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 1-8, & 11-17 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 & 11 recite determining the calculated value depending on at least one first parameter and second parameter of the vehicle device and an input value associated with the vehicle device, 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 operating a vehicle device which is dependent on at least one calculated value and comprises the following steps, an analytical correlation, calculating the first parameter by a first neural network and calculating the second parameter by a second neural network, iteratively redetermining the calculated value using the prior calculated value as the input value associated with the vehicle device” language, “determining the calculated value depending on at least one first parameter and second parameter of the vehicle device and an input value associated with the vehicle device” in the context of this claim encompasses the user calculating a temperature of a vehicle device based on different parameters of the vehicle device. 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 operating a vehicle device which is dependent on at least one calculated value and comprises the following steps, an analytical correlation, calculating the first parameter by a first neural network and calculating the second parameter by a second neural network, iteratively redetermining the calculated value using the prior calculated value as the input value associated with the vehicle device”. The devices are recited at a high-level of generality (i.e., device configured to calculate a temperature of a vehicle device) 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 operating a vehicle device which is dependent on at least one calculated value and comprises the following steps, an analytical correlation, calculating the first parameter by a first neural network and calculating the second parameter by a second neural network, iteratively redetermining the calculated value using the prior calculated value as the input value associated with the vehicle device,”, 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 2-8, & 12-17, in the context of this claim encompasses the user calculating other values relating the parameters of the vehicle device. 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 calculate a temperature of a vehicle device) 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.
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) 1-5, 7-14, & 16-18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by DE 102020214234A1 (“Hilali”).
As per claim 1 Hilali discloses
A method for operating a vehicle device which is dependent on at least one calculated value and comprises the following steps (see at least Hilali, para. [0063]: In a fourth step S4, the calculated hotspot temperature of the rotor 3 and the stator 4 is output and used, for example, by a control unit to control the electric machine 2.):
determining the calculated value with an analytical correlation depending on at least one first parameter and second parameter of the vehicle device and an input value associated with the vehicle device (see at least Hilali, para. [0046]: The hybrid model 125 comprises a physical model 1251 for calculating a first contribution to heat transfer at rotor 3, and a third artificial intelligence model 1252 for calculating a second contribution to heat transfer at rotor 3. The temperature of rotor 3 is calculated based on the first and second contributions regarding heat transfer at rotor 3. The third artificial intelligence model 1252 can be trained to receive and take into account information 7 about serial variation. & para. [0052]: The computing device 12 divides 107 the summed contributions to heat transfer at rotor 3 by the specific heat capacity C<sub>th,rot</sub>of rotor 3, where the specific heat capacity C<sub>th,rot</sub>of rotor 3 is the ratio of the heat supplied or removed and the resulting temperature change: This quantity is integrated over time 108 so that the instantaneous hotspot temperature of rotor 3 is calculated 109.);
calculating the first parameter by a first neural network (see at least Hilali, para. [0045]: The second artificial intelligence model 124 is trained to calculate a temperature T<sub>S/R< /sub>of the stator 4 towards the rotor 3, which is provided to the hybrid model 120.); and
calculating the second parameter by a second neural network (see at least Hilali, para. [0046]: The hybrid model 125 comprises a physical model 1251 for calculating a first contribution to heat transfer at rotor 3, and a third artificial intelligence model 1252 for calculating a second contribution to heat transfer at rotor 3. & para. [0051]: The third artificial intelligence model 1252 calculates based on the input variables I<sub>1</sub>, ...I<sub>n</sub>a second contribution to heat transfer at rotor 3. The first contribution to heat transfer at rotor 3 is summed with the second contribution to heat transfer at rotor 3 = 106. In particular, the heat losses within rotor 3 represent a second contribution to the heat transfer at rotor 3.).
As per claim 2 Hilali discloses
further comprising at least one of measuring or calculating the input value based on a physical quantity of the vehicle device (see at least Hilali, para. [0042-0043]: The plausibilized stator temperature T<sub>stator, NTC</sub>serves as an input variable for a first artificial intelligence model 123 and a second artificial intelligence model 124…The input variables I<sub>1</sub>, ..., I<sub>n</sub> can include currents of the electric machine 2, voltages of the electric machine 2, a DC link voltage of a battery coupled to the electric machine 2, an effective phase current of the electric machine 2, a pulse width modulation frequency, a speed of the electric machine 2, a torque of the electric machine 2, modulation variables of the electric machine 2, an ambient temperature of the electric machine 2 and/or a gearbox temperature of a gearbox coupled to the electric machine 2.).
As per claim 3 Hilali discloses
further comprising training the first and second neural networks separately from one another (see at least Hilali, para. [0030]: According to a further embodiment of the method for determining a hotspot temperature of a rotor of an electric machine and a hotspot temperature of a stator of the electric machine, the first artificial intelligence model and the second artificial intelligence model are trained using test bench data of the electric machine. This allows for the consideration of serial variation effects and machine and sensor aging effects, which are then included in the temperature calculation. para. [0054-0056]: Fig. 4 shows a schematic block diagram to illustrate a training procedure of the third artificial intelligence model 1252. By separating the data into a static component (stationary loss characteristic) and a dynamic component, the training of the third artificial intelligence model 1252 is facilitated and its size is reduced. For each type of electrical machine 2, a separate artificial intelligence model is preferably trained.).
As per claim 4 Hilali discloses
wherein the determined calculated value is the input value of a further determination of the calculated value temporally following the determination (see at least Hilali, para. [0049-0050]: The computer 12 calculates the difference between the current temperature of the coolant 5 and the last calculated hotspot temperature of the rotor 3: The computer 12 also calculates the difference between the current temperature of the stator 4 and the last calculated hotspot temperature of the rotor 3…By multiplying 104 with a specific thermal conductivity of the coolant 5 stored in a first lookup table 102, a heat flow from the coolant 5 to the rotor 3 is calculated. Furthermore, by multiplying 105 with a specific thermal conductivity of the stator 4 stored in a second look-up table 103, a heat flow from the stator 4 to the rotor 3 is calculated. The two calculated heat flows represent a first contribution to the, in particular convective, heat transfer at rotor 3.).
As per claim 5 Hilali discloses
wherein the analytical correlation is represented by an n-th order polynomial (see at least Hilali, para. [0052]: The computing device 12 divides 107 the summed contributions to heat transfer at rotor 3 by the specific heat capacity C<sub>th,rot</sub>of rotor 3, where the specific heat capacity C<sub>th,rot</sub>of rotor 3 is the ratio of the heat supplied or removed and the resulting temperature change: This quantity is integrated over time 108 so that the instantaneous hotspot temperature of rotor 3 is calculated 109.).
As per claim 7 Hilali discloses
wherein a number of neural networks used to determine the calculated value and including the first and second neural networks is equal to n+1 (see at least Hilali, para. [0026-0027]: According to a further embodiment of the device for determining a hotspot temperature of a rotor of an electric machine and a hotspot temperature of a stator of the electric machine, the first artificial intelligence model and/or the second artificial intelligence model comprise artificial neural networks or a Gaussian process regressor…According to another embodiment of the device for determining a hotspot temperature of a rotor of an electric machine and a hotspot temperature of a stator of the electric machine, the third artificial intelligence model comprises a Gaussian process regressor. para. [0052]: The computing device 12 divides 107 the summed contributions to heat transfer at rotor 3 by the specific heat capacity C<sub>th,rot</sub>of rotor 3, where the specific heat capacity C<sub>th,rot</sub>of rotor 3 is the ratio of the heat supplied or removed and the resulting temperature change: This quantity is integrated over time 108 so that the instantaneous hotspot temperature of rotor 3 is calculated 109.).
As per claim 8 Hilali discloses
wherein at least one of: the calculated value indicates a temperature change, the first parameter indicates a heat input, the second parameter indicates a heat dissipation or the input value indicates a temperature (see at least Hilali, para. [0055]: For this purpose, a time integration is performed to calculate a difference between the current hotspot temperature of rotor 3 and the previous hotspot temperature of rotor 3 (A): This is then multiplied by the specific heat capacity C<sub>th,rot</sub>of rotor 3 (B): By subtracting the first contribution, the heat loss P<sub>loss</sub>at the rotor is finally calculated as a label. This label is used to train the third artificial intelligence model 1252. The trained third artificial intelligence model 1252 for heat loss modeling is then used forward as a building block in the overall hybrid model to calculate the hotspot temperature of rotor 3.).
As per claim 9 Hilali discloses
A vehicle device which is operable according to the method of claim 1 (see at least Hilali, para. [0002]: For electric machines in the automotive sector, determining the rotor temperature and the stator temperature is important in order to avoid negative effects such as overheating and thus premature derating. para. [0063]: In a fourth step S4, the calculated hotspot temperature of the rotor 3 and the stator 4 is output and used, for example, by a control unit to control the electric machine 2.).
As per claim 10 Hilali discloses
wherein the vehicle device is a drive train device of a vehicle drive train (see at least Hilali, para. [0039]: The calculated hotspot temperatures of rotor 3 and stator 4 can be output via interface 11. For example, a control unit can receive the calculated hotspot temperatures of the rotor 3 and the stator 4 and use them to control the electric machine 2.).
As per claim 11 Hilali discloses
A method for operating a vehicle device which is dependent on at least one calculated value and comprises the following steps (see at least Hilali, para. [0063]: In a fourth step S4, the calculated hotspot temperature of the rotor 3 and the stator 4 is output and used, for example, by a control unit to control the electric machine 2.):
determining the calculated value with an analytical correlation depending on at least one first parameter and second parameter of the vehicle device and an input value associated with the vehicle device (see at least Hilali, para. [0046]: The hybrid model 125 comprises a physical model 1251 for calculating a first contribution to heat transfer at rotor 3, and a third artificial intelligence model 1252 for calculating a second contribution to heat transfer at rotor 3. The temperature of rotor 3 is calculated based on the first and second contributions regarding heat transfer at rotor 3. The third artificial intelligence model 1252 can be trained to receive and take into account information 7 about serial variation. & para. [0052]: The computing device 12 divides 107 the summed contributions to heat transfer at rotor 3 by the specific heat capacity C<sub>th,rot</sub>of rotor 3, where the specific heat capacity C<sub>th,rot</sub>of rotor 3 is the ratio of the heat supplied or removed and the resulting temperature change: This quantity is integrated over time 108 so that the instantaneous hotspot temperature of rotor 3 is calculated 109.);
calculating the first parameter by a first neural network (see at least Hilali, para. [0045]: The second artificial intelligence model 124 is trained to calculate a temperature T<sub>S/R< /sub>of the stator 4 towards the rotor 3, which is provided to the hybrid model 120.); and
calculating the second parameter by a second neural network (see at least Hilali, para. [0046]: The hybrid model 125 comprises a physical model 1251 for calculating a first contribution to heat transfer at rotor 3, and a third artificial intelligence model 1252 for calculating a second contribution to heat transfer at rotor 3. & para. [0051]: The third artificial intelligence model 1252 calculates based on the input variables I<sub>1</sub>, ...I<sub>n</sub>a second contribution to heat transfer at rotor 3. The first contribution to heat transfer at rotor 3 is summed with the second contribution to heat transfer at rotor 3 = 106. In particular, the heat losses within rotor 3 represent a second contribution to the heat transfer at rotor 3.), and
iteratively redetermining the calculated value using the prior calculated value as the input value associated with the vehicle device (see at least Hilali, para. [0051]: The third artificial intelligence model 1252 calculates based on the input variables I<sub>1< /sub>, ... I<sub>n</sub>a second contribution to heat transfer at rotor 3. The first contribution to heat transfer at rotor 3 is summed with the second contribution to heat transfer at rotor 3 = 106. In particular, the heat losses within rotor 3 represent a second contribution to the heat transfer at rotor 3.).
As per claim 12 Hilali discloses
further comprising at least one of measuring or calculating the input value based on a physical quantity of the vehicle device (see at least Hilali, para. [0042-0043]: The plausibilized stator temperature T<sub>stator, NTC</sub>serves as an input variable for a first artificial intelligence model 123 and a second artificial intelligence model 124…The input variables I<sub>1</sub>, ..., I<sub>n</sub> can include currents of the electric machine 2, voltages of the electric machine 2, a DC link voltage of a battery coupled to the electric machine 2, an effective phase current of the electric machine 2, a pulse width modulation frequency, a speed of the electric machine 2, a torque of the electric machine 2, modulation variables of the electric machine 2, an ambient temperature of the electric machine 2 and/or a gearbox temperature of a gearbox coupled to the electric machine 2.).
As per claim 13 Hilali discloses
further comprising training the first and second neural networks separately from one another (see at least Hilali, para. [0030]: According to a further embodiment of the method for determining a hotspot temperature of a rotor of an electric machine and a hotspot temperature of a stator of the electric machine, the first artificial intelligence model and the second artificial intelligence model are trained using test bench data of the electric machine. This allows for the consideration of serial variation effects and machine and sensor aging effects, which are then included in the temperature calculation. para. [0054-0056]: Fig. 4 shows a schematic block diagram to illustrate a training procedure of the third artificial intelligence model 1252. By separating the data into a static component (stationary loss characteristic) and a dynamic component, the training of the third artificial intelligence model 1252 is facilitated and its size is reduced. For each type of electrical machine 2, a separate artificial intelligence model is preferably trained.).
As per claim 14 Hilali discloses
wherein the analytical correlation is represented by an n-th order polynomial (see at least Hilali, para. [0052]: The computing device 12 divides 107 the summed contributions to heat transfer at rotor 3 by the specific heat capacity C<sub>th,rot</sub>of rotor 3, where the specific heat capacity C<sub>th,rot</sub>of rotor 3 is the ratio of the heat supplied or removed and the resulting temperature change: This quantity is integrated over time 108 so that the instantaneous hotspot temperature of rotor 3 is calculated 109.).
As per claim 16 Hilali discloses
wherein a number of neural networks used to determine the calculated value and including the first and second neural networks is equal to n+1 (see at least Hilali, para. [0026-0027]: According to a further embodiment of the device for determining a hotspot temperature of a rotor of an electric machine and a hotspot temperature of a stator of the electric machine, the first artificial intelligence model and/or the second artificial intelligence model comprise artificial neural networks or a Gaussian process regressor…According to another embodiment of the device for determining a hotspot temperature of a rotor of an electric machine and a hotspot temperature of a stator of the electric machine, the third artificial intelligence model comprises a Gaussian process regressor. para. [0052]: The computing device 12 divides 107 the summed contributions to heat transfer at rotor 3 by the specific heat capacity C<sub>th,rot</sub>of rotor 3, where the specific heat capacity C<sub>th,rot</sub>of rotor 3 is the ratio of the heat supplied or removed and the resulting temperature change: This quantity is integrated over time 108 so that the instantaneous hotspot temperature of rotor 3 is calculated 109.).
As per claim 17 Hilali discloses
wherein at least one of: the calculated value indicates a temperature change, the first parameter indicates a heat input, the second parameter indicates a heat dissipation or the input value indicates a temperature (see at least Hilali, para. [0055]: For this purpose, a time integration is performed to calculate a difference between the current hotspot temperature of rotor 3 and the previous hotspot temperature of rotor 3 (A): This is then multiplied by the specific heat capacity C<sub>th,rot</sub>of rotor 3 (B): By subtracting the first contribution, the heat loss P<sub>loss</sub>at the rotor is finally calculated as a label. This label is used to train the third artificial intelligence model 1252. The trained third artificial intelligence model 1252 for heat loss modeling is then used forward as a building block in the overall hybrid model to calculate the hotspot temperature of rotor 3.).
As per claim 18 Hilali discloses
A vehicle device which is operable according to the method of claim 11 (see at least Hilali, para. [0002]: For electric machines in the automotive sector, determining the rotor temperature and the stator temperature is important in order to avoid negative effects such as overheating and thus premature derating. para. [0063]: In a fourth step S4, the calculated hotspot temperature of the rotor 3 and the stator 4 is output and used, for example, by a control unit to control the electric machine 2.).
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) 6 & 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hilali, in view of US 2019/0238139A1 (“Haneda”).
As per claim 6 Hilali does not explicitly disclose
wherein a number of parameters of the analytical correlation including the first and second parameters is equal to n+1.
Haneda teaches
wherein a number of parameters of the analytical correlation including the first and second parameters is equal to n+1 (see at least Haneda, para. [0125-0127]: The obtained estimation value can be used in various processes. In a case where the resonator device 2 is an oscillator, the compensation process that reduces fluctuation of the oscillation frequency accompanied by a change in temperature is performed based on the estimation value. The compensation process may be performed using approximation or may be performed using calculation that takes the estimation value as input. & para. [0143]: After the loss function E is obtained by the forward pass, each parameter is updated using a partial derivative of the loss function E as illustrated in Expressions (7) to (12). In Expressions (7) to (12), a value that is suffixed with “+1” denotes a value after the update process. For example, b+1 denotes the value of b after the update process. In addition, η denotes a learning rate. It is desirable that the learning rate is not constant and is changed depending on the status of learning.).
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 Hilali to incorporate the teaching of wherein a number of parameters of the analytical correlation including the first and second parameters is equal to n+1 of Haneda, with a reasonable expectation of success in order for improvement in terms of accuracy (see at least Haneda, para. [0153]).
As per claim 15 Hilali does not explicitly disclose
wherein a number of parameters of the analytical correlation including the first and second parameters is equal to n+1.
Haneda teaches
wherein a number of parameters of the analytical correlation including the first and second parameters is equal to n+1 (see at least Haneda, para. [0125-0127]: The obtained estimation value can be used in various processes. In a case where the resonator device 2 is an oscillator, the compensation process that reduces fluctuation of the oscillation frequency accompanied by a change in temperature is performed based on the estimation value. The compensation process may be performed using approximation or may be performed using calculation that takes the estimation value as input. & para. [0143]: After the loss function E is obtained by the forward pass, each parameter is updated using a partial derivative of the loss function E as illustrated in Expressions (7) to (12). In Expressions (7) to (12), a value that is suffixed with “+1” denotes a value after the update process. For example, b+1 denotes the value of b after the update process. In addition, η denotes a learning rate. It is desirable that the learning rate is not constant and is changed depending on the status of learning.).
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 Hilali to incorporate the teaching of wherein a number of parameters of the analytical correlation including the first and second parameters is equal to n+1 of Haneda, with a reasonable expectation of success in order for improvement in terms of accuracy (see at least Haneda, para. [0153]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED ABDO ALGEHAIM whose telephone number is (571)272-3628. The examiner can normally be reached Monday-Friday 8-5PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fadey Jabr can be reached at 571-272-1516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MOHAMED ABDO ALGEHAIM/Primary Examiner, Art Unit 3668
Prosecution Insights