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. Claim Interpretation The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non- structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “ machine learning (ML) module ” in claims 1 and 15 . Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. According to MPEP 2181, II, B, “In cases involving a special purpose computer-implemented means-plus-function limitation, the Federal Circuit has consistently required that the structure be more than simply a general purpose computer or microprocessor and that the specification must disclose an algorithm for performing the claimed function. See, e.g., Noah Systems Inc. v. Intuit Inc., 675 F.3d 1302, 1312, 102 USPQ2d 1410, 1417 (Fed. Cir. 2012); Aristocrat, 521 F.3d at 1333, 86 USPQ2d at 1239. Image… the specification must sufficiently disclose an algorithm to transform a general purpose microprocessor to a special purpose computer so that a person of ordinary skill in the art can implement the disclosed algorithm to achieve the claimed function. Aristocrat, 521 F.3d at 1338, 86 USPQ2d at 1241.” A review of the specification shows that the following appears to be the corresponding algorithm for performing the claimed function as described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation: Figs. 3A and 3B and paras. [00 61 ]-[00 65 ]. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph . 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- 11 and 15-20 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. Specifically, representative Claim 1 recites: A system for an automated programming of a battery agnostic battery management system (BMS), comprising: a processor of a BMS programming server node connected to at least one controller over a network and configured to host a machine learning (ML) module; a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: receive sensory data from a sensor array attached to a battery module comprising at least one BMS coupled to the at least one controller; parse the sensory data to derive a plurality of features; query a local BMS database to retrieve local historical BMS data collected from the battery module; generate at least one feature vector based on the plurality of features and the historical BMS data; and provide at least one feature vector to the ML module for generating a predictive model configured to output at least one BMS programming parameter for re-programming of the at least one BMS . The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements.” Step 1: under the Step 1 of the eligibility analysis, we determine whether the claims are 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 ( Machine ). Step 2A, Prong One: under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted 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 groupings of subject matter when recited as such in a claim limitation that falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations and mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion. . For example, the limitations of “ parse the sensory data to derive a plurality of features (see para. [0063] of instant application)” is mental process (observation/evaluation), “query a local BMS database to retrieve local historical BMS data collected from the battery module (see para. [0043] of instant application)” is mental processes (observation/evaluation) based on retrieve local historical BMS data collected from the battery module, which is insignificant solution activity (data gathering). Further, the limitations of “an automated programming of a battery agnostic (see paras. [0030]-[0031] of instant application),” “a machine learning (ML) (see paras. [0030]-[0031] of instant application),” “generate at least one feature vector based on the plurality of features and the historical BMS data (see paras. [0058]-[0064])” is mathematical calculations. A feature vector is algorithm for pattern recognition, classification and regression tasks , which is mathematical calculations (See Wikipedia). Further, limitation of “provide at least one feature vector to the ML module for generating a predictive model configured to output at least one BMS programming parameter for re-programming of the at least one BMS (see para. [0063] of instant application)” is mathematical calculations (i.e. provide at least one feature vector to the ML module for generating a predictive model) related to insignificant post solution activity (i.e. configured to output at least one BMS programming parameter for re-programming of the at least one BMS ). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mathematical calculations and human mind, then it falls within the “Mental Processes” and “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea . Similar limitations comprise the abstract ideas of Claims 15 and 20 . Step 2A, Prong Two: 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. This judicial exception is not integrated into a practical application. Therefore, none of the additional elements indicate a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B. Step 2B: The above claims comprise the following additional elements: In Claim 1: a system for battery management system BMS ( preamble ) ; a processor; a processor of a server; a memory; a step of receiving sensory data from a sensor array attached to a battery module comprising at least one BMS coupled to the at least one controller; In Claim 15: a method for an automated programming of a battery agnostic battery management system BMS ( preamble ) ; a step of receiving sensory data from a sensor array attached to a battery module comprising at least one BMS coupled to the at least one controller; and In Claim 20: a non-transitory computer readable medium comprising instructions, that when read by a processor (preamble); a step of receiving sensory data from a sensor array attached to a battery module comprising at least one BMS coupled to the at least one controller. The additional elements such as a system for an automated programming of battery management system BMS, a processor of a BMS programming server, a memory, and a non-transitory computer-readable medium are recited at a high-level of generality without descriptions of its specific structure/features to perform the claimed features for producing the mathematical or mental process addressed above ( MPEP 2106.05 (d)) . The additional elements of “ receiving sensory data from a sensor array attached to a battery module comprising at least one BMS coupled to the at least one controller ” is not meaningful and represent insignificant (gathering data) extra-solution activity to perform abstract idea. see MPEP 2106.05(g). Further, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these additional elements/steps are well-understood, routine, and conventional in the relevant based on the prior art of record (Herring, Aykol (US 2020/0269722 A1)). For example, Herring and Aykol teach a sensor array attached to a battery module comprising at least one BMS coupled to the at least one controller (Fig. 1 and para. [0052] of Herring; Fig. 4 and paras. [0031], [0035[, [0047], [0056]-[0060] of Aykol ). Regarding claims 2-11 and 16-19, All features recited in these claims are abstract ideas, as all features found in these claims are directed towards mathematical calculations steps and/or insignificant solution activity. The explanation for the rejection of Claims 2-11 and 16-19 therefore are incorporated herein and applied to Claims 1, 15, and 20. These claims therefore stand rejected for similar reasons as explained in above Claims 1, 15, and 20. 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. Claims 1-5 and 8 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Herring et al. (US 2021/0229568 A1, hereinafter referred to as “ Herring ”). Regarding claim 1, Herring discloses a system for an automated programming of a battery agnostic battery management system (BMS) ( Fig. 2, 170 and Fig. 3, 170A-C ), comprising: a processor (Fig. 3, 312 ) of a BMS programming server node ( Fig. 3, 310 ) connected to at least one controller over a network ( Fig. 3, 314 ) and configured to host a machine learning (ML) module ( Fig. 3, 342 ); a memory ( Fig. 3, 316) on which are stored machine-readable instructions ( Fig. 3, 318 ) that when executed by the processor ( Fig. 3, 312) , cause the processor ( Fig. 3, 312 ) to: receive sensory data ( para. [0052]: The one or more sensors can be configured to detect, and/or sense in real-time ) from a sensor array ( Fig. 1, 120 ) attached to a battery module ( Fig. 1, 115 ) comprising at least one BMS coupled to the at least one controller ( Fig. 1, 110 ); parse the sensory data to derive a plurality of features ( para. [0027]: analyzing stored data , providing stored data, organizing stored data, and so on; [0054]: the vehicle sensor(s) 121 can be configured to detect, and/or sense one or more characteristics of the vehicle 100 ); query a local BMS database (Fig. 2, 240 ) to retrieve local historical BMS data collected from the battery module ( para. [0027]: analyzing stored data, providing stored data, organizing stored data, and so on; para. [0033]: a historical speed of the vehicle 100, and a charging history of the battery 115 of the vehicle ); generate at least one feature vector based on the plurality of features ( Fig. 2 and para. [0025]: battery characteristic prediction module 254 ) and the historical BMS data ( para. [0033]: a historical speed of the vehicle 100, and a charging history of the battery 115 of the vehicle ); and provide at least one feature vector to the ML module ( Fig. 2, 242 ) for generating a predictive model ( paras. [0025]-[0027]: battery characteristic prediction module 254 ) configured to output at least one BMS programming parameter for re-programming of the at least one BMS ( para. [0027]: the data store 240 includes an active machine learning model 242 that utilizes one or more model weights to determine one or more characteristics of the battery 115, note that the feature of learning model reads on “re-programing”). Regarding claim 2, Herring discloses all the limitation of claim 1, in addition, Herring discloses that the instructions further cause the processor ( Fig. 2, 110) to provide the at least one BMS programming parameter ( para. [0027]: the data store 240 includes an active machine learning model 242 that utilizes one or more model weights to determine one or more characteristics of the battery 115 ) to the controller ( Fig. 2, 242 ) configured to generate at least one signal to re-program the at least one BMS based on the at least one programming parameter ( para. [0027]: the data store 240 includes an active machine learning model 242 that utilizes one or more model weights to determine one or more characteristics of the battery 115, note that the feature of learning model reads on “re-program based on the at least one programming parameter”). Regarding claim 3, Herring discloses all the limitation of claim 1, in addition, Herring discloses that the instructions further cause the processor ( Fig. 2, 110) to retrieve remote BMS data ( Fig. 3, 320 ) from at least one remote BMS database, wherein the remote BMS data ( Fig. 3, 320) is collected at least one remote battery module ( Fig. 3, 310 ) ( para. [0042]: by utilizing an external device, such as the external device 310, to remotely generate model weights by training the active machine learning model 342 ). Regarding claim 4, Herring discloses all the limitation of claim 3, in addition, Herring discloses that the instructions further cause the processor ( Fig. 2, 110) to generate the at least one feature vector based on the plurality of features ( para. [0027]: the data store 240 includes an active machine learning model 242 that utilizes one or more model weights to determine one or more characteristics of the battery 115 ), the historical BMS ( para. [0033]: the driving characteristics of the user may include the historical distance that the vehicle 100 typically travels between destinations, a historical speed of the vehicle 100, and a charging history of the battery 115 of the vehicle ) combined with the remote BMS data ( Fig. 3, 320 ). Regarding claim 5, Herring discloses all the limitation of claim 1, in addition, Herring discloses that the instructions further cause the processor (Fig. 2, 110 ) to acquire the sensory data from the sensor array ( Fig. 1, 120 ) periodically based on pre-set time intervals ( para. [0027[:the data store 240 includes an active machine learning model 242 that utilizes one or more model weights to determine one or more characteristics of the battery 115; para. [0042]: the active machine learning model 242 of the BMS system 170 can be updated periodically with improved model weights to improve the active machine learning model 242 over time, not that the above feature of “data store 240“ in para. [0027] and “updated periodically with improved model weights to improve the active machine learning model 242 over time” reads on “acquire the sensory data from the sensor array periodically based on pre-set time intervals” because updating periodically with improved model over the time relies on sensing interval). Regarding claim 8, Herring discloses all the limitation of claim 1, in addition, Herring discloses that the instructions further cause the processor ( Fig. 2, 110 ) to record the at least one programming parameter ( Fig. 2, 210) along with the sensory data ( Fig. 2, 120 ) on the local BMS database ( Fig. 2, 240 ) for training of the predictive model ( Fig. 2, 254 ). 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 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Herring in view of Parent et al. (US 2022/0262221 A1, hereinafter referred to as “Parent”). Regarding claim 6, Herring teaches all the limitation of claim 1, in addition, Herring teaches that the instructions further cause the processor ( Fig. 1, 110 ) to continuously monitor current sensory data received ( para. [0052]: the one or more sensors can be configured to detect, and/or sense in real-time ) from at least one sensor of the sensor array ( Fig. 1, 120 ). Herring does not specifically teach monitoring sensor data determining if at least one reading of the at least one sensor deviates from a previous reading of the at least one sensor by a margin exceeding a pre-set threshold value. However, Parent teaches monitoring sensor data determining if at least one reading of the at least one sensor deviates from a previous reading of the at least one sensor by a margin exceeding a pre-set threshold value ( para. [0055]: based on normal fluctuations of the real-time sensor data: para. [0061]; determine whether the sensor specific abnormality value exceeds a threshold value ). Herring and Parent are both considered to be pertinent to the claimed invention because they are in the same filed of monitoring sensor data . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the monitoring sensor data such as is described in Paren into Herring, in order to determine a sensor specific abnormality value for each node of the plurality of sensory nodes ( Parent , para. [0005]). Regarding claim 7, Herring in view of Parent teaches all the limitation of claim 6, in addition, Herring teaches that the instructions further cause the processor ( Fig. 2, 110 ) to generate an updated feature vector based on the current sensory data ( para. [0052]: the one or more sensors can be configured to detect, and/or sense in real-time ) and reprogram the at least one BMS by the controller based on the at least one programming parameter produced by the predictive model in response to the updated feature vector ( para. [0027]: the data store 240 includes an active machine learning model 242 that utilizes one or more model weights to determine one or more characteristics of the battery 115; para. [0042]: th e active machine learning model 242 of the BMS system 170 can be updated periodically with improved model weights to improve the active machine learning model 242 over time, note that the above feature of “learning model” in paras. [0027] and para. [0042] reads on “updated feature vector” and “re-programing”). Herring does not specifically teach being responsive to the at least one reading deviating from the previous reading by the margin exceeding a pre-set threshold value. However, Parent teaches being responsive to the at least one reading deviating from the previous reading by the margin exceeding a pre-set threshold value ( para. [0055]: based on normal fluctuations of the real-time sensor data: para. [0061]; determine whether the sensor specific abnormality value exceeds a threshold value ). Herring and Parent are both considered to be pertinent to the claimed invention because they are in the same filed of monitoring sensor data . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the being responsive to the at least one reading deviating from the previous reading by the margin exceeding a pre-set threshold value such as is described in Parent into Herring, in order to determine a sensor specific abnormality value for each node of the plurality of sensory nodes ( Parent , para. [0005]). Claims 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Herring in view of Senn et al. (US 2022/0065939 A1, hereinafter referred to as “ Senn ”) and Wang et al. (CN 114814630 A, hereinafter referred to as “Wang”). Regarding claim 9, Herring discloses all the limitation of claim 1. Herring does not specifically teach that the instructions further cause the processor to provide State of Health (SOH) data outputted by the ML module to the controller. However, Senn teaches that instructions further cause the processor ( para. [0049]: one or more processor ) to provide State of Health (SOH) and State of Safety (SOS) data outputted by the ML module ( para. [0030]: FIG. 3 illustrates a flow chart for an embodiment of the present disclosure to generate a state of health indication for a battery. In order to determine a SoH for a battery, battery characteristics and process parameters for the battery are received to be used as training data 40 ) to the controller ( para. [0071]: at least one controller coupled to other components of the transportation vehicle ). Herring and Senn are both considered to be pertinent to the claimed invention because they are in the same filed of monitoring battery state . Therefore, it would have been obvious to Herring and Senn one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the providing State of Health (SOH) data such as is described in Senn into Herring, in order to improving battery life, understanding battery operation, and gaining increased performance from the battery ( Senn , para. [0004]). Herring and Senn do not specifically teach State of Safety (SOS) However, Wang teach State of Safety (SOS) ( page 11, lines 6-7: the gradient utilization of the battery, and the safety of the end of the life cycle of the gradient utilization of the battery can be improved ). Herring and Wang are both considered to be analogous to the claimed invention because they are in the same filed of monitoring battery status . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the State of Safety (SOS) data such as is described in Wang into Herring, in order to guarantee the driving range and safe operation because the safety in the application of the retired battery is sensitive to the health state (Wang, page 2, lines 8-9). Regarding claim 10, Herring in view of Senn and Wang discloses all the limitation of claim 9, in addition Senn teaches that the instructions further cause the processor to, responsive to the SOH or SOS being below a corresponding threshold, cause the controller generate a deactivation signal to the at least one battery management system ( para. [0038]: the SoH indications can comprise relative terminology of various states, including “bad”, “medium”, or “good” SoH indications. The various states can correspond to threshold percentage values of the SoH or SoC , note that since Senn teaches that the SoH indications can comprise relative terminology of various states, including “bad”, “medium”, or “good” SoH indications basded on threshold, responsive to the SOH or SOS being below a corresponding threshold, causing the controller generate a deactivation signal to the at least one battery management system would be an obvious variation of such method). Herring and Senn are both considered to be analogous to the claimed invention because they are in the same filed of monitoring battery state . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the generating the deactivation signal to the at least one battery management system such as is described in Senn into Herring, in order to improve battery life, understanding battery operation, and gaining increased performance from the battery ( Senn , para. [0004]). Regarding claim 11, Herring in view of Senn and Wang discloses all the limitation of claim 9, in addition Senn teaches that the instructions further cause the processor to send a notification to a user device responsive to the SOH or SOS being below the corresponding threshold ( para. [0038]: the SoH indications can comprise relative terminology of various states, including “bad”, “medium”, or “good” SoH indications. The various states can correspond to threshold percentage values of the SoH or SoC; para. [0055]: a server transmits data, e.g., an HTML page, to a user device, e.g., for purposes of displaying data to and receiving user input from a user interacting with the device, which acts as a client. Data generated at the user device, e.g., a result of the user interaction, can be received at the server from the device., note the above feature of “the SoH indications can comprise relative terminology of various states, including “bad”, “medium”, or “good” SoH indications” in para. [0038] and “a server transmits data, e.g., an HTML page, to a user device, e.g., for purposes of displaying data to and receiving user input from a user interacting with the device” in para. [0055] reads on “send a notification to a user device responsive to the SOH or SOS being below the corresponding threshold”). Herring and Senn are both considered to be analogous to the claimed invention because they are in the same filed of monitoring battery state . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the sending the notification to a user device responsive to the SOH or SOS such as is described in Senn into Herring, in order to improving battery life, understanding battery operation, and gaining increased performance from the battery ( Senn , para. [0004]). Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Herring in view of Kim et al. (US 2014/0306662 A1, hereinafter referred to as “Kim”). Regarding claim 12, Herring discloses all the limitation of claim 1. Herring does not specifically teach that the battery module configured to use a plurality of hot swappable batteries connected in parallel at different states of charge without pre-balancing; the BMS configured to use a hot swappable mode for activation of a current limiter function configured to keeps down a flow of current until a difference in voltage at the plurality of hot swappable batteries drops below a preset threshold. However, Kim teaches that the battery module configured to use a plurality of hot swappable batteries connected in parallel at different states of charge without pre-balancing ( para. [0122]: FGS. 13A and 13B illustrate two exemplary hot swapping protection circuits 1300A and 1300B each providing an active solution for protecting against hot swapping the battery stack into the BMS board ); the BMS ( Fig. 1, BMS 100 ) configured to use a hot swappable mode ( para. [0122]: see above ) for activation of a current limiter function configured to keeps down a flow of current until a difference in voltage at the plurality of hot swappable batteries drops below a preset threshold ( para. [0121]: FIG. 12 illustrates an exemplary hot swapping protection circuit 1200 providing a mechanical solution for protecting against hot swapping the battery stack into the BMS board. A mechanical approach may result in the most cost effective solution. The circuit 1200 includes a monitoring integrated circuit 1210, a battery cell 1220, a resistor 1230, and a capacitor 1240. The resistor 1230 may be a 10Ω resistor used to pre-charge the CVIN capacitor 1240 to the battery voltage, limiting the inrush current ). Herring and Kim are both considered to be analogous to the claimed invention because they are in the same filed of battery management system. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the plurality of hot swappable batteries such as is described in Kim into Herring, in order to monitor the balancing current of the monitoring device and calculating the voltage drop based on the monitored balancing current to avoid overcharging or undercharging the one of the plurality of the cells during balancing (Kim. para. [0006]). Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Herring in view of Cheng et al. (US 2015/0295420 A1, hereinafter referred to as “Cheng”). Regarding claim 13, Herring teaches all the limitation of claim 1, in addition, Herring teaches a remotely programmable BMS ( Fig. 2, 170 and Fig. 3, 170A-C; para. [0018]: the BMS system includes at least one processor that is in communication with a battery that includes a plurality of cells ). Herring does not specifically teach that a BMS with a current limiter configured to use batteries of matching chemistry and series, wherein the batteries comprising unmatched parameters of: capacities; state of health; form factor; and discharge rate. However, Cheng teach that a BMS ( Fig. 1 ,112 and para. [0025]: A battery management system (BMS) control section 112 ) with a current limiter configured to use batteries of matching chemistry and series ( para. [0019]: the Li battery assembly 104 shown comprises first Li battery cells 104A connected in series with second Li battery cells 104B. Again, the Li battery assembly 104 may include a single cell or battery, or it may include any number of multiple cells or batteries to be matched with LA battery 102A in voltage level. Etc ; para. [0022]: the total voltage of the Li battery assembly 104 must be less than the total voltage of the LA batteries 102A and 102B. This ensures that electric current always flows from LA batteries 102A and 102B to Li batteries 104A and 104B, note that the above feature of “Li battery assembly 104 may include a single cell or battery, or it may include any number of multiple cells or batteries to be matched with LA battery 102A in voltage level”in para. [0019] and “that electric current always flows from LA batteries 102A and 102B to Li batteries 104A and 104B” in para. [0022] reads on “BMS with a current limiter configured to use batteries of matching chemistry and series”), wherein the batteries comprising unmatched parameters of: capacities; state of health; form factor; and discharge rate ( para. [0003]: the choice of battery chemistry to be used in a given device is typically a compromise of characteristics, such as weight, size, capacity, charge/discharge current, charge time, safety, heat generation, reliability, life span ). Herring and Cheng are both considered to be analogous to the claimed invention because they are in the same filed of battery management system. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the BMS with a current limiter such as is described in Cheng into Herring, in order to provide a careful selection of the cells and the accompanying voltage level between the portions having different chemistries (Cheng, para. [0006]). Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Herring in view of loffe et al. (US 2022/0123574 A1, hereinafter referred to as “ loffe ”) and Cheng. Regarding claim 14, Herring teaches all the limitation of claim 1. Herring does not specifically teach a plurality of programmable tunable boosters configured to provide for simultaneous operation of batteries of different chemistry and series groups, wherein the programmable tunable boosters are further configured to be tuned to match voltage at the batteries to accommodate paralleled arrangement of the batteries at high voltage. However, Ioffe teaches a plurality of programmable tunable boosters ( para.[0007]: booster unit configured to boost the charging current above the high-C charging current, according to the user preferences ) configured to provide for simultaneous operation of batteries of different chemistry and series groups ( para. [0047]: user interface 120 may be configured to identify the type of battery chemistry (and optionally the distribution of chemistry types in batteries 90 comprising cells with multiple types of chemistry ) and to adjust the charging profile accordingly, utilizing both main charging unit 80 and booster unit 110 with respect to the respective battery chemistry ), wherein the programmable tunable boosters ( para. [0007]: see above ) are further configured to be tuned to match voltage at the batteries at high voltage ( para. [0044 ]: booster unit 110 may be configured to provide additional energy 115 in a temporary boost 115A of higher current above the CC charging current… high charging currents , e.g., providing charging rate higher than 4 C, e.g., 5 C, 10 C, 20 C, 30 C, 50 C, etc., may be provided over a narrow SoC range, e.g., for short charging periods, e.g., according to user preferences , enhancing the level of voltage achieved in the short charging period …booster unit 110 may be further configured to extend a boosting period into an extended constant voltage charging period, that may be pre-defined and/or correspond to user preferences; para. [0080]: the booster unit may charge the battery cells at the certain voltage (state of the battery cells) with a booster charging current, note that the above feature of “enhancing the level of voltage achieved in the short charging period” and “an extended constant voltage charging period, that may be pre-defined and/or correspond to user preferences” in para. [0044] reads on “programmable tunable boosters are further configured to be tuned to match voltage at the batteries at high voltage”). Herring and loffe both considered to be analogous to the claimed invention because they are in the same filed of battery system. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the a plurality of programmable tunable boosters such as is described in Loffe into Herring, in order to provide one or more batteries having metalloid-based anodes having a Si, Ge, and/or Sn-based anode active material for charging ( Ioffe , para. [0005]). Herring and loffe do not specifically teaches the batteries to accommodate paralleled arrangement of the batteries. However, Cheng teaches that batteries to accommodate paralleled arrangement of the batteries ( para. [0007]: t he second battery assembly is electrically connected in parallel with the first battery assembly;para . [0009]: battery system includes a lead-acid battery assembly connected in parallel with a lithium-ion battery assembly ; para, [0020]: The LA battery assembly 102 is connected in parallel with the Li battery assembly 104 ). Herring and Cheng are both considered to be analogous to the claimed invention because they are in the same filed of battery management system. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the batteries to accommodate paralleled arrangement of the batteries. such as is described in Cheng into Herring, in order to provide a careful selection of the cells and the accompanying voltage level between the portions having different chemistries (Cheng, para. [0006]). Regarding claim 15 , it is a method type claim having similar limitations as of claim 1 above . Therefore, it is rejected under the same rationale as of claim 1 above. . Regarding claim 1 6 , it is dependent on claim 15 and has similar limitations as of claim 2 above. Therefore, it is rejected under the same rational as of claim 2 above. Regarding claim 1 7 , it is dependent on claim 15 and has similar limitations as of claim 3 above. Therefore, it is rejected under the same rational as of claim 3 above. Regarding claim 1 8 , it is dependent on claim 15 and has similar limitations as of claim 4 above. Therefore, it is rejected under the same rational as of claim 4 above. Regarding claim 19 , it is dependent on claim 15 and has similar limitations as of claim 6 above. Therefore, it is rejected under the same rational as of claim 6 above. Regarding claim 20 , it is a computer program product 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 limitation s of a non-transitory computer readable medium ( para. [0026]: random-access memory (RAM), read-only memory (ROM), a hard disk drive, a flash memory, or other suitable memory ) comprising instructions, that when read by a processor ( para. [0026]: processor(s) ), taught by Herring. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Klein at al. (US 2024/0144078 A1) teaches that methods and systems of optimizing battery charging are disclosed. Battery state sensors are used to determine anode overpotential of a battery multiple times during multiple charge cycles. In a first phase, a reinforcement learning model (e.g., actor-critic model) is trained with rewards given throughout each charge cycle of the battery to optimize training. Palombini at al. (US 2023/0415603 A1) teaches that a system for charger management for electrical vertical takeoff and landing aircrafts includes a sensor connected to the first charger. The sensor is configured to detect a battery metric and transmit the metric to a computing device. The computing device is connected to a mesh network. The mesh network contains many aircrafts connected to chargers. The charger management system manages the charging of the aircraft. Lee at al. (US 2020/0074297 A1) teaches that a method of controlling a battery is disclosed. The method includes training an artificial neural network to calculate an internal characteristic parameter value of the battery corresponding to a sensed input/output parameter value using training data, sensing the input/output parameter value of the battery, acquiring the characteristic parameter value corresponding to the sensed input/output parameter value using the trained artificial neural network, and controlling charging or discharging of the battery based on the acquired characteristic parameter value. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT SANGKYUNG LEE whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-3669 . 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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. /SANGKYUNG LEE/ Examiner, Art Unit 2858 /LEE E RODAK/ Supervisory Patent Examiner, Art Unit 2858