DETAILED ACTION
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 .
Status of Claims
2. This Office Action is in response to the initial filing of application #19/005505 on 12/30/2024.
3. Claims 1-20 are currently pending and are considered below.
Information Disclosure Statement
4. The Applicant is respectfully reminded that each individual associated with the filing and prosecution of a patent application has a duty of candor and good faith in dealing with the Office, which includes a duty to disclose to the Office all information known to that individual to be material to patentability as defined in 37 CFR 1.56.
Claim Rejections - 35 USC § 101
5. 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.
6. Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. Representative claim 1, recites a computing system, which is a statutory class, the system comprising:
one or more processors;
a database; and
a non-transitory, computer-readable medium storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising:
periodically providing a reporting data request to each data source of a plurality of
data sources, the reporting data request requesting data indicative of a number of
impressions associated with content provided to the plurality of data sources;
receiving reporting data from the plurality of data sources in response to the
reporting data request;
processing the reporting data into a data format usable by the database;
storing the reporting data in the database; and
in response to receiving a request from a user to generate a report:
processing the reporting data stored in the database using a machine-learned
model to generate a model output, wherein the model output is indicative of one or
more actions a user can take based on the reporting data; and
outputting at least a portion of the reporting data and the model output for
display to the user.
The steps of
periodically providing a reporting data request to each data source of a plurality of
data sources, the reporting data request requesting data indicative of a number of
impressions associated with content provided to the plurality of data sources;
receiving reporting data from the plurality of data sources in response to the
reporting data request;
processing the reporting data into a data format usable by the database;
storing the reporting data in the database; and
in response to receiving a request from a user to generate a report:
processing the reporting data stored in the database using a machine-learned
model to generate a model output, wherein the model output is indicative of one or
more actions a user can take based on the reporting data; and
outputting at least a portion of the reporting data and the model output for
display to the user,
as drafted, is a process that, under its broadest reasonable interpretation, covers a method of organizing human activity. Given the broadest reasonable interpretation, the claim recites a system for surfacing impression data. The above identified method steps recite commercial interactions such as sales activities and/or tailored personalized marketing relating to providing data associated with the person.
If a claim limitation, under its broadest reasonable interpretation, covers commercial interaction such as tailored personalized marketing, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of one or more processors, a database, a non-transitory, computer-readable medium and a machine-learned model. The one or more processors is recited at a high-level of generality (i.e., as a generic processor performing a generic computer functions of periodically providing a reporting data request to each data source; receiving reporting data from the plurality of data sources;
processing the reporting data into a data format usable by the database; storing the reporting data in the database; and processing the reporting data stored in the database; and outputting at least a portion of the reporting data and the model output for display to the user) such that they amount to no more than mere instructions to apply the exception using generic computer components. As for the limitation training a machine learning model to generate a demand forecast, this feature is considered math, and therefore is a part of the abstract idea. Because the machine learning model in this claim is used as a tool for improving the abstract idea, rather than improving any technical feature or function, it is not sufficient to integrate the judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of
one or more processors, a database, a non-transitory, computer-readable medium and a machine-learned model amount to no more than mere instructions to apply the exception using generic computer components. The additional elements are similar to the additional elements found by courts to be mere instructions to apply an exception because they do no more than merely invoke computers or machinery to perform an existing process such as: a common business method or mathematical algorithm being applied on a general purpose computer (Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 US 208, 223; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334); providing a user with tailored information like advertisements based on information known about the user such as a location, address, or personal characteristics and a time of day is a fundamental practice long prevalent in our system); In re Morsa, 809 F. App’x 913, 917 (Fed. Cir. 2020). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Thus, considered as an ordered combination, the additional elements add nothing that is not already present when the steps are considered separately. That is, one or more processors, a database, a non-transitory, computer-readable medium and a machine-learned model, performing commercial interactions including: periodically providing a reporting data request to each data source; receiving reporting data from the plurality of data sources; processing the reporting data into a data format usable by the database; storing the reporting data in the database; and processing the reporting data stored in the database; and outputting at least a portion of the reporting data and the model output for display to the user, amount to mere instructions to apply the steps to a computer comprising of a processor.
Thus, claims 1, 19 and 20 are not eligible.
As for dependent claim 2, this claim recites the operations further comprising providing the content to the plurality of data sources, wherein the content is selected from a plurality of content stored in the database, as drafted, is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind or using pen and paper but for the recitation of generic computer components. For example but for the “one or more processors, a database, a non-transitory, computer-readable medium and a machine-learned model” language in claim 1. The claim falls into the mental process grouping of abstract ideas. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea itself.
As for dependent claims 3 and 4, these claims recite limitations that further define the same abstract idea in claims 1 and 3, therefore, they are considered patent ineligible for the reasons given above. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea itself.
As for dependent claim 5, this claim recites the operations further comprising providing authentication credentials to each data source of the plurality of data sources; and creating an authenticated connection between the computing system and each data source of the plurality of data sources based on the providing of the authentication credentials to each data source of the plurality of data sources; wherein the reporting data is received over the authenticated connection, as drafted, is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind or using pen and paper but for the recitation of generic computer components. For example but for the “one or more processors, a database, a non-transitory, computer-readable medium and a machine-learned model” language in claim 1. The claim falls into the mental process grouping of abstract ideas. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea itself.
As for dependent claims 6-18, these claims recite limitations that further define the same abstract idea in claims 1, 5, 7, 9, 10, 12-14 and 16, therefore, they are considered patent ineligible for the reasons given above. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea itself.
Claims 1-20 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
7. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
8. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
9. The factual inquiries 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.
10. Claims 1-4, 9, 11-12, 15-16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Donia et al. (U.S. Patent No. 11,151,653) (hereinafter ‘Donia’) in view of Mishra et al. (U.S. Pub. No. 2020/0226325 (hereinafter ‘Mishra’).
Claims 1, 19 and 20: Donia discloses a computing system, a computer-implemented method and a non-transitory, computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations, the operations comprising:
one or more processors, Donia teaches processors (see at least column 2 lines 13-17);
a database, Donia teaches in (column 6 lines 61-65), and
a non-transitory, computer-readable medium storing instructions that, when executed by the one or more processors, Donia teaches computer-readable media (see at least column 4 lines 54-59), cause the one or more processors to perform operations, the operations comprising:
periodically providing a reporting data request to each data source of a plurality of data sources, the reporting data request requesting data indicative of a number of impressions associated with content provided to the plurality of data sources, Donia teaches a user request module 406 configured to receive and process requests for data from the users 440 and a content retrieval module 408 configured to retrieve the requested data from the drug specific relational database 430 (see at least column 8 lines 28-67; column 9 lines 43-61; column 13line 62 through column 14 line 2 and see Figure 2C element 106);
receiving reporting data from the plurality of data sources in response to the reporting data request, Donia teaches a user request module 406 configured to receive and process requests for data from the users 440 and a content retrieval module 408 configured to retrieve the requested data from the drug specific relational database 430 (see at least column 8 lines 28-67; column 9 lines 43-61; column 13line 62 through column 14 line 2 and see Figure 2C element 106);
processing the reporting data into a data format usable by the database; and storing the reporting data in the database, Donia teaches module 104 either pulls or receives data having any known format, software code within aggregation module 104 may recognize the format of the data and determine its relevance based on one or more predetermined rules. If relevant, the data may be reformatted, as necessary and stored in database 130 in a format suitable for presentation as described hereinafter (see at least column 9 lines 15-28 and column 16 lines 9-40); and
in response to receiving a request from a user to generate a report, Donia teaches a user request module 106 configured to receive and process requests for data from the users 140 and a content retrieval module 108 configured to retrieve the requested data from the drug specific relational database (see at least column 9lines 43-61).
While Donia teaches the limitations mentioned above, Donia does not explicitly teach processing the reporting data stored in the database using a machine-learned model to generate a model output, wherein the model output is indicative of one or more actions a user can take based on the reporting data; and outputting at least a portion of the reporting data and the model output for
display to the user.
However, Mishra teaches processing the reporting data stored in the database using a machine-learned model to generate a model output, wherein the model output is indicative of one or more actions a user can take based on the reporting data, Mishra teaches to generate a structured technical report using a list of
confirmed technical entities and corresponding technical entity value parameters, consistent with method 200 and/or method 300. Report generation logical circuit 914 may store the structured technical report on data store 902 and or cause graphical user interface 920 to display the structured technical report (see at least paragraph 0060); and
outputting at least a portion of the reporting data and the model output for
display to the user, Mishra teaches Steps 112 through 116 provide a mechanism for providing a predicted list of technical entities to a user through the graphical user
interface, and enabling the user to refine the model by identifying which predicted technical entities are actually technical entities of interest, and thus should appear in the
structured version of the technical report (see at least paragraph 0044-0047). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Donia to modify to include the teaching of Mishra in order to refine the pattern matching model and generate a structured technical report.
Claim 2: Donia in view of Mishra disclose the system according to claim 1, and Mishra further teaches the operations further comprising providing the content to the plurality of data sources, wherein the content is selected from a plurality of content stored in the database, Mishra teaches Steps 112 through 116 provide a mechanism for providing a predicted list of technical entities to a user through the graphical user interface, and enabling the user to refine the model by identifying which predicted technical entities are actually technical entities of interest, and thus should appear in the structured version of the technical report (see at least paragraph 0044-0047). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Donia to modify to include the teaching of Mishra in order to refine the pattern matching model and generate a structured technical report.
Claim 3: Donia in view of Mishra disclose the system according to claim 1, and Donia further teaches further teaches wherein processing the reporting data comprises converting the reporting data from a format incompatible for use by the computing system to a format compatible for use by the computing system, Donia teaches process of matching against entities in a database, for example, drug names, ensures that data is usable in the context of the system described herein. System for Granting Access to Arbitrary Configurations of (see at least column 16 lines 36-40).
Claim 4: Donia in view of Mishra disclose the system according to claim 3, and Mishra further teaches further teaches wherein the converting is performed based on which data source of the plurality of data sources the reporting data was received from,
Donia teaches process of matching against entities in a database, for example, drug names, ensures that data is usable in the context of the system described herein. System for Granting Access to Arbitrary Configurations of (see at least column 16 lines 36-40).
Claim 9: Donia in view of Mishra disclose the system according to claim 1, and Donia further teaches further teaches further teaches wherein the reporting data is event-level impression data, Donia teaches upon user interaction, a "Summary Row" can be expanded to show each individual piece of information, as well as provide the ability to open and extend a "Tertiary Row" containing more information that cannot otherwise fit in the "Summary Columns." (see at least column 16 line 58 through column 17 line 3).
Claim 11: Donia in view of Mishra disclose the system according to claim 10, and Donia further teaches further teaches further teaches wherein the event payload comprises data indicative of credit attribution for at least one impression described by the event-level impression data, Donia teaches upon user interaction, a "Summary Row" can be expanded to show each individual piece of information, as well as provide the ability to open and extend a "Tertiary Row" containing more information that cannot otherwise fit in the "Summary Columns." (see at least column 16 line 58 through column 17 line 3).
Claim 12: Donia in view of Mishra disclose the system according to claim 9, and Donia further teaches further teaches further teaches wherein the computing system further comprises a multi-party event hub software layer implemented by a second set of instructions stored in the non-transitory, computer-readable medium, Donia teaches the network environment may be deployed utilizing in
a multi-tier architecture, including a web browsers, a Web server/application server and one or more databases (see at least column 6 lines 51-65).
Claim 15: Donia in view of Mishra disclose the system according to claim 14, and Donia further teaches further teaches further teaches wherein processing the reporting data includes processing the event-level impression data to generate an attribution data payload for storage in the database, Donia teaches upon user interaction, a "Summary Row" can be expanded to show each individual piece of information, as well as provide the ability to open and extend a "Tertiary Row" containing more information that cannot otherwise fit in the "Summary Columns." (see at least column 16 line 58 through column 17 line 3).
Claim 16: Donia in view of Mishra disclose the system according to claim 14, and Donia further teaches further teaches further teaches wherein the event-level impression data includes data indicative of an aggregation of data from at least one data source of the plurality of data sources, Donia teaches upon user interaction, a "Summary Row" can be expanded to show each individual piece of information, as well as provide the ability to open and extend a "Tertiary Row" containing more information that cannot otherwise fit in the "Summary Columns." (see at least column 16 line 58 through column 17 line 3).
Claim 18: Donia in view of Mishra disclose the system according to claim 1, and Donia further teaches further teaches further teaches wherein the reporting data includes credit attribution data, Donia teaches upon user interaction, a "Summary Row" can be expanded to show each individual piece of information, as well as provide the ability to open and extend a "Tertiary Row" containing more information that cannot otherwise fit in the "Summary Columns." (see at least column 16 line 58 through column 17 line 3).
11. Claims 5-6 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Donia et al. (U.S. Patent No. 11,151,653) (hereinafter ‘Donia’) in view of Mishra et al. (U.S. Pub. No. 2020/0226325 (hereinafter ‘Mishra’) and further in view of Bahl (U.S. Pub. No. 2025/0055878).
Claim 5: Donia in view of Mishra disclose the system according to claim 1, however, the combination of Donia and Mishra do not explicitly teach the operations further comprising: providing authentication credentials to each data source of the plurality of data sources; and creating an authenticated connection between the computing system and each data source of the plurality of data sources based on the providing of the authentication credentials to each data source of the plurality of data sources; wherein the reporting data is received over the authenticated connection.
However, Bahl teaches Any communication between the system 130 and the end
Point device(s) 140 may be subject to an authentication protocol allowing the system 130 to maintain security by permitting only authenticated users ( or processes) to access the protected resources of the system 130, which may include servers, databases, applications, and/or any of the components described herein (see at least paragraphs 0021 and 0051). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Donia in view of Mishra to modify to include the teaching of Bahl in order to maintain security by allowing only authenticated users to access protected resources of the system.
Claim 6: Donia in view of Mishra and further in view of Bahl disclose the system according to claim 5, and Bahl further teaches, wherein the authentication credentials for a respective data source is unique to the respective data source, Bahl teaches Any communication between the system 130 and the end Point device(s) 140 may be subject to an authentication protocol allowing the system 130 to maintain security by permitting only authenticated users ( or processes) to access the protected resources of the system 130, which may include servers, databases, applications, and/or any of the components described herein (see at least paragraphs 0021 and 0051). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Donia in view of Mishra to modify to include the teaching of Bahl in order to maintain security by allowing only authenticated users to access protected resources of the system.
Claim 13: Donia in view of Mishra and further in view of Bahl disclose the system according to claim 12, and Bahl further teaches further teaches wherein the multi-party event hub software layer receives the reporting data, Bahl teaches
creating a first training set comprising the set of data segments associated with at least one of a customer event hub, an enterprise event hub, at least one application programming interface (API), messaging data, or at least one data packet. For example, and in some embodiments, the customer event hub described herein may comprise an application associated with all the user accounts' events for a client (including all the application operated by the client and the associated events within each), such as by ingesting data for each event from any source, whereby each event of each user account associated with a client may be tracked and data may be generated and stored. Similarly, the enterprise event hub may act in a similar manner to the customer event hub in that the enterprise event hub is an application associated with all the applications of a client (e.g., an enterprise), which is configured to ingest data for each of the applications of the client, tracking each of the events, and generating data from each of the tracked events (see at least paragraph 0098). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Donia in view of Mishra to modify to include the teaching of Bahl in order to tracked and store data by the system.
Claim 14: Donia in view of Mishra and further in view of Bahl disclose the system according to claim 13, and Bahl further teaches wherein the multi-party event hub software layer processes the reporting data into the data format usable by the database, Bahl teaches creating a first training set comprising the set of data segments associated with at least one of a customer event hub, an enterprise event hub, at least one application programming interface (API), messaging data, or at least one data packet. For example, and in some embodiments, the customer event hub described herein may comprise an application associated with all the user accounts' events for a client (including all the application operated by the client and the associated events within each), such as by ingesting data for each event from any source, whereby each event of each user account associated with a client may be tracked and data may be generated and stored. Similarly, the enterprise event hub may act in a similar manner to the customer event hub in that the enterprise event hub is an application associated with all the applications of a client (e.g., an enterprise), which is configured to ingest data for each of the applications of the client, tracking each of the events, and generating data from each of the tracked events (see at least paragraph 0098). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Donia in view of Mishra to modify to include the teaching of Bahl in order to tracked and store data by the system.
12. Claims 7-8, 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Donia et al. (U.S. Patent No. 11,151,653) (hereinafter ‘Donia’) in view of Mishra et al. (U.S. Pub. No. 2020/0226325 (hereinafter ‘Mishra’) and further in view of Bahl (U.S. Pub. No. 2025/0055878, and further in view of Sundaresan (U.S. Pub. No. 2021/0250166).
Claim 7: Donia in view of Mishra and further in view of Bahl disclose the system according to claim 5, but the combination of Donia, Mishra and Bahl do not explicitly teach wherein providing authentication credentials to each data source includes providing a token allowing sending of impression data to the computing system. However, Sundaresan teaches the hashed token is used to compare against any subsequently submitted data item by a client-side code. In some embodiments, a subsequently submitted event data item is considered as new if the hashed token corresponding to the event does not match with any of previously submitted events (see at least paragraphs 0012, 0022 and 0046). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Donia in view of Mishra and Bahl to modify to include the teaching of Sundaresan in order to prevent
injection of unauthorized versions of the ML model into the peer to peer network 202 (see at least paragraph 0047).
Claim 8: Donia in view of Mishra and further in view of Bahl and further in view of Sundaresan disclose the system according to claim 7, and Sundaresan teaches wherein the authentication credentials include a terms of service, wherein the token is provided once a publisher accepts the terms of service, Sundaresan teaches the hashed token is used to compare against any subsequently submitted data item by a client-side code. In some embodiments, a subsequently submitted event data item is considered as new if the hashed token corresponding to the event does not match with any of previously submitted events (see at least paragraphs 0012, 0022 and 0046). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Donia in view of Mishra and Bahl to modify to include the teaching of Sundaresan in order to prevent injection of unauthorized versions of the ML model into the peer to peer network 202 (see at least paragraph 0047).
Claim 10: Donia in view of Mishra and further in view of Bahl and further in view of Sundaresan disclose the system according to claim 7, and Sundaresan teaches wherein the event-level impression data includes an event-level hash ID and an event payload, Sundaresan teaches the hashed token is used to compare against any subsequently submitted data item by a client-side code. In some embodiments, a subsequently submitted event data item is considered as new if the hashed token corresponding to the event does not match with any of previously submitted events (see at least paragraphs 0012, 0022 and 0046). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Donia in view of Mishra and Bahl to modify to include the teaching of Sundaresan in order to prevent injection of unauthorized versions of the ML model into the peer to peer network 202 (see at least paragraph 0047).
Claim 17: Donia in view of Mishra and further in view of Bahl and further in view of Sundaresan disclose the system according to claim 16, and Sundaresan teaches wherein the aggregation of data includes a plurality of event hashed identifications and a plurality of event payloads, wherein each event payload of the plurality of event payloads is associated with one event hashed identification of the plurality of event hashed identifications, Sundaresan teaches the hashed token is used to compare against any subsequently submitted data item by a client-side code. In some embodiments, a subsequently submitted event data item is considered as new if the hashed token corresponding to the event does not match with any of previously submitted events (see at least paragraphs 0012, 0022 and 0046). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Donia in view of Mishra and Bahl to modify to include the teaching of Sundaresan in order to prevent injection of unauthorized versions of the ML model into the peer to peer network 202 (see at least paragraph 0047).
Conclusion
13. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
14. Telford et al. (U.S. Pub. No. 2019/0026491) discloses user events are processed to estimate a unique user count. An identifier hash, having a bucket index portion denoting one of a plurality hash buckets, is generated for each of the user events. At a processing node, each of the user events is allocated to one of a plurality of processing threads based on the bucket index portion of its identifier hash. A unique user count is estimated as follows: for each user event satisfying at least one query parameter, 1) determine a run length of a second portion of its identifier hash, 2) compare it with a value of the hash bucket denoted by the bucket index portion of that identifier hash, and 3) if the determined run length is greater, change that hash bucket value at that node to match the determined run length. The hash bucket values are used to estimate the unique user count (see at least the Abstract).
15. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARILYN G MACASIANO whose telephone number is (571)270-5205. The examiner can normally be reached Monday-Friday 12:00-9:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, llana Spar can be reached at 571)270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARILYN G MACASIANO/Primary Examiner, Art Unit 3622 04/15/2026
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