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 .
Response to Amendment
Claims 1-14 are amended.
Claim 15 is new.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a processing unit configured to”, and “a determination space creation unit” in claim 8; “a determination space selection unit” and “a determination unit” in claim 10; “an update unit” in claim 13; “a validity evaluation unit” in claim 14.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-15 are rejected as failing to define the invention in the manner required by 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claims 1-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 and similarly 8 recites “acquiring output waveform data obtained by a plurality of measurements of changes in a physical quantity of a tire of the vehicle for a plurality of different road surface states of the road, and corresponding running conditions of the vehicle when the measurements are made for the output waveform data” (emphasis added) it is not clear how “acquiring output waveform data” is obtained by “corresponding running conditions of the vehicle when the measurements are made for the output waveform data” as it is not clear what the running conditions are being corresponded to or how that obtains output waveform data.
Further, regarding “performing machine learning using the training data group by group,” (emphasis added), it is unclear what is meant by “group by group” as is not clear if “group by group” used in the colloquial sense of one by one / one after the other, and even so the only “group” described in the claim are the plurality of groups from classifying the waveform data not training data, there is no grouped training data as per the claim “generating training data by associating the output waveform data with road surface information indicating a corresponding road surface state”, and there is no link between the training data and the plurality of groups from classifying the waveform data. This also similarly applies to claims 4 and 8-9.
Claim 4 and similarly 9 recites “an important range”, “changes more greatly”, and “an important part” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Thus it is not clear what “determining, for each group, an important range of the output waveform data in which the output waveform data changes more greatly depending on the road surface state than other ranges” is describing. It is also not clear what “other ranges” refers to.
Further claim 4 recites “wherein the performing machine learning using the first training data” however there is no previous recitation of the performing machine learning using the first training data, examiner suggests “performing machine learning using the first training data” to be consistent with the rest of the claim or changing both to ‘wherein the performing machine learning uses the first training data’, with “second training data” in the following instance.
Claim 6 depends from claim 1 yet recites “A method for determining a road surface state from output waveform data obtained by a measurement of changes in a physical quantity of a tire of a vehicle running on a road, using the plurality of determination spaces created by the method according to claim 1” which are already recited in claim 1 thus making it unclear if these are new elements and which is being referred to.
Regarding claim 8 where applicant acts as his or her own lexicographer to specifically define a term of a claim contrary to its ordinary meaning, the written description must clearly redefine the claim term and set forth the uncommon definition so as to put one reasonably skilled in the art on notice that the applicant intended to so redefine that claim term. Process Control Corp. v. HydReclaim Corp., 190 F.3d 1350, 1357, 52 USPQ2d 1029, 1033 (Fed. Cir. 1999). The term “a memory ” is used by the claim to “a acquiring data,” while the accepted usage is “storing data.” The term is indefinite because the specification does not clearly redefine the term.
Claim 10 depends from claim 1 yet recites “determining a road surface state from output waveform data obtained by measuring changes in a physical quantity of a tire of a vehicle running on a road, using the plurality of determination spaces crated by the method according to claim 1” and “a running condition of the vehicle” which are already recited in claim 1 thus making it unclear if these are new elements and which is being referred to.
In claims 10-15 limitations “a determination unit” in claim 10; “an update unit” in claim 13; “a validity evaluation unit” have been evaluated under the three-prong test set forth in MPEP § 2181, subsection I, but the result is inconclusive. Thus, it is unclear whether this limitation should be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because while the specification describes “determination space creation unit 15 is implemented as hardware or software” no description is given of the a determination unit”, “an update unit”, “a validity evaluation unit” leaving them as a black box merely described by the function they perfrom. The boundaries of this claim limitation are ambiguous; therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
In response to this rejection, applicant must clarify whether this limitation should be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Mere assertion regarding applicant’s intent to invoke or not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph is insufficient. Applicant may:
(a) Amend the claim to clearly invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, by reciting “means” or a generic placeholder for means, or by reciting “step.” The “means,” generic placeholder, or “step” must be modified by functional language, and must not be modified by sufficient structure, material, or acts for performing the claimed function;
(b) Present a sufficient showing that 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, should apply because the claim limitation recites a function to be performed and does not recite sufficient structure, material, or acts to perform that function;
(c) Amend the claim to clearly avoid invoking 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, by deleting the function or by reciting sufficient structure, material or acts to perform the recited function; or
(d) Present a sufficient showing that 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, does not apply because the limitation does not recite a function or does recite a function along with sufficient structure, material or acts to perform that function.
Claims 2-7 and 9-15 are rejected based on their inherited deficiencies.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under step 1, claims 1-15 belong to a statutory category.
Under Step 2A prong 1, the claims as a whole are identified as being directed to a judicial exception as claim 1 and similarly 8 recite(s) “a method for creating determination spaces for determining a road surface state of a road on which a vehicle is running”, “corresponding running conditions of the vehicle when the measurements are made for the output waveform data”, “classifying the output waveform data into a plurality of groups based on the corresponding running conditions; generating training data by associating the output waveform data with road surface information indicating a corresponding road surface state;” and “thereby creating a plurality of determination spaces associated with the corresponding running conditions” which are directed to mathematical concepts and/or mental processes based on applicant’s specification for examples see Par. 26, 28-29, 47, 57 “calculate(d)”.
Under Step 2A prong 2, evaluating whether the claim as a whole integrates the exception into a practical application of that exception, the judicial exception is not integrated into a practical application because “the method comprising: acquiring output waveform data obtained by a plurality of measurements of changes in a physical quantity of a tire of the vehicle for a plurality of different road surface states of the road,” are considered to be data gathering steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity. The elements of “an apparatus for”, “the apparatus comprising: a memory for”, “a processing unit configured to”, and “a determination space creation unit” are considered to be generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. The additional element(s) of “performing machine learning using the training data group by group,” are using generic AI/ML technology to perform data evaluations or calculations, as identified under Prong 1 above. The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general-purpose computer. See MPEP 2106.05(f). Additionally, the use of the “performing machine learning using the training data group by group,” merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of “performing machine learning using the training data group by group,” to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2.
Under Step 2B, evaluating additional elements to determine whether they amount to an inventive concept both individually and in combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because “the method comprising: acquiring output waveform data obtained by a plurality of measurements of changes in a physical quantity of a tire of the vehicle for a plurality of different road surface states of the road,” are considered to be adding insignificant extra-solution activity to the judicial exception per MPEP 2106.05(g) (ii) and are well-understood, routine, conventional activities/elements previously known to the industry per MPEP 2106.05(d)(I and see prior art of record). The elements of “an apparatus for”, “the apparatus comprising: a memory for”, “a processing unit configured to”, and “a determination space creation unit” are considered to be well-understood, routine, conventional activities/elements previously known to the industry per MPEP 2106.05(d). The elements of “performing machine learning using the training data group by group,” are considered to be nothing more than mere instructions to implement the abstract idea on a general purpose computer per MPEP 2106.05(f), merely indicates a field of use or technological environment in which the judicial exception is performed per MPEP 2106.05(h), and are well-understood, routine, conventional activities/elements previously known to the industry per MPEP 2106.05(d).
Claims 2-3, 6-7, 11, further describe the abstract ideas cited above.
In claim 4 and similarly 9, “wherein the training data includes first training data and second training data”, “determining, for each group, an important range of the output waveform data in which the output waveform data changes more greatly depending on the road surface state than other ranges; extracting an important part of the output waveform data corresponding to in the important ranges; generating, for each group, second training data by associating the important part of the output waveform data with the road surface information;” and “thereby creating the plurality of determination spaces associated with the corresponding running conditions” further describe the abstract ideas cited above.
The judicial exception is not integrated into a practical application and does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because “wherein the performing machine learning using the first training data in which the output waveform data is associated with the road surface information group by group” and “performing, machine learning using the second training data group by group,” are using generic AI/ML technology to perform data evaluations or calculations, as identified under Prong 1 above. The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general-purpose computer and merely indicates a field of use or technological environment in which the judicial exception is performed. Performing steps that are otherwise abstract does not integrate the abstract idea into a practical application. Nor are they sufficient to amount to significantly more than the judicial exception because they are nothing more than mere instructions to implement the abstract idea on a general purpose computer per MPEP 2106.05(f), merely indicates a field of use or technological environment in which the judicial exception is performed per MPEP 2106.05(h), and are well-understood, routine, conventional activities/elements previously known to the industry per MPEP 2106.05(d).
Claim 5 and 12 further describe the abstract ideas cited above and are not integrated into a practical application and does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because “wherein the output waveform data is an output of a piezoelectric sensor attached to an inside surface of the tire” are considered to be data gathering steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity and are considered to be adding insignificant extra-solution activity to the judicial exception per MPEP 2106.05(g) (ii) and are well-understood, routine, conventional activities/elements previously known to the industry per MPEP 2106.05(d)(I and see prior art of record).
Claim 10 further describes the abstract ideas cited above. The additional elements of “An apparatus for”, “a storage unit storing the plurality of determination spaces”, “a determination space selection unit configured to” and “a determination unit” are not integrated into a practical application and does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are considered to be generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer and are well-understood, routine, conventional activities/elements previously known to the industry per MPEP 2106.05(d). The element “a measurement device” is considered to be data gathering steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity and are considered to be adding insignificant extra-solution activity to the judicial exception per MPEP 2106.05(g) (ii) and are well-understood, routine, conventional activities/elements previously known to the industry per MPEP 2106.05(d)(I and see prior art of record).
Claim 13 and 14 further describe the abstract ideas cited above. The additional elements of “an update unit” and “a validity evaluation unit” are not integrated into a practical application and does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are considered to be generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer and are well-understood, routine, conventional activities/elements previously known to the industry per MPEP 2106.05(d).
Claim 15 further describes the abstract ideas cited above. The additional elements of “perform machine learning using the updated training data” are not integrated into a practical application and does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are considered to be are using generic AI/ML technology to perform data evaluations or calculations, as identified under Prong 1 above. The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general-purpose computer and merely indicates a field of use or technological environment in which the judicial exception is performed. Performing steps that are otherwise abstract does not integrate the abstract idea into a practical application. Nor are they sufficient to amount to significantly more than the judicial exception because they are nothing more than mere instructions to implement the abstract idea on a general-purpose computer per MPEP 2106.05(f), merely indicates a field of use or technological environment in which the judicial exception is performed per MPEP 2106.05(h), and are well-understood, routine, conventional activities/elements previously known to the industry per MPEP 2106.05(d).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-4, 6-11 and 13-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by (HANATSUKA US 20200380185 A1).
In claim 1, HANATSUKA discloses a method for creating determination spaces for determining a road surface state of a road (See abstract “state of the road surface”) on which a vehicle (See abstract “vehicle”) is running, the method comprising: acquiring output waveform data (Par. 2 “time series waveform”) obtained by a plurality of measurements (Par. 2 “detected by an acceleration” See Fig. 3) of changes in a physical quantity (Par. 2 “Vibration”) of a tire (Par. 2 “tire”) of the vehicle for a plurality of different road surface states (Par. 2 “a plurality of road surface states”) of the road, and corresponding running conditions (Par. 8 “braking/driving force”) of the vehicle when the measurements are made for the output waveform data (Par. 2 and 8 “time window”); classifying the output waveform data into a plurality of groups based on the corresponding running conditions (Par. 84 “dry road” “snow road”); generating training data by associating the output waveform data with road surface information indicating a corresponding road surface state (abstract “road surface models are constructed with learning data comprising time series waveform data of tire vibration obtained by causing a vehicle mounted with a tire provided with an acceleration sensor to travel on road surfaces in multiple road surface states”); and performing, machine learning using the training data group by group (Par. 91 “learning by a support vector machine” also see Fig. 7 S15-17 repeated), thereby creating a plurality of determination spaces associated with the space corresponding running conditions (Par. 37 ”constituted for respective road surface states” “road surface HMM”).
In claim 2, HANATSUKA further discloses normalizing the output waveform data for each group, wherein the generating the training data includes associating the normalized output waveform data with the road surface information (Par. 50-54 “Gauss distribution”).
In claim 3, HANATSUKA further discloses herein the running conditions include at least one of a running speed of the vehicle and a wear level of the tire (Par. 29 “vehicle body speed” Par. 30 “state of the tire”).
In claim 4, HANATSUKA further discloses wherein the training data includes first training data and second training data (Par. 39 “learning of dividing the tire vibration into five states”), and wherein the machine learning includes performing machine learning using the first training data in which the output waveform data is associated with the road surface information group by group (Par. 91 “learning by a support vector machine”), and determining, for each group, an important range of the output waveform data in which the output waveform data changes more greatly depending on the road surface state than other ranges (Par. 43-44 “range of breaking/driving force”); extracting an important part of the output waveform data corresponding to in the important ranges (Par. 43-44 “force J applied to the tire”); generating, for each group, second training data by associating the important part of the output waveform data with the road surface information (Par. 39 “learning of dividing the tire vibration into five states”); and performing, machine learning using the second training data group by group, thereby creating the plurality of determination spaces associated with the corresponding running conditions (Par. 37 ”constituted for respective road surface states” “road surface HMM”).
In claim 6, HANATSUKA further discloses obtaining determination target data, which is the output waveform data for determining the road surface state (Par. 8), and a determination running condition, which is a running condition of the vehicle when the measurement is made for of the determination target data (Par. 8 “braking/driving force”); selecting, from among the plurality of determination spaces, all applicable determination spaces which are associate with the running conditions including the determination running condition and applying the applicable determination spaces to the determination target data (Fig. 5, also see Par. 84); and determining the road surface state of the road according to the road surface information associated with the determination target data in the applicable determination spaces (Par. 84).
In claim 7, HANATSUKA discloses all of claim 6. HANATSUKA further discloses creating new training data by associating the determination target data with the road surface state determined by applying the applicable determination spaces thereto (Par. 44); and updating the applicable determination spaces using the new training (Par. 44).
In claim 8, HANATSUKA discloses an apparatus (Fig. 1) for creating determination spaces for determining a road surface state of a road (See abstract “state of the road surface”) on which a vehicle is running (See abstract “vehicle”), the apparatus comprising: a memory (Fig. 1, 17) for acquiring output waveform data (Par. 2 “time series waveform”) obtained by a plurality of measurements (Par. 2 “detected by an acceleration” See Fig. 3) of changes in a physical quantity (Par. 2 “Vibration”) of a tire (Par. 2 “tire”) of the vehicle for a plurality of different road surface states of the road states (Par. 2 “a plurality of road surface states”), and corresponding running conditions (Par. 8 “braking/driving force”) of the vehicle when the measurements were made for output waveform data (Par. 2 and 8 “time window”); a processing unit (Fig. 1, Fig. 1, 16) configured to classify the output waveform data into a plurality of groups based on the corresponding running conditions (Par. 84 “dry road” “snow road”) to generate training data by associating the output waveform data with road surface information indicating a corresponding road surface state (abstract “road surface models are constructed with learning data comprising time series waveform data of tire vibration obtained by causing a vehicle mounted with a tire provided with an acceleration sensor to travel on road surfaces in multiple road surface states”); and a determination space creation unit (Fig. 1, 18) configured to perform machine learning using the training data group by group (Par. 91 “learning by a support vector machine” also see Fig. 7 S15-17 repeated), thereby creating a plurality of determination spaces associated with the corresponding running conditions (Par. 37 ”constituted for respective road surface states” “road surface HMM”).
In claim 9, HANATSUKA further discloses wherein the training data includes first training data and second training data (Par. 39 “learning of dividing the tire vibration into five states”), and wherein the machine learning includes performing machine learning using the first training data in which the output waveform data is associated with the road surface information group by group (Par. 91 “learning by a support vector machine”), and determining, for each group, an important range of the output waveform data in which the output waveform data changes more greatly depending on the road surface state than other ranges (Par. 43-44 “range of breaking/driving force”); extracting an important part of the output waveform data corresponding to in the important ranges (Par. 43-44 “force J applied to the tire”); generating, for each group, second training data by associating the important part of the output waveform data with the road surface information (Par. 39 “learning of dividing the tire vibration into five states”); and performing, machine learning using the second training data group by group, thereby creating the plurality of determination spaces associated with the corresponding running conditions (Par. 37 ”constituted for respective road surface states” “road surface HMM”).
In claim 10, HANATSUKA discloses all of claim 1. HANATSUKA further discloses the additional elements of an apparatus (Fig. 1); a measurement device (Fig. 1, 11); a storage unit (Fig. 1, 17); a determination space selection unit (Fig. 1, HMM); a determination unit (Fig. 1, 19).
In claim 11, HANATSUKA discloses all of claim 10. HANATSUKA further discloses wherein the determination unit is configured to obtain the running condition from vehicle equipment or to calculate the running condition from the output waveform data received from the measurement device (Par. 8).
In claim 13, HANATSUKA discloses all of claim 10. HANATSUKA further discloses an update unit (Fig. 1, 18) for updating the determination spaces stored in the storage unit, wherein the update unit is configured to: create new training data by associating the determination target data with road surface information corresponding to of the road surface state determined by the determination unit (Par. 44); and update the applicable determination spaces using the new training data (Par. 44).
In claim 14, HANATSUKA discloses all of claim 13. HANATSUKA further discloses a validity evaluation unit (Fig. 1, 18 and Par. 72 “correcting the influence”) configured to obtain information indicating whether the road surface state determined by the determination unit is valid wherein the update unit is further configured to create the new training data (Par. 72), only if the road surface state determined by the determination unit is valid, by associating the road surface state with the determination target data as the road surface information (Par. 72-73).
In claim 15, HANATSUKA discloses all of claim 13. HANATSUKA further discloses wherein the update unit is further configured to: associate the new training data with the determination running condition (Par. 43-44); update a set of the training data used to generate the applicable determination spaces by adding the new training data thereto, thereby generating the updated training data for the running condition corresponding to the determination running condition (Par. 44); and perform machine learning using the updated training data, thereby creating updated determination spaces corresponding to the applicable determination spaces (Par. 91 “learning by a support vector machine” also see Fig. 7 S15-17 repeated).
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.
Claim(s) 5 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over HANATSUKA in view of WAKAO (JP 2010274906 A) see translation attached.
In claim 5, HANATSUKA further discloses wherein the output waveform data is an output of an acceleration sensor (Par. 26) attached to an inside surface of the tire (see Fig. 2, 11).
HANATSUKA does not explicitly disclose that the acceleration sensor is a piezoelectric sensor as claimed.
WAKAO teaches examples of the acceleration sensor include a piezoelectric acceleration sensor (Page 3 “a piezoelectric acceleration sensor”).
It would have been obvious to one of ordinary skill in the art before the invention was filed to have a piezoelectric sensor as taught by WAKAO for the acceleration sensor of HANATSUKA because WAKAO shows that a piezoelectric sensor is an equivalent structure known in the art. Therefore, because these two sensors were art-recognized equivalents before the invention was filed, one of ordinary skill in the art would have found it obvious to substitute a piezoelectric acceleration sensor for an acceleration sensor.
In claim 12, HANATSUKA discloses all of claim 10. HANATSUKA further discloses wherein the running condition includes a running speed of the vehicle and a wear level of the tire at the time of the measurement (Par. 29 “vehicle body speed” Par. 30 “state of the tire”), wherein the measurement device includes is an acceleration sensor (par. 26) attached to an inside surface of the tire (see Fig. 2, 11) and outputs time-series data of a deformation speed of the tire as the output waveform data (Par. 30 “state of the tire”), and wherein the determination unit is further configured to calculate the running speed of the vehicle from periodicity of the output waveform data and the wear level of the tire from an output value of the output waveform data (Par. 29-30).
HANATSUKA does not explicitly disclose that the acceleration sensor is a piezoelectric sensor as claimed.
WAKAO teaches examples of the acceleration sensor include a piezoelectric acceleration sensor (Page 3 “a piezoelectric acceleration sensor”).
It would have been obvious to one of ordinary skill in the art before the invention was filed to have a piezoelectric sensor as taught by WAKAO for the acceleration sensor of HANATSUKA because WAKAO shows that a piezoelectric sensor is an equivalent structure known in the art. Therefore, because these two sensors were art-recognized equivalents before the invention was filed, one of ordinary skill in the art would have found it obvious to substitute a piezoelectric acceleration sensor for an acceleration sensor.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20200346655 A1, US 6704636 B2
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON J BECKER whose telephone number is (571)431-0689. The examiner can normally be reached M-F 9:30-5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby Turner can be reached at (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/B.J.B/ Examiner, Art Unit 2857
/SHELBY A TURNER/ Supervisory Patent Examiner, Art Unit 2857
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