DETAILED ACTION
This office action addresses Applicant’s response filed on 2 June 2026. Claims 1-15 and 21-24 are pending.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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) 1-10, 22, and 23 is/are rejected under 35 U.S.C. 103 as obvious over Wolfe (WO 2022/241267) in view of Galin (US 2018/0201142), Faries (US 2014/0266054), and Baxter (US 2010/0134067).
Regarding claim 1, Wolfe discloses a charging network comprising: a charging system configured to be electrically coupled with a power source (Fig. 1; ¶3), the charging system comprising: a grid interconnect configured to operably couple with a power grid, a battery energy storage system electrically coupled with the grid interconnect, and a charging station remote from the grid interconnect and battery energy storage system, the charging station electrically coupled with the grid interconnect through a first connection line and the battery energy storage system through a second connection line (Figs. 1 and 2, electric vehicle chargers/charging stations connected to electrical grid and energy storage systems through multiple connection lines, the energy storage systems also electrically coupled to the grid), the first connection line is separated from the second connection line (Fig. 2, line from 280 or from 211 are separate from line from 230 to 210), the charging station further comprising:
a power distribution assembly configured to convert an alternating current (AC) to a direct current (DC) downstream of the first connection line and the second connection line (¶56); and
an electric resource connection line configured to transfer power from the charging station to an electric resource, wherein a supplemented power load including a first amount of alternating current (AC) power transferred through the first connection line and a second amount of direct current (DC) power from the battery energy storage system through the second connection line are transferred from the charging station through the electric resource connection line, wherein the supplemented power load exceeds the defined power value of the grid interconnect with AC power from the grid and DC power from the battery energy storage system (¶¶34, 37, 50, 58, 67).
a computing system operably coupled with the battery energy storage system and the charging station in parallel (Fig. 1), the computing system including a processor and associated memory, the memory storing instructions that, when implemented by the processor, configure the computing system (Fig. 10, ¶40) to: determine a requested power load by the charging station at a defined time (Fig. 4; ¶73), determine a defined power value of the grid interconnect (Fig. 3; ¶¶34, 67, grid connection limit), determine one or more charging parameters based at least in part on the requested power load by the charging station at the defined time and the defined power value of the grid interconnect, and an expected power load over a defined period (¶¶46, 60, 73-74) by executing a peak shaving estimation algorithm that calculates a specific peak shave target for the battery energy storage system, the target being selected to minimize utility demand charges while permitting the supplemented power load to exceed the defined power value of the grid interconnect (¶¶34, 50, 64, 67);
and generate one or more commands for the battery energy storage system or the charging station to provide a power load based on the one or more charging parameters (¶¶46, 60, 73-74), wherein the one or more commands cause the power load provided to the charging station to be varied over time during a charging session based at least in part on vehicle session data received from the charging station (¶¶57, 63).
Wolfe does not appear to explicitly disclose that the first connection line is separated from the second connection line at the charging station. Galin discloses a charging station remote from the grid interconnect and battery energy storage system, the charging station electrically coupled with the grid interconnect through a first connection line and the battery energy storage system through a second connection line, the first connection line is separated from the second connection line at the charging station (Fig. 1A, 111/113 and 101/106 connected to 102 through different lines; Fig. 7, 704/705 and 701/713 connected to 702 through different lines); the charging station further comprising: an electric resource connection line configured to transfer power from the charging station to an electric resource, wherein a combined power load including a first amount of AC power transferred through the first connection line and a second amount of DC power from the battery energy storage system through the second connection line are transferred from the charging station through the electric resource connection line (Fig. 2, merger 218 combining AC and DC power on output line 260 to electric resource; Fig. 7; ¶¶85, 132, 133), wherein the supplemented power load exceeds the defined power value of the grid interconnect with AC power from the grid and DC power from the battery energy storage system (Fig. 8, blocks 823, 829, 830; ¶¶133, 137);
and generate one or more commands for the battery energy storage system or the charging station to provide a power load based on the one or more charging parameters, wherein the one or more commands cause the power load provided to the charging station to be varied over time during a charging session based at least in part on vehicle session data received from the charging station (¶¶77-79, 82, 115, 197-199).
It would have been obvious to persons having ordinary skill in the art before the effective filing date of the application to combine the teachings of Wolfe and Galin, because doing so would have involved merely the routine use of a known technique to improve similar devices in the same way to achieve the predictable results of improving management of power flow from grid and battery supply to a charger through independent connections from the grid and battery. KSR Int’l Co. v. Teleflex Inc., 82 U.S.P.Q.2d 1385, 1396. Wolfe discloses a charger that receives power from both the grid and a battery and provides power to loads (EVs). Galin teaches that the grid and battery should be connected to the charger through separate lines to allow multiple configurable power paths from different sources to loads. The teachings of Galin are directly applicable to Wolfe in the same way, so that Wolfe’s system would similarly connect chargers to the grid and to the batteries using separate connection lines to allow improved management of power flow from multiple sources to loads.
If Wolfe does is found to be unclear regarding executing a peak shaving estimation algorithm that calculates a specific peak shave target for the battery energy storage system, the target being selected to minimize utility demand charges, Faries also discloses executing a peak shaving estimation algorithm that calculates a specific peak shave target for the battery energy storage system, the target being selected to minimize utility demand charges while permitting the supplemented power load to exceed the defined power value of the grid interconnect (¶¶2, 20, 22, 23). It would have been obvious to persons having ordinary skill in the art before the effective filing date of the application to combine the teachings of Wolfe, Galin, and Faries, because doing so would have involved merely the routine combination of known elements according to known techniques to produce merely the predictable results of smoothing demand to minimize peak demand charges. KSR Int’l Co. v. Teleflex Inc., 82 U.S.P.Q.2d 1385, 1395. Wolfe discloses an EV charging site that includes batteries for peak shaving and allowing supplemented power to exceed grid limits. Faries provides additional explicit teaching of peak shaving algorithms for minimizing utility demand charges while allowing supplemented power to exceed grid limits. The teachings of Faries are directly applicable to Wolfe in the same way, so that Wolfe would similarly use a peak shaving algorithm in order to smooth demand and minimize peak demand charges.
If Wolfe and/or Galin are found to be unclear regarding the one or more commands causing the power load provided to the charging station to be varied over time during a charging session based at least in part on vehicle session data received from the charging station, Baxter also discloses these limitations (¶50). It would have been obvious to persons having ordinary skill in the art before the effective filing date of the application to combine the teachings of Wolfe, Galin, Faries, and Baxter, because doing so would have involved merely the routine combination of known elements according to known techniques to produce merely the predictable results of sharing power between charging stations while avoiding excessive total power draw. KSR Int’l Co. v. Teleflex Inc., 82 U.S.P.Q.2d 1385, 1395.. KSR Int’l Co. v. Teleflex Inc., 82 U.S.P.Q.2d 1385, 1396. Wolfe and Galin disclose controlling varying power provided to charging stations during a charging session. Baxter provides further explicit teaching of dynamically varying power allocated to charging stations during a charging session to allow the stations to share power while avoiding excessive total power draw. The teachings of Baxter are directly applicable to Wolfe in the same way, so that Wolfe would similarly dynamically allocate power to charging stations during charging sessions to share power between stations while avoiding excessive total power draw.
Regarding claim 2, Wolfe discloses that the one or more commands for the battery energy storage system to provide a power load based on the one or more charging parameters further includes providing an additional amount of power to supplement power provided by the grid (¶¶34, 67).
Regarding claim 3, Wolfe discloses predicting vehicle flow over a defined period, wherein determining the one or more charging parameters is based at least in part on the predicted vehicle flow (¶73).
Regarding claim 4, Wolfe discloses determining an expected power load over a defined period, wherein the one or more charging parameters are at least partially based on the expected power load over a defined period (¶73).
Regarding claims 5, Wolfe discloses producing a peak shave target for the battery energy storage system based on a peak shaving estimation algorithm, wherein the one or more charging parameters are at least partially based on the peak shave target for the battery energy storage system (¶¶3, 50, 64, 71). If Wolfe is found to be unclear regarding these limitations, Faries discloses the same (¶¶2, 20, 22, 23). Motivation to combine remains consistent with claim 1.
Regarding claim 6, Wolfe discloses that the one or more commands for the battery energy storage system to provide a power load based on the one or more charging parameters includes generating a power limit for the charging station to maintain the power load from the grid interconnect below a specified value (¶34).
Regarding claim 7, Wolfe discloses that the power load provided to the charging station is varied over time (¶¶34, 73).
Regarding claim 8, Wolfe discloses that the battery energy storage system is remote from the charging station (Figs. 1 and 2).
Regarding claim 9, Wolfe discloses a first meter operably coupled with a first connection line upstream of the grid interconnect; and a second meter operably coupled with a second connection line between the grid interconnect and the battery energy storage system (¶¶7, 45, 55, 62-63).
Regarding claim 10, Wolfe discloses that a machine-learned model is used to determine the one or more charging parameters (¶73).
Regarding claim 22, Wolfe discloses that the power load is determined prior to supplying the power load (¶¶65, 73-74).
Regarding claim 23, Wolfe discloses that the power load exceeds the defined power value of the grid interconnect (¶¶34, 67).
Claim(s) 11-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wolfe in view of Galin and Guo (US 2022/0407310).
Regarding claim 11, Wolfe discloses a method for operating a charging network, the method comprising: receiving a requested power load by a charging station at a defined time (Fig. 4, ¶73); determining a defined power value of a grid interconnect, the defined power value being different than an amount of power received from a power grid (Fig. 3; ¶¶34, 67, grid connection limit); determining one or more charging parameters based at least in part on the requested power load by the charging station at the defined time and the defined power value of the grid interconnect, wherein a machine-learned model is used to determine the one or more charging parameters, and wherein the one or more charging parameters are at least partially based on a peak shaving algorithm that produces a peak shave target for the battery energy storage system (¶¶3, 7, 46, 50, 60, 64, 71, 73-74); and generating one or more commands for a battery energy storage system and the charging station to provide a power load based on the one or more charging parameters (¶¶46, 60, 73-74);
receiving, at the charging station, a first amount of alternating current (AC) power through a first connection line and a second amount of direct current (DC) power from a battery energy storage system through a second connection line (Fig. 2; ¶¶56, 58, 59, 67), the first connection line separated from the second connection line at the charging system (Fig. 2, connection from 280/211 separate from connection from 230a), wherein the charging station receives the second amount of power to supplement power provided by the first line (¶¶34, 67) to form a supplemented power load, wherein the supplemented power load exceeds the defined power value of the grid interconnect with AC power from the grid and DC power from the battery energy storage system (¶¶34, 37, 50, 58, 67); and
converting at least one of the first amount of power from DC power to AC power or the second amount of AC power to DC power downstream of the first connection line and the second connection line (¶56); and
transferring the supplemented power load to an electric resource through a single electric resource connection line (¶¶34, 37, 67), wherein the amount of power is varied over time during the charging session (¶¶57, 63).
If Wolfe is found to be unclear regarding the first connection line separated from the second connection line at the charging system and that the charging station receives the second amount of power to supplement power provided by the first line, the charging station forming a supplemented power load, and transferring the supplemented power load to an electric resource through a single electric resource connection line, Galin also discloses:
receiving, at the charging station, a first amount of AC power transferred through a first connection line and a second amount of DC power a the battery energy storage system through the second connection line, the first connection line separated from the second connection line at the charging system, wherein the charging station receives the second amount of power to supplement power provided by the first line to form a supplemented power load, wherein the supplemented power load exceeds the defined power value of the grid interconnect with AC power from the grid and DC power from the battery energy storage system (Fig. 1A, 111/113 and 101/106 connected to 102 through different lines; Fig. 7, 704/705 and 701/713 connected to 702 through different lines; Fig. 8, blocks 823, 829, 830; ¶¶65, 67, 85, 132-133, 137)
converting at least one of the first amount of power from DC power to AC power or the second amount of AC power to DC power downstream of the first connection line and the second connection line (Fig. 7; ¶¶53, 55); and
transferring the supplemented power load to an electric resource through a single electric resource connection line (Figs. 2 and 7), wherein the amount of power is varied over time during the charging session (¶¶77-79, 82, 115, 197-199).
It would have been obvious to persons having ordinary skill in the art before the effective filing date of the application to combine the teachings of Wolfe and Galin, because doing so would have involved merely the routine use of a known technique to improve similar devices in the same way to achieve the predictable results of improving management of power flow from grid and battery supply to a charger through independent connections from the grid and battery. KSR Int’l Co. v. Teleflex Inc., 82 U.S.P.Q.2d 1385, 1396. Wolfe discloses a charger that receives power from both the grid and a battery and provides power to loads (EVs). Galin teaches that the grid and battery should be connected to the charger through separate lines to allow multiple configurable power paths from different sources to loads. The teachings of Galin are directly applicable to Wolfe in the same way, so that Wolfe’s system would similarly connect chargers to the grid and to the batteries using separate connection lines to allow improved management of power flow from multiple sources to loads.
Wolfe does not appear to explicitly disclose receiving, at a site computing system, a machine-learned model from a remote computing device over a network; storing the machine-learned model in the memory of the site computing system; and executing the machine-learned model locally at the site computing system using one or more processors of the site computing system to determine the one or more charging parameters, wherein the one or more charging parameters are at least partially based on a peak shaving estimation algorithm that produces a peak shave target for the battery energy storage system. Guo discloses receiving, at a site computing system, a machine-learned model from a remote computing device over a network; storing the machine-learned model in the memory of the site computing system; and executing the machine-learned model locally at the site computing system using one or more processors of the site computing system to determine the one or more charging parameters (¶¶70-73), wherein the one or more charging parameters are at least partially based on a peak shaving estimation algorithm that produces a peak shave target for the battery energy storage system (¶¶15, 72). It would have been obvious to persons having ordinary skill in the art before the effective filing date of the application to combine the teachings of Wolfe, Galin, and Guo, because doing so would have involved merely the routine use of a known technique to improve similar devices in the same way to achieve the predictable results of adaptively controlling power delivery based on demand and available power. KSR Int’l Co. v. Teleflex Inc., 82 U.S.P.Q.2d 1385, 1396. Wolfe discloses controlling charge parameters based on peak-shaving. Guo provides further details of a peak-shaving algorithm. The teachings of Guo are directly applicable to Wolfe in the same way, so that Wolfe would similarly control charge parameters adaptively to account for demand and available power.
Regarding claim 12, Wolfe discloses that the one or more commands for the battery energy storage system to provide a power load based on the one or more charging parameters further includes providing an additional amount of power to supplement power provided by the grid (¶¶34, 67).
Regarding claim 13, Wolfe discloses predicting vehicle flow over a defined period, wherein determining the one or more charging parameters is based at least in part on the predicted vehicle flow (¶73).
Regarding claim 14, Wolfe discloses determining an expected power load over a defined period, wherein the one or more charging parameters are at least partially based on the expected power load over a defined period (¶73).
Regarding claim 15, Wolfe discloses producing a peak shave target for the battery energy storage system based on a peak shaving estimation algorithm, wherein the one or more charging parameters are at least partially based on the peak shave target for the battery energy storage system (¶¶3, 50, 64, 71). If Wolfe is found to be unclear regarding these limitations, Guo discloses the same (¶¶15, 72). Motivation to combine remains consistent with claim 11.
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wolfe in view of Galin, Kruszelnicki (US 2018/0339597), and Keith (US 5,462,439).
Regarding claim 21, Wolfe discloses a charging network comprising: a charging system configured to be electrically coupled with a power source (Fig. 1; ¶3), the charging system comprising: a grid interconnect, a battery energy storage system electrically coupled with the grid interconnect, a charging station electrically coupled with the grid interconnect and the battery energy storage system in parallel (Figs. 1 and 2, electric vehicle chargers/charging stations connected to electrical grid and energy storage systems in parallel, the energy storage systems also electrically coupled to the grid); and
a computing system operably coupled with the battery energy storage system and the charging station, the computing system including a processor and associated memory, the memory storing instructions that, when implemented by the processor, configure the computing system (Fig. 10; ¶40) to: determine a requested power load by the charging station at a defined time (Fig. 4; ¶73), determine a defined power value of the grid interconnect (Fig. 3; ¶¶34, 67, grid connection limit), determine one or more charging parameters based at least in part on the requested power load by the charging station at the defined time and the defined power value of the grid interconnect using a machine-learned model trained on historical data (¶¶46, 60, 73-74, 110); and generate one or more commands for the battery energy storage system or the charging station to provide a power load based on the one or more charging parameters (¶¶46, 60, 73-74), wherein the power load includes a first amount of alternating current (AC) power through a first connection line and a second amount of direct current (DC) power from a battery energy storage system through a second connection line (Fig. 2; ¶¶56, 58, 59, 67), the first connection line separated from the second connection line (Fig. 2, connection from 280/211 separate from connection from 230a); and
wherein the charging station is configured to convert at least one of the first amount of power from DC power to AC power or the second amount of AC power to DC power downstream of the first connection line and the second connection line (¶56).
Wolfe does not appear to explicitly disclose that the first connection line is separated from the second connection line between the grid interconnect and the charging station and the second connection line is separated from the first connection line between the battery energy storage system and the charging station. Galin discloses that the power load includes a first amount of alternating current (AC) power through a first connection line and a second amount of direct current (DC) power from a battery energy storage system through a second connection line, the first connection line separated from the second connection line between the grid interconnect and the charging station and the second connection line separated from the first connection line between the battery energy storage system and the charging station (Fig. 1A, 111/113 and 101/106 connected to 102 through different lines; Fig. 5; Fig. 7, 704/705 and 701/713 connected to 702 through different lines; ¶¶65, 67, 85, 132, 133).
It would have been obvious to persons having ordinary skill in the art before the effective filing date of the application to combine the teachings of Wolfe and Galin, because doing so would have involved merely the routine use of a known technique to improve similar devices in the same way to achieve the predictable results of improving management of power flow from grid and battery supply to a charger through independent connections from the grid and battery. KSR Int’l Co. v. Teleflex Inc., 82 U.S.P.Q.2d 1385, 1396. Wolfe discloses a charger that receives power from both the grid and a battery and provides power to loads (EVs). Galin teaches that the grid and battery should be connected to the charger through separate lines to allow multiple configurable power paths from different sources to loads. The teachings of Galin are directly applicable to Wolfe in the same way, so that Wolfe’s system would similarly connect chargers to the grid and to the batteries using separate connection lines to allow improved management of power flow from multiple sources to loads.
Wolfe does not appear to explicitly disclose a remote power management module located in the remote electric resource and configured to electrically couple with the charging station through a single electric resource connection line, the remote power management module further configured to control an amount of power or a timing of power being transferred into or out of a remote electric resource. Kruszelnicki discloses these limitations (¶58). It would have been obvious to persons having ordinary skill in the art before the effective filing date of the application to combine the teachings of Wolfe, Galin, and Kruszelnicki, because doing so would have involved merely the routine combination of known elements according to known techniques to produce merely the predictable results of controlling charging in accordance with the battery’s management system. KSR Int’l Co. v. Teleflex Inc., 82 U.S.P.Q.2d 1385, 1395. Wolfe discloses EV charging stations. Kruszelnicki teaches that the EV battery management system connects with the charging station to control charging based on battery conditions. The teachings of Kruszelnicki are directly applicable to Wolfe in the same way, so that Wolfe’s charging stations would similarly charge EVs in accordance with the EV’s battery management.
Wolfe does not appear to explicitly disclose that at least one of the one or more commands is transmitted to the remote power management module when communicatively coupled with the charging system and is configured to provide self-contained control of power transfer and safety measures when communication with the charging system is interrupted. Keith discloses these limitations (col. 7, lines 32-35; col. 8, lines 1-20; col. 23, line 62 to col. 24, line 8; col. 25, lines 9-23). It would have been obvious to persons having ordinary skill in the art before the effective filing date of the application to combine the teachings of Wolfe, Galin, Kruszelnicki, and Keith, because doing so would have involved merely the routine use of a known technique to improve similar devices in the same way to achieve the predictable results of allowing the EV to control charging without requiring ongoing communication with the charging station. KSR Int’l Co. v. Teleflex Inc., 82 U.S.P.Q.2d 1385, 1396. Wolfe discloses EV charging. Keith teaches that the EV has a charging controller that controls charging. The teachings of Keith are directly applicable to Wolfe in the same way, so that Wolfe would similarly use an EV charging controller to allow the EV to control charging without requiring ongoing communication with the charging station.
Claim(s) 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wolfe in view of Galin, Faries, Baxter, and Kruszelnicki.
Regarding claim 24, Wolfe discloses a remote power management module configured to electrically couple with the charging station through a single electric resource connection line, the remote power management module further configured to control an amount of power or a timing of power being transferred into or out of a remote electric resource (¶¶57, 74, computing devices connected to charging stations remotely or physically via a network, such as serial, LAN, etc. to control distribution of allowed amounts of power); if Wolfe is found to be unclear regarding these limitations, Kruszelnicki also discloses the same (¶58). It would have been obvious to persons having ordinary skill in the art before the effective filing date of the application to combine the teachings of Wolfe, Galin, Faries, Baxter, and Kruszelnicki, because doing so would have involved merely the routine combination of known elements according to known techniques to produce merely the predictable results of controlling charging in accordance with the battery’s management system. KSR Int’l Co. v. Teleflex Inc., 82 U.S.P.Q.2d 1385, 1395. Wolfe discloses EV charging stations. Kruszelnicki teaches that the EV battery management system connects with the charging station to control charging based on battery conditions. The teachings of Kruszelnicki are directly applicable to Wolfe in the same way, so that Wolfe’s charging stations would similarly charge EVs in accordance with the EV’s battery management.
Response to Arguments
Applicant's arguments filed 2 June 2026 have been fully considered but they are not persuasive.
Newly-added limitations are addressed above in the rejections, in some cases using newly-cited art.
Applicant asserts that Wolfe and Galin fail to teach the limitations of claim 1. Remarks 11. The examiner disagrees. Applicant simply states that Wolfe and Galin fail to teach the recited limitations, and does not address the rejection or the cited portions of the art. Wolfe and Galin clearly teach the recited limitations in the manner set forth in the rejection. Applicant further asserts that the examiner fails to explain why persons having ordinary skill in the art would have been motivated to combine Wolfe and Galin. Remarks 15. The examiner disagrees. The portion of the Office Action quoted in the Remarks explicitly addresses the motivation. The claimed invention is a textbook case of obviousness: as stated in the rejection, Wolfe teaches the overall charging site architecture except for the connection line separation at the charging station, and Galin teaches separate charging lines at the charging station that allows better independent control of power flow from different power sources and combinations of power sources. Persons having ordinary skill in the art would clearly recognize the claimed invention as obvious.
Applicant repeats the same form of argument for claim 11 (broadly stating that the prior art fails to teach the recited limitations, and alleging that the examine fails to provide a motivation to combine). Remarks 17. The examiner disagrees for the same reasons stated above. Regarding the additional limitations pertaining to the machine-learning model for peak shaving, as discussed above in the rejection, Guo clearly and explicitly teaches all of the recited limitations, including the new limitations added in the most recent amendment. Regarding the further combination with Guo, peak shaving itself is extremely well-known, and Wolfe already teaches the general concept. Guo’s adaptive machine-learning-based peak shaving algorithm is a clear and obvious improvement that allows adaptive control of power delivery according to the peak shaving algorithm. Furthermore, applying machine-learning to improve existing algorithms is a routine improvement strategy well-known to persons having ordinary skill in the art.
Applicant repeats the same form of argument for claim 21 (broadly stating that the prior art fails to teach the recited limitations, and alleging that the examine fails to provide a motivation to combine). Remarks 22. The examiner disagrees for the same reasons stated above. Regarding the further combination with Kruszelnicki, as stated in the rejection, Wolfe teaches EV charging stations, and Kruszelnicki teaches that the EV battery management system connects with the charging station to control charging based on battery conditions. Persons having ordinary skill in the art would clearly recognize that allowing the EV’s battery management system, which has information on the battery conditions, to control the charging of the battery, is an obvious modification.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARIC LIN whose telephone number is (571)270-3090. The examiner can normally be reached M-F 07:30-17:00 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jack Chiang can be reached at 571-272-7483. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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12 August 2026
/ARIC LIN/ Examiner, Art Unit 2851
/JACK CHIANG/ Supervisory Patent Examiner, Art Unit 2851
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