 50-51-The road surface condition predicting unit 30 includes a statistical data generating means 31, an actual statistical data storage means 32, a predicted statistical data generating means 33, and a road surface condition predicting means 34. The road surface condition predicting unit 30 predicts road surface conditions at the location Lr within a predetermined range at future time tp (p>0), using the vehicular information obtained at t−n to time t−1.; The statistical data generating means 31 takes out the mr units of data on estimated road surface conditions obtained at the location Lr within the predetermined range at time t(r)k (k=−n1 to −1) before the present time t0 from the data stored in the data storage means 22 of the server 20, counts the numbers of the vehicles having estimated the DRY road surface, WET road surface, SNOW road surface, and ICE road surface, respectively,”).
Regarding Claim 13,
A controller (7) comprising a processor configured to perform the method according to one of the preceding claims. (Hanatsuka Par. 32-33- The server 20 and the road surface condition predicting unit 30 are installed in a road surface condition management center 2. The road surface condition estimating means 13, the vehicular information collecting means 14, and the road surface condition predicting unit 30 are constituted, for instance, by computer software. The acceleration sensor 11 is disposed approximately in the middle”).
Regarding Claim 14,
A computer program comprising instructions which, when the computer program is performed by a processor, cause a processor to carry out the method according to one of the preceding claims. (Hanatsuka Par. 32-33- The server 20 and the road surface condition predicting unit 30 are installed in a road surface condition management center 2. The road surface condition estimating means 13, the vehicular information collecting means 14, and the road surface condition predicting unit 30 are constituted, for instance, by computer software. The acceleration sensor 11 is disposed approximately in the middle”)
Regarding Claim 15,
A computer-readable medium on which the computer program according to claim 14 is stored. (Hanatsuka Par. 42-44- The server 20 receives the vehicular information, including the data on estimated road surface conditions, sent from the respective vehicles W1 (i=1 to N) by its receiver 21, classifies these data into data at the respective times for the location within the predetermined range, stores them in the data storage means 22, and transmits the prediction data on the road surface condition for the location within the predetermined range predicted by the road surface condition predicting unit 30 to the registered vehicles.”)
Claims 4-5 and 9 is rejected under 35 U.S.C. 103 as being unpatentable over Hanatsuka et al., US Publication No. 20170241778A1, [hereinafter Hanatsuka], in view of Hanatsuka et al., US Publication No. 20120330493A1, [hereinafter Hanatsuka2], and in further view of Wilgar et al., US Publication No. 20210134082A1, [hereinafter Wilgar] .
Regarding Claim 4, Hanatsuka in view of Hanatsuka2 teach The method according to one of the preceding claims,…
wherein the measured data (9) and/or measured data (25) on the basis of the measured data (9) are input as input data (19) into a smoothing filter (18) to obtain output data (21) smoothed against the input data (19); wherein a difference is formed between the input data (19) and the output data (21); wherein the noise values (13) are determined from the difference (Wilgar Par. 4- In these embodiments, the tire feature data may include at least one of a radial acceleration profile, peak radial displacement, and a contact patch. Receiving, by the transceiver, one or more parameters from a vehicle control unit may include receiving a wheel speed, and configuring, in dependence upon the one or more parameters, the data processing unit may include configuring, in dependence upon the wheel speed or another vehicle-provided parameter, the accelerometer, the sampling rate of the accelerometer, signal capture parameters, window function parameters of a road strike waveform, a filter frequency band, and/or Fast Fourier Transform (FFT)/Goertzel parameters. In these embodiments, extracting, from the signals, by the data processing unit, the tire feature data may include calculating a contact patch length based on a rotational period of the tire.; Par. 34-The radial acceleration signal is then conditioned to make processing easier by isolating each strike in the acceleration profile, low-pass filtering the waveform, inverting the waveform, and normalizing the waveform for speed, the result of which is shown in FIG. 6.; Par. 46; Par.49-53.”) .
Hanatsuka, Hanatsuka2 and Wilgar are directed to road condition analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Hanatsuka in view of Hanatsuka2, as taught by Wilgar, by utilizing frequency analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Hanatsuka in view of Hanatsuka2 with the motivation of enabling system logic on the vehicle to determine how the TMS should process the signal from the accelerometer (Wilgar Par. 26).
Regarding Claim 5 The method according to claim 4, …
wherein the noise values (13) are determined by squaring the difference. (Hanatsuka2 Par. 79- Also, in the foregoing embodiment, the power values xk(t) of filtered waves are used for the feature vector X, but the mean value μk and standard deviation σk of the time-varying dispersion of the power values xk(t) of filtered waves may be used instead. The time-varying dispersion can be expressed as log[xk 2(t)+xk-1 2(t)]. In this case, the dimensions of the feature vector x are: “number of frequency bands (6)×number of parameters (2)=12”.”)
Hanatsuka and Hanatsuka2 are directed to road condition analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Hanatsuka, as taught by Hanatsuka2, by utilizing vibration analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Hanatsuka with the motivation of improving the accuracy in determining a road surface condition (Hanatsuka2 Par. 11).
Regarding Claim 9 – Hanatsuka in view of Hanatsuka2 teach The method according to claim 7, referring back to claim 4…
wherein the filter data (25) is input into the smoothing filter (18) as the input data (19). (Wilgar Par. 4- In these embodiments, the tire feature data may include at least one of a radial acceleration profile, peak radial displacement, and a contact patch. Receiving, by the transceiver, one or more parameters from a vehicle control unit may include receiving a wheel speed, and configuring, in dependence upon the one or more parameters, the data processing unit may include configuring, in dependence upon the wheel speed or another vehicle-provided parameter, the accelerometer, the sampling rate of the accelerometer, signal capture parameters, window function parameters of a road strike waveform, a filter frequency band, and/or Fast Fourier Transform (FFT)/Goertzel parameters. In these embodiments, extracting, from the signals, by the data processing unit, the tire feature data may include calculating a contact patch length based on a rotational period of the tire.; Par. 34-The radial acceleration signal is then conditioned to make processing easier by isolating each strike in the acceleration profile, low-pass filtering the waveform, inverting the waveform, and normalizing the waveform for speed, the result of which is shown in FIG. 6.; Par. 46; Par.49-53.”) .
Hanatsuka, Hanatsuka2 and Wilgar are directed to road condition analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Hanatsuka in view of Hanatsuka2, as taught by Wilgar, by utilizing frequency analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Hanatsuka in view of Hanatsuka2 with the motivation of enabling system logic on the vehicle to determine how the TMS should process the signal from the accelerometer (Wilgar Par. 26).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Publication No. 20230100827A1 to Zhou et al.- Abstract-“ It is advantageous for a vehicle to detect road wetness or related environmental conditions. This is particularly true for self-driving vehicles, which can then adjust the manner of automated operation of the vehicle to increase safety by reducing speed, braking earlier, adjusting internal estimates of road traction parameters, or adjusting autonomous operation in some other manner. It is difficult to directly measure road wetness (e.g., using spectroscopy or other methods directed at the road surface), however, it is possible to indirectly estimate road wetness based on road noise audio signals detected via one or more microphones disposed on the vehicle. The location of the microphones, the type of post-processing applied to the audio signals, or other factors can be adapted to increase the useful road wetness-related content of such audio signals while reducing the presence of engine noise, road noise, or other confounding signals.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
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Applicants are invited to contact the Office to schedule an in-person interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
Sincerely,
/CHESIREE A WALTON/ Examiner, Art Unit 3624
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