DETAILED ACTION
This Office Action is in response to Applicants application filing received on May 13, 2025. Claim(s) 1-20 is/are currently pending in the instant application. The Applicant claims foreign priority to Korea application 10-2024-0070853 filed on May 30, 2024.
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 .
Information Disclosure Statement
The Examiner acknowledges the Applicants filing of IDS references on ***. The references have been considered at this time. A copy of the annotated IDS sheet is included in this correspondence.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in Korea on May 30, 2024. It is noted, however, that applicant has not filed a certified copy of the Korean application as required by 37 CFR 1.55.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-20 are directed to one of the four statutory classes of invention (e.g. process, machine, manufacture, or composition of matter). The claims include a system or “apparatus”, method or “process” and is a method or scheduling charging or discharging of an electric vehicle (Step 1: YES).
The Examiner has identified independent method Claim 12 as the claim that represents the claimed invention for analysis and is similar to independent system Claim 1. Claim 12 recites the limitations of (abstract ideas highlighted in italics and additional elements highlighted in bold)
creating a plurality of electric vehicle groups by clustering a plurality of electric vehicles according to a state of charge (SoC) in a first time slot;
setting a first charging and discharging schedule for an electric vehicle group, wherein a total cost of an energy cost and a battery wear cost of the electric vehicle group is minimized in the first time slot; and
setting a second charging and discharging schedule for an individual electric vehicle of the electric vehicle group based on the first charging and discharging schedule in the first time slot.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as “Mental Processes”. Creating a grouping of vehicles, setting a first schedule for the group based on certain cost minimization, and setting a second schedule based on the prior schedule recites a concept performed in the human mind. But for the “computer device”, “memory storing instructions”, and “processor” as part of the claim preamble, the claim encompasses creating a charge / discharge schedule for electric vehicles based on minimizing cost and changing or updating the schedule at a later time using his/her mind. The mere nomina recitation of a generic computer applied does not take the claim out of the mental processes grouping. Accordingly, the claim recites an abstract idea. The apparatus for scheduling charging and discharging of an electric vehicle in Claim 1 is just applying generic computer components to the recited abstract limitations. Claim 1 is also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as “Certain Methods of Organizing Human Activity”. Creating a grouping of vehicles, setting a first schedule for the group based on certain cost minimization, and setting a second schedule based on the prior schedule recites a fundamental economic practice. The concept of optimizing a charging and discharging schedules for electric vehicles involves charging when electric rates and lower and discharging (selling power) when rates are higher with the goal being to minimize the overall cost with respect to the stored electrical charge. The apparatus for scheduling charging and discharging of an electric vehicle in Claim 1 is just applying generic computer components to the recited abstract limitations. Claim 1 is also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as “Mathematical Concepts ”. Creating a grouping of vehicles, setting a first schedule for the group based on certain cost minimization, and setting a second schedule based on the prior schedule recites a mathematical formula or calculation. [0083] of the Applicants disclosure points to the energy cost and the battery wear cost as an objective function to optimize and ultimately minimize the total cost by solving the objective function. While a specific formula is not presented the Applicant has stated that use of an objective function (e.g. linear programming) which is mathematical formulas and equations. Accordingly, the claim recites an abstract idea. The apparatus for scheduling charging and discharging of an electric vehicle in Claim 1 is just applying generic computer components to the recited abstract limitations. Claim 1 is also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. In particular, the claims only recite a memory storing executable instructions and a processor to execute the instructions (Claim 1) and/or a computing device, memory storing executable instructions, and a processor executing the stored instructions (preamble) (claim 12). The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1 and 12 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Applicant’s specification para. [0070] about implementation using general purpose or special purpose computing devices (The electric vehicle charging and discharging scheduling apparatus 100 according to the embodiment may be implemented in a logic circuit by hardware, firmware, software, or a combination thereof, and may also be implemented using a general-purpose or special-purpose computer. The apparatus may be implemented using a hardwired device, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or the like. In addition, the apparatus 100 may be implemented as a system on chip (SoC) including one or more processors and controllers.) and MPEP 2106.05(f) where applying a computer as a tool is not indicative of significantly more.<< Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus claims 1 and 12 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-11 and 13-20 further define the abstract idea that is present in their respective independent claims 1 and 12 and thus correspond to Mental Processes, Certain Methods of Organizing Human Activity, and/or Mathematical Concepts and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. The dependent claims include steps or processes which are similar to that disclosed in MPEP 2106.05(d), (f), (g), and/or (h) which include activities and functions the courts have determined to be well-understood, routine, and conventional when claimed in a generic manner, or as insignificant extra solution activity, or as merely indicating a field of use or technological environment in which to apply the judicial exception.
Claims 2 and 7 are not more than MPEP 2106.05(f)(2) i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
Claims 3 and 4 are determining a vehicle group using the scheduled entry time which equates to MPEP 2106.05(d)II. ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.");
Claims 5 and 6 are similar to ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.");
Claim 8 is similar to MPEP2106.05(f)(2) v. Requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015);
Claim 9-11 are drawn to MPEP 2106.05(f)(2) i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
Claims 13-20 are identical or near identical to claims 2, 3, 6, 8, 9, 10, 11, and 4 respectively.
Therefore, the claims 2-11 and 13-20 are directed to an abstract idea. Thus, the claims 1-20 are not patent-eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kiessling et al. U.S. Publication 2024/0116388 A1 (hereafter Kiessling)
Regarding claim 1, Kiessling discloses clustering, via the processor, a plurality of electric vehicles into an electrical vehicle group according to a state of charge (SoC) for a time slot (see at least [0040] a customer provides fleet parameters and preferred duty cycles to the optimizer system. Fleet parameters may include a number of vehicles, types of vehicles, target states of charge, vehicle duty cycles, vehicle ranges, vehicle battery sizes, a number and availability status of parking stations, power output limits of charging stations, and other parameters. A vehicle duty cycle may include a vehicle use schedule, an arrival time at a charging station, or a duration of stay at the charging station. A charging schedule may be created at step 220 for an electric vehicle based on the charging method plan and the customer-provided parameters at the current time.);
setting, via the processor, a first charging and discharging schedule for the electric vehicle group, wherein a total cost of an energy cost and a battery wear cost of the electric vehicle group is minimized for the time slot (see at least [0041] At step 230, the one or more fleets or one or more charging depots may execute a charging schedule during a time interval N. A distinct charging schedule may be executed for each electric vehicle in each electric vehicle fleet. The charging schedule may be based on a charging method plan for the electric vehicle fleet to which the electric vehicle belongs or the charging depot at which the vehicle is plugged in. The charging schedule may be updated based on updates to customer-provided parameters or real time event updates. [0028] A fleet-based charging method plan may coordinate charging schedules between one or more electric vehicles and one or more charging stations to decrease energy costs, increase revenue from vehicle to grid transfer to power ancillary services, or reduce electric vehicle down time.);
setting, via the processor, a second charging and discharging schedule for an electric vehicle of the electric vehicle group based on the first charging and discharging schedule for the time slot (see at least [0042] An event update from an electric vehicle may further comprise telematics information, for example location, movements, status, or behavior of a vehicle or fleet of electric vehicles. An event update from a charging station may comprise arrival or departure of an electric vehicle, a state of charge of the electric vehicle, a charging rate, a power output limit, and power metrics. An event update from an automated grid signal may include an Automatic Generation Control (AGC) signal.); and
charging the electric vehicle based on the first charging and discharging schedule or the second charging or discharging schedule (see at least [0045] The charging schedules for one or more electric vehicles may be updated at step 280. The charging schedules may be updated based on real time event updates or updates to input parameters including the states of one or more electric vehicles plugged into one or more charging stations, the AGC signal, the ancillary service energy market demand, improved grid stabilization and utilization, or increased gross contribution. Parameters may be used individually or in any combination. Updates to gross contribution are described in further detail with respect to FIG. 3.).
Regarding claim 2, Kiessling discloses wherein the instructions further comprise clustering, via the processor, the plurality of electric vehicles for a next time slot by applying the SoC changed according to the first charging and discharging schedule and the second charging and discharging schedule (see at least [0008] a method is provided that comprises measuring power consumption of a site over a time period, determining a consumption trend indicator for the power consumption during the time period, and determining a charging rate for an electric vehicle at the site based on the consumption trend indicator. [0026] a charging method plan may be implemented by a fleet of ride sharing autonomous electric vehicles. The charging method plan may provide guidelines to one or more electric vehicles in the fleet regarding departure times from a charging depot and arrival times to a charging depot. The charging method plan may be used to set one or more charging schedules for one or more electric vehicles. A charging schedule may comprise periods of charging, periods of discharging, power levels or rates of charging, and power levels or rates of discharging.).
Regarding claim 3, Kiessling discloses wherein the instructions further comprise determining, via the processor, an electric vehicle group for an entering electric vehicle using a scheduled entry time and a SoC of the entering electric vehicle (see at least [0048] Detected vehicle parameters may include vehicle states of charge, vehicle entry times at charging stations, predicted durations of stay at the charging stations, and target states of charge. Customer-provided parameters may include fleet vehicle compositions, nominal state of charge trajectories for one or more electric vehicles, predicted vehicle schedules, predicted states of charge, predicted vehicle locations, and predicted vehicle travel distances. Predicted parameters may include an AGC signal and a market demand for ancillary service energy.).
Regarding claim 4, Kiessling discloses wherein the instructions further comprise determining, via the processor, the electric vehicle group for the entering electric vehicle according to the SoC in the time slot corresponding to the scheduled entry time of the entering electric vehicle (see at least [0048] Detected vehicle parameters may include vehicle states of charge, vehicle entry times at charging stations, predicted durations of stay at the charging stations, and target states of charge.).
Regarding claim 5, Kiessling discloses wherein the instructions further comprise setting, via the processor, the first charging and discharging schedule using an electric vehicle whose SoC is equal to or greater than a preset minimum SoC for the electric vehicle group (see at least [0048] Customer-provided parameters may include fleet vehicle compositions, nominal state of charge trajectories for one or more electric vehicles, predicted vehicle schedules, predicted states of charge, predicted vehicle locations, and predicted vehicle travel distances. Predicted parameters may include an AGC signal and a market demand for ancillary service energy. [0050] increasing deviation from vehicle state of charge nominal trajectories may increase payment received from an ancillary service provider. Receipt of payment from the ancillary service provider may comprise delivering one or more committed state of charge schedules to the fleet customer, delivering power from one or more electric vehicles to the ancillary service provider, and securing payment from the ancillary service provider for power provided minus penalties for deviations from vehicle state of charge nominal trajectories. ).
Regarding claim 6, Kiessling discloses wherein the instructions further comprise determining, via the processor, an electric vehicle group for an exiting electric vehicle using a scheduled exit time and a target SoC of the exiting electric vehicle (see at least [0057] One or more of the scheduling logic, the vehicle to grid revenue logic, automatic generation control signal logic, power output accuracy logic, power cost minimization logic, state of charge optimization logic, or real-time optimization logic may use parameters stored in the database 480 or may store determined or improved parameters in the database. [0058] Previous values for any parameters, for example the AGC signal, the power cost, the environmental signal, the vehicle duration of stay, the rate of charging or discharging, the vehicle state of charge, the nominal state of charge trajectory, or any other measured or provided parameter, may be stored in the database and used to train a machine learning system, such as a neural network, to predict a current or future parameter. Using scheduling logic to plan charge and discharge schedules while also considering duration of stay (i.e. entry and exit times for charging)).
Regarding claim 7, Kiessling discloses wherein the instructions further comprise determining, via the processor, the electric vehicle group for the exiting electric vehicle according to the target SoC in the time slot corresponding to the scheduled exit time of the exiting electric vehicle (see at least [0034] These parameters may include fleet size and composition; predicted vehicle usage schedules and locations; desired duty cycle; vehicle types, charge capacities, and ranges; vehicle target states of charge; number of charging stations; capacity of charging stations; total power consumed by the site (e.g., the total power used by controlled and uncontrolled loads powered by the site); available power for charging at the site; or maximum power load of the site or its charging stations. The optimizer system may provide a charging method plan to a fleet 150 based on the parameters provided by the customer, predicted environmental variables, time-dependent cost of energy, power-consumption-magnitude-dependent cost of energy or power, and predicted revenue from vehicle to grid (V2G) discharging. [0058] Previous values for any parameters, for example the AGC signal, the power cost, the environmental signal, the vehicle duration of stay, the rate of charging or discharging, the vehicle state of charge, the nominal state of charge trajectory, or any other measured or provided parameter, may be stored in the database and used to train a machine learning system, such as a neural network, to predict a current or future parameter.).
Regarding claim 8, Kiessling discloses wherein the instructions further comprise setting, via the processor, the first charging and discharging schedule according to a representative battery capacity and a charging and discharging efficiency set for the electric vehicle group (see at least [0038] The optimizer system may utilize bidirectional charging efficiency to increase efficiency of charging schedules. While plugged into a charging station, an electric vehicle may be charging (e.g., storing energy into a battery) or discharging (e.g., providing energy to an ancillary service provider). The optimizer system may instruct an electric vehicle to charge based on an AGC signal indicating system load. In some embodiments, the optimizer system may provide instructions to the electric vehicle to charge while system load is low and energy costs are low. In some embodiments, the optimizer system may provide instructions to the electric vehicle to discharge when system load is high and energy costs are high. Discharging while energy costs are high may increase revenue to a fleet charging operator by selling energy to ancillary service providers at times of high demand.).
Regarding claim 9, Kiessling discloses wherein the instructions further comprise setting, via the processor, the first charging and discharging schedule, wherein a sum of the battery wear costs caused by movement between one electric vehicle group to another electric vehicle group according to the clustering is minimized (see at least Fig. 7 [0090] Certain charging stations can oscillate with respect to the power they dispense at certain power levels. EV chargers can interact unusually with battery management systems when close to completing charging (e.g., when nearing final state of charge (SOC)). For example, different EV buses with different battery management systems can produce power curve oscillations that vary with their different states of charge, such as when they nearly reach full charge, the BMS can turn charging on and off at unpredictable times. Additionally, grid power levels can fluctuate based on power electronics used at certain power levels. These fluctuations and oscillations are relatively unpredictable, but there are patterns at which power levels they occur, and the state of the overall system impacts the patterns. Embodiments of the present disclosure can help mitigate the effects of those fluctuations and oscillations by controlling the power drawn by EV chargers to levels that take into account those changes in consumption and reduce the chance that those fluctuations will drive the total power consumption over the threshold peak consumption value.).
Regarding claim 10, Kiessling discloses wherein the instructions further comprise setting, via the processor, the second charging and discharging schedule to follow a target SoC of an electric vehicle based on the first charging and discharging schedule (see at least [0034] These parameters may include fleet size and composition; predicted vehicle usage schedules and locations; desired duty cycle; vehicle types, charge capacities, and ranges; vehicle target states of charge; number of charging stations; capacity of charging stations; total power consumed by the site (e.g., the total power used by controlled and uncontrolled loads powered by the site); available power for charging at the site; or maximum power load of the site or its charging stations. The optimizer system may provide a charging method plan to a fleet 150 based on the parameters provided by the customer, predicted environmental variables, time-dependent cost of energy, power-consumption-magnitude-dependent cost of energy or power, and predicted revenue from vehicle to grid (V2G) discharging.).
Regarding claim 11, Kiessling discloses wherein the instructions further comprise setting, via the processor, the second charging and discharging schedule, wherein a sum of a charging amount and a discharging amount of the electric vehicle through the second charging and discharging schedule is equal to a charging amount and a discharging amount calculated through the first charging and discharging schedule (see at least Fig. 7 [0096] exemplary charging and discharging schedules for a plurality of electric vehicles plugged into a plurality of charging stations.).
Claim 12 is substantially similar to claim 1 and therefore rejected under the same rationale.
Claim 13 is substantially similar to claim 2 and therefore rejected under the same rationale.
Claim 14 is substantially similar to claim 3 and therefore rejected under the same rationale.
Claim 15 is substantially similar to claim 6 and therefore rejected under the same rationale.
Claim 16 is substantially similar to claim 8 and therefore rejected under the same rationale.
Claim 17 is substantially similar to claim 9 and therefore rejected under the same rationale.
Claim 18 is substantially similar to claim 10 and therefore rejected under the same rationale.
Claim 19 is substantially similar to claim 11 and therefore rejected under the same rationale.
Claim 20 is substantially similar to claim 4 and therefore rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The cited prior art generally refers to charging and discharging plans and schedules for electric vehicles and associated methods and systems.
U.S. Publication 2016/0159239 A1 - A method for controlling charging and discharging of an electric vehicle, the method comprising: first, analyzing information such as the battery status and the history default rate of electric vehicles applying for joining a power-grid charging and discharging service, and screening out an electric vehicle that can participate in the charging and discharging service for electric vehicles in the future; then, determining an optimal combination state of a generator set and the electric vehicle by using a method of electric energy transmission cost comparison; and further monitoring in real time the status of the electric vehicle during charging and discharging, and performing real-time power control on the electric vehicle, whereby an electric vehicle aggregator not only can meet the requirements on the charging and discharging service of the power system, but also can implement energy management and real-time control of the electric vehicles during charging and discharging, thereby reducing the effect of charging and discharging on the vehicle-mounted power battery.
U.S. Publication 2025/0018825 A1 - Electric vehicle supply equipment includes at least first and second connectors, and a controller. The controller, responsive to detecting the first and second connectors are engaged with first and second vehicles respectively, synchronizes electric power from the first and second vehicles and supplies the electric power to an entity other than the first and second vehicles.
U.S. Publication 2022/0024337 A1 - A device for charging and discharging a drive energy store of a hybrid or electric vehicle includes a frequency measuring module which is designed to measure a network frequency of an energy supply network; a control module which is designed to control a charge process of the drive energy store from the energy supply network or a discharge process of the drive energy store into the energy supply network on the basis of the measured network frequency in order to produce a control power; and a first communication module which is designed to communicate, as the master, with the hybrid or electric vehicle in order to produce the control power.
U.S. Publication 2022/0176845 A1 - A battery management system, including: a current measuring unit measuring a cell current (CC) flowing through battery cells (BCs) in series, a voltage measuring unit measuring a cell voltage of each BC, and a control unit to: classify the BCs into at least one group based on a state of charge (SOC) of each BC estimated in a previous cycle (PCY), estimate an average polarization voltage of a current cycle (CCY) of each group, based on an average SOC of the PCY, a first CC of each group, and a second CC of each group, using a first Kalman filter, the first CC being measured in the PCY, and the second CC being measured in the CCY, estimate the SOC of the CCY of each BC, based on the first CC, the second CC, and the average polarization voltage of the CCY of each group, using a second Kalman filter.
U.S. Publication 2025/0018819 A1 - A charging and discharging system including a charging and discharging device to which a vehicle energy storage apparatus of a vehicle is connected and which supplies electric power to an electric power load includes a first charging and discharging device, which is the charging and discharging device to which a first vehicle energy storage apparatus being the vehicle energy storage apparatus is connected and which is electrically connected to a second charging and discharging device being another charging and discharging device to which a second vehicle energy storage apparatus being another vehicle energy storage apparatus is connected. The first charging and discharging device includes a control section controlling the first voltage output from the first charging and discharging device and the first phase of the first voltage in such a way as to match the second voltage output from the second charging and discharging device and the second phase of the second voltage.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DYLAN C WHITE whose telephone number is (571)272-1406. The examiner can normally be reached M-F 7:30-4:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached at (571)272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DYLAN C WHITE/Primary Examiner, Art Unit 3625 July 22, 2026
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