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 5/29/2026. Claims 1-20 are currently pending and have been examined. Claims 1-5, 8-9, 15 and 19 have been amended. Claims 1-20 are pending and have been rejected as follows.
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 1-7 are a method, claims 8-14 are a computer readable medium, and claims 15-20 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 1-20 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 8 as a representative claim) recite:
A non-transitory computer-readable medium comprising instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:
receiving, via a client device, a user selection of a first item to purchase online and pick up via an in-store pickup;
clustering, by a computing system, a plurality of items into item clusters based on selection patterns using a clustering model;
determining, utilizing a machine learning model, an item categorization of the first item corresponding to one or more of the item clusters;
accessing physical store traffic modeling for a store, wherein the physical store traffic modeling comprises determining a store-specific traffic prediction model based on user encounters and user selections at the store;
determining, based on the item categorization and the physical store traffic modeling, an item selection metric comprising a predicted rate of user selection relative to predicted user encounters during the in-store pickup, wherein the item selection metric is determine utilizing the item categorization and the physical store traffic modeling in connection with a time-series model;
generating an analysis of historical return of items, wherein the analysis distinguishes between online purchase return rates and in-store purchase return rates;
generating, utilizing a delayed in-situ collaborative filter engine
generating a time-series item categorization by applying the time-series model fitted to the item categorization, wherein the time-series item categorization indicates an optimization of the item categorization with respect to online purchases and in-store purchases;
and weighting the item selection metric generated utilizing the item categorization and the physical store traffic modeling against the time-series item categorization, the analysis of historical return of items, inventory data for the second item, and utilizing an optimization algorithm that maximizes a likelihood of selection of the second item and minimizes a likelihood of return of the second item based on the historical return of items;
and generating, for presentation via the client device, a graphical user interface output identifying the second item for in-store viewing and comprising a selectable option to reserve the second item, based on the determination of the second item by the delayed in-situ collaborative filter engine.
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 determining an item to recommend to a user to view in the store based on item data, store traffic data, and return data. The steps under its broadest reasonable interpretation specifically fall under sales 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 computer-implemented method comprising: (claim 1)
A non-transitory computer-readable medium comprising instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising: (claim 8)
A system comprising: one or more memory devices comprising a client device and a delayed in-situ collaborative filter recommendation engine; and one or more processors configured to cause the system to: (Claim 15)
receiving, via a client device, a user selection of a first item to purchase online and pick up via an in-store pickup;
clustering, by a computing system, a plurality of items into item clusters based on selection patterns using a clustering model;
determining, utilizing a machine learning model, an item categorization of the first item corresponding to one or more of the item clusters;
accessing physical store traffic modeling for a store, wherein the physical store traffic modeling comprises determining a store-specific traffic prediction model based on user encounters and user selections at the store;
determining, based on the item categorization and the physical store traffic modeling, an item selection metric comprising a predicted rate of user selection relative to predicted user encounters during the in-store pickup, wherein the item selection metric is determine utilizing the item categorization and the physical store traffic modeling in connection with a time-series model;
generating an analysis of historical return of items, wherein the analysis distinguishes between online purchase return rates and in-store purchase return rates;
generating, utilizing a delayed in-situ collaborative filter engine
generating a time-series item categorization by applying the time-series model fitted to the item categorization, wherein the time-series item categorization indicates an optimization of the item categorization with respect to online purchases and in-store purchases;
and weighting the item selection metric generated utilizing the item categorization and the physical store traffic modeling against the time-series item categorization, the analysis of historical return of items, inventory data for the second item, and utilizing an optimization algorithm that maximizes a likelihood of selection of the second item and minimizes a likelihood of return of the second item based on the historical return of items;
and generating, for presentation via the client device, a graphical user interface output identifying the second item for in-store viewing and comprising a selectable option to reserve the second item, based on the determination of the second item by the delayed in-situ collaborative filter engine.
The additional elements of 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).
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.
Dependent claims 2-7, 9-14, and 16-20 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 2 recites receiving user interaction with the selectable option, wherein the user interaction comprises a selection of an accept button of the second item for in-store viewing; generating instructions for retrieving and reserving the second item to transmit to an administrator device corresponding to a physical location selected by the client device; and providing the instructions to the administrator device to retrieve or reserve the second item. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 3 recites clustering the plurality of items into the item clusters based on similar historical selection patterns comprising digital items that tend to be bought together, digital items that attract a similar amount of traffic, digital items that adhere to similar trends, and digital items that are selected at similar dates or times; selecting a cluster corresponding to the first item; and utilizing the delayed in-situ collaborative filter engine to fit the time-series model to the cluster. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 4 recites accessing a third-party system to access third-party system data comprising the item categorization, the physical store traffic modeling for the store, the historical return of items, and the inventory data; and determining the second item to view based on the third-party system data. While the claims recite a third-party system, the additional element is recited at a high level of generality. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 5 recites the physical store traffic modeling generates an item selection metric for items at the store based on a rate of user selection of the items at the store relative to a predicted number of user encounters for the items at the store; and determining the second item to view in the store by utilizing the delayed in-situ collaborative filter engine which utilizes the time-series model to process the item selection metric and to compare rate of returns for online purchases relative to in-store purchases. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 6 recites determining a traffic prediction model for the second item as a function of a general traffic prediction model for the store; and modulating the traffic prediction model for the second item by a time-series traffic forecasting model derived as a function of the item clusters. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 7 recites wherein the analysis of historical returns comprises determining an overall return rate and an online purchase return rate for the second item. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 9 recites wherein the operations further comprise providing for presentation via a graphical user interface of the client device, the graphical user interface output comprising a name of the second item, a description of the second item, an image of the second item, an option to accept the second item and an option to decline the second item. While the claim recites the additional element of an administrator device, the additional element does not integrate the judicial exception into a practical application.
Claim 10 recites wherein clustering the plurality of items into the item clusters based on similar historical selection patterns; selecting a cluster corresponding to the first item; and utilizing the delayed in-situ collaborative filter engine to fit a time-series model to the cluster. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 11 recites wherein the delayed in-situ collaborative filter engine determines the second item to view in the store based on similarities between items at the store and similarities between users associated with the items at the store. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 12 recites wherein accessing the physical store traffic modeling comprises generating an item selection metric for items at the store based on a rate of user selection of the items at the store relative to a predicted number of user encounters for the items at the store. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 13 recites wherein the operations further comprise: determining a traffic prediction model for the second item as a function of a general traffic prediction model for the store; and modulating the traffic prediction model for the second item by a time-series traffic forecasting model derived as a function of the item clusters. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 14 recites wherein generating the analysis of historical returns comprises determining an overall return rate and an online purchase return rate for the second item. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claims 16-20 recite parallel claim language and therefore are also rejected for the reasons set forth for claims 9-14. Therefore claims 1-20 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.
The claims as amended are found to overcome the prior art rejection for the reasons set forth below.
With respect to claim 1, claim 1 now recites the additional claimed features of:
accessing physical store traffic modeling for a store, wherein the physical store traffic modeling comprises determining a store-specific traffic prediction model based on user encounters and user selections at the store;
determining, based on the item categorization and the physical store traffic modeling, an item selection metric comprising a predicted rate of user selection relative to predicted user encounters during the in-store pickup, wherein the item selection metric is determine utilizing the item categorization and the physical store traffic modeling in connection with a time-series model;
generating an analysis of historical return of items, wherein the analysis distinguishes between online purchase return rates and in-store purchase return rates;
generating, utilizing a delayed in-situ collaborative filter engine a second item to view in the store based on weighting a variety of data signals that provide context for the second item being an optimal recommendation item, wherein weighting the variety of data signals comprises:
generating a time-series item categorization by applying the time-series model fitted to the item categorization, wherein the time-series item categorization indicates an optimization of the item categorization with respect to online purchases and in-store purchases; and
With respect to claims 8 and 15, the claims now recite the additional claimed features of:
accessing physical store traffic modeling for a store, wherein the physical store traffic modeling comprises determining a store-specific traffic prediction model based on user encounters and user selections at the store;
determining, based on the item categorization and the physical store traffic modeling, an item selection metric comprising a predicted rate of user selection relative to predicted user encounters during the in-store pickup, wherein the item selection metric is determine utilizing the item categorization and the physical store traffic modeling in connection with a time-series model;
generating an analysis of historical return of items, wherein the analysis distinguishes between online purchase return rates and in-store purchase return rates;
generating, utilizing a delayed in-situ collaborative filter engine a second item to view in the store based on weighting a variety of data signals that provide context for the second item being an optimal recommendation item, wherein weighting the variety of data signals comprises:
generating a time-series item categorization by applying the time-series model fitted to the item categorization, wherein the time-series item categorization indicates an optimization of the item categorization with respect to online purchases and in-store purchases: and weighting the item selection metric generated utilizing the item categorization and the physical store traffic modeling;
against the time-series item categorization, the analysis of historical return of items, and inventory data for the second item, and utilizing an optimization algorithm that maximizes a likelihood of selection of the second item and minimizes a likelihood of return of the second item based on the historical return of items;
The closest prior art was found to be as follows:
Mamgain (US 20160275592) discloses a recommendation system identify items based on the category of items a user wishes to purchase [0029]. The system may access inventory information to determine if the requested item is in stock [0038]. Then the e-commerce system 42 at 261 may obtain recommended items 25 from the recommendation system 49 and present the recommended items 25 to the customer via customer computing device 30. To this end, the e-commerce system 42 may send the recommendation request to the recommendation system 49 in order to identify which items are being requested by the customer as well as which customer is requesting the items 24 [0051] and an additional item may be reserved [Abstract]. The reference does not disclose access physical store traffic modeling for a store; determine an item selection metric comprising a predicted rate of user selection relative to predicted user encounters during the in-store pickup utilizing a time series model and based on the item categorization and the physical store traffic modeling; generate an analysis of historical return of items; utilize the delayed in-situ collaborative filter recommendation engine to determine a recommendation of a second item to view in the store based on an optimization equation comprising weighting the item selection metric, the analysis of historical return of items.”
Shipman (US 20170262815) discloses enabling a user to buy parts and pick them up at a store location [0128], the prioritization of parts may also be informed by historical rates of part returns (e.g., a part that is frequently returned may be ranked lower than a part that is very rarely returned [0124], and the ranking may be based on identifying orders of priority of the required parts and the recommended parts, wherein causing presentation of the interface facilitating purchase of the required parts, the required tools, and the recommended parts includes causing presentation of the required parts and the recommended parts in the corresponding orders of priority. In some such embodiments, the orders of priority of the required parts and the recommended parts are based on one or more of: price, inventory levels, vendor-negotiated deals, geography, or historical rates of part returns. [0010]. However while the reference teaches the recommendation of a second item and ranking the items based on inventory and rate of return, the reference does not disclose “access physical store traffic modeling for the a store; determine an item selection metric comprising a predicted rate of user selection relative to predicted user encounters during the in-store pickup utilizing a time series model and based on the item categorization and the physical store traffic modeling; utilize the delayed in-situ collaborative filter recommendation engine to determine a recommendation of a second item to view in the store based on an optimization equation comprising weighting the item selection metric, the analysis of historical return of items, and inventory data for the second item.”
Paolella (US20200394697) discloses determining a predicted rate of consumption of a product based on historical inventory levels, historical traffic levels, and a current traffic level. The historical inventory levels may be determined from the inventory system 110 for the retail location. The historical traffic level and the current traffic level may be determined by the retail traffic system 114 for the current location. The machine-learning model 130 may determine the predicted rate of consumption of the product, which may be specific to the retail location [0034]. While the reference reviews foot traffic of a store to determine consumption rate of the product for a specific location, the reference does not disclose “generating an analysis of historical return of items; utilize the delayed in-situ collaborative filter recommendation engine to determine a recommendation of a second item to view in the store based on an optimization equation comprising weighting the item selection metric, the analysis of historical return of items, and inventory data for the second item”.
Ouimet (US 20050273377) discloses [Abstract] A computer system models customer response using observable data. The observable data includes transaction, product, price, and promotion. The computer system receives data observable from customer responses. A set of factors including customer traffic within a store, selecting a product, and quantity of selected product is defined as expected values, each in terms of a set of parameters related to customer buying decision. A likelihood function is defined for each of the set of factors. The parameters are solved using the observable data and associated likelihood function. The customer response model is time series of unit sales defined by a product combination of the expected value of customer traffic and the expected value of selecting a product and the expected value of quantity of selected product. A linear relationship is given between different products which includes a constant of proportionality that determines affinity and cannibalization relationships between the products. While the reference discloses using foot traffic data to determined the expectancy of the user to purchase a product and then determine an appropriate promotion for identified products, the reference does not disclose “utilize the delayed in-situ collaborative filter recommendation engine to determine a recommendation of a second item to view in the store based on an optimization equation comprising weighting the item selection metric, the analysis of historical return of items, and inventory data for the second item;.”
Maan (US 11017238) discloses [Col. 19 lines 55-Col. 20 lines 6] For example, based on the captured video/images, the server 316 (or some other device connected thereto) may be configured to store and/or present information regarding the number of customers located within the retail location to the user to provide information about customer traffic and compared to sales data for a corresponding time period to provide information about customer-to-sales conversion rates. A customer traffic heat map image correlated to time of day over the course of a period of time may be presented to the merchant. In another embodiment, the server 316 may be configured to count customer traffic, binary events, such as objects moving in camera frame, to discern customer traffic indirectly and optionally correlating the binary events to sales data to customer purchase data over the same time period to determine a specific conversion rate or to determine an ambiguous spectrum, such as “high conversion rate” and “low conversion rate.” While the reference reviews foot traffic of a store to determine conversion rates for periods of time, the reference does not disclose “utilize the delayed in-situ collaborative filter recommendation engine to determine a recommendation of a second item to view in the store based on an optimization equation comprising weighting the item selection metric, the analysis of historical return of items, and inventory data for the second item”.
As claims 1, 8, and 15 are free of prior art, dependent claims 2-7, 9-14, and 16-20 are also free of prior art by virtue of dependency.
Response to Arguments
Applicant's arguments filed 5/29/2026 have been fully considered but they are not persuasive.
I. The Currently Amended Claims Are Not Directed to an Abstract Idea Under Alice Step 2A Prong One.
The examiner asserts that as amended the claims still recite an abstract idea. The identified limitations of “"clustering, by a computing system, a plurality of items into item clusters based on selection patterns using a clustering model," "determining, utilizing a machine learning model, an item categorization of the first item corresponding to one or more of the item clusters," "accessing physical store traffic modeling for a store," "determining, based on the item categorization and the physical store traffic modeling, an item selection metric comprising a predicted rate of user selection relative to predicted user encounters during the in-store pickup," "generating an analysis of historical return of items, wherein the analysis distinguishes between online purchase return rates and in-store purchase return rates," "generating, utilizing a delayed in-situ collaborative filter engine, a second item to view in the store based on weighting a variety of data signals that provide context for the second item being an optimal recommendation item..." and "generating, for presentation via the client device, a graphical user interface output identifying the second item for in-store viewing and comprising a selectable option to reserve the second item." are performed by the computer, but does not recite additional elements that implement the technology itself. These steps performed by the computer(s)/device(s) do culminate in the presentation of items for in-store viewing and options that a user can select to reserve such item. The recommendation, viewing and reservation of an item is marketing and sales activities as described in MPEP 2106.04(a)(2).
II. The currently amended independent claims are patent eligible in light of Ex parte Szostak
The examiner first notes that the decision of Ex parte Szostak is a non-precedential decision and the comparison of the instant application to that of the PTAB decision is not found to be persuasive. The analysis under 35 USC 101 above has followed the two-part analysis set forth in MPEP 2106.
It is noted by the examiner, that the recitation of the machine learning in the instant application does not recite a transformation of data and then a use of that transformed data via machine learning, which as note by applicant, was emphasized by the Board. This transformation resulted in an improvement to the machine learning itself and integrated the judicial exception into a practical application. That is not the case in that instant application. As stated by applicant in the remarks, the recommendation in across the online and in store environments are improved. This is an improvement to the abstract idea, and not an improvement to machine learning itself. With respect to the instant specification, here it also states that the recommendation is improved by the steps recited in the claimed invention. However, the conclusion that the evaluation process recited in the claims improved efficiency is merely consequential in nature. If the information is clustered together, then the input into the system is reduced and will consequentially require less computing resources.
III. The currently amended independent claims are patent eligible in light of Ex parte Desjardins
The examiner does not find the remarks directed to Ex parte Desjardins to be persuasive. The instant claimed invention does not recite an improvement to the machine learning itself, but instead, as stated in the remarks and specification are directed to improving the recommendation. The steps of the claimed invention use machine learning and the delayed in-situ collaborative filter engine at a high level. Unlike in Desjardins where the claimed invention set forth the manner in which the machine learning itself was improved, the processing of the item information, the item selection metrics, the historical return data, and item categorization are merely processed, organized and then fed into machine learning that is recited in the claims as merely “utilized”. While the claim goes on to recite time series item categorizations using time series modeling and weight the item selection metric, the claim then merely utilizes the optimization algorithm to maximize a likelihood of selection and minimizes a likelihood of return. This again is rooted in the abstract idea and at most recites machine learning at a light level of utilization and not concrete steps for improvement, as was the case in Desjardins.
As to the discussion of the specification, as noted above in section II, the improvement of the instant claimed invention lies in the digital predictions/recommendations not an improvement to the machine learning functionality itself and evaluating items within a cluster while it limits the amount of date required to be processed by the system, the result is merely consequential. If less information is required to be processed by the system, then the system will require less resources. This does not improve the computer technology itself. This is also true of the delayed in-situ recommendations. As discussed above, the improvement lies in the recommendation and not in the delayed in situ engine. In concert with the reasons discussed above regarding the amended language and lack of language directed to improving the machine learning, the discussion directed to the specification is not found to be persuasive.
IV. The currently amended independent claims recite a practical application in accordance with Step 2A Prong 2 per the holding in McRO.
The examiner first notes, with respect to the remarks here regarding the abstract idea and improvement of the efficiency relative to conventional systems, the examiner maintains the remarks set forth above directed to these remarks.
Further, with respect to the McRo, first, in the case of McRo, the courts did not find the claims to recite an abstract idea. The claims were so rooted in computer technology of lip synchronization, that no abstract idea was recited. Even such, the examiner does not find the comparison of the instant application to McRo to be persuasive, as stated above, the instant application is not rooted in computer technology. McRo recited an improvement to the computer technology of animation and lip syncing. The examiner finds that the combined specific rules that render information into a specific format and then used to create a desired result of the instant application lies in the abstract idea.
V. The currently amended independent claims recite a practical application in accordance with Step 2A Prong 2 per holding in Ex Parte Hannun
The examiner first notes that the decision of Ex parte Hannun is a non-precedential decision and the comparison of the instant application to that of the PTAB decision is not found to be persuasive. The analysis under 35 USC 101 above has followed the two-part analysis set forth in MPEP 2106.
The examiner does note, as discussed above, the models recited in the claimed invention lie in the abstract idea except for the machine learning model and the delayed in-situ recommendation system which are recited at a high level of generality and do not integrate the judicial exception into a practical application. These additional elements are merely utilized in the claimed invention, not integrated. The comparison of the instant application to that of Ex parte Hannun is not persuasive.
VI. The currently amended claims are patent eligible in light of the memo “Reminders on evaluating subject matter eligibility of claims under 35 USC 101…”
The examiner maintains that when considered as a whole, the claims are still rejected under 35 USC 101. The additional elements considered along with the limitations that recite an abstract idea do not amount to significantly more. As stated above, the steps recited that generate/determine different modality outputs are part of the abstract idea. As to the abstract idea, again the solution of more accurately predicting a second item based on receiving a user selection of a first item to purchase online by clustering, determining an item categorization, accessing store traffic modeling, determining item selection…, generating an analysis of historical return items, and to determine the second item to view in the store are part of the abstract idea and at most improve the abstract idea itself. Lastly, the examiner does not find the claimed invention to fall into the more likely than not consideration discussed in the reference memo. The examiner has concluded the claims are not eligible.
Relevant Art Not Cited
NPL: “Ecommerce Return Rates: Statistics and Tips to Improve Yours” which discloses the analysis of varying return rates for online purchases, in store purchases, and the return rate variance across industries.
NPL: “Pricing and Return Strategy: Whether to Adopt a Cross-Channel Return Option”? which discloses “that the return rate for online channels is roughly over 30% on average, which is much higher than that of physical channels at 8.89%.” on page 1.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to 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 7/2/2026
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