DETAILED ACTION
This is a response to Application # 18/893,770 filed on September 23, 2024 in which claims 1-20 were presented for examination.
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 .
Status of Claims
Claims 1-20 are pending, of which claims 1-5, 13-16, and 20 are rejected under 35 U.S.C. § 101; claim 12 is rejected under 35 U.S.C. § 112(b); claims 1-3, 5, 13-15, and 20 are rejected under 35 U.S.C. § 102(a)(1); and claims 4, 6, 8, 11, 12, and 16-18 are rejected under 35 U.S.C. § 103.
Information Disclosure Statement
The information disclosure statement filed September 23, 2024 complies with the provisions of 37 C.F.R. § 1.97, 1.98 and MPEP § 609. It has been placed in the application file and the information referred to therein has been considered as to the merits.
Priority
Receipt is acknowledged of certified copies of papers required by 37 C.F.R. § 1.55.
Claim Interpretation
Claim 13 recites a method claim including the step of “inputting an input value, related to driving of a rack configured to be movable by a driving force generated from a wheel actuator driven in response to manipulation of a steering wheel to move a wheel of the vehicle, to an artificial neural network model.” (Emphasis added).
The broadest reasonable interpretation of this limitation does not require the inputting of the input value to be performed because it does not expressly require any manipulation of the steering wheel to occur. See Ex parte Schulhauser, 2013-007847 (PTAB 2016) (precedential) where the board held that when method steps are to be carried out only upon the occurrence of a condition precedent, the broadest reasonable interpretation holds that those steps are not required to be performed. (id. at *7). See, e.g., Ex parte Heil (PTAB 2018) (App. S.N. 12/512,669), at 6; Ex parte Frost (PTAB 2018) (App. S.N. 12/785,052) at 7; Ex parte Dawson (PTAB 2018) (App. S.N. 12/103,472) at 6; and Ex parte Candelore (PTAB 2017) (App. S.N. 14/281,158) at 5 (supporting the interpretation that “in response to” limitations are conditional).
Claim Rejections - 35 U.S.C. § 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 1-5, 13-16, and 20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter.
Regarding claims 1-5, 13-16, and 20, these claims are directed to an abstract idea without significantly more.
101 Analysis – Step 1
The claims recite, when considered individually or as a whole, a method, system, and computer program for comparing values related to the steering of a vehicle. Therefore, claims 1-5, 13-16, and 20 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the § 101 rejection. Representative claim 1 recites:
1. A device comprising:
a memory configured to store one or more instructions; and
a processor configured to execute the one or more instructions comprising:
inputting an input value, related to driving of a rack configured to be movable by a driving force generated from a wheel actuator driven in response to manipulation of a steering wheel to move a wheel of a vehicle, to an artificial neural network model;
obtaining one or more estimation values, related to steering of the vehicle, from the artificial neural network model; and
detecting whether a steering motor included in a steer-by-wire system of the vehicle and configured to provide a reaction force against the manipulation of the steering wheel is in an abnormal state by comparing the one or more estimation values, obtained from the artificial neural network model, with one or more actual measurement values related to the steering of the vehicle.
Co-pending independent claims 13 and 20 do not include any additional subject matter.
The examiner submits that the foregoing bolded limitation/s constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, this is recited at a high level of generality and, therefore, includes a driver estimating what the steering wheel should feel like based on past driving experience and then comparing that to measurements taken at a high level (e.g., the steering is stiff) in the human mind to determine that the steering is abnormal. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claims, as a whole, integrate the abstract into a practical application. As noted in the 2019 PEG, 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 use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
1. A device comprising:
a memory configured to store one or more instructions; and
a processor configured to execute the one or more instructions comprising:
inputting an input value, related to driving of a rack configured to be movable by a driving force generated from a wheel actuator driven in response to manipulation of a steering wheel to move a wheel of a vehicle, to an artificial neural network model;
obtaining one or more estimation values, related to steering of the vehicle, from the artificial neural network model; and
detecting whether a steering motor included in a steer-by-wire system of the vehicle and configured to provide a reaction force against the manipulation of the steering wheel is in an abnormal state by comparing the one or more estimation values, obtained from the artificial neural network model, with one or more actual measurement values related to the steering of the vehicle.
For the following reasons, the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of device, the memory, and the processor, the examiner submits that these limitations are instructions to “apply it.” See MPEP § 2106.05(f).
In particular, the device, memory, and processor are recited at a high level of generality (i.e. as general computing equipment), and amounts to merely applying the abstract idea.
Regarding the additional limitations of “inputting an input value, related to driving of a rack configured to be movable by a driving force generated from a wheel actuator driven in response to manipulation of a steering wheel to move a wheel of a vehicle, to an artificial neural network model; obtaining one or more estimation values, related to steering of the vehicle, from the artificial neural network model,” and that values are “obtained from the artificial neural network model” the examiner submits that these are mere data gathering. See MPEP § 2106.05(g).
In particular, the inputting and obtaining steps are recited at a high level of generality (i.e. as a general means of gathering vehicle and data for use in the detecting step), and amounts to mere data gathering.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation/s as an ordered combination or as a whole, the limitations add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception. See MPEP § 2106.05.
Accordingly, the additional limitations do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the device, memory, and processor amount to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP § 2106.05(f).
And as discussed above, the additional limitations of the inputting and obtaining steps, the examiner submits that these limitations are well-understood, routine, and conventional. See MPEP § 2106.05(d) and Van Ende et al., US Publication 2022/0289287
Hence, the claim is not patent eligible.
Dependent claims 2-5 and 14-16 do not recite any further limitations that cause the claims to be directed towards statutory subject matter. The claims merely recite: [repeat the judicial exception]. Each of the further limitations expound upon the [repeat judicial exception] and do not recite additional elements integrating the mental process into a practical application or additional elements that are not well-understood, routine or conventional. Therefore, dependent claims 2-5 and 14-16 are similarly rejected as being directed towards non-statutory subject matter.
Therefore, claims 1-5, 13-16, and 20 are ineligible under 35 U.S.C. § 101.
Regarding claims 6-12 and 17-19, these claims include “something more” and are, therefore, not rejected under 35 U.S.C § 101. Specifically, claims 6 and 17 explain the details of the claimed invention in a manner that expresses the unconventional technical solution to a technical problem. See MPEP § 2106.05. This is in contrast to the high level of generality of rejected claims 1-5, 13-16, and 20.
Claim Rejections - 35 U.S.C. § 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.
Claim 12 is rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding claim 12¸ this claim incudes the limitation “the artificial neural network model is configured to alternately perform learning of the generator and the discriminator,” where the use of the term “alternately” causes the limitation to be subject to two, mutually exclusive interpretations. (Emphasis added).
First, the term “alternately” may indicate that artificial neural network model is configured to perform either learning of the generator or learning of the discriminator. In other words, the two functions are alternatives.
Second, the term “alternately” may be interpreted to impose an order on the performance of learning of the generator and learning of the discriminator; namely that that it learns the generator and then it learns the discriminator. In other words, the artificial neural networks alternates between the two functions.
“[I]f a claim is amenable to two or more plausible claim constructions, the USPTO is justified in requiring the applicant to more precisely define the metes and bounds of the claimed invention by holding the claim unpatentable under 35 U.S.C. § 112, second paragraph, as indefinite.” Ex parte Miyazaki, 89 USPQ2d 1207, 1211 (BPAI 2008) (precedential). See also Ex parte McAward, Appeal 2015-006416 (PTAB 2017) (precedential) (affirming the holding in Ex parte Miyazaki).
Therefore, this claim is indefinite.
For purposes of examination, the examiner shall apply the first interpretation as this is the broader interpretation and, thus, comports with the broadest reasonable interpretation standard set for in MPEP § 2111.
Claim Rejections - 35 U.S.C. § 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 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)(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.
(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 1-3, 5, 13-15, and 20 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by Van Ende et al., US Publication 2022/0289287 (hereinafter Van Ende).
Regarding claim 1, Van Ende discloses a device comprising “a memory configured to store one or more instructions; and a processor configured to execute the one or more instructions” (Van Ende ¶ 20) where the system is executed on a microprocessor, which is known to a person of ordinary skill in the art to include at least memory in the form of registers. Additionally, Van Ende discloses “inputting an input value, related to driving of a rack configured to be movable by a driving force generated from a wheel actuator driven in response to manipulation of a steering wheel to move a wheel of a vehicle, to an artificial neural network model; (Van Ende ¶¶ 21, 24) where state variables are input into an artificial neural network (Van Ende ¶ 24), which include steering wheel torque. (Van Ende ¶ 21). Further, Van Ende discloses “obtaining one or more estimation values, related to steering of the vehicle, from the artificial neural network model” (Van Ende ¶ 33) where the estimated current steering wheel torque is output. Finally, Van Ende discloses “detecting whether a steering motor included in a steer-by-wire system of the vehicle and configured to provide a reaction force against the manipulation of the steering wheel is in an abnormal state by comparing the one or more estimation values, obtained from the artificial neural network model, with one or more actual measurement values related to the steering of the vehicle” (Van Ende ¶¶ 38, 42) where an error function (i.e., a function that determines an abnormal state) is based on the difference 14 (Van Ende ¶ 42), where the difference 14 is the difference between the reference value output and the estimated steering wheel torque.
Regarding claim 13, it merely recites a method for performed by the system of claim 1. The method comprises steps performed by computer software modules for performing the various functions. Van Ende comprises computer software modules for performing the same functions. Thus, claim 13 is rejected using the same rationale set forth in the above rejection for claim 1.
Regarding claim 20, it merely recites a non-transitory computer-readable storage medium for embodying the system of claim 1. The medium comprises computer software modules for performing the various functions. Van Ende comprises computer software modules for performing the same functions. Thus, claim 20 is rejected using the same rationale set forth in the above rejection for claim 1.
Regarding claims 2 and 14, Van Ende discloses the limitations contained in parent claims 1 and 13 for the reasons discussed above. In addition, Van Ende discloses “wherein the input value related to the driving of the rack comprises a propulsive force of the rack.” (Van Ende ¶ 31).
Regarding claims 3 and 15, Van Ende discloses the limitations contained in parent claims 1 and 13 for the reasons discussed above. In addition, Van Ende discloses “the one or more estimation values obtained from the artificial neural network model include a first estimation value for a first residual which is a difference between a command value of a steering torque applied to the steering wheel to correspond to the input value and a measurement value of the steering torque; and the one or more actual measurement values include a first actual measurement value which is an actual measurement value for the first residual” (Van Ende ¶ 33) where each of these values was obtained, as discussed above, and indicating that they may represent the steering torque value.
Regarding claim 5, Van Ende discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Van Ende discloses “wherein the processor is configured to obtain the input value through a controller area network (CAN) of the vehicle.” (Van Ende ¶ 17).
Claim Rejections - 35 U.S.C. § 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 of this title, 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.
This application currently names joint inventors. In considering patentability of the claims, the Examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicants are advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 4 and 16 are rejected under 35 U.S.C. § 103 as being unpatentable over Van Ende in view of Bahena et al., US Publication 2023/0014442 (hereinafter Bahena).
Regarding claims 4 and 16, Van Ende discloses the limitations contained in parent claims 3 and 14 for the reasons discussed above. In addition, Van Ende does not appear to explicitly disclose “the one or more estimation values obtained from the artificial neural network model further include a second estimation value for a second residual which is a difference between a command value of a motor torque transmitted to the steering motor to correspond to the input value and a measurement value of the motor torque; and the one or more actual measurement values further include a second actual measurement value which is an actual measurement value for the second residual.”
However, Bahena discloses a steer-by-wire system including “the one or more estimation values obtained from the … neural network model further include a second estimation value for a second residual which is a difference between a command value of a motor torque transmitted to the steering motor to correspond to the input value and a measurement value of the motor torque” (Bahena ¶¶ 55, 67 72) where the overshoot position determiner determines how to adjust the torque applied by the EPS system to the steering column for assisting the driver (i.e., the motor torque) based on the overshoot position, which is the estimated position as compared to the actual position. Bahena indicates that this may be performed by a neural network.
A person of ordinary skill in the art prior to the effective filing date of the present application would have recognized that when Bahena was combined with Van Ende, the neural network calculations of Bahena would be performed by the artificial neural network of Van Ende. Therefore, the combination of Van Ende and Bahena at least teaches and/or suggests the claimed limitation “the one or more estimation values obtained from the artificial neural network model further include a second estimation value for a second residual which is a difference between a command value of a motor torque transmitted to the steering motor to correspond to the input value and a measurement value of the motor torque,” rendering it obvious.
Finally, Bahena discloses “the one or more actual measurement values further include a second actual measurement value which is an actual measurement value for the second residual.” (Bahena ¶ 41).
Van Ende and Bahena are analogous art because they are from the “same field of endeavor,” namely that of steer-by-wire systems.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Van Ende and Bahena before him or her to modify the artificial neural network of Van Ende to include the calculations of Bahena.
The motivation/rationale for doing so would have been that of applying a known technique to a known device. See KSR Int’l Co. v. Teleflex Inc., 550 US 398, 82 USPQ2d 1385, 1396 (U.S. 2007) and MPEP § 2143(I)(D). Van Ende teaches the “base device” for using an artificial neural network to calculate values related to a steer-by-wire system. Further, Bahena teaches the “known technique” of using a neural network to calculate values related to a steer-by-wire system that is applicable to the base device of Van Ende. One of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in an improved system.
Claims 6, 8, 11, 12, 17, and 18 are rejected under 35 U.S.C. § 103 as being unpatentable over Van Ende in view of Zhang et al., US Publication 2020/0089244 (hereinafter Zhang).
Regarding claims 6 and 17, Van Ende discloses the limitations contained in parent claims 1 and 13 for the reasons discussed above. In addition, Van Ende does not appear to explicitly disclose “
6, 17. The device of claim 1,13 wherein the artificial neural network model is comprised in a generative adversarial network (GAN) including a generator configured to receive the input value, related to the driving of the rack, and generate the one or more estimation values; and
a discriminator configured to, in response to the input value, related to the driving of the rack, and actual measurement data including the one or more actual measurement values, output a discrimination value for the actual measurement data.
However, Zhang discloses a steer-by-wire vehicle using a neural network “wherein the … neural network model is comprised in a generative adversarial network (GAN) including a generator configured to receive the input value … and generate the one or more estimation values; and a discriminator configured to, in response to the input value … and actual measurement data including the one or more actual measurement values, output a discrimination value for the actual measurement data.” (Zhang ¶ 85)
A person of ordinary skill in the art prior to the effective filing date of the present invention would have recognized that when Zhang was combined with Van Ende, the GAN and discriminator or Zhang would be part of the artificial neural network that uses data related to the driving rack of Van Ende. Therefore, the combination of Van Ende and Zhang at least teaches and/or suggests the claimed limitations “wherein the artificial neural network model is comprised in a generative adversarial network (GAN) including a generator configured to receive the input value, related to the driving of the rack, and generate the one or more estimation values; and a discriminator configured to, in response to the input value, related to the driving of the rack, and actual measurement data including the one or more actual measurement values, output a discrimination value for the actual measurement data,” rendering them obvious.
Van Ende and Zhang are analogous art because they are from the “same field of endeavor,” namely that of steer-by-wire systems.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Van Ende and Zhang before him or her to modify the artificial neural network of Van Ende to include the GAN and discriminator of Zhang.
The motivation/rationale for doing so would have been that of applying a known technique to a known device. See KSR Int’l Co. v. Teleflex Inc., 550 US 398, 82 USPQ2d 1385, 1396 (U.S. 2007) and MPEP § 2143(I)(D). Van Ende teaches the “base device” for using a neural network in a steer-by-wire system. Further, Zhang teaches the “known technique” of a neural network including a GAN and a discriminator that is applicable to the base device of Van Ende. One of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in an improved system.
Regarding claim 8, the combination of Van Ende and Zhang discloses the limitations contained in parent claims 6 for the reasons discussed above. In addition, the combination of Van Ende and Zhang discloses “wherein the processor is configured to input error data related to a difference between the one or more estimation values and the one or more actual measurement values to an abnormality detection model, and determine whether the steering motor is in the abnormal state based on an output of the abnormality detection model” (Van Ende ¶¶ 42-43) by determining the optimization criteria from the comparison unless the error is below a predetermined threshold.
Regarding claim 11, the combination of Van Ende and Zhang discloses the limitations contained in parent claim 6 for the reasons discussed above. In addition, the combination of Van Ende and Zhang discloses “wherein the discriminator is further configured to, in response to the input value and estimation data including the one or more estimation values, output a discrimination value for the estimation data.” (Zhang ¶ 92).
Regarding claim 12, the combination of Van Ende and Zhang discloses the limitations contained in parent claim 11 for the reasons discussed above. In addition, the combination of Van Ende and Zhang discloses “the artificial neural network model is configured to alternately perform learning of the generator and the discriminator.” (Zhang ¶ 85). Further, the combination of Van Ende and Zhang at least teaches and/or suggests “the processor is configured to obtain the input value and the actual measurement value used for the learning of the generator and the discriminator when the vehicle and the steering motor are in a normal state” (Zhang ¶ 85) where this is performed in all states.
Regarding claim 18, the combination of Van Ende and Zhang discloses the limitations contained in parent claim 17 for the reasons discussed above. In addition, the combination of Van Ende and Zhang discloses “wherein the detecting of whether the steering motor is in the abnormal state comprises: by inputting the actual measurement data to the discriminator, obtaining the discrimination value for the actual measurement data generated by the discriminator.” (Zhang ¶ 91). Further, the combination of Van Ende and Zhang discloses “by inputting error data including the discrimination value for the actual measurement data and values related to a difference between the one or more estimation values and the one or more actual measurement values to an abnormality detection model, obtaining an output from the abnormality detection model.” (Zhang ¶ 92)
Allowable Subject Matter
Claims 7, 9, 10, and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Specifically, while each of these claims refer to elements that are individually well-known in the art, each would require further modifying the Zhang reference, which itself is a modifying reference. Therefore, it is the examiner’s opinion that such a modification of a modifying reference would only occur through the use of impermissible hindsight.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Sun, US Publication 2024/0059350, System and method for using a neural network to detect abnormalities in the amount of steering torque applied by the user of a steer-by-wire vehicle.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW R DYER whose telephone number is (571)270-3790. The examiner can normally be reached Monday-Thursday 7:30-4:30.
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/ANDREW R DYER/Primary Examiner, Art Unit 3662