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 .
The following is a Final Office Action in response to communications received on 9/4/2026. Claims 1, 3-8, 10-15, 17-20 are currently pending and have been examined. Claims 1, 4, 8, 11, 15, and 18 been amended. Claims 2, 9, and 16 have been cancelled.
Claim Objections
Claims 1, 8, and 15 are objected to because of the following informalities: the claims recite “receiving, from the model serving system, a response generated by executing the machine-learned language model on the prompt, wherein the machine- learned language model is configured as a transformer architecture”. The claim in a preceding limitation recites “as a transformer architecture” and therefore the examiner believes the second recitation should state “the transformer architecture”. Appropriate correction is required.
Claim Rejections - 35 USC § 101
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.
Step 1: The claims 1, 3-7 are a method, claims 8, 10-14 are a computer readable medium, and claims 15, 17-20 are a system. Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101. However, the claims 1, 3-8, 10-15, 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1: The independent claims (1, 8 and 15, taking claim 1 as a representative claim) recite:
A method comprising:
receiving, from a client device of a user, an order including a list of ordered items from the client device;
obtaining session data for the user that includes browsing history for the user during a session established between the client device and a web application or a mobile application, the session data maintained in a database;
presenting an interface on the client device, the interface including an interface element prompting the user to request identification of one or more purposes of the order;
responsive to receiving an indication that the user interacted with the interface element, invoking a machine-learned language model configured as a transformer architecture with one or more attention layers to identify one or more purposes of the order by:
generating a prompt for input to the machine-learned language model, the prompt specifying a natural language description of at least the list of ordered items in the order, the session data obtained for the user, and a request to identify one or more purposes of the ordered items;
providing the prompt to a model serving system for execution by the machine- learned language model, wherein the machine-learned language model is a large language model (LLM);
receiving, from the model serving system, a response generated by executing the machine-learned language model on the prompt, wherein the machine- learned language model is configured as a transformer architecture;
and parsing the response from the model serving system to extract at least a first identified purpose and a second identified purpose of the order including the list of ordered items;
presenting a set of carouselson an interface on the client device, wherein the set of carousels comprises at least a first carousel corresponding to the first identified purpose and a second carousel corresponding to the second identified purpose, wherein the first carousel comprises a first title and a first description of the first identified purpose as natural language text and a first set of item interface elements associated with one or more items arranged horizontally to one another, and wherein the second carousel is displayed below the first carousel on the interface and comprises a second title and a second description of the second identified purpose as natural language text and a second set of item interface elements associated with one or more items arranged horizontally to one another, wherein the first identified purpose and the second identified purpose are presented while maintaining a current view of the first set of item interface elements and the second set of item interface elements;
responsive to receiving an indication that the user interacted with an item associated with an item interface element of the first or second set, adding the item to the order for the user;
and fine-tuning the machine-learned language model by:
obtaining the indication that the user added the item, wherein the item interface element is associated with an identified purpose of the first or second identified purposes;
generating one or more training examples using the identified purpose and the item interface element presented in the set of carousels that the user interacted with, wherein the one or more training examples include session data for the user, the ordered items for the user, and the one or more purposes identified for the order;
generating estimated outputs by applying the machine-learned language model to other session data for the user and another list of ordered items;
computing a loss function indicating a difference between the estimated outputs and the identified purpose of the one or more training examples to obtain one or more error terms;
and backpropagating the one or more error terms to update one or more parameters of the machine-learned language model.
These limitations, except for the italicized portions, under their broadest reasonable interpretations, recite certain methods of organizing human activity for managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as well as commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). The claimed invention recites steps for receiving a list of items a user wishes to purchase, generating, providing a parsing a prompt based on the set of items, generating and displaying recommended items to a user based on the received information. The steps under its broadest reasonable interpretation specifically fall under sales activities and marketing activities. The Examiner notes that although the claim limitations are summarized, the analysis regarding subject matter eligibility considers the entirety of the claim and all of the claim elements individually, as a whole, and in ordered combination.
Prong 2: This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of
A method (claim 1)
A non-transitory computer-readable storage medium comprising stored instructions executable by a processor, the instructions when executed causing the processor to: (claim 8)
The computer system comprising: a processor; and a non-transitory computer-readable medium storing instructions that, when executed by the processor, cause the processor to: (claim 15)
receiving, from a client device of a user, an order including a list of ordered items from the client device;
obtaining session data for the user that includes browsing history for the user during a session established between the client device and a web application or a mobile application, the session data maintained in a database;
presenting an interface on the client device, the interface including an interface element prompting the user to request identification of one or more purposes of the order;
responsive to receiving an indication that the user interacted with the interface element, invoking a machine-learned language model configured as a transformer architecture with one or more attention layers to identify one or more purposes of the order by:
generating a prompt for input to the machine-learned language model, the prompt specifying a natural language description of at least the list of ordered items in the order, the session data obtained for the user, and a request to identify one or more purposes of the ordered items;
providing the prompt to a model serving system for execution by the machine- learned language model, wherein the machine-learned language model is a large language model (LLM);
receiving, from the model serving system, a response generated by executing the machine-learned language model on the prompt, wherein the machine- learned language model is configured as a transformer architecture;
and parsing the response from the model serving system to extract at least a first identified purpose and a second identified purpose of the order including the list of ordered items;
presenting a set of carouselson an interface on the client device, wherein the set of carousels comprises at least a first carousel corresponding to the first identified purpose and a second carousel corresponding to the second identified purpose, wherein the first carousel comprises a first title and a first description of the first identified purpose as natural language text and a first set of item interface elements associated with one or more items arranged horizontally to one another, and wherein the second carousel is displayed below the first carousel on the interface and comprises a second title and a second description of the second identified purpose as natural language text and a second set of item interface elements associated with one or more items arranged horizontally to one another, wherein the first identified purpose and the second identified purpose are presented while maintaining a current view of the first set of item interface elements and the second set of item interface elements;
responsive to receiving an indication that the user interacted with an item associated with an item interface element of the first or second set, adding the item to the order for the user;
and fine-tuning the machine-learned language model by:
obtaining the indication that the user added the item, wherein the item interface element is associated with an identified purpose of the first or second identified purposes;
generating one or more training examples using the identified purpose and the item interface element presented in the set of carousels that the user interacted with, wherein the one or more training examples include session data for the user, the ordered items for the user, and the one or more purposes identified for the order;
generating estimated outputs by applying the machine-learned language model to other session data for the user and another list of ordered items;
computing a loss function indicating a difference between the estimated outputs and the identified purpose of the one or more training examples to obtain one or more error terms;
and backpropagating the one or more error terms to update one or more parameters of the machine-learned language model.
The additional elements emphasized above are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application – MPEP 2106.05(f).
Examiner notes the emphasized portions of “generating a prompt for input to the machine-learned language model, the prompt specifying a natural language description of at least the list of ordered items in the order, the session data obtained for the user, and a request to identify one or more purposes of the ordered items; providing the prompt to a model serving system for execution by the machine- learned language model, wherein the machine-learned language model is a large language model (LLM); receiving, from the model serving system, a response generated by executing the machine-learned language model on the prompt, wherein the machine- learned language model is configured as a transformer architecture; and parsing the response from the model serving system to extract at least a first identified purpose and a second identified purpose of the order including the list of ordered items; and fine-tuning the machine-learned language model […]; generating estimated outputs by applying the machine-learned language model to other session data for the user and another list of ordered items; provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. 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. That the model is machine learned is used to generally apply the abstract idea without placing any limits on how the machine learned model functions. Rather, these limitations only recite the outcome of “to extract the identified purposes of the order including the list of ordered items; generating one or more recommendations from the identified one or more purposes of the order” and do not include any details about how the “extracting/parsing” is accomplished. See MPEP 2106.05(f) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis.
Accordingly, these additional elements when considered individually or as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The independent claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong two, the additional elements in the claims amount to no more than mere instructions to apply the judicial exception using a generic computer component.
Even when considered as an ordered combination, the additional elements of claim 1, 8, and 15 do not add anything that is not already present when they are considered individually. Therefore, under Step 2B, there are no meaningful limitations in claims 1, 8, and 15 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (see MPEP 2106.05). As such, independent claims 1, 8, and 15 are ineligible.
Dependent claims 3-7, 10-14, and 17-20 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. §101 because the additional recited limitations fail to establish that the claims are not directed to the same abstract idea of Independent Claims 1, 8 and 15 without significantly more.
Claim 3 recites wherein presenting the set of carousels of items on the interface on the client device comprises presenting text describing a basis for which the carousel of items have been recommended. The limitation merely further limits the abstract idea and does not further integrate the judicial exception into a practical application.
Claim 4 recites wherein generating the one or more recommendations from the one or more purposes include: identifying a basket of items for fulfilling a respective purpose, and generating an equivalent basket of recommendations that is a more cost-effective or healthier alternative to the basket of items for fulfilling the purpose. The limitation merely further limits the abstract idea and does not further integrate the judicial exception into a practical application.
Claim 5 recites wherein the one or more identified purposes include fulfillment of one or more recipes or tasks. The limitation merely further limits the abstract idea and does not further integrate the judicial exception into a practical application.
Claim 6 recites receiving, from the client device, session history of the user including a list of viewed items from the client device, search query history, and user data, and wherein the prompt includes the session history of the user. The limitation merely further limits the abstract idea and does not further integrate the judicial exception into a practical application.
Claim 7 recites wherein generating one or more recommendations comprises:
accessing an availability model, wherein the availability model is a second machine learning model trained to predict an availability of an item from an item database;
for a candidate item, applying the availability model to generate the predicted availability; and responsive to the predicted availability being above a threshold, generating the candidate item as a recommendation. The limitation merely further limits the abstract idea. The recitation of the second machine learning model is recited at a high level of generality for applying the abstract idea and does not further integrate the judicial exception into a practical application.
Claims 9-14 and 16-20 recite parallel claim language and therefore are rejected for the reasons set forth above. For these reasons claims 1, 3-8, 10-15, 17-20 are rejected under 35 USC 101.
Subject Matter Free of Prior Art
Claims 1, 8 and 15 are determined to have overcome the prior art of rejection and are free of prior art, however the claims remain rejected under 35 USC 101, as set forth above. All dependent claims are also free of prior art by virtue of dependency, but remain rejected under 35 USC 101.
Taking amended claim 1 as a representative claim, the claims as amended are found to overcome the prior art rejection for the reasons set forth below.
Claim 1 now recites the additional claimed features of:
and parsing the response from the model serving system to extract at least a first identified purpose and a second identified purpose of the order including the list of ordered items;
presenting a set of carousels on an interface on the client device, wherein the set of carousels comprises at least a first carousel corresponding to the first identified purpose and a second carousel corresponding to the second identified purpose, wherein the first carousel comprises a first title and a first description of the first identified purpose as natural language text and a first set of item interface elements associated with one or more items arranged horizontally to one another, and wherein the second carousel is displayed below the first carousel on the interface and comprises a second title and a second description of the second identified purpose as natural language text and a second set of item interface elements associated with one or more items arranged horizontally to one another, wherein the first identified purpose and the second identified purpose are presented while maintaining a current view of the first set of item interface elements and the second set of item interface elements;
responsive to receiving an indication that the user interacted with an item associated with an item interface element of the first or second set, adding the item to the order for the user;
The closest prior art was found to be as follows:
Allen (US 20180293644) discloses determining an objective of a shopping list based on the items input into the shopping list. Allen states [0027] "In some embodiments of the present invention, shopping list analysis program 106 also queries subject expertise sources 122 to accurately determine individual items associated with high level shopping list entries. For example, a shopping list may include an entry of a high level, result based concept such as "make chocolate chip cookies." In this example, shopping list analysis program 106 identifies that the user wishes to make homemade chocolate chip cookies instead of buying premade chocolate chip cookies. Shopping list analysis program 106 identifies the hierarchy of categories related to the entry and queries one of the plurality of subject expertise sources related to food recipes to identify the ingredients necessary for homemade chocolate chip cookies." and see Fig. 1, para [0023]. However Allen does not disclose “presenting a set of carousels on an interface on the client device, wherein the set of carousels comprises at least a first carousel corresponding to the first identified purpose and a second carousel corresponding to the second identified purpose, wherein the first carousel comprises a first title and a first description of the first identified purpose as natural language text and a first set of item interface elements associated with one or more items arranged horizontally to one another, and wherein the second carousel is displayed below the first carousel on the interface and comprises a second title and a second description of the second identified purpose as natural language text and a second set of item interface elements associated with one or more items arranged horizontally to one another, wherein the first identified purpose and the second identified purpose are presented while maintaining a current view of the first set of item interface elements and the second set of item interface elements” as now required by the claimed invention.
Schillace (US 20240202452) discloses [0024] The prompt generator 128, receives the task objective and task request and utilizes them to generate one or more prompts for the ML model. The prompt generator 128 may generate one or more prompts that when processed a ML model, such as a generative large language model (LLM), provide sufficient context for the ML model to generate model output responsive to the task objective associated with the input. However the reference does not disclose “presenting a set of carousels on an interface on the client device, wherein the set of carousels comprises at least a first carousel corresponding to the first identified purpose and a second carousel corresponding to the second identified purpose, wherein the first carousel comprises a first title and a first description of the first identified purpose as natural language text and a first set of item interface elements associated with one or more items arranged horizontally to one another, and wherein the second carousel is displayed below the first carousel on the interface and comprises a second title and a second description of the second identified purpose as natural language text and a second set of item interface elements associated with one or more items arranged horizontally to one another, wherein the first identified purpose and the second identified purpose are presented while maintaining a current view of the first set of item interface elements and the second set of item interface elements” as required by the claimed invention.
Dong (US 11947912) discloses [Col. 9 lines 60-Col. 10 lines 10] FIG. 4 is a block diagram of a memory layer that may be used to incorporate entity data into named entity recognition processing, in accordance with various aspects of the present disclosure. Memory layer 316 may be implemented as an attention head of transformer model 306. Generally, in an attention head of a transformer model, given a query q and a set of key-value pairs (K, V), attention can be generalized to compute a weighted sum of the values dependent on the query and the corresponding keys. The query determines which values to focus on. In some examples, the query may be described as “attending to” the values. The memory layer(s) 316 may be searched to return entity embeddings 318 using a query/key/′value architecture, as described below. However the reference does not disclose “presenting a set of carousels on an interface on the client device, wherein the set of carousels comprises at least a first carousel corresponding to the first identified purpose and a second carousel corresponding to the second identified purpose, wherein the first carousel comprises a first title and a first description of the first identified purpose as natural language text and a first set of item interface elements associated with one or more items arranged horizontally to one another, and wherein the second carousel is displayed below the first carousel on the interface and comprises a second title and a second description of the second identified purpose as natural language text and a second set of item interface elements associated with one or more items arranged horizontally to one another, wherein the first identified purpose and the second identified purpose are presented while maintaining a current view of the first set of item interface elements and the second set of item interface elements” as required by the claimed invention.
Forouzandehmehr (US 20230245209) discloses [0143] In some embodiments, dynamically building the mixed-intent basket can include generating complementary item recommendation in real-time and/or near real-time as each item is added and/or detected in the basket. In a number of embodiments, an advantage of receiving respective real-time complementary item predictions on-the-fly as the baskets are built or filled in real-time by the users can include receiving a different or diversified set of complementary item recommendations for each timestamp of a browsing session, for each basket or cart, for each modification of an active order, for each platform-level using different electronic devices, and/or another suitable measure of diversity on a website carousel display and shown in Figure 10. However the reference does not disclose “ presenting a set of carousels on an interface on the client device, wherein the set of carousels comprises at least a first carousel corresponding to the first identified purpose and a second carousel corresponding to the second identified purpose, wherein the first carousel comprises a first title and a first description of the first identified purpose as natural language text and a first set of item interface elements associated with one or more items arranged horizontally to one another, and wherein the second carousel is displayed below the first carousel on the interface and comprises a second title and a second description of the second identified purpose as natural language text and a second set of item interface elements associated with one or more items arranged horizontally to one another, wherein the first identified purpose and the second identified purpose are presented while maintaining a current view of the first set of item interface elements and the second set of item interface elements” as required by the claimed invention.
Yeh (US 10088331) discloses FIG. 9 illustrates an example optimized display 900 of products on a user interface 121 of a user computing device 120. In this example, the items from the shopping list 800 (size 1 diapers 810a, milk 810b, paper towels 810c, laundry detergent 810d, hand soap 810e, baby wipes 810f, rice cereal 810g, applies 810h, and bananas 810i) are displayed in a vertical list with one or more products corresponding to each item displayed in a horizontal list on each vertical line. In another example, the vertical listing of extends beyond the visible user interface 121 screen. In this example, a scroll bar element 820 is presented to allow the user to view different portions of the user interface 121. However the reference does not disclose “ presenting a set of carousels on an interface on the client device, wherein the set of carousels comprises at least a first carousel corresponding to the first identified purpose and a second carousel corresponding to the second identified purpose, wherein the first carousel comprises a first title and a first description of the first identified purpose as natural language text and a first set of item interface elements associated with one or more items arranged horizontally to one another, and wherein the second carousel is displayed below the first carousel on the interface and comprises a second title and a second description of the second identified purpose as natural language text and a second set of item interface elements associated with one or more items arranged horizontally to one another, wherein the first identified purpose and the second identified purpose are presented while maintaining a current view of the first set of item interface elements and the second set of item interface elements” as required by the claimed invention.
“Making Recommendations Better: The Role of User Online Purchase Intention Identification” discloses improving item recommendations based on understanding the intention of the user’s purchase (section 5-conclusion). However the reference does not disclose “ presenting a set of carousels on an interface on the client device, wherein the set of carousels comprises at least a first carousel corresponding to the first identified purpose and a second carousel corresponding to the second identified purpose, wherein the first carousel comprises a first title and a first description of the first identified purpose as natural language text and a first set of item interface elements associated with one or more items arranged horizontally to one another, and wherein the second carousel is displayed below the first carousel on the interface and comprises a second title and a second description of the second identified purpose as natural language text and a second set of item interface elements associated with one or more items arranged horizontally to one another, wherein the first identified purpose and the second identified purpose are presented while maintaining a current view of the first set of item interface elements and the second set of item interface elements” as required by the claimed invention.
It was found that no references alone or in combination, neither anticipates, reasonable teaches, nor renders obvious the below noted features of Applicant’s invention. The features of claim 1 (and parallel claims 8 and 15) in combination that overcome the prior art are:
and parsing the response from the model serving system to extract at least a first identified purpose and a second identified purpose of the order including the list of ordered items;
presenting a set of carousels on an interface on the client device, wherein the set of carousels comprises at least a first carousel corresponding to the first identified purpose and a second carousel corresponding to the second identified purpose, wherein the first carousel comprises a first title and a first description of the first identified purpose as natural language text and a first set of item interface elements associated with one or more items arranged horizontally to one another, and wherein the second carousel is displayed below the first carousel on the interface and comprises a second title and a second description of the second identified purpose as natural language text and a second set of item interface elements associated with one or more items arranged horizontally to one another, wherein the first identified purpose and the second identified purpose are presented while maintaining a current view of the first set of item interface elements and the second set of item interface elements;
responsive to receiving an indication that the user interacted with an item associated with an item interface element of the first or second set, adding the item to the order for the user;
Therefore, none of the cited references disclose or render obvious each and every feature of the claimed invention and the claimed invention is determined to be free of the prior art. Although individually the claimed features could be taught, any combination of references would teach the claimed limitations using a piecemeal analysis, since references would only be combined and deemed obvious based on knowledge gleaned from the applicant's disclosure. Such a reconstruction is improper (i.e., hindsight reasoning). See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). The examiner emphasizes that it is the interrelationship of the limitations that renders these claims free of the prior art/additional art.
Therefore, it is hereby asserted by the Examiner that, in light of the above, that the claims are free of prior art as the references do not anticipate the claims and do not render obvious any further modification of the references to a person of ordinary skill in art.
Response to Arguments
Applicant's arguments filed 9/4/2026 with respect to 35 USC 101 have been fully considered but they are not persuasive.
With respect to the remarks directed to 35 USC 101, the examiner first asserts the rejection has been updated above to address the claims as amended. With respect to the remarks directed to the alleged improvement to the user interface system, the examiner asserts that the improvement to the information presented addressing the user’s intent and recommendations at most improves the information on the interface and not the interface functionality itself. The improvement here would lie in the abstract idea. Furthermore, the arrangement of the information improves the user shopping experience and is not a technical solution to a technical problem. An improved arrangement of information to allow the shopper to more easily access information does not provide a technical solution to a technical problem.
With respect to the remarks directed to Core Wireless, the examiner asserts the claimed invention does not provide a technical solution to a technical problem like in Core Wireless. In the Core Wireless, the claimed invention improves interfaces for devices with small screens. That is, the level of technical detail provided improved the interface to operate in a manner it could not have operated unless the concrete process and the recited computer architecture were executed. In contrast, as discussed above, the claimed invention is improving what is displayed on the interface, not the interface functionality itself being improved.
With respect to the amended claims addressing the fine tuning of the machine learned language model, the examiner asserts the limitations does not recite more than “already available [technology], with [its] already available basic functions, to use as [a] tool[] in executing the claimed process.” SAP Am., 898 F.3d at 1169–70. We think those cases are equally applicable in the machine learning context. [ Recentive, page 15]. Furthermore, as stated in Recentive, the requirements that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement. Recentive’s own representations about the nature of machine learning vitiate this argument: Iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning [page 12]. The same is true in the instant application. The fine tuning the machine learned model is merely training machine learning in the manner intended for machine learning to learn.
For at least these reasons, the claim all remain rejected under 35 USC 101.
Related Art Not Cited
Jayaram (US 9299099) discloses the process of the collecting context information form a user about what they wish to do during a shopping trip (shopping trip objectives).
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VICTORIA E. FRUNZI whose telephone number is (571)270-1031. The examiner can normally be reached Monday- Friday 7-4 (EST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached at (571) 272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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VICTORIA E. FRUNZI
Primary Examiner
Art Unit TC 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 9/15/2026
Prosecution Insights