CTNF 18/338,427 CTNF 86550 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. Specification 06-16 AIA Applicant is reminded of the proper language and format for an abstract of the disclosure. The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details. The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided. The abstract of the disclosure is objected to because it contain phrases which can be implied, i.e., “are disclosed”. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b). 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. 07-15-aia AIA Claim(s) 1- 4, 7, 9-12, 14-15, and 19-20 i s/are rejected under 35 U.S.C. 102( a)(1) a s being a nticipated b y G HANTOUS (US Pub. No. 2019/0072618). R egarding claim 1 , GHANTOUS discloses a method for adapting a charging protocol for charging batteries of a plurality of electric vehicles ( ¶ 0036 : batteries and associated charging conditions or other operating conditions are evaluated by a computational model that classifies or characterizes the battery and associated conditions. Such battery model may classify batteries according to any of many different considerations ; ¶ 0038 : Systems and apparatus may be designed and/or configured to carry out analysis of battery physical phenomena, and characterize batteries ; ¶ 0041 : Examples of electronic devices include…battery powered vehicles ; ¶ 0079 : Aspects of this disclosure pertain to models or similar tools for characterizing or classifying a battery and/or a battery's characteristics (e.g., charge characteristics) at various points in the battery's life, some or all of which may be when the battery is in service, as for example when the battery is installed in an electronic device such as… an automobile ; ¶ 0086 : the battery is evaluated in situ using a model. That is, the battery need not be removed from its electronic device in order to be classified by the model. Collection of battery parameter values and application to a battery model may be conducted in a way that is unobtrusive, e.g., during charging and in a way that does not significantly slow or modify the charging ), the method comprising: storing, in a memory of a server ( ¶ 0104 : logic for executing the battery model resides at a remote location (e.g., a cloud or a server under the control the battery manufacturer, the device manufacturer, or the battery charging/monitoring system provider) ; ¶ 0148 : The apparatus that operates the battery control logic may be the same apparatus used to collect the battery data or may be a distinct apparatus such as a mobile device, a server, or a distributed collection of remote processing devices. In some implementations, a cloud-based application is used to store and operate the battery control logic. In certain embodiments, the apparatus used to collect battery data is also used to adaptively charge a battery ; ¶ 0157 : control circuitry may operate on a remote server or a cloud-based application. In some cases, control circuitry may be coupled to monitoring circuitry and/or charging circuitry via wireless or wired communication. In some cases, control circuitry may be configured to store identified parameter values on a remote server ; a memory is implied), a baseline charging protocol ( ¶ 0027 : FIGS. 5a-b depict current and voltage waveforms resulting from charging a battery using a constant-current constant-voltage (CCCV) technique and an adapted CCCV technique which includes a plurality of constant charge pulses. Adapting a CCCV technique may involve modifying one or more characteristics of a constant-current portion of the charge process. An example of such modification includes changing the magnitude of the applied current, which may involve changing the current magnitude or duration in one or more steps if the constant-current portion is implemented as current steps. Another example may involve changing the criteria for transitioning from the constant-current to the constant-voltage portion of the charge process ; ¶ 0077 : FIG. 5a illustrates current and voltage of a battery as a function of time illustrating the conventional charging method known as constant-current, constant-voltage (CCCV). When charging a rechargeable battery (e.g., a lithium-ion type rechargeable battery) using CCCV, the charging sequence includes a constant-current (CC) charging mode until the terminal voltage of the battery/cell is at about a maximum amplitude (for example, about 4.2V to 4.5V for certain lithium-ion type rechargeable batteries) at which point the charging sequence changes from the constant-current charging mode to a constant-voltage (CV) charging mode. In the CV mode, a constant voltage is applied to the terminals of the battery. Generally when charging a rechargeable battery using the CCCV technique, the charging circuitry changes from the CC charging mode to the CV charging mode when the state of charge (SOC) of the battery is at, e.g., about 60-80%, although in some embodiments, as described herein, a charging circuitry does not enter a CV charging mode until the battery charge is greater than about 90% SOC or greater than about 95% SOC. Adaptive charging may be employed to adjust the CC and/or CV portions of a CCCV charging process. Adaptive charging may also be employed to adjust the transition from the CC to the CV portion of the process ; ¶ 0080 : A simple pictorial example of a two-dimensional battery model is represented in FIG. 5c. As shown, the model classifies charge process parameters for a particular battery—in this particular case, values of state of charge and charge current. Using these charge parameters, the model classifies the charging process according to a particular state: safe, safe and slow, potentially unsafe, and known unsafe ; ¶ 0085 : multiple batteries of the same type (e.g., the same size, format, chemistry, manufacture, and batch) may use the same model, at least initially ; ¶ 0087 : battery charge process characteristics provided by a model may inform decisions about future performance of battery and/or actions to be taken to address potential safety hazards, performance degradation, charging procedures, etc. For example, a particular model for a particular battery may indicate that the battery can or should be charged with a current of greater than 0.5 A when the battery's state of charge is 40% at a given temperature. The same model may indicate that the battery can be comfortably and appropriately charged with a current of about 0.6 A when the battery's state of charge is 80% at the same temperature. Still further, the model may indicate that the battery definitely should not be charged with a current of greater than 0.5 A when the battery's state of charge is 90% also at the same temperature. In certain embodiments, the battery charge characteristics are used to adapt charge process parameters (e.g., as with adaptive charging described herein) ; it is implied that an initial model would specify a “baseline charging protocol” comprising, e.g., a CCCV charging process); determining, with a processor of the server (¶ 0104, 0148, 0157: see above; ¶ 0047), a plurality of alternative charging protocols by modifying the baseline charging protocol (¶ 0077, 0087: see above); transmitting, with a transceiver of the server (¶ 0157: control circuitry may operate on a remote server or a cloud-based application. In some cases, control circuitry may be coupled to monitoring circuitry and/or charging circuitry via wireless or wired communication ), to each respective electric vehicle from a subset of the plurality of electric vehicles ( ¶ 0007 : the battery model accurately provides the predicted effect only for the battery or for a group of batteries of the same battery type ; ¶ 0036 : the battery model is applicable to a single battery or a group of batteries sharing similar characteristics such as the same battery chemistry, the same manufacturer, the same batch ; ¶ 0099 : To develop a battery model, a group of batteries such as those of a particular battery type provided by a particular vendor is analyzed for such factors as the state of health of the battery under various conditions ), a respective alternative charging protocol from the plurality of alternative charging protocols, the subset of the plurality of electric vehicles being configured to charge batteries thereof according to the plurality of alternative charging protocols ( ¶ 0044 : A charge process may be conducted under the control of charging circuitry which may be part of the battery charging system or the battery control logic. In certain embodiments, charging circuitry adapts, adjusts and/or controls the amplitude, pulse width, duty cycle, or other parameter of charging or discharging current pulses and/or it adjusts and/or controls the conditions of a constant voltage portion of the charge process. It may perform any such function to control or adjust a feature of the battery such as the battery's overall charging rate, cycle life, etc. It may perform these functions to control or adjust a more specific characteristic of the battery such as the battery's relaxation time, characteristics of the decay of the terminal voltage (e.g., the rate of decay or the shape of the decay curve), propensity to plate metallic lithium, etc. For example, as depicted in FIG. 2, charging circuitry may adapt, adjust and/or control the amplitude and pulse width of the discharge pulse to reduce or minimize the “overshoot” or “undershoot” of the decay of the terminal voltage of the battery ; ¶ 0066 : Adaptive charging as described herein refers to charging processes that make use of feedback related to battery conditions, environmental conditions, user behavior, user preferences, battery diagnostic information, physical properties of the battery and the like. Using adaptive charging techniques, one or more characteristics of a charge signal may be continuously or periodically adjusted or controlled while charging a battery. Such adjustment or control may be performed to maintain battery parameter values within a selected range. Generally, adaptive charging is used to optimize the battery's cycle life, optimize its charge speed, minimize its swelling, and/or keep it operating within safe boundaries. For example, adaptive charging may keep a battery charge process operating in a regime where minimal or no battery degradation, such as from metal plating (e.g., metallic lithium plating), occurs or is likely to occur. Various types of battery parameter values may be captured and used for adaptive charging ); receiving, with the transceiver, battery data from the subset of the plurality of electric vehicles, the battery data having been measured during or after charging the batteries of the subset of the plurality of electric vehicles according to the plurality of alternative charging protocols ( ¶ 0066 : Some of these parameter values are obtained by measuring parameters directly associated with the battery, charging system, and/or the device powered by the battery. For example, parameter values may be obtained from battery terminal voltage measurements, battery temperature, battery size, and the like. In some cases, adaptive charging also makes use of parameters not directly associated with a battery. Examples include user information and environmental information. Examples of indirectly obtained battery parameters that may be used for adaptive charging include charge pulse voltage, partial relaxation time, the state of charge, and the battery's state of health. In some cases, ranges of acceptable values of each of these parameters may vary as a function of the state of charge during the charge portion of a battery cycle. The parameters values may also vary from cycle-to-cycle over the battery's life. In some cases, adaptive charging may also make use of the current charging parameters such as the current or voltage that is being applied to a battery by charging circuitry ; ¶ 0105 : battery control logic may monitor or otherwise determine parameters associated with a charging process. Some of these may be used as battery charging process parameters, e.g., inputs to a battery model. These parameters may be provided directly or indirectly from the measurement circuitry. See e.g., FIG. 1. Raw battery measurements taken by the measurement circuitry include but are not limited to temperature, voltage, current, charge passed, and time from or between events. In some embodiments, raw battery measurements are passed to the control circuitry, which may employ logic to determine parameters such as SOC, SOH, overpotential, and PRT. Depending on the design of the battery model, any raw or derived measurements and parameters may be used by battery models. As an example, measurements such as a battery's SOC are made directly by measurement circuitry and passed to a battery model. Raw and/or derived parameters may be stored locally or on a remote device and provided, at an appropriate time, to the battery model for analysis and ultimately to provide a battery charge process classification. The analyzed present and/or historical data and/or battery charge process classification may be fed back into the battery control logic to allow for, e.g., adaptive charging, safety alerts, etc ); and updating, with the processor, the baseline charging protocol based on the battery data (¶ 0079 : models are developed or refined using information about a battery collected while the battery is in service. Regardless of how a model is generated and/or updated, it may classify a battery in a way that identifies different charge process regimes or characteristics of the battery ; ¶ 0097 : the boundary determination is made (and periodically updated) using an optimization scheme that takes, e.g., various EIS measurements as inputs and determines how they relate to a defined specification (e.g., charge time, cycle life, health and aging progression, etc.). The boundary determination or update may iterate this process using present and historical data and inputs, if necessary, to adjust the model, which allows for updating an optimal charge process configuration ). Regarding claim 2 , GHANTOUS discloses the baseline charging protocol and the plurality of alternative charging protocols include charging according to a respective current-voltage profile that sets a charging current depending on a battery voltage (¶ 0027, 0077-0078). Regarding claim 3 , GHANTOUS discloses the baseline charging protocol and the plurality of alternative charging protocols include selecting the respective current-voltage profile from a plurality of baseline current-voltage profiles depending on a current battery temperature, each of plurality of baseline current-voltage profiles being associated with a different respective battery temperature (¶ 0042, 0047, 0056, 0059, 0066, 0080, 0087-0088). Regarding claim 4 , GHANTOUS discloses determining the plurality of alternative charging protocols further comprising: modifying the current-voltage profile of the baseline charging protocol (¶ 0027, 0077-0078). Regarding claim 7 , GHANTOUS discloses the subset of the plurality of electric vehicles are configured to charge the batteries thereof according to the plurality of alternative charging protocols for a predetermined period of time; and the battery data is measured during or after predetermined period of time (¶ 0066, 0097). Regarding claim 9 , GHANTOUS discloses the battery data includes at least one of (i) battery current measurements, (ii) battery voltage measurements, (iii) battery temperature measurements, and (iv) battery state of charge measurements (¶ 0066, 0105). Regarding claim 10 , GHANTOUS discloses updating the baseline charging protocol further comprising: determining, based on the battery data, a modification to the baseline charging protocol that will result in at least one of (i) a slower battery aging and (ii) a faster charging time, compared to the baseline charging protocol; and updating the baseline charging protocol according the determined modification (¶ 0047, 0066, 0080, 0097). Regarding claim 11 , GHANTOUS discloses determining the modification to the baseline charging protocol further comprising: identifying, based on the battery data, an alternative charging protocol from the plurality of alternative charging protocols that resulted in at least one of (i) a slower battery aging and (ii) a faster charging time, compared to the baseline charging protocol; and determining the modification to the baseline charging protocol based on the identified alternative charging protocol (¶ 0047, 0066, 0080, 0097). Regarding claim 12 , GHANTOUS discloses updating the baseline charging protocol further comprising: determining, based on the battery data, updated internal state limits for a battery model; and updating the baseline charging protocol by applying the battery model with the updated internal state limits (¶ 0079-0087, 0097). Regarding claim 14 , GHANTOUS discloses transmitting, with the transceiver, the updated baseline charging protocol to the plurality of electric vehicles, the plurality of electric vehicles being configured to charge the batteries thereof according to the updated baseline charging protocol (¶ 0044, 0066, 0157). Regarding claim 15 , GHANTOUS discloses selecting, with the processor, the subset of the plurality of electric vehicles from the plurality of electric vehicles (¶ 0007, 0036, 0099). Regarding claim 19 , GHANTOUS discloses the method is iterated periodically to further update the baseline charging protocol, each iteration of the method including selecting a new subset of the plurality of electric vehicles from the plurality of electric vehicles and determining a new plurality of alternative charging protocols (¶ 0079, 0085, 0097). Regarding claim 20 , GHANTOUS discloses a method for adapting a charging protocol for charging a battery of an electric vehicle ( ¶ 0036 : batteries and associated charging conditions or other operating conditions are evaluated by a computational model that classifies or characterizes the battery and associated conditions. Such battery model may classify batteries according to any of many different considerations ; ¶ 0038 : Systems and apparatus may be designed and/or configured to carry out analysis of battery physical phenomena, and characterize batteries ; ¶ 0041 : Examples of electronic devices include…battery powered vehicles ; ¶ 0079 : Aspects of this disclosure pertain to models or similar tools for characterizing or classifying a battery and/or a battery's characteristics (e.g., charge characteristics) at various points in the battery's life, some or all of which may be when the battery is in service, as for example when the battery is installed in an electronic device such as… an automobile ; ¶ 0086 : the battery is evaluated in situ using a model. That is, the battery need not be removed from its electronic device in order to be classified by the model. Collection of battery parameter values and application to a battery model may be conducted in a way that is unobtrusive, e.g., during charging and in a way that does not significantly slow or modify the charging ), the method comprising: receiving, with a transceiver (¶ 0157: control circuitry may operate on a remote server or a cloud-based application. In some cases, control circuitry may be coupled to monitoring circuitry and/or charging circuitry via wireless or wired communication ) of a battery management system (comprising at least charging circuitry 112 and monitoring circuitry 114 as shown in Fig. 1; ¶ 0012-0013 : Another aspect of the disclosure pertains to battery charging systems for classifying and adjusting use of a battery. Such systems may be characterized by the following elements: charging and/or monitoring circuitry designed or configured to apply a charge signal to the battery, and measure a voltage at the terminals of the battery; and control circuitry, coupled to the charging and/or monitoring circuitry, and designed or configured to cause the system to: (a) obtain values of one or more charge process parameters currently applied to or to be applied to the battery; (b) provide the current values of the one or more charge process parameters to a battery model designed or configured to characterize the battery and the one or more charge process parameters; (c) receive from the battery model, a battery charge process characteristic that provides a predicted effect of charging the battery under conditions indicated by the one or more process parameters currently applied to or to be applied to the battery, and (d) based on the battery charge process characteristic received from the model, (i) adjust a charge process used to charge the battery ; ¶ 0023 : FIG. 1 illustrates, in block diagram form a “battery charging system” or a “battery monitoring system” in conjunction with a battery where charging circuitry 112 (including, e.g., a voltage source and/or current source) responds to control circuitry 116 which receives battery information from monitoring circuitry 114 (including, e.g., a voltmeter and/or a current meter) ), an alternative charging protocol ( ¶ 0044 : A charge process may be conducted under the control of charging circuitry which may be part of the battery charging system or the battery control logic. In certain embodiments, charging circuitry adapts, adjusts and/or controls the amplitude, pulse width, duty cycle, or other parameter of charging or discharging current pulses and/or it adjusts and/or controls the conditions of a constant voltage portion of the charge process. It may perform any such function to control or adjust a feature of the battery such as the battery's overall charging rate, cycle life, etc. It may perform these functions to control or adjust a more specific characteristic of the battery such as the battery's relaxation time, characteristics of the decay of the terminal voltage (e.g., the rate of decay or the shape of the decay curve), propensity to plate metallic lithium, etc. For example, as depicted in FIG. 2, charging circuitry may adapt, adjust and/or control the amplitude and pulse width of the discharge pulse to reduce or minimize the “overshoot” or “undershoot” of the decay of the terminal voltage of the battery ; ¶ 0066 : Adaptive charging as described herein refers to charging processes that make use of feedback related to battery conditions, environmental conditions, user behavior, user preferences, battery diagnostic information, physical properties of the battery and the like. Using adaptive charging techniques, one or more characteristics of a charge signal may be continuously or periodically adjusted or controlled while charging a battery. Such adjustment or control may be performed to maintain battery parameter values within a selected range. Generally, adaptive charging is used to optimize the battery's cycle life, optimize its charge speed, minimize its swelling, and/or keep it operating within safe boundaries. For example, adaptive charging may keep a battery charge process operating in a regime where minimal or no battery degradation, such as from metal plating (e.g., metallic lithium plating), occurs or is likely to occur. Various types of battery parameter values may be captured and used for adaptive charging ) from a server ( ¶ 0104 : logic for executing the battery model resides at a remote location (e.g., a cloud or a server under the control the battery manufacturer, the device manufacturer, or the battery charging/monitoring system provider) ; ¶ 0148 : The apparatus that operates the battery control logic may be the same apparatus used to collect the battery data or may be a distinct apparatus such as a mobile device, a server, or a distributed collection of remote processing devices. In some implementations, a cloud-based application is used to store and operate the battery control logic. In certain embodiments, the apparatus used to collect battery data is also used to adaptively charge a battery ; ¶ 0157 : control circuitry may operate on a remote server or a cloud-based application. In some cases, control circuitry may be coupled to monitoring circuitry and/or charging circuitry via wireless or wired communication. In some cases, control circuitry may be configured to store identified parameter values on a remote server ); controlling, with a processor of the battery management system (¶ 0104: logic for executing the battery model resides entirely on the device or the device charger (e.g., with a battery pack or a processor used by the device for other purposes) ; ¶ 0159), a charging of the battery according to the alternative charging protocol (¶ 0012-0013, 0023: see above); measuring, with at least one sensor of the battery management system (¶ 0149: measurement circuitry 114 coupled to the battery, to measure voltage, current, and/or other battery parameter values that may be used for adaptive charging ; ¶ 0154), battery data during or after the charging of the battery according to the alternative charging protocol ( ¶ 0066 : Some of these parameter values are obtained by measuring parameters directly associated with the battery, charging system, and/or the device powered by the battery. For example, parameter values may be obtained from battery terminal voltage measurements, battery temperature, battery size, and the like. In some cases, adaptive charging also makes use of parameters not directly associated with a battery. Examples include user information and environmental information. Examples of indirectly obtained battery parameters that may be used for adaptive charging include charge pulse voltage, partial relaxation time, the state of charge, and the battery's state of health. In some cases, ranges of acceptable values of each of these parameters may vary as a function of the state of charge during the charge portion of a battery cycle. The parameters values may also vary from cycle-to-cycle over the battery's life. In some cases, adaptive charging may also make use of the current charging parameters such as the current or voltage that is being applied to a battery by charging circuitry ; ¶ 0105 : battery control logic may monitor or otherwise determine parameters associated with a charging process. Some of these may be used as battery charging process parameters, e.g., inputs to a battery model. These parameters may be provided directly or indirectly from the measurement circuitry. See e.g., FIG. 1. Raw battery measurements taken by the measurement circuitry include but are not limited to temperature, voltage, current, charge passed, and time from or between events. In some embodiments, raw battery measurements are passed to the control circuitry, which may employ logic to determine parameters such as SOC, SOH, overpotential, and PRT. Depending on the design of the battery model, any raw or derived measurements and parameters may be used by battery models. As an example, measurements such as a battery's SOC are made directly by measurement circuitry and passed to a battery model. Raw and/or derived parameters may be stored locally or on a remote device and provided, at an appropriate time, to the battery model for analysis and ultimately to provide a battery charge process classification. The analyzed present and/or historical data and/or battery charge process classification may be fed back into the battery control logic to allow for, e.g., adaptive charging, safety alerts, etc ); transmitting, with the transceiver, the battery data to the server (¶ 0066, 0104-0105, 0148, 0157: see above); and receiving, with the transceiver, an updated baseline charging protocol (¶ 0079 : models are developed or refined using information about a battery collected while the battery is in service. Regardless of how a model is generated and/or updated, it may classify a battery in a way that identifies different charge process regimes or characteristics of the battery ; ¶ 0097 : the boundary determination is made (and periodically updated) using an optimization scheme that takes, e.g., various EIS measurements as inputs and determines how they relate to a defined specification (e.g., charge time, cycle life, health and aging progression, etc.). The boundary determination or update may iterate this process using present and historical data and inputs, if necessary, to adjust the model, which allows for updating an optimal charge process configuration ), the updated baseline charging protocol having been determined based on the battery data and further battery data from further electric vehicles (¶ 0036, 0038, 0041, 0079, 0086: see above) . 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. 07-22-aia AIA Claim (s) 5-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over GHANTOUS as applied to claim s 1-4, 7, 9-12, 14-15, and 19-20 above, and further in view of KISHIYAMA (US Pub. No. 2013/0307475) . Regarding claim 5 , GHANTOUS discloses the method as applied to claim 4, and further discloses the modifying the current-voltage profile further comprising: applying a battery model to determine a modified current-voltage profile (¶ 0077, 0087). GHANTOUS fails to disclose determining a modified current-voltage profile that will result in charging time that is within a predetermined threshold difference from a charging time that results from the current-voltage profile of the baseline charging protocol. KISHIYAMA discloses determining a modified current-voltage profile that will result in charging time that is within a predetermined threshold difference from a charging time that results from the current-voltage profile of the baseline charging protocol (¶ 0006, 0022, 0029). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the charging time within a predetermined threshold difference of KISHIYAMA into the method of GHANTOUS to produce an expected result of a method including a charging time that is within a predetermined threshold difference. The modification would be obvious because one of ordinary skill in the art would be motivated to provide a slower charging option that may be better for the battery while still finishing charge within a desired time (KISHIYAMA, ¶ 0003). Regarding claim 6 , GHANTOUS discloses applying the battery model further comprising: modifying at least one internal state limit in the battery model (¶ 0079-0087, 0097) . 07-22-aia AIA Claim (s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over GHANTOUS as applied to claim s 1-4, 7, 9-12, 14-15, and 19-20 above, and further in view of HORTOP (US Pub. No. 2019/0039467) . Regarding claim 8 , GHANTOUS discloses the method as applied to claim 7, but fails to disclose the predetermined period of time is at least one week. HORTOP discloses the predetermined period of time is at least one week (¶ 0004). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the predetermined period of time is at least one week as disclosed in HORTOP into the method of GHANTOUS to produce an expected result of a method including a predetermined period of time is at least one week. The modification would be obvious because one of ordinary skill in the art would be motivated to generate preferred charging schemes that can be applied to a particular battery at given times so as to provide enhanced battery life (HORTOP, ¶ 0004) . 07-22-aia AIA Claim (s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over GHANTOUS as applied to claim s 1-4, 7, 9-12, 14-15, and 19-20 above, and further in view of YAZAMI (US Pub. No. 2021/0167620) . Regarding claim 13 , GHANTOUS discloses the method as applied to claim 1, but fails to disclose updating the baseline charging protocol further comprising: determining a gradient based on the battery data; and updating the baseline charging protocol based on the gradient. YAZAMI discloses updating the baseline charging protocol further comprising: determining a gradient based on the battery data; and updating the baseline charging protocol based on the gradient (¶ 0018-0020, 0105, 0122-0127, 0199-0206, 0219, claims 1, 4, and 13). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate updating the baseline charging protocol based on the gradient as disclosed in YAZAMI into the method of GHANTOUS to produce an expected result of a method comprising updating the baseline charging protocol based on a gradient. The modification would be obvious because one of ordinary skill in the art would be motivated to propose a new charging protocol which allows fast charging for batteries with improved performances (YAZAMI, ¶ 0004) . 07-22-aia AIA Claim (s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over GHANTOUS as applied to claim s 1-4, 7, 9-12, 14-15, and 19-20 above, and further in view of ADAGOUDA (US Pub. No. 2016/0259014) . Regarding claim 16 , GHANTOUS discloses the method as applied to claim 15, but fails to disclose selecting the subset of the plurality of electric vehicles further comprising: selecting the subset of the plurality of electric vehicles as a random subset of the plurality of electric vehicles. ADAGOUDA discloses selecting the subset of the plurality of [batteries] as a random subset of the plurality of [batteries] (¶ 0211-0212). It would be obvious to apply the random selection of ADAGOUDA to the electric vehicles in the method of GHANTOUS. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate selecting a random subset of the plurality of electric vehicles into the method of GHANTOUS to produce an expected result of a method including selecting a random subset of a plurality of electric vehicles. The modification would be obvious because one of ordinary skill in the art would be motivated to provide highly representative samples . 07-22-aia AIA Claim (s) 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over GHANTOUS as applied to claim s 1-4, 7, 9-12, 14-15, and 19-20 above, and further in view of ARYA (WO 2020/208653 A1) . Regarding claim 17 , GHANTOUS discloses the method as applied to claim 15, but fails to disclose selecting the subset of the plurality of electric vehicles further comprising: selecting the subset of the plurality of electric vehicles depending on at least one of (i) environmental conditions, (ii) driving styles, and (iii) charging behaviors associated with the plurality of electric vehicles. ARYA discloses selecting the subset of the plurality of electric vehicles depending on driving styles (¶ 0017-0021). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate selecting the subset of vehicles based on driving styles as disclosed in ARYA into the method of GHANTOUS to produce an expected result of a method including selecting the subset of vehicles based on driving styles. The modification would be obvious because one of ordinary skill in the art would be motivated to reduce charge wait time and/or incentivize desirable battery usage patterns (ARYA, ¶ 0086-0087). Regarding claim 18 , GHANTOUS discloses the plurality of electric vehicles are configured to measure data including at least one of (i) environmental condition data, (ii) driving style data, and (iii) charging behavior data, the method further comprising: receiving, with the transceiver, the data from the plurality of electric vehicles (¶ 0066). Conclusion 07-96 The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANUEL HERNANDEZ whose telephone number is (571)270-7916. The examiner can normally be reached Monday-Friday 9a-5p ET. 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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. /Manuel Hernandez/Examiner, Art Unit 2859 3/29/2026 Application/Control Number: 18/338,427 Page 2 Art Unit: 2859 Application/Control Number: 18/338,427 Page 3 Art Unit: 2859 Application/Control Number: 18/338,427 Page 4 Art Unit: 2859 Application/Control Number: 18/338,427 Page 5 Art Unit: 2859 Application/Control Number: 18/338,427 Page 6 Art Unit: 2859 Application/Control Number: 18/338,427 Page 7 Art Unit: 2859 Application/Control Number: 18/338,427 Page 8 Art Unit: 2859 Application/Control Number: 18/338,427 Page 9 Art Unit: 2859 Application/Control Number: 18/338,427 Page 10 Art Unit: 2859 Application/Control Number: 18/338,427 Page 11 Art Unit: 2859 Application/Control Number: 18/338,427 Page 12 Art Unit: 2859 Application/Control Number: 18/338,427 Page 13 Art Unit: 2859 Application/Control Number: 18/338,427 Page 14 Art Unit: 2859 Application/Control Number: 18/338,427 Page 15 Art Unit: 2859 Application/Control Number: 18/338,427 Page 16 Art Unit: 2859 Application/Control Number: 18/338,427 Page 17 Art Unit: 2859 Application/Control Number: 18/338,427 Page 18 Art Unit: 2859 Application/Control Number: 18/338,427 Page 19 Art Unit: 2859 Application/Control Number: 18/338,427 Page 20 Art Unit: 2859 Application/Control Number: 18/338,427 Page 21 Art Unit: 2859 Application/Control Number: 18/338,427 Page 22 Art Unit: 2859
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