CTNF 18/583,182 CTNF 95628 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 07-04-01 AIA 07-04 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 1-8, 13-16, and 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. Claim 1 recites: A method for battery analysis in an event detection system, comprising: recording, by a processor, a characteristic of a battery in a control panel of the event detection system at predetermined intervals; determining, by the processor via a prediction model using the recorded characteristics, a trend associated with a performance characteristic of the battery; and generating, by the processor , an alert based on the trend associated with the performance characteristic. Claim 9 recites: A controller for battery analysis in an event detection system, comprising: a memory; and a processor configured to execute executable instructions stored in the memory to: record: a current of a battery and a temperature of the battery at predetermined intervals across different frequencies; and a timestamp of each recorded current and temperature, wherein the battery is included in a control panel of the event detection system; determine an impedance for each frequency; cause a trend associated with a performance characteristic of the battery to be determined via an equivalent circuit model using each impedance, each temperature, and each time stamp at each predetermined interval; predict, based on the trend, that the performance characteristic is to exceed a predetermined threshold within a predetermined time period; and generate an alert based on the prediction. Claim 15 recites: A non-transitory computer readable medium having computer readable instructions stored thereon that are executable by a processor to: record: a no-load voltage of a fully charged battery and a temperature of the battery at predetermined intervals; and a timestamp of each recorded no-load voltage and temperature, wherein the battery is included in a control panel of an event detection system; cause a trend associated with a performance characteristic of the battery to be determined via a machine-learning model using each no-load voltage, each temperature, and each time stamp at each predetermined interval; predict, based on the trend, that the performance characteristic is to exceed a predetermined threshold within a predetermined time period; and generate an alert based on the prediction. The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”. 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 (process). 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 grouping of subject matter when recited as such in a claim limitation, that covers mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion. For example, steps of “determine an impedance for each frequency (determination) ; determining a trend (determination based on data) ; determining via a prediction model using the recorded characteristics, a trend associated with a performance characteristic of the battery (determination based on data) ; and generating an alert based on the trend associated with the performance characteristic (announcing findings) ” are treated by the Examiner as belonging to mental process grouping . Examiner also notes that an equivalent circuit can be drawn and analyzed or represented as an equation. Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception. The above claims comprise the following additional elements: Claim 1: A method for battery analysis in an event detection system, comprising: recording, by a processor, a characteristic of a battery in a control panel of the event detection system at predetermined intervals; Claim 9: A controller for battery analysis in an event detection system, comprising: a memory; and a processor configured to execute executable instructions stored in the memory to: record: a current of a battery and a temperature of the battery at predetermined intervals across different frequencies; and a timestamp of each recorded current and temperature, wherein the battery is included in a control panel of the event detection system; Claim 15: A non-transitory computer readable medium having computer readable instructions stored thereon that are executable by a processor to: record: a no-load voltage of a fully charged battery and a temperature of the battery at predetermined intervals; and a timestamp of each recorded no-load voltage and temperature, wherein the battery is included in a control panel of an event detection system; a machine learning model. The additional element in the preamble of “A method/controller for battery analysis in an event detection system” is not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use. Recording a characteristic of a battery in a control panel of the event detection system at predetermined intervals or frequencies represents a mere data gathering step and only adds an insignificant extra-solution activity to the judicial exception. A non-transitory computer readable medium (generic memory) and a processor (generic processor) are generally recited and are not qualified as particular machines. In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B. However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis). The claims, therefore, are not patent eligible. With regards to the dependent claims, claims 2-8, 13, 14, 16 and 20 provide additional features/steps which are part of an expanded algorithm, so these limitations should be considered part of an expanded abstract idea of the independent claims. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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 (i.e., changing from AIA to pre-AIA) 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. 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-aia AIA Claim(s) 1-4, 6, and 8 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Porebski (US 20070046261 A1) . Regarding Claim 1, Porebski teaches a method for battery analysis in an event detection system, comprising: recording, by a processor, a characteristic of a battery in a control panel of the event detection system at predetermined intervals (Porebski [0062] Each of the monitoring parameters may provide useful information in determining the condition of the battery. For example, the battery device identifier may uniquely identify the battery and/or a data location in memory associated with the battery device. Also see [0086] The digital signals may be processed and stored within the master and slave monitoring units in the form of end results, which may involve, for example, the DC voltage/temperature, current and/or multiple signal processing. Also see [0199] According to an exemplary embodiment and/or exemplary method of the present invention, a measurement log may be generated using a snap shot time and interval defined by the user. The time interval may be set, for example, from 1 day up to up to 30 days.) ; determining, by the processor via a prediction model using the recorded characteristics, a trend associated with a performance characteristic of the battery (Porebski Also see [0066] The relation of certain measured parameters to the battery actual State of Health (SOH), State of Charge (SOC), life expectation, etc. is often discussed but one thing that has emerged from the discussion is the growing consensus that analyzing the trending of these parameters rather than precise actual value may be a useful tool to determine whether a system might be within a failure zone. Accordingly, an exemplary monitoring system according to the present invention may focus on trend analysis to provide a more simple and inexpensive tool than is available with other prior systems. Also see [0114] This record may then be used to perform impedance trending analysis and to generate an impedance alarm.) ; and generating, by the processor, an alert based on the trend associated with the performance characteristic (Porebski [0065] Accordingly, software resident on a remote computer may maintain a database containing battery data collected throughout the entire life cycle of the battery, which may be combined with other data and/or processing to generate reports, warnings and/or alarms based on predefined criteria. Also see [0130] Certain information may be transferred between the exemplary string monitoring unit 1300 and the user interface 1304. For example, in a set up mode, the user may perform functions, such as, for example, […] set up impedance trend alarm threshold, or set up all specific alarm thresholds.) . Regarding Claim 2, Porebski further teaches predicting, by the processor based on the trend, that the performance characteristic is to exceed a predetermined threshold within a predetermined time period (Porebski [0130] Certain information may be transferred between the exemplary string monitoring unit 1300 and the user interface 1304. For example, in a set up mode, the user may perform functions, such as, for example, […] set up impedance trend alarm threshold, or set up all specific alarm thresholds. Also see [0008] The exemplary string monitoring system may also evaluate the battery state of health (SOH) via a trending of float current and/or Coup de Fouet recording over time. A trend in context is evaluating the parameter of interest over time ) . Regarding Claim 3, Porebski further teaches generating, by the processor, the alert in response to the trend indicating the performance characteristic is to exceed the predetermined threshold within the predetermined time period (Porebski [0065] Accordingly, software resident on a remote computer may maintain a database containing battery data collected throughout the entire life cycle of the battery, which may be combined with other data and/or processing to generate reports, warnings and/or alarms based on predefined criteria. Also see [0130] Certain information may be transferred between the exemplary string monitoring unit 1300 and the user interface 1304. For example, in a set up mode, the user may perform functions, such as, for example, […] set up impedance trend alarm threshold, or set up all specific alarm thresholds.) . Regarding Claim 4, Porebski further teaches wherein the trend in the performance characteristic indicates a decrease in the performance characteristic of the battery over time (Porebski [0066] The relation of certain measured parameters to the battery actual State of Health (SOH), State of Charge (SOC), life expectation, etc. is often discussed but one thing that has emerged from the discussion is the growing consensus that analyzing the trending of these parameters rather than precise actual value may be a useful tool to determine whether a system might be within a failure zone.) . Regarding Claim 6, Porebski further teaches wherein the prediction model is an equivalent circuit model (Porebski [0068] A number of simplified battery models may provide an electrical equivalent circuit that may be used to perform battery ohmic parameters trending.) . Regarding Claim 8, Porebski further teaches recording, by the processor, a no-load voltage of the battery when the battery is fully charged, a temperature of the battery, and a timestamp of each recorded no-load voltage and temperature at each predetermined interval (Porebski [0110] The exemplary string monitoring unit 1300 may perform certain signal processing functions, including, for example, test operations with respect to voltage, current and temperature. Also see [0104] The system timer 1303 provides time and date stamping functions so that the time and date of collection may be assigned therewith. The communications interface 1304 provides information and data transfer to and from the exemplary string monitoring unit 1300. In this regard, the communications interface 1304 may transfer data in real time or in a time-slotted manner. Also see [0199] According to an exemplary embodiment and/or exemplary method of the present invention, a measurement log may be generated using a snap shot time and interval defined by the user. The time interval may be set, for example, from 1 day up to up to 30 days.) . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim (s) 5, 7, 9-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Porebski (as stated above) in view of Garcia et al. (US 20190187212 A1), hereinafter “Garcia” . Regarding Claim 5, Porebski is not relied upon to teach wherein the prediction model is a machine-learning model that updates as further data is collected. Garcia teaches wherein the prediction model is a machine-learning model that updates as further data is collected (Garcia [0042] This information is then used by the present disclosure to construct an aging model or a multiplicity of aging models considering different failure mechanisms of the battery through learning algorithms (e.g., relevance vector machines (RVM), fuzzy logic, and neural networks). These aging models will age the battery in distinct manners and at different rates. ) . It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application to modify Porebski (as stated above) in view of Garcia, to explicitly teach wherein the prediction model is a machine-learning model that updates as further data is collected, to describe the specific prediction model used in Porebski, as a machine learning model is known to extrapolate outputs from specific inputs (Garcia [0120] While different regression and machine learning techniques can here be used to estimate internal parameters of the considered aging model. Also see MPEP 2143 I. D. Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results;) . Regarding Claim 7, Porebski further teaches recording, by the processor, a current of the battery, a temperature of the battery, and a timestamp of each recorded current and temperature at each predetermined interval (Porebski [0110] The exemplary string monitoring unit 1300 may perform certain signal processing functions, including, for example, test operations with respect to voltage, current and temperature. Also see [0104] The system timer 1303 provides time and date stamping functions so that the time and date of collection may be assigned therewith. The communications interface 1304 provides information and data transfer to and from the exemplary string monitoring unit 1300. In this regard, the communications interface 1304 may transfer data in real time or in a time-slotted manner. Also see [0199] According to an exemplary embodiment and/or exemplary method of the present invention, a measurement log may be generated using a snap shot time and interval defined by the user. The time interval may be set, for example, from 1 day up to up to 30 days.) . Porebski (as stated above) is not relied upon to explicitly teach measurements across different frequencies. Garcia teaches measurements across different frequencies (Garcia [0088] Measurements used in the present disclosure to derive these internal battery parameters 224 are impedance spectra data. It is useful to recall that at each sample instance k, an impedance spectrum I.sub.s[k] of the battery under observation is collected. An impedance spectra represents the impedance of a battery over a range of frequencies, and therefore its frequency response.) . It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Porebski (as stated above) in view of Garcia to explicitly teach measurements across different frequencies, to improve accuracy, because measured battery parameters, such as temperature are known to vary with frequency (Garcia [0088-0092], specifically see [0089] Temperature sensing may be useful in interpreting impedance data since the spectra change relative to temperature shifts.). Regarding Claim 9, Porebski teaches a controller (Porebski [0104] a system controller 1302) for battery analysis in an event detection system, comprising: a memory (Porebski [0104] a C51 base processor with both internal and external memory for data processing and logging) ; and a processor configured to execute executable instructions stored in the memory (Porebski [0104] The system controller 1302 provides analog-to-digital conversion (ADC) and digital signal processing (DSP), and may be implemented, for example, via a C51 base processor with both internal and external memory for data processing and logging.) to: record: a current of a battery and a temperature of the battery at predetermined intervals (Porebski [0062] Each of the monitoring parameters may provide useful information in determining the condition of the battery. For example, the battery device identifier may uniquely identify the battery and/or a data location in memory associated with the battery device. Also see [0086] The digital signals may be processed and stored within the master and slave monitoring units in the form of end results, which may involve, for example, the DC voltage/temperature, current and/or multiple signal processing. Also see [0199] According to an exemplary embodiment and/or exemplary method of the present invention, a measurement log may be generated using a snap shot time and interval defined by the user. The time interval may be set, for example, from 1 day up to up to 30 days.) ; and a timestamp of each recorded current and temperature, wherein the battery is included in a control panel of the event detection system (Porebski [0110] The exemplary string monitoring unit 1300 may perform certain signal processing functions, including, for example, test operations with respect to voltage, current and temperature. Also see [0104] The system timer 1303 provides time and date stamping functions so that the time and date of collection may be assigned therewith. The communications interface 1304 provides information and data transfer to and from the exemplary string monitoring unit 1300. In this regard, the communications interface 1304 may transfer data in real time or in a time-slotted manner.) ; determine an impedance (Porebski [0114] This option may also be used to perform impedance measurements by a serviceman. […] Both parameters, AC current and AC voltage drop, for each battery are then measured by the exemplary string monitoring unit 1300 and recorded in memory. This record may then be used to perform impedance trending analysis and to generate an impedance alarm.) ; cause a trend associated with a performance characteristic of the battery to be determined via an equivalent circuit model (Porebski [0068] A number of simplified battery models may provide an electrical equivalent circuit that may be used to perform battery ohmic parameters trending.) using each impedance, each temperature, and each time stamp at each predetermined interval (Porebski Also see [0066] The relation of certain measured parameters to the battery actual State of Health (SOH), State of Charge (SOC), life expectation, etc. is often discussed but one thing that has emerged from the discussion is the growing consensus that analyzing the trending of these parameters rather than precise actual value may be a useful tool to determine whether a system might be within a failure zone. Accordingly, an exemplary monitoring system according to the present invention may focus on trend analysis to provide a more simple and inexpensive tool than is available with other prior systems. Also see [0114] This record may then be used to perform impedance trending analysis and to generate an impedance alarm. Also see [0199] According to an exemplary embodiment and/or exemplary method of the present invention, a measurement log may be generated using a snap shot time and interval defined by the user. The time interval may be set, for example, from 1 day up to up to 30 days.) ; predict, based on the trend, that the performance characteristic is to exceed a predetermined threshold within a predetermined time period (Porebski [0130] Certain information may be transferred between the exemplary string monitoring unit 1300 and the user interface 1304. For example, in a set up mode, the user may perform functions, such as, for example, […] set up impedance trend alarm threshold, or set up all specific alarm thresholds. Also see [0008] The exemplary string monitoring system may also evaluate the battery state of health (SOH) via a trending of float current and/or Coup de Fouet recording over time. A trend in context is evaluating the parameter of interest over time ) ; and generate an alert based on the prediction (Porebski [0065] Accordingly, software resident on a remote computer may maintain a database containing battery data collected throughout the entire life cycle of the battery, which may be combined with other data and/or processing to generate reports, warnings and/or alarms based on predefined criteria. Also see [0130] Certain information may be transferred between the exemplary string monitoring unit 1300 and the user interface 1304. For example, in a set up mode, the user may perform functions, such as, for example, […] set up impedance trend alarm threshold, or set up all specific alarm thresholds.) . Porebski is not relied upon to explicitly teach measurements and analysis across different frequencies. Garcia teaches measurements and analysis across different frequencies (Garcia [0088] Measurements used in the present disclosure to derive these internal battery parameters 224 are impedance spectra data. It is useful to recall that at each sample instance k, an impedance spectrum I.sub.s[k] of the battery under observation is collected. An impedance spectra represents the impedance of a battery over a range of frequencies, and therefore its frequency response.) . It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Porebski in view of Garcia to explicitly teach measurements and analysis across different frequencies, to improve accuracy, because measured battery parameters, such as temperature are known to vary with frequency (Garcia [0088-0092], specifically see [0089] Temperature sensing may be useful in interpreting impedance data since the spectra change relative to temperature shifts.). Regarding Claim 10, Porebski in view of Garcia (as stated above) further teaches wherein the processor is configured to execute the instructions to cause a variable electric current to be generated and transmitted to the battery to cause the battery to generate the current (Porebski [0114] The exemplary string monitoring unit 1300 may include provision for a scheduled, short duration system loading with a known AC or pulse current.) . Regarding Claim 11, Porebski in view of Garcia (as stated above) further teaches wherein the variable electric current is a pulsed alternating current (AC) at different current levels (Porebski [0114] The exemplary string monitoring unit 1300 may include provision for a scheduled, short duration system loading with a known AC or pulse current. AC or DC current may be used depending on the desired test ) . Regarding Claim 12, Porebski in view of Garcia (as stated above) further teaches wherein the variable electrical current is an intermittent pulsating direct current (DC) (Porebski [0114] The exemplary string monitoring unit 1300 may include provision for a scheduled, short duration system loading with a known AC or pulse current. [0115] The DC voltage and DC current may be used to calculate energy removed from battery during discharge or added to the battery during charge. AC or DC current may be used depending on the desired test ) . Regarding Claim 13, Porebski in view of Garcia (as stated above) further teaches wherein the controller is: to record a current and a temperature of the battery over time (Porebski [0086] The digital signals may be processed and stored within the master and slave monitoring units in the form of end results, which may involve, for example, the DC voltage/temperature, current and/or multiple signal processing.) ; to record a current and a temperature of other batteries over time (Porebski [0086] In this regard, the certain signal processing may be performed with respect to these end results. For example, the battery DC voltages may be compared against one another or against predefined threshold values, ambient and individual battery temperatures may be compared against one another or against predefined threshold values, and/or logs and alarms may be generated therefrom. There may be a plurality of batteries being monitored and analyzed ) ; and located remotely from the battery and the other batteries (Porebski [0006] An exemplary battery monitoring system may be provided, which measures various parameters for a single or multiple batteries, and includes a data processing and/or storage unit which may be configured to be local and/or remote to the battery via a wireless connection or a combination of a wireless and wired connection. Also see [0065] Accordingly, software resident on a remote computer may maintain a database containing battery data collected throughout the entire life cycle of the battery, which may be combined with other data and/or processing to generate reports, warnings and/or alarms based on predefined criteria.) . Regarding Claim 14, Porebski in view of Garcia (as stated above) further teaches wherein the processor is configured to execute the instructions to cause the trend to be displayed via a user interface (Porebski [0092] For example, the master monitoring unit 1151 may […] provide an external visual display to indicate status and/or certain actions/requirements.) . Regarding Claim 15, Porebski teaches a non-transitory computer readable medium having computer readable instructions stored thereon that are executable by a processor (Porebski [0104] a C51 base processor with both internal and external memory for data processing and logging.) to: record: a no-load voltage of a fully charged battery and a temperature of the battery at predetermined intervals (Porebski [0062] Each of the monitoring parameters may provide useful information in determining the condition of the battery. For example, the battery device identifier may uniquely identify the battery and/or a data location in memory associated with the battery device. Also see [0086] The digital signals may be processed and stored within the master and slave monitoring units in the form of end results, which may involve, for example, the DC voltage/temperature, current and/or multiple signal processing. Also see [0199] According to an exemplary embodiment and/or exemplary method of the present invention, a measurement log may be generated using a snap shot time and interval defined by the user. The time interval may be set, for example, from 1 day up to up to 30 days.) ; and a timestamp of each recorded no-load voltage and temperature, wherein the battery is included in a control panel of an event detection system (Porebski [0110] The exemplary string monitoring unit 1300 may perform certain signal processing functions, including, for example, test operations with respect to voltage, current and temperature. Also see [0104] The system timer 1303 provides time and date stamping functions so that the time and date of collection may be assigned therewith. The communications interface 1304 provides information and data transfer to and from the exemplary string monitoring unit 1300. In this regard, the communications interface 1304 may transfer data in real time or in a time-slotted manner.) ; cause a trend associated with a performance characteristic of the battery to be determined via a model using each no-load voltage, each temperature, and each time stamp at each predetermined interval (Porebski Also see [0066] The relation of certain measured parameters to the battery actual State of Health (SOH), State of Charge (SOC), life expectation, etc. is often discussed but one thing that has emerged from the discussion is the growing consensus that analyzing the trending of these parameters rather than precise actual value may be a useful tool to determine whether a system might be within a failure zone. Accordingly, an exemplary monitoring system according to the present invention may focus on trend analysis to provide a more simple and inexpensive tool than is available with other prior systems. Also see [0114] This record may then be used to perform impedance trending analysis and to generate an impedance alarm. Also see [0199] According to an exemplary embodiment and/or exemplary method of the present invention, a measurement log may be generated using a snap shot time and interval defined by the user. The time interval may be set, for example, from 1 day up to up to 30 days.) ; predict, based on the trend, that the performance characteristic is to exceed a predetermined threshold within a predetermined time period (Porebski [0130] Certain information may be transferred between the exemplary string monitoring unit 1300 and the user interface 1304. For example, in a set up mode, the user may perform functions, such as, for example, […] set up impedance trend alarm threshold, or set up all specific alarm thresholds. Also see [0008] The exemplary string monitoring system may also evaluate the battery state of health (SOH) via a trending of float current and/or Coup de Fouet recording over time. A trend in context is evaluating the parameter of interest over time ) ; and generate an alert based on the prediction (Porebski [0065] Accordingly, software resident on a remote computer may maintain a database containing battery data collected throughout the entire life cycle of the battery, which may be combined with other data and/or processing to generate reports, warnings and/or alarms based on predefined criteria. Also see [0130] Certain information may be transferred between the exemplary string monitoring unit 1300 and the user interface 1304. For example, in a set up mode, the user may perform functions, such as, for example, […] set up impedance trend alarm threshold, or set up all specific alarm thresholds.) . Porebski is not relied upon to teach a machine-learning model. Garcia teaches a machine-learning model (Garcia [0042] This information is then used by the present disclosure to construct an aging model or a multiplicity of aging models considering different failure mechanisms of the battery through learning algorithms (e.g., relevance vector machines (RVM), fuzzy logic, and neural networks). These aging models will age the battery in distinct manners and at different rates. ) . It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application to modify Porebski in view of Garcia, to explicitly teach a machine-learning model, to describe the specific prediction model used in Porebski, as a machine learning model is known to extrapolate outputs from specific inputs (Garcia [0120] While different regression and machine learning techniques can here be used to estimate internal parameters of the considered aging model. Also see MPEP 2143 I. D. Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results;) . Regarding Claim 16, Porebski in view of Garcia (as stated above) further teaches to determine whether the battery is fully charged (Porebski [0055] a charge/discharge test stage 204, an open cell voltage (OCV) stage 205. Also see [0060] FIG. 4 shows a graph of open cell voltage (OVC) and a battery's charge state. Here, FIG. 4 shows that if the voltage reading is 2.14 volts per cell (VPC) (or 12.84+V per module), the battery is practically at 100% charge. Monitoring the charge of the battery inherently indicates whether the battery is fully charged or not. ) . Regarding Claim 17, Porebski in view of Garcia (as stated above) further teaches wherein in response to the battery not being fully charged, the computer readable instructions are executable by the processor to cause the battery to be charged by a power supply unit included in the control panel (Porebski [0082] The AC current source (ACCS) 1101 generates an AC current flow through the string arrangements S1 and S2 so that a battery resistance and/or conductance test may be performed for the batteries attached thereof. […] The AC voltage component causes an AC current to flow through the battery, which charges the battery during the positive half of the sine wave and discharges the battery during the negative half of the sine wave (See FIG. 10).. As part of the testing the battery is charged and discharged ) . Regarding Claim 18, Porebski in view of Garcia (as stated above) further teaches to cause the battery to be charged by the power supply unit for a predetermined amount of time (Porebski [0082] The AC current source (ACCS) 1101 generates an AC current flow through the string arrangements S1 and S2 so that a battery resistance and/or conductance test may be performed for the batteries attached thereof. […] The AC voltage component causes an AC current to flow through the battery, which charges the battery during the positive half of the sine wave and discharges the battery during the negative half of the sine wave (See FIG. 10). The battery charge/discharged cycles last for the determined period of the applied waveform. ) . Regarding Claim 19, Porebski in view of Garcia (as stated above) further teaches to cause the battery to be charged by the power supply unit until the battery is fully charged (Porebski [0116] After the battery is fully charged, the string current input may be used to perform a float current test. The battery is fully charged for at least part of the monitoring ) . Regarding Claim 20, Porebski in view of Garcia (as stated above) further teaches wherein in response to the battery being fully charged, the computer readable instructions are executable by the processor to record the no-load voltage, the temperature, and the timestamp at each of the predetermined intervals (Porebski Also see [0066] The relation of certain measured parameters to the battery actual State of Health (SOH), State of Charge (SOC), life expectation, etc. is often discussed but one thing that has emerged from the discussion is the growing consensus that analyzing the trending of these parameters rather than precise actual value may be a useful tool to determine whether a system might be within a failure zone. Accordingly, an exemplary monitoring system according to the present invention may focus on trend analysis to provide a more simple and inexpensive tool than is available with other prior systems. Also see [0114] This record may then be used to perform impedance trending analysis and to generate an impedance alarm. Also see [0199] According to an exemplary embodiment and/or exemplary method of the present invention, a measurement log may be generated using a snap shot time and interval defined by the user. The time interval may be set, for example, from 1 day up to up to 30 days.) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Vapurcuyan et al. (US 20180089983 A1) discloses a Remote Battery Monitor. Stanley (US 20100201322 A1) discloses a Battery Analysis System. Li et al. (US 20220283242 A1) discloses a Method And Device For Managing Battery Data. Plestid et al. (US 20100036628 A1) discloses Systems And Methods For Monitoring Deterioration Of A Rechargeable Battery. Liu (CN 111007410 A) discloses a Vehicle Battery Diagnosis Method And Device. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /CHRISTIAN T BRYANT/Examiner, Art Unit 2857 Application/Control Number: 18/583,182 Page 2 Art Unit: 2857 Application/Control Number: 18/583,182 Page 3 Art Unit: 2857 Application/Control Number: 18/583,182 Page 4 Art Unit: 2857 Application/Control Number: 18/583,182 Page 5 Art Unit: 2857 Application/Control Number: 18/583,182 Page 6 Art Unit: 2857 Application/Control Number: 18/583,182 Page 7 Art Unit: 2857 Application/Control Number: 18/583,182 Page 8 Art Unit: 2857 Application/Control Number: 18/583,182 Page 9 Art Unit: 2857 Application/Control Number: 18/583,182 Page 10 Art Unit: 2857 Application/Control Number: 18/583,182 Page 11 Art Unit: 2857 Application/Control Number: 18/583,182 Page 12 Art Unit: 2857 Application/Control Number: 18/583,182 Page 13 Art Unit: 2857 Application/Control Number: 18/583,182 Page 14 Art Unit: 2857 Application/Control Number: 18/583,182 Page 15 Art Unit: 2857 Application/Control Number: 18/583,182 Page 16 Art Unit: 2857 Application/Control Number: 18/583,182 Page 17 Art Unit: 2857 Application/Control Number: 18/583,182 Page 18 Art Unit: 2857 Application/Control Number: 18/583,182 Page 19 Art Unit: 2857 Application/Control Number: 18/583,182 Page 20 Art Unit: 2857 Application/Control Number: 18/583,182 Page 21 Art Unit: 2857 Application/Control Number: 18/583,182 Page 22 Art Unit: 2857 Application/Control Number: 18/583,182 Page 23 Art Unit: 2857
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