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 Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1- 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Step 1: According to the first part of the analysis, in the instant case, claims 1- 6 are directed to a n information handling system , claim s 7-14 are directed to a method, and claim s 15-20 are directed to a n article of manufacture . Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Regarding claim 1 : A method comprising: an information handling system receiving information relating to a plurality of batteries, wherein the information includes environmental data and performance data; the information handling system training a machine learning model based on the received information; and based on the machine learning model, the information handling system performing a predictive analysis on at least one other battery to determine a likelihood of failure. Step 2A Prong 1 : “ an information handling system receiving information relating to a plurality of batteries, wherein the information includes environmental data and performance data ” is directed to mental step of data gathering. “the information handling system training a machine learning model based on the received information” is directed to math because the training a machine learning model by an information handling system is fundamentally based on mathematics. When a system trains on data, it is translating input information into numerical representations and applying mathematical transformations to learn the patterns within it. “ based on the machine learning model, the information handling system performing a predictive analysis on at least one other battery to determine a likelihood of failure ” is directed to math because the machine learning algorit h ms use mathematical logic to identify patterns in training data and apply that pattern recognition to new data . The system uses statistical models to analyze current and historical facts, predicting future, unknown events, such as the likelihood of failure . The accuracy of the battery’s failure prediction is evaluated using mathematical error metrics like Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination. Each limitation recites in the claim is a process that, under BRI covers performance of the limitation in the mind but for the recitation of a generic “measurement” which is a mere indication of the field of use. Nothing in the claim elements precludes the steps from practically being performed in the mind. Thus, the claim recites a mental process. Further, the claim recites the step of " the information handling system training a machine learning model based on the received information; and based on the machine learning model, the information handling system performing a predictive analysis on at least one other battery to determine a likelihood of failure ” which as drafted, under BRI recites a mathematical calculation. The grouping of "mathematical concepts” in the 2019 PED includes "mathematical calculations" as an exemplar of an abstract idea. 2019 PEG Section |, 84 Fed. Reg. at 52. Thus, the recited limitation falls into the "mathematical concept" grouping of abstract ideas. This limitation also falls into the “mental process” group of abstract ideas, because the recited mathematical calculation is simple enough that it can be practically performed in the human mind, e.g., scientists and engineers have been solving the Arrhenius equation in their minds since it was first proposed in 1889. Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation. See October Update at Section I(C)(i) and (iii). Additional Elements: Step 2A Prong 2 : “an information handling system receiving information relating to a plurality of batteries, wherein the information includes environmental data and performance data” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)). “the information handling system training a machine learning model based on the received information” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)). “based on the machine learning model, the information handling system performing a predictive analysis on at least one other battery to determine a likelihood of failure” is directed to insignificant activity and does not integrate the judicial exception into a practical application. See MPEP 2106.05(g) . The claim is merely selecting data, manipulating or analyzing the data using math and mental process, and displaying the results. This is similar to electric power : MPEP 2106.05(h) vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A ., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016). Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field. Claim 7 recites the additional element(s) of using generic AI/ML technology, i.e. *** training a machine learning model ***, to perform data evaluations or calculations, as identified under Prong 1 above. The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer. See MPEP 2106.05(f). Additionally, the use of the *** training a machine learning model *** merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of *** training a machine learning model *** to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2. The claim as a whole does not meet any of the following criteria to integrate the judicial exception into a practical application: An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; an additional element effects a transformation or reduction of a particular article to a different state or thing; and an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Step 2B : “an information handling system receiving information relating to a plurality of batteries, wherein the information includes environmental data and performance data” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)). “the information handling system training a machine learning model based on the received information” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)). “based on the machine learning model, the information handling system performing a predictive analysis on at least one other battery to determine a likelihood of failure” is directed to insignificant activity and does not amount to significantly more than the judicial exception in the claim. See MPEP 2106.05(g) and 2106.05(d)(ii), third list, (iv). T he claim is therefore ineligible under 35 USC 101. Claim 1 is similar to claim 7 but recites an information handling system comprising: at least one processor; and a network interface adapter; wherein the information handling system is configured to perform the steps as in claim 7 . These additional elements fail to integrate the abstract idea into a practical application. These limitations are recited at a high level of generality and do not add significantly more to the judicial exception. These elements are generic computing devices that perform generic functions. Using generic computer elements to perform an abstract idea does not integrate an abstract idea into a practical application. See 2019 Guidance, 84 Fed. Reg. at 55. Moreover, “the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.” Alice, 573 U.S. at 223; see also FairWarninglP, LLCv. latric SysInc., 839 F.3d 1089, 1096 (Fed. Cir. 2016) (citation omitted) (“[T]he use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter”). On the record before us, we are not persuaded that the hardware of claim 1 integrates the abstract idea into a practical application. Nor are we persuaded that the additional elements are anything more than well-understood, routine, and conventional so as to impart subject matter eligibility to claim 1. Claim 1 5 is directed to an abstract idea similar to claim 7 . The additional elements (i.e., a n article of manufacture comprising a non-transitory, computer-readable medium having computer-executable instructions thereon that are executable by a processor of an information handling system for perform ing the steps as in claim 7 ) are recited at a high level of generality, necessary, routine, or conventional to facilitate the application of the abstract idea. When considered separately and in combination, they do not add significantly more to the abstract idea. See Alice Corp. and 2014 Interim Guidance. Regarding claim s 2 and 16 , “wherein the at least one other battery is a component of a hyper-converged infrastructure (HCI) system” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)). Regarding claim s 3 and 17 , “wherein the at least one other battery is a component of an edge gateway of the HCI system” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).. Regarding claim s 4 and 18 , “wherein the battery is a component of an uninterruptible power supply (UPS)” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)). Regarding claim s 5 and 19 , “wherein the environmental data includes temperature data” is mer e ly to abstract idea of identifying data. Regarding claim s 6 and 20 , “ wherein the performance data includes a number of discharge cycles ” is directed to math because battery performance, particular l y cycle life is determined using mathematical models, statistical analysis, and engineering formulas to predict how long a battery will last before its capacity falls below a usable threshold . Regarding claim 8, “ sending a warning message in response to the likelihood of failure being above a threshold likelihood ” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)). Regarding claim 9, “ wherein the warning message includes data regarding a most-significant factor contributing to the likelihood of failure being above the threshold likelihood ” is directed to math because this process de s cribes a quan titative risk management process based on mathematical modeling to estimate the probability of failure and identify the primary driver of that risk . Regarding claim 10, “ dispatching a replacement battery in response to the likelihood of failure being above a threshold likelihood ” is directed to math because this process transforms battery heath indicators into a quan titative, probabilistic estimate of failure, allowing for informed, calculated maintenance decisions . Regarding claim 11, “ enabling a spare battery in response to the likelihood of failure being above a threshold likelihood ” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)). Regarding claim 12, “ wherein the machine learning model is configured to implement a linear model ” . The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer. See MPEP 2106.05(f). Additionally, the use of the machine learning model merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of the machine learning model to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2. Regarding claim 13, “ wherein the information relating to the plurality of batteries is received from one or more management controllers ” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)). Regarding claim 14, “ wherein the information relating to the plurality of batteries is received from one or more host operating system software agents ” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)). Hence the claims 1- 20 are treated as ineligible subject matter under 35 U.S.C. § 101. 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) 1, 5, 7, 13, 115, 17, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable ove r Manning et al. (KR 20100065392 A) in view of Divyansh et al. ( WO 2022047204 A1 ) . Regarding claim s 1, 7, and 15, Manning et al. disclose an information handling system (1 2 ) and method comprising: at least one processor ( 84 ) ; and a network interface adapter ( 85 ) ; wherein the information handling system is configured to: receive, via the network interface adapter (85) , information relating to a plurality of batteries wherein the information includes environmental data and performance data (Fig.2, para. [0019] , [0041]: As described above, tracking / reporting configuration 62 includes one or more selected battery parameters 26 that define what battery performance related data is available at the cellular devices 12, 14, 15, 16, 17 to collect; Selected associated data parameters 28 specifying what corresponding environment / operating condition information to collect ) . Manning et al. fail to disclose train a machine learning model based on the received information; and based on the machine learning model, perform a predictive analysis on at least one other battery to determine a likelihood of failure. Divyansh et al. teach train a machine learning model based on the received information (para. [0027]- [00 28 ]: In supervised machine learning, first machine learning model(s) 110 and second machine learning model 114 may be trained using labelled training data, i.e., input data (e.g., time-series historical battery attributes) and associated output data (i.e., battery conditions and remaining battery life predictions ) ); and based on the machine learning model, perform a predictive analysis on at least one other battery to determine a likelihood of failure ( para. [00 69 |: a prediction may be made to predict when the battery will fail or degrade within the next 90 days . To predict the fail status, machine learning models such as a recurrent neural network, a long short-term memory (LSTM), or the like may be utilized. ). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to incorporate train a machine learning model based on the received information; and based on the machine learning model, perform a predictive analysis on at least one other battery to determine a likelihood of failure of Divyansh et al. with the an information handling system of Manning et al. for the purposes of providing a server may include a receiving unit to obtain a set of battery attributes associated with a battery of a client device and a prediction unit to predict a battery condition by applying at least one first machine learning model to the set of battery attributes ( Divyansh et al., abstract ). Regarding claims 5 and 19, Manning et al. disclose wherein the environmental data includes temperature data (para. [0022]: battery temperature). Regarding claim 13, Manning et al. disclose wherein the information relating to the plurality of batteries is received from one or more management controllers (Fig.1, para. [0019]: manager module 40). Regarding claim 14, Manning et al. disclose wherein the information relating to the plurality of batteries is received from one or more host operating system software agents (para. [0026]) . Claim(s) 2- 4 and 16-1 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Manning et al. (KR 20100065392 A) in view of Divyansh et al. ( WO 2022047204 A1 ) as applied to claim s 1 and 15 above, and further in view of Kenney et al. (US 11,567,810) . Regarding claims 2-3 and 16-17, the combination of Manning et al. and Divyansh et al. fail to disclose wherein the at least one other battery is a component of a hyper-converged infrastructure (HCI) system , wherein the at least one other battery is a component of an edge gateway of the HCI system. Kenney et al. teach wherein the at least one other battery (Col.4, lines 17: a power source may be a battery) is a component of a hyper-converged infrastructure (HCI) system (Fig.3B, Col.30, lines 48-Col.31, line 10, storage system 306 comprise a hyper-converged infrastructure (HCI), storage system 306 support AI ), wherein the at least one other battery (Col.4, lines 17: a power source may be a battery) is a component of an edge gateway (Col. 65, lines 31-34: gateway computer and/or edge server) of the HCI system (Fig.3B, Col.30, lines 48-Col.31, line 10, storage system 306 comprise a hyper-converged infrastructure (HCI), storage system 306 support AI ) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to incorporate wherein the at least one other battery is a component of a hyper-converged infrastructure (HCI) system, wherein the at least one other battery is a component of an edge gateway of the HCI system of Kenney et al. with the an information handling system of Manning et al. in view of Divyansh et al. for the purposes of providing a unique power source that maintains the state of the RAM after main power l oss to the NVRAM device ( Kenny et al.,Col.4, lines 9-17 ). Regarding claims 4 and 1 8 , Kenny et al. teach the battery is a component of an uninterruptible power supply (UPS) ( Col4, lines 9-17: the NVRAM device may receive or include a unique power source that maintains the state of the RAM after main power loss to the NVRAM device. Such a power source may be a battery , one or more capacitors, or the like. In response to a power loss, the NVRAM device may be configured to write the contents of the RAM to a persistent storage, such as the storage drives 171 A-F ) . Claim(s) 6 , 8-1 0 , and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Manning et al. (KR 20100065392 A) in view of Divyansh et al. ( WO 2022047204 A1 ) as applied to claim s 1 and 15 above, and further in view of Sourabh et al. (WO 2022/033668 A1) . Regarding claims 6 and 20, the combination of Manning et al. and Divyansh et al. fail to disclose wherein the performance data includes a number of discharge cycles. Sourabh et al. teach wherein the performance data includes a number of discharge cycles (page 6, lines 28-30: The historical battery performance data may comprise a battery cycle count value representing the number of full discharge/charge cycles associated with the battery of the mobile electronic device ). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to incorporate wherein the performance data includes a number of discharge cycles of Sourabh et al. with the information handling system of Manning et al. in view of Divyansh et al. for the purposes of providing a method for determining condition of a battery of a mobile electronic device ( Sourabh et al., abstract ). Regarding claim 8, Sourabh et al. disclose sending a warning message in response to the likelihood of failure being above a threshold likelihood (para. [0040]: processor 202 to send a noti fication including a recommendation to the client device based on the predicted remaining life , para. [0069]: The performance prediction model may predict when a battery can show up a fail-status or degraded-status within three months ) . Regarding claim 9, Sourabh et al. disclose wherein the warning message includes data regarding a most-significant factor contributing to the likelihood of failure being above the threshold likelihood (para. [0040]: processor 202 to send a noti fication including a recommendation to the client device based on the predicted remaining life , para. [0069]: The performance prediction model may predict when a battery can show up a fail-status or degraded-status within three months, for instance and therefore should be replaced. In an example, the performance prediction model may deliver a probability of failure or degradation of the battery, a failure type or a degraded status, a timespan for the failure or the degradation, or the like. ) . Regarding claim 10, Sourabh et al. disclose dispatching a replacement battery in response to the likelihood of failure being above a threshold likelihood (para. [0069]: The performance prediction model may predict when a battery can show up a fail-status or degraded-status within three months, for instance and therefore should be replaced. ) . Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Manning et al. (KR 20100065392 A) in view of Divyansh et al. ( WO 2022047204 A1 ) as applied to claim s 7 above, and further in view of Liu et al. (US 2022/0109309 A1) . Regarding claim 11, the combination of Manning et al. and Divyansh et al. fail to disclose enabling a spare battery in response to the likelihood of failure being above a threshold likelihood. Liu et al. teach enabling a spare battery in response to the likelihood of failure being above a threshold likelihood (para. [0059]: preventing high inrush currents flowing into capacitive loads from battery sources may help minimize a likelihood of catastrophic failure of the battery , para. [0264]: master battery pack 2102 a enables spare battery pack 2104 a and disables bad battery pack 2103 a via messages 2163 and 2162 ) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to incorporate enabling a spare battery in response to the likelihood of failure being above a threshold likelihood of Lui et al. with the information handling system of Manning et al. in view of Divyansh et al. for the purposes of providing battery protection and providing improved efficiency, and provide a better user experience than previous battery technologies ( Lui et al., para. [0004] ). Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Manning et al. (KR 20100065392 A) in view of Divyansh et al. ( WO 2022047204 A1 ) as applied to claim s 7 above, and further in view of Zhang et al. ( CN 113466718 A ) . Regarding claim 12, the combination of Manning et al. and Divyansh et al. fail to disclose wherein the machine learning model is configured to implement a linear model. Zhang et al. teach wherein the machine learning model is configured to implement a linear model ( page 9, para. [0067]: S501: introducing the training set into the p-based learning device; training the p-based learning device, wherein each base learning device can use various machine learning method, comprising supporting vector regression, neural network, decision tree, limit tree, K neighbor model, linear model ). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to incorporate wherein the machine learning model is configured to implement a linear model of Zhang et al. with the information handling system of Manning et al. in view of Divyansh et al. for the purposes of providing a method for estimat ing the battery SOC and correcting the mobile phone battery management system ( Zhang et al., abstract ). Contact Information Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT JOHN H LE whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-2275 . The examiner can normally be reached on Monday-Friday from 7:00am – 3:30pm Eastern Time. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A. Turner can be reached on (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 an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /JOHN H LE/ Primary Examiner, Art Unit 2857