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 .
Election/Restrictions
Claims 15-17 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected subcombination, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 06/29/2026.
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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 storage unit” and “a wheel slip estimation unit,” plus their respective functions in claim 1; “a receiving unit,” “a storage unit,” “a wheel slip estimation unit,” and “a driving speed estimation unit,” plus their respective functions in claim 8; “a pre-processing unit” plus the respective function in claim 12; and “a post-processing unit” plus the respective function in claim 18.
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. See specification [0041] showing processor, memory, and non-transitory computer readable media as examples of the embodiments.
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 Objections
Claims 8-14 and 18-20 are objected to because of the following informalities:
In claim 8, lines 9-10, “a driving speed” should be --the driving speed-- to avoid creating another antecedent basis.
In claim 18, line 2-3, “wheel slip rate information” should be ----the wheel slip rate information-- to avoid creating another antecedent basis.
In claim 19, line 3 ‘the wheel slip rate” should be --a wheel slip rate-- to avoid the issue of lack of antecedent basis.
The other claim(s) not discussed above, or depending on the above claim(s), are objected to for inheriting the issue(s) from their linking claim(s).
Appropriate correction is required.
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 2, 10, and 19 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.
Regarding claim 2, it recite “each wheel” in the end. It is too broad without clear boundaries. For examination purpose, --each of a plurality of wheels in a vehicle-- is assumed.
Regarding claim 10, it recite “each wheel” in the end. It is too broad without clear boundaries. For examination purpose, --each of a plurality of wheels in the vehicle-- is assumed.
Regarding claim 19, it recite “each wheel” in the end. It is too broad without clear boundaries. For examination purpose, --each of a plurality of wheels in the vehicle-- is assumed.
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 1-13 and 18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 4-6 of U.S. Patent No. 12427997 in view of Coerman et al. (US 20240369592 A1), Nourizadeh et al. ("In situ slip estimation for mobile robots in outdoor environments" J Field Robotics. 2023;40:467-482, first published December 2022), and/or LI et al. (CN 115587526 A). Claim 4 of the patent teaches substantially the current independent claims 1 and 8. Any differences are well-known or obvious in view of Coerman. The current dependent claims 2-7, 9-13 and 18 are also obvious over the cited prior art. The rejections under 35 USC 102 and 103 below are incorporated herein by reference to address any differences.
Claims 1-13 and 18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 2 and 10-16 of U.S. Patent No. 12115861 in view of Coerman et al. (US 20240369592 A1), Nourizadeh et al. ("In situ slip estimation for mobile robots in outdoor environments" J Field Robotics. 2023;40:467-482, first published December 2022), and/or LI et al. (CN 115587526 A). Claims 2 and 10 of the patent teach substantially the current independent claims 1 and 8. Any differences are well-known or obvious in view of Coerman. The current dependent claims 2-7, 9-13 and 18 are also obvious over the cited prior art. The rejections under 35 USC 102 and 103 below are incorporated herein by reference to address any differences.
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.
MPEP 2106 outlines a two-part analysis for Subject Matter Eligibility as shown in the chart below.
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645
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Step 1, the claimed invention must be to one of the four statutory categories. 35 U.S.C. 101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter.
Step 2, the claimed invention also must qualify as patent-eligible subject matter, i.e., the claim must not be directed to a judicial exception unless the claim as a whole includes additional limitations amounting to significantly more than the exception.
Step 2A is a two-prong inquiry, as shown in the chart below.
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881
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Prong One asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. If the claim recites a judicial exception (i.e., an abstract idea enumerated in MPEP § 2106.04(a), a law of nature, or a natural phenomenon), the claim requires further analysis in Prong Two. If the claim does not recite a judicial exception (a law of nature, natural phenomenon, or abstract idea), then the claim cannot be directed to a judicial exception (Step 2A: NO), and thus the claim is eligible at Pathway B without further analysis. Abstract ideas can be grouped as, e.g., mathematical concepts, certain methods of organizing human activity, and mental processes.
Prong Two asks does the claim recite additional elements that integrate the judicial exception into a practical application? If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B.
Claims 1-14 and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding claim 1, Step 1: Is the claim to a process, machine, manufacture or composition of matter? Yes.
Step 2A: Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea (judicially recognized exceptions)? Yes (see analysis below).
Prong one: Whether the claim recites a judicial exception? (Yes).
The claim recites:
1. An apparatus for estimating wheel slip rate, the apparatus comprising:
a storage unit storing a wheel slip estimation model; and
a wheel slip estimation unit estimating wheel slip information using the wheel slip estimation model based on driving information.
The claim is directed to an abstract idea because it recites the limitations as bold-faced above. These limitations are directed to mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; and/or mental processes – concepts performed in the human mind (or with a pen and paper).
Prong two: Whether the claim recites additional elements that integrate the exception into a practical application of that exception? (No). The claim recites additional elements as underlined in the claim above. However, these are to invoke a generic computer or components for its computing power to facilitate the application of the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements are insufficient to integrate the abstract idea into a practical application of the abstract idea.
Step 2B: Does the claim recite additional elements (other than the judicial exception) that amount to significantly more than the judicial exception? No (see analysis below).
The claim does not include additional elements that are sufficient to make the claim significantly more than the judicial exception. As discussed with respect to Step 2A Prong Two above, the additional element(s) in the claim are to invoke a generic computer for its computing power to facilitate the application of the abstract idea, and/or to indicate the data source or environment, which is a field of use. Also, it is routine and conventional to invoke a computer for data processing. See MPEP 2106.05(d). Considered as a whole, the claim does not amount to significantly more than the abstract idea.
Claim 8 is similarly rejected by analogy to claim 1. Note that the “receiving unit” is to invoke a generic computer component to collect data necessary for the abstract. It is insufficient to make the claim eligible. See MPEP 2106.05(d), (f), and (g).
Dependent claims 2-7, 9-14, and 18-20 when analyzed as a whole respectively are held to be patent ineligible under 35 U.S.C. 101 because they either extend (or add more details to) the abstract idea or the additional recited limitation(s) (if any) fail(s) to establish that the claim(s) is/are not directed to an abstract idea, as discussed below: there is no additional element(s) in the dependent claims that sufficiently integrates the abstract idea into a practical application of, or makes the claims significantly more than, the judicial exception (abstract idea). The additional element(s) (if any) are mere instructions to apply an except, field of use, and/or insignificant extra-solution activities (applied to Step 2A_Prong Two and Step 2B; see MPEP 2016.05(f)-(h)) and/or well-understood, routine, or conventional (applied to Step 2B; see MPEP 2106.05(d)) to facilitate the application of the abstract idea.
Claim Rejections - 35 USC § 102
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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 8-10, and 18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Coerman et al. (US 20240369592 A1; hereinafter “Coerman”).
Regarding claim 1, Coerman teaches an apparatus for estimating wheel slip rate (i.e., “computer hardware and software”; see [0059]; “The artificial neural network can use a given torque of an electric drive of the land vehicle to determine four slip values”; see [0024]), the apparatus comprising:
a storage unit (this is implied in the computer; see [0059]) storing a wheel slip estimation model (i.e., “The artificial neural network can use a given torque of an electric drive of the land vehicle to determine four slip values”; see [0024]); and
a wheel slip estimation unit (this is implied in the computer; see [0059]) estimating wheel slip information using the wheel slip estimation model based on driving information (i.e., “The acceleration of the vehicle body in the vehicle longitudinal direction, the acceleration of the vehicle body in the vehicle lateral direction, the acceleration of the vehicle body in the vehicle vertical direction, the vehicle mass, wheel braking torques and drive torques can be used as inputs for this. The artificial neural network can output the slip ratio for each wheel”; see [0025]).
Regarding claim 2, Coerman further teaches:
wherein the driving information includes at least one of an engine torque, a number of revolutions per minute (RPM) of an engine, a longitudinal acceleration, a lateral acceleration, a yaw-rate, and a wheel rotation speed of each wheel (i.e., “The acceleration of the vehicle body in the vehicle longitudinal direction, the acceleration of the vehicle body in the vehicle lateral direction, the acceleration of the vehicle body in the vehicle vertical direction, the vehicle mass, wheel braking torques and drive torques can be used as inputs for this. The artificial neural network can output the slip ratio for each wheel”; see [0025]).
Regarding claim 8, Coerman teaches an apparatus for estimating a driving speed (i.e., “computer hardware and software”; see [0059]; “improving an estimation of a ground reference speed of a land vehicle”; see [0008]), comprising:
a receiving unit (this is implied in the computer; ; see [0059]) acquiring driving information of a vehicle (i.e., “The acceleration of the vehicle body in the vehicle longitudinal direction, the acceleration of the vehicle body in the vehicle lateral direction, the acceleration of the vehicle body in the vehicle vertical direction, the vehicle mass, wheel braking torques and drive torques can be used as inputs”; see [0025]);
a storage unit (this is implied in the computer; see [0059]) storing a wheel slip estimation model (i.e., “The artificial neural network can use a given torque of an electric drive of the land vehicle to determine four slip values”; see [0024]);
a wheel slip estimation unit (this is implied in the computer; see [0059]) estimating wheel slip information using the wheel slip estimation model based on the driving information (i.e., “The acceleration of the vehicle body in the vehicle longitudinal direction, the acceleration of the vehicle body in the vehicle lateral direction, the acceleration of the vehicle body in the vehicle vertical direction, the vehicle mass, wheel braking torques and drive torques can be used as inputs for this. The artificial neural network can output the slip ratio for each wheel”; see [0025]); and
a driving speed estimation unit (this is implied in the computer; see [0059]) estimating a driving speed of the vehicle based on the wheel slip information (i.e., “The artificial neural network can use a given torque of an electric drive of the land vehicle to determine four slip values. Using this and the curve-adjusted wheel speeds, four virtual wheel speeds can be determined that are ideally identical to the “real” vehicle speed, which compensate for the respective wheel slip that can be safely attributed to the motor torque or a braking torque”; see [0024]).
Regarding claim 9, Coerman further teaches:
wherein the receiving unit receives the driving information using a network provided in the vehicle (i.e., “vehicle data provided via a CAN bus of the land vehicle”; see [0003]).
Regarding claim 10, Coerman further teaches:
wherein the driving information includes at least one of an engine torque, an engine speed, a longitudinal acceleration, a lateral acceleration, a yaw-rate, and a wheel rotation speed of each wheel (i.e., “The acceleration of the vehicle body in the vehicle longitudinal direction, the acceleration of the vehicle body in the vehicle lateral direction, the acceleration of the vehicle body in the vehicle vertical direction, the vehicle mass, wheel braking torques and drive torques can be used as inputs for this. The artificial neural network can output the slip ratio for each wheel”; see [0025]).
Regarding claim 18, Coerman further teaches:
a post-processing unit (this is implied in the computer; see [0059]) configured to post-process wheel slip rate information estimated by the wheel slip estimation unit (i.e., “The purpose of this network is to underestimate the slip ratio (when the vehicle accelerates, the speed is less than or equal to the actual speed, and when the vehicle brakes, the speed is greater than or equal to the actual speed). The purpose of this procedure is to always have a safety factor. For example, if too high a slip ratio is estimated in a braking scenario (i.e. excessively low wheel speeds), the brake pressure could be erroneously reduced, reducing braking performance. This safety factor is achieved by rescaling the network outputs so that 95% of the data set is underestimated”; see [0024]) and the driving speed estimated by the driving speed estimation unit (i.e., “In block 60, the speed values V1 to V4 are weighted and combined when estimating the true speed VRef”; see [0062]).
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 12 is rejected under 35 U.S.C. 103 as being unpatentable over Coerman.
Regarding claim 12, the prior art applied to the preceding linking claim(s) teaches the features of the linking claim(s).
Coerman does not explicitly disclose:
a pre-processing unit configured to receive and pre-process the driving information from the receiving unit, and then to transmit the driving information to the wheel slip estimation unit.
However, it is well-known to preprocess raw data, such as cleaning, noise reduction, time adjusting, etc. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a pre-processing unit configured to receive and pre-process the driving information from the receiving unit, and then to transmit the driving information to the wheel slip estimation unit, as claimed. The rationale would be to help providing better quality of data for the estimation.
Claims 3-5, 7, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Coerman in view of Nourizadeh et al. ("In situ slip estimation for mobile robots in outdoor environments" J Field Robotics. 2023;40:467-482, first published December 2022; hereinafter “Nourizadeh”).
Regarding claims 3 and 4, the prior art applied to the preceding linking claim(s) teaches the features of the linking claim(s).
Coerman does not explicitly disclose:
(claim 3) wherein the wheel slip estimation model is learned using a deep learning network.
(claim 4) wherein the wheel slip estimation model is learned using a Long-Short Term Memory (LSTM) network.
But Nourizadeh:
wherein the wheel slip estimation model is learned using a Long-Short Term Memory (LSTM) network (i.e., “Explore the functionality of deep learning algorithms for slip estimation on uneven terrains. Different deep learning algorithms based on LSTM are developed”; see p. 468, col. 2, ¶ 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Coerman in view of Nourizadeh, such that the wheel slip estimation model is learned using a deep learning network, and the wheel slip estimation model is learned using a Long-Short Term Memory (LSTM) network, as claimed. The rationale would be to use a known type of artificial neural network for its capability of estimating a wheel slip rate.
Regarding claim 5, the prior art applied to the preceding linking claim(s) teaches the features of the linking claim(s).
Coerman does not explicitly disclose:
wherein the LSTM network has a sampling time of 20 milliseconds and a window size of 30 samples.
However, the sampling rate and window size are tunable configurations for the LSTM where the input is time series data (see Nourizadeh, p. 471, col. 1, ¶ 3). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the configurations such that the LSTM network has a sampling time of 20 milliseconds and a window size of 30 samples, as claimed. The rationale would be to optimizing the configurations for the LSTM for estimating the wheel slip rate based on user preference and desired level of accuracy by tuning the sampling rate and window size.
Regarding claim 7, the prior art applied to the preceding linking claim(s) teaches the features of the linking claim(s).
Coerman does not explicitly disclose:
wherein the wheel slip estimation model is learned by applying a wheel slip rate determined using a global positioning system (GPS) as a correct value.
But Nourizadeh teaches:
wherein the wheel slip estimation model is learned by applying a wheel slip rate determined using a global positioning system (GPS) as a correct value (i.e., “An RTK-GPS (U-Blox C94-M8P, 5 Hz) was used to measure the robot's velocity for calculating the slip ratio as the ground truth data”; see p. 472, col. 2, ¶ 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Coerman in view of Nourizadeh, such that the wheel slip estimation model is learned by applying a wheel slip rate determined using a global positioning system (GPS) as a correct value, as claimed. The rationale would be to facilitate the training of artificial neural network.
Regarding claim 11, the claim recites the same substantive further limitations as claim 3 and is rejected by applying the same teachings.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Coerman in view of KASHEFHAGHIGHI et al. (US 20220067489 A1; hereinafter “KASHEFHAGHIGHI”).
Regarding claim 6, the prior art applied to the preceding linking claim(s) teaches the features of the linking claim(s).
Coerman does not explicitly disclose:
wherein, in the wheel slip estimation model, at least one of a smooth L1 loss function and a Gaussian negative log likelihood (NLL) loss function is applied as a loss function.
But KASHEFHAGHIGHI teaches:
a smooth L1 loss function for artificial neural network training (i.e., “It can use one or more loss functions such as logistic regression/log loss, multi-class cross-entropy/softmax loss, binary cross-entropy loss, mean-squared error loss, L1 loss, L2 loss, smooth L1 loss, and Huber loss”; see [0063]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Coerman in view of KASHEFHAGHIGHI, such that in the wheel slip estimation model, at least one of a smooth L1 loss function and a Gaussian negative log likelihood (NLL) loss function is applied as a loss function, as claimed. The rationale would be to use a known loss function for training the artificial neural network.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over LI et al. (CN 115587526 A; machine translation provided; hereinafter “LI”).
Regarding claim 13, the prior art applied to the preceding linking claim(s) teaches the features of the linking claim(s).
Coerman does not explicitly disclose:
wherein the pre-processing unit is configured to pre-process the driving information received, using standardization.
But LI teaches:
pre-processing the driving information received for a prediction model, using standardization (i.e., “in order to reduce the model complexity, in the pre-processing stage, different standardization method is adopted for different input characteristics in the training set, ensuring the model of the resolution of each feature is substantially consistent, the normalized vehicle real-time speed, vehicle real-time acceleration and real-time rotating speed of the engine to form input characteristic data”; see translation p. 4, upper section).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Coerman in view of LI, such that the pre-processing unit is configured to pre-process the driving information received, using standardization, as claimed. The rationale would be to help reducing the model complexity and ensuring model resolution consistency (see LI, translation p. 4, upper section).
Notes
Claims 14, 19, and 20 distinguish over the closest prior art of record as discussed below.
Regarding claim 14, the closest prior art of record fails to teach the feature: “wherein the wheel slip information includes an average and a variance of wheel slip rates,” in combination with the rest of the claim limitations as claimed and defined by the Applicant. Coerman simply teaches estimating a wheel slip rate by an artificial neural network. It does not teach or suggest estimating an average and a variance of wheel slip rates as claimed. There is no motivation to do so, either. None of the prior art of record, singly or in combination, teaches or suggests the feature at issues.
Regarding claim 19, the closest prior art of record fails to teach the feature: “wherein the post-processing unit is configured to fix the driving speed and the wheel slip rate to '0' (zero) when a product of a wheel rotational angular velocity and a dynamic radius of each wheel is a preset value or less,” in combination with the rest of the claim limitations as claimed and defined by the Applicant. Coerman simply teaches post-processing the estimated wheel slip rates and the driving speed to estimate a true driving speed. It does not teach or suggest the particular post-processing as claimed. There is no motivation to do so, either. None of the prior art of record, singly or in combination, teaches or suggests the feature at issues.
Regarding claim 20, the closest prior art of record fails to teach the feature: “wherein the post-processing unit is configured to post-process the driving speed using an exponential moving average,” in combination with the rest of the claim limitations as claimed and defined by the Applicant. Coerman simply teaches post-processing the estimated wheel slip rates and the driving speed to estimate a true driving speed. It does not teach or suggest the particular post-processing as claimed. There is no motivation to do so, either. None of the prior art of record, singly or in combination, teaches or suggests the feature at issues.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
LIU et al. (CN 113135191 A) teaches a track vehicle slip rate estimation method based on road classification and machine learning, involving a slip rate discrete estimation model and a slip rate regression model.
CHEN et al. (CN 114572224 A) teaches a method of estimating a road maximum adhesion coefficient, involving calculating a longitudinal slip rate based on longitudinal speed obtained through a three-degree-of-freedom nonlinear vehicle dynamics mathematical model.
ZHENG et al. (CN 113335293 A) teaches a high-speed road surface detection system, involving estimating a slip rate based on a vehicle acceleration and a wheel angular velocity.
MIAO et al. (CN 113044041 A) teaches a vehicle speed and tire slippage rate estimation method, involving obtaining wheel speed information of the four wheels; combining a front wheel rotating angle and a vehicle driving state to calculate a vehicle speed estimation value; and calculating four wheel slip rate according to the vehicle speed estimation value.
YI et al. (US 20200023852 A1) teaches a method and device for estimating the road surface friction coefficient of a tire, involving acquiring state information of a vehicle including at least one of engine state information, transmission state information, and chassis state information from sensors on the vehicle; and estimating a longitudinal slip ratio, normal force, and longitudinal force for a tire mounted on each wheel of the vehicle by using the acquired state information of the vehicle.
Zhang et al. (US 20200086877 A1) teaches an acceleration slip regulation method, involving determining a wheel velocity of a vehicle, a yaw angular velocity of the vehicle, and a steering wheel angle of the vehicle; determining a wheel acceleration of the vehicle based on the wheel velocity of the vehicle; and determining a wheel slip rate based on the wheel velocity of the vehicle, the vehicle velocity of the vehicle, and the steering wheel angle and the yaw angular velocity of the vehicle.
Basri et al. ("A Hybrid Deep Learning Approach for Vehicle Wheel Slip Prediction in Off-Road Environments" 2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE)) teaches a hybrid Deep Learning approach for identifying the terrain type on which the vehicle is driving, and estimating the wheel slip on uneven and unstructured surfaces.
Conclusion
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/JOHN C KUAN/Primary Examiner, Art Unit 2857
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