DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This final action is in response to Applicant’s filing dated October 13, 2025. Claims 1-20 are currently pending and have been considered, as provided in more detail below.
*Examiner Note: Claim language is bolded. Cited References and Applicant’s arguments are italicized. Examiner interpretations are preceded with an asterisk *.
Response to Arguments
Applicant’s arguments filed 10/13/2025 have been fully considered but are considered moot because the arguments are directed toward subject matter that has necessitated a new ground of rejection that does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Response to Amendment
Regarding the rejections under 35 USC 101, Applicant has amended the claims to overcome the rejections. Therefore, the rejections under 35 USC 101 have been withdrawn.
Regarding the rejections under 35 USC 103, amendments made to the claims have necessitated a new ground of rejection as outlined below.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1-3, 6-10, 13-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Abbaraju (US 2024/0391352A1) in view of Yonemoto (US 2018/0358663A1) and further in view of Vidhi (US 2020/0094691A1).
Regarding claim 1, Abbaraju discloses a memory (Fig. 2, 218 and see at least para. [0048] of
Abbaraju which discloses “The computer-readable media 218 may include volatile and nonvolatile memory and/or removable and non-removable media implemented in any type of technology for storage of information” and see at least para. [0049] of Abbaraju which discloses “The computer-readable media 218”) configured to store computer-executable components (see at least para. [0049] of Abbaraju which discloses “The computer-readable media 218 may be used to store any number of functional components that are executable by the processor(s) 216”); and a processor (Fig. 2, 216 and see at least para. [0045] of Abbaraju which discloses “the processors 216 may include one or more ECUs (electronic control units) or any of various other types of computing devices. “ECU” is a generic term for any embedded processing system that controls one or more of the systems, subsystems, or components in a vehicle”) that executes at least one of the computer-executable components (see at least para. [0058] of Abbaraju which discloses “any number of functional components that are executable by the processors 240” and see at least para. [0049] of Abbaraju which discloses “functional components that are executable by the processor(s) 216. In many implementations, these functional components comprise instructions or programs that are executable by the processor(s) 216 and that, when executed, specifically program the processor(s) 216 to perform the actions attributed herein to the vehicle computing device 104”), determines, via execution of a first machine learning model (Fig. 1, 126(1) and see at least para. [0002] of Abbaraju which discloses “a first machine learning model configured for predicting the state of health of the battery. The vehicle may determine an estimated battery state of health based at least on the first machine learning model, and may receive control information while traversing the route based on the estimated battery state of health to at least partially minimize battery degradation during traversal of the route”), that the battery is predicted to experience expedited (see at least para. [0064] of Abbaraju which discloses “the EOL for impedance is when the impedance has increased to 200 percent over the original battery impedance 310 measured when the battery was new”, *This increased impedance is linked to expedited degradation since battery degradation causes impedance to increase) degradation (see at least para. [0020] of Abbaraju which discloses “the predicted degradation of the battery SOH of the particular battery, such as based on the selected route(s)”and see at least para. [0042] of Abbaraju which discloses “information regarding battery discharge and possible degradation of the battery may be sent, via the RSUs 109 along the route, to the service computing device 108. This information may include vehicle travel speed, sudden accelerations, stops, severe weather, and any other events that may cause battery degradation”) due to a pattern of battery usage (Abbaraju discloses predicting degradation of battery SOH for a particular battery based on the selected route and associated operating conditions. Abbaraju further discloses that information such as travel speed, sudden accelerations, stops, severe weather, and other events that may cause battery degradation is collected and used in the SOH management system, which reflects degradation associated with usage and operating patterns); determines, via execution of a second machine learning model (Fig. 1, 126 (2) and see at least para. [0031] of Abbaraju which discloses “machine learning models (MLMs) 126” and see at least claim 3 of Abbaraju which discloses “second machine learning models from a plurality of charging stations, respectively, each second machine learning model having been trained based on data associated with the respective charging station”).
Abbaraju may not explicitly disclose a charging history of a battery of a vehicle.
However, in the same field of endeavor, Yonemoto discloses a charging history (see at least
para. [0029] of Yonemoto which discloses “The history storage unit 104 stores time information relating to an arbitrary timing or an arbitrary period from the past to the present, past battery state parameters”) of a battery (Fig. 1, 1 and see at least para. [0027] of Yonemoto which discloses “a battery system 1”) of a vehicle (see at least para. [0027] of Yonemoto which discloses a “vehicle”).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the system of Abbaraju to include a component that accesses a charging history; as taught in Yonemoto with a reasonable expectation of success in order to improve th accuracy and robustness of the degradation information and resulting control decisions to more effectively mange the EV battery health over time. See para. [0027] and [0029] of Yonemoto for motivation.
Abbaraju, as modified by Yonemoto, may not explicitly disclose a discharge routine for
discharging the battery to a power source that counteracts the predicted expedited degradation, wherein the power source comprises at least one of a power grid or a vehicular charging station; and controls the battery to discharge to the power source according to the discharge routine.
However, in the same field of endeavor, Vidhi discloses a discharge routine for discharging
the battery to a power source (see at least para. [0035] of Vidhi which discloses “power discharged from the battery 60 that is supplied to the power grid 58 and provides data characterizing the supplied power to the charge control application”) that counteracts the predicted expedited degradation (see at least para. [0015] of Vidhi which discloses “the V2G interface and the charging server can be configured/programmed to curtail (reduce/limit) battery degradation of the battery in the EV while the EV is parked in the long-term parking area”, *curtailing the battery degradation is counteracting the predicted expedited degradation), wherein the power source comprises at least one of a power grid (Fig. 1, 58 and see at least para. [0023] of Vidhi which discloses “the power grid 58 and discharge the battery 60 of the EV 52 and supply the discharged power to the power grid 58. That is, the V2G interface 54 can provide an interface for V2G services”) or a vehicular charging station (Fig. 1, 54 and see at least para. [0003] of Vidhi which describes a “charging station, also called an electric recharging point, charging point, charge point, ECS (Electronic Charging Station) and EVSE (electric vehicle supply equipment), is an element in an infrastructure that supplies electric power for the recharging of electric vehicles” and see at least para. [0023] of Vidhi which describes “The V2G interface 54 can be representative of a charging station that provides a V2G system that can charge the battery 60 of the EV 52 with power from the power grid 58 and discharge the battery 60 of the EV 52 and supply the discharged power to the power grid 58”); and controls the battery to discharge to the power source according to the discharge routine (see at least para. [0057] of Vidhi which discloses “the V2G interface 102 measures an amount of power discharged from the battery of the EV 104 that is supplied to the power grid and provides data characterizing the supplied power to the charge control application 120. In response, the plan module 134 can query a utility server via the network 112 for a present (e.g., near real-time) credit value (e.g., per kilowatt hour) for power supplied to the power grid”).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the system of Abbaraju, as modified by Yonemoto, to include a discharge routine for discharging the battery to a power source that counteracts the predicted expedited degradation, wherein the power source comprises at least one of a power grid or a vehicular charging station; and controls the battery to discharge to the power source according to the discharge routine; as taught in Vidhi with a reasonable expectation of success in order to facilitate increased accuracy of the degradation information and to more effectively manage degradation both during driving and during parking using known V2G infrastructure. See para. [0015] and [0057] of Vidhi for motivation.
Regarding claim 2, Abbaraju, as modified by Yonemoto and Vidhi discloses wherein the at
least one of the computer- executable components further determines, via execution of the second machine learning model (Fig. 1, 126 (2) and see at least para. [0031] of Abbaraju which discloses “machine learning models (MLMs) 126” and see at least claim 3 of Abbaraju which discloses “second machine learning models from a plurality of charging stations, respectively, each second machine learning model having been trained based on data associated with the respective charging station”) on the charging history and on a driving history (see at least para. [0064] of Abbaraju which discloses “estimating the SOH (as opposed to measuring the SOH) may be complicated as multiple factors such as ambient environment, charge and discharge cycles, driving patterns, and the like, may contribute differently towards aging effects on the battery”) of the vehicle, the discharge routine that counteracts the predicted expedited degradation (see at least para. [0015] of Vidhi which discloses “the V2G interface and the charging server can be configured/programmed to curtail (reduce/limit) battery degradation of the battery in the EV while the EV is parked in the long-term parking area”, *curtailing the battery degradation is counteracting the predicted expedited degradation).
Regarding claim 3, Abbaraju, as modified by Yonemoto and Vidhi discloses wherein the
discharge routine (see at least para. [0035] of Vidhi which discloses “power discharged from the battery 60 that is supplied to the power grid 58 and provides data characterizing the supplied power to the charge control application”) comprises at least one of a recommended time or a recommended date (see at least para. [0029] of Vidhi which discloses “the “expected return time” denotes a date and time that the operator is expected to return to the parking spot 56 of the parking area, detach the EV 52 from the V2G interface 54 and drive away”) to be suitable for periodic discharging of the battery (see at least para. [0023] of Vidhi which discloses “discharging of the battery 60”), and wherein the discharge routine further comprises a recommended discharge amount to be periodically discharged at the at least one of the recommended time or the recommended date (see at least para. [0029] and [0050] of Vidhi which discloses “a time and date that the operator is expecting to return to the EV 104. Alternatively, the charge control application 120 can receive information corresponding to the expected return time 124”).
Regarding claim 6, Abbaraju, as modified by Yonemoto and Vidhi discloses wherein the
vehicle is docked at the vehicular charging station, and wherein the at least one of the computer-executable components further comprise: controls the battery to discharge the recommended discharge amount to the vehicular charging station at the at least one of the recommended time or the recommended date (see at least para. [0023] of Vidhi which discloses “The V2G interface 54 can be representative of a charging station that provides a V2G system that can charge the battery 60 of the EV 52 with power from the power grid 58 and discharge the battery 60 of the EV 52 and supply the discharged power to the power grid 58. That is, the V2G interface 54 can provide an interface for V2G services. The V2G interface 54 can include a computing device (e.g., a processor and memory or a controller) to control the charging and discharging of the battery 60 of the EV 52”).
Regarding claim 7, Abbaraju, as modified by Yonemoto and Vidhi discloses wherein the
vehicle is equipped with autonomous driving controls, and wherein the at least one of the computer-executable components further: controls, via the autonomous driving controls (see at least para. [0074] of Abbaraju which discloses “the vehicle is autonomous or semi-autonomous, the vehicle control program may receive the vehicle control information, and may control the speed of the vehicle 102”), the vehicle to travel to and dock at the vehicular charging station prior to the at least one of the recommended time or the recommended date (see at least para. [0029] and [0050] of Vidhi which discloses “a time and date that the operator is expecting to return to the EV 104. Alternatively, the charge control application 120 can receive information corresponding to the expected return time 124”).
Regarding claim 8, Abbaraju discloses accessing, by a device (see at least para. [0045] of
Abbaraju which discloses “device 104 may include one or more processors 216, one or more computer-readable media 218, one or more communication interfaces” and see at least para. [0049] of Abbaraju which discloses “The computer-readable media 218 may be used to store any number of functional components that are executable by the processor(s) 216”) operatively coupled to a processor (Fig. 2, 216 and see at least para. [0045] of Abbaraju which discloses “the processors 216 may include one or more ECUs (electronic control units) or any of various other types of computing devices. “ECU” is a generic term for any embedded processing system that controls one or more of the systems, subsystems, or components in a vehicle”), determining, by the device and via execution of a first machine learning model (Fig. 1, 126(1) and see at least para. [0002] of Abbaraju which discloses “a first machine learning model configured for predicting the state of health of the battery. The vehicle may determine an estimated battery state of health based at least on the first machine learning model, and may receive control information while traversing the route based on the estimated battery state of health to at least partially minimize battery degradation during traversal of the route”) that the battery is predicted to experience expedited (see at least para. [0064] of Abbaraju which discloses “the EOL for impedance is when the impedance has increased to 200 percent over the original battery impedance 310 measured when the battery was new”, *This increased impedance is linked to expedited degradation since battery degradation causes impedance to increase) degradation (see at least para. [0020] of Abbaraju which discloses “the predicted degradation of the battery SOH of the particular battery, such as based on the selected route(s)”and see at least para. [0042] of Abbaraju which discloses “information regarding battery discharge and possible degradation of the battery may be sent, via the RSUs 109 along the route, to the service computing device 108. This information may include vehicle travel speed, sudden accelerations, stops, severe weather, and any other events that may cause battery degradation”) due to a pattern of battery usage (Abbaraju discloses predicting degradation of battery SOH for a particular battery based on the selected route and associated operating conditions. Abbaraju further discloses that information such as travel speed, sudden accelerations, stops, severe weather, and other events that may cause battery degradation is collected and used in the SOH management system, which reflects degradation associated with usage and operating patterns); determining, by the device, via execution of a second machine learning model (Fig. 1, 126 (2) and see at least para. [0031] of Abbaraju which discloses “machine learning models (MLMs) 126” and see at least claim 3 of Abbaraju which discloses “second machine learning models from a plurality of charging stations, respectively, each second machine learning model having been trained based on data associated with the respective charging station”).
Abbaraju may not explicitly disclose a charging history of a battery of a vehicle.
However, in the same field of endeavor, Yonemoto discloses a charging history (see at least
para. [0029] of Yonemoto which discloses “The history storage unit 104 stores time information relating to an arbitrary timing or an arbitrary period from the past to the present, past battery state parameters”) of a battery (Fig. 1, 1 and see at least para. [0027] of Yonemoto which discloses “a battery system 1”) of a vehicle (see at least para. [0027] of Yonemoto which discloses a “vehicle”).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the system of Abbaraju to include a component that accesses a charging history; as taught in Yonemoto with a reasonable expectation of success in order to improve the accuracy and robustness of the degradation information and resulting control decisions to more effectively mange the EV battery health over time. See para. [0027] and [0029] of Yonemoto for motivation.
Abbaraju, as modified by Yonemoto, may not explicitly disclose a discharge routine for
discharging the battery to a power source that counteracts the predicted expedited degradation, wherein the power source comprises at least one of a power grid or a vehicular charging station; and controlling, by the device, the battery to discharge to the power source according to the discharge routine.
However, in the same field of endeavor, Vidhi discloses a discharge routine for discharging
the battery to a power source (see at least para. [0035] of Vidhi which discloses “power discharged from the battery 60 that is supplied to the power grid 58 and provides data characterizing the supplied power to the charge control application”) that counteracts the predicted expedited degradation (see at least para. [0015] of Vidhi which discloses “the V2G interface and the charging server can be configured/programmed to curtail (reduce/limit) battery degradation of the battery in the EV while the EV is parked in the long-term parking area”, *curtailing the battery degradation is counteracting the predicted expedited degradation), wherein the power source comprises at least one of a power grid (Fig. 1, 58 and see at least para. [0023] of Vidhi which discloses “the power grid 58 and discharge the battery 60 of the EV 52 and supply the discharged power to the power grid 58. That is, the V2G interface 54 can provide an interface for V2G services”) or a vehicular charging station (Fig. 1, 54 and see at least para. [0003] of Vidhi which describes a “charging station, also called an electric recharging point, charging point, charge point, ECS (Electronic Charging Station) and EVSE (electric vehicle supply equipment), is an element in an infrastructure that supplies electric power for the recharging of electric vehicles” and see at least para. [0023] of Vidhi which describes “The V2G interface 54 can be representative of a charging station that provides a V2G system that can charge the battery 60 of the EV 52 with power from the power grid 58 and discharge the battery 60 of the EV 52 and supply the discharged power to the power grid 58”); and controlling, by the device, the battery to discharge to the power source according to the discharge routine (see at least para. [0057] of Vidhi which discloses “the V2G interface 102 measures an amount of power discharged from the battery of the EV 104 that is supplied to the power grid and provides data characterizing the supplied power to the charge control application 120. In response, the plan module 134 can query a utility server via the network 112 for a present (e.g., near real-time) credit value (e.g., per kilowatt hour) for power supplied to the power grid”).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the system of Abbaraju, as modified by Yonemoto, to include a discharge routine for discharging the battery to a power source that counteracts the predicted expedited degradation, wherein the power source comprises at least one of a power grid or a vehicular charging station; and controlling, by the device, the battery to discharge to the power source according to the discharge routine; as taught in Vidhi with a reasonable expectation of success in order to facilitate increased accuracy of the degradation information and to more effectively manage degradation both during driving and during parking using known V2G infrastructure. See para. [0015] and [0057] of Vidhi for motivation.
Regarding claim 9, Abbaraju, as modified by Yonemoto and Vidhi discloses wherein the
determining the discharge routine comprises determining by the device, via execution of the second machine learning model (Fig. 1, 126 (2) and see at least para. [0031] of Abbaraju which discloses “machine learning models (MLMs) 126” and see at least claim 3 of Abbaraju which discloses “second machine learning models from a plurality of charging stations, respectively, each second machine learning model having been trained based on data associated with the respective charging station”) on the charging history and on a driving history (see at least para. [0064] of Abbaraju which discloses “estimating the SOH (as opposed to measuring the SOH) may be complicated as multiple factors such as ambient environment, charge and discharge cycles, driving patterns, and the like, may contribute differently towards aging effects on the battery”) of the vehicle, the discharge routine that counteracts the predicted expedited degradation (see at least para. [0015] of Vidhi which discloses “the V2G interface and the charging server can be configured/programmed to curtail (reduce/limit) battery degradation of the battery in the EV while the EV is parked in the long-term parking area”, *curtailing the battery degradation is counteracting the predicted expedited degradation).
Regarding claim 10, Abbaraju, as modified by Yonemoto and Vidhi discloses wherein the
discharge routine (see at least para. [0035] of Vidhi which discloses “power discharged from the battery 60 that is supplied to the power grid 58 and provides data characterizing the supplied power to the charge control application”) comprises at least one of a recommended time or a recommended date (see at least para. [0029] of Vidhi which discloses “the “expected return time” denotes a date and time that the operator is expected to return to the parking spot 56 of the parking area, detach the EV 52 from the V2G interface 54 and drive away”) to be suitable for periodic discharging of the battery (see at least para. [0023] of Vidhi which discloses “discharging of the battery 60”), and wherein the discharge routine further comprises a recommended discharge amount to be periodically discharged at the at least one of the recommended time or the recommended date (see at least para. [0029] and [0050] of Vidhi which discloses “a time and date that the operator is expecting to return to the EV 104. Alternatively, the charge control application 120 can receive information corresponding to the expected return time 124”).
Regarding claim 13, Abbaraju, as modified by Yonemoto and Vidhi discloses wherein the
vehicle is docked at the vehicular charging station, and further comprising: controlling by the device, the battery to discharge the recommended discharge amount to the vehicular charging station at the at least one of the recommended time or the recommended date (see at least para. [0023] of Vidhi which discloses “The V2G interface 54 can be representative of a charging station that provides a V2G system that can charge the battery 60 of the EV 52 with power from the power grid 58 and discharge the battery 60 of the EV 52 and supply the discharged power to the power grid 58. That is, the V2G interface 54 can provide an interface for V2G services. The V2G interface 54 can include a computing device (e.g., a processor and memory or a controller) to control the charging and discharging of the battery 60 of the EV 52”).
Regarding claim 14, Abbaraju, as modified by Yonemoto and Vidhi discloses wherein the
vehicle is equipped with autonomous driving controls, and further comprising: controlling, by the device and via the autonomous driving controls (see at least para. [0074] of Abbaraju which discloses “the vehicle is autonomous or semi-autonomous, the vehicle control program may receive the vehicle control information, and may control the speed of the vehicle 102”), the vehicle to travel to and dock at the vehicular charging station prior to the at least one of the recommended time or the recommended date (see at least para. [0029] and [0050] of Vidhi which discloses “a time and date that the operator is expecting to return to the EV 104. Alternatively, the charge control application 120 can receive information corresponding to the expected return time 124”).
Regarding claim 15, Abbaraju discloses A computer program product (see at least para.
[0049] of Abbaraju which discloses “one or more computer programs, applications, executable code, or portions thereof. Further, during use, some portions of this program may be executed on multiple separate processors 216 of the vehicle computing device(s) 104”) for facilitating strategic discharging of vehicular batteries, the computer program product comprising a non-transitory computer- readable (see at least para. [0048] of Abbaraju which discloses “non-transitory medium to the extent that, when mentioned, non-transitory computer-readable media exclude media such as energy, carrier signals, electromagnetic waves, and/or signals per se. In some cases, the computer-readable media“) memory (see at least para. [0048] of Abbaraju which discloses “The computer-readable media 218 may include volatile and nonvolatile memory and/or removable and non-removable media implemented in any type of technology for storage of information” having program instructions embodied therewith, wherein the program instructions are executable by a processor (Fig. 2, 216 and see at least para. [0045] of Abbaraju which discloses “the processors 216 may include one or more ECUs (electronic control units) or any of various other types of computing devices. “ECU” is a generic term for any embedded processing system that controls one or more of the systems, subsystems, or components in a vehicle”), and wherein execution of the program instructions (see at least para. [0058] of Abbaraju which discloses “any number of functional components that are executable by the processors 240” and see at least para. [0049] of Abbaraju which discloses “functional components that are executable by the processor(s) 216. In many implementations, these functional components comprise instructions or programs that are executable by the processor(s) 216 and that, when executed, specifically program the processor(s) 216 to perform the actions attributed herein to the vehicle computing device 104”) determine, via execution of a first machine learning model (Fig. 1, 126 (2) and see at least para. [0031] of Abbaraju which discloses “machine learning models (MLMs) 126” and see at least claim 3 of Abbaraju which discloses “second machine learning models from a plurality of charging stations, respectively, each second machine learning model having been trained based on data associated with the respective charging station”). that the battery is predicted to experience expedited (see at least para. [0064] of Abbaraju which discloses “the EOL for impedance is when the impedance has increased to 200 percent over the original battery impedance 310 measured when the battery was new”, *This increased impedance is linked to expedited degradation since battery degradation causes impedance to increase) degradation (see at least para. [0020] of Abbaraju which discloses “the predicted degradation of the battery SOH of the particular battery, such as based on the selected route(s)”and see at least para. [0042] of Abbaraju which discloses “information regarding battery discharge and possible degradation of the battery may be sent, via the RSUs 109 along the route, to the service computing device 108. This information may include vehicle travel speed, sudden accelerations, stops, severe weather, and any other events that may cause battery degradation”) due to a pattern of battery usage (Abbaraju discloses predicting degradation of battery SOH for a particular battery based on the selected route and associated operating conditions. Abbaraju further discloses that information such as travel speed, sudden accelerations, stops, severe weather, and other events that may cause battery degradation is collected and used in the SOH management system, which reflects degradation associated with usage and operating patterns); determine, via execution of a second machine learning model (Fig. 1, 126 (2) and see at least para. [0031] of Abbaraju which discloses “machine learning models (MLMs) 126” and see at least claim 3 of Abbaraju which discloses “second machine learning models from a plurality of charging stations, respectively, each second machine learning model having been trained based on data associated with the respective charging station”).
Abbaraju may not explicitly disclose a charging history of a battery of a vehicle.
However, in the same field of endeavor, Yonemoto discloses a charging history (see at least
para. [0029] of Yonemoto which discloses “The history storage unit 104 stores time information relating to an arbitrary timing or an arbitrary period from the past to the present, past battery state parameters”) of a battery (Fig. 1, 1 and see at least para. [0027] of Yonemoto which discloses “a battery system 1”) of a vehicle (see at least para. [0027] of Yonemoto which discloses a “vehicle”).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the system of Abbaraju to include a component that accesses a charging history; as taught in Yonemoto with a reasonable expectation of success in order to improve the accuracy and robustness of the degradation information and resulting control decisions to more effectively mange the EV battery health over time. See para. [0027] and [0029] of Yonemoto for motivation.
Abbaraju, as modified by Yonemoto, may not explicitly disclose a discharge routine for
discharging the battery to a power source that counteracts the predicted expedited degradation, wherein the power source comprises at least one of a power grid or a vehicular charging station; and control the battery to discharge to the power source according to the discharge routine.
However, in the same field of endeavor, Vidhi discloses a discharge routine for discharging
the battery to a power source (see at least para. [0035] of Vidhi which discloses “power discharged from the battery 60 that is supplied to the power grid 58 and provides data characterizing the supplied power to the charge control application”) that counteracts the predicted expedited degradation (see at least para. [0015] of Vidhi which discloses “the V2G interface and the charging server can be configured/programmed to curtail (reduce/limit) battery degradation of the battery in the EV while the EV is parked in the long-term parking area”, *curtailing the battery degradation is counteracting the predicted expedited degradation), wherein the power source comprises at least one of a power grid (Fig. 1, 58 and see at least para. [0023] of Vidhi which discloses “the power grid 58 and discharge the battery 60 of the EV 52 and supply the discharged power to the power grid 58. That is, the V2G interface 54 can provide an interface for V2G services”) or a vehicular charging station (Fig. 1, 54 and see at least para. [0003] of Vidhi which describes a “charging station, also called an electric recharging point, charging point, charge point, ECS (Electronic Charging Station) and EVSE (electric vehicle supply equipment), is an element in an infrastructure that supplies electric power for the recharging of electric vehicles” and see at least para. [0023] of Vidhi which describes “The V2G interface 54 can be representative of a charging station that provides a V2G system that can charge the battery 60 of the EV 52 with power from the power grid 58 and discharge the battery 60 of the EV 52 and supply the discharged power to the power grid 58”); and control the battery to discharge to the power source according to the discharge routine (see at least para. [0057] of Vidhi which discloses “the V2G interface 102 measures an amount of power discharged from the battery of the EV 104 that is supplied to the power grid and provides data characterizing the supplied power to the charge control application 120. In response, the plan module 134 can query a utility server via the network 112 for a present (e.g., near real-time) credit value (e.g., per kilowatt hour) for power supplied to the power grid”).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the system of Abbaraju, as modified by Yonemoto, to include a discharge routine for discharging the battery to a power source that counteracts the predicted expedited degradation, wherein the power source comprises at least one of a power grid or a vehicular charging station; and control the battery to discharge to the power source according to the discharge routine; as taught in Vidhi with a reasonable expectation of success in order to facilitate increased accuracy of the degradation information and to more effectively manage degradation both during driving and during parking using known V2G infrastructure. See para. [0015] and [0057] of Vidhi for motivation.
Regarding claim 16, Abbaraju, as modified by Yonemoto and Vidhi discloses wherein the
determining the discharge routine comprises: determining via execution of the second machine learning model (Fig. 1, 126 (2) and see at least para. [0031] of Abbaraju which discloses “machine learning models (MLMs) 126” and see at least claim 3 of Abbaraju which discloses “second machine learning models from a plurality of charging stations, respectively, each second machine learning model having been trained based on data associated with the respective charging station”) on the charging history and on a driving history (see at least para. [0064] of Abbaraju which discloses “estimating the SOH (as opposed to measuring the SOH) may be complicated as multiple factors such as ambient environment, charge and discharge cycles, driving patterns, and the like, may contribute differently towards aging effects on the battery”) of the vehicle, the discharge routine that is likely to counteracts the predicted expedited degradation (see at least para. [0015] of Vidhi which discloses “the V2G interface and the charging server can be configured/programmed to curtail (reduce/limit) battery degradation of the battery in the EV while the EV is parked in the long-term parking area”, *curtailing the battery degradation is counteracting the predicted expedited degradation).
Regarding claim 17, Abbaraju, as modified by Yonemoto and Vidhi discloses wherein the
discharge routine (see at least para. [0035] of Vidhi which discloses “power discharged from the battery 60 that is supplied to the power grid 58 and provides data characterizing the supplied power to the charge control application”) comprises at least one of a recommended time or a recommended date (see at least para. [0029] of Vidhi which discloses “the “expected return time” denotes a date and time that the operator is expected to return to the parking spot 56 of the parking area, detach the EV 52 from the V2G interface 54 and drive away”) to be suitable for periodic discharging of the battery (see at least para. [0023] of Vidhi which discloses “discharging of the battery 60”), and wherein the discharge routine further comprises a recommended discharge amount to be periodically discharged at the at least one of the recommended time or the recommended date (see at least para. [0029] and [0050] of Vidhi which discloses “a time and date that the operator is expecting to return to the EV 104. Alternatively, the charge control application 120 can receive information corresponding to the expected return time 124”).
Regarding claim 19, Abbaraju, as modified by Yonemoto and Vidhi discloses wherein the
vehicle is docked at the vehicular charging station, and wherein the program instructions are further executable to cause the processor to: control the battery to discharge the recommended discharge amount to the vehicular charging station at the at least one of the recommended time or the recommended date (see at least para. [0023] of Vidhi which discloses “The V2G interface 54 can be representative of a charging station that provides a V2G system that can charge the battery 60 of the EV 52 with power from the power grid 58 and discharge the battery 60 of the EV 52 and supply the discharged power to the power grid 58. That is, the V2G interface 54 can provide an interface for V2G services. The V2G interface 54 can include a computing device (e.g., a processor and memory or a controller) to control the charging and discharging of the battery 60 of the EV 52”).
Regarding claim 20, Abbaraju, as modified by Yonemoto and Vidhi discloses wherein the
vehicle is equipped with autonomous driving controls (see at least para. [0074] of Abbaraju which discloses “the vehicle is autonomous or semi-autonomous, the vehicle control program may receive the vehicle control information, and may control the speed of the vehicle 102”), and wherein the program instructions are further executable to cause the processor to: control, via the autonomous driving controls, the vehicle to travel to and dock at the vehicular charging station prior to the at least one of the recommended time or the recommended date(see at least para. [0029] and [0050] of Vidhi which discloses “a time and date that the operator is expecting to return to the EV 104. Alternatively, the charge control application 120 can receive information corresponding to the expected return time 124”).
Claims 4-5, 11-12 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Abbaraju (US 2024/0391352A1) in view of Yonemoto (US 2018/0358663A1) and further in view of Vidhi (US 2020/0094691A1) and further in view of Nakajima et al. (US 2021/0004879 A1).
Regarding claim 4, Abbaraju as modified by Yonemoto and Vidhi discloses the computer-
executable components (see at least para. [0058] of Abbaraju which discloses “any number of functional components that are executable by the processors 240” and see at least para. [0049] of Abbaraju which discloses “functional components that are executable by the processor(s) 216. In many implementations, these functional components comprise instructions or programs that are executable by the processor(s) 216 and that, when executed, specifically program the processor(s) 216 to perform the actions attributed herein to the vehicle computing device 104”) has the discharge routine (see at least para. [0035] of Vidhi which discloses “power discharged from the battery 60 that is supplied to the power grid 58 and provides data characterizing the supplied power to the charge control application”).
Abbaraju as modified by Yonemoto and Vidhi may not explicitly disclose visually rendering
the discharge routine on an electronic display.
However, in the same field of endeavor, Nakajima et al. discloses visually renders the
discharge routine (see at least para. [0098] of Nakajima which discloses “The display section 307 may display a list of, for example, the deterioration class and price of a plurality of batteries 200 to the user 60 who has returned the battery 200. The display section 307 may visually inform the user 60 of the accommodating location of the battery 200 which is recommended or selected by the management device 400, for example by displaying an image of the battery-accommodating section 301 and flashing a particular accommodating location in the image”, *Examiner interprets this to be a display of the discharge routine on screen) on an electronic display (Fig. 6, 307 and see at least para. [0097] of Nakajima which discloses “The display section 307 displays the reward information received from the management device 400. For example, the display section 307 may display a barcode for a communication terminal of the user 60 to read, and thereby cause the reward information to be displayed on the communication terminal. Instead of or in addition to the display section 307, the station 300 may include a print section configured to print and eject document on which the reward information is described based on the instruction from the management device 400” and see at least para. [0122] of Nakajima which discloses “The reward information stored in the reward information storage section 437 is output to the station 300 when the user 60 returns the battery 200 to the station 300, and then the management device 400 judges that the method in which the user 60 uses the battery 200 is good or judges that the location where the battery 200 returned to the station 300 is accommodated by the user 60 is appropriate”).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to further modify the system of Abbaraju as modified by Yonemoto and Vidhi, to include visually renders the discharge routine on an electronic display, as taught in Nakajima with a reasonable expectation of success in order to increase an operator’s awareness of best practices regarding the battery management. See para. [0097]-[0098] of Nakajima for motivation.
Regarding claim 5, Abbaraju as modified by Yonemoto and Vidhi, discloses the computer-
executable components (see at least para. [0058] of Abbaraju which discloses “any number of functional components that are executable by the processors 240” and see at least para. [0049] of Abbaraju which discloses “functional components that are executable by the processor(s) 216. In many implementations, these functional components comprise instructions or programs that are executable by the processor(s) 216 and that, when executed, specifically program the processor(s) 216 to perform the actions attributed herein to the vehicle computing device 104”).
Abbaraju as modified by Yonemoto and Vidhi may not explicitly disclose may not explicitly
disclose visually renders on the electronic display a reward associated with the discharge routine.
However, in the same field of endeavor, Nakajima et al. discloses visually renders on the
electronic display a reward (Fig. 6, 307 and see at least para. [0097] of Nakajima which discloses “The display section 307 displays the reward information received from the management device 400. For example, the display section 307 may display a barcode for a communication terminal of the user 60 to read, and thereby cause the reward information to be displayed on the communication terminal) associated with the discharge routine (see at least para. [0098] of Nakajima which discloses “The display section 307 may display a list of, for example, the deterioration class and price of a plurality of batteries 200 to the user 60 who has returned the battery 200. The display section 307 may visually inform the user 60 of the accommodating location of the battery 200 which is recommended or selected by the management device 400, for example by displaying an image of the battery-accommodating section 301 and flashing a particular accommodating location in the image”, *Examiner interprets this to be a display of the discharge routine on screen).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to further modify the system of Abbaraju as modified by Yonemoto and Vidhi to include visually renders on the electronic display a reward associated with the discharge routine, as taught in Nakajima with a reasonable expectation of success in order to incentivize an operator and to increase an operator’s awareness of best practices regarding the battery management. See para. [0096]-[0097] of Nakajima for motivation.
Regarding claim 11, Abbaraju as modified by Yonemoto and Vidhi discloses further
comprising: the device (see at least para. [0045] of Abbaraju which discloses “device 104 may include one or more processors 216, one or more computer-readable media 218, one or more communication interfaces” and see at least para. [0049] of Abbaraju which discloses “The computer-readable media 218 may be used to store any number of functional components that are executable by the processor(s) 216”), the discharge routine (see at least para. [0035] of Vidhi which discloses “power discharged from the battery 60 that is supplied to the power grid 58 and provides data characterizing the supplied power to the charge control application”).
Abbaraju as modified by Yonemoto and Vidhi may not explicitly disclose visually rendering
the discharge routine on an electronic display.
However, in the same field of endeavor, Nakajima et al. discloses visually rendering the
discharge routine (see at least para. [0098] of Nakajima which discloses “The display section 307 may display a list of, for example, the deterioration class and price of a plurality of batteries 200 to the user 60 who has returned the battery 200. The display section 307 may visually inform the user 60 of the accommodating location of the battery 200 which is recommended or selected by the management device 400, for example by displaying an image of the battery-accommodating section 301 and flashing a particular accommodating location in the image”, *Examiner interprets this to be a display of the discharge routine on screen) on an electronic display (Fig. 6, 307 and see at least para. [0097] of Nakajima which discloses “The display section 307 displays the reward information received from the management device 400. For example, the display section 307 may display a barcode for a communication terminal of the user 60 to read, and thereby cause the reward information to be displayed on the communication terminal. Instead of or in addition to the display section 307, the station 300 may include a print section configured to print and eject document on which the reward information is described based on the instruction from the management device 400” and see at least para. [0122] of Nakajima which discloses “The reward information stored in the reward information storage section 437 is output to the station 300 when the user 60 returns the battery 200 to the station 300, and then the management device 400 judges that the method in which the user 60 uses the battery 200 is good or judges that the location where the battery 200 returned to the station 300 is accommodated by the user 60 is appropriate”).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to further modify the system of Abbaraju as modified by Yonemoto and Vidhi, to include visually rendering the discharge routine on an electronic display, as taught in Nakajima with a reasonable expectation of success in order to increase an operator’s awareness of best practices regarding the battery management. See para. [0097]-[0098] of Nakajima for motivation.
Regarding claim 12, Abbaraju as modified by Yonemoto and Vidhi discloses further
comprising: the device (see at least para. [0045] of Abbaraju which discloses “device 104 may include one or more processors 216, one or more computer-readable media 218, one or more communication interfaces” and see at least para. [0049] of Abbaraju which discloses “The computer-readable media 218 may be used to store any number of functional components that are executable by the processor(s) 216”).
Abbaraju as modified by Yonemoto and Vidhi may not explicitly disclose visually rendering
on the electronic display, a reward associated with the discharge routine.
However, in the same field of endeavor, Nakajima et al. discloses visually rendering on the
electronic display a reward (Fig. 6, 307 and see at least para. [0097] of Nakajima which discloses “The display section 307 displays the reward information received from the management device 400. For example, the display section 307 may display a barcode for a communication terminal of the user 60 to read, and thereby cause the reward information to be displayed on the communication terminal)I associated with the discharge routine (see at least para. [0098] of Nakajima which discloses “The display section 307 may display a list of, for example, the deterioration class and price of a plurality of batteries 200 to the user 60 who has returned the battery 200. The display section 307 may visually inform the user 60 of the accommodating location of the battery 200 which is recommended or selected by the management device 400, for example by displaying an image of the battery-accommodating section 301 and flashing a particular accommodating location in the image”, *Examiner interprets this to be a display of the discharge routine on screen).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to further modify the system of Abbaraju as modified by Yonemoto and Vidhi, to include visually rendering on the electronic display a reward associated with the discharge routine, as taught in Nakajima with a reasonable expectation of success in order to incentivize an operator and to increase an operator’s awareness of best practices regarding the battery management. See para. [0096]-[0097] of Nakajima for motivation.
Regarding claim 18, Abbaraju as modified by Yonemoto and Vidhi, discloses wherein the
program instructions (see at least para. [0058] of Abbaraju which discloses “any number of functional components that are executable by the processors 240” and see at least para. [0049] of Abbaraju which discloses “functional components that are executable by the processor(s) 216. In many implementations, these functional components comprise instructions or programs that are executable by the processor(s) 216 and that, when executed, specifically program the processor(s) 216 to perform the actions attributed herein to the vehicle computing device 104”) are further executable to cause the processor (Fig. 2, 216 and see at least para. [0045] of Abbaraju which discloses “the processors 216 may include one or more ECUs (electronic control units) or any of various other types of computing devices. “ECU” is a generic term for any embedded processing system that controls one or more of the systems, subsystems, or components in a vehicle”) to: render the discharge routine (see at least para. [0035] of Vidhi which discloses “power discharged from the battery 60 that is supplied to the power grid 58 and provides data characterizing the supplied power to the charge control application”).
Abbaraju as modified by Yonemoto and Vidhi, may not explicitly disclose visually render the
discharge routine and a reward associated with the discharge routine on an electronic display.
However, in the same field of endeavor, Nakajima et al. discloses visually render the
discharge routine (see at least para. [0098] of Nakajima which discloses “The display section 307 may display a list of, for example, the deterioration class and price of a plurality of batteries 200 to the user 60 who has returned the battery 200. The display section 307 may visually inform the user 60 of the accommodating location of the battery 200 which is recommended or selected by the management device 400, for example by displaying an image of the battery-accommodating section 301 and flashing a particular accommodating location in the image”, *Examiner interprets this to be a display of the discharge routine on screen) and a reward (Fig. 6, 307 and see at least para. [0097] of Nakajima which discloses “The display section 307 displays the reward information received from the management device 400. For example, the display section 307 may display a barcode for a communication terminal of the user 60 to read, and thereby cause the reward information to be displayed on the communication terminal) associated with the discharge routine on an electronic display (Fig. 6, 307 and see at least para. [0097] of Nakajima which discloses “The display section 307 displays the reward information received from the management device 400. For example, the display section 307 may display a barcode for a communication terminal of the user 60 to read, and thereby cause the reward information to be displayed on the communication terminal. Instead of or in addition to the display section 307, the station 300 may include a print section configured to print and eject document on which the reward information is described based on the instruction from the management device 400” and see at least para. [0122] of Nakajima which discloses “The reward information stored in the reward information storage section 437 is output to the station 300 when the user 60 returns the battery 200 to the station 300, and then the management device 400 judges that the method in which the user 60 uses the battery 200 is good or judges that the location where the battery 200 returned to the station 300 is accommodated by the user 60 is appropriate”).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to further modify the system of Abbaraju as modified by Yonemoto and Vidhi, to include visually renders the discharge routine on an electronic display, as taught in Nakajima with a reasonable expectation of success in order to incentivize and operator and to increase an operator’s awareness of best practices regarding the battery management. See para. [0097]-[0098] of Nakajima for motivation.
Additional Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Yang et al. (US 9,241, 249) discloses a processor 220 that is in communication with a tangible, non-transitory computer-readable storage medium 225 (e.g., computer memory) by way of a communication bus 205 or other such computing infrastructure and instructions 228 that are stored in the computer-readable storage medium 225 include a device locator module 229 that includes computer-executable instructions that are executable by the processor 220. Dixt (US 2021/0335059 A1) disclose a computing system that receives data of performance parameters and determines corresponding levels of degradation and rates of change of degradation for the respective like components. A fleet-level of degradation for groups of like components is generated based on analysis of the combined degradations of the like components in the respective group.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/DANA D IVEY/Examiner, Art Unit 3662 /D.D.I/January 22, 2026
/JELANI A SMITH/Supervisory Patent Examiner, Art Unit 3662