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
This action is in response to application filed 03/03/2026.
Claims 36-55 are pending in this application.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive. Applicant asserts that the prior art of record fails to disclose “verification information for verifying the model comprises configurations/instructions/semantics information for verifying the model" and "receiving a SECOND MESSAGE… the SECOND MESSAGE comprising a report associated with verifying the model based on the verification information received with the FIRST MESSAGE." Examiner respectfully disagrees. The claim merely recites the verification of model is performed by configuration or instructions or semantic information. According to applicant’s specification “…wherein the configuration for verifying the model may comprise one or more information elements in the group of: a. an identity or an identifier of a model to which the configuration for verification is applicable to or associated to; b. an indication to verify a model; c. an instruction to verify a model; and d. a recommendation to verify a model (see applicant’s specification [0225]). Hasegawa disclose when a model verification request (e.g. indication/instruction/recommendation) with a license ID (e.g. configuration/instruction information) attached is input from a user apparatus, a license/model management apparatus identifies a model corresponding to the license ID, transmits, to the user apparatus, test data corresponding to the model, and compares an output result corresponding to the test data and received from the user apparatus with an output value by a prestored test data-trained model to detect whether a user model is falsified or no ([0056]). The first management apparatus 41(e.g. second network node) identifies a model ID corresponding to the individual license ID to determine whether the user model is fraudulent or not based on the error tolerable range in order to notify the result to the user apparatus 42 ([0199]). In other words, Hasegawa discloses using a verification request (e.g. instruction) with a license ID (e.g. configuration/identity of model) in order to verify the model. The user apparatus is notified of the verification result. Therefore, the prior art of record disclose every limitation of the claim.
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.
Claim(s) 36-37, 39, 44-45, 47, 50-51 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hasegawa (US 2020/0082056 A1).
Regarding claim 36, Hasegawa discloses a method performed by a first network node (fig. 4: User Apparatus 42), the method comprising:
transmitting a FIRST MESSAGE towards a second network node, the FIRST MESSAGE comprising verification information for verifying a model ([0193]: the user apparatus 42 implements the model into the own apparatus or any other terminal or apparatus (end user) (S122), and transmits, to the first management apparatus 41, the usage registration request and the model verification request (S124), wherein the verification information comprises configurations/instructions/semantics information for verifying the model ([0056]: When a model verification request with a license ID attached is input from a user apparatus, a license/model management apparatus identifies a model corresponding to the license ID, transmits, to the user apparatus, test data corresponding to the model, and compares an output result corresponding to the test data and received from the user apparatus with an output value by a prestored test data-trained model to detect whether a user model is falsified or no); and
receiving a SECOND MESSAGE transmitted by the second network node, the SECOND MESSAGE comprising a report associated with verifying the model, wherein the report is based on the verification information received with the FIRST MESSAGE ([0199]: based on the model verification request, the first management apparatus 41(e.g. second network node) identifies a model ID corresponding to the individual license ID, executes the user model using test data corresponding to the model ID, and compares the output data with an output value by the normal test data-trained model to determine whether the user model is fraudulent or not based on the error tolerable range (S126) in order to notify the result to the user apparatus 42 (S128).
Regarding claim 37, Hasegawa discloses the method of claim 36 wherein the verification information provided to the second network node is intended for verifying that the model can perform as per tested and validated performance at the first network node or at least as per an acceptable performance level ([0199]: based on the model verification request, the first management apparatus 41(e.g. second network node) identifies a model ID corresponding to the individual license ID, executes the user model using test data corresponding to the model ID, and compares the output data with an output value by the normal test data-trained model to determine whether the user model is fraudulent or not based on the error tolerable range (S126)
Regarding claim 39, Hasegawa discloses the method of claim 36 further comprising: providing, either with the FIRST MESSAGE or with a THIRD MESSAGE, the model to the second network node and the verification information for verifying the model associated to the model provided by the first network node ([0183]: When receiving the completion notification of license registration, the licensor apparatus transmits a model/test data registration request (e.g. Third Message) to the first management apparatus 41 (S114). The pre-trained model generated in process S106, the test data generated in process S104, license terms, and the like are attached to the model/test data registration request).
Regarding claim 44, Hasegawa discloses the method of claim 36 wherein the configuration for verifying the model may comprise one or more information elements in the group of: an identity or an identifier of the model to which the configuration for verification is applicable to or associated to; an indication to verify the model; an instruction to verify the model; and a recommendation to verify the model ([0199]: the first management apparatus 41 identifies a model ID corresponding to the individual license ID, executes the user model using test data corresponding to the model ID, and compares the output data with an output value by the normal test data-trained model to determine whether the user model is fraudulent or not based on the error tolerable range (S126) in order to notify the result to the user apparatus 42 (S128). The case where the user model is determined to be fraudulent is considered to be a case where the user model is falsified or a case where the user model is damaged).
Regarding claim 45, Hasegawa discloses the method of claim 36 wherein the verification information for verifying the model comprises one or more information related to verifying the model in the group of: one or more conditions or events to be fulfilled for triggering the verification of the model indicated by the first network node; one or more instructions or policies or recommendations related to verification of the model indicated by the first network node; a request to transmit to the first network node a report comprising information associated to the verification of the model indicated by the first network node; one or more conditions or events to be fulfilled for transmitting the report to the first network node comprising information associated to the verification of the model indicated by the first network node; one or more conditions or events to be fulfilled for transmitting/forwarding the configuration for verifying the model ([0199]: the first management apparatus 41 identifies a model ID corresponding to the individual license ID, executes the user model using test data corresponding to the model ID, and compares the output data with an output value by the normal test data-trained model to determine whether the user model is fraudulent or not based on the error tolerable range (S126) in order to notify the result to the user apparatus 42 (S128). [0237]: When receiving the completion notification of test data generation from the test data generation unit 308, the model learning unit 312 reads, from the fraud detection data holding unit 309, the test data corresponding to the model ID, inputs the test data to the generated pre-trained model to perform learning in order to generate a test data-trained model); weight factors for each input needed by the model, namely revealing the importance/priority of each input type with respect to the process of inference carried out by the model; frequency and/or frequency ranges and/or cumulative amount of samples in a given time window, with which each type of input is assumed to be received in order to allow the model to perform according to its tested performance or according to a sufficiently good performance level established by the first node; frequency and/or frequency ranges and/or cumulative number of samples in a given time window, with which each type of output is assumed to be generated in order to allow the model to perform according to its tested performance or according to a sufficiently good performance level established by the first node; and semantics of the inputs needed at the model and/or of the outputs generated by the model.
Regarding claim 47, Hasegawa discloses a method performed by a second network node, the method comprising: receiving a FIRST MESSAGE transmitted by a first network node, the FIRST MESSAGE comprising verification information for verifying a model, wherein the verification information for verifying the model comprises configurations/instructions/semantics information for verifying the model ([0193]: the user apparatus 42 implements the model into the own apparatus or any other terminal or apparatus (end user) (S122), and transmits, to the first management apparatus 41, the usage registration request and the model verification request (S124), wherein the verification information comprises configurations/instructions/semantics information for verifying the model ([0056]: When a model verification request with a license ID attached is input from a user apparatus, a license/model management apparatus identifies a model corresponding to the license ID, transmits, to the user apparatus, test data corresponding to the model, and compares an output result corresponding to the test data and received from the user apparatus with an output value by a prestored test data-trained model to detect whether a user model is falsified or no); and transmitting a SECOND MESSAGE towards the first network node, the SECOND MESSAGE comprising a report associated with verifying the model, wherein the report is based on the verification information received within the FIRST MESSAGE ([0199]: based on the model verification request, the first management apparatus 41(e.g. second network node) identifies a model ID corresponding to the individual license ID, executes the user model using test data corresponding to the model ID, and compares the output data with an output value by the normal test data-trained model to determine whether the user model is fraudulent or not based on the error tolerable range (S126) in order to notify the result to the user apparatus 42 (S128).
Regarding claim 50, Hasegawa discloses the method of claim 47 further comprising: receiving, either with the FIRST MESSAGE or with a THIRD MESSAGE, the model from the first network node ([0183]: When receiving the completion notification of license registration, the licensor apparatus transmits a model/test data registration request (e.g. Third Message) to the first management apparatus 41 (S114). The pre-trained model generated in process S106, the test data generated in process S104, license terms, and the like are attached to the model/test data registration request).
Regarding claim 51, Hasegawa discloses the method of claim 50 wherein the configuration for verifying the model provided with the FIRST MESSAGE is associated to the model provided by the first network node to the second network node ([0056]: When a model verification request with a license ID attached is input from a user apparatus, a license/model management apparatus identifies a model corresponding to the license ID, transmits, to the user apparatus, test data corresponding to the model, and compares an output result corresponding to the test data and received from the user apparatus with an output value by a prestored test data-trained model to detect whether a user model is falsified or no).
Regarding claim 54; the claim is interpreted and rejected for the same reason as set forth in claim 36.
Regarding claim 55; the claim is interpreted and rejected for the same reason as set forth in claim 47.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 38, 48 are rejected under 35 U.S.C. 103 as being unpatentable over Hasegawa in view of Kar et al. (US 2021/0287050 A1).
Regarding claim 38, Hasegawa discloses the method of claim 36.
However, Hasegawa does not disclose wherein receiving the SECOND MESSAGE from the second network node comprises receiving the report associated with verifying the model without prior transmission of the FIRST MESSAGE to the second network node.
In an analogous art, Kar discloses wherein receiving the SECOND MESSAGE from the second network node comprises receiving the report associated with verifying the model without prior transmission of the FIRST MESSAGE to the second network node ([0053]: If the output produced by the ML model under test 150 in response to the test data is accurate, a successful test is recorded, else, if the output is inaccurate, a failed test is recorded at 714 and the model report 168 regarding the success or failure of the ML model under test 150 is produced at 716. The model report 168 including the test results may be conveyed to the users at 718. The results can convey if the ML model under test 150 is robust or if additional training or modifications are required).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Hasegawa to comprise “wherein receiving the SECOND MESSAGE from the second network node comprises receiving the report associated with verifying the model without prior transmission of the FIRST MESSAGE to the second network node” taught by Kar.
One of ordinary skilled in the art would have been motivated because it would have enabled to test ML models for accuracy and robustness (Kar, [0017]).
Regarding claim 48, Hasegawa discloses the method of claim 47.
However, Hasegawa does not disclose wherein the SECOND MESSAGE is transmitted towards the first network node without previously receiving the FIRST MESSAGE from the first network node, wherein the second network node, without previous configurations/instructions/semantics from the first network node, verifies the model and notifies the first network node of inconsistencies between the instructions/configurations/semantics relative to the inputs and/or outputs, provided prior to using the model, and the actual availability of inputs and/or outputs over connected interfaces to the second network node.
In an analogous art, Kar wherein the SECOND MESSAGE is transmitted towards the first network node without previously receiving the FIRST MESSAGE from the first network node, wherein the second network node, without previous configurations/instructions/semantics from the first network node, verifies the model and notifies the first network node of inconsistencies between the instructions/configurations/semantics relative to the inputs and/or outputs, provided prior to using the model, and the actual availability of inputs and/or outputs over connected interfaces to the second network node ([0053]: If the output produced by the ML model under test 150 in response to the test data is accurate, a successful test is recorded, else, if the output is inaccurate, a failed test is recorded at 714 and the model report 168 regarding the success or failure of the ML model under test 150 is produced at 716. The model report 168 including the test results may be conveyed to the users at 718. The results can convey if the ML model under test 150 is robust or if additional training or modifications are required).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Hasegawa to comprise “wherein the SECOND MESSAGE is transmitted towards the first network node without previously receiving the FIRST MESSAGE from the first network node, wherein the second network node, without previous configurations/instructions/semantics from the first network node, verifies the model and notifies the first network node of inconsistencies between the instructions/configurations/semantics relative to the inputs and/or outputs, provided prior to using the model, and the actual availability of inputs and/or outputs over connected interfaces to the second network node” taught by Kar.
One of ordinary skilled in the art would have been motivated because it would have enabled to test ML models for accuracy and robustness (Kar, [0017]).
Claims 40-43 are rejected under 35 U.S.C. 103 as being unpatentable Hasegawa in view of Guo et al. (US 2021/0295162 A1).
Regarding claim 40, Hasegawa discloses the method of claim 36.
However, Hasegawa does not disclose further comprising: providing to the second network node, either with the FIRST MESSAGE or with the THIRD MESSAGE, a set of reference data samples which can be used to verify the model.
In an analogous art, Guo discloses further comprising: providing to the second network node, either with the FIRST MESSAGE or with the THIRD MESSAGE, a set of reference data samples which can be used to verify the model ([0020]: The server may obtain a training sample in a training set for model training and a reference sample, and train a deep neural network model based on the training sample in the training set to obtain a trained deep neural network model; perform data verification on all reference samples in a reference set based on the trained deep neural network model to obtain a model prediction value of each of all the reference samples).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Hasegawa to comprise “further comprising: providing to the second network node, either with the FIRST MESSAGE or with the THIRD MESSAGE, a set of reference data samples which can be used to verify the model” taught by Guo.
One of ordinary skilled in the art would have been motivated because it would have enabled performing data verification on all reference samples (Guo, [0077]).
Regarding claim 41, Hasegawa-Guo discloses the method of claim 40 wherein the set of reference data samples is provided with the FIRST MESSAGE as part of the configuration for verifying the model (Guo, ([0020]: The server may obtain a training sample in a training set for model training and a reference sample, and train a deep neural network model based on the training sample in the training set to obtain a trained deep neural network model). The same rationale applies as in claim 40.
Regarding claim 42, Hasegawa-Guo discloses the method of claim 40 wherein the provided reference data samples are explicitly associated to one or more models (Guo, [0025]: Perform data verification on all reference samples in a reference set based on the trained deep neural network model to obtain a model prediction value of each of all the reference samples). The same rationale applies as in claim 40.
Regarding claim 43, Hasegawa-Guo discloses the method of claim 40 wherein the provided reference data samples comprise one or more of: a set of reference input-output pairs, where each reference output value represents that output that is expected to obtain for the corresponding reference input data when provided to the model for verification; and reference state-action pairs, wherein the reference action represents either the expected output of the model or the decision of an AIML algorithm using the model, when feeding the model with the reference state (Guo, [0020]: perform data verification on all reference samples in a reference set based on the trained deep neural network model to obtain a model prediction value of each of all the reference samples, where the reference set includes a verification set and/or a test set; calculate a difference measurement index between the model prediction value of each reference sample and a real annotation corresponding to the reference sample). The same rationale applies as in claim 40.
Claims 46 are rejected under 35 U.S.C. 103 as being unpatentable over Hasegawa in view of Kang et al. (US 2024/0205929 A1 – Priority Date 05/11/2021).
Regarding claim 46, Hasegawa discloses the method of claim 36.
However, Hasegawa does not disclose wherein the first network node comprises one or more of: an Operation and Management, OAM, node; and a Service and Management Orchestration, SMO, node, while the second network node comprises one or more of: a Radio Access Network, RAN, node; a Next Generation Radio Access Network, NG-RAN, node; a function of the RAN node; a network node realizing at least in part a Non-Real Time Radio Intelligent Controller, RIC; a network node realizing at least in part a Near-Real Time RIC; a Core Network node; and a Cloud-based centralized training node.
In an analogous art, Kang discloses wherein the first network node comprises one or more of: an Operation and Management, OAM, node; and a Service and Management Orchestration, SMO, node, while the second network node comprises one or more of: a Radio Access Network, RAN, node ([0324]: Validation data: This refers to a data set for verifying a model for which learning has already been completed. In other words, it usually refers to a data set used to prevent over-fitting of the training data set. Fig. 15, [0336]: FIG. 15 may be implemented through cooperation of two or more entities among a RAN, a network node, an OAM of network operator, or a UE. For example, one entity may perform some of the functions of FIG. 15 and other entities may perform the remaining functions) a Next Generation Radio Access Network, NG-RAN, node; a function of the RAN node; a network node realizing at least in part a Non-Real Time Radio Intelligent Controller, RIC; a network node realizing at least in part a Near-Real Time RIC; a Core Network node; and a Cloud-based centralized training node.
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Hasegawa to comprise “wherein the first network node comprises one or more of: an Operation and Management, OAM, node; and a Service and Management Orchestration, SMO, node, while the second network node comprises one or more of: a Radio Access Network, RAN, node; a Next Generation Radio Access Network, NG-RAN, node; a function of the RAN node; a network node realizing at least in part a Non-Real Time Radio Intelligent Controller, RIC; a network node realizing at least in part a Near-Real Time RIC; a Core Network node; and a Cloud-based centralized training node taught by Kang.
One of ordinary skilled in the art would have been motivated because it would have enabled to initially deploy a trained, validated, and tested AI model (Kang, [0318]).
Claims 49 are rejected under 35 U.S.C. 103 as being unpatentable over Hasegawa in view of Hyde et al. (US 2024/0298225 A1 – Priority Date 10/21/2021).
Regarding claim 49, Hasegawa discloses the method of claim 47.
However, Hasegawa does not disclose further comprising signaling the result of the verification/testing/validation process to any other external node or system in a network, to enable a system diagnostic and system optimization.
In an analogous art, Hyde discloses further comprising signaling the result of the verification/testing/validation process to any other external node or system in a network, to enable a system diagnostic and system optimization ([0088]: predicted information provided by various AI/ML models is accompanied with metrics to allow a network node, such as an OAM or RAN node, to know accuracy and error bounds for various training reports 414 or analytics reports 416. This will allow individual RAN nodes or OAM nodes to make better decisions under currently prevailing conditions. Also, it helps to improve the existing AI/ML models, such as ML model 406 of the MDA ML system 400. [0249] Example 6 may include NG-RAN could provide periodic feedback to OAM to improve the AI model training if model training occurs in OAM).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Hasegawa to comprise “further comprising signaling the result of the verification/testing/validation process to any other external node or system in a network, to enable a system diagnostic and system optimization” taught by Hyde.
One of ordinary skilled in the art would have been motivated because it would have enabled to provide analytics report and recommended actions to enable the necessary actions for network and service operation (Hyde, [0025]).
Claims 52-53 are rejected under 35 U.S.C. 103 as being unpatentable over Hasegawa in view of Samdanis et al. (WO 2021/023388 A1).
Regarding claim 52, Hasegawa discloses the method of claims 47.
However, Hasegawa does not disclose further comprising one or more of: transmitting a FOURTH MESSAGE towards a third network node, the FOURTH MESSAGE comprising at least part of configurations/instructions/semantics information for verifying the model received from the first network node; and receiving a FIFTH MESSAGE transmitted by the third network node, the FIFTH MESSAGE comprising a report associated to verifying a model based on the configurations/instructions/semantics information received with the FOURTH MESSAGE.
In an analogous art, Samdanis further comprising one or more of: transmitting a FOURTH MESSAGE towards a third network node, the FOURTH MESSAGE comprising at least part of configurations/instructions/semantics information for verifying the model received from the first network node (pg. 16, [0002]-[0003]: each of the messages between NEF and NWDAF for updating, validating and modifying (e.g. the lower portion of Fig. 2), the analytics model is identified by the unique analytics model ID. NEF maps the analytics model ID to an identifier of the AF (or a combination of the identifier of the AF and an identifier of an application on the AF) and vice versa. The AF may request performance data for validation, i.e. to examine if the Analytics Model performs as expected (step 11 ). In addition or alternatively, such process can be triggered once certain conditions are met, e.g. periodic or upon a performance deviation observation at the NWDAF); and receiving a FIFTH MESSAGE transmitted by the third network node, the FIFTH MESSAGE comprising a report associated to verifying a model based on the configurations/instructions/semantics information received with the FOURTH MESSAGE (pg. 16, [0003]: The AF may request performance data for validation, i.e. to examine if the Analytics Model performs as expected (step 11). In addition or alternatively, such process can be triggered once certain conditions are met, e.g. periodic or upon a performance deviation observation at the NWDAF. In any of these cases, the NWDAF prepares and formats the validation data (step 12) including optionally a root cause analysis to identify potential faulty data analytics sources. It may report also the data sources of the analytics (e.g. the AMF instance in case of UE mobility analytics). Such validation reporting is forwarded to the AF via the NEF, which may provide an appropriate data abstraction, e.g. hiding network topology or other mobile network operator internal network or data details).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Hasegawa to comprise “further comprising one or more of: transmitting a FOURTH MESSAGE towards a third network node, the FOURTH MESSAGE comprising at least part of configurations/instructions/semantics information for verifying the model received from the first network node; and receiving a FIFTH MESSAGE transmitted by the third network node, the FIFTH MESSAGE comprising a report associated to verifying a model based on the configurations/instructions/semantics information received with the FOURTH MESSAGE” taught by Samdanis.
One of ordinary skilled in the art would have been motivated because it would have enabled to expose model training and validation information to/from the AF, in order to be able to trigger model re-training and maintenance when needed (Samdanis, pg. 10, [0005]).
Regarding claim 53, Hasegawa-Samdanis discloses the method of claim 52 further comprising: forwarding the report received from the third network node to the first network node via the SECOND MESSAGE (Samdanis, pg. 16, [0003]: It may report also the data sources of the analytics (e.g. the AMF instance in case of UE mobility analytics). Such validation reporting is forwarded to the AF via the NEF, which may provide an appropriate data abstraction, e.g. hiding network topology or other mobile network operator internal network or data details). The same rationale applies as in claim 52.
Additional References
The prior art made of record and not relied upon is considered pertinent to applicants disclosure.
Takasaki et al., US 2022/0343219: Parallel Cross Validation in Collaborative Machine Learning.
Yao et al., US 2023/0141237 A1: Techniques for Management Data Analytics (MDA) Process and Service.
Feki et al., US 2024/0152814 A1: Training Data Characterization and Optimization for a Positioning Task.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUAN C TURRIATE GASTULO whose telephone number is (571)272-6707. The examiner can normally be reached Monday - Friday 8 am-4 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian J Gillis can be reached at 571-272-7952. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.C.T/Examiner, Art Unit 2446
/BRIAN J. GILLIS/Supervisory Patent Examiner, Art Unit 2446
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