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
This Office Action is in response to the communication filed on 10/29/2025.
Claims 1-3, 10-11 and 16-17 have been amended.
4. Claims 1-20 are currently pending and are considered below.
Information Disclosure Statement
5. 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
6. 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.
7. 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 system, which is a statutory class, comprising at least one processor, and a non-transitory computer-readable medium storing instructions that, when executed by the at least one processor, cause the system to:
generate a member information embedding reflecting information stored in a member profile, wherein the member profile comprises a set of profile attributes for a member;
generate a member activity embedding reflecting member activity data associated with the member,
generate a member embedding based on the member information embedding and the member activity embedding:
generate a content item embedding, reflecting information about a content item;
determine a similarity score indicating a similarity between the content item embedding and the member embedding: and
generate a target content item group based on determining that the similarity score satisfies a threshold similarity score; and
filter the member of the target content item group based on a filtering threshold.
The steps of,
generate a member information embedding reflecting information stored in a member profile, wherein the member profile comprises a set of profile attributes for a member;
generate a member activity embedding reflecting member activity data associated with the member,
generate a member embedding based on the member information embedding and the member activity embedding:
generate a content item embedding, reflecting information about a content item;
determine a similarity score indicating a similarity between the content item embedding and the member embedding: and
generate a target content item group based on determining that the similarity score satisfies a threshold similarity score; and
filter the member of the target content item group based on a filtering threshold,
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 generating a content item group comprising members that have interest in a content item. The above identified system steps recite commercial interactions such as sales activities and/or tailored personalized marketing relating to generate a target content item group corresponding to the content item.
If a claim limitation, under its broadest reasonable interpretation, covers commercial interaction such as commercial interaction, 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 at least one processor, memory, a publisher device. The at least one processor, memory, a publisher device is recited at a high level of generality (i.e., as a generic processor performing a generic computer functions of
generate a member information embedding; generate a content item embedding; determine a similarity score ; and generate a target content item group) such that they amount to no more than mere instructions to apply the exception using generic computer components. As for the limitation using a large language model to analyze raw material and a wide and deep model to generate an output layer, this feature is considered math, and therefore is a part of the abstract idea. Because the large language model and a wide and deep 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
at least one processor, memory, a publisher device 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); generating a second menu from a first menu and sending the menu to the second location as performed by a generic computer components (Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44). 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 already present when the steps are considered separately. That is, at least one processor, memory, a publisher device, performing commercial interactions including
generating a member information embedding; generate a content item embedding; determine a similarity score; and generate a target content item group, amount to mere instructions to apply the steps to a computer comprising of a processor.
Thus, independent claims 1, 10 and 16 are not eligible.
As for dependent claims 2-9, 11-15 and 17-20, these claims recite limitations that further define the same abstract idea in claims 1, 10 and 16, to generate a target content item group corresponding to the content item. 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
8. 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.
9. 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.
10. 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.
11. Claims 1-3, 5-6, 8-20 are rejected under 35 U.S.C. 103 as being unpatentable over Drake et al. (U.S. Pub. No. 2019/0236640) (hereinafter “Drake’) in view of Chen et al. (U.S. Pub. No. 2022/0374761) (hereinafter ‘Chen’) and further in view of Christakopoulou et al. (U.S. Pub. No. 2022/0394336) (hereinafter ‘Chris’).
Claims 1, 10 and 16: Drake discloses a system, a computer-implemented method and a non-transitory computer readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to:, the system comprising:
at least one processor, (see at least paragraph 0058); and
a non-transitory computer readable medium, (see at least paragraph 0060) storing instructions that, when executed by the at least one processor, cause the system to:
determine a similarity score indicating a similarity between the content item embedding and the member embedding, Drake teaches a system may analyze the engagement metadata of the current interaction relative to profile data associated with additional engagement entities that are registered with the IRL-MPA system. The IRL-MPA system may assign each additional engagement entity with a similarity score that quantifies a degree of correlation between engagement metadata of the current interaction and profile data of each additional engagement entity (see at least paragraphs 0031, 0097 and 0101-0108); and
generate a target content item group based on determining that the similarity score satisfies a threshold similarity score, Drake teaches generate the subset of audience members by aggregating individual audience members with similarity scores that are greater than or equal to a predetermined similarity threshold (see at least paragraphs 0029-0030 and 0101-0108).
While Drake teaches all the limitations mentioned above, Drake is silent on the generating of the embedding data. Drake does not teach the following limitations:
generate a member activity embedding reflecting member activity data associated with the member; generate a member embedding based on the member information embedding and the member activity embedding; and generate a content item embedding, reflecting information about a content item.
However, Chen teaches generate a member activity embedding reflecting member activity data associated with the member, Chen teaches generating a predicted list of one or more content items of interest for recommending to the user (see at least paragraphs 0008-010);
generate a member embedding based on the member information embedding and the member activity embedding, Chen teaches generating an embedding profile vector based on the embedding model, the embedding profile vector including a vector representation of one or more user interaction data of a second type (see at least paragraphs 0008-0010); and
generate a content item embedding, reflecting information about a content item, Chen teaches generating a content embedding vector based on the embedding model (see at least paragraphs 0008-0010). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Drake to modify to include the teaching of Chen in order to identify content, generate a predicted list of one or more content items of interest for recommend to the user.
While Drake in view of Chen teaches the limitations mentioned above, Drake and Chen does not explicitly teach generate a member information embedding reflecting information stored in a member profile, wherein the member profile comprises a set of profile attributes for a member generate a member embedding based on the member information embedding and the member activity embedding; and filter the member of the target content item group based on a filtering threshold. However, Chrsi teaches generate a member information embedding reflecting information stored in a member profile, wherein the member profile comprises a set of profile attributes for a member generate a member embedding based on the member information embedding and the member activity embedding, Chris teaches generate an aggregate preference profile based at least in part on the first preference profile, the second preference profile, and the group preference profile and to identify content items based at least in part on the aggregate preference profile, and Chris further teaches in the process flow 500, the electronic device 102 may obtain the preference profile of the first-second user group, such as from the memory 204 (502). The electronic device 102 may then identify filter criteria (504). The filter criteria may correspond to a particular attribute that the electronic device 102 would like to filter group recommendations based on such as genre, actor, tag, and the like. For example, for explanatory purposes, the electronic device 102 may select the genre filter criteria. (see at least the Abstract and paragraphs 0047-0051). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Drake in view of Chen to modify to include the teaching of Chris in order to generate group recommendations that correspond to a particular filter criteria, such as genre, actor, tag ( e.g., editorial tag), and the like.; and
filter the member of the target content item group based on a filtering threshold, Chris further teaches in the process flow 500, the electronic device 102 may obtain the preference profile of the first-second user group, such as from the memory 204 (502). The electronic device 102 may then identify filter criteria (504). The filter criteria may correspond to a particular attribute that the electronic device 102 would like to filter group recommendations based on such as genre, actor, tag, and the like. For example, for explanatory purposes, the electronic device 102 may select the genre filter criteria. (see at least the Abstract and paragraphs 0047-0051). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Drake in view of Chen to modify to include the teaching of Chris in order to generate group recommendations that correspond to a particular filter criteria, such as genre, actor, tag ( e.g., editorial tag), and the like.
Claims 2, 11, 14, 17 and 20: Drake in view of Chen and further in view of Chris disclose the system and method according to claims 1 and 10, and Drake further teaches further storing instructions that, when executed by the at least one processor, cause the system to:
generate the member information {embedding (taught by Chen)} by using a large language model to analyze raw text data from the member information, wherein a member information model comprises the large language model, Drake teaches the IRL-MPA system may use one or more trained machine learning models to analyze for data patterns between the engagement metadata of a current interaction and the dataset of historical interactions. The analytics module 320 may use one or more trained machine learning models to analyze data patterns between engagement metadata of a current interaction and profile data associated with additional engagement entities registered with the IRL-MPA system (see at least paragraphs 0029-0032 and 0076); and
generate the content item embedding by using the large language model to analyze raw text data reflecting information about the content item, Drake teaches the analytics module 320 may use one or more trained machine learning models to analyze data patterns between engagement metadata of a current interaction and profile data associated with additional engagement entities registered with the IRL-MPA system (see at least paragraphs 0016, 0029-0032, 0038 and 0076).
Claim 3: Drake in view of Chen and further in view of Chris disclose the system according to claim 1, and Drake further teaches further storing instructions that, when executed by the at least one processor, cause the system to:
determine the filtering threshold for the target content item group, Drake teaches the curated IRL-media data may comprise of a refined presentation of the raw IRL-media data; and, further include one or more product-service offerings embedded as marketing elements, and one or more corresponding engagement requests associated with engagement entities (i.e., producer, merchant, or curator) associated with the curated IRL-media data (see at least paragraphs 0014, 0043, 0087 and 0091); and
generating, based on the filtering threshold and the target content item group, filtered members comprising members within an entity that influence outcomes related to the content item, Drake teaches the curated IRL-media data may comprise of a refined presentation of the raw IRL-media data; and, further include one or more product-service offerings embedded as marketing elements, and one or more corresponding engagement requests associated with engagement entities (i.e., producer, merchant, or curator) associated with the curated IRL-media data (see at least paragraphs 0014, 0043, 0087 and 0091).
Claims 5, 13, 15 and 19: Drake in view of Chen and further in view of Chris disclose the system and method according to claims 4, 12 and 18, and Drake further teaches wherein the member activity data comprises at least one of content item engagement, publisher profile views, or publisher connections, Drake teaches the IRL-MPA system may receive an engagement request from an audience member to interact with a merchant associated with a product-service offering that is embedded as a marketing element within curated IRL-media data, or a curator of the curated IRL-media data. In this instance, the IRL-MPA system may monitor each respective interaction on the engagement platform, and further provide the merchant or curator (i.e., engagement entities) with recommendations relating to additional producers that may present their product-service offerings as marketing elements within curated IRL-media data (see at least paragraphs 0013, 0045-0046 and 0067).
Claim 6: Drake in view of Chen and further in view of Chris disclose the system according to claim 1, and Drake further teaches further storing instructions that, when executed by the at least one processor, cause the system to generate the member embedding by:
generating a member outreach embedding reflecting outreach data associated with the member, Drake teaches generates raw IRL media data. In one example, the producer may further curate, via the IRL-MPA system, the raw IRL-media data to generate a curated IRL-media data. Alternatively, the producer may transmit the raw IRL-media data to the IRL-MPA system and request curating services from a curating merchant ( see at least paragraphs 0038-0039); and
generate the member embedding based on the member information embedding, the member activity embedding, and the member outreach embedding, Drake teaches the product-service offering may include curating services of raw IRL-media data (see at least paragraphs 0038-0039).
Claim 8: Drake in view of Chen and further in view of Chris disclose the system according to claim 1, and Drake further teaches further storing instructions that, when executed by the at least one processor, cause the system to provide, for display via a content item management user interface of a publisher device, the target content item group comprising the member, Drake teaches the IRL-MPA system may receive an engagement request from an audience member to interact with a merchant associated with a product-service offering that is embedded as a marketing element within curated IRL-media data, or a curator of the curated IRL-media data. In this instance, the IRL-MPA system may monitor each respective interaction on the engagement platform, and further provide the merchant or curator (i.e., engagement entities) with recommendations relating to additional producers that may present their product-service offerings as marketing elements within curated IRL-media data (see at least paragraphs 0013, 0045-0046 and 0067).
Claim 9: Drake in view of Chen and further in view of Chris disclose the system according to claim 1, and Drake further teaches further storing instructions that, when executed by the at least one processor, cause the system to provide, for display via a content item management user interface of a publisher device, filtered members corresponding to the target content item by:
determining a category of the content item, Drake teaches the producer may
indicate a preference for product-service offerings related to a set of a product, a category of products, a service, a category of services, an event, a category of events, a
merchant, a category of merchants, a place, a category of places, or any combination thereof (see at least paragraphs 0069-0072);
generating, using an intent model, an intent score predicting a level of intent that the member has in the category of the content item, Drake teaches generate and assign an AI-score for the engagement entity that numerically qualifies a relevance of an engagement entity's interaction with the audience member on the engagement plat form (see at least paragraphs 0073-0080);
generating an aggregated intent score based on intent scores from members of an entity, wherein the intent scores comprises the intent score and the entity comprises the member, Drake teaches the analytics module 320 may assign a similarity score to each audience member within the dataset of historical interactions based on a degree of correlation with the engagement metadata of the current interaction. The analytics module 320 may further generate a subset of audience members by aggregating individual audience members that have a similarity score that is greater than or equal to a predetermined similarity threshold. The predetermined similarity threshold may be set by an administrator of the IRL-MPA system 302 (see at least paragraphs 0073-0080);
determining that an aggregated intent score corresponding to the entity satisfies an entity intent threshold score, Drake teaches generate and assign an AI-score for the engagement entity that numerically qualifies a relevance of an engagement entity's interaction with the audience member on the engagement plat form (see at least paragraphs 0073-0080); and
providing, for display via the content item management user interface, a member within the entity as a filtered member, Drake teaches generate and assign an AI-score for the engagement entity that numerically qualifies a relevance of an engagement entity's interaction with the audience member on the engagement plat form (see at least paragraphs 0073-0080).
Claims 12 and 18: Drake in view of Chen and further in view of Chris disclose the system and method according to claims 1 and 10, and Drake further teaches further comprising generating the member activity embedding by using a neural network to analyze member activity data corresponding with the member, Drake teaches the IRL-MPA system may use one or more trained machine learning models to analyze for data patterns between the engagement metadata of a current interaction and the dataset of historical interactions. The analytics module 320 may use one or more trained machine learning models to analyze data patterns between engagement metadata of a current interaction and profile data associated with additional engagement entities registered with the IRL-MPA system (see at least paragraphs 0029-0032 and 0076).
12. Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Drake et al. (U.S. Pub. No. 2019/0236640) (hereinafter “Drake’) in view of Chen et al. (U.S. Pub. No. 2022/0374761) (hereinafter ‘Chen’) and Christakopoulou et al. (U.S. Pub. No. 2022/0394336) (hereinafter ‘Chris’) and further in view of Cai et al. (U.S. Pub. No. 2019/0349321) (hereinafter ‘Cai’).
Claim 4: Drake in view of Chen and further in view of Chris disclose the system, method and the apparatus according to claims 1, 10 and 16, but is silent on further storing instructions that, when executed by the at least one processor, cause the system to generate the member activity embedding by using a multilayer perceptron to analyze member activity data corresponding with the member. However, Cai teaches the platform is configured for: receiving an interaction data set including data representing an interaction type, an interaction length, a number of clients, a number of internal executives, and an interaction description text including the natural language message data; inputting the interaction data set into a machine learning classification model including a multilayer perceptron to generate a relevant interaction score; and generating signals for outputting the sentiment score, wherein the sentiment score represents a relevant interaction score for an interaction corresponding to the interaction data set (see at least paragraphs 0189 and 0198). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Drake in view of Chen and further in view of Chris to modify to include the teaching of Cai in order to generate relevant interaction score.
13. Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Drake et al. (U.S. Pub. No. 2019/0236640) (hereinafter “Drake’) in view of Chen et al. (U.S. Pub. No. 2022/0374761) (hereinafter ‘Chen’) and Christakopoulou et al. (U.S. Pub. No. 2022/0394336) (hereinafter ‘Chris’) and further in view of Zhang et al. (U.S. Patent No. 10/740825) (hereinafter ‘Zhang’).
Claim 7: Drake in view of Chen and further in view of Chris disclose the system according to claim 1, and Drake further teaches further storing instructions that, when executed by the at least one processor, cause the system to generate the member embedding by:
using a wide and deep model to generate an output layer capturing interactions between the member information embedding and the member activity embedding, wherein the member embedding model comprises the wide and deep model, Drake teaches the IRL-MPA system may use one or more trained machine learning models to analyze for data patterns between the engagement metadata of a current interaction and the dataset of historical interactions. The analytics module 320 may use one or more trained machine learning models to analyze data patterns between engagement metadata of a current interaction and profile data associated with additional engagement entities registered with the IRL-MPA system (see at least paragraphs 0029-0032 and 0076).
Drake teaches the limitation above but does not explicitly teach generating the member embedding based on the output layer by extracting a dense vector representation from the output layer.
However, Zhang teaches the embedding module 260 applies machine learning techniques to generate an embedding model 270 that includes embedding vectors for entities of the online system 140 that describes the entities in latent space. As used herein, latent space is a vector space where each dimension or axis of the vector space is a latent or inferred characteristic of the objects in the space. Latent characteristics are characteristics that are not observed, but are rather inferred through a mathematical model from other variables that can be observed. In some embodiments, the embedding model 270 includes user embeddings (or user embedding vectors) for users of the online system 140, cluster embeddings for clusters of users of the online system 140, and word embeddings (see at least column 7 line 34 through column 8 line 37). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Drake in view of Chen and further in view of Chris to modify to include the teaching of Zhang in order to cluster users based on the embedding vectors for targeting of content items.
Response to Arguments
14. Applicant’s arguments, filed on 10/29/2025, with respect to the rejection of claims 2, 11 and 17 under 35 U.S.C. 112(b) have been fully considered and are persuasive. The rejection of claims 2, 11 and 17 has been withdrawn.
15. Applicant's arguments filed on 10/29/2025 with respect to the rejection of claims 1-20 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
16. Applicant argued “…The representative claim is similar to Example 39 of the Revised Guidance, which is directed to facial expression recognition based on McRO, Inc. dba Planet Blue V. Bandai Namco Games America Inc., 120 USPQ2d 1091 (Fed. Cir. 2016). As stated in page 2 of the Bahr Memo, in a discussion of the teachings of McRO, "[a]n 'improvement in computer-related technology' is not limited to mprovements in the operation of a computer or a computer network per se, but may also be claimed as a set of 'rules' (basically mathematical relationships) that improve computer-related technology by allowing computer performance of a function not previously performable by a computer." The Bahr Memo further states "[a]n indication that a claim is directed to an improvement in computer-related technology may include-(1) a teaching in the specification about how the claimed invention improves a computer or other technology….” Remarks pages 15-16
17. Examiner notes that in Example 39 uses training data of digital facial images that are transformed various ways to create a first training set used to train a neural network. Then, a second training set is created from the first training set and non-facial images that were incorrectly detected as facial images during the first stage. 2019 Subject Matter Eligibility Examples: Here, the training data is not transformed or curated in any particular way in order to train the machine learning model. Nor is the model trained in stages to improve its accuracy as in Example 39.
18. Applicant argued “…Claim 3 of Example 47 recites a method of using an artificial neural network (ANN) to detect malicious network packets. The method includes training the ANN based on input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm. Then, one or more anomalies are detected in network traffic using the trained ANN. Next, at least one detected anomaly is determined to be associated with one or more malicious network packets. The source address of the malicious network packets is determined, one or more malicious network packets are dropped, and then future traffic from the source address is blocked. Though Claim 3 of Example 47 is determined to recite the abstract idea of mathematical concepts and mental processes, the claim is nonetheless deemed to be eligible under 35 U.S.C. § 101 because the claim as a whole integrates the alleged judicial exception into a practical application…” Remarks page 19
20. Examiner notes that the claims in claim 3 of Example 47 is directed to detecting anomalies in network traffic, detecting anomaly with network packets, then detecting a source address, then dropping the malicious network packets in real time and blocking future traffic from the source addresses therefore enhancing security by acting in real time to proactively prevent network intrusions. The instant claim predicts, based on data of the patient, an optimal value for at least one of the first time window, the lower evaluation window, and the lower threshold for determining patient discharge readiness.
21. Applicant’s arguments filed on 10/29/2025 with respect to the rejection of claims 1-20 under 35 U.S.C. 102/103(a) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
22. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
23. O’Malley (U.S. Patent No. 12,094018) discloses a perceptron method (see at least Column 12 line 66 through column 13 line 15).
24. Smith et al. (U.S. Patent No. 7,818,206) discloses tracking user activity at a terminal on a communication network and, more particularly, to methods and systems for generating user profiles based on user activity a communication terminal (see at least paragraph 2).
25. 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 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.
26. 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.
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/MARILYN G MACASIANO/Primary Examiner, Art Unit 3622 02/24/2026