DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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.
Claim 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
A claim that recites an abstract idea, a law of nature, or a natural phenomenon is directed to a judicial exception. Abstract ideas include the following groupings of subject matter, when recited as such in a claim limitation: (a) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; (b) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and (c) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion). See the 2019 Revised Patent Subject Matter Eligibility Guidance.
Even when a judicial element is recited in the claim, an additional claim element(s) that integrates the judicial exception into a practical application of that exception renders the claim eligible under §101. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The following examples are indicative that an additional element or combination of elements may integrate the judicial exception into a practical application:
the additional element(s) reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
the additional element(s) that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
the additional element(s) implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
the additional element(s) effects a transformation or reduction of a particular article to a different state or thing; and
the additional element(s) applies or uses 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 more than a drafting effort designed to monopolize the exception.
Examples in which the judicial exception has not been integrated into a practical application include:
the additional element(s) merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
the additional element(s) adds insignificant extra-solution activity to the judicial exception; and
the additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
See the 2019 Revised Patent Subject Matter Eligibility Guidance.
Claims 1, 7, & 11-12 recite detecting a shifting operation of the vehicle clutch, determine the clutch temperature depending on the detection of a shifting operation, outputting the determined clutch temperature and determine the clutch temperature, as drafted, is a device & process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer elements. The claim is practically able to be performed in the mind. For example, but for the “A method for determining a clutch temperature of a vehicle clutch by means of a neural network, the method comprising, activating the neural network, inputting at least one value of at least one operating parameter of the vehicle clutch as input data into the neural network, the neural network, deactivating the neural network, A method for training a neural network (12) adapted to determine a clutch temperature (KT) of a vehicle clutch, wherein the method comprises the following steps, A control device (10) for determining a clutch temperature of a vehicle clutch, the control device comprising, a computer-readable storage medium, on which a neural network for determining the clutch temperature is stored; a detection device, an activation device, an input device, an output device, a deactivation device, providing (TS 1) at least one value (l 8a, 18b, 18c) of an operating parameter of the vehicle clutch as input data (20a); providing (TS2) values for the clutch temperature as output data; and training (TS3) the neural network (12) with the input data (20a) and the output data in order to learn a relationship between the input data and the clutch temperature” language, “detecting a shifting operation of the vehicle clutch, determine the clutch temperature depending on the detection of a shifting operation, outputting the determined clutch temperature and determine the clutch temperature” in the context of this claim encompasses the user calculating a clutch temperature using sensors while vehicle is tested. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – using “A method for determining a clutch temperature of a vehicle clutch by means of a neural network, the method comprising, activating the neural network, inputting at least one value of at least one operating parameter of the vehicle clutch as input data into the neural network, the neural network, deactivating the neural network, A method for training a neural network (12) adapted to determine a clutch temperature (KT) of a vehicle clutch, wherein the method comprises the following steps, A control device (10) for determining a clutch temperature of a vehicle clutch, the control device comprising, a computer-readable storage medium, on which a neural network for determining the clutch temperature is stored; a detection device, an activation device, an input device, an output device, a deactivation device, providing (TS 1) at least one value (l 8a, 18b, 18c) of an operating parameter of the vehicle clutch as input data (20a); providing (TS2) values for the clutch temperature as output data; and training (TS3) the neural network (12) with the input data (20a) and the output data in order to learn a relationship between the input data and the clutch temperature”. The devices are recited at a high-level of generality (i.e., device configured to detect temperature of a clutch) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using “A method for determining a clutch temperature of a vehicle clutch by means of a neural network, the method comprising, activating the neural network, inputting at least one value of at least one operating parameter of the vehicle clutch as input data into the neural network, the neural network, deactivating the neural network, A method for training a neural network (12) adapted to determine a clutch temperature (KT) of a vehicle clutch, wherein the method comprises the following steps, A control device (10) for determining a clutch temperature of a vehicle clutch, the control device comprising, a computer-readable storage medium, on which a neural network for determining the clutch temperature is stored; a detection device, an activation device, an input device, an output device, a deactivation device, providing (TS 1) at least one value (l 8a, 18b, 18c) of an operating parameter of the vehicle clutch as input data (20a); providing (TS2) values for the clutch temperature as output data; and training (TS3) the neural network (12) with the input data (20a) and the output data in order to learn a relationship between the input data and the clutch temperature,”, amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Similarly for claims 2-6, 8-10, & 13, is a device and process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, in the context of this claim encompasses the user utilizing different input data to the neural network at predetermined time intervals to detect the temperature of the clutch. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The devices are recited at a high-level of generality (i.e., device configured to detect temperature of a clutch) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claims 11-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
"Computer programs claimed as computer listings per se, i.e., the descriptions or expressions of the programs, are not physical 'things.' They are neither computer components nor statutory processes, as they are not 'acts' being performed." MPEP §2106.01 I. Because the claims recite only abstractions that are neither "things" nor "acts," the claims are not within one of the four statutory classes of invention. Because the claims are not within one of the four statutory classes of invention, the claims are rejected under 35 U.S.C. §101.
Regarding Claims 11-12, require a computer readable storage medium, which stores a program. The specification does not set forth what constitutes the computer readable storage medium as being non-transitory, and therefore, in view of the ordinary and customary meaning of computer readable media and in accordance with the broadest reasonable interpretation of the claim, said device could be directed towards a transitory propagating signal per se and considered to be non-statutory subject matter. The claim states that these tangible devices are stored in a computer program product which can be interpreted as software, in that case hardware cannot be stored in software (program product). See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) and Interim Examination Instructions for Evaluating Subject Matter Eligibility Under 35 U.S.C. 101, Aug 24, 2009, p. 2. Please refer to MPEP 2111.01 and the USPTO’s “Subject Matter Eligibility of Computer Readable Media” memorandum dated January 26, 2010, http://www.uspto.gov/patents/law/notices/101_crm_20100127.pdf.
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)(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.
Claim(s) 7 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2019/0195292A1 (“Pan”).
As per claim 7 Pan discloses
A method for training a neural network (12) adapted to determine a clutch temperature (KT) of a vehicle clutch (see at least Pan, para. [0044]: The simulation data is used to train a machine learning process, such as a neural network or other type of machine-learning technique known in the art, to generate a surrogate model that can be executed in real time by the ECM 140 of the machine 10 to predict local peak temperatures in the friction clutch 80-90 under the current operating conditions.), wherein the method comprises the following steps:
providing (TSl) at least one value (18a, 18b, 18c) of an operating parameter of the vehicle clutch as input data (20a) (see at least Pan, para. [0051]: As the machine control parameters are collected at the block 234, control may pass to a block236 where any necessary calculations are performed to convert collected machine operating parameters to input parameters for the surrogate model of the friction clutch 80-90. Such calculations or conversions may be necessary where operating parameter conversions have been generated at the block 218 as discussed above.);
providing (TS2) values for the clutch temperature as output data (see at least Pan, para. [0052]: With the machine operating parameters collected at the block 234 and any necessary parameter conversions are performed at the block 236, control may pass to a block 238 where the surrogate model input parameters are input to the surrogate model to determine the local peak temperature of the friction clutch 80-90 at the current operating conditions. As discussed above, the surrogate model was developed from the FEA simulation data to determine the local peak temperature without executing the calculations necessary in an FEA model simulation. Therefore, the surrogate model does not require an undue amount of processing resources of the ECM 140. After executing the surrogate model with the current machine operating parameters at the block 238, control may pass toa block 240 where local peak temperature data output by the surrogate model is transmitted to a machine monitoring location that may be remote from the machine 10.); and
training (TS2) the neural network (12) with the input data (20a) and the output data in order to learn a relationship between the input data and the clutch temperature (see at least Pan, para. [0044]: After the comprehensive simulation data is generated at the block 214, control may pass back to a block 216 where a surrogate model for the friction clutch 80-90 is generated using the simulation results from the block 214. The simulation data is used to train a machine learning process, such as a neural network or other type of machine-learning technique known in the art, to generate a surrogate model that can be executed in real time by the ECM 140 of the machine 10 to predict local peak temperatures in the friction clutch 80-90 under the current operating conditions. During the training, the machine learning process performs the necessary calculations on the simulation data to identify the critical combinations of operating conditions and streamline the local peak temperature discriminations that will be performed by the ECM 140. para. [0052]: After executing the surrogate model with the current machine operating parameters at the block 238, control may pass toa block 240 where local peak temperature data output by the surrogate model is transmitted to a machine monitoring location that may be remote from the machine 10.).
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.
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. Applicant is advised of the obligation under 37 CFR 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2, & 9-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pan, in view of US 2023/0394286A1 (“Qin”).
As per claim 1 Pan discloses
A method for determining a clutch temperature of a vehicle clutch by means of a neural network (12) (see at least Pan, para. [0044]: The simulation data is used to train a machine learning process, such as a neural network or other type of machine-learning technique known in the art, to generate a surrogate model that can be executed in real time by the ECM 140 of the machine 10 to predict local peak temperatures in the friction clutch 80-90 under the current operating conditions.), the method comprising:
detecting (BS1) a shifting operation (24) of the vehicle clutch (see at least Pan, para. [0049]: The manual or automatic input can cause the ECM 140 to transmit clutch control currents to one or more of the clutch control valves 120-130 to engage the corresponding friction clutches 80-90 and shift the transmission 12 to one of the gears according to the truth table 92.For example, the ECM 140 may transmit control signals to the clutch control valves 120, 126, 130 to engage the friction clutches 80, 86, 90, respectively, to shift into the first forward gear 1F.);
activating (BS2) the neural network (12) to determine the clutch temperature, depending on the detection of a shifting operation (24) (see at least Pan, para. [0050]: After the transmission 12 is engaged at the block 232, control passes to a block 234 where the machine operating parameters are detected and collected by the ECM 140. The machine operating parameters can include various operating parameters that can be used to determine the local peak temperatures in the friction clutch 80-90 as discussed above. The clutch control current transmitted to the clutch control valves 120-130 of the friction clutch 80-90 being monitored may be provided by the transmission control module being executed by the ECM 140.);
inputting (BS3) at least one value (18a, 18b, 18c) of at least one operating parameter of the vehicle clutch as input data (20a) into the neural network (12) (see at least Pan, para. [0051]: As the machine control parameters are collected at the block 234, control may pass to a block236 where any necessary calculations are performed to convert collected machine operating parameters to input parameters for the surrogate model of the friction clutch 80-90. Such calculations or conversions may be necessary where operating parameter conversions have been generated at the block 218 as discussed above.);
determining (BS4) a clutch temperature (KT) by the neural network (12), based on the input data (20a) and a relationship of the at least one operating parameter to the clutch temperature (KT) learned from the neural network (12) (see at least Pan, para. [0052]: With the machine operating parameters collected at the block 234 and any necessary parameter conversions are performed at the block 236, control may pass to a block 238 where the surrogate model input parameters are input to the surrogate model to determine the local peak temperature of the friction clutch 80-90 at the current operating conditions. As discussed above, the surrogate model was developed from the FEA simulation data to determine the local peak temperature without executing the calculations necessary in an FEA model simulation. Therefore, the surrogate model does not require an undue amount of processing resources of the ECM 140. After executing the surrogate model with the current machine operating parameters at the block 238, control may pass toa block 240 where local peak temperature data output by the surrogate model is transmitted to a machine monitoring location that may be remote from the machine 10.);
outputting (BSS) the determined clutch temperature (KT) by the neural network (12) (see at least Pan, para. [0052]: After executing the surrogate model with the current machine operating parameters at the block 238, control may pass toa block 240 where local peak temperature data output by the surrogate model is transmitted to a machine monitoring location that may be remote from the machine 10.),
completing calculation from neural network to determine the clutch temperature, depending on the determined clutch temperature (KT) (see at least Pan, para. [0044]: After the comprehensive simulation data is generated at the block 214, control may pass back to a block 216 where a surrogate model for the friction clutch 80-90 is generated using the simulation results from the block 214. The simulation data is used to train a machine learning process, such as a neural network or other type of machine-learning technique known in the art, to generate a surrogate model that can be executed in real time by the ECM 140 of the machine 10 to predict local peak temperatures in the friction clutch 80-90 under the current operating conditions. During the training, the machine learning process performs the necessary calculations on the simulation data to identify the critical combinations of operating conditions and streamline the local peak temperature discriminations that will be performed by the ECM 140. para. [0052]: After executing the surrogate model with the current machine operating parameters at the block 238, control may pass toa block 240 where local peak temperature data output by the surrogate model is transmitted to a machine monitoring location that may be remote from the machine 10.).
However Pan does not explicitly disclose
deactivating (BS6) the neural network (12).
Qin teaches
deactivating (BS6) the neural network (see at least Qin, para. [0066]: When the error conditions are met, the current training process is stopped, and the network parameters of the current neural network are temporarily solidified and stored in the model file.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pan to incorporate the teaching of deactivating (BS6) the neural network of Qin with a reasonable expectation of success in order for the temperature of the battery to be stabilized, and avoiding the battery overheating (see at least Qin, para. [0066]).
As per claim 2 Pan teaches
wherein reengaging the clutch is performed when the determined clutch temperature (KT) is determined to be below a defined threshold value (see at least Pan, para. [0056]: After the friction clutch 80-90 is disengaged and the transmission 12 is shifted at the block 246, control may pass to a block 248 to determine whether the ECM 140 is still receiving commands to engage the transmission 12. If the transmission 12 is to remain engaged, control may pass back to the block 234 to continue detecting and collecting machine operating parameter values and evaluating the local peak temperature of the friction clutch 80-90. If the transmission 12 is disengaged at the block 248, the routine 230 may cease execution until the transmission 12 is reengaged.).
However Pan does not explicitly disclose
wherein deactivating the neural network (12) is performed when the determined temperature is determined to be below a defined threshold value.
Qin teaches
wherein deactivating the neural network (12) is performed when the determined temperature is determined to be below a defined threshold value (see at least Qin, para. [0066]: The data matrix is used as a training data set for the training of a deep learning model. According to the set thresholds (for example, the current threshold is 3 A and the allowable error is 0.25 A, the temperature threshold is 60 degrees and the allowable error is 1 degree), the deep learning model training process is guided and the feed-forward neural network parameters are updated and iterated. When the error conditions are met, the current training process is stopped, and the network parameters of the current neural network are temporarily solidified and stored in the model file.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pan to incorporate the teaching of wherein deactivating the neural network (12) is performed when the determined temperature is determined to be below a defined threshold value of Qin with a reasonable expectation of success in order for the temperature of the battery to be stabilized, and avoiding the battery overheating (see at least Qin, para. [0066]).
As per claim 9 Pan discloses
wherein training the neural network (12) comprises: providing (TS 1) at least one value (18a, 18b, l 8c) of an operating parameter of the vehicle clutch as input data (20a) (see at least Pan, para. [0051]: As the machine control parameters are collected at the block 234, control may pass to a block236 where any necessary calculations are performed to convert collected machine operating parameters to input parameters for the surrogate model of the friction clutch 80-90. Such calculations or conversions may be necessary where operating parameter conversions have been generated at the block 218 as discussed above.);
providing (TS2) values for the clutch temperature as output data (see at least Pan, para. [0052]: With the machine operating parameters collected at the block 234 and any necessary parameter conversions are performed at the block 236, control may pass to a block 238 where the surrogate model input parameters are input to the surrogate model to determine the local peak temperature of the friction clutch 80-90 at the current operating conditions. As discussed above, the surrogate model was developed from the FEA simulation data to determine the local peak temperature without executing the calculations necessary in an FEA model simulation. Therefore, the surrogate model does not require an undue amount of processing resources of the ECM 140. After executing the surrogate model with the current machine operating parameters at the block 238, control may pass toa block 240 where local peak temperature data output by the surrogate model is transmitted to a machine monitoring location that may be remote from the machine 10.); and
training (TS3) the neural network (12) with the input data (20a) and the output data in order to learn a relationship between the input data and the clutch temperature (see at least Pan, para. [0044]: After the comprehensive simulation data is generated at the block 214, control may pass back to a block 216 where a surrogate model for the friction clutch 80-90 is generated using the simulation results from the block 214. The simulation data is used to train a machine learning process, such as a neural network or other type of machine-learning technique known in the art, to generate a surrogate model that can be executed in real time by the ECM 140 of the machine 10 to predict local peak temperatures in the friction clutch 80-90 under the current operating conditions. During the training, the machine learning process performs the necessary calculations on the simulation data to identify the critical combinations of operating conditions and streamline the local peak temperature discriminations that will be performed by the ECM 140. para. [0052]: After executing the surrogate model with the current machine operating parameters at the block 238, control may pass toa block 240 where local peak temperature data output by the surrogate model is transmitted to a machine monitoring location that may be remote from the machine 10.).
As per claim 10 Pan discloses
comprising: selecting the at least one operating parameter of the vehicle clutch from at least one of (i) a torque of a drive axis of a vehicle engine, which is in mechanical operative connection with the vehicle clutch, (ii) a speed difference between two rotating clutch elements of the vehicle clutch, (iii) a rotational speed of a rotating clutch element of the vehicle clutch, (iv) a mechanical pressure acting on a clutch element of the vehicle clutch, (v) a current intensity of an electrical current flowing through a clutch element of the vehicle clutch, and (vi) a sump temperature of a vehicle transmission at the start of a shifting operation of the vehicle clutch (see at least Pan, para. [0044-0045]: As discussed, operating parameter values of relevant operating parameters will be input into the surrogate model generated at the block 216 to determine the local peak temperatures of the friction clutch 80-90 in real time. Some of the relevant operating parameters may be readily measured and input to the surrogate model. For example, the rotational speeds of the input member or shaft 16 and the output member or shaft 20 may be measured by the transmission speed sensors 152, 154 discussed above and have the sensed speeds input to the surrogate model. Other operating parameter values are not readily directly measured in real time. For example, it may not be practical to place a pressure sensor inside the friction clutch 80-90 to sense a pressure in a hydraulic piston. Also, torque of the engine output shaft that determines a load on the friction clutch 80-90 may not be able to be directly measured without reducing the torque and the efficiency of the engine 14. Consequently, such operating parameter values must be derived from other measurable or known operating parameters. ).
As per claim 11 Pan discloses
A control device (10) for determining a clutch temperature of a vehicle clutch (see at least Pan, para. [0044]: The simulation data is used to train a machine learning process, such as a neural network or other type of machine-learning technique known in the art, to generate a surrogate model that can be executed in real time by the ECM 140 of the machine 10 to predict local peak temperatures in the friction clutch 80-90 under the current operating conditions.), the control device comprising:
a computer-readable storage medium (14), on which a neural network (12) for determining the clutch temperature is stored (see at least Pan, para. [0044]: The simulation data is used to train a machine learning process, such as a neural network or other type of machine-learning technique known in the art, to generate a surrogate model that can be executed in real time by the ECM 140 of the machine 10 to predict local peak temperatures in the friction clutch 80-90 under the current operating conditions.);
a detection device (22) for detecting a shifting operation (24) of the vehicle clutch (see at least Pan, para. [0049]: The manual or automatic input can cause the ECM 140 to transmit clutch control currents to one or more of the clutch control valves 120-130 to engage the corresponding friction clutches 80-90 and shift the transmission 12 to one of the gears according to the truth table 92.For example, the ECM 140 may transmit control signals to the clutch control valves 120, 126, 130 to engage the friction clutches 80, 86, 90, respectively, to shift into the first forward gear 1F.);
an activation device (28) for activating (30) the neural network (12), depending on the detection (24) of a shifting operation (see at least Pan, para. [0050]: After the transmission 12 is engaged at the block 232, control passes to a block 234 where the machine operating parameters are detected and collected by the ECM 140. The machine operating parameters can include various operating parameters that can be used to determine the local peak temperatures in the friction clutch 80-90 as discussed above. The clutch control current transmitted to the clutch control valves 120-130 of the friction clutch 80-90 being monitored may be provided by the transmission control module being executed by the ECM 140.);
an input device (16) for inputting input data (20a, 20b) into the neural network (l2), wherein the input data (20a, 20b) comprises at least one value ( 18a, 18b, 18c) of at least one operating parameter of the vehicle clutch (see at least Pan, para. [0051]: As the machine control parameters are collected at the block 234, control may pass to a block236 where any necessary calculations are performed to convert collected machine operating parameters to input parameters for the surrogate model of the friction clutch 80-90. Such calculations or conversions may be necessary where operating parameter conversions have been generated at the block 218 as discussed above.);
an output device (40) for outputting the determined clutch temperature (KT) by the neural network (12) (see at least Pan, para. [0052]: With the machine operating parameters collected at the block 234 and any necessary parameter conversions are performed at the block 236, control may pass to a block 238 where the surrogate model input parameters are input to the surrogate model to determine the local peak temperature of the friction clutch 80-90 at the current operating conditions. As discussed above, the surrogate model was developed from the FEA simulation data to determine the local peak temperature without executing the calculations necessary in an FEA model simulation. Therefore, the surrogate model does not require an undue amount of processing resources of the ECM 140. After executing the surrogate model with the current machine operating parameters at the block 238, control may pass toa block 240 where local peak temperature data output by the surrogate model is transmitted to a machine monitoring location that may be remote from the machine 10.); and
completing calculation from neural network to determine the clutch temperature, depending on the determined clutch temperature (KT) (see at least Pan, para. [0044]: After the comprehensive simulation data is generated at the block 214, control may pass back to a block 216 where a surrogate model for the friction clutch 80-90 is generated using the simulation results from the block 214. The simulation data is used to train a machine learning process, such as a neural network or other type of machine-learning technique known in the art, to generate a surrogate model that can be executed in real time by the ECM 140 of the machine 10 to predict local peak temperatures in the friction clutch 80-90 under the current operating conditions. During the training, the machine learning process performs the necessary calculations on the simulation data to identify the critical combinations of operating conditions and streamline the local peak temperature discriminations that will be performed by the ECM 140. para. [0052]: After executing the surrogate model with the current machine operating parameters at the block 238, control may pass toa block 240 where local peak temperature data output by the surrogate model is transmitted to a machine monitoring location that may be remote from the machine 10.).
However Pan does not explicitly disclose
a deactivation device (42) for deactivating (44) the neural network (12).
Qin teaches
a deactivation device (42) for deactivating (44) the neural network (12) (see at least Qin, para. [0066]: When the error conditions are met, the current training process is stopped, and the network parameters of the current neural network are temporarily solidified and stored in the model file.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pan to incorporate the teaching of a deactivation device (42) for deactivating (44) the neural network (12) of Qin with a reasonable expectation of success in order for the temperature of the battery to be stabilized, and avoiding the battery overheating (see at least Qin, para. [0066]).
As per claim 12 Pan discloses
wherein the clutch temperature (KT) is determined by a neural network (12) configured to perform the following steps: detecting (BS 1) a shifting operation (24) of the vehicle clutch (see at least Pan, para. [0049]: The manual or automatic input can cause the ECM 140 to transmit clutch control currents to one or more of the clutch control valves 120-130 to engage the corresponding friction clutches 80-90 and shift the transmission 12 to one of the gears according to the truth table 92.For example, the ECM 140 may transmit control signals to the clutch control valves 120, 126, 130 to engage the friction clutches 80, 86, 90, respectively, to shift into the first forward gear 1F.);
activating (BS2) the neural network (12) to determine the clutch temperature depending on the detection of a shifting operation (24) (see at least Pan, para. [0050]: After the transmission 12 is engaged at the block 232, control passes to a block 234 where the machine operating parameters are detected and collected by the ECM 140. The machine operating parameters can include various operating parameters that can be used to determine the local peak temperatures in the friction clutch 80-90 as discussed above. The clutch control current transmitted to the clutch control valves 120-130 of the friction clutch 80-90 being monitored may be provided by the transmission control module being executed by the ECM 140.);
inputting (BS3) at least one value (18a, 18b, 18c) of at least one operating parameter of the vehicle clutch as input data (20a) into the neural network (12) (see at least Pan, para. [0051]: As the machine control parameters are collected at the block 234, control may pass to a block236 where any necessary calculations are performed to convert collected machine operating parameters to input parameters for the surrogate model of the friction clutch 80-90. Such calculations or conversions may be necessary where operating parameter conversions have been generated at the block 218 as discussed above.);
determining (BS4) a clutch temperature (KT) by the neural network (12), based on the input data (20a) and a relationship of the at least one operating parameter to the clutch temperature (KT) learned from the neural network (12) (see at least Pan, para. [0052]: With the machine operating parameters collected at the block 234 and any necessary parameter conversions are performed at the block 236, control may pass to a block 238 where the surrogate model input parameters are input to the surrogate model to determine the local peak temperature of the friction clutch 80-90 at the current operating conditions. As discussed above, the surrogate model was developed from the FEA simulation data to determine the local peak temperature without executing the calculations necessary in an FEA model simulation. Therefore, the surrogate model does not require an undue amount of processing resources of the ECM 140. After executing the surrogate model with the current machine operating parameters at the block 238, control may pass toa block 240 where local peak temperature data output by the surrogate model is transmitted to a machine monitoring location that may be remote from the machine 10.);
outputting (BS5) the determined clutch temperature (KT) by the neural network (12) (see at least Pan, para. [0052]: After executing the surrogate model with the current machine operating parameters at the block 238, control may pass toa block 240 where local peak temperature data output by the surrogate model is transmitted to a machine monitoring location that may be remote from the machine 10.); and
completing calculation from neural network to determine the clutch temperature, depending on the determined clutch temperature (KT) (see at least Pan, para. [0044]: After the comprehensive simulation data is generated at the block 214, control may pass back to a block 216 where a surrogate model for the friction clutch 80-90 is generated using the simulation results from the block 214. The simulation data is used to train a machine learning process, such as a neural network or other type of machine-learning technique known in the art, to generate a surrogate model that can be executed in real time by the ECM 140 of the machine 10 to predict local peak temperatures in the friction clutch 80-90 under the current operating conditions. During the training, the machine learning process performs the necessary calculations on the simulation data to identify the critical combinations of operating conditions and streamline the local peak temperature discriminations that will be performed by the ECM 140. para. [0052]: After executing the surrogate model with the current machine operating parameters at the block 238, control may pass toa block 240 where local peak temperature data output by the surrogate model is transmitted to a machine monitoring location that may be remote from the machine 10.);
the neural network (l2) was trained according to the following steps:
providing (TS1) at least one value (l 8a, 18b, 18c) of an operating parameter of the vehicle clutch as input data (20a) (see at least Pan, para. [0051]: As the machine control parameters are collected at the block 234, control may pass to a block236 where any necessary calculations are performed to convert collected machine operating parameters to input parameters for the surrogate model of the friction clutch 80-90. Such calculations or conversions may be necessary where operating parameter conversions have been generated at the block 218 as discussed above.);
providing (TS2) values for the clutch temperature as output data (see at least Pan, para. [0052]: With the machine operating parameters collected at the block 234 and any necessary parameter conversions are performed at the block 236, control may pass to a block 238 where the surrogate model input parameters are input to the surrogate model to determine the local peak temperature of the friction clutch 80-90 at the current operating conditions. As discussed above, the surrogate model was developed from the FEA simulation data to determine the local peak temperature without executing the calculations necessary in an FEA model simulation. Therefore, the surrogate model does not require an undue amount of processing resources of the ECM 140. After executing the surrogate model with the current machine operating parameters at the block 238, control may pass toa block 240 where local peak temperature data output by the surrogate model is transmitted to a machine monitoring location that may be remote from the machine 10.); and
training (TS3) the neural network (12) with the input data (20a) and the output data in order to learn a relationship between the input data and the clutch temperature (see at least Pan, para. [0044]: After the comprehensive simulation data is generated at the block 214, control may pass back to a block 216 where a surrogate model for the friction clutch 80-90 is generated using the simulation results from the block 214. The simulation data is used to train a machine learning process, such as a neural network or other type of machine-learning technique known in the art, to generate a surrogate model that can be executed in real time by the ECM 140 of the machine 10 to predict local peak temperatures in the friction clutch 80-90 under the current operating conditions. During the training, the machine learning process performs the necessary calculations on the simulation data to identify the critical combinations of operating conditions and streamline the local peak temperature discriminations that will be performed by the ECM 140. para. [0052]: After executing the surrogate model with the current machine operating parameters at the block 238, control may pass toa block 240 where local peak temperature data output by the surrogate model is transmitted to a machine monitoring location that may be remote from the machine 10.).
However Pan does not explicitly disclose
deactivating (BS6) the neural network (12).
Qin teaches
deactivating (BS6) the neural network (see at least Qin, para. [0066]: When the error conditions are met, the current training process is stopped, and the network parameters of the current neural network are temporarily solidified and stored in the model file.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pan to incorporate the teaching of deactivating (BS6) the neural network of Qin with a reasonable expectation of success in order for the temperature of the battery to be stabilized, and avoiding the battery overheating (see at least Qin, para. [0066]).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pan, in view of Qin, in view of US 11525490B1 (“Mahmud”).
As per claim 3 Pan does not explicitly disclose
wherein determining the clutch temperature is performed by the neural network (12) within a predetermined time interval.
Mahmud teaches
wherein determining the clutch temperature is performed by the neural network (12) within a predetermined time interval (see at least Mahmud, col. 12 lines 35-44: In one form, the clutch temperature estimation module 304 uses one or more counters to track the power increase duration and the power decrease duration. In one variation, the clutch temperature estimation module 304 is configured to determine the approximated temperature increase/decrease without tracking the power increase duration or the power decrease duration.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pan to incorporate the teaching of determining the clutch temperature is performed by the neural network (12) within a predetermined time interval of Mahmud with a reasonable expectation of success in order to improve accuracy of the initial clutch temperature (see at least Mahmud, col. 11 lines 39-40).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pan, in view of Qin, in view of Mahmud, in view of US 2014/0172212A1 (“Park”).
As per claim 4 Pan does not explicitly disclose
comprising specifying the time interval is based on the determined clutch temperature.
Park teaches
comprising specifying the time interval is based on the determined clutch temperature (see at least Park, para. [0025-0029]: The setting of a learning period of the engine clutch may further include reducing a DC learning period value that is set by the DC number of times value of the hybrid vehicle to correspond to the setting temperature exceeding accumulation number of times or the setting temperature exceeding accumulation time; and setting the DC learning period value that is reduced by the setting temperature exceeding accumulation number of times or the setting temperature exceeding accumulation time as a new learning period of the engine clutch.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pan to incorporate the teaching of specifying the time interval is based on the determined clutch temperature of Park with a reasonable expectation of success in order for unnecessary learning entry to be prevented, and due to an insufficient learning period, a vehicle aging effect is not reflected to the control of the engine clutch and thus a phenomenon that drivability is aggravated can be prevented (see at least Park, para. [0032]).
Claim(s) 5-6, & 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pan, in view of Qin, in view of CN 103967963A (“Huifang”).
As per claim 5 Pan does not explicitly disclose
wherein the method further comprises the following steps:
determining at least one input value (38) representative of a power supplied to the vehicle clutch, wherein determining the at least one input value (38) is based on a processing of chronologically consecutive values (32a, 32b, 32c, 32d) for the power supplied to the vehicle clutch; and
inputting the at least one input value (38) as input data (20b) into the neural network (12).
Huifang teaches
wherein the method further comprises the following steps: determining at least one input value (38) representative of a power supplied to the vehicle clutch, wherein determining the at least one input value (38) is based on a processing of chronologically consecutive values (32a, 32b, 32c, 32d) for the power supplied to the vehicle clutch (see at least Huifang, para. [0067]: The clutch slip power is then derived from the formula, where Tq<sub> cl </sub> and Tq<sub> c2 </sub> are the torques transmitted by clutch 1 and clutch 2 under slip conditions, respectively; n<sub> In </sub>, n<sub> cl </sub>, and n<sub> c2 </sub> are the speeds of the transmission input shaft, the speed of the driven plate of clutch 1, and the speed of the drive plate of clutch 2, respectively. Afterwards, the prediction module input variable group lnP is constructed based on the clutch oil outlet oil temperature and clutch slip power at the current and past moments, which); and
inputting the at least one input value (38) as input data (20b) into the neural network (12) (see at least Huifang, para. [0062]: The trained BP neural network is tested using the normalized input variable group matrix lnTE_l of the test sample set to obtain the normalized test output variable matrix BPTE_l, which is the normalized predicted value of the actual temperature of the wet clutch arranged in time series.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pan to incorporate the teaching of determining at least one input value (38) representative of a power supplied to the vehicle clutch, wherein determining the at least one input value (38) is based on a processing of chronologically consecutive values (32a, 32b, 32c, 32d) for the power supplied to the vehicle clutch; and inputting the at least one input value (38) as input data (20b) into the neural network (12) of Huifang with a reasonable expectation of success in order for accurate predictions without increasing hardware costs (see at least Huifang, para. [0034]).
As per claim 6 Pan does not explicitly disclose
wherein the at least one input value (38) is determined at predetermined time intervals
Huifang teaches
wherein the at least one input value (38) is determined at predetermined time intervals (see at least Huifang, para. [0013]: Step 1.2: First, convert the existing data of the clutch 1 piston pressure per unit area, the clutch 2 piston pressure per unit area, the transmission input shaft speed, the clutch 1 driven plate speed, and the clutch 2 driven plate speed during the specified time period into the clutch slip power during the corresponding time period. Then, combined with the clutch oil outlet temperature during the corresponding time period, construct a data sample set of the input variable group matrix In and the output variable matrix Out arranged in time.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pan to incorporate the teaching of wherein the at least one input value (38) is determined at predetermined time intervals of Huifang with a reasonable expectation of success in order for accurate predictions without increasing hardware costs (see at least Huifang, para. [0034]).
As per claim 13 Pan does not explicitly disclose
wherein: training the neural network (12) uses at least one specific input value (38) representative for a power supplied to the vehicle clutch, and determining the at least one input value (38) includes processing chronologically consecutive values (32a, 32b, 32c, 32d) for the power supplied to the vehicle clutch.
Huifang teaches
wherein: training the neural network (12) uses at least one specific input value (38) representative for a power supplied to the vehicle clutch, and determining the at least one input value (38) includes processing chronologically consecutive values (32a, 32b, 32c, 32d) for the power supplied to the vehicle clutch (see at least Huifang, para. [0067]: The clutch slip power is then derived from the formula, where Tq<sub> cl </sub> and Tq<sub> c2 </sub> are the torques transmitted by clutch 1 and clutch 2 under slip conditions, respectively; n<sub> In </sub>, n<sub> cl </sub>, and n<sub> c2 </sub> are the speeds of the transmission input shaft, the speed of the driven plate of clutch 1, and the speed of the drive plate of clutch 2, respectively. Afterwards, the prediction module input variable group lnP is constructed based on the clutch oil outlet oil temperature and clutch slip power at the current and past moments, which).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pan to incorporate the teaching of wherein: training the neural network (12) uses at least one specific input value (38) representative for a power supplied to the vehicle clutch, and determining the at least one input value (38) includes processing chronologically consecutive values (32a, 32b, 32c, 32d) for the power supplied to the vehicle clutch of Huifang with a reasonable expectation of success in order for accurate predictions without increasing hardware costs (see at least Huifang, para. [0034]).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pan, in view of Huifang.
As per claim 8 Pan does not explicitly disclose
wherein training the neural network (12) further uses at least one specific input value (38) representative for a power supplied to the vehicle clutch, and wherein determining the at least one input value (38) includes processing chronologically consecutive values (32a, 32b, 32c, 32d) for the power supplied to the vehicle clutch.
Huifang teaches
wherein training the neural network (12) further uses at least one specific input value (38) representative for a power supplied to the vehicle clutch, and wherein determining the at least one input value (38) includes processing chronologically consecutive values (32a, 32b, 32c, 32d) for the power supplied to the vehicle clutch (see at least Huifang, para. [0067]: The clutch slip power is then derived from the formula, where Tq<sub> cl </sub> and Tq<sub> c2 </sub> are the torques transmitted by clutch 1 and clutch 2 under slip conditions, respectively; n<sub> In </sub>, n<sub> cl </sub>, and n<sub> c2 </sub> are the speeds of the transmission input shaft, the speed of the driven plate of clutch 1, and the speed of the drive plate of clutch 2, respectively. Afterwards, the prediction module input variable group lnP is constructed based on the clutch oil outlet oil temperature and clutch slip power at the current and past moments, which).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pan to incorporate the teaching of wherein training the neural network (12) further uses at least one specific input value (38) representative for a power supplied to the vehicle clutch, and wherein determining the at least one input value (38) includes processing chronologically consecutive values (32a, 32b, 32c, 32d) for the power supplied to the vehicle clutch of Huifang with a reasonable expectation of success in order for accurate predictions without increasing hardware costs (see at least Huifang, para. [0034]).
Conclusion
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/MOHAMED ABDO ALGEHAIM/Primary Examiner, Art Unit 3668
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