DETAILED ACTIONS
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/03/2026 has been entered.
Response to Amendment
This office action is in response to the amendments/arguments submitted by the Applicant(s) on 06/03/2026
Status of the Claims
Claims 1, 4-13,18-19,21-22, and 25-27 are pending.
Claims 1 is amended.
Claim 27 is new.
Response to Arguments
Rejections Under 35 USC § 102
Applicant's arguments, see remarks pages 6-9, filed 04/28/2026 with respect to the rejection(s) of Claims under 35 U.S.C.§103 has been considered, and are moot
because a new ground of rejection with new prior art is set forth below.
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 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.
Claims 1, 4, 6-13,18,21-22, and 25-26 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Suh et al. (US 2013/0166233 A1, hereinafter Suh).
Regarding claim 1, Suh teaches,
A test system for determining battery performance during development of a battery configuration in a test environment (Suh, Figure 1, “[0002] Embodiments of the present invention relate to a device for predicting the lifetime of a secondary battery and a method thereof. [0003] To estimate a lifetime of a secondary battery during development of the secondary battery”)
the test system comprising at least one communication interface (Suh, Figure 4,” a data input unit 110”) and at least one processing device (Suh, Figure 4,” a data processing unit 120 (e.g., a processor)”)
wherein the test system is configured for receiving operating data indicative of at least one test protocol via the communication interface (Suh, Figure 4, “data input unit 110”),
wherein the test system is configured for receiving battery performance input data via the communication interface (Suh, Figure 5,6a “a data input step (S11)” [0061] As shown in FIG. 6a, in the data input step (S11), normal lifetime test data of the first battery and accelerated lifetime test data of the second battery are illustrated for the capacity (mAh) relative to the number of charge and/or discharge cycles”.)
wherein the processing device is configured for determining at least one predicted time series of at least one state variable indicative of battery performance based on the battery performance input data (Suh, Figure 5,“a parameter extraction step (S12)”. [0070] As shown in FIGS. 6b and 6c, in the parameter extraction step (S12), slope parameters A are extracted from the input normal lifetime test data and accelerated lifetime test data, respectively. Here, FIG. 6b illustrates the transformation result for the normal lifetime test data, and FIG. 6c illustrates the transformation result for the accelerated lifetime test data”), and on the operating data using at least one data driven model and wherein the test system is configured for providing at least parts of the predicted time series of the state variable (Suh, figure 5, “an acceleration factor calculation step (S13),Figure 4, [0052] In addition, the acceleration factor calculation unit 122 calculates the acceleration factor (AF) using the extracted parameters and an equation associated with a semi-empirical lifetime model or an equation associated with a statistical lifetime analysis model. [0074] As shown in FIG. 6d, in the acceleration factor calculation step (S13), the acceleration factor (AF) is calculated using the extracted slope parameter A”),
wherein the at least one test protocol comprises information about at least one battery performance test, wherein the battery performance test comprises at least one sequence of different charge cycles and/or discharge cycles (Suh, Figure 4-6, [0048] Normal lifetime test data (e.g., first battery life test data of a first battery) obtained from a test conducted for a given amount of time, such as residual capacity ratio, cycle numbers, charge and/or discharge currents, charge and/or discharge cut-off voltage, or the like, may be input to the data input unit 110. In addition, accelerated lifetime test data ( e.g., second battery life test data of a second battery) obtained from a test conducted for a given amount of time, such as residual capacity ratio, cycle numbers, charge and/or discharge currents, charge and/or discharge cut-off voltages, or the like, may also be input to the data input unit 110”). , and
wherein the test system is configured to predict a battery lifetime (Suh, figure 5, predict lifetime estimation step(S14). [0022] FIG. 5 is a flowchart illustrating an accelerated lifetime estimation method for predicting the lifetime of a secondary
battery according to an embodiment of the present invention” [0060] FIGS. 6a to 6e illustrate an example of an accelerated lifetime estimation method for predicting the lifetime of a secondary battery according to an embodiment of the
present invention”); and
categorize a battery (Suh, [0034] Here, the conditions for the accelerated lifetime estimation method and the normal lifetime estimation method of the battery may vary according to various types of batteries. The battery is typically classified into a prismatic battery,a pouch type battery, and a cylindrical battery. In addition,
since batteries of the same type may have different materials and composition ratios, evaluation conditions optimized according to characteristics of the battery manufactured are empirically determined”)
depending on the state variable at a future time point if the state variable fulfills or does not fulfill predetermined or predefined conditions (Suh, [0035] In addition, if the cycle life of a battery is predicted according to normal conditions and accelerated conditions according to the present invention, and it is determined that
the predicted cycle life of the battery lies in a lifetime tolerance range (Spec In), batteries can be manufactured according to the same various and complex specification factors of the tested battery. If it is determined that the predicted cycle life of the battery lies outside of a lifetime tolerance range (Spec Out), the various and complex specification factors of the 'tested battery are varied to manufacture batteries with the desired cycle life”)
Regarding claim 4, Suh teaches the test system according to claim 1,
Suh further teaches wherein the operating data indicative of at least one test protocol comprises at least one sequence of different charge cycles and/or discharge cycles (Suh, Figures 3-6, [0048] “Normal lifetime test data (e.g., first battery life test data of a first battery) obtained from a test conducted for a given amount of time, such as residual capacity ratio, cycle numbers, charge and/or discharge currents, charge and/or discharge cut-off voltage, or the like, may be input to the data input unit 110” see figure 6d ,same is done for the accelerated test).
Regarding claim 6, Suh teaches the test system according to claim 1,
Suh further teaches, wherein the processing device is configured for using the test protocol as an input parameter for determining the predicted time series of the state variable with the data driven model ((Suh, Figure 5,6a “a data input step (S11)” [0061] As shown in FIG. 6a, in the data input step (S11), normal lifetime test data of the first battery and accelerated lifetime test data of the second battery are illustrated for the capacity (mAh) relative to the number of charge and/or discharge cycles”.)
, and/or wherein the processing device comprises a plurality of data driven models, wherein the processing device is configured for selecting one of the data driven models for determining the predicted time series of the state variable depending on the test protocol. (Suh, figure 5, “an acceleration factor calculation step (S13),Figure 4, [0052] In addition, the acceleration factor calculation unit 122 calculates the acceleration factor (AF) using the extracted parameters and an equation associated with a semi-empirical lifetime model or an equation associated with a statistical lifetime analysis model. [0074] As shown in FIG. 6d, in the acceleration factor calculation step (S13), the acceleration factor (AF) is calculated using the extracted slope parameter A”).
Regarding claim 7, Suh teaches the test system according to claim 1,
Suh further teaches wherein the processing device is configured for using the battery performance input data as input parameter for determining the predicted time series of the state variable with the data driven model. (Suh, Figure 4,” a data input unit 110” Suh, figure 5, “an acceleration factor calculation step (S13),Figure 4, [0052] “In addition, the acceleration factor calculation unit 122 calculates the acceleration factor (AF) using the extracted parameters and an equation associated with a semi-empirical lifetime model or an equation associated with a statistical lifetime analysis model”).
Regarding claim 8, Suh teaches the test system according to claim 1,
Suh further teaches wherein the processing device comprises a plurality of data driven models wherein the processing device is configured for analyzing the battery performance input data, ((Suh, Figure 4,” a data input unit 110” Suh, figure 5, “an acceleration factor calculation step (S13),Figure 4, [0052] “In addition, the acceleration factor calculation unit 122 calculates the acceleration factor (AF) using the extracted parameters and an equation associated with a semi-empirical lifetime model or an equation associated with a statistical lifetime analysis model”).”).
wherein the analyzing comprises determining at least one material characteristic, wherein at least one of the data driven models is selected based on the material characteristic. (Suh,[ 0034] “Here, the conditions for the accelerated lifetime
estimation method and the normal lifetime estimation method of the battery may vary according to various types of batteries. In addition, since batteries of the same type may have different materials and composition ratios, evaluation conditions optimized according to characteristics of the battery manufactured are
empirically determined”. See table 3, table 5, for different material battery test results, ([0124] Table3 below shows normal evaluation time periods and accelerated evaluation time periods conducted on pouch type batteries. In a case of an 'A' group, a negative electrode active material relative to a positive electrode active material was 1.6 times or greater and a volume energy density was 500 Wh/L. In a case of an' B' group, a negative electrode active material relative to a positive electrode active material was 1.6 times or less, and a current density ranges from 2.8 to 2.99. In a case of an 'C' group, a negative electrode active material relative to a positive electrode active material was 1.6 times or less and a current density was 2.8 or less”.).
Regarding claim 9, Suh teaches the test system according to claim 1,
Suh further teaches wherein the battery performance input data comprises data generated in response to the test protocol. model ((Suh, Figure 5,6a “a data input step (S11)” [0061] As shown in FIG. 6a, in the data input step (S11), normal lifetime test data of the first battery and accelerated lifetime test data of the second battery are illustrated for the capacity (mAh) relative to the number of charge and/or discharge cycles”).
Regarding claim 10, Suh teaches the test system according to claim 1,
Suh further teaches wherein the test protocol is predefined. (Suh, [0003] In order to estimate a lifetime of a secondary battery during development of the secondary battery, charge and discharge operations are repeatedly performed under various
conditions and time periods, which are similar to those for a case where the secondary battery is actually used, and a lifetime of the secondary battery is estimated by measuring a cycle life (residual capacity ratio) whenever measurement is made.”).
Regarding claim 11, Suh teaches the test system according to claim 1,
Suh further teaches wherein the data driven model was parametrized based on operating data indicative of the at least one test protocol and battery performance input data. (Suh, figure 5, “an acceleration factor calculation step (S13),Figure 4, [0052] In addition, the acceleration factor calculation unit 122 calculates the acceleration factor (AF) using the extracted parameters and an equation associated with a semi-empirical lifetime model or an equation associated with a statistical lifetime analysis model. [0074] As shown in FIG. 6d, in the acceleration factor calculation step (S13), the acceleration factor (AF) is calculated using the extracted slope parameter A”).
Regarding claim 12, Suh teaches the test system according to claim 1,
Suh further teaches wherein the data driven model uses knowledge of past and future charge-discharge-cycles following the at least one test protocol (Suh, [0034] Here, the conditions for the accelerated lifetime estimation method and the normal lifetime estimation method of the battery may vary according to various types of batteries.” (Suh, Figure 4-6, [0048] Normal lifetime test data (e.g., first battery life test data of a first battery) obtained from a test conducted for a given amount of time, such as residual capacity ratio, cycle numbers, charge and/or discharge currents, charge and/or discharge cut-off voltage, or the like, may be input to the data input unit 110. In addition, accelerated lifetime test data ( e.g., second battery life test data of a second battery) obtained from a test conducted for a given amount of time, such as residual capacity ratio, cycle numbers, charge and/or discharge currents, charge and/or discharge cut-off voltages, or the like, may also be input to the data input unit 110”)).
to predict future battery performance (Suh, figure 5, predict lifetime estimation step(S14). [0022] FIG. 5 is a flowchart illustrating an accelerated lifetime estimation method for predicting the lifetime of a secondary
battery according to an embodiment of the present invention”)
Regarding claim 13, Suh teaches the test system according to claim 1,
Suh further teaches wherein the data driven model has a time memory and/or the data driven model is a time dependent model (Suh, [0047] As shown in FIG. 4, an accelerated lifetime estimation device 100 according to an embodiment of the invention includes a data input unit 110, a data processing unit 120 ( e.g.,
a processor), a storage unit 130”) [0038] FIGS. 3a to 3/ are detailed conceptual diagrams illustrating an accelerated lifetime estimation method for predicting
the lifetime of a battery according to an embodiment of the present invention, in which the X axis indicates the evaluation time period”).
Regarding claim 14, Suh teaches the test system according to claim 1,
Suh further teaches wherein the test protocol comprises information about at least one battery performance test, wherein the battery performance test comprises at least one sequence of different charge cycles and/or discharge cycles, wherein in the battery performance test discharge-charge curves are determined for each cycle. . (Suh, Figure 4-6, [0048] Normal lifetime test data (e.g., first battery life test data of a first battery) obtained from a test conducted for a given amount of time, such as residual capacity ratio, cycle numbers, charge and/or discharge currents, charge and/or discharge cut-off voltage, or the like, may be input to the data input unit 110. In addition, accelerated lifetime test data ( e.g., second battery life test data of a second battery) obtained from a test conducted for a given amount of time, such as residual capacity ratio, cycle numbers, charge and/or discharge currents, charge and/or discharge cut-off voltages, or the like, may also be input to the data input unit 110”).
Regarding claim 18, Suh teaches the test system according to claim 1,
Suh further teaches A test rig (Suh, [0047] “As shown in FIG. 4, an accelerated lifetime estimation device 100 according to an embodiment of the invention”), configured for performing at least one battery performance test on at least one battery based on at least one test protocol wherein the battery performance test comprises at least one sequence of different charge cycles and/or discharge cycles
, wherein the battery performance test comprises determining of discharge- charge curves for each cycle, wherein the test rig comprises at least one communication interface configured for providing operating data indicative of the test protocol and battery performance input data to at least one test system according to claim 1. (Suh, Figure 4-6, [0048] Normal lifetime test data (e.g., first battery life test data of a first battery) obtained from a test conducted for a given amount of time, such as residual capacity ratio, cycle numbers, charge and/or discharge currents, charge and/or discharge cut-off voltage, or the like, may be input to the data input unit 110. In addition, accelerated lifetime test data (e.g., second battery life test data of a second battery) obtained from a test conducted for a given amount of time, such as residual capacity ratio, cycle numbers, charge and/or discharge currents, charge and/or discharge cut-off voltages, or the like, may also be input to the data input unit 110”).
Regarding claim 21, Suh teaches the test system according to claim 1,
Suh further teaches A computer implemented method for determining battery performance during development of a battery configuration in a test environment (Suh, [0003] In order to estimate a lifetime of a secondary battery during development of the secondary battery, charge and discharge operations are repeatedly performed under various conditions and time periods”), wherein in the method at least one test system according to claim 1 is used, the method comprising:
a) retrieving operating data indicative of at least one test protocol via at least one communication interface (Suh, Figure 4, “data input unit 110”);
b) retrieving battery performance input data via the communication interface (Suh, Figure 5,6a “a data input step (S11)” [0061] As shown in FIG. 6a, in the data input step (S11), normal lifetime test data of the first battery and accelerated lifetime test data of the second battery are illustrated for the capacity (mAh) relative to the number of charge and/or discharge cycles”.)
c) determining a predicted time series of a state variable indicative of battery performance based on the battery performance input data and on the operating data using a data driven model by using a processing device (Suh, figure 5, predict lifetime estimation step(S14). [0022] FIG. 5 is a flowchart illustrating an accelerated lifetime estimation method for predicting the lifetime of a secondary
battery according to an embodiment of the present invention” [0060] FIGS. 6a to 6e illustrate an example of an accelerated lifetime estimation method for predicting the lifetime of a secondary battery according to an embodiment of the
present invention”);
d) providing at least parts of the predicted time series of the state variable. (Suh, Figure 1, Figure 4, [0056] In addition, the display unit 140 displays various kinds of data of the data input unit 110 and the data processing unit 120. That is to say, the display unit 140 displays not only the test data input to the data input unit 110, but also the parameter, the acceleration factor (AF), and the estimated
normal ( or standard) cycle lifetime data. The display unit 140 may be generally an LCD monitor and equivalents thereof, but aspects of the present invention are not limited thereto”).
Regarding claim 22, Suh teaches the test system according to claim 21,
Suh further teaches A computer program for determining battery performance during development of a battery configuration in a test environment, configured for causing a computer or computer network to perform the method for determining battery performance (Suh, Figure 4-5, [0050], The data processing unit 120 may be implemented by a program running on a computer, software and equivalents thereof, but aspects of the present invention are not limited thereto. [0052] In addition, the acceleration factor calculation unit 122 calculates the acceleration factor (AF) using the extracted parameters and an equation associated with a semi-empirical lifetime model or an equation associated with a statistical lifetime analysis model”),
.during development of a battery configuration in a test environment according to the preceding claim, when executed on the computer or computer network, wherein the computer program is configured to perform at least steps a) to d) of the method for determining battery performance during development of a battery configuration in a test environment according to claim 21the preceding claim. ((Suh, [0003] In order to estimate a lifetime of a secondary battery during development of the secondary battery, charge and discharge operations are repeatedly performed under various conditions and time periods”),
Regarding claim 25, Suh teaches the test system according to claim 21,
Suh further teaches wherein the battery performance input data comprises metadata relating to one or more of cathode material and cell set-up.(Suh, table 3, table 5 , [0129] Table 5 below shows results of accelerated lifetime tests conducted on cylindrical batteries at room temperature. Here, a lithium cobalt oxide (LCO) type cylindrical battery including a large amount of cobalt (Co) as a positive electrode
active material was selected as the cylindrical battery to be evaluated”).
Regarding claim 26, Suh teaches the test system according to claim 21,
Suh further teaches wherein in the battery performance test discharge-charge curves are determined for each cycle. (Suh, figure 6, figure 8b,[0062] Here, the charge and/or discharge currents and charge and/or discharge cut-off voltages for accelerated lifetime tests may be set to be higher than those for normal
lifetime tests. Therefore, as the number of charge and/or discharge cycles of the secondary battery increases, while the capacity of the secondary battery slowly decreases at the normal lifetime test data curve, the capacity of the secondary
battery sharply decreases at the accelerated lifetime test data curve”).
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.
Claims 5, 19, and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Suh and in view of Aliyev et al. (US 2017/0108551 A1, hereinafter Aliyev, previously cited).
Regarding claim 5, Suh teaches the test system according to claim 1,
Suh further teaches a model being used to estimate life time see (Suh, Figure 4-5, [0050], The data processing unit 120 may be implemented by a program running on a computer, software and equivalents thereof, but aspects of the present invention are not limited thereto. [0052] In addition, the acceleration factor calculation unit 122 calculates the acceleration factor (AF) using the extracted parameters and an equation associated with a semi-empirical
lifetime model or an equation associated with a statistical lifetime analysis model”),
Suh is silent on wherein the data driven model was trained on at least one training dataset, wherein the training dataset comprises time series of historical data of charge and discharge cycles of at least one known battery configuration and at least one known test protocol.
However, Aliyev teaches wherein the data driven model was trained on at least one training dataset, wherein the training dataset comprises time series of historical data of charge and discharge cycles of at least one known battery configuration and at least one known test protocol. (Aliyev, Figure 4, [0038] In one particular embodiment, at least a portion of the battery test management system 26 may be implemented as a specially configured battery test support vector machine (SVM) that has been trained using certain types of battery test data”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Suh’s calculation model for predicting lifetime to incorporate a machine learning SVM model as taught by Aliyev to train and estimate battery lifetime (Aliyev, [0038]). It would have been obvious to a person of ordinary skill to include the well-known vector machine (SVM)model along with the other machine learning network, in order to yield the predicted results of generating accurate battery lifetime, yet with higher accuracy (KSR).
Regarding claim 19, Suh teaches the test system according to claim 1,
Suh further teaches a model being used to estimate life time see (Suh, Figure 4-5, [0050], The data processing unit 120 may be implemented by a program running on a computer, software and equivalents thereof, but aspects of the present invention are not limited thereto. [0052] In addition, the acceleration factor calculation unit 122 calculates the acceleration factor (AF) using the extracted parameters and an equation associated with a semi-empirical
lifetime model or an equation associated with a statistical lifetime analysis model”),
Suh is silent on wherein the data driven model was trained on at least one training dataset, wherein the training dataset comprises time series of historical data of charge and discharge cycles of at least one known battery configuration and at least one known test protocol.
However, Aliyev teaches wherein the data driven model was trained on at least one training dataset, wherein the training dataset comprises time series of historical data of charge and discharge cycles of at least one known battery configuration and at least one known test protocol. (Aliyev, Figure 4, [0038] In one particular embodiment, at least a portion of the battery test management system 26 may be implemented as a specially configured battery test support vector machine (SVM) that has been trained using certain types of battery test data”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Suh’s calculation model for predicting lifetime to incorporate a machine learning SVM model as taught by Aliyev to train and estimate battery lifetime (Aliyev, [0038]). It would have been obvious to a person of ordinary skill to include the well-known vector machine (SVM)model along with the other machine learning network, in order to yield the predicted results of generating accurate battery lifetime, yet with higher accuracy (KSR).
Regarding claim 27, Suh teaches the test system according to claim 1,
Suh is silent on wherein the data driven model comprises at least one recurrent neural network.
However, Aliyev teaches wherein the data driven model comprises at least one recurrent neural network. (Aliyev, Figure 4, [0038] In one particular embodiment, at least a portion of the battery test management system 26 may be implemented as a specially configured battery test support vector machine (SVM) that has been trained using certain types of battery test data”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Suh’s calculation model for predicting lifetime to incorporate a machine learning SVM model as taught by Aliyev to train and estimate battery lifetime (Aliyev, [0038]). It would have been obvious to a person of ordinary skill to include the well-known vector machine (SVM)model along with the other machine learning network, in order to yield the predicted results of generating accurate battery lifetime, yet with higher accuracy (KSR).
Conclusions
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gering, Kevin L. (US 2013/0090900 A1) recites “A method, system, and computer-readable medium are described for characterizing performance loss of an object undergoing an arbitrary aging condition. The method comprises collecting baseline aging data from the object for at least one known baseline aging condition over time, determining baseline multiple sigmoid model parameters from the baseline data, and determining performance loss data of the object over time through multiple sigmoid model parameters associated with the object undergoing the arbitrary aging condition using a differential deviation-from-baseline approach from the baseline multiple sigmoid model parameters. The system comprises an object, monitoring hardware configured to sample performance characteristics of the object, and a processor coupled to the monitoring hardware. The processor is configured to determine performance loss data for the arbitrary aging condition from a comparison of the performance characteristics of the object deviating from baseline performance characteristics associated with a baseline aging condition” (abstract).
Steingart et al. (US 2019/0064123 A1) discloses “Systems and methods for prediction of state of charge (SOH), state of health (SOC) and other characteristics of batteries using acoustic signals, includes determining acoustic data at two or more states of charge and determining a reduced acoustic data set representative of the acoustic data at the two or more states of charge. The reduced acoustic data set includes time of flight (TOF) shift, total signal amplitude, or other data points related to the states of charge. Machine learning models use at least the reduced acoustic dataset in conjunction with non-acoustic data such as voltage and temperature for predicting the characteristics of any other independent battery” (abstract).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DILARA SULTANA whose telephone number is (571)272-3861. The examiner can normally be reached Mon-Fri, 9:00AM-6 PM.
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, EMAN ALKAFAWI can be reached on (571) 272-4448. 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.
/DILARA SULTANA/Examiner, Art Unit 2858
07/21/2026
/EMAN A ALKAFAWI/Supervisory Patent Examiner, Art Unit 2858 7/24/2026
Prosecution Insights