DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
The applicant respectfully argues
Claim 1 in the present application is directed to improvements in the function of computers and to the automobile technologies.
The examiner respectfully argues
The examiner acknowledges that invention in the present application may improve in the function of computers and to the automobile technologies. The invention of the present application is different than that claimed in claim 1. Claim 1, as presently present does not actually do anything after determining/detecting that the solenoid valve may be abnormal, so as to move the claimed invention past the implementation of an abstract idea on a generic computer device (i.e. an actuation of an actuator, signaling/notifying via. a malfunction indicator lamp, switching the suspension to a service (or limp home) mode, etc.) This being the case, it seems that claim 1 is merely implementing an abstract idea on a generic computer device without significantly more. Further, claims 1, 10 and 17 are further rejected under a new grounds of rejection as explained below.
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 1-7, 9-15 and 17 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.
Each of claims 1-7, 9-15 and 17 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of claims 1-7, 9-15 and 17 recites at least one step or instruction for an abstract idea, which is grouped as a mental process under the 2019 PEG or a certain method of organizing human activity under the 2019 PEG.
Claim 1 recites, inter alia,
inputting input data representing a state of an electronically controlled suspension (ECS) system into an artificial neural network;
obtaining at least one estimated value of a physical quantity representing an output of the ECS system from the artificial neural network model;
detecting abnormality of a solenoid valve included in the ECS system of a vehicle by comparing the at least one estimated value of the physical quantity obtained from the artificial neural network model with at least one measured value of the physical quantity measured by one or more sensors,
input error data related to a difference between the estimated value of the physical quantity obtained from the artificial neural network model and the measured value of the physical quantity measured by the one or more sensors into an abnormality detection model to determine whether the solenoid valve included in the ECS system is abnormal.
Claim 10 recites, inter alia,
inputting, by a processor, input data representing a state of an electronically controlled suspension (ECS) system into an artificial neural network model and
obtaining, by the processor, an estimated value of a physical quantity representing an output of the ECS system from the artificial neural network model; and
detecting abnormality of a solenoid valve included in the ECS system of a vehicle by comparing, by the processor, the estimated value of the physical quantity obtained from the artificial neural network model with a measured value of the physical quantity measured by one or more sensors
inputting, by the processor, the measurement data including the input data representing the state of the ESC system and the measured value of the physical quantity measured by the one or more sensors into the discriminator and obtaining the discrimination value for the measurement data generated by the discriminator; and
inputting, by the processor, error data including the discrimination value for the measurement data and a value related to a difference between the estimated value of the physical quantity obtained from the artificial neural network model and the measured value of the physical quantity measured by the one or more sensors into an abnormality detection model to determine whether the solenoid included in the ECS system is abnormal based on an output of the abnormality detection model.
Claim 17 recites, inter alia,
inputting input data representing a state of an electronically controlled suspension (ECS) system into an artificial neural network model,
obtaining at least one estimated value of a physical quantity representing an output of the ECS system from the artificial neural network model,
detecting abnormality of a solenoid valve included in the ECS system of a vehicle by comparing the at least one estimated value of the physical quantity obtained from the artificial neural network model with at least one measured value of the physical quantity measured by one or more sensors
inputting, by the processor, the measurement data including the input data representing the state of the ESC system and the measured value of the physical quantity measured by the one or more sensors into the discriminator and obtaining the discrimination value for the measurement data generated by the discriminator; and
inputting, by the processor, error data including the discrimination value for the measurement data and a value related to a difference between the estimated value of the physical quantity obtained from the artificial neural network model and the measured value of the physical quantity measured by the one or more sensors into an abnormality detection model to determine whether the solenoid valve included in the ECS system is abnormal based on an output of the abnormality detection model.
Here, the steps of “inputting input data representing a state of an electronically controlled suspension (ECS) system into an artificial neural network”, “obtaining at least one estimated value of a physical quantity representing an output of the ECS system from the artificial neural network model”, and “detecting abnormality of a solenoid valve included in the ECS system of a vehicle by comparing the at least one estimated value of the physical quantity obtained from the artificial neural network model with at least one measured value of the physical quantity measured by one or more sensors”, as recited in claim 1, and similarly, mutatis mutandis, as recited in claims 10 and 17 comprise the abstract ideas of comprise the mathematical operations of computing/calculating/comparing estimated/measured values. Further, it has been held by the courts that performing mathematical operations (mathematical operations/algorithm/relations) are considered to be an abstract idea (i.e. a mental process), and as such is ineligible subject matter. Further, the step of determining whether the solenoid valve included in the ECS system is abnormal, as recited in claims1, 10 and 17 has been determined to be post solution activity, without significantly more (i.e. nothing further happens due to the determination of the solenoid valve included in the ECS system being abnormal).
Accordingly, each of claims 1, 10 and 17 recite an abstract idea.
Specifically, Claim 1 recites
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 input data representing a state of an electronically controlled suspension (ECS) system into an artificial neural network model,
obtaining at least one estimated value of a physical quantity representing an output of the ECS system from the artificial neural network model,
detecting abnormality of a solenoid valve included in the ECS system of a vehicle by comparing the at least one estimated value of the physical quantity obtained from the artificial neural network model with at least one measured value of the physical quantity measured by one or more sensors,
wherein the processor is configured to input error data related to a difference between the estimated value of the physical quantity obtained from the artificial neural network model and the measured value of the physical quantity measured by the one or more sensors into an abnormality detection model to determine whether the solenoid valve included in the ECS system is abnormal.
The recited limitations of
a memory,
a processor,
an electronically controlled suspension (ECS) system,
a solenoid,
vehicle, and
one or more sensors
as recited in claim 1, are additional claim elements, however, these additional claim elements fail to meaningfully limit the claim.
Specifically, the recitation of the following claim limitation
inputting input data representing a state of an electronically controlled suspension (ECS) system into an artificial neural network model, and
obtaining at least one estimated value of a physical quantity representing an output of the ECS system from the artificial neural network model
input error data related to a difference between the estimated value of the physical quantity obtained from the artificial neural network model and the measured value of the physical quantity measured by the one or more sensors into an abnormality detection model
is merely pre-solution data gathering. Furthermore, the courts have held that using mathematical algorithms/relationships to update or convert data is ineligible subject matter. See e.g. Parker v. Flook; Gottschaulk v. Benson
The claim limitations of
detecting abnormality of a solenoid valve included in the ECS system of a vehicle, and
determine whether the solenoid valve included in the ECS system is abnormal.
are merely post solution activity, which fails to meaning limit the claim (note: the claim does not require that anything actually happens after detecting abnormality of a solenoid valve via. comparing estimated/measured values).
The claims also include additional structural elements (a memory, a processor, an electronically controlled suspension (ECS) system, a solenoid, vehicle, and one or more sensors) which are well-known and understood, routine, and conventional elements to those having ordinary skill within the relevant art. These elements do not meaningfully limit the claim.
Mutatis mutandis claims 10 and 17.
Accordingly, as indicated above, each of the above-identified claims recites an abstract idea.
Further, dependent claims 2-7, 9 and 11-15 merely include limitations that either further define the abstract idea (and thus don’t make the abstract idea any less abstract) or amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they’re merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
Step 2A, Prong 2
The above-identified abstract idea in each of independent claims 1, 10, and 17 (and their respective dependent claims 2-7, 9 and 11-15) are not integrated into a practical application under 2019 PEG because the additional elements (identified above in independent claims 1, 10, and 17), either alone or in combination, generally link the use of the above-identified abstract idea to a particular technological environment or field of use. More specifically, the additional elements of: (a memory, a processor, an electronically controlled suspension (ECS) system, a solenoid, vehicle, and one or more sensors) are generically recited structural elements and generically recited computer elements in independent claims 1, 10, and 17 (and their respective dependent claims) which do not improve the functioning of a computer, or any other technology or technical field. Nor do these above-identified additional elements serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified additional elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract idea identified above in independent claims 1, 10, and 17 (and their respective dependent claims) is not integrated into a practical application under 2019 PEG.
Moreover, the above-identified abstract idea is not integrated into a practical application under 2019 PEG because the claimed method and system merely implements the above-identified abstract idea (e.g., mental process and certain method of organizing human activity) using rules (e.g., computer instructions) executed by a computer (e.g., a processor as claimed). In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in independent claims 1, 10, and 17 (and their respective dependent claims) is not integrated into a practical application under the 2019 PEG.
Accordingly, independent Claims 1, 10, and 17 (and their respective dependent claims) are each directed to an abstract idea under 2019 PEG.
Step 2B
None of claims 1-7, 9-15 and 17 include additional elements that are sufficient to amount to significantly more than the abstract idea for at least the following reasons.
These claims require the additional elements of: a memory, a processor, an electronically controlled suspension (ECS) system, a solenoid, vehicle, and one or more sensors as recited in independent claims 1, and 10, and a processor, an electronically controlled suspension (ECS) system, a solenoid, vehicle, and one or more sensors as recited in independent claim 17.
The above-identified additional elements are generically claimed structural components (i.e. an electronically controlled suspension (ECS) system, a solenoid, a vehicle, etc.) and generically claimed computer components (a processor) which enable the above-identified abstract idea(s) to be conducted by performing the basic functions of automating mental tasks (such as mathematical functions/operations (including comparing estimated/measured values)). The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, Versata Dev. Group, Inc. v. SAP Am., Inc. , 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
The Applicant’s specification, in [page 9, ln 8-21], states that “Referring to FIG. 2, the device 100 for detecting the abnormality of the solenoid valve of the ECS system according to the embodiment of the present disclosure may include a memory 110 and a processor 120.
The memory 110 stores one or more instructions. The one or more instructions may be executed by the processor 120.
The memory 110 may include a hardware device configured to store and execute program instructions. For example, the memory 110 may include a storage medium, such as a read-only memory (ROM), a random access memory (RAM), a flash memory, etc. Further, the memory 110 may include a magnetic medium such as a floppy disk or a magnetic tape, an optical medium such as a compact disc read only memory (CD-ROM) or a digital video disc (DVD), a magneto-optical medium such as a floptical disk, etc.
The processor 120 executes the one or more instructions. For example, the processor 120 may be a hardware unit that performs calculations and control within a computer. The processor 120 may include at least one arithmetic logic unit (ALU) and a register" This being the case, it seems that the processor is a generic processor, which is well understood, routine and conventional.
Accordingly, in light of Applicant’s specification, the claimed term “processor” is reasonably construed as to constitute a generic computing device. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear, from the claims themselves and the specification, that these limitations require no improved computer resources, just already available computers, with their already available basic functions, to use as tools in executing the claimed process.
Furthermore, Applicant’s specification does not describe any special programming or algorithms required for the processor. This lack of disclosure is acceptable under 35 U.S.C. §112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the computer arts. By omitting any specialized programming or algorithms, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the computer industry or arts. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional elements because it describes these additional elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a) (see Berkheimer memo from April 19, 2018, (III)(A)(1) on page 3). Adding hardware that performs “‘well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible (TLI Communications).
The recitation of the above-identified additional limitations in claims 1-7, 9-15 and 17 amounts to mere instructions to implement the abstract idea on a computer. Simply using 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 (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Here, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. v. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
For at least the above reasons, the methods and systems of claims 10-15 and 1-7, 9 and 17, respectively, are directed to applying an abstract idea (e.g., mental process or certain method of organizing human activity) on a general purpose computer without (i) improving the performance of the computer itself (as in McRO, Bascom and Enfish), or (ii) providing a technical solution to a problem in a technical field (as in DDR). In other words, none of claims 1-7, 9-15 and 17 provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in independent claims 1, 10 and 17 (and their dependent claims) do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment. That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. As such, the above-identified additional elements, when viewed as whole, do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, claims 1-7, 9-15 and 17 merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself (as in Bascom and Enfish), or (ii) provide a technical solution to a problem in a technical field (as in DDR).
Therefore, none of the claims 1-7, 9-15 and 17 amounts to significantly more than the abstract idea itself.
Accordingly, claims 1-7, 9-15 and 17 are not patent eligible and rejected under 35 U.S.C. 101 as being directed to abstract ideas implemented on a generic computer in view of the Supreme Court Decision in Alice Corporation Pty. Ltd. v. CLS Bank International, et al. and 2019 PEG.
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 1-7, 9-15 and 17 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 re claim 1, claim 1 recites, inter alia,
detecting abnormality of a solenoid valve included in the ECS system of a vehicle by comparing the at least one estimated value of the physical quantity obtained from the artificial neural network model with at least one measured value of the physical quantity measured by one or more sensors,
wherein the processor is configured to input error data related to a difference between the estimated value of the physical quantity obtained from the artificial neural network model and the measured value of the physical quantity measured by the one or more sensors into an abnormality detection model to determine whether the solenoid valve included in the ECS system is abnormal,
in lines 8-16. More specifically, the step of “detecting abnormality of a solenoid valve included in the ECS system” as recited in line 8 and the step of “input error data…into an abnormality detection model to determine whether the solenoid valve included in the ECS system is abnormal” as recited in lines 12-16 appear to be problematic. It seems that an abnormality of the solenoid valve is detected in line 8, however, in lines 12-16, it also seems that an abnormality of the solenoid valve is determined. While the examiner acknowledges that the steps taken to detect the abnormality of the solenoid valve, as recited in lines 8-11, appear to be different from those taken to determine an abnormality of the solenoid valve, as recited in lines 12-16, it also seems that the steps as recited in lines 8-11 arrive at the same conclusion as those as recited in lines 12-16. Further, the claim does not seem to distinguish any differences from the abnormality of the solenoid valve as in lines 8-11 from that as recited in lines 12-16 (i.e. it seems that the same sort of abnormality of the solenoid valve is determined using two different methods/steps). This being the case, it is unclear in the claim exactly what benefit that that the steps taken to detect the abnormality of the solenoid valve, as recited in lines 8-11 have over the steps as recited in lines 8-11, or vice versa. Additionally, since it seems that there are two different methods for determining an abnormality of the solenoid valve with in the claim, it is unclear in the claim what benefit is derived from determining an abnormality of the solenoid valve as recited in lines 12-16, since an abnormality of the solenoid valve has already been detected, as recited in lines 8-11. Claims 2-7 and 9 are further rejected for dependence upon a rejected claim.
In re claims 10 and 17, claims 10 and 17 recite limitations substantially similar to those as recited above in respect to claim 1, and are therefore, similarly rejected on substantially the same grounds as claim 1, as recited above, mutatis mutandis. Claims 11-15 are further rejected for dependence upon a rejected claim.
Claim Rejections - 35 USC § 103
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 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.
Claims 1-2, 4-5, 10-11, 13-14 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Bruno et al. (U.S. 20210291611) in view of Singh et al. (U.S. 20220155783).
Note: see the 35 U.S.C. 112 above regarding claims 1, 10 and 17.
In re claim 1, Bruno teaches a device comprising:
a memory (fig. 1; estimator module 14 is further coupled in reading and writing with a memory module M; [0035]) configured to store one or more instructions (fig. 1; estimator module 14 is arranged to execute a computer program or group of programs, for example stored locally; [0040]; Here, in [0040], it is understood that the instructions present within the programs are stored locally, which means that they either stored directly in the estimator module or in the connected memory module); and
a processor (necessarily present) configured to execute the one or more instructions comprising:
inputting input data representing a state of an electronically controlled suspension (ECS) system into a model (in step 120, the estimator module 14 acquires a respective mean driving current value of the control valve of each shock absorber…determined through the Skyhook control model;[0045]),
obtaining at least one estimated value of a physical quantity representing an output of the ECS system (fig. 4; in step 120, the estimator module 14 acquires a respective mean driving current value of the control valve of each shock absorber for which the degradation (i.e., of the front and rear shock absorbers) determined through the Skyhook control model implemented in the processing module 12 is estimated; [0045]; note: fig. 4 shows the flow chart, however, the steps in the flow chart are not written, and instead are labeled numerically; further note: Here, the estimator module 14 estimates a mean driving current value of the control valve for each shock absorber) from the model,
detecting abnormality of a solenoid valve included in the ECS system of a vehicle by comparing (In step 150, the estimator module 14 checks whether the mean driving current value of the control valve of each shock absorber …is between said lower threshold value N.sub.inf and said upper threshold value N.sub.sup. If this is the case, the estimator module concludes that there is no degradation of the shock absorber at step 160. If not, the estimator module detects a possible degradation condition and in step 170 identifies the shock absorber for which the driving current of the respective control valve is not consistent with the expected one; [0047]; Here, the checking is a comparison, with the outcome being used to determine degradation/no degradation, which is suggests an abnormality) the at least one estimated value of the physical quantity obtained from the model with at least one measured value of the physical quantity (in step 140, the estimator module 14 determines a road severity index (RSI) based on the vertical accelerations measured by the accelerometric sensors located at the front wheel hubs, and, through the reference model indicative of the nominal relation between the road severity index RSI and the mean driving current I of the control valve of each shock absorber, it determines the respective expected driving current or, preferably, an expected driving current range between a lower threshold value and an upper threshold value (represented in FIG. 3 by the N.sub.inf and N.sub.sup curves); [0046]; Here, the estimator module 14 uses vertical accelerations measured by the accelerometric sensors to determine an expected driving current or preferably an expected driving current range (driving current range between a lower threshold value and an upper threshold value)) measured by one or more sensors (in step 140, the estimator module 14 determines…based on the vertical accelerations measured by the accelerometric sensors located at the front wheel hubs…; [0046] ).
Bruno lacks
inputting input data representing a state of an electronically controlled suspension (ECS) system into an artificial neural network model,
obtaining at least one estimated value of a physical quantity representing an output of the ECS system from the artificial neural network model,
detecting abnormality of a solenoid valve included in the ECS system of a vehicle by comparing the at least one estimated value of the physical quantity obtained from the artificial neural network model with at least one measured value of the physical quantity measured by one or more sensors.
Singh teaches an analogous vehicular device (fig. 1) that uses a neural network (abstract; fig. 2a-2b) to control a vehicle suspension system (as indicated via. fig. 4; [0038]) and further teaches
inputting input data representing a state of an electronically controlled suspension (ECS) system into an artificial neural network model (The NN controller 36 receives the desired response or reference signal 52, vehicle state information and disturbances 56, and generates the control signal 54… For example, in an active suspension system 40, the sensors 28 and actuators 30 receive information about suspension component position and movement, as well as overall vehicle 10 pitch, roll, yaw, velocity, speed, acceleration, and the like.; [0041]; In an exemplary NN controller 36 for use in an active suspension system 40, the NN controller 36 receives inputs from the sensors 28 and actuators 30 of the active suspension system 40 in an input layer 101. The input layer for an ANN control system 36 for controlling an exemplary active suspension system 40 of the present disclosure includes eight neurons 48.; [0043]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the teachings of Bruno, to incorporate an artificial neural network model, as clearly suggested and taught by Singh, in order to continuously and actively command…one or more actuators of an active suspension system to alter position in response to a vehicle state information and disturbances in the shape of a road surface over which the vehicle is driving ([0018]) and in order to substantially eliminate the disturbances 56, the processor 20 continuously and actively commands one or more actuators 30 of an active control system 12 of the vehicle 10, such as the active suspension system 40 to alter position in response to the vehicle state information and disturbances 56 in a shape of a road surface 210 over which the vehicle 10 is driving ([0058]).
Further, one having ordinary skill in the art would have found it obvious before the effective filing date of the claimed invention to modify the teachings of Bruno by employing an artificial neural network model (as opposed to using the skyhook control model) as the means for controlling a vehicle suspension system in the manner as taught by Singh since both references teach art equivalent means for controlling a vehicle suspension.
Note: claim 1 further recites the limitation of
wherein the processor is configured to input error data related to a difference between the estimated value of the physical quantity obtained from the artificial neural network model and the measured value of the physical quantity measured by the one or more sensors into an abnormality detection model to determine whether the solenoid valve included in the ECS system is abnormal.
Here, the limitation of inputting error data related to a difference between the estimated value of the physical quantity obtained from the artificial neural network model and the measured value of the physical quantity measured by the one or more sensors, is essentially comparing the at least one estimated value of the physical quantity obtained from the artificial neural network model with the at least one measured value of the physical quantity measured by the one or more sensors. It is further understood that the comparison is performed via. a processor or controller. The newly amended claim limitation further requires that an abnormality of a solenoid valve is then detected/determined based upon this comparison/difference. This being the case, and the similarity in the claim language presented in lines 8-11 and also 12-16, it is likely that one having an ordinary level of would have arrived at the above claim limitation.
It is in this way that the teachings of Bruno, as modified by Singh, arrive at the claimed invention.
In re claim 2, Bruno as modified by Singh teaches the device of claim 1, and Bruno further teaches wherein
the input data comprises one or more signals obtained through a controller area network (CAN) of the vehicle (this particular operating condition is easily identifiable in real time by a suspension control system including a processing module coupled to an on-board CAN bus through which a vehicle lateral dynamics control unit and/or vehicle lateral dynamics sensors such as yaw and lateral acceleration sensors are in communication; [0020]).
In re claim 4, Bruno as modified by Singh teaches the device of claim 1, and Bruno further teaches wherein
the input data includes a vertical acceleration of a wheel of the vehicle and a vertical acceleration of a body of the vehicle (processing module 12 is coupled to sensor assemblies 20, 22 (generally including accelerometers), respectively coupled to the vehicle body and wheel hubs on at least one vehicle axle, preferably the front axle, adapted to detect relative acceleration or relative movement between the vehicle body and the wheel hub; [0029]; note: as indicated via. fig. 1, data from processing module 12 is feed back into estimator 14, and thus acceleration data is considered to be input date for estimator 14).
In re claim 5, Bruno as modified by Singh teaches the device of claim 4, and Bruno further teaches wherein
the input data further includes at least one of
a speed of the wheel of the vehicle (acquiring respective relative acceleration or speed data of at least the front wheels of the vehicle with respect to the vehicle body; [claim 1]),
a steering angle of the vehicle,
a steering angular velocity of the vehicle,
a displacement of an accelerator pedal of the vehicle,
a displacement of a brake pedal of the vehicle, and
a lateral acceleration of the vehicle.
In re claim 10, see claim 1 above, mutatis mutandis.
In re claim 11, see claims 2 and 10 above.
In re claim 13, see claims 4 and 10 above.
In re claim 14, see claims 5 and 10 above.
In re claim 17, see claim 1 above, mutatis mutandis.
Claims 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Bruno et al. (U.S. 20210291611) in view of Singh et al. (U.S. 20220155783) and in further view of Kanda et. al. (U.S. 20150290995).
In re claim 3, Bruno as modified by Singh teaches the device of claim 1, but lack wherein
the physical quantity includes a damping force of the ECS system.
Kanda teaches an analogous vehicle suspension control system (abstract; fig. 1-2) and further teaches
an analogous physical quantity includes a damping force of the ECS system (a skyhook control means (90, 25) for controlling the damping force of the variable damper (6) according to the computed sprung velocity (S.sub.2) and the computed stroke speed (Ss); [0028]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the teachings of Bruno, to incorporate wherein the physical quantity includes a damping force of the ECS system, as clearly suggested and taught by Kanda, in order to improve the reliability of the suspension system ([0015]) and in order to contribute to the improvement of the ride quality ([0017]).
In re claim 12, see claim 3 above.
Claims 6-7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Bruno et al. (U.S. 20210291611) in view of Singh et al. (U.S. 20220155783) and in further view of Yan et al. (U.S. 20200322366).
In re claim 6, Bruno as modified by Singh teaches the device of claim 1, but lack wherein
the artificial neural network model includes
a generative adversarial network (GAN) including a generator configured to
receive the input data representing the state of the ESC system and
generate the estimated value based on the received input data.
Yan teaches an industrial control system for use with vehicles (although some embodiments are focused on gas turbines, any of the embodiments described herein could be applied to other types of systems including…autonomous vehicles (including automobiles, trucks, drones, submarines, etc.); [0086]) and further teaches
the artificial neural network model includes
a generative adversarial network (GAN) (fig. 3; complementary Generative Adversarial Network (“GAN”) 345; [0040]) including a generator (fig. 3; generator network 346; [0040]) configured to
receive the input data representing the state of the ESC system (generator network 346 may be trained to learn a generative distribution that is close to the complementary distribution of the normal data; [0040]; Here, the training data used to train the generator network 346 is considered to be the input data) and
generate the estimated value based on the received input data (producing novel candidates of synthetic abnormal operation data that the discriminator network 347 interprets as not synthesized (are part of a true data distribution); [0040]; Here, the production of novel candidate values of abnormal operation data, is essentially an estimated value based upon complementary distribution of the normal data (i.e. abnormal operation data is close to the complementary distribution of the normal data)).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the teachings of Bruno, to incorporate wherein the artificial neural network model includes a generative adversarial network (GAN), as clearly suggested and taught by Yan, in order to improve the reliability of the suspension system ([0015]) and in order to let a system use supervised AD methods (achieving improved detection performance) ([0040]).
In re claim 7, Bruno as modified by Singh, and further modified by Yan teach the device of claim 6, wherein
the artificial neural network model further includes
a discriminator (fig. 3; discriminator network 347; [0040]) configured to
receive measurement data (as indicated in fig. 3; normal operation data samples) including
the input data representing the state of the system (fig. 3; a discriminator network 347 is trained to distinguish the complementary samples from the real normal data; [0040]; Here, the complementary samples is input data representing the state of the system, which is different from actual system data.) and
the measured value of the physical quantity measured by the one or more sensors (fig. 3; a discriminator network 347 is trained to distinguish the complementary samples from the real normal data; [0040]; Here, the real normal data is sensor data, measured by one or more sensors, as suggested via. fig. 1 and fig. 3 and also [0028], which states “The abnormality detection model 155 may, for example, monitor streams of data from the monitoring nodes 110 comprising data from sensor nodes, actuator nodes, and/or any other critical monitoring nodes (e.g., sensor nodes MN.sub.1 through MN.sub.N) and automatically output global and local abnormality status signal”.) and
output a discrimination value (fig. 3, decision) for the measurement data (as indicated in fig. 3, decision real normal or generated abnormal).
Yan lacks
an ESC system
Bruno further teaches
an ESC system, as explained above.
However, one having an ordinary level of skill within the art, would recognize that Bruno as modified by Yan, would result in the using the sensor input data of the ESC system of Bruno, as the input data in the discriminator as taught by Yan, thus arriving at the claimed invention.
Motivation to combine is given above in claim 6 above.
In re claim 15, see claims 6-7 and 10 above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 JOHN D BAILEY whose telephone number is (571)272-5692. The examiner can normally be reached M-F 8-5.
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/JOHN D BAILEY/Examiner, Art Unit 3747
/LOGAN M KRAFT/Supervisory Patent Examiner, Art Unit 3747