DETAILED ACTION
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 .
Information Disclosure Statement
The information disclosure statements (IDSs) were submitted on 08/05/2026 and 06/17/2026. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Responses to Amendments and Arguments
The amendments filed 07/08/2026 have been entered. Claims 1, 5-9 are amended, and Claims 4 and 11 are canceled. Claims 1-3, 5-10 and 12 remain pending in the application.
Applicant's amendments filed 07/08/2026 with respect to the interpretation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph have been fully considered and are persuasive. Thus, the interpretation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph has been withdrawn.
Applicant's amendments filed 07/08/2026 with respect to the rejection of
claims 1-15 under 35 U.S.C. 112(b) or 112 (pre-AIA ), 2nd paragraph have been fully considered and are persuasive. Thus, the rejection of claims 1-12 under 35 U.S.C. 112(b) or 112 (pre-AIA ), 2nd paragraph has been withdrawn.
Applicant's argument and amendments filed 07/08/2026 with respect to the rejection of claim 1-12 under 35 U.S.C. 101 have been fully considered, but are not persuasive. Therefore, the rejection under 35 U.S.C. 101 is withdrawn.
On page 6 of the Remarks, Applicant alleges that claims 1 and 9 have been amended to include the subject matter of dependent claims 4 and 11, respectively, and to further require that the target charge/discharge profile is acquired through the activation process of a battery cell by controlling a charger/discharger to perform the plurality of charge/discharge control sections of the activation process according to a preset sequence. Examiner respectfully disagrees.
The limitation of “wherein the target charge/discharge profile being acquired through the activation process of a battery cell by controlling a charger/discharger to perform the plurality of charge/discharge control section of the activation process according to a preset sequence” is indicative of a practical application integrated into abstract idea, because “controlling a charger/discharger to perform the plurality of charge/discharge control section of the activation process according to a preset sequence” is an insignificant extra-solution activity merely recited to input routine data (i.e., the target charge/discharge profile), which is performed by a generic computer function of a generic computer component, where the target charge/discharge profile and the plurality of charge/discharge control section are not provided with the specific structure/configuration of their data themselves, but are indicative of routine data used for perform abstract idea. The additional element of the data processor is a high-level of generality merely recited to perform a generic computer function of a generic computer component.
Applicant’s amendments and arguments filed 07/08/2026, with respect to the rejection under 35 U.S.C. 103 have been fully considered and are persuasive, because no prior art teaches “inputting, … a target charge/discharge profile … wherein the target charge/discharge profile being acquired through the activation process of a battery cell by controlling a charger/discharger to perform the plurality of charge/discharge control section of the activation process according to a preset sequence”.
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.
The current 35 USC 101 analysis is based on the current guidance (Federal Register vol. 79, No. 241. pp. 74618-74633). The analysis follows several steps. Step 1 determines whether the claim belongs to a valid statutory class. Step 2A prong 1 identifies whether an abstract idea is claimed. Step 2A prong 2 determines whether any abstract idea is integrated into a practical application. If the abstract idea is integrated into a practical application the claim is patent eligible under 35 USC 101. Last, step 2B determines whether the claims contain something significantly more than the abstract idea. In most cases the existence of a practical application predicates the existence of an additional element that is significantly more.
The 35 USC 101 analysis between each element of claims and its combination is presented in the table below
Claim number and elements
Judicial exception (Step 2A Prong one)
Practical application (Step 2A Prong two)/ Significantly more (Step 2B)
Claim 1
Step 1: Yes, statutory class
Step 2A Prong two: No / Step 2B: No
A battery charge/discharge profile analysis method, comprising:
Step2A Prong one: Yes
training a machine learning model using a plurality of training charge/discharge profiles as a training dataset,
wherein each training charge/discharge profile includes training section classification information, and the training section classification information is a dataset in which an identification number of any one of a plurality of charge/discharge control sections performed in a sequential order in an activation process is allocated to corresponding time indices of the plurality of charge/discharge control sections;
“training a machine learning model using a plurality of training charge/discharge profiles …” is an insignificant pre-solution activity to train a machine learning model using routine data (training charge/discharge profiles), which is indicative of executing a computer program itself performed by a generic computer component.
“a machine learning model” is indicative of a mathematical relation/computer algorithm itself.
“training section classification information” is routine data collected to perform abstract idea.
inputting, by a data processor, a target charge/discharge profile to the machine learning model, wherein the target charge/discharge profile being acquired through the activation process of a battery cell by controlling a charger/discharger to perform the plurality of charge/discharge control section of the activation process according to a preset sequence; and
abstract idea
mathematical concept
“inputting, by a data processor, a target charge/discharge profile …” is an insignificant extra-solution activity to perform abstract idea, and the function/operation related to the inputting step may be performed by a generic computer functions of a generic computer component.
The step of acquiring a target charge/discharge profile through the activation process of a battery cell is a math process and/or data processing, which may be executed by a generic computer to execute/calculate such data as profile using the machine learning model (para 0067-0089).
The data processor is a high level of generality.
acquiring, by a data processor, target section classification information for the input target charge/discharge profile from the machine learning model,
wherein the target section classification information is a dataset in which the identification number of any one of the plurality of charge/discharge control sections is allocated to the corresponding time indices of the target charge/discharge profile; and
abstract idea
mathematical concept
“acquiring, by a data processor, target section classification information …” is a math process and/or data processing, which may be executed by a generic computer to execute/calculate such data as profile using the machine learning model, where the machine learning model is a computer program/algorithm itself (para 0067-0089).
determining, by the data processor, if the target charge/discharge profile is abnormal by comparing the identification numbers allocated to the time indexes of the target section classification information according to a sequence of the time indexes of the target section classification information.
abstract idea
mathematical concept
“acquiring, by a data processor, target section classification information …” is a math/mental process and/or data processing.
Claims 1-12 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. Claims 1-12 are directed to an abstract idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception as addressed below and presented in the above table.
Step 2A: Prong One
Regarding Claim 1, the limitations recited in Claim 1, as drafted, are processes that, under its broadest reasonable interpretation, cover performance of the limitation in the mathematical calculations and/or the mind, as presented in the above table. Nothing in the claim elements precludes the step from practically being performed in the mind and/or the mathematical calculations. For example, “a target charge/discharge profile acquired through the activation process of a battery cell” in the context of this claim may encompass manually calculating/acquiring or inferring the target charge/discharge profile, which is indicative of a mathematical process and/or data processing itself using the machine learning model (i.e., mathematical algorithm related to executing data processing ), where the target charge/discharge profile is indicative of dataset resulted from the mathematical calculation and/or data processing executed by a generic computer using the machine learning model. (MPEP 2106.04(a)(2)). In “wherein each training charge/discharge profile includes training section classification information, and the training section classification information is a dataset in which an identification number of any one of a plurality of charge/discharge control sections performed in a sequential order in an activation process is allocated to corresponding time indices”, the training charge/discharge profiles and the training section classification information are indicative of routine data used for calculating/acquiring or inferring the target charge/discharge profile. Similarly, “acquiring target section classification information for the input target charge/discharge profile from the machine learning model, wherein the target section classification information is a dataset in which the identification number of any one of the plurality of charge/discharge control sections is allocated to the corresponding time indices of the target charge/discharge profile” in the context of this claim may encompass manually calculating or inferring the target section classification information based on the calculated result (i.e., the target charge/discharge profile) using the machine learning model, which is indicative of a mathematical process and/or data processing itself using the machine learning model (i.e., mathematical algorithm related to executing data processing ). (See at least paragraphs 0067-0089 of US Publication No. US 20240288500 A1 of the instant application) The target section classification information is indicative of dataset resulted from the mathematical calculation and/or data processing executed by a generic computer using the machine learning model. (MPEP 2106.04(a)(2)). Similarly, The limitation of “determining, by the processor, if the target charge/discharge profile is abnormal by comparing the identification numbers allocated to the time indexes of the target section classification information according to a sequence of the time indexes of the target section classification information” may encompass manually calculating or inferring the abnormal determination with respect to the target charge/discharge profile based on the comparison process, which is indicative of a mathematical process and/or data processing itself using the machine learning model. (MPEP 2106.04(a)(2)).
Step 2A: Prong Two
This judicial exception is abstract ideal itself and not integrated into a practical application. In particular, the specification details use of a processor to perform mathematical calculations and/or data processing of “a target charge/discharge profile acquired through the activation process of a battery cell” and “acquiring target section classification information for the input target charge/discharge profile from the machine learning model, wherein the target section classification information is a dataset in which the identification number of any one of the plurality of charge/discharge control sections is allocated to the corresponding time indices of the target charge/discharge profile”. The additional element of the data processor is a high-level of generality merely recited to perform a generic computer function of a generic computer component. The limitation of “training a machine learning model using a plurality of training charge/discharge profiles as a training dataset” is an insignificant extra-solution activity necessary to execute the machine learning model using routine data (i.e., the training charge/discharge profiles), which is indicative of executing a computer program itself performed by a generic computer component. See MPEP 2106.05(g). The limitation of “inputting a target charge/discharge profile to the machine learning model” is an insignificant extra-solution activity to perform abstract idea, and the function/operation related to the inputting step is performed by a generic computer function of a generic computer component. The limitation of “wherein the target charge/discharge profile being acquired through the activation process of a battery cell by controlling a charger/discharger to perform the plurality of charge/discharge control section of the activation process according to a preset sequence” is an insignificant extra-solution activity merely recited to input routine data (i.e., the target charge/discharge profile), which is performed by a generic computer function of a generic computer component, where the target charge/discharge profile and the plurality of charge/discharge control section are not provided with the specific structure/configuration of their data themselves, but are indicative of routine data used for perform abstract idea. Claim 1 does not present tangible or physical elements/components and/or integration of improvements to be indicative of specific features/structure/acts how and or with what to, for example, acquire a target charge/discharge profile and/or acquire target section classification information. (See MPEP 2106.04(d)). Further, claim 1 does not present tangible or physical elements/components and/or integration of improvements to be indicative of specific features/structure/acts how/what to, for example, acquire a target charge/discharge profile and/or acquire target section classification information. Claim 1 does not present a technical solution to a technical problem by providing an improvement to the functioning of computer, or to any other technology or technical field related to a battery charge/discharge profile analysis. (See MPEP 2106.04(d)). Therefore, there is no showing of integration into a practical application such as an improvement to the functioning of a computer, or to any other technology or technical field, or use of a particular machine.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitation of “training a machine learning model using a plurality of training charge/discharge profiles as a training dataset” is an insignificant extra-solution activity necessary to execute the machine learning model using routine data (i.e., the training charge/discharge profiles), which is indicative of executing a computer program itself performed by a generic computer component. See MPEP 2106.05(g). The limitation of “inputting a target charge/discharge profile to the machine learning model” is an insignificant extra-solution activity to perform abstract idea, and the function/operation related to the inputting step is performed by a generic computer functions of a generic computer component. The limitation of “wherein the target charge/discharge profile being acquired through the activation process of a battery cell by controlling a charger/discharger to perform the plurality of charge/discharge control section of the activation process according to a preset sequence” is an insignificant extra-solution activity merely recited to input routine data (i.e., the target charge/discharge profile), which is performed by a generic computer function of a generic computer component, where the target charge/discharge profile and the plurality of charge/discharge control section are not provided with the specific structure/configuration of their data themselves, but are indicative of routine data used for perform abstract idea. See MPEP 2106.05(g). As discussed above, with respect to integration of the abstract idea into a practical application, using the processing circuity of the sensor to perform “training a machine learning model using a plurality of training charge/discharge profiles as a training dataset, wherein each training charge/discharge profile includes training section classification information, and the training section classification information is a dataset in which an identification number of any one of a plurality of charge/discharge control sections performed in a sequential order in an activation process is allocated to corresponding time indices”, “inputting a target charge/discharge profile acquired through the activation process of a battery cell to the machine learning model” and “acquiring target section classification information for the input target charge/discharge profile from the machine learning model, wherein the target section classification information is a dataset in which the identification number of any one of the plurality of charge/discharge control sections is allocated to the corresponding time indices of the target charge/discharge profile” amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept cannot provide statutory eligibility. Claim 1 is not patent eligible.
Regarding Claims 2-3, 5-8, the limitations are further directed to an abstract idea, as described in claim 1.
Regarding Claim 9, it is an apparatus type claim having similar limitations as of claim 1 above. Therefore, it is rejected under the same rationale as of claim 1 above. The additional element of the memory is a high-level of generality recited to merely perform a generic computer function of a generic computer component. The additional limitation of “store a plurality of training charge/discharge profiles” is an insignificant pre-solution activity to merely perform a generic computer function (i.e., storing data) of a generic computer component.
Regarding Claim 10, the limitations are further directed to an abstract idea, as described in claim 1 or 9. For the reasons described above with respect to Claim 3, the judicial exceptions are not meaningfully integrated into a practical application, or amount to significantly more than the abstract idea.
Regarding Claim 12, it is a system type claim depending on claim 9 and has similar limitations as of claim 9 above. Therefore, it is rejected under the same rationale as of claim 9 above.
Citation of Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
BAEK et al. (US 20130268466 A1) teaches predicting a lifetime of a battery cell, including a learning data input unit, the learning data input unit being configured to receive at least one learning measurement factor and at least one learning factor, a target data input unit, the target data input unit being configured to receive at least one target factor, a machine learning unit, the machine learning unit being coupled to the learning data input unit, the machine learning unit assigning weights to respective ones of the learning factors input to the learning data input unit, and a lifetime prediction unit, the lifetime prediction unit being coupled to the target data input unit and the machine learning unit, the lifetime prediction unit using the weights assigned by the machine learning unit to predict one or more characteristics indicative of the lifetime of the target battery cell.
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 BYUNG RO LEE whose telephone number is (571)272-3707. The examiner can normally be reached on Monday-Friday 8:30am-4:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lee Rodak can be reached on (571) 270-5628. The fax phone number for the organization where this application or proceeding is assigned is 571-273-2555.
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/BYUNG RO LEE/Examiner, Art Unit 2858
/LEE E RODAK/Supervisory Patent Examiner, Art Unit 2858
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