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 8-16 have been presented for examination based on the amendment filed on 12/15/2025.
Claims 8-16 are rejected under 35 U.S.C. 101.
Claims 8-16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US PGPUB No. US 20230273265 A1 by LI; Weihan et al.
This action is made Final.
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Response to Arguments
(Argument 1) Applicant has argued in Remarks Pg.9-10:
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(Response 1) Applicant has argued that determination of state of health(SOH) and state of charge (SOC) from the model has practical application as indicated above. The claim is not directed to any of the argued practical applications and how the SOH/SOC may be used to achieve/improve on the technology. Even if simply amended that SOC/SOH derived is used for above application without disclosing how the vulnerabilities are identified/improved on or what is done to improve the maintenance based on SOC/SOH, the limitation will remain an idea of solution (See MPEP 2106.05(f)(1)).
(Argument 2) Applicant has argued in Remarks Pg.11:
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(Response 2) The generic recitation of the measuring apparatus is considered as mere data gathering means (See MPEP 2106.05(g)). No details are claimed for what makes this additional element which integrates the abstract idea into practical application and adds significantly more.
(Argument 3) Applicant has argued in Remarks Pg.12-13:
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(Response 3) Weihan teaches capacity as Ampere-hour (Ah) and indicates the data collected is timestamped voltage data ([0054]). Weihan also teaches current is kept constant ([0058]-[0062]). It is well known that power = current x voltage (timestamped here). Hence the power (represented by timestamped voltage measurements) is captured and used in Fig.7 encoder/decoder. For this reason applicant’s argument is not persuasive.
(Argument 4) Applicant has argued in Remarks Pg.13-14:
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(Response 4) First, it is unclear what limitation is not taught in the claim. Secondly the argument related to Weihan generating extrapolation for future capacities is not countered in current claim. i.e. there is nothing in the claim such extrapolation for future capacity is explicitly discounted in the claim. Third, applicant themselves have argued on Remarks Pg. 10 stated:
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This appears to be that extrapolated information is used for future detection and maintenance.
Further Weihan states in [0101]:
[0101] The neural network NN2 can, for example, consist of n = 4 cells (for both the decoder and the encoder). The cells can have as a hidden dimension size, the size of the desired output sequence, for example, 108 nodes for the encoder, and 78 accounts for the decoder. The neural network NN2 can once again be trained, as previously described, with an optimiser, for example “Adam”, for example using the mean absolute error. The input sequence represents a past capacity series, while the output sequence represents a future capacity series.
The future capacity series in Ah (Ampere-Hour) is directly proportional to voltage in energy use calculation. Specifically total energy (Watt-hours (Wh)) = Capacity (Ah) x Voltage). So a projection of future capacity can also predict the voltage for any given energy usage. Examiner respectfully maintains the rejection.
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 9-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental process without any additional elements that provide a practical application or amount to significantly more than the abstract idea.
Claims 8 and 9:
Step 1: the claims 8 and 9 are drawn to a system and a method respectively, falling under one of the four statutory categories of invention.
Step 2A, Prong 1: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. The limitations are bolded for abstract idea/judicial exception identification. Performing analysis on claim 8 but is applicable to claim 9 as well.
Claim 8
Mapping Under Step 2A Prong 1
8.An energy storage management system for an energy storage having at least one storage unit, the energy storage management system comprising a data storage for storing a respective measured temporal course of current and voltage or of power and voltage of the respective storage unit, characterized in that the energy storage management system further comprises a simulation system for simulating a performance of the respective storage unit of the energy storage, wherein the simulation system comprises:
at least one respective model for a respective storage unit of the energy storage, the storage unit being assigned to the model, wherein the model includes an encoder-decoder model having an encoder and a decoder, wherein the encoder-decoder model includes at least one recurrent neural network or at least one neural network having a transformer architecture,
wherein the encoder is configured for processing an encoder input sequence that describes a measured temporal course of current and voltage, or of power and voltage of the storage unit that is assigned to the model,
wherein the processing of the encoder input sequence includes generating an initial state of the model from the encoder input sequence, and wherein the decoder is configured for processing a decoder input sequence while starting from the initial state of the model, the decoder input sequence describing a temporal course that is to be simulated of the current or of the power of the storage unit that is assigned to the model, wherein the processing of the decoder input sequence includes generating a decoder output sequence from the decoder input sequence while starting from the initial state of the model, the decoder output sequence describing a simulated temporal course of the voltage of the storage unit that is assigned to the model, wherein said simulated temporal course of the voltage is a temporal course of voltage simulated for said temporal course of the current or of the power of the storage unit.
The structural components is now limited to a data storage for the energy storage management system.
Abstract Idea/Mathematical Concept/Mental step: The model here is a recites recurrent neural network or at least one neural network having a transformer architecture which is a mathematical construct and therefore an abstract idea as in MPEP 2106.04(a)(2)(I) given broadest reasonable interpretation. It may also be considered a mental process/step to design such a model based on observations of datum. See MPEP 2106.04(a)(2)(III).
See Step 2A Prong 2 & 2B.
Abstract Idea/Mathematical Concept: executing the mathematical model (e.g. encoder-decoder model) to predict an output (voltage) is a mathematical concept.
Under its broadest reasonable interpretation, these covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper or perform mathematical concept to generate the output. Also the mathematical concepts disclosed may also be performed in the mind or with the aid of pencil and paper.
Step 2A, Prong 2: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). As per (1) the additional elements are identified as bolded parts of the limitations in column 1 of the table below, and as per (2) the evaluation is shown in the mapping section of the table.
In accordance with this step, the judicial exception is not integrated into a practical application.
Claim 1
Mapping Under Step 2A Prong 2
8.An energy storage management system for an energy storage having at least one storage unit, the energy storage management system comprising a data storage for storing a respective measured temporal course of current and voltage or of power and voltage of the respective storage unit, characterized in that the energy storage management system further comprises a simulation system for simulating a performance of the respective storage unit of the energy storage, wherein the simulation system comprises:
at least one respective model for a respective storage unit of the energy storage, the storage unit being assigned to the model, wherein the model includes an encoder-decoder model having an encoder and a decoder, wherein the encoder-decoder model includes at least one recurrent neural network or at least one neural network having a transformer architecture,
wherein the encoder is configured for processing an encoder input sequence that describes a measured temporal course of current and voltage, or of power and voltage of the storage unit that is assigned to the model,
wherein the processing of the encoder input sequence includes generating an initial state of the model from the encoder input sequence, and wherein the decoder is configured for processing a decoder input sequence while starting from the initial state of the model, the decoder input sequence describing a temporal course that is to be simulated of the current or of the power of the storage unit that is assigned to the model, wherein the processing of the decoder input sequence includes generating a decoder output sequence from the decoder input sequence while starting from the initial state of the model, the decoder output sequence describing a simulated temporal course of the voltage of the storage unit that is assigned to the mode, wherein said simulated temporal course of the voltage is a temporal course of voltage simulated for said temporal course of the current or of the power of the storage unit.
Under MPEP 2106.05(g) the additional component is generic database and is used merely a means for generic data gathering.
See Step 2A Prong 1 above.
Even if encoder & decoder are considered as additional elements (software at best) they are considered as generic elements that do not integrate the abstract idea into practical application. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also O/P Techs. v.
Under MPEP 2106.05(g) determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. In this case the processing an encoder input sequence that describes a measured temporal course of current and voltage or power is mere data gathering at best and then processing is simply the judicial exception.
See Step 2A Prong 1 above.
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
The system claim does not disclose any structural components. The additional elements do not add significantly more and at best are field of use (of simulating a performance of at least one storage unit of an energy storage) of the judicial exception (mathematical modeling using encoder-decoder at highest level of generality). Claim 8 also discloses a data storage in most abstract manner and at best is considered as generic computer component. See MPEP 2106.05(b) & 2106.05(f). The remaining limitation are rejected likewise. Claim 9 discloses a non-computer implemented method and is rejected likewise. The claims 1, 8-9 are therefore considered to be patent ineligible.
Claims 10-16 recite various aspects of the encoder/decoder mathematical concept to generate an output in the field of battery performance and further add to the judicial exception. This type of limitation merely confines the use of the abstract idea to a particular technological environment and thus fails to add an inventive concept to the claims. MPEP 2106.05(g) & (h).
Claim 10 additionally recites a measuring apparatus. The generic recitation of the measuring apparatus is considered as mere data gathering means (See MPEP 2106.05(g)). No details are claimed for what makes this additional element which integrates the abstract idea into practical application and adds significantly more.
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Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 8-16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US PGPUB No. US 20230273265 A1 by LI; Weihan et al.
Regarding Claims 8 (Amended, Rejection Updated 2/17/2026)
(Claim 8) An energy storage management system for an energy storage having at least one storage unit (Weihan: Fig.3/4 storage unit as memory) , the energy storage management (Weihan : Fig.3-4) system comprising a data storage for storing a respective measured temporal course of current and voltage or of power and voltage of the respective storage unit:
characterized in that the energy storage management system further comprises a simulation system for simulating a performance of the respective storage unit of the energy storage (Weihan: Fig.2 & [0067]-[0071] describing the simulation system), wherein the simulation system comprises:
at least one respective model for a respective storage unit of the energy storage (Weihan: Fig.4) , the storage unit being assigned to the model (Weihan: Fig.4 Vehicle battery assigned to the capacity estimation model) , wherein the model includes an encoder-decoder model having an encoder and a decoder (Weihan: Fig.7 showing encoder and decoder) , wherein the encoder-decoder model includes at least one recurrent neural network (Weihan : [0083]) or at least one neural network having a transformer architecture,
wherein the encoder is configured for processing an encoder input sequence that describes a measured temporal course of current and voltage (Weihan: [0054] "...[0054] During a normal charging process of the battery cell, a voltage is now determined repeatedly in step 100. A normal charging process means that this does not take place under laboratory conditions, but rather during normal operation. This voltage, or a value representing this voltage, is assigned a time stamp in step 200...."; this would be similar to known curves discussed in [0014] Q-V curve; [0019], [0022] charge curves in prior art) , or of power and voltage (Weihan : alternately as in [0101] "... The input sequence represents a past capacity series..." – capacity is in Ampere-Hour(Ah) which is related to power-voltage) of the storage unit that is assigned to the model (Weihan: [0054] Figs.3 and 7-8) , wherein the processing of the encoder input sequence includes generating an initial state of the model from the encoder input sequence (Weihan: Figs.3, 7-8 & [0084]-[0104] as initial input sequence generating initial state as “Coded vector”) , and
wherein the decoder is configured for processing a decoder input sequence while starting from the initial state of the model (Weihan: Fig.3 shows the state (status) based operation flow as performed in Fig.7-8) , the decoder input sequence describing a temporal course that is to be simulated of the current or of the power of the storage unit that is assigned to the model (Weihan: [Bolded for Emphasis added to address arguments] Power is equal to voltage x current; Fig.7 shows decoder processing input (voltage) sequence in time steps [0054]-[0055]; Further [0061] states "...[0061] In one form of embodiment of the aspect, the determination of an applied voltage 100, and the assignment 200 to a time stamp, take place under a constant charging current...." therefore for the constant current, temporal course of voltage change is directly proportional to temporal course of the power change being assigned to the model) , wherein the processing of the decoder input sequence includes generating a decoder output sequence from the decoder input sequence while starting from the initial state of the model (Weihan: Fig.7 initial state as coded vector output from encoder and as input to the decoder) , the decoder output sequence describing a simulated temporal course of the voltage of the storage unit that is assigned to the model (Weihan: [0085] & Fig.7; [0101] "... output sequence represents a future capacity series...." ), wherein said simulated temporal course of the voltage is a temporal course of voltage simulated for said temporal course of the current or of the power of the storage unit (Weihan: [0054]-[0055][0061] temporal course of power is proportional to time stamped voltage when the current is constant, [0088] [0101], Fig.7).
Regarding Claim 9 (Amended, Rejection Updated 2/17/2026)
(Claim 9) (Currently Amended) A method for simulating a performance of at least one storage unit of an energy storage by the simulation system of the energy storage management system according to claim 8, (Weihan: Fig.7-8, Fig.2 [0057]) is rejected in similar manner as the claim 8 above. Further , wherein the method includes the step of: processing an encoder input sequence by encoder and generating an initial state of the model (Weihan: Fig.7 as initial input sequence generating initial state as “Coded vector”), wherein the encoder input sequence describes a measured temporal course of current and voltage (Weihan: [0014] Q-V curve; [0019], [0022] charge curves) , or of power and voltage (Weihan : alternately as in [0101] "... The input sequence represents a past capacity series..." – capacity is in Ampere-Hour(Ah) which is related to power-voltage) of the storage unit assigned to the model (Weihan: Fig.4 Vehicle battery assigned to the capacity estimation model; Fig.7 showing encoder and decoder); and
processing of the decoder input sequence by the decoder includes generating a decoder output sequence (Weihan: Fig.7 initial state as coded vector output from encoder and as input to the decoder) , wherein the decoder input sequence describes a temporal course to be simulated of the current or of the power of the storage unit assigned to the model (Weihan : [0098]-[0104]) the decoder output sequence describing a simulated temporal course of the voltage of the storage unit that is assigned to the model (Weihan: [0085] & Fig.7; [0101] "... output sequence represents a future capacity series...." ), wherein said simulated temporal course of the voltage is a temporal course of voltage simulated for said temporal course of the current or of the power of the storage unit (Weihan: [0054]-[0055][0061] temporal course of power is proportional to time stamped voltage when the current is constant, [0088] [0101], Fig.7).
Regarding Claim 10 (New)
Weihan teaches the energy storage management system of The energy storage management system of wherein the energy storage management system includes a measuring apparatus (Weihan: [0097] as sensor in "... As shown in FIG. 4, sensor values, for example from a battery management system (of a vehicle), can be obtained....") for measuring current and voltage or power and voltage of the respective storage unit of the energy storage (Weihan: [0056] "... [0056] As a result, the neural network NN1 provides a (first) indicator SOH in step 500, based on the voltages obtained in step 400 and associated time stamps, wherein the (first) indicator SOH is a measure for the nominal capacitance at the end of the last measured applied voltage...."; current as discussed in [0059]-[0062] being measured constant current) , wherein the energy storage management system is configured for storing respective measured temporal courses of current and voltage or of power and voltage of the respective storage unit in the data storage ( Weihan: [0104] "... [0104] In one form of embodiment of the aspect, determined voltages and time stamps of a charging process are in each case stored as a time series, wherein the multiplicity of time series of different charging processes are supplied to the further neural network NN2....").
Regarding Claim 11 (New)
Weihan teaches The energy storage management system of claim 8, wherein the encoder input sequence describes the measured temporal course of current (Weihan: [0014] Q-V curve; [0019], [0022] charge curves; Also see current in [0056]-[062]) and voltage (Weihan : [0104] "... [0104] In one form of embodiment of the aspect, determined voltages and time stamps of a charging process are in each case stored as a time series, wherein the multiplicity of time series of different charging processes are supplied to the further neural network NN2....") or of power and voltage of the storage unit assigned to the model and a measured temporal course of a temperature, and wherein the decoder input sequence describes the temporal course to be simulated of the current or of the power of the storage unit assigned to the model (Weihan : Fig.1, [0101] ) and a temporal course to be simulated of a temperature.
Regarding Claim 12 (New)
Weihan teaches wherein at least one of the respective encoder or the respective decoder is a recurrent neural network having an Long Short-Term Memory Network (LSTM) architecture or a Gated Recurrent Unit (GRU) architecture, or is a Convolutional Neural Network (CNN) (Weihan: [0090] teaching LSTM-RNN) .
Regarding Claim 13 (New)
Weihan teaches wherein the simulation system is configured for predicting whether the respective storage unit of the energy storage fulfills a load scenario, wherein the predicting comprises: processing a respective encoder input sequence by the encoder of the model to which the respective storage unit is assigned (Weihan: Fig.7 as input sequence) , wherein the encoder input sequence describes a last measured temporal course of current and voltage (Weihan : [0014] Q-V curve; [0019], [0022] charge curves)) or of power and voltage (Weihan : alternately as in [0101] "... The input sequence represents a past capacity series..." – capacity is in Ampere-Hour(Ah) which is related to power-voltage) of the storage unit assigned to the model, wherein the processing of the encoder input sequence comprises generating an initial state of the model from the encoder input sequence (Weihan: Fig.7 as initial input sequence generating initial state as “Coded vector”); and, starting from the initial state of the model, processing a respective decoder input sequence by the decoder of the model (Weihan: Fig.7 as initial input sequence is temporal t1-tn), to which the respective storage unit is assigned, wherein the decoder input sequence in accordance with the load scenario describes a temporal course to be simulated of the current or of the power (Weihan : alternately as in [0101] "... The input sequence represents a past capacity series..." ) of the storage unit assigned to the model, wherein the processing of the decoder input sequence comprises, while starting from the initial state of the model, generating a decoder output sequence from the decoder input sequence, wherein the decoder output sequence describes a simulated temporal course of the voltage of the storage unit assigned to the model (Weihan: Fig.7 [0100]-[0107]) ; and checking whether the generated decoder output sequence fulfills the load scenario (Weihan: [0085] & Fig.7; [0101] "... output sequence represents a future capacity series...." ), and if the decoder output sequence fulfills the load scenario, predicting that the load scenario can be fulfilled, and if the decoder output sequence does not fulfill the load scenario, predicting that the load scenario cannot be fulfilled ((Weihan: [0082] [0092] [0093] & [0101] as error vector to decide whether prediction warrants correcting the model/load can be fullfilled) .
Regarding Claim 14 (New)
Weihan teaches wherein the simulation system is configured for estimating a state parameter that indicates a state of the respective storage unit of the energy storage, wherein the estimating of the state parameter includes: processing a respective encoder input sequence by the encoder of the model to which the respective storage unit is assigned, wherein the encoder input sequence describes a last measured temporal (Weihan: Fig.7 as initial input sequence is temporal t1-tn & [0084]-[0085] only accounts for last input in the LSTM cell), course of current and voltage or of power and voltage of the storage unit assigned to the model, wherein the processing of the encoder input sequence includes generating an initial state of the model from the encoder input sequence (Weihan: Fig.7 as initial input sequence generating initial state as “Coded vector”); and, starting from the initial state of the model, processing a respective decoder input sequence by the decoder of the model to which the respective storage is assigned, wherein the decoder input sequence describes a temporal course to be simulated of the current or of the power of the storage unit (Weihan : alternately as in [0101] "... The input sequence represents a past capacity series..." ) assigned to the model, wherein the processing of the decoder input sequence includes, while starting from the initial state of the model, generating a decoder output sequence from the decoder input sequence (Weihan: [0085] & Fig.7; [0101] "... output sequence represents a future capacity series...." ), wherein the decoder output sequence describes a simulated temporal course of the voltage of the storage unit assigned to the model (Weihan: [0085] & Fig.7; [0101] "... output sequence represents a future capacity series...." ); and determining an estimated value of the state parameter based on the temporal course to be simulated of the current or of the power and based on the associated simulated course of the voltage (Weihan: [0098] "... These time series of voltages and time, as previously described in the context of the first method, or another measure of a state-of-health indicator SOH of a cell, are supplied to a further neural network NN2 in step 600.:...").
Regarding Claim 15 (New)
Weihan teaches wherein the simulation system is configured for comparing the simulated temporal course of the voltage to a further measured temporal course of the voltage of the storage unit assigned to the model, and for determining an indicator of a rating of the model from the result of the comparison (Weihan: [0082] [0092] [0093] & [0101] as error vector).
Regarding Claim 16 (New)
Weihan teaches wherein the simulation system is configured for training the respective model based on training data (Weihan: [0080]-[0082]) , wherein the training data for the respective model include a plurality of training sequences (Weihan: [0084]-[0088] & Fig.3) , wherein a respective training sequence describes a measured temporal course of current (Weihan : [0059]-[0062]) and voltage Weihan: [0098] "... These time series of voltages and time, as previously described in the context of the first method, or another measure of a state-of-health indicator SOH of a cell, are supplied to a further neural network NN2 in step 600.:...") or of power and voltage of the storage unit assigned to the model, wherein the training of the respective model comprises, for a respective training sequence(Weihan: [0084]-[0088] & Fig.3); processing an encoder input sequence by the encoder (Weihan: Fig.7 initial sequence) , wherein the encoder input sequence corresponds to a first section of the training sequence (Weihan : [0014] Q-V curve; [0019], [0022] charge curves)) and describes a measured temporal course of current and voltage or of power (Weihan : alternately as in [0101] "... The input sequence represents a past capacity series..." – capacity is in Ampere-Hour(Ah) which is related to power-voltage) and voltage of the storage unit assigned to the model, wherein the processing of the encoder input sequence includes generating an initial state of the model from the encoder input sequence (Weihan: Fig.7 as initial input sequence generating initial state as “Coded vector”); starting from the initial state of the model, processing a decoder input sequence by the decoder (Weihan: Fig.7 as initial input sequence is temporal t1-tn), wherein the decoder input sequence corresponds to a second section of the training sequence and describes a measured temporal course of the current or of the power of the storage unit assigned to the model (Weihan: Fig.7-8 as initial input sequence is temporal t1-tn used in the decoder; training based on error in NN1 and NN2 - [0082] [0092] [0093] & [0101] ) , wherein the processing of the decoder input sequence includes, while starting from the initial state of the model, generating a decoder output sequence from the decoder input sequence, wherein the decoder output sequence describes a simulated temporal course of the voltage of the storage unit assigned to the model (Weihan: Figs.7-8 and [0088]-[0107] as mapped in claim 1) ; and adapting the model based on deviations of the generated decoder output sequence from a temporal course of the voltage according to the second section of the training sequence (Weihan: [0082] [0092] [0093] & [0101] as optimizing the current model based on error) .
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Communication
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AKASH SAXENA
Primary Examiner
Art Unit 2188
/AKASH SAXENA/Primary Examiner, Art Unit 2188 Wednesday, February 25, 2026