DETAILED ACTION
Claims 1-17 are presented for examination.
Claims 1-4, 6, 10-12 and 14 have been amended.
Claims 16 and 17 are new.
This office action is in response to the RCE submitted on 5-May-2026.
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed Application No. KR10-2022-0070536, filed on 06/10/2022.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Examiner’s Note
The prior art rejections below cite particular paragraphs, columns, and/or line numbers in the references for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art.
Response to Arguments – 35 USC 101
On pgs. 8-10 of the Applicant/Arguments Remarks, Applicant argues the amended claims have overcome the rejection under 35 USC 101.
The applicant argues the claimed invention is directed to an improvement in the technology
Examiner disagrees that the improvement is to a technological improvement. A proper statement of the rule as given by Enfish: For that reason, the first step in the Alice inquiry in this case asks whether the focus of the claims is on the specific asserted improvement in computer capabilities or, instead, on a process that qualifies as an "abstract idea" for which computers are invoked merely as a tool. (see Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336 (Fed. Cir. 2016)).
The Court’s analysis of the claim hinged on the “self-referential table” limitation being an improvement over the conventional technology and not invoking the computer as a tool.
In our instant application, the limitations are directed to gathering data and providing them to a deep learning model to determine an output. The claimed improvement is an improvement on the mental process, but invokes a computer as a tool to perform the mental process. It is important to note, the judicial exception alone cannot provide the improvement (see MPEP 2106.05(a) paragraph 6).
MPEP 2106.05(a) further states: To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. See MPEP § 2106.05(f) for more information about mere instructions to apply an exception.
On pg. 9 the applicant further argues that the claimed inventions is integrated into a practical application. The applicant further argues the invention resembles Claim 3 of Example 47, related to actual operation of an apparatus.
The examiner respectfully disagrees. Determining a performance target as recited in claim 1 is a mental process that is not integrated into a practical application. Determining in and of itself is abstract. The claim language never integrates the determining into a practical application. Claim 3 of Example 47 provides very specific actions based on the trained model, mainly dropping, blocking of real time traffic. The claim language in both the independent and dependent claims do not integrate the abstract idea into any application.
The applicant is additionally reminded of 2106.05(f) of the MPEP:
Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on "the draftsman’s art").
Response to Arguments – 35 USC 103
On pgs. 11 of the Applicant/Arguments Remarks, Applicant argues the amended claims overcome the rejection under 35 USC 103.
The applicant argues the combination of Lee and Jang doesn’t teach the training deep learning model based on input and output data as claimed.
The examiner respectfully disagrees. In fact, Lee, specifically models the slide resistance and discusses it in the Abstract as well as throughout the paper. For example, “By using our model, the time spent on the up and down motions, the current, and the lifting resistance could be predicted with 4 %, 11 %, 3 % and 4 % of error, respectively, comparing with the test data.” (Pg. 1, Abstract) Modeling the overall glass run as a damper system doesn’t preclude the model from modeling the various features of the glass, of which slide resistance is one of the most prominent features.
Lee and Jang are in the same field of endeavor of automotive part modeling and prediction. Lee provides details about the various different parameters to be modeled and simulated for power windows. It would have been obvious to a person of ordinary skill in the art to replace Lee’s model with a deep learning model as is common in the industry evidenced by Jang’s work with predictable results.
Applicant additionally argues that Gerbetz being combined with Lee and Jang wouldn’t teach the claimed controller configured to train the learning model.
The examiner respectfully disagrees. The controller is a generic computer. The combination of Lee and Jang teaches the method in claim 1 as explained earlier.
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1
Step 1: Statutory class – machine.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature or natural phenomenon?
Yes
“3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).” MPEP § 2106.04(a).
The claims are directed to an abstract idea of data processing and analysis. The claim recites:
determine performance of a target power window based on an output of the trained deep learning model
The determine limitation is a mental process of evaluation, judgement and mathematical calculations. By way of example, one can mentally evaluate criteria based on the output of the neural network to determine results such as performance of power windows.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The additional elements are:
a memory storage configured to store a deep learning model and trained updates thereto; and
a controller configured to
train the deep learning model based on training data including input data and output data a slide resistance of a glass run, a stroke distance of a door glass, a weight of the door glass, a torque of a motor, and an operation history of the power window; the output data includes an operating current and an operating time of the motor
The memory and controller configured to is mere instructions to apply an exception on a generic computer. MPEP § 2106.05(f).
The train…to limitation provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer. Additionally, it is merely indicating a field of use or technological environment in which the judicial exception is performed. This type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. MPEP § 2106.05(h).
Step 2B: Does the claim recite additional elements that amount to significantly more than judicial exception?
No, as discussed with respect to Step 2A, the additional limitation are mere instructions to apply an exception on a generic computer and a general purpose computer. They do not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B. Further, in regards to step 2B and as cited above in step 2A, MPEP 2106.05(g) “Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir.2011)” is merely data gathering. The additional elements have been considered both individually and as an ordered combination in the significantly more consideration. This claim is ineligible.
Claim 2 recites an input device configured to input: first input data including the slide resistance of the glass run, the stroke distance of the door glass, the weight of the door glass, the torque of the motor, and the operation history of the power window as real data for the target power window, which is mere instructions to apply an exception on a generic computer under Step 2A Prong 2 and 2B. Therefore, the claim is considered ineligible under 35 USC 101.
Claim 3 recites the controller is further configured to, which is mere instructions to apply an exception on a generic computer under Step 2A Prong 2 and 2B.
Obtain first output data including the operating current and the operating time of the motor as the performance of the target power window by inputting the real data into the deep learning model, which is mere data entry under Step 2A Prong Two and 2B.
determine second input data by replacing first partial data of the first input data with second partial data of the training data, based on the first output data failing to satisfy a predefined condition, which is a mental process under Step 2A Prong One.
obtain second output data by inputting the second input data into the trained deep learning model; and output information on the replaced second partial data, based on the second output data satisfying the predefined condition, which is mere data entry and output under Step 2A Prong Two and 2B.
Therefore, the claim is considered ineligible under 35 USC 101.
Claim 4 recites first partial data includes the slide resistance of the glass run and the torque of the motor in the real data, and wherein the second partial data includes a slide resistance of the glass run and a torque of the motor included in the training data, which is data entry under Step 2A Prong Two and 2B. Therefore, the claim is considered ineligible under 35 USC 101.
Claim 5 select training data including values similar to the stroke distance and weight of the door glass in the real data from among a plurality of pieces of training data, and replace the slide resistance of the glass run and the torque of the motor in the real data with a slide resistance of the glass run and a torque of the motor in the selected training data, which is a mental process under Step 2A Prong One. Therefore, the claim is considered ineligible under 35 USC 101.
Claim 6 recites the controller is further configured to, which is mere instructions to apply an exception on a generic computer under Step 2A Prong 2 and 2B.
determine that the determined performance of the target power window does not satisfy the predefined condition when (a) the determined operating current of the motor is greater than a reference current, and/or (b) the determined operating time of the motor is greater than a reference time, which is a mathematical/mental process under Step 2A Prong One. Therefore, the claim is considered ineligible under 35 USC 101.
Claim 7 recites an output device configured to output the determined performance of the target power window, which is mere instructions to apply an exception on a generic computer under Step 2A Prong 2 and 2B. Therefore, the claim is considered ineligible under 35 USC 101.
Claim 8 recites the controller is further configured to, which is mere instructions to apply an exception on a generic computer under Step 2A Prong 2 and 2B.
output the slide resistance of the glass run and the torque of the motor, which is mere data transmission under Step 2A Prong Two and 2B.
the slide resistance of the glass run and the torque of the motor being replaced via the output device when the re-determined performance of the target power window satisfies the designer's requirements, which is a mathematical/mental process under Step 2A Prong One. Therefore, the claim is considered ineligible under 35 USC 101.
Claim 9 recites the deep learning model is implemented with a Long Short Term Memory (LSTM), which is a mathematical/mental process under Step 2A Prong One. Therefore, the claim is considered ineligible under 35 USC 101.
Claims 10-15 are method claims and recite substantially the same elements as apparatus claims 1, 2 and 3, 4-6, and 8 respectively, and are rejected on the same grounds under 35 U.S.C. 101.
Claim 16 recites the controller is further configured to: output the performance of the target power window including the operating current and the operating time of the motor, which is mere data transmission under Step 2A Prong Two and 2B.
based on a determination that the operating current and the operating time do not exceed predefined reference values, determine a first slide resistance of the glass run and a first torque of the motor, included in the input data provided to the deep learning model for obtaining performance of the target power window, as an optimal slide resistance of the glass run and an optimal torque of the motor, which is a mathematical/mental process under Step 2A Prong One.
Therefore, the claim is considered ineligible under 35 USC 101.
Claim 17 is a method claim reciting limitations similar to claim 16 and is rejected under the same rationale.
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 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (NUMERICAL MODELING AND DYNAMIC SIMULATION OF AUTOMOTIVE POWER WINDOW SYSTEM WITH A SINGLE REGULATOR) in view of Jang et al. (A feasible strain-history extraction method using machine learning for the durability evaluation of automotive parts) and further in view of Gerbetz (US20020190680A1).
Regarding Claim 1, Lee teaches an apparatus for determining performance of a power window, comprising:
wherein the input data includes a slide resistance of a glass run, (Pg. 1, Abstract, “We modeled the glass run”).
a stroke distance of a door glass (Pg. 10, Simulation Results and Analysis, "Therefore, checking the changes in the sealing part and door glass overlap length caused by the door glass stroke is important for understanding changes in the load and moment obtained in the analysis results. An increase in the overlap length caused by an increase in stroke is reflected in an increase in the size of the boundary load").
a weight of the door glass (Pg. 1, Introduction, "Previous analyses have focused mainly on noise and vibration, which occur when the glass door is lifted, as well as the noise and vibration characteristics and strength of lightweight materials for door plates, for which durability and light weight are major concerns").
a torque of a motor (Pg. 11, Motor Response, "Figure 13 shows the changes in motor torque and wire speed during the door glass lifting process.").
an operation history of the power window (Pg. 1, Abstract, "By using our model, the time spent on the up and down motions, the current, and the lifting resistance could be predicted with 4 %, 11 %, 3 % and 4 % of error, respectively, comparing with the test data." The test data incorporates historical operation data).
the output data includes an operating current and an operating time of the motor determine performance of a target power window (Pg1, Abstract, "By using our model, the time spent on the up and down motions, the current, and the lifting resistance could be predicted with 4 %, 11 %, 3 % and 4 % of error, respectively" EN: the output current and operating time indicate performance measures of the power window).
However, Lee doesn’t appear to explicitly teach:
a memory storage configured to store a deep learning model and trained updates thereto; and a controller configured to:
train the deep learning model to based on training data including input data and output data,
based on an output of the trained deep learning model
Jang teaches train the deep learning model to predict the performance of a power window using (Pg. 1, Abstract, "It was suggested that the data ranges for machine-learning training be larger than that in real application to minimize discrepancies around peaks").
based on the deep learning model and trained updates thereto (Pg. 3, Learning and Prediction Process, "LSTM algorithm was selected for the machine-learning algorithm in this study. The algorithm is known to be a type of recurrent neural network that can learn order dependence in sequential data owing to the recurrent connection to the hidden state. The LSTM is widely used for model evaluation as the time-series data are employed in multiple fields in mechanical engineering").
However Lee and Jang do not seem to explicitly teach
a memory storage configured to store a deep learning model and trained updates thereto; and a controller configured to
Gerbetz teaches a memory storage configured to store a deep learning model and trained updates thereto; and a controller configured to ([0019] “ Computer 302 comprises a processor 304, which can be a microprocessor, microcontroller, application specific integrated circuit (ASIC) or other electronic device. Memory 306 can be non-volatile memory such as read only memory (ROM) or electrically erasable programmable ROM (EEPROM), and contain stored instructions, tables, data, and the like, to be utilized by processor 304”).
Lee, Jang and Gerbetz are analogous art because they are from the same field of endeavor in automotive parts analysis and optimization. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art, to combine Lee, Jang and Gerbetz to benefit from machine learning algorithm and training data for accurate predictions. “it was suggested that the data ranges for machine-learning training be larger than that in real application to minimize discrepancies around peaks.” (Lee, Abstract) In addition to better control mechanism for power windows operations. “The prior art anti-pinch safety systems outlined above rely on pre-programmed limits in window velocity or electric motor torque to signal that pinched condition exists. The problem with these systems is that an abrupt load on the window can develop, which is not due to a pinched condition, but to other normal conditions can develop, with the anti-pinch safety system halting window operation.” (Gerbetz [0006])
Regarding Claim 2, Lee in view of Jang and in further view of Gerbetz teaches the apparatus of claim 1. Jang further teaches an input device configured to input: first input data including (Pg. 1, Introduction, "The other way to collect the critical strain histories is computer-aided engineering (CAE) techniques, which include multibody dynamics simulations and finite element analysis (FEA). They identify highly stressed elements and their corresponding strain histories based on the external loading conditions [16-18]," and Pg. 3, Learning and Prediction Process, “the test data obtained from the associated sensors serves as inputs, while that from the strain gages as well as spring and damping forces is assigned to be the outputs”).
the slide resistance of the glass run, the stroke distance of the door glass, the weight of the door glass, the torque of the motor, and the operation history of the power window as real data for the target power window (Please see claim 1 above).
Regarding Claim 9, Lee in view of Jang and in further view of Gerbetz teaches the apparatus of claim 1. Jang further teaches the deep learning model is implemented with a Long Short Term Memory (LSTM) (Pg. 2, Introduction, "The purpose of this study is easy prediction of strain without using complex and time-consuming simulations and sensors attached in critical locations. Moreover, performance depending on how to prepare and learn data, details on machine learning for strain-history and optimal LSTM parameters are provided").
Claims 10-11 are method claims that recite limitations similar to claims 1-2 and are rejected under the same rationale.
Claims 3-8 and 12-17 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (NUMERICAL MODELING AND DYNAMIC SIMULATION OF AUTOMOTIVE POWER WINDOW SYSTEM WITH A SINGLE REGULATOR) in view of Jang et al. (A feasible strain-history extraction method using machine learning for the durability evaluation of automotive parts) and further in view of Gerbetz (US20020190680A1) and further in view of McConaghy et al. (US20090083680A1)
Regarding Claim 3, Lee in view of Jang and in further view of Gerbetz teaches the apparatus of claim 2. Lee further teaches the controller is further configured to obtain first output data including the operating current and the operating time of the motor as the performance of the target power window by inputting the real data into the deep learning model (Pg. 1, Abstract, "By using our model, the time spent on the up and down motions, the current, and the lifting resistance could be predicted with 4 %, 11 %, 3 % and 4 % of error, respectively").
McConaghy teaches determine second input data by replacing first partial data of the first input data with second partial data of the training data, based on the first output data failing to satisfy a predefined condition; obtain second output data by inputting the second input data into the trained deep learning model; and output information on the replaced second partial data, based on the second output data satisfying the predefined condition (Fig. 9, [0091] “These last two variants are illustrated at FIG. 9 where initial sample vectors are generated at step 400, the scalar cost of each vector is measured at step 402, a regression model is built at step 404, optimization on the regression model is done at steps 406 and 408, the cost are measured at step 410, an update to the initial sample vectors and their scalar cost is made at step 412 and finally, at step 414, verification is made if a termination criteria has been hit. In general, the algorithm designer's decision making for which approach is the most appropriate is dependent on the relative time taken for model building, vs. inner optimization, vs. calculating the true cost.”)
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Lee, Jang, Gerbetz, and McConaghy are analogous art because they are from the same field of endeavor in machine learning and optimization. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art, to combine Lee, Jang, Gerbetz and McConaghy to benefit from augmenting input values to achieve more accurate and efficient utilization of machine learning algorithm and training. “Optimization is a challenge due to the nature of the particular design problem. The time taken to compute/simulate/measure the value of the single cost function of a single ECD candidate design can take minutes or more. Therefore only a limited number of candidate designs can actually be examined given the resources at hand. The single cost function is usually a blackbox, which means that it is possibly non-convex, non-differentiable and possibly non-continuous. Consequently, this precludes the use of optimization algorithms that might take advantage of those properties. That is, it is not possible to use algorithms that exploit many simplifying assumptions.” (McConaghy, [0012])
Regarding Claim 4, Lee in view of Jang and in further view of Gerbetz teaches the apparatus of claim 2. Gerbetz further teaches the first partial data includes the slide resistance of the glass run (Fig. 5, and [0005] "In a pinched condition, the presence of a foreign object between the window and sash represents a frictional force that is opposite in direction to the applied motor torque. As a result, the electric motor draws additional current to compensate for the increased frictional force. The represents a frictional force that is opposite in direction to the applied motor torque. As a result, the electric motor draws additional current to compensate for the increased frictional force. The anti-pinch safety system monitors the current drawn by the electric motor and recognizes the pinched condition when the current exceeds a predetermined limit").
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the torque of the motor in the real data (Fig 5, shows the updating of the motor torque when thresholds are met, and [0016] "Pinch threshold 308, although input into memory 306 as a current or voltage value, can be calculated based on a spring constant 212 of the window lift mechanism 200")
McConaghy teaches wherein the second partial data includes a slide resistance of the glass run and a torque of the motor included in the training data (Fig. 5, [0005 and 0016]. EN: McConaghy’s Fig. 9 and [0091] show the inclusion of the first input values as second input values to correct for model inaccuracies and improve training efficiency and accuracy).
For motivation to combine see claim 3.
Regarding Claim 5, Lee in view of Jang and in further view of Gerbetz and further in view of McConaghy teaches the apparatus of claim 4. Jang further teaches the controller is further configured to: select training data including values similar to the stroke distance and weight of the door glass in the real data from among a plurality of pieces of training data (Pg. 5, results and discussion, "Furthermore, considering that the data sources for learning and verification were obtained from the PG. test program in this study, their range and the distribution of data are supposed to be similar to each other," and Pg. 7, "From the comparison of LSTM_All and LSTM_Indv (Fig. 5) and analysis of the network performance for prediction of diverse sensors (Fig. 11), it is found that training a neural network with a combination of PG. data is more beneficial in order to achieve a better prediction in automotive durability evaluation. Since each PG. has a specific direction to which a large amount of wheel force is generated, various kinds of PG. data in consideration of dominant directions need to be learned for strain estimations of automotive durability. To increase accuracy, the data range of learning data should be carefully determined" Jang describes the importance of having multiple similar values for training the LSTM model to achieve more accurate results).
While Lee teaches replace the slide resistance of the glass run and the torque of the motor in the real data with a slide resistance of the glass run and a torque of the motor in the selected training data (Fig 11 shows the iteration of the simulation where the motor torque and friction coefficient (included variables in the status variables) are recalculated based on the simulation model with ranges that are similar to the original values).
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Regarding Claim 6, Lee in view of Jang and in further view of Gerbetz and further in view of McConaghy teaches the apparatus of claim 4. Gerbetz further teaches the controller is further configured to: determine that the determined performance of the target power window does not satisfy the predefined condition when
(a) the determined operating current of the motor is greater than a reference current ([0016] "Pinch threshold 308, although input into memory 306 as a current or voltage value, can be calculated based on a spring constant 212 of the window lift mechanism 200").
(b) the determined operating time of the motor is greater than a reference time ([0004] "Generally the window is moved at a constant velocity. In a pinched condition, however, the velocity abruptly drops. The sensors can also detect changes in velocity over time, and in either case the anti-pinch safety system recognizes the pinched condition and reverses the upward travel of the window," and "The prior art anti-pinch safety systems outlined above rely on pre-programmed limits in window velocity or electric motor torque to signal that pinched condition exists." The window velocity limits are time limits).
Regarding Claim 7, Lee in view of Jang and in further view of Gerbetz and further in view of McConaghy teaches the apparatus of claim 4. Lee further teaches an output device configured to output the determined performance of the target power window (Lee, Fig 12-22 show the output of the simulation predictions).
Regarding Claim 8, Lee in view of Jang and in further view of Gerbetz and further in view of McConaghy teaches the apparatus of claim 7. Gerbetz further teaches the controller is further configured to output the slide resistance of the glass run and the torque of the motor (Fig. 6 shows the motor torque. It also shows the pinch factor which is directly calculated from the slide resistance).
the slide resistance of the glass run and the torque of the motor being replaced via the output device when the re-determined performance of the target power window satisfies the designer's requirements (Fig. 6 steps 620-626 shows the controller updating the stored values when the condition is met. Lee teaches the prediction as explained in Claim 1 above).
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Claims 12-15 are method claims and recite substantially the same elements as apparatus claims 3 and 4, 5-6, and 8 respectively, and are rejected on the same grounds.
Regarding Claim 16, Lee in view of Jang and in further view of Gerbetz teaches the apparatus of claim 1. Lee teaches the controller is further configured to: output the performance of the target power window including the operating current and the operating time of the motor (Pg. 1, Abstract, "By using our model, the time spent on the up and down motions, the current, and the lifting resistance could be predicted with 4 %, 11 %, 3 % and 4 % of error, respectively").
McConaghy teaches based on a determination that the operating current and the operating time do not exceed predefined reference values ([0098] “The processor module 124 then proceeds to do multi-objective optimization of the surrogate models by optimizing each performance metric in accordance with pre-determined criteria and by maximizing the uncertainty of each performance metric. The multi-objective optimization, in accordance with pre-determined conditions, provides a number of non-dominated candidate designs that form a second set of candidate designs. Subsequently, the processor module 124 calculates a value and uncertainty for each candidate design of the second set of candidate designs and adds these candidate designs and their values to the first set of candidate designs to obtain an augmented set of candidate designs. Finally, the processor module 124 compares the candidate designs of the augmented set to determine if they meet pre-determined target criteria. If they do, the processor module 124 outputs the candidate designs of the augmented set to a computer-readable medium 126, which can be accessed by a user for further analysis or use.”)
determine a first slide resistance of the glass run and a first torque of the motor, included in the input data provided to the deep learning model for obtaining performance of the target power window, as an optimal slide resistance of the glass run and an optimal torque of the motor ([0098] “If they do, the processor module 124 outputs the candidate designs of the augmented set” EN: The candidates that produced the outcome meeting the pre-determined criteria determine the optimal values required)
For motivation to combine please see claim 3.
Claim 17 is a method claim reciting limitations similar to claim 16 and is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Patalano et al (Automotive power window system design: object-oriented modelling and design of experiments integration within a digital pattern approach): Discloses experiment design to determine automotive PW performance.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMIR DARWISH whose telephone number is (571)272-4779. The examiner can normally be reached 7:30-5:30 M-Thurs.
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/A.E.D./Examiner, Art Unit 2199
/LEWIS A BULLOCK JR/Supervisory Patent Examiner, Art Unit 2199
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