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 .
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.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/08/2025 has been entered.
Response to Amendment
This non-final action is in response to amendment filed on 12/08/2025.
Claims 1, 3, 12, 14 are amended.
Claims 4-11, 15-20 are previously presented.
Claims 2, 13 are canceled.
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, 3-12, 14-22 are rejected under 35 U.S.C. 101 because the claimed inventions are directed to a judicial exception without significantly more, as determined by the Subject Matter Eligibility Test detailed below.
Step 1
Step 1 of the Subject Matter Eligibility Test entails considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter.
Independent claims -1 and 12 are directed towards a system and a method, respectively. Therefore, each of the independent claims 1 and 12, and the corresponding dependent claims 3-11 and 14-22 are directed to a statutory category of invention under step 1.
Step 2A, Prong 1
If the claim recites a statutory category of invention, the claim requires further analysis in Step
2A. Step 2A of the Subject Matter Eligibility Test is a two-prong inquiry. In Prong 1, examiners evaluate whether the claim recites a judicial exception.
Regarding Prong 1, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 recites abstract limitations, including those shown in bold below.
A system comprising:
a controller programmed to:
collect a vehicle noise through a device;
convert the vehicle noise to a spectrogram;
obtain a fingerprint of the vehicle noise including one or more features of the spectrogram by extracting one or more spectrogram peaks from the spectrogram, wherein the fingerprint comprises the spectrogram peaks, peak frequencies of the spectrogram peaks, and time difference between the peak frequencies;
compare the fingerprint of the vehicle noise and predetermined fingerprints associated with different classifications, wherein each predetermined fingerprint comprises predetermined peak frequencies, predetermined wavelengths, or both, associated with a predetermined vehicle noise;
identify, using a trained machine learning model, a classification of the vehicle noise based on the comparison of the fingerprint of the vehicle noise and the predetermined fingerprints, wherein the classification comprises probabilities of two or more types of the vehicle noise, and the machine learning model is trained using a random forest classifier based on sample vehicle noises and sounds of normal vehicle, wherein each sample vehicle noise corresponds to a single type of vehicle noise, to generate the classification comprising the probabilities of two or more types of the vehicle noises in response to determining that the fingerprint of the vehicle noise is not similar to the predetermined fingerprints, and wherein the predetermined fingerprints correspond to at least one type of vehicle noise different from the sounds of normal vehicle;
determine one or more issues of one or more vehicle parts based on the classification;
display, via a user interface, the issues of the one or more vehicle parts associated with the classification; and
schedule an appointment for vehicle maintenance for the one or more issues of the one or more vehicle parts.
These limitations, as drafted, describe a system that, under its broadest reasonable interpretation, covers performance of the limitations in the mind, or by a human using pen and paper, and therefore recites mental processes. For example, “compare the fingerprint of the vehicle noise and predetermined fingerprints associated with different classifications, wherein each predetermined fingerprint comprises predetermined peak frequencies, predetermined wavelengths, or both, associated with a predetermined vehicle noise” may be interpreted as a mental process of considering the similarities and differences between multiple datasets, similar to playing a “find the differences”-type puzzle game. Additionally, “identify a classification of the vehicle noise based on the comparison of the fingerprint of the vehicle noise and the predetermined fingerprints, wherein the classification comprises probabilities of two or more types of the vehicle noise” may be interpreted as a mental determination made according to observable data, such as observing a squeaky noise and then determining that the observed noise may be due to brakes or a fault in the steering system, based on historical knowledge of vehicle circumstances that may contribute to squeaky sounds. The trained machine learning recited in this limitation will be addressed under step 2A, prong 2. Additionally, “determine one or more issues of one or more vehicle parts based on the classification” may be interpreted as a mental process of determining, in one’s mind, a problem with the brakes or the steering system, after mentally determining those possibly classifications of a vehicle noise. Additionally, “schedule an appointment for vehicle maintenance for the one or more issues of the one or more vehicle parts” may be interpreted as a mental process of determining a time that one is available for a vehicle maintenance appointment, and mentally noting or writing down the scheduled time of the appointment. Thus, the claim recites an abstract idea.
Claim 12 recites abstract limitations analogous to those identified above with respect to claim 1, and therefore recites abstract ideas per the same analysis.
Step 2A, Prong 2
If the claim recites a judicial exception in Step 2A, Prong 1, the claim requires further analysis in Step 2A, Prong 2. In Step 2A, Prong 2, examiners evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
Regarding Prong 2, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in MPEP § 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra-solution activity, or generally linking the use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”.
Claim 1 recites additional elements including those underlined below.
A system comprising:
a controller programmed to:
collect a vehicle noise through a device;
convert the vehicle noise to a spectrogram;
obtain a fingerprint of the vehicle noise including one or more features of the spectrogram by extracting one or more spectrogram peaks from the spectrogram, wherein the fingerprint comprises the spectrogram peaks, peak frequencies of the spectrogram peaks, and time difference between the peak frequencies;
compare the fingerprint of the vehicle noise and predetermined fingerprints associated with different classifications, wherein each predetermined fingerprint comprises predetermined peak frequencies, predetermined wavelengths, or both, associated with a predetermined vehicle noise;
identify, using a trained machine learning model, a classification of the vehicle noise based on the comparison of the fingerprint of the vehicle noise and the predetermined fingerprints, wherein the classification comprises probabilities of two or more types of the vehicle noise, and the machine learning model is trained using a random forest classifier based on sample vehicle noises and sounds of normal vehicle, wherein each sample vehicle noise corresponds to a single type of vehicle noise, to generate the classification comprising the probabilities of two or more types of the vehicle noises in response to determining that the fingerprint of the vehicle noise is not similar to the predetermined fingerprints, and wherein the predetermined fingerprints correspond to at least one type of vehicle noise different from the sounds of normal vehicle;
determine one or more issues of one or more vehicle parts based on the classification;
display, via a user interface, the issues of the one or more vehicle parts associated with the classification; and
schedule an appointment for vehicle maintenance for the one or more issues of the one or more vehicle parts.
The recitation of collect a vehicle noise and obtain a fingerprint of the vehicle noise including one or more features of the spectrogram by extracting one or more spectrogram peaks from the spectrogram, wherein the fingerprint comprises the spectrogram peaks, peak frequencies of the spectrogram peaks, and time difference between the peak frequencies amount to mere data receiving, which is a form of insignificant extra-solution activity. The recitation of display, via a user interface, the issues of the one or more vehicle parts associated with the classification amounts to sending or displaying information, which is a form of insignificant extra-solution activity. The recitations of a controller, a device, convert the vehicle noise to a spectrogram, and using a trained machine learning model amount to mere instructions to implement an abstract idea or other exception on a computer. The recitation of the machine learning model is trained using a random forest classifier based on sample vehicle noises and sounds of normal vehicle, wherein each sample vehicle noise corresponds to a single type of vehicle noise, to generate the classification comprising the probabilities of two or more types of the vehicle noises in response to determining that the fingerprint of the vehicle noise is not similar to the predetermined fingerprints, and wherein the predetermined fingerprints correspond to at least one type of vehicle noise different from the sounds of normal vehicle is recited at a high level of generality and amounts to confining the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B
If the additional elements do not integrate the exception into a practical application in step 2A Prong 2, then the claim is directed to the recited judicial exception, and requires further analysis under Step 2B to determine whether it provides an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
As discussed above, the additional elements of a controller, a device, convert the vehicle noise to a spectrogram, and using a trained machine learning model amount to mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
As discussed above, the machine learning model is trained using a random forest classifier based on sample vehicle noises and sounds of normal vehicle, wherein each sample vehicle noise corresponds to a single type of vehicle noise, to generate the classification comprising the probabilities of two or more types of the vehicle noises in response to determining that the fingerprint of the vehicle noise is not similar to the predetermined fingerprints, and wherein the predetermined fingerprints correspond to at least one type of vehicle noise different from the sounds of normal vehicle amounts to merely indicating a field of use or technological environment in which to apply a judicial exception, which does not amount to significantly more than the exception itself (see MPEP § 2106.05(h)).
As discussed above, collect a vehicle noise and obtain a fingerprint of the vehicle noise including one or more features of the spectrogram by extracting one or more spectrogram peaks from the spectrogram, wherein the fingerprint comprises the spectrogram peaks, peak frequencies of the spectrogram peaks, and time difference between the peak frequencies amount to insignificant extra-solution activity. MPEP § 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
As discussed above, display, via a user interface, the issues of the one or more vehicle parts associated with the classification amounts to insignificant extra-solution activity. MPEP 2106.05(d)(II), and the cases cited therein, including in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data (i.e., notifying an individual of changes to the at least one route) is a well understood, routine, and conventional function. Note that, even if the notification was displayed on a display device, this display would be considered insignificant extra-solution activity.
Thus, even when viewed as an ordered combination, nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea.
Dependent claims 3-11 and 14-20 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the various limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine, and conventional additional elements that do not integrate the judicial exception into a practical application (i.e., further characterizing the data acquisition steps, and displaying information – another form of insignificant extra-solution activity). Therefore, dependent claims 3-11 and 14-20 are not patent eligible under the same rationale as provided for in the rejection of independent claims 1 and 12.
Response to Arguments
Applicant's arguments filed 05/01/2026 have been fully considered.
Regarding the arguments provided for the 35 U.S.C. §101 rejection of claims 1, 3-12, 14-20 (from remarks pages 7-12), the applicant's arguments have been considered but are not persuasive.
(A) applicant argues, "Similarly, claim 1 recites… Thus, Applicant respectfully submits that amended independent claims 1 and 12 integrate the alleged judicial exception into a practical application to impose a meaningful limitation and should be held eligible in Prong Two of Revised Step 2A."
As to point (A), Examiner respectfully disagrees. The Appeals Review Panel decision in Ex Parte Desjardins relies upon the disclosure of the technological improvements to the machine learning model identified in the specification of the invention application. Examiner notes that the instant application specification, as much as can be reasonably searched with relevant keywords, appears not to contain a description of how the claimed invention’s use of single-type vehicle noise sample training data provides improvement to computational requirements, CPU usage, memory capacity, or storage demands. Examiner notes that the claimed invention, when viewed as a whole, comprises judicial exceptions not integrated into practical application. Options for integrating the identified judicial exceptions into practical application may include actuating vehicle controls in response to the abstract processing occurring in the system (e.g., the vehicle autonomously driving itself to the scheduled appointment). However, the invention as currently amended does not provide the necessary inventive concept or practical application needed to withdraw the rejection under 35 U.S.C. § 101.
Regarding the arguments provided for the 35 U.S.C. § 103 rejections of claims 1, 3-12, 14-20, the applicant’s arguments have been fully considered and are persuasive. The 35 U.S.C. § 103 rejection of claims 1, 3-12, 14-20 has been withdrawn.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20200234517 A1 CAMPANELLA; CHARLIE et al. discloses capturing and processing vehicle engine audio data to detect engine conditions using trained models.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELLE ROSE KNUDSON whose telephone number is (703)756-1742. The examiner can normally be reached 1000-1700 ET M-F.
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/ELLE ROSE KNUDSON/Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
8/11/26
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