DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 18 is objected to because of the following informalities: Claim 18 is dependent on itself. Appropriate correction is required.
ART REJECTION:
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 (i.e., changing from AIA to pre-AIA ) 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1,4-10, and 13-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sekine et al(USPat 5,586,028)) in view of Yuan et al(USPGPUB 2024/0218911 A1).
-- In considering claim 1, the claimed subject matter that is met by Sekine et al(Sekine) includes:
1) one or more sensors including a microphone, configured to collect a temporal sequence of sensor data corresponding to vibrations recorded by the microphone is met by the road noise detecting means(1), which is embodied by a microphone(1), the microphone being on a wheel house(11) with a diaphragm, the wheel house being physically connected to a tire(12)(see: Sekine, column 11, lines 1-25);
2) a neural network circuit coupled to the sensor, the neural network circuit configured to receive the sensor data and generate one or more output data items based on analysis of the sensor data, the one or more output data items indicating a condition of a road, the vehicle, and/or a component of the vehicle is met by the Neural network model, which has a three layer structure, as seen in figure 8 of Sekine, and executed by CPU and memory, not shown(see: Sekine, column 12, lines 36-51).
- Sekine does not teach:
1) the sensors including a microphone, physically coupled to a tire of a vehicle.
Although not specifically taught by Sekine, use of microphones physically connected to tires, for the purpose of detecting a road condition, is well known. In related art, Yuan et al(Yuan) teaches a wear detection system, wherein a microphone unit(216) is mounted inside tires of a vehicle, and disposed to collect sound signals from the road to detect a road condition(see: Yuan, sec[0028]).
Since the use of microphones which are physically coupled to a tire of a vehicle is well known, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the microphone unit(216) of Yuan, into the system of Sekine, and mounted inside the tires of the vehicle, since this would have provided a means of providing the most accurate road condition detecting scenario, because the microphones would be directly in the tires, thereby providing a closer proximity to recording vibrations and sounds based on road conditions.
-- With regards to claim 4,
1) the neural network circuit includes an analog hardware circuit is met by determination block(4), comprising an analog neural network(see: Sekine, column 10, lines 33-48).
-- With regards to claim 5, Sekine does not teach:
1) the neural network circuit is configured to implement a convolutional neural network(CNN), a recurrent neural network(RNN), and transformer, and/or an autoencoder.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to substitute a CNN, RNN, transformer, or autoencoder into the neural network of Sekine, since they are art related equivalents that one of ordinary skill would have recognized the most advantageous Neural Network to apply in the system.
-- With regards to claim 6,
1) the analysis of the sensor data, by the neural network circuit includes:
i) processing the sensor data to identify frequency components within the sensor data is met by the frequency analysis means extracting sound pressure levels of predetermined frequency components of the road noise such that the road noise is detected by the road noise detecting means(see: Sekine, column 5, lines 50-54);
ii) identifying a condition of the road based on the identified frequency components in the sensor data is met by the road noise detected by the road noise detecting means(see: Sekine, column 5, lines 50-54).
-- With regards to claim 7,
1) the neural network circuit is configured to identify a change in a condition of the road based on analysis of the identified frequency components in the sensor data and in response to the identification that the condition of the road has changed from a first road condition to a second road condition, transmitting an alert indicating the change in the condition of the road, wherein the second road condition is different from the first road condition is met by the frequency analysis inputting normalized sound pressure levels to a neural network, and for determining the condition of the road surface, based on an output from the neural network, wherein the frequency components are predetermined so as to determine different level ranges(see: Sekine, column 5, lines 50-65).
-- With regards to claim 8,
1) the identified frequency components include frequency components within a frequency range between 10 Hz- 10 kHz would have been readily set by the setting means which sets in advance, upper and lower limit values of the range(see: column 5 lines 55-65). Therefore, it would have constituted matter of design choice to one of ordinary skill before the effective filing date of the claimed invention, to set the limits at 10 Hz and 10 kHz, or any other upper and lower range limit, as desired.
-- Claim 9 recites a method that substantially corresponds to the subject matter of claim 1, and therefore, is met for the reasons as discussed in the rejection of claim 1 above, as well as:
1) transmitting the temporal sequence of sensor data to a neural network circuit that is coupled to the one or more sensors is met by the frequency analysis device(22) analyzing the frequency of time series data of the road noise detected by the microphone(21)(see: Sekine, column 13, lines 58 et seq; column 14 lines 1-13).
-- Claim 10 depends from claim 9, and recites essentially the same subject matter as that of claim 4. Therefore, claim 10 is met for the reasons as discussed in the rejection of claims 4 and 9 above.
-- Claim 13 depends from claim 9, and recites essentially the same subject matter as that of claim 5. Therefore, claim 13 is met for the reasons as discussed in the rejection of claims 5 and 9 above.
-- Claim 14 depends from claim 9, and recites essentially the same subject matter as that of claim 6. Therefore, claim 14 is met for the reasons as discussed in the rejection of claims 6 and 9 above.
-- Claim 15 depends from claim 14, and recites essentially the same subject matter as that of claim 7. Therefore, claim 15 is met for the reasons as discussed in the rejection of claims 7 and 14 above.
-- Claim 16 depends from claim 14, and recites essentially the same subject matter as that of claim 8. Therefore, claim 16 is met for the reasons as discussed in the rejection of claims 8 and 14 above.
-- Claim 17 recites a method that substantially corresponds to the subject matter of claim 1. Therefore, claim 17 is met for the reasons as discussed in the rejection of claim 1 above, as well as:
Although the claimed converting the data samples into a plurality of parallel data items and providing those data items as a plurality of inputs to a neural network is not specifically disclosed, it would have been met by the analog neural network inherently inputting parallel data samples in the system of Sekine, which is common for analog neural network systems.
-- Claim 18 recites essentially the same subject matter as that of claim 5, and therefore is met for the reasons as discussed in the rejection of claim 5 above.
-- Claim 19 depends from claim 18, and recites essentially the same subject matter as that of claim 4. Therefore, claim 19 is met for the reasons as discussed in the rejection of claims 4 and 18 above.
Allowable Subject Matter
Claims 2,3,11, and 12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
The primary reasons for indicating allowable subject matter in claims 2-3, and 11-12, is because the prior art failed to teach or suggest the use of microphone, including a diaphragm that is deformable in a particular direction, being physically mounted to a tire such that a change in aire pressure causes the microphone to record vibrations corresponding to the deformation of the diaphragm. As well, the prior art fails to teach or suggest the microphone located within a environmentally isolated are chamber, interior to a tire, such that the microphone is isolated from changes in air pressure
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
In related prior art, Otabe et al(USPat 5,765,119) teaches a devices for estimating hydroplaning, occurrence for a vehicle, wherein microphones are utilized to detect road noise, so as to determine the road surface condition so as to prepare the vehicle for countermeasures such as ant-lockt braking and power steering(see: Otabe, column 3, lines 62 et seq; column 4 lines 1-15).
Pal et al(USPat 5,434,783) teaches a road nots determining system, wherein a microphone and piezoelectric element detect noise and vibration state based on condition of the roadway(see: Pal, column 3, lines 52-64).
Castelaz et al(USPat 5,003,490) teaches the use of a neural network signal processor processes waveforms for determining road noise in a vehicle system.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DARYL C POPE whose telephone number is (571)272-2959. The examiner can normally be reached 9AM - 5PM M-F.
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/DARYL C POPE/Primary Examiner, Art Unit 2686