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 .
Detailed Action
Claims 1-20 are pending
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 1-3, 5, 8-11 and 17-20 are rejected under 35 U.S.C. 102(a)(1) as being
anticipated by Lee et al. (US 20210144076 A1) hereinafter Lee.
Regarding claims 1 and 8, Lee discloses a model supervision processing method, comprising:
receiving, by a first network device, first information from a second network device, wherein the first information comprises a data analysis result (para. [0055], [0059] [0063]-[0064], Consumer NF device 102 (first network device) receives analytics form NWDF 101 (second network device). NWDF 101 analyzes collected network data and provides an analytics response containing the result analytics information (i.e. first information data analysis result) to consumer NF device 102); and
sending, by the first network device, second information to the second network device or a third network device, wherein the second information is used to indicate a usage status of the data analysis result by the first network device (para. [0045]-[0046],[0063]-[0064],Consumer NF device102 provides feedback to analytics device 101, and the feedback includes Analytics Usage,(i.e. consumer device 102 used the analytics information), identifying whether consumer device 102 used the analytics information the analytics information ID is information representing analytics information related to feedback. The analytics information is used is information for identifying whether the consumer network function device 102 has used analytics information for the first network data (i.e. the feedback corresponds to the claimed second information and the Analytics Usage information indicates the usage status).
Regarding claim 2, claim 1 is incorporated. Lee further discloses , wherein the status of using the data analysis result is used to assist the second network device or the third network device in evaluating first performance information of a first model, the first model is a model obtained by the third network device through a model training process, and the first model is used for generating the data analysis result by the second network device; wherein the second information further comprises at least one of the following information: type information or description information of the first network operation; first time information, wherein the first time information is used to indicate a time range for performing the first network operation; first location information, wherein the first location information is used to indicate a location range for performing the first network operation; or first object information, wherein the first object information is used to indicate an object that performs the first network operation, and the object comprises at least one of a user equipment object, an area object, or a network element object (para. [0070] In step (vi), the network data analytics function device 101 evaluates the analytics information of the first network data based on the feedback obtained from the consumer network function device 102 and the second network data collected in step (v). Further, the network data analytics function device 101 may change the analytics method used when generating analytics information of the first network data for a specific use case or a specific network function device based on the evaluation result. For example, the network data analytics function device 101 may update the analytics model used when generating analytics information of the first network data (e.g., optimize or additionally train the analytics mode evaluates analytics and updates model (evaluates analytics and updates model). [0089] Feedback on the analytics information may be provided to the entity training the analytics model. For example, when the network data analytics function device 101 trains an analytics model by itself, feedback on the analytics information provided by the consumer network function device 102 may be provided to the network data analytics function device 101. However, in the case of training the analytics model in the network storage function device 104 connected to the network data analytics function device 101, the feedback on the analytics information provided by the consumer network function device 102 is the network storage function device 104-- provides feedback to the model-training entity, including separate device 104 (i.e. third network device). [0065] The type of the consumer network function device is information for identifying what kind of network function device (e.g., AMF or SMF) the consumer network function device 102 is. The use case indication is information for identifying a purpose of an action performed by the consumer network function device 102 according to analytics information on the first network data. The use case indication represents a plurality of use cases--identifies the purpose of the performed action).
Regarding claims 3 and 9, claim 1 is incorporated. Lee further discloses wherein the second information comprises first indication information, and the first indication information is used to indicate whether the first network device has performed a first network operation based on the data analysis result (para.[0064] [0070] connects the usage indication to the consumer performing “one or more actions based on the analytics information)
Regarding claim 5, claim 3 is incorporated. Lee further discloses wherein in the second information further comprises at least one of the following data analysis task information corresponding to the data analysis result: task identification information; conditional limitation information of a task; or analysis object information of a task (para. [0063] Here, the feedback message is the analytics information ID (Analytics ID), whether the analytics information is used (Analytics Usage), the type of the consumer network function device (NF Type), a use case indication, and an evaluation value., Notification Target Address (+Notification Correlation Id), a timestamp for feedback, and the like.
[0064] The analytics information ID is information representing analytics information related to feedback. The analytics information is used is information for identifying whether the consumer network function device 102 has used analytics information for the first network data. The analytics information may be enabled when the consumer network function device 102 performs one or more actions based on the analytics information).
Regarding claim 10, claim 8 is incorporated. Lee further discloses wherein the second information comprises first indication information, and the first indication information is used to indicate whether the first network device has performed a first network operation based on the data analysis result (para. [0047] The network data analytics function device 101 may collect second network data from the second provider network function device 103-2 in order to evaluate the analytics information of the first network data. The second provider network function device 103-2 may be a network function device that provides second network data necessary for evaluating analytics information of the network data [0048] In this case, the first provider network function device 103-1 may be the same as or different from the second provider network function device 103-2. Also, the first network data may be the same as or different from the second network data. [0049] The network data analytics function device 101 may evaluate analytics information of the first network data by using the feedback and the second network data. In addition, the network data analytics function device 101 may change the analytics method for the first network data based on the evaluation of the analytics information of the first network data. See also para. [0069]-[0070])
Regarding claim 11, claim10 is incorporated. Claim 11 corresponds to claims 2 and 5, is therefore rejected for similar reasoning.
Regarding independent claim 17, the claim corresponds to independent claim 1 and claim 2, therefore rejected for similar reasoning.
Regarding claims 18-20, claim 1 is incorporated. Lee discloses network-side device, comprising a processor and a memory, wherein the memory stores a program or instructions capable of running on the processor, and when the program or instructions are executed by the processor, the steps of the model supervision processing method according to claim 1 are implemented (para. [0035], and Figs. 3 and 5)
Claim Rejections - 35 USC § 103
5. In the event the determination of the status of the application as subject to AlA 35 U.S.C. 102 and 103 (or as subject to pre-AlA 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.
6. 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 of this title, 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.
7. Claims 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over
Lee et al. (US 20210144076 A1) hereinafter Lee in view of in view of 3GPP
TR23.700-81 V1.0.0 “3rd Generation Partnership Project; Technical Specification
Group Services and System Aspects; Study of Enablers for Network Automation
for 5G, 5G System (SGS); Phase 3 (Release 18)” hereinafter 3GPP.
Regarding claims 4 and 12, claim 3 is incorporated. Lee may not explicitly disclose second indication information, wherein the second indication information is used to indicate whether the first network operation affects evaluation of the first performance information of the first model, or whether the first network operation affects evaluation of correctness of the data analysis result. However, 3GPP disclose, wherein the second information further comprises: second indication information, wherein the second indication information is used to indicate whether the first network operation affects evaluation of the first performance information of the first model, or whether the first network operation affects evaluation of correctness of the data analysis result
Figure 6.4.2-1: Model drift determined at MTLF NWDAF. 1. A consumer requests analytics including an Analytics ID as per TS 23.288 [5]. For example, a consumer can request analytics for an Analytics ID for NF load. The request may include a minimum accuracy may denote a confidence level of the analytics requested. The consumer may include an indication that it can provide feedback on an action taken. 2. The AnLF request an ML model from the MTLF NWDAF to derive analytics for the Analytics ID requested if the AnLF has no ML model available for the requested Analytics ID. 3. The MTLF NWDAF provides the current trained ML model for the Analytics ID. 4. The MTLF provides the requested ML model to the AnLF NWDAF. Based on the feedback indication provided by the consumer the MTLF may include in the request to provide feedback when a consumer determines an action based on analytics provided by the AnLF. The feedback is used to determine whether drift detection should be carried out for an ML model (see step 10). The MTLF includes a notification correlation ID in the request. 5. The AnLF NWDAF derives analytics. 6. The AnLF NWDAF provides requested analytics to the consumer. The AnLF also forwards the feedback request received from the MTLF. 7. The consumer may determine an action based on the received analytics. For example, if the analytics indicate a high load at a UPF function the consumer, i.e. SMF, may select a less loaded UPF. 8. Based on the feedback indication the consumer determines to report a network behavior change including a time where the action was taken. NOTE: How the consumer determines that the action taken changes the behavior of the network is implementation specific. 9. The consumer reports to the AnLF/MTLF that the action t will change the behavior of the network. 10. The MTLF determines based on the received feedback to evaluate the accuracy of the ML model. Alternatively, the MTLF at regular intervals initiates an ML model drift detection as described in steps 12-14…)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Lee and include second indication information, wherein the second indication information is used to indicate whether the first network operation affects evaluation of the first performance information of the first model, or whether the first network operation affects evaluation of correctness of the data analysis result using the teaching of 3GPP. One would have been motivated to do so in order to accurately evaluate network performance.
8. Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Lee et al.
(US 20210144076 A1) hereinafter Lee in view of Patil et al. (US20210014141A1)
hereinafter Patil.
Regarding claim 6, claim 1 is incorporated. Lee may not explicitly disclose wherein the first information further comprises at least one of the following information corresponding to the data analysis result: confidence information, wherein the confidence information is used to indicate a credibility degree of the data analysis result by the second network device; second performance information of the first model; or
an interested-data type, wherein the interested-data type is used for determining the second indication information. However, Patil discloses wherein the first information further comprises at least one of the following information corresponding to the data analysis result: confidence information, wherein the confidence information is used to indicate a credibility degree of the data analysis result by the second network device; second performance information of the first model; or an interested-data type, wherein the interested-data type is used for determining the second indication information (para. [0063]-[0067] and Fig. 5B, reports identifier, “action(s) taken” and the extent to which analytics influenced the change; a change indicator, specific parameter changes, and event/start/ suspend/resume stop timestamps. [0074] describes training system 615 generating, tuning and validating models, then forwarding them to NWDAF. [0013] describes a probability assertion expressing confidence in statics/ predictions. 0058 NSDAF 268 provides analytics report 450 to commissioning NF 410-1 including an estimated service experience [0070] NWDAF indicates a confidence factor in subsequent report [0076] permits a low-confidence indication in the analytics report)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Lee and include second indication information, wherein the second indication information is used to indicate whether the first network operation affects evaluation of the first performance information of the first model, or whether the first network operation affects evaluation of correctness of the data analysis result using the teaching of Patil. One would have been motivated to do so in order to generate analytic reports that include unique analytic report identifiers to enable tracking of feedback from commissioning network functions.
9. Claims 7 and 16 rejected under 35 U.S.C. 103 as being unpatentable over Lee
et al. (US 20210144076 A1) hereinafter Lee in view of Wang et al.
(US 20160366663 A1) hereinafter Wang.
Regarding claim 7, claim 1 is incorporated. Lee discloses
wherein the sending, by the first network device, second information to the second network device or a third network device comprises: sending, by the first network device, N pieces of second information to the second network device or the third network device (see Lee para 0062-0064 and [0089]-[0090]) Lee may not explicitly disclose wherein N is an integer greater than 1, and the N pieces of second information are determined by the first network device based on N pieces of first information; wherein the sending, by the first network device, N pieces of second information to the second network device or the third network device comprises: sending, by the first network device, the N pieces of second information to the second network device or the third network device when a preset reporting time point or an ending moment of a preset reporting time period is reached; or sending, by the first network device, the N pieces of second information to the second network device or the third network device when the first network device determines that the number of pieces of unsent second information reaches N; wherein the method further comprises: sending, by the first network device, at least one of the following information to the second network device or the third network device: the number N of pieces of the second information; or the preset reporting time point or the preset reporting time period. However, Wang discloses wherein N is an integer greater than 1, and the N pieces of second information are determined by the first network device based on N pieces of first information; wherein the sending, by the first network device, N pieces of second information to the second network device or the third network device comprises: sending, by the first network device, the N pieces of second information to the second network device or the third network device when a preset reporting time point or an ending moment of a preset reporting time period is reached; or sending, by the first network device, the N pieces of second information to the second network device or the third network device when the first network device determines that the number of pieces of unsent second information reaches N; wherein the method further comprises: sending, by the first network device, at least one of the following information to the second network device or the third network device: the number N of pieces of the second information; or the preset reporting time point or the preset reporting time period (see para. [0012], [0025] ] the notification events are stored until either the specified number of notifications have been batched, or until a duration, which commences (starts) after the first notification was generated, has expired. Following these events, the batched notifications are sent. .. For example, assume the number of notifications is set to five (5) without setting a duration. If the notifications reach four (4) and stop coming, then there is no way for the stored four (4) notifications to be sent. In contrast, by making the duration mandatory if the number of notifications to be batched is used, the batched notifications may be sent even if the number is not reached [0035] Fig 4 steps 46-50, a batch notification system and method, it should be appreciated in view of the present disclosure that one embodiment of a batch notification method, the method depicted in FIG. 4 and denoted as method 44, comprises configuring a batch notification based on setting a first value, the first value corresponding to a specified number of notifications to be stored before providing a batch notification responsive to setting the first value, setting a second value, the second value corresponding to a duration commencing from generation of a first notification and ending at a specified temporal value (48); and automatically providing one or more notifications over a network compliant with a oneM2M specification based on the first and second values (50)).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Lee and include wherein N is an integer greater than 1, and the N pieces of second information are determined by the first network device based on N pieces of first information; wherein the sending, by the first network device, N pieces of second information to the second network device or the third network device comprises: sending, by the first network device, the N pieces of second information to the second network device or the third network device when a preset reporting time point or an ending moment of a preset reporting time period is reached; or sending, by the first network device, the N pieces of second information to the second network device or the third network device when the first network device determines that the number of pieces of unsent second information reaches N; wherein the method further comprises: sending, by the first network device, at least one of the following information to the second network device or the third network device: the number N of pieces of the second information; or the preset reporting time point or the preset reporting time period using the teaching of Wang. One would have been motivated to do so in order to perform batch notification in an efficient manner.
Regarding claim 16, claim 8 is incorporated. Claim 16 corresponds to claim 7 and is therefore rejected for similar reasoning
10. Claims 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over
Lee et al. (US 20210144076 A1) hereinafter Lee in view of Lee et al
(US20220108214 A1) hereinafter Lee1.
Regarding claim 13, claim 1 is incorporated. Lee discloses [0047]-[0049] provide collection and evaluation operation. [0069]-[0070] expressly evaluate analytics using the collected second network data and feedback then update the analytics model based on evaluation. Lee may not explicitly disclose collecting, by the second network device, target data based on the second information; and evaluating, by the second network device, the first performance information of the first model based on the target data; wherein in a case that first indication information in the second information indicates that the first network device has performed a first network operation based on the data analysis result, the target data comprises data unrelated to the first network operation. However, Lee1 discloses collecting, by the second network device, target data based on the second information; and evaluating, by the second network device, the first performance information of the first model based on the target data; wherein in a case that first indication information in the second information indicates that the first network device has performed a first network operation based on the data analysis result, the target data comprises data unrelated to the first network operation (para.[0378]-[0379] the NWDAF device 101 may collect second network data from the provider NF device 103 or the OAM device 105….the NWDAF device 101 may evaluate the analytics information of the first network data based on the feedback obtained from the consumer NF device 102 and the second network data collected in operation (v). In addition, the NWDAF device 101 may change an analytics method used when generating the analytics information of the first network data with respect to a specific use case or a specific NF device based on an evaluation result. For example, the NWDAF device 101 may update an analytics model used when generating the analytics information of the first network data (for example, optimizing or additionally training the analytics model).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Lee and include collecting, by the second network device, target data based on the second information; and evaluating, by the second network device, the first performance information of the first model based on the target data; wherein in a case that first indication information in the second information indicates that the first network device has performed a first network operation based on the data analysis result, the target data comprises data unrelated to the first network operation using the teaching of Lee1. One would have been motivated to do so in order to improve accuracy of the analysis result of the network data provided by the machine learning model.
Regarding claim 14, claim13 is incorporated. Lee discloses providing temporal filtering (para. [0086] changing the analytics method of network data does not use all of the network data collected from the provider network function device 103 as it is, and filtering (ex, temporal filtering-acquired at a specific time) Alternatively, it may mean using network data to which only network data generated at a specific time is used, or spatial filtering-acquired at a specific location or analyzed using only network data generated at a specific location) is applied. [0090] the feedback is information on whether the consumer network function device 102 uses the analytics information of the network data, and information on the performance change (e.g., processing speed, whether or not the accuracy is changed or the degree of change) of the consumer network function device 102 when the analytics information of the network data is applied, satisfaction with the analytics information of the network data, the purpose of using the analytics information of the network data, and temporal or spatial information about the use of the network data. Lee may not explicitly disclose determining, by the second network device based on first time information in the second information, data unrelated to the first time information as the target data; determining, by the second network device based on first object information in the second information, data unrelated to the first object information as the target data; or determining, by the second network device based on first location information in the second information, data unrelated to the first location information as the target data; or, wherein the collecting, by the second network device, target data based on the second information comprises:
obtaining, by the second network device based on data analysis task information corresponding to the second information, full-set data for evaluating the first performance information from a fourth network device; and removing, by the second network device based on first time information in the second information, data related to the first time information from the full-set data, so as to obtain the target data; and/or removing, by the second network device based on first object information in the second information, data related to the first object information from the full-set data, so as to obtain the target data; and/or removing, by the second network device based on first location information in the second information, data related to the first location information from the full-set data, so as to obtain the target data. However, Lee1 discloses determining, by the second network device based on first time information in the second information, data unrelated to the first time information as the target data; determining, by the second network device based on first object information in the second information, data unrelated to the first object information as the target data; or determining, by the second network device based on first location information in the second information, data unrelated to the first location information as the target data; or, wherein the collecting, by the second network device, target data based on the second information comprises: obtaining, by the second network device based on data analysis task information corresponding to the second information, full-set data for evaluating the first performance information from a fourth network device; and removing, by the second network device based on first time information in the second information, data related to the first time information from the full-set data, so as to obtain the target data; and/or removing, by the second network device based on first object information in the second information, data related to the first object information from the full-set data, so as to obtain the target data; and/or removing, by the second network device based on first location information in the second information, data related to the first location information from the full-set data, so as to obtain the target data (para. [0372] the feedback service operation may include at least one of an Analytic ID, analytics usage, a type of a consumer NF device (NF type), a use case indication, an evaluation value, a notification target address (+ notification correlation ID), and a time stamp for feedback. [0373] The Analytic ID may be information indicating analytics information related to feedback. The analytics usage may be information for identifying whether the consumer NF device 102 uses the analytics information on the first network data. The analytics usage may be enabled when the consumer NF device 102 performs one or a plurality of actions according to the analytics information. [0378] provide NF 103/OAM 105 supplies second network data --the NWDAF device 101 may collect second network data from the provider NF device 103 or the OAM device 105 [0090] provides deletion based on a UE list/ time window. The NWDAF deice 101 may detect and delete based on abnormal behavior analytics of user equipment (UE) including an abnormal UE list and observed time widow input data from abnormal UE(s). In addition, the NWDAF device 101 may generate a new model without input data related to the abnormal UE list during the observed time window and/or generate an analytics result for network data and then may transmit the new model or the network data to the subscribed NWDAF device 101 or update the new model or the network data).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Lee and include determining, by the second network device based on first time information in the second information, data unrelated to the first time information as the target data; determining, by the second network device based on first object information in the second information, data unrelated to the first object information as the target data; or determining, by the second network device based on first location information in the second information, data unrelated to the first location information as the target data; or, wherein the collecting, by the second network device, target data based on the second information comprises: obtaining, by the second network device based on data analysis task information corresponding to the second information, full-set data for evaluating the first performance information from a fourth network device; and removing, by the second network device based on first time information in the second information, data related to the first time information from the full-set data, so as to obtain the target data; and/or removing, by the second network device based on first object information in the second information, data related to the first object information from the full-set data, so as to obtain the target data; and/or removing, by the second network device based on first location information in the second information, data related to the first location information from the full-set data, so as to obtain the target data using the teaching of Lee1. One would have been motivated to do so in order to improve accuracy of the analysis result of the network data provided by the machine learning model.
Regarding claim 15, claim 13 is incorporated. Lee discloses wherein in a case that first indication information in the second information indicates that the first network device has not performed a first network operation based on the data analysis result operations (para. [0047] The network data analytics function device 101 may collect second network data from the second provider network function device 103-2 in order to evaluate the analytics information of the first network data. The second provider network function device 103-2 may be a network function device that provides second network data necessary for evaluating analytics information of the network data. [0048] In this case, the first provider network function device 103-1 may be the same as or different from the second provider network function device 103-2. Also, the first network data may be the same as or different from the second network data. [0049] The network data analytics function device 101 may evaluate analytics information of the first network data by using the feedback and the second network data. In addition, the network data analytics function device 101 may change the analytics method for the first network data based on the evaluation of the analytics information of the first network data. See also para. [0069]-[0070]). Lee may not explicitly disclose the target data comprises full-set data that is used for evaluating the first performance information and that is obtained by the second network device from a fourth network device based on data analysis task information corresponding to the second information. However, Lee1 discloses the target data comprises full-set data that is used for evaluating the first performance information and that is obtained by the second network device from a fourth network device based on data analysis task information corresponding to the second information(para. [0372] the feedback service operation may include at least one of an Analytic ID, analytics usage, a type of a consumer NF device (NF type), a use case indication, an evaluation value, a notification target address (+ notification correlation ID), and a time stamp for feedback. [0373] The Analytic ID may be information indicating analytics information related to feedback. The analytics usage may be information for identifying whether the consumer NF device 102 uses the analytics information on the first network data. The analytics usage may be enabled when the consumer NF device 102 performs one or a plurality of actions according to the analytics information. [0378] provide NF 103/OAM 105 supplies second network data --the NWDAF device 101 may collect second network data from the provider NF device 103 or the OAM device 105. [0379] the NWDAF device 101 may evaluate the analytics information of the first network data based on the feedback obtained from the consumer NF device 102 and the second network data collected in operation (v). In addition, the NWDAF device 101 may change an analytics method used when generating the analytics information of the first network data with respect to a specific use case or a specific NF device based on an evaluation result. For example, the NWDAF device 101 may update an analytics model used when generating the analytics information of the first network data (for example, optimizing or additionally training the analytics model).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Lee and include wherein in a case that first indication information in the second information indicates that the first network device has not performed a first network operation based on the data analysis result, the target data comprises full-set data that is used for evaluating the first performance information and that is obtained by the second network device from a fourth network device based on data analysis task information corresponding to the second information using the teaching of Lee1. One would have been motivated to do so in order to improve accuracy of the analysis result of the network data provided by the machine learning model.
Conclusion
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kidest Mendaye whose telephone number is (571)272-2603. The examiner can normally be reached on Monday through Friday 7:00 am-5:00pm EST. 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, Ario Etienne, can be reached on (571) 272-4001. 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.
09/28/2026
/KIDEST MENDAYE/
Examiner, Art Unit 2457
/ARIO ETIENNE/Supervisory Patent Examiner, Art Unit 2457
