DETAILED ACTION Non-Final Rejection 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. Claim Objections Claims 1-16 are objected to because of the informalities: should be change to Claim 1: a first controller configured to obtain state data [..]; a second controller configured to generate prediction data [..]; [..]the second controller configured to determine [..]; a communication unit configured to transmit the state data [..]; Claim 2: [..]the second controller is further configured to compress[..]; Claim 3: [..]the communication unit is further configured to transmit [..]; Claims 5-6: [..]second controller is further configured to predict [..] Claim 7: [..] the second controller is further configured to generate [..]; Claim 8: [...]the second controller is further configured to determine[..] Claim 9:[..] a battery management apparatus configured to generate[..]; Claim 10: [..]the battery management apparatus is further configured to compress[..]; [..]the battery management apparatus configured to transmit[..]; Claim 12: [..] the battery management apparatus is further configured to predict[..] Claim 13: [..] the battery management apparatus is further configured to generate[..]; [..]the battery management apparatus configured to determine[..]; Claim 14: [..]the server is further configured to extract[..]; Claim 15: [..]the server is further configured to measure[..]; Claim 16: [..]the battery management apparatus is further configured to update[..]. Appropriate correction is required. 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 1-16 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Each of claims 1- 16 falls within one of the four statutory categories. See MPEP § 2106.03. For example, each of claims 1- 16 falls within category of machine, i.e., a “concrete thing, consisting of parts, or of certain devices and combination of devices.” Digitech , 758 F.3d at 1348–49, 111 USPQ2d at 1719 (quoting Burr v. Duryee , 68 U.S. 531, 570, 17 L. Ed. 650, 657 (1863)). Regarding Claims 1-8 Step 2A – Prong 1 Exemplary claim 1 is directed to an abstract idea of determines the state of the battery. The abstract idea is set forth or described by the following italicized limitations: 1. A battery management apparatus comprising: a first controller obtaining state data comprising a measurement value corresponding to a state of a battery; a second controller generating prediction data for predicting the state of the battery by applying at least a part of the state data to machine learning , wherein the second controller d etermines the state of the battery by comparing the prediction data with the state data; and a communication unit transmitting the state data to a server based on a result of determining the state of the battery . The italicized limitations above represent combination of mathematical concepts (i.e., a process that can be performed by mathematical relationships or rules or idea) and mental step (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment). Therefore, the italicized limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 Revised Patent Subject Matter Eligibility Guidance. For example, the limitations “[..] predicting the state of the battery [..]; [..] determines the state of the battery by comparing the prediction data [..];” are combination of mathematical concepts (i.e., a process that can be performed by mathematical relationships or rules or idea) and mental step (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment), see 2106.04(a)(2). Limitations (are considered together as a single abstract idea for further analysis. (discussing Bilski v. Kappos , 561 U.S. 593 (2010)). Step 2A – Prong 2 Claims 1 does not include additional elements (when considered individually, as an ordered combination, and/or within the claim as a whole) that are sufficient to integrate the abstract idea into a practical application. For example, additional first element is “ a first controller obtaining state data comprising a measurement value corresponding to a state of a battery; a communication unit transmitting the state data to a server based on a result of determining the state of the battery ” to be performed, at least in-part, these additional elements appear to only add insignificant extra-solution activity (e.g., data gathering) and only generally link the abstract idea to a particular field. Therefore, this element individually or as a whole does not provide a practical application. See MPEP 2106.05(g) The 2 nd additional element of “a machine learning in limitations are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept and it is recited a computer component at a high level of generality. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. See MPEP 2106.05(f ). . The 3rd additional element is “A battery management apparatus comprising: a first controller, a second controller”. This element amounts to mere use of a generic battery monitoring system, which is well understood routine and conventional (see background of current discloser and IDS and PTO 892) and this element individually does not provide a practical application. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. see MPEP 2106.05(d). In view of the “ additional element” individually does not provide a practical application of the abstract idea. Furthermore, the “additional elements” in combination amount to a generic system with extra solution activity. The combination of additional elements does no more than generally link the use of the abstract idea to a particular technological environment, and for this additional reason, the combination of additional elements does not provide a practical application of the abstract idea. Step 2B Claims1 does not include additional elements, when considered individually and as an ordered combination, that are sufficient to amount to significantly more than the abstract idea. For example, the limitation of Claim 1 contains additional elements that is, i.e. A battery management apparatus, controllers, server”, generic devices, which are well understood, routine and conventional (see background of current discloser and IDS and PTO 892) and MPEP 2106.05(d)) The reasons for reaching this conclusion are substantially the same as the reasons given above in § Step 2A – Prong 2. For brevity only, those reasons are not repeated in this section. See MPEP §§ 2106.05(g) and MPEP §§2106.05(II). Dependent Claims 2- 8 Dependent claims 2- 8 fail to cure this deficiency of independent claim 1 (set forth above) and are rejected accordingly. Particularly, claim s 2-8 recite limitations that represent (in addition to the limitations already noted above) either the abstract idea or an additional element that is merely extra-solution activity, mere use of instructions and/or generic computer component(s) as a tool to implement the abstract idea, and/or merely limits the abstract idea to a particular technological environment. For example, the limitations of Claims 2-4: insignificant extra-solution activity (e.g., data gathering) For example, the limitations of Claim 6 in limitations are at best mere instructions to “apply” the abstract ideas to machine learning, which cannot provide an inventive concept and it is recited a computer component at a high level of generality. For example, the limitations of Claims 5, 7-8 a re a combination of mental step and mathematical concept( abstract idea). Regarding Claims 9-16 Step 2A – Prong 1 Exemplary claim 9 is directed to an abstract idea of determining whether the battery is in an anomaly state . The abstract idea is set forth or described by the following italicized limitations: 9. A battery testing system comprising: a battery management apparatus generating prediction data for predicting a state of a battery by applying at least a part of state data comprising a measurement value resulting from measuring the state of the battery to machine learning, wherein the battery management apparatus determines the state of the battery by comparing the prediction data with state data of the battery , and wherein the battery management apparatus transmits the state data to a server based on a result of determining the state of the battery; and a server determining whether the battery is in an anomaly state based on the state data of the battery . . The italicized limitations above represent combination of mathematical concepts (i.e., a process that can be performed by mathematical relationships or rules or idea) and mental step (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment). Therefore, the italicized limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 Revised Patent Subject Matter Eligibility Guidance. For example, the limitations “[..] predicting a state of a battery [..]; [..] determines the state of the battery [..]; [..] determining whether the battery is in an anomaly state[..] ” are combination of mathematical concepts (i.e., a process that can be performed by mathematical relationships or rules or idea) and mental step (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment), see 2106.04(a)(2). Limitations (are considered together as a single abstract idea for further analysis. (discussing Bilski v. Kappos , 561 U.S. 593 (2010)). Step 2A – Prong 2 Claims 9 does not include additional elements (when considered individually, as an ordered combination, and/or within the claim as a whole) that are sufficient to integrate the abstract idea into a practical application. For example, additional first element is “ a measurement value resulting from measuring the state of the battery; wherein the battery management apparatus transmits the state data to a server based on a result of determining the state of the battery ” to be performed, at least in-part, these additional elements appear to only add insignificant extra-solution activity (e.g., data gathering) and only generally link the abstract idea to a particular field. Therefore, this element individually or as a whole does not provide a practical application. See MPEP 2106.05(g) The 2 nd additional element of “a machine learning ” in limitations are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept and it is recited a computer component at a high level of generality. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. See MPEP 2106.05(f ).. The 3rd additional element is “ A battery testing system comprising: a battery management apparatus ”. This element amounts to mere use of a generic battery monitoring system, which is well understood routine and conventional (see background of current discloser and IDS and PTO 892) and this element individually does not provide a practical application. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. see MPEP 2106.05(d). In view of the “ additional element” individually does not provide a practical application of the abstract idea. Furthermore, the “additional elements” in combination amount to a generic system with extra solution activity. The combination of additional elements does no more than generally link the use of the abstract idea to a particular technological environment, and for this additional reason, the combination of additional elements does not provide a practical application of the abstract idea. Step 2B Claims 9 does not include additional elements, when considered individually and as an ordered combination, that are sufficient to amount to significantly more than the abstract idea. For example, the limitation of Claim 9 contains additional elements that is, i.e. battery testing system , server”, generic devices, which are well understood, routine and conventional (see background of current discloser and IDS and PTO 892) and MPEP 2106.05(d)) The reasons for reaching this conclusion are substantially the same as the reasons given above in § Step 2A – Prong 2. For brevity only, those reasons are not repeated in this section. See MPEP §§ 2106.05(g) and MPEP §§2106.05(II). Dependent Claims 10 - 16 Dependent claims 10-16 fail to cure this deficiency of independent claim 9 (set forth above) and are rejected accordingly. Particularly, claims 10-16 recite limitations that represent (in addition to the limitations already noted above) either the abstract idea or an additional element that is merely extra-solution activity, mere use of instructions and/or generic computer component(s) as a tool to implement the abstract idea, and/or merely limits the abstract idea to a particular technological environment. For example, the limitations of Claims 10 - 11 (the state data comprises a measurement value comprising voltage , current, and temperature of the battery, wherein the voltage, current, and temperature of the battery are measured cumulatively,), 15 : insignificant extra-solution activity (e.g., data gathering) For example, the limitations of Claim 12 and 16 in limitations are at best mere instructions to “apply” the abstract ideas to machine learning, which cannot provide an inventive concept and it is recited a computer component at a high level of generality. For example, the limitations of Claims 11(wherein the state data comprises the measurement value and a state of health (SOH) of the battery, and wherein the SOH of the battery is calculated based on the measurement value) , 13-14: a re a combination of mental step and mathematical concept( abstract idea). Claim Rejections - 35 USC § 102 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. Claim(s) 1,4-5 and 7-8 is/are rejected under 35 U.S.C. 102 (a)( 1) as being anticipated by Holme (US 2020/0164763) . Regarding Claim 1 . Holme teaches a battery management apparatus comprising (fig s . 1 -2 ; fig. 3 A ; abstract) : a first controller ( 209 : fig. 3 A ) obtaining state data comprising a measurement value corresponding to a state of a battery (BMS sensors/controllers 209 generate data relating to physical characteristics of the battery 208, such as temperature, voltage, current, pressure:[0096]-[0097]) ; a second controller ( 10: fig. 3A; Device 10 may be part of the BMS 210: [0096] ) generating prediction data for predicting the state of the battery ( prediction component 36 reside at device 10: [0105] ) by applying at least a part of the state data to machine learning ( predicted battery state(s):[0026], [0083], [0086], [0010], [0127] ) , wherein the second controller (10: fig. 3A) determines the state of the battery by comparing the prediction data with the state data ( FIG. 6A is a plot showing predictions of battery cell voltages versus time for a new battery cell. The solid lines show the actual (measured) voltages vs. time, in response to a predetermined charging and discharging current applied to the battery. The predicted voltage values using the trained model are indicated by “x.” The voltage predictions were generated using a trained Random Forest model, trained from an initial training set (ITS) of battery currents and corresponding voltages for the new battery cell: [0039] -[ 0041]; fig. 6A-C; ) ; and a communication unit (18: fig. 3A) transmitting the state data to a server based on a result of determining the state of the battery ( 38: fig. 2; 62: fig 3A; The predicted battery state(s) are passed to the prediction module 30, which are then stored in local memory, sent to the BMS processor for modulating a controller of the battery, and/or sent to the vehicle telematics device, telematics device accessible over the vehicle's control area network. Network interface 18 may also include a wireless connection capability to a network 61 for accessing other devices 62, such as a mobile device, or a server hosting a vehicle and/or battery manufacturer resources sit : [0025 ] , [ 0093]- [0095] ) . Regarding Claim 4 . Holme further teaches the measurement value comprises voltage, current, and temperature of the battery, wherein the voltage, current, and temperature of the battery are measured cumulatively ( [0015] , [0151] ) , and wherein the state data comprises the measurement value and a state of health (SOH) of the battery ( [0024] , [0151] ) , and wherein the SOH of the battery is calculated based on the measurement value ( [0015], [0024], [0045] , [0151] ). Regarding Claim 5 . Holme further teaches the prediction data comprises a prediction voltage of the battery ( fig. 6A-C) , wherein the second controller predicts the prediction voltage of the battery by providing past data included in the state data and measured current and temperature of the battery, included in the state data, to a machine learning mode ( [0147], [0149] , [0151] ) , and wherein the measured current and temperature of the battery are measured at current time ( [0147]-[0148], [0151]) . Regarding Claim 7 . Holme further teaches the second controller generates an anomaly score by applying the state data and the prediction data to an anomaly detection algorithm( issuing a warning of a present abnormal condition of the battery, cooling the battery, heating the battery, or producing a maximum number of driving miles available before the battery reaches 80% discharge: [0087]; claims 18-19; [0250]-[0260]). Regarding Claim 8 . Holme further teaches the second controller determines the battery to be in an anomaly state when a rate percentage of anomaly scores equal to or greater than a threshold value among anomaly values generated for a predetermined time is equal to or greater than a specific predetermined percentage , and Wherein the percentage of the anomaly scores has anomaly values that are equal to or greater than an anomaly threshold value(issuing a warning of a present abnormal condition of the battery, cooling the battery, heating the battery, or producing a maximum number of driving miles available before the battery reaches 80% discharge: [0087]; claims 18-19; [0250]-[0260]). Claim(s) 9 and 11 is/are rejected under 35 U.S.C. 102(a)( 1)as being anticipated by Aliyev et al. (US 2017/010855 1 ). Regarding Claim 9 . Aliyev teaches a battery testing system comprising (fig. 2) a battery management apparatus (22: fig.2) generating prediction data for predicting a state of a battery by applying at least a part of state data comprising a measurement value resulting from measuring the state of the battery to machine learning( 46: fig. 2; statistical model ( svm ): [0039]; [0045]-[0046],[0056]-[0058]), wherein the battery management apparatus determines the state of the battery by comparing the prediction data with state data of the battery( 48: fig. 2; [0067]; fig.6), and wherein the battery management apparatus transmits the state data to a server based on a result of determining the state of the battery( 38: fig. 2); and a server (38: fig.2) determining whether the battery is in an anomaly state based on the state data of the battery( 52, 56: fig. 2; provide user-perceivable indications relating to the operation of the battery:[0033], [0047]). Regarding Claim 11 : Aliyev further teaches the state data comprises a measurement value comprising voltage , current, and temperature of the battery, wherein the voltage, current, and temperature of the battery are measured cumulatively([0034]), and wherein the state data comprises the measurement value and a state of health (SOH) of the battery( life: [ 0006], [0024]), and wherein the SOH of the battery is calculated based on the measurement value( [0024], [0034]). 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. Claim(s) 2-3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Holme (US 2020/0164763) in view of Jo et al. (US 2022/0268844 ) . Regarding Claims 2-3 . Holme silent about the second controller compresses, upon determining the battery to be in an anomaly state, the state data obtained for a predetermined time before and after determining the anomaly state, wherein the communication unit transmits the compressed state data of the battery to the server. However, Jo teaches the second controller ( 100: fig.3) compresses(310: fig.3 ), upon determining the battery to be in an anomaly state(308(yes): fig. 3 ), the state data obtained for a predetermined time before and after determining the anomaly state(310: fig. 3), wherein the communication unit transmits the compressed state data (312: fig. 3)of the battery to the server(200: fig. 3) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the invention of Holme, the second controller compresses, upon determining the battery to be in an anomaly state, the state data obtained for a predetermined time before and after determining the anomaly state, wherein the communication unit transmits the compressed state data of the battery to the serve, as taught by Jo, so as to identify a state of an accident and analyze a cause of the accident by rapidly securing stored data without a loss in an event of an emergency on the battery . Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Holme (US 2020/0164763) in view of Lee et al. (US 2022/0206078). Regarding Claim 6 . Holme further teaches the second controller predicts the prediction voltage of the battery by providing the state data to the machine learning model, and wherein the machine learning model is based on a an artificial neural network (ANN) or other types of machine learning models ([0083]). Holme silent about machine learning model is based on a long short term memory (LSTM) algorithm. However lee teaches machine learning model is based on a long short term memory (LSTM) algorithm for estimation( ([0027],650: fig. 6 , [0078]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the invention of Holme, machine learning model is based on a long short term memory (LSTM) algorithm, as taught by Lee, so as to estimate aging state of the battery in real time . Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aliyev in view of Jo et al. (US 2022/0268844) . Regarding Claim 10 . Aliyev silent about the battery management apparatus compresses, upon determining the battery to be in the anomaly state, the state data obtained for a predetermined time before and after determining the anomaly state, and wherein, battery management apparatus transmits, upon determining the battery to be in the anomaly state, the compressed state data to the server. However, Jo teaches the battery management apparatus compresses(310: fig.3 ), upon determining the battery to be in the anomaly state(308(yes): fig. 3 ), the state data obtained for a predetermined time before and after determining the anomaly state(310: fig. 3), and wherein, battery management apparatus transmits(312: fig. 3), upon determining the battery to be in the anomaly state, the compressed state data to the server(200: fig. 3). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the invention of Aliyev , the battery management apparatus compresses, upon determining the battery to be in the anomaly state, the state data obtained for a predetermined time before and after determining the anomaly state, and wherein, battery management apparatus transmits, upon determining the battery to be in the anomaly state, the compressed state data to the server, as taught by Jo, so as to identify a state of an accident and analyze a cause of the accident by rapidly securing stored data without a loss in an event of an emergency on the battery . Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aliyev in view s of Holme and Lee et al. ( US 2022/0206078) Regarding Claim 12 . Aliyev silent about the battery management apparatus predicts a prediction voltage of the battery by providing past data included in the state data and measured current and temperature of the battery, included in the state data to a long short term memory (LSTM) algorithm, wherein the measured current and temperature of the battery are measured at current time. However, Holme teaches the battery management apparatus predicts a prediction voltage of the battery (fig. 6A-C) by providing past data included in the state data and measured current and temperature of the battery, included in the state data to an artificial neural network ( [0147], [0149], [0151]) , wherein the measured current and temperature of the battery are measured at current time(([0147]-[0148], [0151])). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the invention of Aliyev , the battery management apparatus predicts a prediction voltage of the battery by providing past data included in the state data and measured current and temperature of the battery, included in the state data to a ANN algorithm, wherein the measured current and temperature of the battery are measured at current time, as taught by Holm so as to predicted battery state in real time . Modified Aliyev silent about machine learning model is based on a long short term memory (LSTM) algorithm. However lee teaches machine learning model is based on a long short term memory (LSTM) algorithm for estimation( ([0027],650: fig. 6 , [0078]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the modified invention of Aliyev, machine learning model is based on a long short term memory (LSTM) algorithm, as taught by Lee, so as to estimate aging state of the battery in real time . Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aliyev in view of Holme . Regarding Claim 13. Aliyev teaches the battery management apparatus generates an anomaly score by applying the state data and the prediction data to an anomaly detection algorithm ( 56: fig. 2) , and Aliyev silent about wherein the battery management apparatus determines the battery to be in the anomaly state when a percentage of anomaly scores generated for a predetermined time is equal to or greater than a predetermined percentage, and wherein the percentage of the anomaly scores has anomaly values that are equal to or greater than an anomaly threshold value. However, Home teaches the battery management apparatus determines the battery to be in the anomaly state when a percentage of anomaly scores generated for a predetermined time is equal to or greater than a predetermined percentage, and wherein the percentage of the anomaly scores has anomaly values that are equal to or greater than an anomaly threshold value( issuing a warning of a present abnormal condition of the battery, cooling the battery, heating the battery, or producing a maximum number of driving miles available before the battery reaches 80% discharge: [0087]; claims 18-19; [0250]-[0260]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the invention of Aliyev , wherein the battery management apparatus determines the battery to be in the anomaly state when a percentage of anomaly scores generated for a predetermined time is equal to or greater than a predetermined percentage, and wherein the percentage of the anomaly scores has anomaly values that are equal to or greater than an anomaly threshold value , as taught by Holm so as to predicted battery state in real time . Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aliyev in view of Holme, further in view of Juve et al. (US 20190285703) Regarding claim 14 . The modified Aliyev silent about the server is further configured to extracts a false alarm when the battery is erroneously determined to be in the anomaly state in the battery management apparatus. However, Juve teaches the server (105(AD): fig. 1; [0037 ])extracts a false alarm when the battery is erroneously determined to be in the anomaly state in the battery management apparatus ([0196]) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to the modified invention of Aliyev , the server is further configured to extracts a false alarm when the battery is erroneously determined to be in the anomaly state in the battery management apparatus, as taught by Juve so as to detect reporting of false electrical performance data by battery unit of electric vehicles . Examiner Notes Three is no prior art rejection over claims 15-16, specifically claim 15, however there is 101 rejection . The closets prior arts fail to teach the limitations of “ the server configure to transmit the SOH value and the threshold value to the battery management apparatus when a frequency of the false alarm is equal to or greater than a threshold frequency ” Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. a) Crymble et al. ( US 20240288501 ) disclose BMS 1330 time shifts communications with external systems 1340 and/or IOT cloud 1350 concerning the fault detected by CMD 1320. For example, BMS 1330 first may store data received from CMD 1320 for later retrieval and passing on of such data to external systems 1340 and/or IOT cloud 1350. In some embodiments, BMS 1330 transmits data concerning faults detected at the monitored battery system to external systems 1340 and/or IOT cloud 1350 upon receiving a respective request from external systems 1340 and/or IOT cloud 1350. b ) Kami (U S 20210091581 ) disclose a control circuit that acquires battery information including information related to a status of the battery, a storage that stores the acquired battery information, and an interface circuit that communicates with a management server via a network. The control circuit transmits the battery information stored in the storage to the management server via the interface circuit. The control circuit receives control information related to control of the battery from the management server via the interface circuit. The control circuit controls the battery according to the received control information. c ) Werner et al. ( US 20200088796 ) disclose Predictive rechargeable battery management is provided, which includes obtaining performance data on a battery cell of multiple rechargeable battery cells within a product, and comparing the performance data of the battery cell to statistical data on battery cell performance of a plurality of battery cells of similar type to the battery cell, and in corresponding condition(s) to the battery cell. Further, the managing includes determining, based on the comparing, that performance of the battery cell is trending away from the statistical data of battery cell performance of the plurality of battery cells. Further, the managing includes performing a battery-related action based on the performance of the battery cell trending away from that of the plurality of battery cells of similar type and in corresponding condition(s) to the battery cell. d ) Srinivasan et al. ( US 12055597 ) disclose determining a battery state can include generating a set of models based on a measured response of a plurality of batteries to an applied load, measuring battery properties of a battery, and using a state estimator to determine a battery state associated with a battery. Contact Information Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT MOHAMMAD K ISLAM whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-0328 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-F 9:00 a.m. - 5:00 p.m. . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FILLIN "SPE Name?" \* MERGEFORMAT Shelby A Turner can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 571-272-6334 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent- center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MOHAMMAD K ISLAM/ Primary Examiner, Art Unit 2857