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 .
Response to Amendment
Applicant' s amendment and response filed 10/27/2025 has been entered and made record. This application contains 20 pending claims.
Claims 1-2, 8, 10, 12, 16, 18, and 20 have been amended.
Response to Arguments
Applicant’s arguments filed 10/27/2025 regarding claims rejections under 35 U.S.C. 101 in claim 1-20 have been fully considered but they are not persuasive.
The applicant argues on pages 6-7 of the remark filed on 10/27/2025 that “… Claim 1 has been amended to clarify a practical application of the SoH with adjusting a charging voltage of the at least one battery at the associated electric vehicle based on the SoH. ...”.
The Examiner respectfully disagrees applicant’s argument. Practical application can be demonstrated by additional elements that are sufficient to integrate the judicial exception into a practical application. The additional elements “at least one data storage configured to store computer program instructions”; “at least one processor communicatively coupled to the at least one data storage”, “the at least one processor is configured to execute the computer program instructions to perform the following”, and “adjusting a charging voltage of the at least one battery at the associated electric vehicle based on the SoH” are not sufficient to integrate the abstract idea into a practical application because they only add insignificant extra-solution activities to the judicial exception. The additional elements “receiving battery data for at least one battery including a driving history of an associated electric vehicle” is considered necessary data gathering and thus, not sufficient to integrate the abstract idea into a practical application. As recited in MPEP section 2106.05(g), necessary data gathering (i.e., receiving battery data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). Therefore, the current claims do not recite additional elements that are indicative of integration of an abstract idea into a practical application.
The applicant argues on page 7 of the remark filed that “… Therefore, Claim 1 includes additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 1 as amended does not fall under any of the subject matter groupings of Step 2A, Prong One. …”.
The Examiner respectfully disagrees applicant’s argument. Significantly more can be demonstrated by additional elements that are not well-understood and conventional that integrate the abstract idea into a practical application. However, the claims do not recite them. The limitations of “at least one data storage configured to store computer program instructions”; “at least one processor communicatively coupled to the at least one data storage”, “the at least one processor is configured to execute the computer program instructions to perform the following”; “receiving battery data for at least one battery including a driving history of an associated electric vehicle”; and “adjusting a charging voltage of the at least one battery at the associated electric vehicle based on the SoH”. Therefore, the claims 1, 12, and 20 do not contain additional elements that are not well-understood and conventional that integrate the abstract idea into a practical application.
Hence, the Examiner submits that the rejections of Claims 1-20 are proper.
Applicant’s arguments filed 10/27/2025 regarding claims rejections under 35 U.S.C. 103 in claim 1, 5, and 10-11 have been fully considered and are persuasive.
The claims 1, 2, and 10 have been amended, and the amended claims limitation necessitate a new ground of rejection. Thus, a newly discovered prior art, “Kim US 20220281345”, will be used in combination with prior arts cited in the previous office action to reject the amended claims limitations.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As to claim 1, the claim recites “A computing system, comprising:
at least one data storage configured to store computer program instructions; and
at least one processor communicatively coupled to the at least one data storage,
the at least one processor is configured to execute the computer program instructions to perform the following, comprising:
receiving battery data for at least one battery including a driving history of an associated electric vehicle;
determining at least one electric vehicle battery usage profile for the at least one battery based on the battery data using a graph convolutional network;
calculating a state of health (SoH) for the at least one battery based on the at least one electric vehicle battery usage profile; and
adjusting a charging voltage of the at least one battery at the associated electric vehicle based on the SoH.”
Under the Step 1 of the eligibility analysis, we determine whether the claim is directed to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process for claim 12, and apparatus for claims 1 and 20).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the bold type portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations.
In claim 1, the step identified in bold type are a mathematical concept, therefore, they are considered to be abstract idea.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The claim comprises the following additional elements:
at least one data storage configured to store computer program instructions; at least one processor communicatively coupled to the at least one data storage, the at least one processor is configured to execute the computer program instructions to perform the following: receiving battery data for at least one battery including a driving history of an associated electric vehicle; and adjusting a charging voltage of the at least one battery at the associated electric vehicle based on the SoH.
The additional elements “at least one data storage configured to store computer program instructions”; “at least one processor communicatively coupled to the at least one data storage”, “the at least one processor is configured to execute the computer program instructions to perform the following”, and “adjusting a charging voltage of the at least one battery at the associated electric vehicle based on the SoH” are not sufficient to integrate the abstract idea into a practical application because they only add insignificant extra-solution activities to the judicial exception. The additional element “receiving battery data for at least one battery including a driving history of an associated electric vehicle” represents necessary data gathering and does not integrate the limitation into a practical application. In addition, a generic processor and a generic data storage are generally recited and therefore, not qualified as particular machines.
In conclusion, the above additional elements, considered individually and in combination with the other claims elements do not reflect an improvement to other technology or technical field, do not reflect improvements to the functioning of the computer itself, do not recite a particular machine, do not effect a transformation or reduction of a particular article to a different state or thing, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claim is directed to a judicial exception and require further analysis under the Step 2B.
The above claim, does not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are generically recited and are well-understood/conventional in a relevant art as evidenced by the prior art of record (Step 2B analysis).
For example, receiving battery data for at least one battery including a driving history of an associated electric vehicle is considered necessary data gathering. As recited in MPEP section 2106.05(g), necessary data gathering (i.e. receiving data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015).
For example, adjusting a charging voltage of the at least one battery at the associated electric vehicle based on the SoH is disclosed by “Kim US 20220281345”, [0049], [0076], [0130]; and “Kumar US 20190176639”, [0021], FIG. 11, [0054], [0149], [0158].
The claim, therefore, is not patent eligible.
Independent claims 12 and 20 recite subject matter that is similar or analogous to that of claim 1, and therefore, the claims are also patent ineligible.
With regards to the dependent claims, claims 2-11 and 13-19 provide additional features/steps which are considered part of an expanded abstract idea of the independent claims, and do not integrate the abstract ideas into a practical application.
The dependent claims are, therefore, also not eligible.
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 and 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Choudhary (US 20220294027, hereinafter Choudhary) in view of Yang (CN 114675186A, hereinafter Yang), and further in view of Kim et al. (US 20220281345, hereinafter, Kim).
As to claim 1, Choudhary teaches at least one data storage configured to store computer program instructions ([0064]); and
at least one processor communicatively coupled to the at least one data storage (FIG. 5, #520, #530 and #540),
the at least one processor is configured to execute the computer program instructions to perform ([0064]) the following, comprising:
receiving battery data for at least one battery ([0034] discloses the battery information may include a current capacity of the battery as measured or determined; [0035] discloses the battery management system determines the degradation rate of the battery);
determining at least one usage profile for the at least one battery based on the battery data using a neural network ([0029] discloses the battery management system trains the machine learning model based on the battery usage/charge data and/or the battery environment data, and the battery analysis model is trained to identify patterns and/or trends involving a degradation rate and a particular usage pattern (i.e., determining a usage profile based on the battery data using a model or neural network - emphasis added by Examiner) being indicative of a quantity or range of remaining charge cycles of a battery); and
providing a state of health (SoH) for the at least one battery based on the at least one usage profile (Abstract discloses The battery management system determines, using a battery analysis model, a quantity of remaining charge cycles of the battery based on the degradation rate (i.e., the degradation rate would reflect state of health (SoH) of the battery - emphasis added by Examiner) and the usage pattern (i.e., usage profile - emphasis added by Examiner); [0029] discloses the battery analysis model is trained to identify patterns or trends involving a degradation rate and a particular usage pattern (i.e., provide a degradation rate would reflects state of health (SoH) for the battery based on the usage profile - emphasis added by Examiner) and a particular operating condition being indicative of a quantity or range of remaining charge cycles).
Choudhary does not explicitly teach receiving battery data for at least one battery including a driving history of an associated electric vehicle; one electric vehicle battery usage profile; and adjusting a charging voltage of the at least one battery at the associated electric vehicle based on the SoH.
Kim teaches teach receiving battery data for at least one battery including a driving history of an associated electric vehicle ([0067] discloses The frequency distribution data represents a driving history of the electric vehicle EVn”);
one electric vehicle battery usage profile ([0004]); and
adjusting a charging voltage of the at least one battery at the associated electric vehicle based on the SoH ([0049] and [0076] disclose controls charging/discharging operation of the battery Bn, and during charging/discharging of the battery Bn, measures voltage, current and temperature of the battery Bn. Examine the distribution of voltage data Vk included in the latest charging characteristic information, and determine a charge capacity change amount by integrating the current data measured in the SOH estimation voltage section, and determine the ratio of the charge capacity change amount to a reference charge capacity change amount as the SOH (i.e., changing or adjusting charge capacity or charge voltage of the battery at the associated the EV based on the SOH - emphasis added by Examiner); [0130]).
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Kim into Choudhary for
collecting battery performance evaluation information while an electric vehicle is being
charged in order to update a control factor used for controlling charging/discharging of the battery. This combination would improve in accurately determining a State Of Health of a battery and efficiently controlling charging/discharging of the battery so that the degradation speed of the battery can be delayed as much as possible and thus extend the service life of the battery.
The combination of Choudhary and Kim does not explicitly teach a graph convolutional network.
Yang teaches a graph convolutional network (Abstract discloses “a lithium ion battery health state prediction method based on a graph neural network.”; [0026] discloses the test set is input into the trained graph convolutional neural network (GCN) or graph attention model to obtain the health status of the two half cycles).
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Yang into Choudhary in view of Kim for the purpose of predicting the health status of lithium-ion batteries based on graph neural network so that the health status of batteries can be evaluated to assist in the safe and reliable operation of the batteries. By combining Choudhary’s identification of a quantity of remaining charge cycles of the battery based on the degradation rate by battery analysis model, with Yang’s obtaining of the health status of a lithium ion battery using a graph convolutional neural network (GCN), the future health state of the battery and a remaining useful life of the battery can be accurately predicted.
As to claim 10, the combination of Choudhary, Kim, and Yang teaches the claimed limitations as discussed in claim 1.
Choudhary teaches wherein the at least one data storage and the at least one processor are embedded in a battery management system ([0061] discloses the battery management system 401 includes one or more devices 500 and/or one or more components of device 500, and as shown in FIG. 5, device 500 includes a bus 510, a processor 520, a memory 530, a storage component 540 (i.e., processor 520, a memory 530, a storage component 540 are embedded in a battery management system - emphasis added by Examiner)).
Choudhary does not explicitly teach in a battery management system of a vehicle.
Yang teaches wherein the at least one data storage and the at least one processor are embedded in a battery management system of a vehicle ([0001] discloses
the field of battery health status management (i.e., a battery management system - emphasis added by Examiner), and in particular to a lithium-ion battery health status prediction method based on graph neural network; [0002] discloses lithium-ion batteries have been recognized by the industry and supported by policies due to their high energy density, low self-discharge rate, high efficiency and stability, and long cycle life, and they are widely used in aerospace, automotive (i.e., a vehicle - emphasis added by Examiner), and power systems).
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Yang into Choudhary for the purpose of managing and predicting the health status of lithium-ion batteries based on graph neural network so that the health status of batteries can be evaluated to assist in the safe and reliable operation of the batteries. By combining Choudhary’s identification of a quantity of remaining charge cycles of the battery based on the degradation rate by battery analysis model, with Yang’s obtaining of the health status of a lithium ion battery using a graph convolutional neural network (GCN), the future health state of the battery and a remaining useful life of the battery can be accurately predicted.
The combination of Choudhary and Kim does not explicitly teach a battery management system of the associated electric vehicle.
Kim teaches a battery management system of the associated electric vehicle ([0002], [0049]).
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Kim into Choudhary for
collecting battery performance evaluation information while an electric vehicle is being
charged in order to update a control factor used for controlling charging/discharging of the battery. This combination would improve in accurately determining a State Of Health of a battery and efficiently controlling charging/discharging of the battery so that the degradation speed of the battery can be delayed as much as possible and thus extend the service life of the battery.
As to claim 11, the combination of Choudhary, Yang, and Kim teaches the claimed limitations as discussed in claim 1.
Choudhary teaches a computer network, wherein the at least one data storage and the at least one processor are embedded in a cloud computing environment and wherein the cloud environment is communicatively coupled to the at least one battery via the computer network ([0050] and [0052] disclose as shown in FIG. 4, environment 400 includes a battery management system 401, which include one or more elements of and/or execute within a cloud computing system 402. The cloud computing system 402 includes one or more elements 403-413; and computing hardware 403 may include one or more processors 407, one or more memories 408, one or more storage components 409 (i.e., processor 407, a memory 408, a storage component 409 are embedded in a battery management system of a cloud computing system 402 - emphasis added by Examiner), and/or one or more networking components 410).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Choudhary, Yang, and Kim, in view of Kranski et al. (US 20220314434, hereinafter Kranski).
As to claim 5, the combination of Choudhary, Kim, and Yang teaches the claimed limitations as discussed in claim 1.
The combination of Choudhary, Yang, and Kim does not explicitly teach wherein the time series encoder is long short-term memory neural network or a gated recurrent units network.
Kranski teaches wherein the time series encoder is long short-term memory neural network or a gated recurrent units network ([0126] discloses features may be extracted from sensor data with long-short term memory model or a convolutional neural network for time-series data (i.e., the time series encoder is long short-term memory neural network - emphasis added by Examiner) like motor current, or a geometric deep learning model for 3D point clouds from depth sensors. The extracted features may be input to an encoder model, like a time contrastive network or convolution neural network).
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Kranski into Choudhary in view of Yang and Kim for the purpose of reducing processing complexity to a degree that supports near real-time sequences of state determination to control model outputs, and reducing the high dimensional data by one or more encoder models which may implement long short term memory model. This combination would improve in efficiently extracting features from sensor data with a convolutional neural network, and the sensor data includes battery level and power consumption.
Examiner’s Note
Regarding Claims 2-4, 6-9 and 12-20, the most pertinent prior arts are “Choudhary US 20220294027”, “Yang CN 114675186A”, “Kranski US 20220314434”, “Budan US 11705590 B1”, “Tu US 20210174170”, “Kranski US 20220318678”, “Kim US 20220281345”, and “Kumar US 20190176639”.
As to claim 2, the prior arts of record, alone or in combination, do not fairly teach or suggest “segmenting the received battery data into subsequences;
generating node representations for the subsequences using at least one time series encoder;
determining a feature-distance adjacency matrix (FDAM) for the node representations;
generating a learned graph representation by applying a graph convolutional network (GCN) on a corresponding graph to the feature-distance adjacency matrix; and
generating one or more labels from the learned graph representation by using a node clustering layer; and
determining the at least one usage profile of the at least one battery based on the one or more generated labels” including all limitations as claimed.
As to claims 12 and 20, Choudhary teaches receiving battery data for at least one battery (Choudhary, [0034], [0035]);
providing a state of health (SoH) for the at least one battery based on the at least one usage profile (Choudhary, Abstract, [0029]).
Kim teaches teach receiving battery data for at least one battery including a driving history of an associated electric vehicle (Kim, [0067]);
one electric vehicle battery usage profile (Kim, [0004]);
calculating a state of health (SoH) for the at least one battery based on the at least one electric vehicle battery usage profile (Kim, Abstract, [0002], [0011], [0015]); and
adjusting a charging voltage of the at least one battery at the associated electric vehicle based on the SoH (Kim, [0049] and [0076], [0130]).
However, the prior arts of record, alone or in combination, do not fairly teach or suggest “segmenting the received battery data into subsequences;
generating node representations for the subsequences using at least one time series encoder;
determining a feature-distance adjacency matrix (FDAM) for the node representations;
generating a learned graph representation by applying a graph convolutional network (GCN) on a corresponding graph to the feature-distance adjacency matrix;
generating one or more labels from the learned graph representation by using a node clustering layer;
determining the at least one electric vehicle battery usage profile of the at least one battery based on the one or more generated labels” including all limitations as claimed.
Dependent claims 3-4, 6-9 and 13-19 are also distinguish over the prior art for at least the same reason as claims 2 and 12.
Examiner notes, however, that claims 1-20 are rejected under 35 U.S.C. 101, and therefore, not patent eligible.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAL CE MANG whose telephone number is (571)272-0370. The examiner can normally be reached Monday to Friday- 8:00-12:00, 1:00-5:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine T Rastovski can be reached at (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LAL CE MANG/Examiner, Art Unit 2863