CTNF 18/525,954 CTNF 97733 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. Information Disclosure Statement The information disclosure statement (IDS) submitted on 08/26/2024 was considered by the examiner. Claim Rejections - 35 USC § 102 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) 1-6, 9-15, and 18-19 is/are rejected under 35 U.S.C. 102 (a)(1) and 102(a)(2) as being anticipated by Kumar (US 11072258 B2) . Regarding claim 1, Kumar teaches An electric vehicle (col 8 lines 31-33 “vehicle 5 is a conventional vehicle with only an engine, or an electric vehicle with only electric machine(s).”) comprising: an electric motor (col 8 lines 34-35 “Electric machine 52 may be a motor or a motor/generator.”) to propel the electric vehicle (col 8 lines 51-52 “Electric machine 52 receives electrical power from a traction battery 58 to provide torque to vehicle wheels 55.”) ; a battery (battery 58) adapted to provide electrical energy to the electric motor for propelling the electric vehicle (col 8 lines 51-52 “Electric machine 52 receives electrical power from a traction battery 58 to provide torque to vehicle wheels 55.”) ; and one or more controllers (controller 12) collectively programmed with the following instructions: measure battery data of the battery (col 9 lines 11-16 “A battery monitoring sensor (BMS) 184 may be coupled to battery 58 for estimating one or more conditions associated with a state of degradation of the battery. Based on input from the BMS 184, controller 12 may calculate an inferred end of life of the battery, as elaborated at FIGS. 3-4.”) ; use the battery data to create a model (col 25 lines 29-34 “the smart adaptation algorithm described at FIG. 7. Changes in performance metrics with respect to EOL-thresholds may be logged and used for future adaptions. In one example, in the context of control theory, the adaptation may include a model predictive control algorithm with adaptive model parameters.”) of storage capacity of the battery (col 20 lines 42-48 “In some examples, instead of computing the EOL of the battery, a state of health (SOH) of the battery may be output, wherein the SOH of a battery can be expressed as a percent of remaining life that varies from 100% for new batteries to 0% for dead batteries. As the battery ages, its internal resistance increases, its internal capacity decreases, and correspondingly its SOH decreases.”) versus distance driven by the electric vehicle (col 12 lines 49-56 “The method enables the predicting of a state of degradation of a vehicle battery based on a rate of change in value of a metric associated with the vehicle battery, from an initial value of the metric at a time of installation in the vehicle system, over a duration of vehicle travel. The predicting is further based on a distance traveled by the vehicle over the duration, the metric derived from a sensed vehicle operating parameter.”) ; assess a reliability of the model (col 16 lines 23-25 “Thresholds and calibration parameters may also be updated if they are found to not deliver targeted accuracy requirements for prediction.”; Figs. 5 and 7, smart adaption) ; if the model is assessed as sufficiently reliable, then perform prognosis on the battery using the model (Fig. 4, steps 414-420) ; and if the model is assessed as not sufficiently reliable (col 26 lines 10-16 “The performance of the diagnostic/prognostic system may also be affected by changes to the set of vehicles being monitored. Changes may be seen as noise factors and classified into several categories. These changes may cause the shape of the distribution functions of battery parameters to evolve, and EOL-thresholds may need to be adapted to maintain acceptable levels of performance.” ) , then adopt a substitute model of storage capacity of the battery versus distance driven by the electric vehicle, the substitute model based on battery data of one or more other vehicles, and perform prognosis on the battery using the substitute model (col 35 lines 64 – col 36 line 7 “In any or all of the preceding examples, additionally or optionally, the vehicle is one of a plurality of vehicles of a fleet, the method further comprising: estimating the plurality of battery metrics for each vehicle of the fleet over at least a threshold duration; and predicting the state of degradation of the vehicle battery responsive to the estimating. In any or all of the preceding examples, additionally or optionally, the method further comprises, updating each of the end of life threshold and the one or more intermediate thresholds of the vehicle responsive to performance of each vehicle of the fleet following the predicting.”) . The plurality of vehicles of the fleet is the one or more vehicles. The optional prediction of the vehicle battery responsive to the estimating (based on the each of the vehicle in the fleet) is the substitute model. Regarding claim 2, Kumar teaches The electric vehicle of Claim 1, further comprising selecting the one or more other vehicles based on the one or more other vehicles having operating characteristics similar to those of the electric vehicle (col 22 lines 50-63 “F(f) represents the number of batteries that failed in vehicles fulfilling the following conditions: (1) The failure mechanism(s) of the batteries is (are) associated with feature f; and (2) the value of feature f identifies a healthy battery at the end of life. N(f,G) represents the total number of batteries in the field that are monitored by the diagnostic/prognostic system with respect to feature f and fall within the group G. The group definition may define battery sizes and may also include vehicle types, electrical content (options) and other constraints. The performance metric is designed to measure and improve system performance for the vehicles or platforms defined by G.”) . The batteries, which belong to the one or more other vehicles, sharing the feature f and in group G, are selected for having operating characteristics similar to those of the electric vehicle. Regarding claim 3, Kumar teaches The electric vehicle of Claim 2, wherein the operating characteristics include average distance driven per day (col 19 lines 32-43 “ the speed of convergence may be further updated based on vehicle driving statistics. The vehicle driving statistics may include, for example, a distance covered over the life of the vehicle (e.g., based on an odometer reading), a number and frequency of service events that have occurred over the life of the vehicle (e.g., how many oil services have occurred, what frequency they were performed, what odometer reading they were performed at), average fuel economy of the vehicle, average speed of the vehicle, average transmission gear usage of the vehicle, average number of miles covered each day, average tire pressure of the vehicle, etc. ”) . The vehicle driving statistics, including average number of miles covered each day, is the average distance driven per day. Regarding claim 4, Kumar teaches The electric vehicle of Claim 2, wherein the operating characteristics include average ambient temperature (col 19 lines 59-63 “In the case of monitoring a propulsion battery in a hybridized or electric vehicle and predicting it's time to end of life, a future (e.g., predicted) driving pattern may be taken into account, such as the terrain, ambient altitude and temperature”; col 11 lines 59-63 “A plausibility strategy may be put into place that only transmits estimates of battery characteristics after measurement has occurred multiple times, or an average of estimates over a defined moving time horizon may be transmitted.”) . Regarding claim 5, Kumar teaches The electric vehicle of Claim 1, wherein the model is assessed as not sufficiently reliable if the battery data has an insufficient number of data measurements (col 13 lines 7-13 “Because the battery statistics only converge to a stable value after a representative amount of batteries have been evaluated over a sufficient amount of time, a monitoring period is associated with each metric. Once the metric has been evaluated over at least the associated monitoring period, it may be used to quantify system performance and calculate further adaptations to the EOL thresholds.”) . The model requiring that the statistics converge to a stable value over a monitoring period is the assessment for sufficient number of data measurements. Regarding claim 6, Kumar teaches The electric vehicle of Claim 1, wherein the model is assessed as not sufficiently reliable if the model demonstrates increasing storage capacity versus distance driven or if the model demonstrates noise (col 26 lines 10-16 “The performance of the diagnostic/prognostic system may also be affected by changes to the set of vehicles being monitored. Changes may be seen as noise factors and classified into several categories. These changes may cause the shape of the distribution functions of battery parameters to evolve, and EOL-thresholds may need to be adapted to maintain acceptable levels of performance.”) . The noise factors, which impact the assessment of the model, are the noise. Regarding claim 9, Kumar teaches The electric vehicle of Claim 1, wherein the prognosis includes using the model or the substitute model in combination with internal resistance measurements of the battery (Fig. 2, battery’s internal resistance 204) . Regarding claim 10, Kumar teaches An electric vehicle (col 8 lines 31-33 “vehicle 5 is a conventional vehicle with only an engine, or an electric vehicle with only electric machine(s).”) comprising: an electric motor (col 8 lines 34-35 “Electric machine 52 may be a motor or a motor/generator.”) to propel the electric vehicle (col 8 lines 51-52 “Electric machine 52 receives electrical power from a traction battery 58 to provide torque to vehicle wheels 55.”) ; a battery (battery 58) adapted to provide electrical energy to the electric motor for propelling the electric vehicle (col 8 lines 51-52 “Electric machine 52 receives electrical power from a traction battery 58 to provide torque to vehicle wheels 55.”) ; and one or more controllers (controller 12) collectively programmed with the following instructions: measure battery data of the battery (col 9 lines 11-16 “A battery monitoring sensor (BMS) 184 may be coupled to battery 58 for estimating one or more conditions associated with a state of degradation of the battery. Based on input from the BMS 184, controller 12 may calculate an inferred end of life of the battery, as elaborated at FIGS. 3-4.”) ; use the battery data to create a model (col 25 lines 29-34 “the smart adaptation algorithm described at FIG. 7. Changes in performance metrics with respect to EOL-thresholds may be logged and used for future adaptions. In one example, in the context of control theory, the adaptation may include a model predictive control algorithm with adaptive model parameters.”) of storage capacity of the battery (col 20 lines 42-48 “In some examples, instead of computing the EOL of the battery, a state of health (SOH) of the battery may be output, wherein the SOH of a battery can be expressed as a percent of remaining life that varies from 100% for new batteries to 0% for dead batteries. As the battery ages, its internal resistance increases, its internal capacity decreases, and correspondingly its SOH decreases.”) versus time in service of the battery in the electric vehicle (col 12 lines 49-56 “The method enables the predicting of a state of degradation of a vehicle battery based on a rate of change in value of a metric associated with the vehicle battery, from an initial value of the metric at a time of installation in the vehicle system, over a duration of vehicle travel. The predicting is further based on a distance traveled by the vehicle over the duration, the metric derived from a sensed vehicle operating parameter.”; col 19 lines 20-25 “the speed of convergence may be further adjusted as a function of a time or duration elapsed since the battery was first installed or operated in the vehicle. As another example, the time or duration elapsed since the battery was last serviced, repaired, or reset may be taken into account.”). The duration, including the time since the battery was first installed, last serviced, or time the vehicle has traveled, is the time in service ; assess a reliability of the model (col 16 lines 23-25 “Thresholds and calibration parameters may also be updated if they are found to not deliver targeted accuracy requirements for prediction.”; Figs. 5 and 7, smart adaption) ; if the model is assessed as sufficiently reliable, then perform prognosis on the battery using the model (Fig. 4, steps 414-420) ; and if the model is assessed as not sufficiently reliable (col 26 lines 10-16 “The performance of the diagnostic/prognostic system may also be affected by changes to the set of vehicles being monitored. Changes may be seen as noise factors and classified into several categories. These changes may cause the shape of the distribution functions of battery parameters to evolve, and EOL-thresholds may need to be adapted to maintain acceptable levels of performance.”) , then adopt a substitute model of storage capacity of the battery versus time in service of the battery in the electric vehicle, the substitute model based on battery data of one or more other vehicles, and perform the prognosis on the battery using the substitute model (col 35 lines 64 – col 36 line 7 “In any or all of the preceding examples, additionally or optionally, the vehicle is one of a plurality of vehicles of a fleet, the method further comprising: estimating the plurality of battery metrics for each vehicle of the fleet over at least a threshold duration; and predicting the state of degradation of the vehicle battery responsive to the estimating. In any or all of the preceding examples, additionally or optionally, the method further comprises, updating each of the end of life threshold and the one or more intermediate thresholds of the vehicle responsive to performance of each vehicle of the fleet following the predicting.”) . The plurality of vehicles of the fleet is the one or more vehicles. The optional prediction of the vehicle battery responsive to the estimating (based on the each of the vehicle in the fleet) is the substitute model. Regarding claim 11, Kumar teaches The electric vehicle of Claim 10, further comprising selecting the one or more other vehicles based on the one or more other vehicles having operating characteristics similar to those of the electric vehicle (col 22 lines 50-63 “F(f) represents the number of batteries that failed in vehicles fulfilling the following conditions: (1) The failure mechanism(s) of the batteries is (are) associated with feature f; and (2) the value of feature f identifies a healthy battery at the end of life. N(f,G) represents the total number of batteries in the field that are monitored by the diagnostic/prognostic system with respect to feature f and fall within the group G. The group definition may define battery sizes and may also include vehicle types, electrical content (options) and other constraints. The performance metric is designed to measure and improve system performance for the vehicles or platforms defined by G.”) . The batteries, which belong to the one or more other vehicles, sharing the feature f and in group G, are selected for having operating characteristics similar to those of the electric vehicle. Regarding claim 12, Kumar teaches The electric vehicle of Claim 11, wherein the operating characteristics include average distance driven per day (col 19 lines 32-43 “ the speed of convergence may be further updated based on vehicle driving statistics. The vehicle driving statistics may include, for example, a distance covered over the life of the vehicle (e.g., based on an odometer reading), a number and frequency of service events that have occurred over the life of the vehicle (e.g., how many oil services have occurred, what frequency they were performed, what odometer reading they were performed at), average fuel economy of the vehicle, average speed of the vehicle, average transmission gear usage of the vehicle, average number of miles covered each day, average tire pressure of the vehicle, etc. ”) . The vehicle driving statistics, including average number of miles covered each day, is the average distance driven per day. Regarding claim 13, Kumar teaches The electric vehicle of Claim 11, wherein the operating characteristics include average ambient temperature (col 19 lines 59-63 “In the case of monitoring a propulsion battery in a hybridized or electric vehicle and predicting it's time to end of life, a future (e.g., predicted) driving pattern may be taken into account, such as the terrain, ambient altitude and temperature”; col 11 lines 59-63 “A plausibility strategy may be put into place that only transmits estimates of battery characteristics after measurement has occurred multiple times, or an average of estimates over a defined moving time horizon may be transmitted.”) . Regarding claim 14, Kumar teaches The electric vehicle of Claim 10, wherein the model is assessed as not sufficiently reliable if the battery data has an insufficient number of data measurements (col 13 lines 7-13 “Because the battery statistics only converge to a stable value after a representative amount of batteries have been evaluated over a sufficient amount of time, a monitoring period is associated with each metric. Once the metric has been evaluated over at least the associated monitoring period, it may be used to quantify system performance and calculate further adaptations to the EOL thresholds.”) . The model requiring that the statistics converge to a stable value over a monitoring period is the assessment for sufficient number of data measurements. Regarding claim 15, Kumar teaches The electric vehicle of Claim 10, wherein the model is assessed as not sufficiently reliable if the model demonstrates increasing storage capacity versus time in service of the battery in the electric vehicle or if the model demonstrates noise (col 26 lines 10-16 “The performance of the diagnostic/prognostic system may also be affected by changes to the set of vehicles being monitored. Changes may be seen as noise factors and classified into several categories. These changes may cause the shape of the distribution functions of battery parameters to evolve, and EOL-thresholds may need to be adapted to maintain acceptable levels of performance.”) . The noise factors, which impact the assessment of the model, are the noise. Regarding claim 18, Kumar teaches The electric vehicle of Claim 10, wherein the prognosis includes using the model or the substitute model in combination with internal resistance measurements of the battery (Fig. 2, battery’s internal resistance 204) . Regarding claim 19, teaches A method for prognosis (Abstract) of a battery (battery 58) of an electric vehicle (col 8 lines 31-33 “vehicle 5 is a conventional vehicle with only an engine, or an electric vehicle with only electric machine(s).”) , the method comprising: through one or more controllers (controller 12) , measuring battery data for the battery (col 9 lines 11-16 “A battery monitoring sensor (BMS) 184 may be coupled to battery 58 for estimating one or more conditions associated with a state of degradation of the battery. Based on input from the BMS 184, controller 12 may calculate an inferred end of life of the battery, as elaborated at FIGS. 3-4.”) ; through one or more controllers (controller 12) , using the battery data to create a model (col 25 lines 29-34 “the smart adaptation algorithm described at FIG. 7. Changes in performance metrics with respect to EOL-thresholds may be logged and used for future adaptions. In one example, in the context of control theory, the adaptation may include a model predictive control algorithm with adaptive model parameters.”) of storage capacity of the battery (col 20 lines 42-48 “In some examples, instead of computing the EOL of the battery, a state of health (SOH) of the battery may be output, wherein the SOH of a battery can be expressed as a percent of remaining life that varies from 100% for new batteries to 0% for dead batteries. As the battery ages, its internal resistance increases, its internal capacity decreases, and correspondingly its SOH decreases.”) versus distance driven by the electric vehicle (col 12 lines 49-56 “The method enables the predicting of a state of degradation of a vehicle battery based on a rate of change in value of a metric associated with the vehicle battery, from an initial value of the metric at a time of installation in the vehicle system, over a duration of vehicle travel. The predicting is further based on a distance traveled by the vehicle over the duration, the metric derived from a sensed vehicle operating parameter.”) or versus time in service of the battery in the electric vehicle; selecting one or more other vehicles (col 35 lines 64 – col 36 line 7 “In any or all of the preceding examples, additionally or optionally, the vehicle is one of a plurality of vehicles of a fleet, the method further comprising: estimating the plurality of battery metrics for each vehicle of the fleet over at least a threshold duration; and predicting the state of degradation of the vehicle battery responsive to the estimating. In any or all of the preceding examples, additionally or optionally, the method further comprises, updating each of the end of life threshold and the one or more intermediate thresholds of the vehicle responsive to performance of each vehicle of the fleet following the predicting. based on the one or more other vehicles having operating characteristics similar to operating characteristics of the electric vehicle (col 22 lines 50-63 “F(f) represents the number of batteries that failed in vehicles fulfilling the following conditions: (1) The failure mechanism(s) of the batteries is (are) associated with feature f; and (2) the value of feature f identifies a healthy battery at the end of life. N(f,G) represents the total number of batteries in the field that are monitored by the diagnostic/prognostic system with respect to feature f and fall within the group G. The group definition may define battery sizes and may also include vehicle types, electrical content (options) and other constraints. The performance metric is designed to measure and improve system performance for the vehicles or platforms defined by G.”) . The batteries, which belong to the one or more other vehicles, sharing the feature f and in group G, are selected for having operating characteristics similar to those of the electric vehicle. ; creating a substitute model of storage capacity of the battery (col 35 lines 64 – col 36 line 7 “In any or all of the preceding examples, additionally or optionally, the vehicle is one of a plurality of vehicles of a fleet, the method further comprising: estimating the plurality of battery metrics for each vehicle of the fleet over at least a threshold duration; and predicting the state of degradation of the vehicle battery responsive to the estimating. In any or all of the preceding examples, additionally or optionally, the method further comprises, updating each of the end of life threshold and the one or more intermediate thresholds of the vehicle responsive to performance of each vehicle of the fleet following the predicting.”) . The plurality of vehicles of the fleet is the one or more vehicles. The optional prediction of the vehicle battery responsive to the estimating (based on the each of the vehicle in the fleet) is the substitute model. versus distance driven by the electric vehicle (col 12 lines 49-56 “The method enables the predicting of a state of degradation of a vehicle battery based on a rate of change in value of a metric associated with the vehicle battery, from an initial value of the metric at a time of installation in the vehicle system, over a duration of vehicle travel. The predicting is further based on a distance traveled by the vehicle over the duration, the metric derived from a sensed vehicle operating parameter.”) or versus time in service of the battery in the electric vehicle based on battery data of the one or more other vehicles; using the model or the substitute model in combination with internal resistance measurements (Fig. 2, battery’s internal resistance 204) of the battery for prognosis of the battery; and identifying one or more failure root causes of degradation of the battery predicted by the prognosis (col 10 lines 14-31 “By using an EOL prediction methodology that assesses multiple battery characteristics associated with battery degradation, as elaborated with reference to the routines of FIGS. 3-4, reliability of the EOL prediction is improved. In addition, the scope of failure mechanisms that can be detected is expanded by relying on multiple parallel paths for failure prediction. In addition, interaction of the different characteristics can be also be accounted for (such as the effect of a rate of corrosion on a rate of sulfation of the battery, and vice versa, a rate of sulfation on the rate of corrosion of the battery). The performance of a battery in a vehicle is a function of a multitude of characteristics. If the end of life is only defined for each characteristic without taking other characteristics into account, the end time to the end of life may be over-estimated, because small degradations in multiple characteristics may cause significant degradation in total battery performance.”; col 14 lines 53 – col 15 line 1 “At 304, the method includes measuring the selected characteristics online during vehicle operation. For example, the selected characteristics may be measured via one or more battery sensors at the determined sampling frequency. For example, internal resistance of the battery may be measured with a hall-based or shunt-based current measurement and a voltage sensor at sampling frequencies greater than 1 kHz. As another example, a loss in battery capacity due to sulfation may be measured with a single voltage measurement after the battery has been fully charged and has been allowed to rest without charging or discharging for a number of hours. The change in open-circuit voltage of the fully charged battery as it ages is a metric for the capacity loss due to sulfation. As another example, internal shorts (and a severity of the short) may be identified based on a degree of voltage relaxation.”) . The assessment of the battery characteristics, wherein select characteristics are tied to types of faults in the battery, is the identification of one or more failure root causes . 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. 07-22-aia AIA Claim (s) 7, 8, 16, 17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kumar as applied to claim s 1, 10, and 19, respectfully , above, and further in view of Lim et al. (US 20230305065 A1) . Regarding claim 7, Kumar teaches The electric vehicle of Claim 1, wherein the prognosis includes comparing projected storage capacities (col 10 lines 48-57 “The controller may be configured to use an algorithm that estimates the rate of convergence of the measured data to the defined thresholds, and uses the estimated rate in addition to a previous history of degradation behavior of the battery, sensed data for parameters relating to the battery, as well as based on mapped vehicle driving statistics (such as real-time vehicle driving statistics, or those compiled over a current vehicle drive cycle), to make a statistical prediction regarding the remaining life of the battery.”) Kumar does not teach the electric vehicle comprising: comparing storage capacities among constituent portions of the battery. Lim teaches an analogous electric vehicle ([0003] “Recently, the demand for portable electronic products such as notebook computers, video cameras and portable telephones has increased sharply, and electric vehicles, energy storage batteries, robots, satellites and the like have been developed in earnest. Accordingly, high-performance batteries allowing repeated charging and discharging are being actively studied.”) comparing: comparing storage capacities (Fig. 2) among constituent portions of the battery ([0019] The battery classifying unit may be configured to generate a target set including the selected at least one target component, generate a plurality of target subsets including at least one target component in the generated target set, generate at least one classification model for classifying the plurality of batteries and the reference cell for each of the plurality of target subsets, and set any one of the plurality of classification models generated for the plurality of target subsets as a representative model for the corresponding cycle.; [0085] “Specifically, the battery classifying unit 140 may be configured to classify a reference cell set to correspond to an abnormal cell and the plurality of batteries into one of the plurality of groups, based on the selected at least one target component”). The components, including battery cells, are the constituent portions of the battery. It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the electric vehicle of Kumar to include the compared constituent portions of the battery of Lim, because it would yield predictable and advantageous results, including indicating differences in constituent portions of the battery, which may advantageously indicate decreases in storage capacity of portions of the battery (e.g. cells) as compared to other portions, and thereby decreases in capacity of the battery itself. Regarding claim 8, Kumar teaches The electric vehicle of Claim 1, wherein the prognosis includes comparing projected storage capacity loss among constituent portions of the battery against a threshold (col 27 lines 28 “A trigger may occur if battery resistance or battery capacity loss exceeds separate thresholds, or if battery resistance and battery capacity loss exceed separate thresholds, or if the values of battery resistance and capacity in “feature space” come within a predefined distance to the end of life threshold as illustrated in FIG. 6.”) . Kumar does not teach the electric vehicle comprising: comparing storage capacities among constituent portions of the battery. Lim teaches an analogous electric vehicle ([0003] “Recently, the demand for portable electronic products such as notebook computers, video cameras and portable telephones has increased sharply, and electric vehicles, energy storage batteries, robots, satellites and the like have been developed in earnest. Accordingly, high- performance batteries allowing repeated charging and discharging are being actively studied.”) comparing: comparing storage capacities (Fig. 2) among constituent portions of the battery ([0019] The battery classifying unit may be configured to generate a target set including the selected at least one target component, generate a plurality of target subsets including at least one target component in the generated target set, generate at least one classification model for classifying the plurality of batteries and the reference cell for each of the plurality of target subsets, and set any one of the plurality of classification models generated for the plurality of target subsets as a representative model for the corresponding cycle.; [0085] “Specifically, the battery classifying unit 140 may be configured to classify a reference cell set to correspond to an abnormal cell and the plurality of batteries into one of the plurality of groups, based on the selected at least one target component”). The components, including battery cells, are the constituent portions of the battery. It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the electric vehicle of Kumar to include the compared constituent portions of the battery of Lim, because it would yield predictable and advantageous results, including indicating differences in constituent portions of the battery, which may advantageously indicate decreases in storage capacity of portions of the battery (e.g. cells) as compared to other portions, and thereby decreases in capacity of the battery itself. Regarding claim 16, Kumar teaches The electric vehicle of Claim 10, wherein the prognosis includes: comparing storage capacities among batteries (Figs. 5 and 7) ; and identifying the battery as faulty based on the comparing (Fig. 3, step 230; col 21 lines 25-26 “Various communication strategies may be used to alert the driver and maintenance personnel to an imminent battery failure”). Kumar does not teach the electric vehicle comprising: comparing storage capacities among constituent portions of the battery. Lim teaches an analogous electric vehicle ([0003] “Recently, the demand for portable electronic products such as notebook computers, video cameras and portable telephones has increased sharply, and electric vehicles, energy storage batteries, robots, satellites and the like have been developed in earnest. Accordingly, high-performance batteries allowing repeated charging and discharging are being actively studied.”) comparing: comparing storage capacities (Fig. 2) among constituent portions of the battery ([0019] The battery classifying unit may be configured to generate a target set including the selected at least one target component, generate a plurality of target subsets including at least one target component in the generated target set, generate at least one classification model for classifying the plurality of batteries and the reference cell for each of the plurality of target subsets, and set any one of the plurality of classification models generated for the plurality of target subsets as a representative model for the corresponding cycle.; [0085] “Specifically, the battery classifying unit 140 may be configured to classify a reference cell set to correspond to an abnormal cell and the plurality of batteries into one of the plurality of groups, based on the selected at least one target component”). The components, including battery cells, are the constituent portions of the battery. It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the electric vehicle of Kumar to include the compared constituent portions of the battery of Lim, because it would yield predictable and advantageous results, including indicating differences in constituent portions of the battery, which may advantageously indicate decreases in storage capacity of portions of the battery (e.g. cells) as compared to other portions, and thereby decreases in capacity of the battery itself. Regarding claim 17, Kumar teaches The electric vehicle of Claim 10, wherein the prognosis includes: comparing storage capacity loss among constituent portions of the battery (col 27 lines 28 “A trigger may occur if battery resistance or battery capacity loss exceeds separate thresholds, or if battery resistance and battery capacity loss exceed separate thresholds, or if the values of battery resistance and capacity in “feature space” come within a predefined distance to the end of life threshold as illustrated in FIG. 6.”) ; and identifying the battery or constituent portions thereof as faulty based on the comparing (Fig. 3, step 230; col 21 lines 25-26 “Various communication strategies may be used to alert the driver and maintenance personnel to an imminent battery failure”) . Kumar does not teach the electric vehicle comprising: comparing storage capacities among constituent portions of the battery. Lim teaches an analogous electric vehicle ([0003] “Recently, the demand for portable electronic products such as notebook computers, video cameras and portable telephones has increased sharply, and electric vehicles, energy storage batteries, robots, satellites and the like have been developed in earnest. Accordingly, high-performance batteries allowing repeated charging and discharging are being actively studied.”) comparing: comparing storage capacities (Fig. 2) among constituent portions of the battery ([0019] The battery classifying unit may be configured to generate a target set including the selected at least one target component, generate a plurality of target subsets including at least one target component in the generated target set, generate at least one classification model for classifying the plurality of batteries and the reference cell for each of the plurality of target subsets, and set any one of the plurality of classification models generated for the plurality of target subsets as a representative model for the corresponding cycle.; [0085] “Specifically, the battery classifying unit 140 may be configured to classify a reference cell set to correspond to an abnormal cell and the plurality of batteries into one of the plurality of groups, based on the selected at least one target component”). The components, including battery cells, are the constituent portions of the battery. It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the electric vehicle of Kumar to include the compared constituent portions of the battery of Lim, because it would yield predictable and advantageous results, including indicating differences in constituent portions of the battery, which may advantageously indicate decreases in storage capacity of portions of the battery (e.g. cells) as compared to other portions, and thereby decreases in capacity of the battery itself. Regarding claim 20, Kumar teaches The method of Claim 19, wherein the prognosis includes: comparing storage capacities (Figs. 5 and 7) or storage capacity loss among constituent portions of the battery; and identifying the battery as faulty based on the comparing (Fig. 3, step 230; col 21 lines 25-26 “Various communication strategies may be used to alert the driver and maintenance personnel to an imminent battery failure”) . Kumar does not teach the electric vehicle comprising: comparing storage capacities among constituent portions of the battery. Lim teaches an analogous electric vehicle ([0003] “Recently, the demand for portable electronic products such as notebook computers, video cameras and portable telephones has increased sharply, and electric vehicles, energy storage batteries, robots, satellites and the like have been developed in earnest. Accordingly, high-performance batteries allowing repeated charging and discharging are being actively studied.”) comparing: comparing storage capacities (Fig. 2) among constituent portions of the battery ([0019] The battery classifying unit may be configured to generate a target set including the selected at least one target component, generate a plurality of target subsets including at least one target component in the generated target set, generate at least one classification model for classifying the plurality of batteries and the reference cell for each of the plurality of target subsets, and set any one of the plurality of classification models generated for the plurality of target subsets as a representative model for the corresponding cycle.; [0085] “Specifically, the battery classifying unit 140 may be configured to classify a reference cell set to correspond to an abnormal cell and the plurality of batteries into one of the plurality of groups, based on the selected at least one target component”). The components, including battery cells, are the constituent portions of the battery. It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the electric vehicle of Kumar to include the compared constituent portions of the battery of Lim, because it would yield predictable and advantageous results, including indicating differences in constituent portions of the battery, which may advantageously indicate decreases in storage capacity of portions of the battery (e.g. cells) as compared to other portions, and thereby decreases in capacity of the battery itself. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN GEISS whose telephone number is (571)270-1248. The examiner can normally be reached Monday - Friday 7:30 am - 4:30 pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached at (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /B.B.G./Examiner, Art Unit 2857 /Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857 Application/Control Number: 18/525,954 Page 2 Art Unit: 2857 Application/Control Number: 18/525,954 Page 3 Art Unit: 2857 Application/Control Number: 18/525,954 Page 4 Art Unit: 2857 Application/Control Number: 18/525,954 Page 5 Art Unit: 2857 Application/Control Number: 18/525,954 Page 6 Art Unit: 2857 Application/Control Number: 18/525,954 Page 7 Art Unit: 2857 Application/Control Number: 18/525,954 Page 8 Art Unit: 2857 Application/Control Number: 18/525,954 Page 9 Art Unit: 2857 Application/Control Number: 18/525,954 Page 10 Art Unit: 2857 Application/Control Number: 18/525,954 Page 11 Art Unit: 2857 Application/Control Number: 18/525,954 Page 12 Art Unit: 2857 Application/Control Number: 18/525,954 Page 13 Art Unit: 2857 Application/Control Number: 18/525,954 Page 14 Art Unit: 2857 Application/Control Number: 18/525,954 Page 15 Art Unit: 2857 Application/Control Number: 18/525,954 Page 16 Art Unit: 2857 Application/Control Number: 18/525,954 Page 17 Art Unit: 2857 Application/Control Number: 18/525,954 Page 18 Art Unit: 2857 Application/Control Number: 18/525,954 Page 19 Art Unit: 2857 Application/Control Number: 18/525,954 Page 20 Art Unit: 2857 Application/Control Number: 18/525,954 Page 21 Art Unit: 2857 Application/Control Number: 18/525,954 Page 22 Art Unit: 2857 Application/Control Number: 18/525,954 Page 23 Art Unit: 2857 Application/Control Number: 18/525,954 Page 24 Art Unit: 2857 Application/Control Number: 18/525,954 Page 25 Art Unit: 2857 Application/Control Number: 18/525,954 Page 26 Art Unit: 2857
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