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
1. This Office Action is in response to the Amendment filed on July 22, 2026, in which paper has been placed of record in the file.
2. Claims 1-6, 8-13, and 15-20 are pending in this application.
Claim Rejections - 35 USC § 101
3. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
4. Claims 1-6, 8-13, and 15-20 are rejected under 35 U.S.C. 101 because the claim invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea) without significantly more.
Regarding independent claim 1, which is analyzed as the following:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites a system for evaluating submitted changes. Thus, the claim is to be a machine, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The claim recites a system for evaluating submitted changes to the control system. The Specification para [0001] discloses that various embodiments of the present technology generally relate to change control systems, tools and processes for performing approval and logging functions and tasks in all types of cloud datacenters. The claim recites the steps: “receive a change package” (receive data), generate a normalized data set that conforms to a defined data schema by extracting and structuring data from the change package according to the defined data schema (analyze data by using mathematical algorithms), “generate, based on the normalized data set, a graph data structure comprising a plurality of nodes and a plurality of edges” (analyze data), “extract, from the graph data structure, a plurality of candidate features” (analyze data), “select one or more candidate features, of the plurality of candidate features, based on evaluation against defined input features of an input layer” (analyze and receive data), “input the features to the input layer” (receive data), “receive one or more similarity scores” (receive data), “in response to the one or more similarity scores satisfying a criterion, reject the change package” (analyze data), and “prevent implementation of the at least one change” (analyze data), constitute analyzing information by steps people go through in their minds and by mathematical algorithms, without more, as drafted, is a process that, under its broadest reasonable interpretation when read in light of the Specification, covers performance of the limitations in the mind, can be practically performed by human in their mind or with pen/paper, but for the recitation of generic computer components. That is, other than reciting “a computer/processor/automatically”, nothing in the claim elements preclude the steps from practically being performed in the mind. The mere nominal recitation of generic computing devices does not take the claim limitation out of the Mental Processes grouping of abstract ideas. Thus, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2), subsection III.
Therefore, the claim recites an abstract idea. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The claim recites the additional elements of “wherein the machine learning model is trained using historical change packages represented as training graph data structures generated according to the defined data schema, the training including mapping features extracted from the training graph data structures to defined input features of the input layer based on labels indicating outcomes of previously accepted and previously rejected changes” and “receive from the trained the machine learning model one or more similarity scores.” The claim also recites that the steps of “receive a change package, generate a normalized data set, generating a graph data structure, extract a plurality of candidate features, select one or more candidate features, input the selected one or more candidate features to the input layer, receive one or more similarity scores, reject the change package, and prevent implementation of the at least one change” are performed by the processing circuitry couple to computer-readable stage media and change control software.
The additional elements “wherein the machine learning model is trained using historical change packages represented as training graph data structures generated according to the defined data schema, the training including mapping features extracted from the training graph data structures to defined input features of the input layer based on labels indicating outcomes of previously accepted and previously rejected changes” and “receive from the trained the machine learning model one or more similarity scores” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
The additional elements “wherein the machine learning model is trained using historical change packages represented as training graph data structures generated according to the defined data schema, the training including mapping features extracted from the training graph data structures to defined input features of the input layer based on labels indicating outcomes of previously accepted and previously rejected changes” and “receive from the trained the machine learning model one or more similarity scores” are used to generally apply the abstract idea without placing any limits on how the machine learning functions. Rather, this limitation only recites the outcome of “output one or more similarity scores” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f).
The additional elements “wherein the machine learning model is trained with historical change control system data” and “wherein the machine learning model evaluates the features from the new graph to output one or more similarity scores” also merely indicate a field of use or technological environment in which the judicial exception is performed. Although the additional elements “wherein the machine learning model is trained using historical change packages represented as training graph data structures generated according to the defined data schema, the training including mapping features extracted from the training graph data structures to defined input features of the input layer based on labels indicating outcomes of previously accepted and previously rejected changes” and “receive from the trained the machine learning model one or more similarity scores” limit the identified judicial exceptions “output one of more similarity score”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning model) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Further, the steps of “receive a change package, normalize data from the change package, generating a new graph, extract features from the edges and nodes of the new graph, map the features to specific inputs of an input layer, input the features to the input layer, reject the change package, and prevent implementation of the at least one change”, are recited as being performed by the processing circuitry couple to computer-readable stage media and change control software. The processing circuitry couple to computer-readable stage media and change control software is recited at a high level of generality and is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). The additional elements recite generic computer components the processing circuitry, computer-readable stage media, and change control software that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- See MPEP 2106.05(f).
Moreover, the additional elements do not effect improvement in the functioning of the processing circuitry, computer-readable stage media, change control software, machine learning model, or other technology, do not recite a particular machine or manufacture that is integral to the claim, and do not transform or reduce a particular article to a different state or thing.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception (Step 2A, Prong One: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole, amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As explained with respect to Step 2A, Prong Two, the additional elements of “wherein the machine learning model is trained using historical change packages represented as training graph data structures generated according to the defined data schema, the training including mapping features extracted from the training graph data structures to defined input features of the input layer based on labels indicating outcomes of previously accepted and previously rejected changes” and “receive from the trained the machine learning model one or more similarity scores” are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
As discussed in Step 2A, Prong Two above, the recitation of the processing circuitry to perform limitations “receive a change package, generate a normalized data set, generating a graph data structure, extract a plurality of candidate features, select one or more candidate features, input the selected one or more candidate features to the input layer, receive one or more similarity scores, reject the change package, and prevent implementation of the at least one change”, amounts to no more than mere instructions to apply the exception using a generic computer component.
(Board Decision 7/28/2025)
“Taking the claim elements separately, the functions performed by the computer at each step of the process are purely conventional. Using a computer to retrieve, select, and apply decision criteria to data and modify the data as a result amounts to electronic data query and retrieval-one of the most basic functions of a computer. All of these computer functions are well-understood, routine, conventional activities previously known to the industry. See Elec. Power Grp., 830 F.3d at 1354; see also In re Katz.
Interactive Call Processing Patent Litig., 639 F.3d 1303, 1316 (Fed. Cir.2011) ("Absent a possible narrower construction of the terms 'processing,' 'receiving,' and 'storing,' those functions can be achieved by any general-purpose computer without special programming"). In short, each step does no more than require a generic computer to perform generic computer functions. As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP Am. Inc. v. InvestPic, LLC, 890 F.3d 1016, 1022 (Fed. Cir. 2018). Considered as an ordered combination, the computer elements of Appellant's claim 1 add nothing that is not already present when the steps are considered separately. The sequence of data reception-analysis-access/display is equally generic and conventional or otherwise held to be abstract. See Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014) (sequence of receiving, selecting, offering for exchange, display, allowing access, and receiving payment recited an abstraction), Inventor Holdings, LLC V. Bed Bath & Beyond, Inc., 876 F.3d 1372, 1378 (Fed. Cir. 2017) (holding that sequence of data retrieval, analysis, modification, generation, display, and transmission was abstract), Two-Way Media Ltd. v. Comcast Cable Commc'ns, LLC, 874 F.3d 1329, 1339 (Fed. Cir. 2017) (holding sequence of processing, routing, controlling, and monitoring was abstract). The ordering of the steps is, therefore, ordinary and conventional.”
“Claim 1 does not, for example, purport to improve the functioning of the processing circuitry, change control software, computer-readable storage media, or machine learning model. As we stated above, claim 1 does not effect an improvement in any other technology or technical field. The Specification spells out different generic equipment and parameters that might be applied using this concept and the particular steps such conventional processing would entail based on the concept of information
access under different scenarios. (See, e.g., Spec. 72). Thus, claim 1 at issue amounts to nothing significantly more than instructions to apply the abstract idea of information access using some unspecified, generic computer. Under our precedents, that is not enough to transform an abstract idea into a patent-eligible invention. See Alice, 573 U.S. at 226.”
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept.
Therefore, the claim is not patent eligible. (Step 2B: NO).
Regarding independent claims 8 and 15, Alice Corp. establishes that the same analysis should be used for all categories of claims. Therefore, independent claim 8 directed to a method, independent claim 15 directed to a medium, are also rejected as ineligible subject matter under 35 U.S.C. 101 for substantially the same reasons as independent method claim 1.
Regarding dependent claims 2-7, 9-14, and 16-20, the dependent claims do not impart patent eligibility to the abstract idea of the independent claim. The dependent claims rather further narrow the abstract idea and the narrower scope does not change the outcome of the two-part Mayo test. Narrowing the scope of the claims is not enough to impart eligibility as it is still interpreted as an abstract idea, a narrower abstract idea.
Regarding dependent claims 2, 9, and 16, the claims recite the additional element wherein the machine learning model determines the one or more similarity scores based on…, which is used to generally apply the abstract idea without placing any limits on how the machine learning functions. Rather, this limitation only recites the outcome of “determines the one or more similarity score” and does not include any details about how the solution is accomplished. See MPEP 2106.05(f). (see claim 1 above). Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 3, 10, and 17, the claims recite the additional elements wherein the program instructions stored on the one or more non-transitory computer-readable stage media to reject the change package based on at least in part on the one or more similarity scores…., which are recited at a high level of generality and are used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). (See claim 1 above). Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 4, 11, and 18, the claims recite the additional element wherein the machine learning model includes at least one of: an artificial neural network, gradient boosting decision trees, and an ensemble random forest, which are well-known machine learning models. See MPEP 2106.05(f). Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 5, 12, and 19, the claims simply refine the abstract idea by further reciting wherein the historical change package further comprises labels identifying compliance failures, outages, anomalous submissions associated with the previously rejected change packages, that fall under the category of Mental process grouping of abstract ideas as described above in the independent claim 1. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 6, 13, and 20, the claims simply refine the abstract idea by further reciting wherein at least one node of the nodes is based at least in part on learned attributes related to one or more of: one or more users associated with the change package…, that fall under the category of Mental process grouping of abstract ideas as described above in the independent claim 1. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Therefore, none of the dependent claims alone or as an ordered combination add limitations that qualify as significantly more than the abstract idea.
Accordingly, claims 1-6, 8-13, and 15-20 are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more and are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Novelty and Non-Obviousness
5. No prior arts were applied to the claims because the Examiner is unaware of any prior arts, alone or in combination, which disclose at least the limitations of “input the selected one or more candidate features to the input layer of the machine learning model, wherein the machine learning model is trained using historical change packages represented as training graph data structures generated according to the defined data schema, the training including mapping features extracted from the training graph data structures to defined input features of the input layer based on labels indicating outcomes of previously accepted and previously rejected changes; receive, from the trained machine learning model, one or more similarity scores indicating similarity between the change package and one or more of the historical change packages; and in response to the one or more similarity scores satisfying a criterion, reject the change package” recited in the independent claims 1, 8, and 15.
Response to Arguments/Amendment
6. Applicant's arguments with respect to claims 1-6, 8-13, and 15-20 have been fully considered but are not persuasive.
Claim Rejections - 35 USC § 101
Claims 1-6, 8-13, and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea) without significantly more.
In response to the Applicant’s arguments, the Examiner submits that the new limitations added to the claims do not overcome the 101 rejection of Abstract idea.
Step 2A-Prong One: The claim recites the following limitations: “receive a change package” (receive data), generate a normalized data set that conforms to a defined data schema by extracting and structuring data from the change package according to the defined data schema (analyze data by using mathematical algorithms), “generate, based on the normalized data set, a graph data structure comprising a plurality of nodes and a plurality of edges” (analyze data), “extract, from the graph data structure, a plurality of candidate features” (analyze data), “select one or more candidate features, of the plurality of candidate features, based on evaluation against defined input features of an input layer” (analyze and receive data), “input the features to the input layer” (receive data), “receive one or more similarity scores” (receive data), “in response to the one or more similarity scores satisfying a criterion, reject the change package” (analyze data), and “prevent implementation of the at least one change” (analyze data), constitute analyzing information by steps people go through in their minds and by mathematical algorithms, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “a computer”, nothing in the claim elements preclude the steps from practically being performed in the mind. The mere nominal recitation of a generic computing device does not take the claim limitation out of the mental processes grouping. Thus, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, opinion). Moreover, the claim recites the additional limitations ““wherein the machine learning model is trained using historical change packages represented as training graph data structures generated according to the defined data schema, the training including mapping features extracted from the training graph data structures to defined input features of the input layer based on labels indicating outcomes of previously accepted and previously rejected changes” and “receive from the trained the machine learning model one or more similarity scores”, which do not preclude the steps from practically being performed in the mind. The Specification references “the machine learning model” and using the well-known machine-learning techniques to evaluate the information (see at least Spec. paras [0008], [0037], [0057]). There is no particular detail about these steps that remove them from the realm of mental processes. Accordingly, the claims recite an abstract idea.
Step 2A-Prong Two: the claims are not integrated into a practical application and do not provide any improvement to the technology.
The additional elements ““wherein the machine learning model is trained using historical change packages represented as training graph data structures generated according to the defined data schema, the training including mapping features extracted from the training graph data structures to defined input features of the input layer based on labels indicating outcomes of previously accepted and previously rejected changes” and “receive from the trained the machine learning model one or more similarity scores” are used to generally apply the abstract idea without placing any limits on how the machine learning functions. Rather, this limitation only recites the outcome of “output one or more similarity scores” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f).
The additional elements “wherein the machine learning model is trained using historical change packages represented as training graph data structures generated according to the defined data schema, the training including mapping features extracted from the training graph data structures to defined input features of the input layer based on labels indicating outcomes of previously accepted and previously rejected changes” and “receive from the trained the machine learning model one or more similarity scores” also merely indicate a field of use or technological environment in which the judicial exception is performed. Although the additional elements ““wherein the machine learning model is trained using historical change packages represented as training graph data structures generated according to the defined data schema, the training including mapping features extracted from the training graph data structures to defined input features of the input layer based on labels indicating outcomes of previously accepted and previously rejected changes” and “receive from the trained the machine learning model one or more similarity scores” limit the identified judicial exceptions “evaluates the features from the new graph to output one of more similarity score”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning model) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Further, the steps of “receive a change package, generate a normalized data set, generating a graph data structure, extract a plurality of candidate features, select one or more candidate features, input the selected one or more candidate features to the input layer, receive one or more similarity scores, reject the change package, and prevent implementation of the at least one change”, are recited as being performed by the processing circuitry couple to computer-readable stage media and change control software. The processing circuitry couple to computer-readable stage media and change control software is recited at a high level of generality and is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). The additional elements recite generic computer components the processing circuitry, computer-readable stage media, and change control software that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- See MPEP 2106.05(f).
In response to the Applicant’s argument that the new features added to the claims “wherein the machine learning model is trained using historical change packages represented as training graph data structures generated according to the defined data schema, the training including mapping features extracted from the training graph data structures to defined input features of the input layer based on labels indicating outcomes of previously accepted and previously rejected changes” and “receive from the trained the machine learning model one or more similarity scores” provide improvement to the technology as Ex parte Desjardins and Ex parte Carmody, the Examiner respectfully disagrees and submits that the claimed features are using the generic machine-learning models to evaluate the information (see at least Spec. paras [0008], [0037], [0057]). The claimed features apply the specific data package using the generic machine learning models, which do not reflect the improvement of the machine learning model itself.
Therefore, the additional elements do not effect improvement in the functioning of the processing circuitry, computer-readable stage media, change control software, machine learning model, or other technology, do not recite a particular machine or manufacture that is integral to the claim, and do not transform or reduce a particular article to a different state or thing.
Accordingly, the claims are not integrated into a practical application.
Step 2B:
As explained with respect to Step 2A, Prong Two, the additional elements of ““wherein the machine learning model is trained using historical change packages represented as training graph data structures generated according to the defined data schema, the training including mapping features extracted from the training graph data structures to defined input features of the input layer based on labels indicating outcomes of previously accepted and previously rejected changes” and “receive from the trained the machine learning model one or more similarity scores” are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
As discussed in Step 2A, Prong Two above, the recitation of the processing circuitry to perform limitations “receive a change package, generate a normalized data set, generating a graph data structure, extract a plurality of candidate features, select one or more candidate features, input the selected one or more candidate features to the input layer, receive one or more similarity scores, reject the change package, and prevent implementation of the at least one change”, amounts to no more than mere instructions to apply the exception using a generic computer component (See also the Board Decision on 07/28/2025).
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept. Therefore, the claims are not eligible.
Accordingly, the 101 rejection is maintained.
Conclusion
7. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
8. Claims 1-6, 8-13, and 15-20 are rejected.
9. The prior arts made of record and not relied upon are considered pertinent to applicant's disclosure:
Cheng et al. (US 11,599,840) disclose a system and method that detects hidden correlation relationships among entities, such as companies and/or individuals.
Kang et al. (US 2021/0097424) disclose apparatuses and methods are disclosed for dynamic selection of features for training a machine learning model.
Rogers et al. (US 2021/0089918) disclose apparatuses and methods for dynamic selection of features for training a machine learning model.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner NGA B NGUYEN whose telephone number is (571) 272-6796. The examiner can normally be reached on Monday-Friday 7AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached on (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NGA B NGUYEN/Primary Examiner, Art Unit 3625 September 29, 2026
