DETAILED ACTION
Claims 1-15 are presented for examination.
Claims 1-4, 6-8, and 10-15 have been amended.
This office action is in response to the amendment submitted on 21-JAN-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.
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. 7-10 of the Applicant/Arguments Remarks, Applicant argues the amended claims have overcome the rejection under 35 USC 101. Examiner respectfully disagrees and finds Claim 2 of Example 47 from the July 2024 Subject Matter Eligibility Examples relevant.
The applicant argues the claimed invention is directed to an improvement in the technology as well as provides a practical application.
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.
Additionally, 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. 8-10 of the Applicant/Arguments Remarks, Applicant argues the amended claims overcome the rejection under 35 USC 103. The applicant argues Lee doesn’t teach slide resistance, but rather models the glass run as a non-linear spring damper system.
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.
The applicant further argues that Lee doesn’t teach or suggest ‘operation history of the power window’.
The examiner respectfully disagrees. Lee utilizes test data to compare against the simulated operation data, “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) As for the deep learning model that is provided by Jang.
The applicant further indicates that none of the references teach the claim as disclosed.
The examiner notes, 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.
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 1-15 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 deep learning model and trained updates thereto.
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 to output the performance of the power window using: 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 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: 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.
determine an operating current and an operating time of the motor as the performance of the target power window by inputting the real data to the deep learning model, which is a mathematical/mental process under Step 2A Prong One. Therefore, the claim is considered ineligible under 35 USC 101.
Claim 4 recites replace the slide resistance of the glass run and the torque of the motor with different values in the real data when the determined performance of the target power window does not satisfy designer's requirements, and re- determine the performance of the target power window as a re-prediction, which is a mathematical/mental process under Step 2A Prong One. 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 designer's requirements 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 system 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 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 1-15 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:
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).
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").
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 predict the performance of a power window using
based on an output of the deep learning model and trained updates thereto
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 Prcoess, "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: (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 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 determine an operating current and an operating time of the motor as the performance of the target power window by inputting the real data to 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").
Regarding Claim 4, Lee in view of Jang and in further view of Gerbetz teaches the apparatus of claim 2. Gerbetz further teaches the controller is further configured to: replace 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 with different values in the real data when the determined performance of the target power window does not satisfy designer's requirements (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")
re-determine the performance of the target power window as a re-prediction ([0021] "Pinch factor 430 is combined with pinch threshold 308 to calculate a modified pinch threshold 432 that takes into account abrupt load changes on window 104 that might otherwise trigger the anti-pinch safety system to reverse or disable window 104 operation").
Regarding Claim 5, Lee in view of Jang and in further view of Gerbetz 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 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 designer's requirements 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 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 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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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 10-15 are system 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
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.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.E.D./Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187