DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Response to Arguments
Regarding Claim Rejections Under 35 U.S.C. 112
Claims 9, 10 , and 11 were rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement.
Original claims 9 and 10 recited the limitation “a plurality of lives”, applicant has amended these claims to read “numbers of charge cycles” to traverse the rejection. Examiner withdraws rejection of claims 9 and 10 under 35 U.S.C. 112(a).
Original claim 11 recited the limitation “the second electric vehicle connects to a charging station prior to the electric vehicle”, wherein the limitation is referring to a plurality of electric vehicles and the underlined portion does not make clear which vehicle is being referred to. Applicant has amended claim 11 to distinguish a first electric vehicle and a second electric vehicle. Examiner withdraws rejection of claim 11 under 35 U.S.C. 112(a).
Regarding Claim Rejections Under 35 U.S.C. 102(a)(1)
Claims 1, 11, 13, and 19 were rejected under 35 U.S.C. 102(a)(a) as being anticipated by Yonetani et al (US 20150256003 A1).
Applicant has amended independent claims 1, 13, and 19 to traverse the existing rejection. Claim 1 has been amended to recite “one or more processors, coupled with memory, to… identify, for each of the plurality of batteries, based on the number of charge cycles, a target state of health” wherein the underlined portion traverses Yonetani. Claims 13 and 19 have been amended in a similar fashion, and similarly traverse Yonetani for the same reason. Applicant provides no further arguments regarding the Yonetani reference.
Regarding Claim Rejections Under 35 U.S.C. 103
Claims 2-5, 7-8, 14-16, and 20 were rejected under 35 U.S.C. 103 over Yonetani modified by Hortop et al (US 20190039467 A1) and evidenced by Zou. Due to their dependence on the amended independent claims, these rejections have been traversed.
Claims 6 and 18 were rejected under 35 U.S.C. 103 over Yonetani modified by Ortzkan et al (US 11977126 B1). Due to their dependence on the amended independent claims, these rejections have been traversed.
Claims 9-10 and 17 were rejected under 35 U.S.C. 103 over Yonetani modified by Hortop and further in view of Logvinov et al (US 20200023747 A1). Due to their dependence on the amended independent claims, these rejections have been traversed.
Claim 12 was rejected under 35 U.S.C. 103 over Yonetani modified by Logvinov and Campbell et al (US 20200223318 A1). Due to their dependence on the amended independent claims, these rejections have been traversed.
Applicant provides no further arguments regarding the Hortop, Zou, Ortskan, Logvinov, or Campbell references.
Applicant's arguments filed 2 January 2026 have been fully considered. New grounds of rejection are presented herein as necessitated by amendment.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 11, 13, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yonetani et al (US 20150256003 A1) modified by Tong et al (US 20220196753 A1),
Regarding claim 1, Yonetani teaches a system, comprising one or more processors, coupled with memory, to: (¶0016 "The power exchange controller 120 can be implemented in the form of a system that includes a processor that is operable to execute instructions that are stored on a computer readable storage device")
identify, for each of a plurality of batteries of a corresponding plurality of electric vehicles:
a state of health; (¶0027 "In step S12, the battery state-of-health of each of the connected EV batteries is detected")
[and a number of charge cycles;]
identify, for each of the plurality of batteries, [based on the number of charge cycles], a target state of health; (¶0027 "step S14,a target state-of-health is determined for the plurality of batteries connected to the charging stations")
and provide, for each of the plurality of batteries, instructions to charge the battery of the electric vehicle based on the state of health of the battery and the target state of health for the battery. (¶0013 "FIG. 6 is a flow diagram of a method of battery deterioration control", FIG 6 ||0029 "calculated power exchange for each connected battery is exchanged between each respective battery and the upper authority 111 in step S18").
Yonetani does not teach and a number of charge cycles; [identify, for each of the plurality of batteries], based on the number of charge cycles, [a target state of health;]
Tong teaches a number of charge cycles; (¶0060 “controller may use battery 904's current state of health, characteristics of the battery (e.g., chemical composition of the battery, type of battery, etc.), historical cycling data relating to the type of battery being reconditioned, etc. and any combination thereof to determine the number of cycles that may need to be performed to achieve the determined target state of health value for the battery 904”)
[identify, for each of the plurality of batteries], based on the number of charge cycles, [a target state of health]. (¶0060 “controller may use battery 904's current state of health, characteristics of the battery (e.g., chemical composition of the battery, type of battery, etc.), historical cycling data relating to the type of battery being reconditioned, etc. and any combination thereof to determine the number of cycles that may need to be performed to achieve the determined target state of health value for the battery 904”)
Therefor it would be obvious to one of ordinary skill in the art, before the effective filing date, to modify Yonetani to identify, for each of the plurality of batteries, based on the number of charge cycles, a target state of health as taught by Tong. Yonetani discloses a power exchange controller which modifies the state of health of a plurality of batteries over time. Similarly Tong discloses a battery re-conditioning system which modifies the state of health of a plurality of batteries to reach a target state of health based on a number of charging cycles. The modification would be obvious because one of ordinary skill in the art would be motivated to accurately determine the state of health of the battery and accurately predict the total lifespan of the battery.
Similarly for claim 13 as applied to a method. (Yonetani ¶0013 “FIG. 6 is a flow diagram of a method of battery deterioration control”)
Similarly for claim 19 as applied to a system, comprising: a data processing system comprising one or more processors, coupled with memory, (Yonetani ¶0016 "The power exchange controller 120 can be implemented in the form of a system that includes a processor that is operable to execute instructions that are stored on a computer readable storage device"),
in communication over a network with at least one of a charging station or an electric vehicle connected to the charging station (Yonetani ¶0016 "The power exchange controller 120 can further include a communications device for exchanging information with other computing devices via a communications network")
Regarding claim 11, Yonetani as modified by Tong teaches the system of claim 1. Yonetani as modified by Tong further teaches comprising the one or more processors to: identify a second state of health of a second battery of a second electric vehicle of the plurality of the electric vehicles, (Yonetani FIG 6 S12 "Determine a state-of-health for each connected battery")
wherein the second electric vehicle connects to a charging station prior to a first electric vehicle of the plurality of the electric vehicles; (Yonetani ¶0023 FIG. 2, conventionally, EVs connect to the power grid 110 at a charging station 130", the plurality of EVs connected to charging stations 130 have to be connected individually and thereby one of the EVs will have to be connected prior to another EV)
identify, based on a second life of the second battery of the second electric vehicle, a second target state of health for the second battery; (Yonetani FIG 6, Yonetani ¶0027 "step S14, a target state-of-health is determined for the plurality of batteries connected to the charging stations. The target state-of-health is based on each state-of-health of all batteries connected")
determine that i) the second target state of health is greater than the second life of the second battery, (Yonetani ¶0028 "step S16, a power exchange for each of the connected EV batteries is determined based on the total power demand and a difference between a battery's respective state-of-health and the target state-of-health", Yonetani ¶0030 "FIG. 4.. the solid lines represent the state-of-health of each of batteries A, B, C as power is exchanged using the battery deterioration control system and method target state-of-health is shown as 95% with the dotted line in FIG. 4" the SOH of battery a is below the target SOH )
and ii) that the target state of health of first battery of the first electric vehicle is less than or equal to the state of health of the first battery; (Yonetani ¶0028 "step S16, a power exchange for each of the connected EV batteries is determined based on the total power demand and a difference between a battery's respective state-of-health and the target state-of-health", Yonetani ¶0030 "FIG. 4... the solid lines represent the state-of-health of each of batteries A, B, C as power is exchanged using the battery deterioration control system and method. target state-of-health is shown as 95% with the dotted line in FIG. 4" the SOH of battery a is below the target SOH );
and instruct, based on the determination, the charging station to charge the first battery prior to the second battery. (Yonetani ¶0013 "FIG. 6 is a flow diagram of a method of battery deterioration control", Yonetani ¶0029 "calculated power exchange for each connected battery is exchanged between each respective battery and the upper authority 111 in step S18")
Claim(s) 2-5, 7-8, 14-16, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yonetani as modified by Tong and further in view of Hortop et al (US 20190039467 A1) and evidenced by evidenced Zou (Zou, Yuan, et al. "Combined state of charge and state of health estimation over lithium-ion battery cell cycle lifespan for electric vehicles." Journal of Power Sources, vol. 273, Jan. 2015, pp. 793-803, https://doi.org/10.1016/j.jpowsour.2014.09.146,]
Regarding claim 2, Yonetani as modified by Tong teaches the system of claim 1. Yonetani as modified by Tong further teaches comprising the one or more processors to, for each of the plurality of batteries: identify the state of health of the battery responsive to a connection between the electric vehicle and a charging station configured to charge the battery; (Yonetani ¶0027 "step S12, the battery state-of-health of each of the connected EV batteries is detected. The battery state-of-health can be detected, for example, by the power exchange controller 120 from an on-board vehicle computer", FIG 2 depicts the connection between EVs A, B, and C to Charging stations 130 which are controlled by power exchange controller 120 external to the vehicle)
generate, [based on a comparison of the state of health of the battery with the target state of health for the battery,] a charge pattern for the battery, (Yonetani ¶0019 "As a non-limiting example of power coordination, the controller 120 may demand power from an EV to decrease a peak energy consumption of an upper authority 111 such as a building if the EV battery has sufficient capacity at a peak consumption time. As another example of coordination, the controller 120 may request power to be exchanged with a connected EV battery to assist with frequency regulation of the power grid 110. As a further example of coordination, the controller 120 may request power to be exchanged from one connected EV battery to another connected EV battery in a vehicle-to-vehicle (V2V) charging operation.")
the charge pattern indicating one or more time intervals to charge the battery and one or more rates at which to charge the battery in the one or more time intervals; (Yonetani ¶0019 "the controller 120 may demand power from an EV to decrease a peak energy consumption of an upper authority 111 such as a building if the EV battery has sufficient capacity at a peak consumption time")
and generate the instruction to charge the battery based on the charge pattern. (Yonetani ¶0019 "power exchange controller 120 is used to coordinate exchange of power between an upper authority 111 and EV batteries connected to the upper authority 111 through the charging stations 130").
Yonetani as modified by Tong does not teach [generate,] based on a comparison of the state of health of the battery with the target state of health for the battery, [a charge pattern for the battery].
Hortop teaches [generate,] based on a comparison of the state of health of the battery with the target state of health for the battery, [a charge pattern for the battery]. (Hortop ¶0018 "HMI 122 communicates with the computer system 150 and can provide functionality, e.g., via a GUI, for powering on the charging circuitry 140, initiating charging, choosing and/or programming a charging scheme, choosing automatic smart charging (in which a charging profile may be selected based on historical metrics and predicted driving expectations as further described herein), overriding a default or present charging scheme, displaying to a user information about thecharging process and state of charge (SOC) of the battery 110")
Therefor it would be obvious to one of ordinary skill in the art, before the effective filing date, to further modify the system as taught by Yonetani modified by Tong to generate, based on a comparison of the state of health of the battery with the target state of health for the battery, a charge pattern for the battery. Yonetani as modified by Tong is a system for charging batteries which measures and predicts the state of health based on the number of charging cycles and generates a charging pattern including delaying charging to a specific time. Hortop similarly teaches a delayed battery charging pattern. Further Hortop ¶0038 discloses "FIG. 4 is a graph illustrating seven exemplary different charge profiles for a battery 110 resulting from exemplary charging schemes according to examples of the disclosure", each charging scheme is designed to charge a battery to a target SOC in a given timeframe. FIG 4 further teaches second charge profile 420, third charge profile 430, and fifth charge profile 450 to reach a target SOC which is not Max SOC. Zou demonstrates that estimations of SOC and SOH are coupled together over the battery lifespan for electric vehicles wherein (p 794) "the inaccurate SOC estimations in turn may mislead the battery SOH calibration. Therefore, simultaneous estimation of SOC and SOH is quite beneficial". The modification would be obvious because one of ordinary skill in the art would be motivated to increase battery lifespan to improve electric vehicle maintenance requirements and reducing user burden.
Similarly for claim 14 as applied to a method, Yonetani as modified by Tong teaches the method of claim 13.
Regarding claim 3, Yonetani as modified by Tong teaches the system of claim 1. Yonetani as modified by Tong further teaches comprising the one or more processors to, for one or more of the plurality of batteries: detect a connection between the electric vehicle and a charging station configured to charge the battery of the electric vehicle; (Yonetani ¶0017 "Each of the charging stations 130 can be an on-board charging station that is disposed within the vehicle and forms a part of the vehicle or may be an off-board charging station to which the vehicle is connected by, for example, a charging cable")
[and determine, based on a comparison of the state of health of the battery with the target state of health for the battery, to delay a charging session to charge the battery. ]
Yonetani modified by Tong does not teach and determine, based on a comparison of the state of health of the battery with the target state of health for the battery, to delay a charging session to charge the battery.
Hortop teaches and determine, based on a comparison of the state of health of the battery with the target state of health for the battery, to delay a charging session to charge the battery. (¶0039 "charge profile 410 in FIG. 4. In this charge profile, charging is delayed until a time t.sub.2 at which time a high charging rate is applied to the battery until it reaches a target SOC at time t.sub.3, which may be a predetermined target time for the vehicle 100").
Therefor it would be obvious to one of ordinary skill in the art, before the effective filing date, to further modify the system as taught by Yonetani modified by Tong to determine, based on a comparison of the state of health of the battery with the target state of health for the battery, to delay a charging session to charge the battery as taught by Hortop. Yonetani as modified by Tong is a system for charging batteries which measures and predicts the state of health based on the number of charging cycles and generates a charging pattern including delaying charging to a specific time. Hortop similarly teaches a delayed battery charging pattern. The modification would be obvious because one of ordinary skill in the art would be motivated to increase battery lifespan to improve electric vehicle maintenance requirements and reduce user burden.
Similarly for claim 15 as applied to a method, Yonetani as modified by Tong teaches the method of claim 13.
Regarding claim 4, Yonetani as modified by Tong teaches the system of claim 1. Yonetani as modified by Tong does not teach [comprising the one or more processors to, for one or more of the plurality of batteries:] determine, based on a comparison of the state of health of the battery with the target state of health for the battery, to delay a charging session to charge the battery; and provide, via a user interface of the electric vehicle, an indication of the delay.
Hortop teaches determine, based on a comparison of the state of health of the battery with the target state of health for the battery, to delay a charging session to charge the battery; (¶0015 "charge profile 410 in FIG. 4. In this charge profile, charging is delayed until a time t.sub.2 at which time a high charging rate is applied to the battery until it reaches a target SOC at time t.sub.3, which may be a predetermined target time for the vehicle 100");
and provide, via a user interface of the electric vehicle, an indication of the delay. (¶0018 "HMI 122 communicates with the computer system 150 and can provide functionality, e.g., via a GUI, for powering on the charging circuitry 140, initiating charging, choosing and/or programming a charging scheme, choosing automatic smart charging (in which a charging profile may be selected based on historical metrics and predicted driving expectations")
The HMI 122 as taught by Hortop can ||0018 "be controlled remotely though wired and/or wireless communication, for example, using a smart phone 124 or a remote computer such as a tablet executing an application (or app) to communicate with and control the charging station 120". The smartphone 124 would be capable of displaying information of the charging circuitry 140 including a delay in charging time. Therefor it would be obvious to one of ordinary skill in the art, before the effective filing date, to further modify the system as taught by Yonetani modified by Tong to determine, based on a comparison of the state of health of the battery with the target state of health for the battery, to delay a charging session to charge the battery as taught by Hortop. Yonetani as modified by Tong is a system for charging batteries which measures and predicts the state of health based on the number of charging cycles and generates a charging pattern including delaying charging to a specific time. Hortop similarly teaches a delayed battery charging pattern. The modification would be obvious because one of ordinary skill in the art would be motivated to increase battery lifespan to improve electric vehicle maintenance requirements and reduce user burden.
Similarly for claim 16 as applied to a method, Yonetani as modified by Tong teaches the method of claim 13.
Regarding claim 5, Yonetani as modified by Tong teaches the system of claim 1. Yonetani as modified by Tong further teaches comprising the one or more processors to, for one or more of the plurality of batteries: [determine, based on a comparison of the state of health of the battery with the target state of health for the battery, to delay a charging session to charge the battery; ]
provide, via a user interface of the electric vehicle, a prompt for authorization to delay the charging session to charge the battery; (Yonetani ¶0026 "if a user requires a full charge at a specific time, the power exchange controller 120 must adjust the power demand for that battery, and in turn, the total power demand, to provide that battery with the requisite power at the requisite time").
and generate, based on input received via the user interface responsive to the prompt, the instruction to charge the battery. (Yonetani ¶0019 "power exchange controller 120 is used to coordinate exchange of power between an upper authority 111 and EV batteries connected to the upper authority 111 through the charging stations 130").
Yonetani as modified by Tong does not teach determine, based on a comparison of the state of health of the battery with the target state of health for the battery, to delay a charging session to charge the battery.
Hortop teaches determine, based on a comparison of the state of health of the battery with the target state of health for the battery, to delay a charging session to charge the battery. (¶0015 "charge profile 410 in FIG. 4. In this charge profile, charging is delayed until a time t.sub.2 at which time a high charging rate is applied to the battery until it reaches a target SOC at time t.sub.3, which may be a predetermined target time for the vehicle 100")
Therefor it would be obvious to one of ordinary skill in the art, before the effective filing date, to further modify the system as taught by Yonetani modified by Tong to determine, based on a comparison of the state of health of the battery with the target state of health for the battery, to delay a charging session to charge the battery as taught by Hortop. Yonetani as modified by Tong is a system for charging batteries which measures and predicts the state of health based on the number of charging cycles and generates a charging pattern including delaying charging to a specific time. Hortop similarly teaches a delayed battery charging pattern. The modification would be obvious because one of ordinary skill in the art would be motivated to increase battery lifespan to improve electric vehicle maintenance requirements and reduce user burden.
Similarly for claim 17 as applied to a method, Yonetani as modified by Tong teaches the method of claim 13.
Regarding claim 7, Yonetani as modified by Tong teaches the system of claim 1. Yonetani as modified by Tong further teaches comprising the one or more processors to, for one or more of the plurality of batteries: determine, based on a comparison of the state of health of the battery with the target state of health for the battery, that the state of health of the battery is less than the target state of health for the battery; (Yonetani ¶0028 "step S16, a power exchange for each of the connected EV batteries is determined based on the total power demand and a difference between a battery's respective state-of-health and the target state-of-health").
[and generate the instruction to delay a charging session for the battery responsive to the state of health of the battery being less than the target state of health for the battery. ]
Yonetani modified by Tong does not teach generate the instruction to delay a charging session for the battery responsive to the state of health of the battery being less than the target state of health for the battery.
Hortop teaches generate the instruction to delay a charging session for the battery responsive to the state of health of the battery being less than the target state of health for the battery. (¶0039 "charge profile 410 in FIG. 4. In this charge profile, charging is delayed until a time t.sub.2 at which time a high charging rate is applied to the battery until it reaches a target SOC at time t.sub.3, which may be a predetermined target time for the vehicle 100")
Zou demonstrates that estimations of SOC and SOH are coupled together over the battery lifespan for electric vehicles wherein (p 794) "the inaccurate SOC estimations in turn may mislead the battery SOH calibration. Therefore, simultaneous estimation of SOC and SOH is quite beneficial". It would be obvious to one of ordinary skill in the art, before the effective filing date, to further modify the system as taught by Yonetani modified by Tong to generate the instruction to delay a charging session for the battery responsive to the state of health of the battery being less than the target state of health for the battery as taught by Hortop. Yonetani as modified by Tong is a system for charging batteries which measures and predicts the state of health based on the number of charging cycles and generates a charging pattern including delaying charging to a specific time. Hortop similarly teaches a delayed battery charging pattern. The modification would be obvious because one of ordinary skill in the art would be motivated to increase battery lifespan to improve electric vehicle maintenance requirements and reduce user burden.
Regarding claim 8, Yonetani as modified by Tong teaches the system of claim 1. Yonetani as modified by Tong further teaches comprising the one or more processors to for one or more of the plurality of batteries: determine, based on a comparison of the state of health of the battery with the target state of health for the battery, that the state of health of the battery is greater than the target state of health for the battery; (Yonetani ¶0028 "step S16, a power exchange for each of the connected EV batteries is determined based on the total power demand and a difference between a battery's respective state-of-health and the target state-of-health").
[and generate, based on the state of health of the battery being greater than the target state of health, the instruction to begin a charging session for the battery responsive to a connection between the electric vehicle and a charging station.]
Yonetani as modified by Tong does not teach and generate, based on the state of health of the battery being greater than the target state of health, the instruction to begin a charging session for the battery responsive to a connection between the electric vehicle and a charging station.
Hortop teaches generate, based on the state of health of the battery being greater than the target state of health, the instruction to begin a charging session for the battery responsive to a connection between the electric vehicle and a charging station. (¶0025 "The charging scheme for the battery is a function of time and may be calculated to achieve a target SOC (e.g., 70% of maximum SOC or other calculated percentage of the maximum SOC) for the battery at the end of a predetermined time period, e.g., at which time driving is intended to commence")
Therefor it would be obvious to one of ordinary skill in the art, before the effective filing date, to further modify the system as taught by Yonetani modified by Tong to generate, based on the state of health of the battery being greater than the target state of health, the instruction to begin a charging session for the battery responsive to a connection between the electric vehicle and a charging station as taught by Hortop. Yonetani as modified by Tong is a system for charging batteries which measures and predicts the state of health based on the number of charging cycles and generates a charging pattern including delaying charging to a specific time. Hortop similarly teaches a delayed battery charging pattern. The modification would be obvious because one of ordinary skill in the art would be motivated to increase battery lifespan to improve electric vehicle maintenance requirements and reduce user burden.
Regarding claim 20, Yonetani modified by Tong teaches the system of claim 19. Yonetani modified by Tong further teaches comprising the data processing system to, for each of the plurality of batteries: identify the state of health of the battery responsive to establishment of a connection between the electric vehicle and the charging station; (¶0027 "step S12, the battery state-of-health of each of the connected EV batteries is detected. The battery state-of-health can be detected, for example, by the power exchange controller 120 from an on-board vehicle computer", FIG 2 depicts the connection between EVs A, B, and C to Charging stations 130 which arecontrolled by power exchange controller 120 external to the vehicle)
generate, [based on a comparison of the state of health of the battery with the target state of health for the battery], a charge pattern for the battery, (¶0019 "As a non-limiting example of power coordination, the controller 120 may demand power from an EV to decrease a peak energy consumption of an upper authority 111 such as a building if the EV battery has sufficient capacity at a peak consumption time. As another example of coordination, the controller 120 may request power to be exchanged with a connected EV battery to assist with frequency regulation of the power grid 110. As a further example of coordination, the controller 120 may request power to be exchanged from one connected EV battery to another connected EV battery in a vehicle-to-vehicle (V2V) charging operation.")
the charge pattern indicating one or more time intervals to charge the battery and one or more rates at which to charge the battery in the one or more time intervals; (Yonetani ¶0019 "the controller 120 may demand power from an EV to decrease a peak energy consumption of an upper authority 111 such as a building if the EV battery has sufficient capacity at a peak consumption time”)
and generate the instruction to charge the battery based on the charge pattern. (Yonetani ¶0019 "power exchange controller 120 is used to coordinate exchange of power between an upper authority 111 and EV batteries connected to the upper authority 111 through the charging stations 130").
Yonetani modified by Tong does not teach [generate,] based on a comparison of the state of health of the battery with the target state of health for the battery, [a charge pattern for the battery].
Hortop teaches [generate,] based on a comparison of the state of health of the battery with the target state of health for the battery, [a charge pattern for the battery]. (¶0018 "HMI 122 communicates with the computer system 150 and can provide functionality, e.g., via a GUI, for powering on the charging circuitry 140, initiating charging, choosing and/or programming a charging scheme, choosing automatic smart charging (in which a charging profile may be selected based on historical metrics and predicted driving expectations as further described herein), overriding a default or present charging scheme, displaying to a user information about the charging process and state of charge (SOC) of the battery 110)
Hortop ¶0038 "FIG. 4 is a graph illustrating seven exemplary different charge profiles for a battery 110 resulting from exemplary charging schemes according to examples of the disclosure", each charging scheme is designed to charge a battery to a target SOC in a given timeframe. FIG 4 further teaches second charge profile 420, third charge profile 430, and fifth charge profile 450 to reach a target SOC which is not Max SOC. Zou demonstrates that estimations of SOC and SOH are coupled together over the battery lifespan for electric vehicles wherein (p 794) "the inaccurate SOC estimations in turn may mislead the battery SOH calibration. Therefore, simultaneous estimation of SOC and SOH is quite beneficial".
Therefor it would be obvious to one of ordinary skill in the art, before the effective filing date, to further modify the system as taught by Yonetani modified by Tong to generate, based on a comparison of the state of health of the battery with the target state of health for the battery, a charge pattern for the battery as taught by Hortop. Yonetani as modified by Tong is a system for charging batteries which measures and predicts the state of health based on the number of charging cycles and generates a charging pattern including delaying charging to a specific time. Hortop similarly teaches a delayed battery charging pattern. The modification would be obvious because one of ordinary skill in the art would be motivated to increase battery lifespan to improve electric vehicle maintenance requirements and reduce user burden.
Claim(s) 6 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yonetani modified by Tong and further in view of Ozkan et al (US 11977126 B1)
Regarding claim 6. Yonetani as modified by Tong teaches the system of claim 1. Yonetani as modified by Tong does not teach comprising the one or more processors to, for each of the plurality of batteries: determine the life of the battery based on a number of charge or discharge cycles performed by the battery.
Ozkan teaches comprising the one or more processors to, for each of the plurality of batteries: determine the life of the battery based on a number of charge or discharge cycles performed by the battery. (col 12 line 8 "At 304, based on the information and one or more battery health metrics associated with one or more battery pack modules, the processing device may predict a first state of health of the one or more battery pack modules. In some embodiments, the first state of health of the one or more battery pack modules may be predicted based on a number of charge cycles").
Therefor it would be obvious to one of ordinary skill in the art, before the effective filing date, to further modify the system as taught by Yonetani as modified by Tong to determine the life of the battery based on a number of charge or discharge cycles performed by the battery as taught by Ozkan. The modification would be obvious because one of ordinary skill in the art would be motivated to track battery degradation over time to more accurately predict battery performance and optimize the battery’s lifespan.
Similarly for claim 18 as applied to a method, Yonetani as modified by Tong teaches the method of claim 13.
Claim(s) 9 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yonetani modified by Tong and further in view of Logvinov et al (US 20200023747 A1)
Regarding claim 9, Yonetani as modified by Tong teaches the system of claim 1. Yonetani as modified by Tong further teaches comprising the one or more processors to: identify a plurality of states of health and a plurality of numbers of charge cycles corresponding to the plurality of batteries corresponding to the plurality of electric vehicles connected to one or more charging stations; (Yonetani ¶0015 "The power exchange controller 120 is in communication with a plurality of EV charging stations 130")
identify a plurality of target states of health for the plurality of batteries based on the plurality of numbers of charge cycles; (Tong ¶0060 “controller may use battery 904's current state of health, characteristics of the battery (e.g., chemical composition of the battery, type of battery, etc.), historical cycling data relating to the type of battery being reconditioned, etc. and any combination thereof to determine the number of cycles that may need to be performed to achieve the determined target state of health value for the battery 904”)
[rank, based on a comparison of the plurality of states of health and the plurality of target states of health, the plurality of electric vehicles for charging by the one or more charging stations;
and generate a schedule to charge the plurality of electric vehicles based on the rank.]
Yonetani as modified by Tong does not teach rank, based on a comparison of the plurality of states of health and the plurality of target states of health, the plurality of electric vehicles for charging by the one or more charging stations; and generate a schedule to charge the plurality of electric vehicles based on the rank
Logvinov teaches rank, based on a comparison of the plurality of states of health and the plurality of target states of health, the plurality of electric vehicles for charging by the one or more charging stations; (¶0106 "if an EV is almost discharged to a point of damaging the battery, such EV may be given priority status to charge among multiple power charging schedules for respective EVs");
and generate a schedule to charge the plurality of electric vehicles based on the rank. (¶0106 "if an EV is almost discharged to a point of damaging the battery, such EV may be given priority status to charge among multiple power charging schedules for respective EVs")
Therefor it would be obvious to one of ordinary skill in the art, before the effective filing date, to further modify the system as taught by Yonetani modified by Tong to rank, based on a comparison of the plurality of states of health and the plurality of target states of health, the plurality of electric vehicles for charging by the one or more charging stations as taught by Logvinov. The modification would be obvious because one of ordinary skill in the art would be motivated to increase battery lifespan to improve electric vehicle maintenance requirements and reduce user burden.
Regarding claim 10, Yonetani as modified by Tong teaches the system of claim 1. Yonetani as modified by Tong further teaches comprising the one or more processors to: identify a plurality of states of health and a plurality of numbers of charge cycles corresponding to the plurality of batteries corresponding to the plurality of electric vehicles connected to one or more charging stations; (Yonetani ¶0015 "The power exchange controller 120 is in communication with a plurality of EV charging stations 130", Tong ¶0060 “controller may use battery 904's current state of health… historical cycling data relating… to determine the number of cycles that may need to be performed to achieve the determined target state of health value for the battery 904”)
identify a plurality of target states of health for the plurality of batteries based on the plurality of numbers of charge cycles; (Yonetani FIG 6 S12 Determine a state-of-health for each connected battery", Tong ¶0060 “controller may use battery 904's current state of health… historical cycling data relating… to determine the number of cycles that may need to be performed to achieve the determined target state of health value for the battery 904”);
[rank, based on a comparison of the plurality of states of health and the plurality of target states of health, the plurality of electric vehicles for charging by the one or more charging stations; identify an operation schedule for the plurality of electric vehicles;
and generate a schedule to charge the plurality of electric vehicles based on the rank and the operation schedule.]
Yonetani as modified by Tong does not teach rank, based on a comparison of the plurality of states of health and the plurality of target states of health, the plurality of electric vehicles for charging by the one or more charging stations; and generate a schedule to charge the plurality of electric vehicles based on the rank
Logvinov teaches rank, based on a comparison of the plurality of states of health and the plurality of target states of health, the plurality of electric vehicles for charging by the one or more charging stations; (¶0106 "if an EV is almost discharged to a point of damaging the battery, such EV may be given priority status to charge among multiple power charging schedules for respective EVs");
and generate a schedule to charge the plurality of electric vehicles based on the rank. (¶0106 "if an EV is almost discharged to a point of damaging the battery, such EV may be given priority status to charge among multiple power charging schedules for respective EVs")
Therefor it would be obvious to one of ordinary skill in the art, before the effective filing date, to further modify the system as taught by Yonetani modified by Tong to rank, based on a comparison of the plurality of states of health and the plurality of target states of health, the plurality of electric vehicles for charging by the one or more charging stations as taught by Logvinov. The modification would be obvious because one of ordinary skill in the art would be motivated to increase battery lifespan to improve electric vehicle maintenance requirements and reduce user burden.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yonetani modified by Tong and further in view of Campbell et al (US 20200223318 A1)
Regarding claim 12, Yonetani as modified by Tong teaches the system of claim 1. Yonetani as modified by Tong further teaches comprising the one or more processors to, for each of the plurality of batteries: [access a model configured based on a chemistry of the battery and trained via machine learning;]
and determine, via the number of charge cycles of the battery input into the model, the target state of health of the battery. (Yonetani ¶0027 "step S14, a target state-of-health is determined for the plurality of batteries connected to the charging stations").
Yonetani modified by Tong does not teach access a model configured based on a chemistry of the battery and trained via machine learning.
Logvinov teaches access a model [configured based on a chemistry of the battery and] trained via machine learning. (¶0104 " the controller 240 may use machine-learning-based analytics to learn behaviors of a variety of entities on the microgrid 202").
Logvinov uses a machine learning technique, as detailed in ¶0104, to learn about power use and battery parameters such that ¶0104 "the controller 240 may learn about such changes in behavior and incorporate such learning into a determination of a power charging schedule". Therefor it would be obvious to one of ordinary skill in the art, before the effective filing date, to further modify the system as taught by Yonetani modified by Tong to access a model trained via machine learning. The modification would be obvious because one of ordinary skill in the art would be motivated to improve user experience by automatically updating the charging schedule.
Yonetani modified by Tong and Logvinov does not teach [access a model] configured based on a chemistry of the battery [and trained via machine learning.]
Campbell teaches [access a model] configured based on a chemistry of the battery [and trained via machine learning.] (¶0120 "observed directly by the master controller 200 through its at least one battery controller 140, while others such as the specifications of each of the plurality of batteries (e.g. chemistry, factory rating, configuration, age) can be input").
Therefor it would be obvious to one of ordinary skill in the art, before the effective filing date, to further modify the system as taught by Yonetani modified by Tong and Logvinov to be configured based on a chemistry of the battery as taught by Campbell. The modification would be obvious because one of ordinary skill in the art would be motivated to allow users with different electric vehicles, using a variety of battery chemistries, to use the charging system.
Prior Art Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the attached PTO-892 Notice of References Cited by Examiner attached to this correspondence.
Yezerets et al (US 20210311129 A1) discloses a system and method for determining the state of health of a battery and controlling charging to reach a target state of health.
Holme et al (US 20200164763 A1) discloses a battery management system for an electric vehicle which predicts a future state of health.
Conclusion
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/LISA KOTOWSKI/Examiner, Art Unit 2859
/TAELOR KIM/Supervisory Patent Examiner, Art Unit 2836
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