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 .
Response to Amendment
The Amendment filed 6/24/2026 has been entered. Claims 1, 5-8, 11-16, and 20-25 remain pending in the application along with added new claims 26-28, and claims 2-4, 9-10, and 17-19 have been canceled. This Office Action is made Final.
Response to Arguments
Applicant's arguments filed 6/24/2026 have been fully considered but they are not persuasive.
Applicant submits on page 9 of remarks that Cella's first neural network 4522, which processes inputs to "predict for at least one of the plurality of vehicles a target energy renewal region," does not disclose "a first model to simulate one or more operating characteristics of the charging site selected from (a) at least one power source including an alternating current station or a direct current power cabinet, (b) a set of output channels, or (c) a set of dispensers, wherein each output channel supplies one or more dispensers of the set of dispensers" (claim 1, as amended). For instance, Applicant respectfully submits that Cella does not disclose any model that simulates operating characteristics selected from power sources, output channels, or dispensers.
The examiner submits that the second neural network 4520 of Cella processes inputs relating to charging or refueling infrastructure and also processes vehicle energy renewal infrastructure usage and demand information for vehicle energy renewal infrastructure facilities within the target energy renewal region to determine at least one parameter 4514 of a charge infrastructure operational plan 4512 that facilitates access by the at least one of the plurality vehicles to renewal energy in the target energy renewal region 4516 (¶[612])..
The intelligent transportation systems of Cella are for the purpose of being applied to real world systems for improved transportation and efficiency (¶[3-4]). The examiner interprets that the intelligent transportation systems of Cella would be ineffective if it did not take into account in its models of usage and demand information including parameters such as users choosing alternating current or direct current methods as they directly affect power availability of the stations and user demand. Further, a person of ordinary skill would know that any supply of charging station would be one of either a direct current or alternating current. Output channels and dispensers can easily be understood by a person of ordinary skill as the charging ports from which the charging stations connect with electric vehicles.
Applicant submits on page 10 that Cella's second neural network 4520, which processes inputs to "determine at least one parameter 4514 of a charge infrastructure operational plan," does not disclose "a second model to simulate charging of one or more batteries of the multiple vehicles based on the one or more operating characteristics simulated by the first model" (claim 1, as amended). Cella's two neural networks operate independently, and Cella does not disclose that the second neural network uses any output from the first neural network.
Cella does not disclose "one or more simulated charging sessions of the one or more batteries based on one or more combinations of the one or more operating characteristics simulated by the first model" (claim 1, as amended). Cella's neural networks predict target energy renewal regions and determine charging plan parameters, but Cella does not disclose any simulated charging sessions, let alone simulated charging sessions "based on one or more combinations of the one or more operating characteristics simulated by the first model" (claim 1, as amended).
The examiner submits that electric vehicles to be charged depend on stations that can supply sufficient power. Hence, the first neural network and second neural network of Cella are not independent. They are complementary.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 8, and 15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 8, and 15 recite
“a first model to simulate one or more operating characteristics of the charging site selected from (a) at least one power source including an alternating current station or a direct current power cabinet, (b) a set of output channels, or (c) a set of dispensers, wherein each output channel supplies one or more dispensers of the set of dispensers.”
The clause “wherein each output channel supplies one or more dispensers of the set of dispensers” appears to link limitations in alternatives (b) output channels and (c) dispensers, thus forcing (b) and (c) to both be selected as operating characteristics of the charging site.
For the purposes of compact prosecution, the examiner interprets the recitation as
“a first model to simulate one or more operating characteristics of the charging site including (a) at least one power source including an alternating current station or a direct current power cabinet, (b) a set of output channels, and (c) a set of dispensers, wherein each output channel supplies one or more dispensers of the set of dispensers.”
Claims 5-7, 11-14, 16, and 20-28 inherit the deficiencies noted with respect to one of claims 1, 8, and 15 above and are thus also rejected under 35 U.S.C. 112(b).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 5-8, 11-16, and 20-28 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cella (US 20200192350 A1).
Regarding independent claim 1, Cella teaches a vehicle charging system (Fig. 45) comprising:
one or more processors and a memory (¶0806: system includes computer readable storage media executed on processors) storing computer-executable instructions that, when executed, cause the one or more processors to:
aggregate available first data associated with states of multiple vehicles (¶0018: classified states of a vehicle) and available second data associated with a charging site and associated with charging the multiple vehicles at the charging site (¶0201: “operational state and energy consumption information from at least one of a plurality of network-enabled vehicles is gathered in real time” is associated with recharging stations via the vehicle transportation system which includes vehicle information) (¶0203: recharging infrastructure comprises recharging stations);
execute a pre-trained learning model to apply a charging policy to the available data to charge the multiple vehicles at the charging site based at least on the available first data and the available second data, the pre-trained learning model configured to take actions and to observe effects of the actions in a simulated charging environment to obtain the charging policy (¶[612]: neural networks serve as models from which a charging plan is developed), wherein the simulated charging environment includes:
(i) a first model to simulate one or more operating characteristics of the charging site selected from (a) at least one power source including an alternating current station or a direct current power cabinet, (b) a set of output channels, or (c) a set of dispensers, wherein each output channel supplies one or more dispensers of the set of dispensers; (¶[612]: “a second neural network 4520 of the hybrid neural network is used to process inputs relating to charging or refueling infrastructure and the like. The second neural network 4520 may process vehicle energy renewal infrastructure usage and demand information for vehicle energy renewal infrastructure facilities within the target energy renewal region to determine at least one parameter 4514 of a charge infrastructure operational plan 4512 that facilitates access by the at least one of the plurality vehicles to renewal energy in the target energy renewal region 4516”)
(The intelligent transportation systems of Cella are for the purpose of being applied to real world systems for improved transportation and efficiency (¶[3-4]). The examiner interprets that the intelligent transportation systems of Cella would be ineffective if it did not take into account in its models of usage and demand information including parameters such as users choosing alternating current or direct current methods as they directly affect power availability of the stations and user demand. Further, a person of ordinary skill would know that any supply of charging station would be one of either a direct current or alternating current. Output channels and dispensers can easily be understood by a person of ordinary skill as the charging ports from which the charging stations connect with electric vehicles)
and
(ii) a second model to simulate charging of one or more batteries of the multiple vehicles based on the one or more operating characteristics simulated by the first model (¶[612]: “A first neural network 4522 of the hybrid neural network may be used to process inputs relating to charge or fuel states of the plurality of vehicles (directly received from the vehicles or through the vehicle information port 4532) … the first neural network 4522 may process inputs comprising vehicle route and stored energy state information for a plurality of vehicles to predict for at least one of the plurality of vehicles a target energy renewal region.”), and wherein the actions taken include:
one or more simulated charging sessions of the one or more batteries based on one or more combinations of the one or more operating characteristics simulated by the first model (¶[612]); and
provide, via a charger of the vehicle charging system, power to charge the multiple vehicles in accordance with the charging policy (¶[203]: “the artificial intelligence system provides a recharging plan that accommodates near-term charging needs for the plurality of rechargeable vehicles based on the optimized at least one parameter”).
Regarding claim 3, Cella teaches the vehicle charging system of claim 1, including a simulated charging environment (¶0203: artificial intelligence system) modeled to include at least one power source chosen from an Alternating Current (AC) station and a Direct Current (DC) power cabinet, wherein the at least one power source is modeled as a set of output channels and a set of dispensers, and wherein each output channel of the set of output channels supplies one or more dispensers of the set of dispensers (models including DC and AC outlets arranged in parallel or in series fall within the scope of the recharging stations of Cella as is common in the layouts of Electric Vehicle Service Equipment).
Regarding claim 5, Cella teaches the vehicle charging system of claim 1, wherein the available first data includes at least one data type chosen from energy rate data, carbon emissions data, and renewable energy source data, and wherein the simulated charging environment includes a charging infrastructure modeled to include at least one object chosen from an object modeling energy rates, an object modeling carbon emissions, and an object modeling renewable energy sources (¶0203: the artificial intelligence system optimizes energy parameters ; ¶0293: parameters include reduced carbon footprint).
Regarding claim 6, Cella teaches the vehicle charging system of claim 1, wherein the simulated charging environment includes the multiple vehicles modeled to include at least one object chosen from an arrival time of a respective vehicle of the multiple vehicles, a state of charge of a battery of the respective vehicle, a charge curve for the battery of the respective vehicle, details of the battery of the respective vehicle, a departure time of the respective vehicle, or a minimum required charge of the respective vehicle (¶0204: artificial system optimizes electricity usage, recharge time, duration of time for recharging, value of charging, and battery charge state ; ¶0293: rider experience parameter includes desired arrival times).
Regarding claim 7, Cella teaches the vehicle charging system of claim 1, wherein the pre-trained learning model includes a Policy Gradient Algorithm (PGA) configured to train a neural network based on at least one set of data chosen from simulated data and a cache of data collected from one or more charging sites (¶0391: parameters for charging are integrated with a system for robotic process automation (RPA). “Through a large training set of observation of human interactions and system states, events, and outcomes, the RPA system may learn to interact with the system in a fashion that mimics that of the human. Learning may be reinforced by training and supervision …”).
Regarding independent claim 8, Cella teaches a method for vehicle charging comprising:
aggregating, by one or more processors coupled with memory, available first data associated with states of multiple vehicles (¶0018: classified states of a vehicle) and available second data associated with a charging site and associated with charging the multiple vehicles at the charging site (¶0201: “operational state and energy consumption information from at least one of a plurality of network-enabled vehicles is gathered in real time” is associated with recharging stations via the vehicle transportation system which includes vehicle information) (¶0203: recharging infrastructure comprises recharging stations);
executing, by the one or more processors (¶0806: system includes computer readable storage media executed on processors), a pre-trained learning model to apply a charging policy to charge the multiple vehicles at the charging site based at least on the available first data and available second data, the pre-trained learning model configured to take actions and to observe effects of the actions in a simulated charging environment to obtain the charging policy (¶[612]: neural networks serve as models from which a charging plan is developed), wherein the simulated charging environment includes:
(i) a first model to simulate one or more operating characteristics of the charging site selected from (a) at least one power source including an alternating current station or a direct current power cabinet, (b) a set of output channels, or (c) a set of dispensers, wherein each output channel supplies one or more dispensers of the set of dispensers; (¶[612]: “a second neural network 4520 of the hybrid neural network is used to process inputs relating to charging or refueling infrastructure and the like. The second neural network 4520 may process vehicle energy renewal infrastructure usage and demand information for vehicle energy renewal infrastructure facilities within the target energy renewal region to determine at least one parameter 4514 of a charge infrastructure operational plan 4512 that facilitates access by the at least one of the plurality vehicles to renewal energy in the target energy renewal region 4516”)
(The intelligent transportation systems of Cella are for the purpose of being applied to real world systems for improved transportation and efficiency (¶[3-4]). The examiner interprets that the intelligent transportation systems of Cella would be ineffective if it did not take into account in its models of usage and demand information including parameters such as users choosing alternating current or direct current methods as they directly affect power availability of the stations and user demand. Further, a person of ordinary skill would know that any supply of charging station would be one of either a direct current or alternating current. Output channels and dispensers can easily be understood by a person of ordinary skill as the charging ports from which the charging stations connect with electric vehicles)
and
(ii) a second model to simulate charging of one or more batteries of the multiple vehicles based on the one or more operating characteristics simulated by the first model (¶[612]: “A first neural network 4522 of the hybrid neural network may be used to process inputs relating to charge or fuel states of the plurality of vehicles (directly received from the vehicles or through the vehicle information port 4532) … the first neural network 4522 may process inputs comprising vehicle route and stored energy state information for a plurality of vehicles to predict for at least one of the plurality of vehicles a target energy renewal region.”), and wherein the actions taken include:
one or more simulated charging sessions of the one or more batteries based on one or more combinations of the one or more operating characteristics simulated by the first model (¶[612]); and
providing, by the one or more processors, via a charger, power to charge the multiple vehicles in accordance with the charging policy (¶[203]: “the artificial intelligence system provides a recharging plan that accommodates near-term charging needs for the plurality of rechargeable vehicles based on the optimized at least one parameter”).
Regarding claim 9, Cella teaches the method of claim 8, wherein the simulated charging environment (¶0203: artificial intelligence system) includes a charging infrastructure modeled to include at least one power source chosen from an Alternating Current (AC) station and a Direct Current (DC) power cabinet, wherein the at least one power source is modeled as a set of output channels and a set of dispensers, and wherein each output channel of the set of output channels supplies one or more dispensers of the set of dispensers (models including DC and AC outlets arranged in parallel or in series fall within the scope of the recharging stations of Cella as is common in the layouts of Electric Vehicle Service Equipment).
Regarding claim 11, Cella teaches the method of claim 8, wherein the alternating current power station is modeled as a single object that includes the set of output channels and the set of dispensers, and wherein a number of the set of output channels is equal to a number of the set of dispensers (each AC power station modeled as a single object including a set of dispensers equal to the number of the set of output channels falls within the scope of Cella).
Regarding claim 12, Cella teaches the method of claim 8, wherein the available first data includes at least one data type chosen from energy rate data, carbon emissions data, and renewable energy source data, and wherein the simulated charging environment includes a charging infrastructure modeled to include at least one object chosen from an object modeling energy rates, an object modeling carbon emissions, and an object modeling renewable energy sources (¶0203: the artificial intelligence system optimizes energy parameters ; ¶0293: parameters include reduced carbon footprint).
Regarding claim 13, Cella teaches the method of claim 8, wherein the simulated charging environment includes the multiple vehicles modeled to include at least one object chosen from an arrival time of a respective vehicle of the multiple vehicles, a state of charge of a battery of the respective vehicle, a charge curve for the battery of the respective vehicle, details of the battery of the respective vehicle, a departure time of the respective vehicle, or a minimum required charge of the respective vehicle (¶0204: artificial system optimizes electricity usage, recharge time, duration of time for recharging, value of charging, and battery charge state ; ¶0293: rider experience parameter includes desired arrival times).
Regarding claim 14, Cella teaches the method of claim 8, wherein the pre-trained learning model includes a Policy Gradient Algorithm (PGA) configured to train a neural network based on at least one set of data chosen from simulated data and a cache of data collected from one or more charging sites (¶0391: parameters for charging are integrated with a system for robotic process automation (RPA). “Through a large training set of observation of human interactions and system states, events, and outcomes, the RPA system may learn to interact with the system in a fashion that mimics that of the human. Learning may be reinforced by training and supervision …”).
Regarding independent claim 15, Cella teaches a method for vehicle charging using reinforcement learning comprising:
training, by one or more processors coupled with memory (¶0806: system includes computer readable storage media executed on processors), a learning model (Fig. 45 and ¶[203]: artificial intelligence system) to (i) obtain a charging policy (recharging plan) (ii) take actions (applying feedback) and (iii) observe effects of the actions taken in a simulated charging environment using at least one set of data chosen from simulated data and a cache of data collected for charging multiple vehicles at one or more charging sites (¶[391): “artificial intelligence/machine learning system may be trained on a training set of data”);
aggregating, by the one or more processors, available first data associated with states of the multiple vehicles (¶0018: classified states of a vehicle) and available second data associated with a charging site and associated with charging the multiple vehicles at the charging site (¶0201: “operational state and energy consumption information from at least one of a plurality of network-enabled vehicles is gathered in real time” ” is associated with recharging stations via the vehicle transportation system which includes vehicle information) (¶0203: recharging infrastructure comprises recharging stations);
executing, by the one or more processors, the learning model to apply the charging policy to the available data to charge the multiple vehicles at the charging site based at least on the available first data and the available second data and within the simulated charging environment (¶[612]: neural networks serve as models from which a charging plan is developed), wherein the simulated charging environment includes:
(i) a first model to simulate one or more operating characteristics of the charging site selected from (a) at least one power source including an alternating current station or a direct current power cabinet, (b) a set of output channels, or (c) a set of dispensers, wherein each output channel supplies one or more dispensers of the set of dispensers; (¶[612]: “a second neural network 4520 of the hybrid neural network is used to process inputs relating to charging or refueling infrastructure and the like. The second neural network 4520 may process vehicle energy renewal infrastructure usage and demand information for vehicle energy renewal infrastructure facilities within the target energy renewal region to determine at least one parameter 4514 of a charge infrastructure operational plan 4512 that facilitates access by the at least one of the plurality vehicles to renewal energy in the target energy renewal region 4516”)
(The intelligent transportation systems of Cella are for the purpose of being applied to real world systems for improved transportation and efficiency (¶[3-4]). The examiner interprets that the intelligent transportation systems of Cella would be ineffective if it did not take into account in its models of usage and demand information including parameters such as users choosing alternating current or direct current methods as they directly affect power availability of the stations and user demand. Further, a person of ordinary skill would know that any supply of charging station would be one of either a direct current or alternating current. Output channels and dispensers can easily be understood by a person of ordinary skill as the charging ports from which the charging stations connect with electric vehicles)
and
(ii) a second model to simulate charging of one or more batteries of the multiple vehicles based on the one or more operating characteristics simulated by the first model (¶[612]: “A first neural network 4522 of the hybrid neural network may be used to process inputs relating to charge or fuel states of the plurality of vehicles (directly received from the vehicles or through the vehicle information port 4532) … the first neural network 4522 may process inputs comprising vehicle route and stored energy state information for a plurality of vehicles to predict for at least one of the plurality of vehicles a target energy renewal region.”), and wherein the actions taken include:
one or more simulated charging sessions of the one or more batteries based on one or more combinations of the one or more operating characteristics simulated by the first model (¶[612]); and
providing, by the one or more processors, via a charger, power to charge the multiple vehicles in accordance with the charging policy (¶[203]: “the artificial intelligence system provides a recharging plan that accommodates near-term charging needs for the plurality of rechargeable vehicles based on the optimized at least one parameter”).
Regarding claim 16, Cella teaches the method of claim 15, wherein the learning model includes a Policy Gradient Algorithm (PGA), the PGA including a neural network that includes a set of parameters that define the charging policy, the set of parameters being updated based on trajectories obtained by the actions taken and the effects observed s given a reward function and an objective function (¶0391: parameters for charging are integrated with a system for robotic process automation (RPA). “Through a large training set of observation of human interactions and system states, events, and outcomes, the RPA system may learn to interact with the system in a fashion that mimics that of the human. Learning may be reinforced by training and supervision …”).
Regarding claim 20, Cella teaches the method of claim 15, wherein the alternating current power station is modeled as a single object that includes the set of output channels and the set of dispensers, and wherein a number of the set of output channels is equal to a number of the set of dispensers (each AC power station modeled as a single object including a set of dispensers equal to the number of the set of output channels falls within the scope of Cella).
Regarding claim 21, Cella teaches the vehicle charging system of claim 1, wherein the one or more simulated charging sessions include simulating multiple adjustments to capacity and load distributions across multiple dispensers of the charging site (Fig. 45 and ¶[612, 624, 388]: the second neural network 4620 is used to process inputs relating to charging or refueling infrastructure and the like. Infrastructure control parameters include routing, battery state, charge value, and charge demand ).
Regarding claim 22, Cella teaches the vehicle charging system of claim 1, wherein simulating the charging of the one or more batteries includes simulating a sequential charging of the one or more batteries (Fig. 45 and ¶[810-811, 612]: “The acts performed as part of the method may be ordered in any suitable way.” “A first neural network 4522 of the hybrid neural network may be used to process inputs relating to charge or fuel states of the plurality of vehicles”).
Regarding claim 23, Cella teaches the method of claim 8, wherein the one or more simulated charging sessions include simulating multiple adjustments to capacity and load distributions across multiple dispensers of the charging site (Fig. 45 and ¶[612, 624, 388]: the second neural network 4620 is used to process inputs relating to charging or refueling infrastructure and the like. Infrastructure control parameters include routing, battery state, charge value, and charge demand ).
Regarding claim 24, Cella teaches the method of claim 8, wherein simulating the charging of the one or more batteries includes simulating a sequential charging of the one or more batteries (Fig. 45 and ¶[810-811, 612]: “The acts performed as part of the method may be ordered in any suitable way.” “A first neural network 4522 of the hybrid neural network may be used to process inputs relating to charge or fuel states of the plurality of vehicles”).
Regarding claim 25, Cella teaches the method of claim 15, wherein the one or more simulated charging sessions include simulating multiple adjustments to capacity and load distributions across multiple dispensers of the charging site (Fig. 45 and ¶[612, 624, 388]: the second neural network 4620 is used to process inputs relating to charging or refueling infrastructure and the like. Infrastructure control parameters include routing, battery state, charge value, and charge demand ).
Regarding claim 26, Cella teaches the vehicle charging system of claim 1, wherein the one or more simulated charging sessions include sequential or parallel simulated charging of multiple electric vehicles, with adjustments to the one or more operating characteristics simulated by the first model (Fig. 45 and ¶[602]: multiple local charging infrastructure systems 4538 and multiple vehicles 4510. An accumulation of vehicles are queued up to use a local charging kiosk. The examiner interprets local as one of many charging infrastructure systems for charging vehicles. As the intelligent transportation systems of Cella are for the purpose of being applied to real world systems for improved transportation and efficiency (¶[3-4]), the examiner interprets that the intelligent transportation systems of Cella would be ineffective if it didn’t simulate the real-world situation of vehicles being charged in sequence or in parallel).
Regarding claim 27, Cella teaches the method of claim 8, wherein the one or more simulated charging sessions include sequential or parallel simulated charging of multiple electric vehicles, with adjustments to the one or more operating characteristics simulated by the first model (Fig. 45 and ¶[602]: multiple local charging infrastructure systems 4538 and multiple vehicles 4510. An accumulation of vehicles are queued up to use a local charging kiosk. The examiner interprets local as one of many charging infrastructure systems for charging vehicles. As the intelligent transportation systems of Cella are for the purpose of being applied to real world systems for improved transportation and efficiency (¶[3-4]), the examiner interprets that the intelligent transportation systems of Cella would be ineffective if it didn’t simulate the real-world situation of vehicles being charged in sequence or in parallel).
Regarding claim 28, Cella teaches the method of claim 15, wherein the one or more simulated charging sessions include sequential or parallel simulated charging of multiple electric vehicles, with adjustments to the one or more operating characteristics simulated by the first model (Fig. 45 and ¶[602]: multiple local charging infrastructure systems 4538 and multiple vehicles 4510. An accumulation of vehicles are queued up to use a local charging kiosk. The examiner interprets local as one of many charging infrastructure systems for charging vehicles. As the intelligent transportation systems of Cella are for the purpose of being applied to real world systems for improved transportation and efficiency (¶[3-4]), the examiner interprets that the intelligent transportation systems of Cella would be ineffective if it didn’t simulate the real-world situation of vehicles being charged in sequence or in parallel).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Von Novak et al. (US 20150102775 A1, published 2015-04-16) teaches a vehicle charging system comprising a processor (Fig. 2 and ¶0022: controller 208) and employing a charging policy (¶0034-0035).
Chang et al. (US 20140210408 A1, published 2014-07-31) teaches EVSEs of different makes and models connected in a series.
Mishra (“L1 and L2 EV Charger Electric Vehicle Service Equipment Design Considerations,” Texas Instruments, Revised June 2021) teaches power configurations for electric vehicle service equipment.
THIS ACTION IS MADE FINAL. 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 Ryu-Sung P. Weinmann whose telephone number is (703)756-5964. The examiner can normally be reached Monday-Friday 9am-5pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Julian Huffman, can be reached at (571) 272-2147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Ryu-Sung P. Weinmann/Examiner, Art Unit 2859 October 13, 2026
/JULIAN D HUFFMAN/Supervisory Patent Examiner, Art Unit 2859
Prosecution Insights