DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1 - 11 is presented for examination.
The specification is objected by minor informalities.
Claim 11 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ).
Claims 1-11 are rejected under 35 U.S.C. 101.
Claims 1-2 and 10-11are rejected under 35 U.S.C. 103 as being unpatentable over Sekar, Vinothkumar, et al. "Inverse design of airfoil using a deep convolutional neural network." Aiaa Journal 57.3 (2019): 993-1003, in the view of Wisley; David (US 11532184 B2).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Sekar, Vinothkumar, et al. "Inverse design of airfoil using a deep convolutional neural network." Aiaa Journal 57.3 (2019): 993-1003, in the view of Wisley; David (US 11532184 B2) further in the view of Manna; Indrajit (US 10339026 B2).
Claims 4, 5, 7, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Sekar, Vinothkumar, et al. "Inverse design of airfoil using a deep convolutional neural network." Aiaa Journal 57.3 (2019): 993-1003, in the view of Wisley; David (US 11532184 B2) further in the view of Koh, Dong-Young, Sung-Jun Jeon, and Seog-Young Han. "Performance prediction of induction motor due to rotor slot shape change using convolution neural network." Energies 15.11 (2022): 4129.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Sekar, Vinothkumar, et al. "Inverse design of airfoil using a deep convolutional neural network." Aiaa Journal 57.3 (2019): 993-1003, in the view of Wisley; David (US 11532184 B2) further in the view of Parekh, Vivek, Dominik Flore, and Sebastian Schoeps. "Deep learning-based prediction of key performance indicators for electrical machines." Ieee Access 9 (2021): 21786-21797.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Sekar, Vinothkumar, et al. "Inverse design of airfoil using a deep convolutional neural network." Aiaa Journal 57.3 (2019): 993-1003, in the view of Wisley; David (US 11532184 B2) in the view of Koh, Dong-Young, Sung-Jun Jeon, and Seog-Young Han. "Performance prediction of induction motor due to rotor slot shape change using convolution neural network." Energies 15.11 (2022): 4129 further in the view of Min seon-gi (KR 101600001 B1) further in the view of Parekh, Vivek, Dominik Flore, and Sebastian Schoeps. "Deep learning-based prediction of key performance indicators for electrical machines." Ieee Access 9 (2021): 21786-21797.
This action is Non final rejection.
Priority
Acknowledgment is made for a claims benefit of a foreign priority of KR10-2022-0132586 and KR10-2022-0132587 filed on 10/14/2022.
Information Disclosure Statement
The IDS filed on 04/13/2023 is reviewed and considered. See attached document.
Specification
The disclosure is objected to because of the following informalities:
[0091] the target performance parameter 110, should be 1100.
Appropriate correction is required.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is
The engineering computation module in claim 11.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 11 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 11 disclosed “engineering computation module” but it does not have any structure which is capable to perform the claimed limitations and the specification does not appear to disclose any structure for the module. Therefore what structure is included in these modules is indefinite.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11 are rejected under 35 U.S.C. 101 because the claim invention recites a judicial exception, which is directed to judicial exception of an abstract idea, as it has not been integrated into practical application and the claim further do not recite significantly more that the judicial exception.
Step 1: Yes, the claims 1-11 are directed to a method and apparatus claims, so they fails within the statutory category of a process and apparatus.
Step 2A: prong 1: Yes, the claims recites abstract ideas. Abstract ideas in the claims are bolded as shown below.
Claim 1 and 11:
acquiring first target performance parameter and second target performance parameter of the mechanical apparatus; (insignificant extra solution activity - data gathering such as such as 'obtaining information'. See MPEP 2106.05(g).)
acquiring first shape information by using first model based on the first target performance parameter and the second target performance parameter; (insignificant extra solution activity - data gathering such as such as 'obtaining information'. See MPEP 2106.05(g).)
acquiring first prediction performance parameter by using second model based on the first shape information; and (under a broadest reasonable interpretation this claim limitations recites a mental process. This limitation does not claim any specific method or program of performing prediction so a human mind can make prediction by observing, analyzing and making judgment on the inputted shape information. According to [0092] predicting of performance is performed using a mathematical concepts so, a human mind can perform this prediction using mathematical concepts by the aid of pen and paper).
updating the first model based on at least one of the first target performance parameter, the second target performance parameter or the first prediction performance parameter (under the broadest reasonable interpretation this claim limitations recites a mental process. A human mind can update a model by using input parameters since the model can be a mathematical model according to [0070] [0073], so a person in the art can make update to this model by performing observation, evaluation and judgment on the model using a pen and paper).
Step 2A: prong 2: No
The above judicially exceptions do not recite additional elements that integrate
the exceptions into a practical application of the exception because the claims do not
have additional elements of a combination of additional elements that apply, rely or use
the judicial exception in a manner that impose a meaningful limit on the judicial
exception.
Claims recites gathering data which is insignificant extra solution activity. Adding
insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in
conjunction with a law of nature or abstract idea such as a step of obtaining information
about credit card transactions so that the information can be analyzed by an abstract
mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366,
1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)).
As of claim 1:
acquiring first target performance parameter and second target performance parameter of the mechanical apparatus; and acquiring first shape information by using first model based on the first target performance parameter and the second target performance parameter; is insignificant extra solution activity - data gathering such as such as 'obtaining information'. See MPEP 2106.05(g).
Step 2B: No, The claims do not cite additional elements which are significantly more than the abstract idea. As it is expanded above the claim merely performs prediction using inputted data and perform model updating without any additional element or any specific method which is not significantly more than the abstract idea. While claim 11, recites additional element of a “advice” but the device is merely used as a tool to implement the claimed invention and merely using of a device is not significantly more.
Regarding dependent claims:
Claim 2:
Comparing…, replacing…. Updating … this claim limitations further define the abstract idea by specifying how updating of the model is performed and this recites a mental process, since a human mind can perform comparation on two parameters and make observation, evaluation and judgment to update the model based on the comparation criterion.
Claim 3:
updating the first model comprises, when a difference between the first target performance parameter and the first prediction performance parameter is less than …. this claim limitation is also further defining the abstract idea by specifying updating criterion without any additional element which is significantly more.
Claim 4 :
wherein, when the mechanical apparatus is a motor, the first target performance parameter comprises at least one of a type of the motor -it father defined the data type used, so it is insignificant extra solution activity - data gathering such as such as 'obtaining information'. See MPEP 2106.05(g).
Claim 5:
wherein the first target performance parameter further comprises target shape information of the mechanical apparatus, a number of slots, winding
information, a fill factor, or a current density, it further defined the data type used, so it is insignificant extra solution activity - data gathering such as such as 'obtaining information'. See MPEP 2106.05(g).
Claim 6:
wherein, when the mechanical apparatus is a motor, the second target performance parameter comprises at least one of material information of the motor, a saturation temperature, volume information, weight information, or material property information, it further defined the data type used, so it is insignificant extra solution activity - data gathering such as such as 'obtaining information'. See MPEP 2106.05(g).
Claim 7:
wherein acquiring the first prediction performance parameter by using the second model based on the first shape information comprises inputting the first shape information… this claim limitation further defines abstract idea with out any additional elements which are significantly more.
Claim 8:
wherein acquiring the first prediction performance parameter by using the second model based on the first shape information comprises additionally inputting the second target performance parameter to the second model and acquiring the first prediction
parameter from the second model, it further defines the abstract idea by specifying the type of parameter used to input into the model to predict output parameters see claim 1 above and in this claim limitation the there no any additional element which is significantly more.
Claim 9:
providing the first shape information ,acquiring… acquiring the first prediction performance parameter comprises inputting the second … this claim limitation also further define the abstract idea of acquiring prediction performance using gather information, this claims limitations does not have any additional element which is significant more, so it is still an abstract idea of a mental process.
Claim 10:
This claim limitation includes additional element of a “A non-transitory computer-readable recording medium”, but this is merely used as a tool to perform the abstract idea and Merly using of a computer and applying abstract ideas into a system without making improvement to the functionality of a computer is not a significantly more.
The dependent claims include the same abstract ideas as recited in the
independent claim and merely incorporate additional details that narrow the scope of abstract ideas and fails to add significantly more than the claims.
Therefore claims 1 -11 are rejected under 35 U.S.C 101 based on the above
prong analysis.
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.
Claims 1-2 and 10-11are rejected under 35 U.S.C. 103 as being unpatentable over Sekar, Vinothkumar, et al. "Inverse design of airfoil using a deep convolutional neural network." Aiaa Journal 57.3 (2019): 993-1003, in the view of Wisley; David (US 11532184 B2).
As of claim 1, Sekar teaches A method for acquiring shape information of a mechanical apparatus based on target performance information, the method comprising: (Abstract, As shown in this paper, the deep learning technique can be used effectively to obtain the airfoil shape from the coefficient of pressure distribution)
acquiring first target performance parameter and second target performance parameter of the mechanical apparatus; ( abstract, and page 995, C. “Data preparation”, In the testing phase, a new pressure coefficient distribution is given to the CNN model, generating an airfoil shape… Data preparation is an important task in machine learning. In the present work, the Cp distribution over the upper and lower surfaces of the airfoil is taken as input to the CNN and the airfoil shape is directly obtained as output). the Cp distribution over the upper and lower surface is considered as first and second performance parameter.
acquiring first shape information by using first model based on the first target performance parameter and the second target performance parameter; (page 1000, IV “conclusion”, The coefficient of pressure distribution Cp is input to the CNN, and the airfoil shape is obtained as output).
acquiring first prediction performance parameter by using second model based on the first shape information; and (page 993, I “introduction” In a forward approach, the airfoil shape and flow conditions are specified; and Navier–Stokes (NS) equations are solved in order to obtain the pressure distribution
PNG
media_image1.png
249
680
media_image1.png
Greyscale
)
Sekar does not explicitly teach updating the first model based on at least one of the first target performance parameter, the second target performance parameter or the first prediction performance parameter.
While Wisley teaches updating the first model based on at least one of the first target performance parameter, the second target performance parameter or the first prediction performance parameter (Col 25 -26 line 64- 67 , line 1-3, The simulated route data and simulated machine operational data may be updated based upon the actual route data and actual machine operational data. Therefore, in second and further simulations under the first and/or second method the machine simulator may more accurately reflect the operation of the at least one work machine 11 on the worksite 14).
Sekar and Wisley are considered to be analogous to the claimed invention since they focus on generating of performance using input parameter. Therefore it would be obvious for a person of ordinary skill in the art before the effective filing date, to integrate Wiseley teaching of updating of a model using target or predicted performance parameter into Sekar model in order to update Sekar’s model to generate a performance information from shape information.
The motivation would have been in order to shows a competitive prediction accuracy very efficient in terms of computational time using CNN model (Sekar, abstract) and in order to improve the accuracy of the machine simulator by performing updating of the model based upon a comparison of simulated monitored operating condition data and actual monitored operating condition data (Wisley, Col. 26 line 35-
45).
Claim 11 is also in the same scope as that of claim 1, with additional elements which Sekar teaches A device for acquiring shape information of a mechanical apparatus based on target performance information, the device comprising an engineering computation module (abstract, perform the inverse design of airfoils using deep convolutional neural networks (CNNs)). Sekar teaches a CNNs (engineering computation module) and it is obvious that CNNs are performed in a computer.
Therefore claim 11 is also rejected under the same rational as claim 1.
As of claim 2, the modified model teaches all the limitations of claim 1, and Wisley also teaches wherein updating the first model comprises: comparing the first target performance parameter and the first prediction performance parameter; (Col 26 line 63-66, Subsequently, the simulated monitored operating condition data may be compared to the actual monitored operating condition data and the machine model and/or simulation algorithm may be updated)
when a difference between the first target performance parameter and the first prediction performance parameter is greater than or equal to a predetermined ratio, replacing a parameter that overlaps the first prediction performance parameter among the first target performance parameter and the second target performance parameter with the first prediction performance parameter; (Col. 27 line 28 – 35, The simulated and actual monitored operating condition data may be compared and if they differ above a monitored operating condition threshold value, for example by the actual spring extension being substantially different to the simulated spring extension, the first stiffness value may be replaced with a second stiffness value. The second stiffness value may be calculated based upon the actual spring extension).
updating the first model based on a parameter that does not overlap the first prediction performance parameter among the first target performance parameter and the second target performance parameter, the first prediction performance parameter and the first shape information (Col. 6 line 23 – 35, The method may comprise: processing the actual monitored operating condition data and simulated monitored operating condition data and determining whether the simulated monitored operating condition data differs from the actual monitored operating condition data along all or part of the route by at least a monitored operating condition threshold value; and processing the machine model and/or simulation algorithm to update the machine model and/or simulation algorithm if the simulated monitored operating condition data differs from the actual monitored operating condition data along all or part of the route by at least a monitored operating condition threshold value).
As of claim 10, the modified model teaches all the limitations of claim 1, and Wisley also teaches A non-transitory computer-readable recording medium having recorded thereon a program (Col. 10, line 60-62, The memory 26 may comprise any suitable computer-accessible or non-transitory storage medium for storing computer program instructions).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Sekar, Vinothkumar, et al. "Inverse design of airfoil using a deep convolutional neural network." Aiaa Journal 57.3 (2019): 993-1003, in the view of Wisley; David (US 11532184 B2) further in the view of Manna; Indrajit (US 10339026 B2).
As of claim 3, the modified model teaches all the limitations of claim 1, but the modified model does not explicitly teach update the first model comprises, when a difference between the first target performance parameter and the first prediction performance parameter is less than a predetermined ratio, updating the first model without using the first prediction performance parameter.
While Manna teaches wherein updating the first model comprises, when a difference between the first target performance parameter and the first prediction performance parameter is less than a predetermined ratio, updating the first model without using the first prediction performance parameter (Col. 7 line 41- 67, the predictive model manager 214 compares the difference between the measured and predicted values of the primary characteristic to one or more predetermined reference thresholds…. If, however, the predictive model manager 214 determined that the difference is not within the ideal range, the predictive model manager 214 determines that the predictive model should be adjusted. To do so, the model management system updater 216 updates the model parameters of the predictive model based on at least one of the measured value of the primary characteristic, the measured values of the secondary characteristic, or the difference).
Manna is considered to be analogous to the claimed invention, since it focus on predicting on physical parameter based on another. Therefore it would be obvious to try by a person of ordinary skill in the art , before the effective filing date to integrate Manna’s teaching of updating a model by comparing the difference between the predicted and target performance parameter with the defined criterion into the modified model in order to update with out using the predicted performance parameter.
The motivation would have been to improve updating and accuracy of the model by avoiding unnecessary model update and parameter by using different criterion to update the model based on the difference between predicted and target parameter compared with threshold. (Manna, Col 9, line 20 -35).
Claims 4, 5, 7, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Sekar, Vinothkumar, et al. "Inverse design of airfoil using a deep convolutional neural network." Aiaa Journal 57.3 (2019): 993-1003, in the view of Wisley; David (US 11532184 B2) further in the view of Koh, Dong-Young, Sung-Jun Jeon, and Seog-Young Han. "Performance prediction of induction motor due to rotor slot shape change using convolution neural network." Energies 15.11 (2022): 4129.
As of claim 4, the modified model teaches all the limitations of claim 1, but the modified model does not explicitly teach when the mechanical apparatus is a motor, the first target performance parameter comprises at least one of a type of the motor, an output, a number of poles, a frequency, a voltage, an operating temperature, or efficiency.
While Koh teaches when the mechanical apparatus is a motor, the first target performance parameter comprises at least one of a type of the motor, an output, a number of poles, a frequency, a voltage, an operating temperature, or efficiency( Section 3.1. “Collect Induction Motor Analysis Learning Data”, Therefore, variables other than the shape and size of the rotor slot were fixed and then learning data were collected. Table 1 shows the specifications of a 2.2 kW-class three-phase squirrel cage induction motor).
Koh is considered to be analogous to the claimed invention since it focus on Performance Prediction of Induction Motor Due to Rotor Slot Shape Change. Therefore it would be obvious for a person of ordinary skill in the art, before the effective filing date to integrate Koh’s teaching of performance parameter including the type of the motor into the modified model to generate performance information from shape information.
The motivation would have been to accurately predict predicting the efficiency, power factor, starting torque, and average torque according to various rotor slot types of induction motors based on the CNN technique, a deep learning technique (Koh , conclusion).
As of claim 5, The modified model teaches all the limitations of claim 4, and Koh also teaches wherein the first target performance parameter further comprises target shape information of the mechanical apparatus, a number of slots, winding information, a fill factor, or a current density ( Section 3.1. “ Collect Induction Motor Analysis Learning Data”,
PNG
media_image2.png
768
1110
media_image2.png
Greyscale
As of claim 7, the modified model teaches all the limitations of claim 1, but it does not explicitly teach wherein acquiring the first prediction performance parameter by using the second model based on the first shape information comprises inputting the first shape information to the second model and acquiring the first prediction performance parameter from the second model.
While Koh teaches wherein acquiring the first prediction performance parameter by using the second model based on the first shape information comprises inputting the first shape information to the second model and acquiring the first prediction performance parameter from the second model (Abstract, We propose a method to predict performance variables according to the rotor slot shape of a three-phase squirrel cage induction motor using a convolution neural network (CNN) algorithm suitable for utilizing image data).
Koh is considered to be analogous to the claimed invention since it focus on Performance Prediction of Induction Motor Due to Rotor Slot Shape Change. Therefore it would be obvious for a person of ordinary skill in the art, before the effective filing date to integrate Koh’s teaching of predict performance variables according to the rotor slot shape of a three-phase squirrel cage induction motor into the modified model to generate performance information from shape information.
The motivation would have been to accurately predict predicting the efficiency, power factor, starting torque, and average torque according to various rotor slot types of induction motors based on the CNN technique, a deep learning technique (Koh , conclusion).
As of claim 9, the modified model teaches all the limitations of claim 1, and Sekar also teaches providing the first shape information; and ( page 999 E. “CNN prediction” For some selected airfoils, the output of the CNNs are showninFig.17 for training and in Fig.18 for testing cases…Fig. 17 CNN-3(144×144×2) prediction of typical training airfoils. (Airfoil name is indicated below the airfoil.)
acquiring second shape information based on the first shape information, (page 999 E. “CNN prediction”, For some selected airfoils, the output of the CNNs are showninFig.17 for training and in Fig.18 for testing cases… Fig. 18 CNN-3(144×144×2) prediction of typical testing airfoils. (Airfoil name is indicated below the airfoil).
The modified model does not explicitly teach wherein acquiring the first prediction performance parameter comprises inputting the second shape information to the second model and acquiring the first prediction performance parameter from the second model.
While Koh teaches wherein acquiring the first prediction performance parameter comprises inputting the second shape information to the second model and acquiring the first prediction performance parameter from the second model (Abstract: We propose a method to predict performance variables according to the rotor slot shape of a three-phase squirrel cage induction motor using a convolution neural network (CNN) algorithm suitable for utilizing image data… Section . 3.1. Collect “Induction Motor Analysis Learning Data”, The training data of Type 1 consist of only the most basic shape, Type 2 is a shape corresponding to the upper part of a double cage slot, and Type 3 is a double cage slot that can be created by a combination of the Type 1 and Type 2 shapes …. To predict the performance variables for the type 3 combined shape, the training data set was classified into three cases, as shown in Table 3).
Koh is considered to be analogous to the claimed invention since it focus on Performance Prediction of Induction Motor Due to Rotor Slot Shape Change. Therefore it would be obvious for a person of ordinary skill in the art, before the effective filing date to integrate Koh’s teaching of predict performance variables according to the rotor slot shape of a three-phase squirrel cage induction motor into the modified model to generate performance information from shape information.
The motivation would have been to accurately predict predicting the efficiency, power factor, starting torque, and average torque according to various rotor slot types of induction motors based on the CNN technique, a deep learning technique (Koh , conclusion).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Sekar, Vinothkumar, et al. "Inverse design of airfoil using a deep convolutional neural network." Aiaa Journal 57.3 (2019): 993-1003, in the view of Wisley; David (US 11532184 B2) further in the view of Parekh, Vivek, Dominik Flore, and Sebastian Schoeps. "Deep learning-based prediction of key performance indicators for electrical machines." Ieee Access 9 (2021): 21786-21797.
As of claim 6, the modified model teaches all the limitations of claim 1, but it does not explicitly teach wherein, when the mechanical apparatus is a motor, the second target performance parameter comprises at least one of material information of the motor, a saturation temperature, volume information, weight information, or material property information.
While Parekh teaches wherein, when the mechanical apparatus is a motor, the second target performance parameter comprises at least one of material information of the motor, a saturation temperature, volume information, weight information, or material property information (
PNG
media_image3.png
393
556
media_image3.png
Greyscale
)
Parekh is considered to be analogous to the claimed invention, since it focus on designing and evaluating of an electronic machine. Therefore it would be obvious for a person of ordinary skill in the art to integrate Parekh’s teaches of use weight information of a motor as performance parameter into the modified model to use the weight information as a second target parameter to predict performance information from shape information.
The motivation would have been to predict KPIs (performance information) for new geometries at much lower computational costs by accurately mapping the input information to the target KPIs (Parekh , conclusion).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Sekar, Vinothkumar, et al. "Inverse design of airfoil using a deep convolutional neural network." Aiaa Journal 57.3 (2019): 993-1003, in the view of Wisley; David (US 11532184 B2) in the view of Koh, Dong-Young, Sung-Jun Jeon, and Seog-Young Han. "Performance prediction of induction motor due to rotor slot shape change using convolution neural network." Energies 15.11 (2022): 4129 further in the view of Min seon-gi (KR 101600001 B1) further in the view of Parekh, Vivek, Dominik Flore, and Sebastian Schoeps. "Deep learning-based prediction of key performance indicators for electrical machines." Ieee Access 9 (2021): 21786-21797.
As of claim 8, the modified model of Sekar- Wisley- Koh teaches all the limitations of claim 7, but the modified model does not explicitly teach wherein acquiring the first prediction performance parameter by using the second model based on the first shape information comprises additionally inputting the second target performance parameter to the second model and acquiring the first prediction parameter from the second model.
While Min seon-gi teaches wherein acquiring the first prediction performance parameter by using the second model based on the first shape information comprises additionally inputting the second target performance parameter to the second model and ( [0084], The performance element (Y(i,k)), which is the input variable to be input into the above objective function, is the above design function Outputs as the result (output) of the function. (Design function setting step S200)).
Min seon-gi is considered to be analogous to the claimed invention, since it teaches Optimal design method for a motor. Therefore it would be obvious for a person of ordinary skill in the art before the effective filing date to integrate Min seon-gi’s teaching of inputting the performance parameter into the objective function which is considered as the second model, into the modified model to predict first prediction performance parameter.
The motivation would have been to obtain optimal design for a motor, using least one performance that numerically represents the performance of the motor and optimize at least one shape element that numerically represents the shape of the motor so as to maximize the capability factor (Min seon-gi, abstract).
The modified model of Sekar- Wisley- Koh- Min seon-gi does not teach acquiring the first prediction parameter from the second model.
While Parekh teaches acquiring the first prediction parameter from the second model (Abstract, In this paper, a data-aided, deep learning based meta-model is employed to predict the KPIs of an electrical machine quickly and with high accuracy to accelerate the full optimization process and reduce its computational costs)
Parekh is considered to be analogous to the claimed invention, since it focus on designing and evaluating of an electronic machine. Therefore it would be obvious for a person of ordinary skill in the art to integrate Parekh’s teaches of predicting of the KPIs of an electrical machine using deep learning based meta-model as a second model into the modified model to predict performance information from shape information.
The motivation would have been to predict KPIs (performance information) for new geometries at much lower computational costs by accurately mapping the input information to the target KPIs (Parekh , conclusion).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
YONEKURA KAZUO (JP 2020173701 A, Date Published 2020-10-22) is similar to the claimed invention since it teaches shape generation device and shape generation method using a generative model machined-trained multiple times by inputting learning shape data and learning performance values.
Einarsson; Carl Jonas Love (US 11416656 B1, Date Published 2022-08-16) is similar to the claimed invention since it teaches generating proposed fabricable designs capable of being fabricated by a fabrication system based on pre-determined design rules, obtaining metadata characterizing the proposed fabricable designs, and updating the pool of the known fabricable designs by adding one or more of the proposed fabricable designs to the pool based, at least in part, on the metadata.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABRHAM A. TAMIRU whose telephone number is (571)272-6987. The examiner can normally be reached Monday - Friday 8:00am - 5:00pm.
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, Ryan Pitaro can be reached at 571 272 4071. 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.
/ABRHAM ALEHEGN TAMIRU/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
Prosecution Insights