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 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, 11 and 16 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.
In Claims 1 and 11, it is unclear what “target data” is referred. Does it refer to a “target time point” has previously been recited.
In claim 16, line 9, “based one of . . .” is vague and maybe intended to be “based on one of . . ..”
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
1. A learning device comprising:
one or more processors (additional element); and
a non-transitory storage medium storing computer-readable instructions that, when executed by the one or more processors, enable the one or more processors to: (additional element)
preprocess one of or any combination of first raw data extracted from a braking device of a vehicle, second raw data including wheel data associated with a wheel of the vehicle, and third raw data measured from an external sensor of the vehicle, to obtain training input data at a target time point at which the braking device operates (additional element – data gathering),
apply the training input data and target data corresponding to squeal noise generated by the training input data, to a squeal noise prediction model to obtain temporary output data (mental process and/or mathematical concept), and
train the squeal noise prediction model, based on a first loss value obtained by applying the temporary output data and the target data to a first loss function associated with regression (mental process and/or mathematical concept).
6. A squeal noise prediction device, comprising:
one or more processors (additional element); and
a non-transitory storage medium storing computer-readable instructions that, when executed by the one or more processors, enable the one or more processors to: (additional element)
apply one of or any combination of braking data extracted from a braking device of a vehicle, wheel data corresponding to a wheel of the vehicle, and external sensor data measured from an external sensor of the vehicle, to a trained squeal noise prediction model to obtain an expected probability indicating a probability that squeal noise of the braking device will be generated (additional element – data gathering),
determine that the squeal noise is generated in the braking device, based on the expected probability being greater than a predetermined threshold, and (evaluation or judgment, which is a mental process and/or mathematical concept if the determination involves math)
determine an oil pressure control mode of the vehicle, based on one of or any combination of a stability control mode of the vehicle, an outside air temperature measured from the external sensor, and a driving time of the vehicle, based on that the squeal noise is generated in the braking device (evaluation or judgment, which is a mental process and/or mathematical concept if the determination involves math).
11. A learning method, comprising:
preprocessing one of or any combination of first raw data extracted from a braking device of a vehicle, second raw data including wheel data associated with a wheel of the vehicle, and third raw data measured from an external sensor of the vehicle, to obtain training input data at a target time point at which the braking device operates (additional element – data gathering);
applying the training input data and target data corresponding to squeal noise generated by the training input data, to a squeal noise prediction model to obtain temporary output data (mental process and/or mathematical concept); and
training the squeal noise prediction model, based on a first loss value obtained by applying the temporary output data and the target data to a first loss function associated with regression (mental process and/or mathematical concept).
16. A squeal noise prediction method, comprising:
applying one of or any combination of braking data extracted from a braking device of a vehicle, wheel data corresponding to a wheel of the vehicle, and external sensor data measured from an external sensor of the vehicle, to a trained squeal noise prediction model to obtain an expected probability indicating a probability that squeal noise of the braking device will be generated (additional element – data gathering);
determining that the squeal noise is generated in the braking device, based on the expected probability being greater than a predetermined threshold (evaluation or judgment, which is a mental process and/or mathematical concept if the determination involves math); and
determining an oil pressure control mode of the vehicle, based one of or any combination of a stability control mode of the vehicle, an outside air temperature measured from the external sensor, and a driving time of the vehicle, based on that the squeal noise is generated in the braking device (evaluation or judgment, which is a mental process and/or mathematical concept if the determination involves math).
101 Analysis - Step 1: Statutory category - Yes
The claim recites a method including at least one step. The claim falls within one of the four statutory categories. MPEP 2106.03 101
Analysis - Step 2A Prong one evaluation: Judicial Exception - Yes - Mental processes
In Step 2A, Prong one of the 2019 Patent Eligibility Guidance (PEG), a claim is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity. The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of "mental processes" because under its broadest reasonable interpretation, the limitations can be "performed in the human mind, or by a human using a pen and paper". See MPEP 2106.04(a)(2)(III)
The claim recites the limitation of applying the training input date and train the squeal noise reduction model in claims 1 and 11 and determining an oil pressure control mode of the vehicle in claim 16 considered the mental process and/or mathematical concept.
This limitation, as drafted, is a simple process that, under its broadest reasonable interpretation, encompasses a person looking at data collected and forming a simple judgement. Thus, the claim recites a mental process.
101 Analysis - Step 2A Prong two evaluation: Practical Application - No
In Step 2A, Prong two of the 2019 PEG, a claim is to be evaluated whether, as a whole, it integrates the recited judicial exception 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, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as: merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a "practical application."
The claim needs additional elements or steps of processors, a non-transitory storage storing computer-readable instruction.
101 Analysis - Step 2B evaluation: Inventive concept - No
In Step 2B of the 2019 PEG, a claim is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate
a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Under the 2019 PEG, a conclusion that an additional element is insignificant extra solution activity in Step 2A should be re-evaluated in Step 2B. Here, the applying/training steps were considered to be insignificant extra-solution activity in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field.
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). Further, the Federal Circuit 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 is a well understood, routine, and conventional function. Accordingly, a conclusion that the collecting/applying/training step is well-understood, routine, conventional activity is supported under Berkheimer. Thus, the claim is ineligible.
Independent claims 6, 11 and 16 were evaluated using same logic as above, although the claims are apparatus's, they are high level generic systems, with the same abstract ideas/mental/mathematical concept identified above and they do not contain any further limitations which further limit or narrow the high level generality or well- understood, routine, conventional activity. Claims 11 and 16 are ineligible.
Dependent claims 2-5, 7-10, 12-15 and 17-20 merely contain data inputs or outputs manipulation, further determining, learning, estimating, detecting, calculating. The adjustment of abstract steering wheel torque feedback is merely further data manipulation which is a form of insignificant extra-solution activity. Claims 2-5, 7-10, 12-15 and 17-20 are ineligible.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 2, 6, 11, 12 and 16 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/627743 (hereinafter SN ‘743). Although the claims at issue are not identical, they are not patentably distinct from each other.
Regarding claim 1, SN ‘743 claims a learning device comprising: one or more processors; and a non-transitory storage medium storing computer-readable instructions that, when executed by the one or more processors, enable the one or more processors to: preprocess one of or any combination of first raw data extracted from a braking device of a vehicle, second raw data including wheel data associated with a wheel of the vehicle, to a squeal noise prediction model to obtain temporary output data, and train the squeal noise prediction model, based on a first loss value obtained by applying the temporary output data and the target data to a first loss function associated with regression (See claim 1). The use of a third raw data measured from an external sensor of the vehicle, to obtain training input data at a target time point at which the braking device operates, apply the training input data and target data corresponding to squeal noise generated by the training input data is not claimed in claim 1. However, applying the third data such as noise data from the outside of the vehicle was claimed. Therefore, it would have been obvious to one having ordinary skill in the art to modify the device with a means for applying the additional data from the external sensor as an engineering expedient.
Regarding claim 2, SN ‘743 claims wherein the instructions further enable the one or more processors to: generate the first raw data including at least one of or any combination of oil pressure data associated with oil pressure (See claim 9) applied to the braking device, disk temperature data, and torque data associated with a torque applied to a disk included in the braking device (See claim 20); generate the second raw data, including at least one of or both of wheel speed data associated with a speed of the wheel and rolling circumference data of a tire combined with the wheel, at the target time point; and generate the third raw data (See claim 2), including at least one of or both of outside air temperature data from the external sensor (See claim 19) and humidity data from the external sensor, at the target time point.
Regarding claim 6, SN ‘743 claims a squeal noise prediction device, comprising: one or more processors; and a non-transitory storage medium storing computer-readable instructions that, when executed by the one or more processors, enable the one or more processors to: apply one of or any combination of braking data extracted from a braking device of a vehicle, wheel data corresponding to a wheel of the vehicle (See claim 9), and external sensor data measured from an external sensor of the vehicle, to a trained squeal noise prediction model to obtain an expected probability indicating a probability that squeal noise of the braking device will be generated, determine that the squeal noise is generated in the braking device (see Clam 9), based on the expected probability being greater than a predetermined threshold, and determine an oil pressure control mode of the vehicle, based on one of or any combination of a stability control mode of the vehicle, an outside air temperature measured from the external sensor, and a driving time of the vehicle, based on that the squeal noise is generated in the braking device (See claim 9).
Regarding claim 11, SN ‘743 claims a learning method, comprising: preprocessing one of or any combination of first raw data extracted from a braking device of a vehicle, second raw data including wheel data associated with a wheel of the vehicle to obtain training input data at a target time point at which the braking device operates (See claim 11); applying the training input data and target data corresponding to squeal noise generated by the training input data, to a squeal noise prediction model to obtain temporary output data (See claim11); and training the squeal noise prediction model, based on a first loss value obtained by applying the temporary output data and the target data to a first loss function associated with regression (See claim 16).
Therefore, it would have been obvious to one having ordinary skill in the art to modify the device with training the squeal noise prediction model as an engineering expedient.
Regarding claim 12, SN ‘743 claims the obtaining of the training input data includes: generating the first raw data including one of or any combination of oil pressure data associated with oil pressure (See claim 19) applied to the braking device, disk temperature data, and torque data associated with a torque applied to a disk included in the braking device; generating the second raw data, including one of or both of wheel speed data associated with a speed of the wheel and rolling circumference data of a tire combined with the wheel, at the target time point; and generating the third raw data, including one of or both of outside air temperature data from the external sensor and humidity data from the external sensor, at the target time point.
Regarding claim 16, SN ‘743 claims a squeal noise prediction method, comprising: applying one of or any combination of braking data extracted from a braking device of a vehicle, wheel data corresponding to a wheel of the vehicle, and external sensor data measured from an external sensor of the vehicle, to a trained squeal noise prediction model to obtain an expected probability indicating a probability that squeal noise of the braking device will be generated (See claim 19); determining that the squeal noise is generated in the braking device, based on the expected probability being greater than a predetermined threshold; and determining an oil pressure control mode of the vehicle (See claim 19), based one of or any combination of a stability control mode of the vehicle, an outside air temperature measured from the external sensor, and a driving time of the vehicle, based on that the squeal noise is generated in the braking device.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Allowable Subject Matter
Claims 3-5, 7-10, 13-15 and 17-20 appear to contain subject matter not shown by the prior art. However, any indication of allowability must be reserved until the issue of 35 U.S.C. 101 has been resolved.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN KWON whose telephone number is (571)272-4846. The examiner can normally be reached M-F; 9A-5P. EST.
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/JOHN KWON/ Primary Examiner, Art Unit 3747 March 7, 2026
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