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 6/26/2026. Claims 1-6, 10-16, and 18-20 are pending, claims 1, 12, and 20 are amended, claims 7 and 17 are cancelled.
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 1-6, 10, and 11 are a method, claims 12-16, 18-19 are a computer program product and claim 20 is 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 1-6, 10-16, 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
3. Step 2A Prong 1: The independent claims (1, 12 and 20 taking claim 1 as a representative claim) recite:
A method, performed at a computer system comprising a processor and, a computer-readable medium, the processor including an aggregation module and a content presentation module, the computer-readable medium storing executable instructions for the processor, the method comprising:
obtaining a set of health scores for a set of individual employees of a user of the computer system;
accessing a machine-learning model of the computer system, wherein the machine- learning model is trained to determine an aggregated health score for the user;
applying, by the aggregation module, the machine-learning model to a seasonality attribute of one or more items not being ordered by the user to infer information about consumption of the one or more items by the set of individual employees;
applying, by the aggregation module, the machine-learning model to the set of health scores, content of a set of orders placed by the user, and the information about consumption of the one or more items to:
determine a baseline diet for the set of individual employees by incorporating demographic data of the set of individual employees, including age, sex, health conditions, and dietary restrictions, to determine what constitutes a healthy element of diet for the set of individual employees, and
generate a value of the aggregated health score for a current time and a plurality of components of the aggregated health score by comparing inferred nutritional factors across items consumed at an environment associated with the user against the determined baseline diet,
wherein the machine-learning model comprises a demographics input that receives demographic data of the set of individual employees and, based on the demographic data, adjusts internal parameters of the machine- learning model to weight nutritional factors differently across distinct employee sub-populations, to generate the value of the aggregated health score for the current time and the plurality of components of the aggregated health score;
obtaining a baseline health score representing a threshold for the aggregated health score;
causing, by the content presentation module, a device associated with the user to display a user interface with a trendline for the aggregated health score overlayed on the baseline health score and a user interface element, the trendline for the aggregated health score including the value of the aggregated health score generated for the current time and a set of values of the aggregated health score generated by the machine-learning model over a defined time period;
ranking a list of items using one or more trends over time associated with one or more of the plurality of components of the aggregated health score and information about each item from the list of items to generate a ranked list of items;
causing, by the content presentation module, the device associated with the user to update the user interface with the ranked list of items for inclusion into a cart of the user via an interaction with the user interface;
receiving, via a network and from the device associated with the user, information about a selection of the user interface element;
responsive to receiving the information about the selection of the user interface element, causing, by the content presentation module, the device associated with the user to update the user interface with trendlines for the plurality of components of the aggregated health score; and
responsive to displaying the trendline for the aggregated health score and the trendlines for the plurality of components of the aggregated health score, causing, by the content presentation module, the device associated with the user to further update the user interface with a set of one or more items ranked based on a predicted impact of each item on one or more of the plurality of components of the aggregated health score, and a prompt for the user to convert on the set of one or more items.
5. Prong 2: This judicial exception is not integrated into a practical application. In
particular, the claims recite the additional elements of
A method, performed at a computer system comprising a processor and, a computer-readable medium, the processor including an aggregation module and a content presentation module, the computer-readable medium storing executable instructions for the processor, the method comprising: (claim 1)
A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor including an aggregation module and a content presentation module, cause the processor to perform steps comprising: (claim 12)
A computer system comprising: a processor including an aggregation module and a content presentation module; and a non-transitory computer-readable storage medium having instructions that, when executed by the processor, cause the computer system to perform steps comprising: (claim 20)
obtaining a set of health scores for a set of individual employees of a user of the computer system;
accessing a machine-learning model of the computer system, wherein the machine- learning model is trained to determine an aggregated health score for the user;
applying, by the aggregation module, the machine-learning model to a seasonality attribute of one or more items not being ordered by the user to infer information about consumption of the one or more items by the set of individual employees;
applying, by the aggregation module, the machine-learning model to the set of health scores, content of a set of orders placed by the user, and the information about consumption of the one or more items to:
determine a baseline diet for the set of individual employees by incorporating demographic data of the set of individual employees, including age, sex, health conditions, and dietary restrictions, to determine what constitutes a healthy element of diet for the set of individual employees, and
generate a value of the aggregated health score for a current time and a plurality of components of the aggregated health score by comparing inferred nutritional factors across items consumed at an environment associated with the user against the determined baseline diet,
wherein the machine-learning model comprises a demographics input that receives demographic data of the set of individual employees and, based on the demographic data, adjusts internal parameters of the machine- learning model to weight nutritional factors differently across distinct employee sub-populations, to generate the value of the aggregated health score for the current time and the plurality of components of the aggregated health score;
obtaining a baseline health score representing a threshold for the aggregated health score;
causing, by the content presentation module, a device associated with the user to display a user interface with a trendline for the aggregated health score overlayed on the baseline health score and a user interface element, the trendline for the aggregated health score including the value of the aggregated health score generated for the current time and a set of values of the aggregated health score generated by the machine-learning model over a defined time period;
ranking a list of items using one or more trends over time associated with one or more of the plurality of components of the aggregated health score and information about each item from the list of items to generate a ranked list of items;
causing, by the content presentation module, the device associated with the user to update the user interface with the ranked list of items for inclusion into a cart of the user via an interaction with the user interface;
receiving, via a network and from the device associated with the user, information about a selection of the user interface element;
responsive to receiving the information about the selection of the user interface element, causing, by the content presentation module, the device associated with the user to update the user interface with trendlines for the plurality of components of the aggregated health score; and
responsive to displaying the trendline for the aggregated health score and the trendlines for the plurality of components of the aggregated health score, causing, by the content presentation module, the device associated with the user to further update the user interface with a set of one or more items ranked based on a predicted impact of each item on one or more of the plurality of components of the aggregated health score, and a prompt for the user to convert on the set of one or more items.
6. The additional elements of emphasized 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. The 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).
7. 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.
8. 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.
9. Even when considered as an ordered combination, the additional elements of claim 1, 12, and 20 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, 12, and 20 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).
10. As such, independent claims 1, 12, and 20 are ineligible.
11. Dependent claims 2-6, 10-11 and 13-16, 18-19 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, 12 and 20 without significantly more.
12. Claim 2 recites wherein obtaining the set of health scores comprises: retrieving
health-related information from an individual account at the computer system of each individual employee of the set of individual employees; and generating, using the retrieved health-related information, a respective health score for each individual employee of the set of individual employees. The claim limitation merely further limits that abstract idea and does not integrate the judicial exception into a practical application.
13. Claim 3 recites wherein obtaining the set of health scores comprises: obtaining, using a health-related survey of each individual employee of the set of individual employees, a respective health score for each individual employee of the set of individual employees. The claim limitation merely further limits that abstract idea and does not integrate the judicial exception into a practical application.
14. Claim 4 recites wherein obtaining the set of health scores comprises: obtaining, using one or more biometric measurements associated with each individual employee of the set of individual employees, a respective health score for each individual employee of the set of individual employees. The claim limitation merely further limits that abstract idea and does not integrate the judicial exception into a practical application.
15. Claim 5 recites further comprising: obtaining information about content of one or more previous orders placed by the user; applying the machine-learning model to the information about the content of the one or more previous orders to generate a training set of health scores for the set of individual employees; and training the machine-learning model, using the training set of health scores. The claim limitation merely further limits that abstract idea and does not integrate the judicial exception into a practical application. The training of the model is recited at a high
level of generality and merely using data of the abstract idea and therefore does not integrate the judicial exception into a practical application.
16. Claim 6 recites further comprising: obtaining information about a portion of content of one or more previous orders placed by the user that was not ingested; applying the machine-learning model to, the information about the portion of content that was not ingested to generate a training set of health scores for the set of individual employees; and training the machine-learning model, using the training set of health scores. The training of the model is recited at a high level of generality and merely using data of the abstract idea and therefore does not integrate the judicial exception into a practical application.
17. Claim 10 recites wherein applying the machine-learning model further comprises: inferring, using a seasonality attribute of one or more items not being ordered by the user, information about consumption of the one or more items by the set of individual employees; and generating, further using the inferred information about consumption of the one or more items, the aggregated health score. The claim limitation merely further limits that abstract idea and does not integrate the judicial exception into a practical
application.
18. Claim 11 recites further comprising: communicating, to a health insurance entity over a network, information about changes of the aggregated health score over time. The claim limitation merely further limits that abstract idea and does not integrate the judicial exception into a practical application.
19. Claims 13-16 and 18-19 recite parallel claim language and are rejected for the same reasons set forth above.
20. For these reasons claims 1-6, 10-16, 18-20 are rejected under 35 USC 101.
Subject Matter Free of Prior Art
Claims 1, 12 and 20 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:
applying, by the aggregation module, the machine-learning model to the set of health scores , content of a set of orders placed by the user, and the information about consumption of the one or more items to:
determine a baseline diet for the set of individual employees by incorporating demographic data of the set of individual employees, including age, sex, health conditions, and dietary restrictions, to determine what constitutes a healthy element of diet for the set of individual employees, and
generate a value of the aggregated health score for a current time and a plurality of components of the aggregated health score by comparing inferred nutritional factors across items consumed at an environment associated with the user against the determined baseline diet,
wherein the machine-learning model comprises a demographics input that receives demographic data of the set of individual employees and, based on the demographic data, adjusts internal parameters of the machine- learning model to weight nutritional factors differently across distinct employee sub-populations, to generate the value of the aggregated health score for the current time and the plurality of components of the aggregated health score;
obtaining a baseline health score representing a threshold for the aggregated health score;
causing, by the content presentation module, a device associated with the user to display a user interface with a trendline for the aggregated health score overlayed on the baseline health score and a user interface element, the trendline for the aggregated health score including the value of the aggregated health score generated for the current time and a set of values of the aggregated health score generated by the machine-learning model over a defined time period;
causing, by the content presentation module, the device associated with the user to update the user interface with the ranked list of items for inclusion into a cart of the user via an interaction with the user interface;
The closest prior art was found to be as follows:
WO 2023/275545 discloses an application for showing an end user how their basket of items compares to a set of nutritional guidelines. The user interface shows the comparison of the user’s current state/choices versus the comparative guideline. The guideline in the reference is a standard value [page 11] “The system surfaces or displays dietary guideline insights on shopping behaviour to motivate each customer to help improve their food choices over time. This makes dietary guidelines such as Eatwell come to life for the customer, as well as making it easy to relate to in a real-life setting. Using Eatwell dietary guidelines, we summarise how customers' purchases match the recommended guidance. While the reference shows a comparison of the user’s basket of a items to a guideline and recommendations as to products that could be purchased, the reference does not disclose at least obtaining a set of health scores for a set of individual employees of a of the computer system; accessing a machine-learning model of the computer system, wherein the machine-learning model is trained to determine an aggregated health score for the user; applying the machine-learning model to the set of health scores and content of a set of orders placed by the to generate a value of the aggregated health score for a current time and a plurality of components of the aggregated health score;
US20140372133 discloses employee health scores and assessments that can be used to determine a baseline for the health of the employees to assess their progress. The reference states [0166] Further, in some embodiments, one or more reports may be generated, after a health assessment of participating employees and/or at suitable intervals, such as yearly, detailing factors that most directly affect overall employee health. These reports may allow the future direction of an employee population's overall health and health behavior to be strategically shaped to allow for effective cost reduction and employee health gains. Reports may continue to be generated at intervals throughout the life of a health management program for an employer. Examples of types of reports that may be generated include, but are not limited to, assessment reports, activity reports, or results reports. A assessment report may be used to provide a health baseline to measure further health progress or assessment reports against. However, the reference does not disclose causing a device associated with the user to display a user interface with a trendline for the aggregated health score overlayed on the baseline health score, the trendline for the aggregated health score including the value of the aggregated health score generated for the current time and a set of values of the aggregated health score generated by the machine-learning model over a defined time period; ranking a list of items using one or more trends over time associated with one or more of the plurality of components of the aggregated health score and information about each item from the list of items to generate a ranked list of items; and causing the device associated with the user to update the user interface with the ranked list of items for inclusion into a cart of the user via an interaction with the user interface as required by the claimed invention.
US 20220058545 discloses displaying aggregate health scores of a group of users (such as a family) and determining trends within the group of users. This can be accomplished through the user of machine learning tools. The reference states [0030] In some embodiments, the instructions cause the one or more processors to generate a trend element, the trend element including a trend of at least one of the people health score, the space health score, the planet health score, or the overall health score over a time period and cause the home screen to include the trend element. [0126] In some embodiments, the building health manager 128 can be configured to search and filter health criteria for displaying health information and scores on a user device 148. Furthermore, various building control operations, e.g., calculations, logic, workflows, automation, machine learning, artificial intelligence, etc. that the building health manager 128 may execute to control the building systems 142, can all incorporate health scores for inputs and outputs of the building control operations. In this regard, the building control operations may execute to account for health and improve health scores. For example, a machine learning algorithm that determines setpoints to use in a zone based on predicted occupancy can incorporate health scores into the setpoint optimization to determine setpoints that result in ideal health scores. However, the reference does not disclose causing a device associated with the user to display a user interface with a trendline for the aggregated health score overlayed on the baseline health score, the trendline for the aggregated health score including the value of the aggregated health score generated for the current time and a set of values of the aggregated health score generated by the machine-learning model over a defined time period; ranking a list of items using one or more trends over time associated with one or more of the plurality of components of the aggregated health score and information about each item from the list of items to generate a ranked list of items; and causing the device associated with the user to update the user interface with the ranked list of items for inclusion into a cart of the user via an interaction with the user interface as required by the claimed invention.
US 20180267700 discloses the overlay of a trendline with a baseline health score at a high level. This is shown in Figure 1. However, the reference does not disclose causing a device associated with the user to display a user interface with a trendline for the aggregated health score overlayed on the baseline health score, the trendline for the aggregated health score including the value of the aggregated health score generated for the current time and a set of values of the aggregated health score generated by the machine-learning model over a defined time period; ranking a list of items using one or more trends over time associated with one or more of the plurality of components of the aggregated health score and information about each item from the list of items to generate a ranked list of items; and causing the device associated with the user to update the user interface with the ranked list of items for inclusion into a cart of the user via an interaction with the user interface as required by the claimed invention.
US20180165418 discloses displaying health score trends for different users in order to identify groupings of users with similar health patterns. The reference shows this in Figure 4b with a diagrammatic representation of health score trends for different individuals, used by the system to identify cohort members similar to the individual. However, the reference does not disclose ranking a list of items using one or more trends over time associated with one or more of the plurality of components of the aggregated health score and information about each item from the list of items to generate a ranked list of items; and causing the device associated with the user to update the user interface with the ranked list of items for inclusion into a cart of the user via an interaction with the user interface as required by the claimed invention.
US 20200219606 discloses identifying recommended items based on nutritional data with respect to an individual and/or family unit. The reference states [0156] The nutrition icon is provided to visually provide percentile information of the amount of nutrient currently contained in the refrigerator on the basis of a preset recommended intake for a nutrient, which is criteria for classifying the nutrition icons. Here, the recommended intake of nutrient may be provided on the basis of the united states department of agriculture (USDA) recommended food portion size. The recommended intake of nutrient may be provided on a daily intake basis, and according to embodiments, the recommended intake of nutrient may be provided on a weekly intake basis. [0157] Users may easily generate a shopping list by identifying the recommended intake for a specific nutrient item on the basis of the percentile information displayed in the nutrition icon, and may have a balanced nutrient intake by visualizing the completion of nutrients for each food group when storing foods in the refrigerator. [0158] The food recommendation area S9-2 provides a food recommendation list. The food recommendation list is provided in order of foods containing nutrients that are currently insufficient in balancing the nutrition of the foods held in the refrigerator. For example, FIG. 16 show lack of “whole grains” in comparison to other nutrients, and thus foods of a grain category are firstly recommended in the food recommendation list. A food recommendation algorithm in the food recommendation process according to the embodiment is determined by a pre-stored program, and the user's preference may be reflected in the food recommendation algorithm. However, the reference does not disclose causing a device associated with the user to display a user interface with a trendline for the aggregated health score overlayed on the baseline health score, the trendline for the aggregated health score including the value of the aggregated health score generated for the current time and a set of values of the aggregated health score generated by the machine-learning model over a defined time period as required by the claimed invention.
The closest NPL of note is “Effectiveness of an Energy Management Training Course on Employee Well-Being: A Randomized Controlled Trial”. The reference is to a study that evaluates the effectiveness of an intervention designed to enhance vitality and purpose in life by assessing changes in employee quality of life (QoL) and health-related behaviors. The measures (page 122-123) include baseline measures of the participants and the results from intervention measures. However, the reference does not disclose causing a device associated with the user to display a user interface with a trendline for the aggregated health score overlayed on the baseline health score, the trendline for the aggregated health score including the value of the aggregated health score generated for the current time and a set of values of the aggregated health score generated by the machine-learning model over a defined time period; ranking a list of items using one or more trends over time associated with one or more of the plurality of components of the aggregated health score and information about each item from the list of items to generate a ranked list of items; and causing the device associated with the user to update the user interface with the ranked list of items for inclusion into a cart of the user via an interaction with the user interface 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 12 and 20) in combination that overcome the prior art are:
applying, by the aggregation module, the machine-learning model to the set of health scores , content of a set of orders placed by the user, and the information about consumption of the one or more items to:
determine a baseline diet for the set of individual employees by incorporating demographic data of the set of individual employees, including age, sex, health conditions, and dietary restrictions, to determine what constitutes a healthy element of diet for the set of individual employees, and
generate a value of the aggregated health score for a current time and a plurality of components of the aggregated health score by comparing inferred nutritional factors across items consumed at an environment associated with the user against the determined baseline diet,
wherein the machine-learning model comprises a demographics input that receives demographic data of the set of individual employees and, based on the demographic data, adjusts internal parameters of the machine- learning model to weight nutritional factors differently across distinct employee sub-populations, to generate the value of the aggregated health score for the current time and the plurality of components of the aggregated health score;
obtaining a baseline health score representing a threshold for the aggregated health score;
causing, by the content presentation module, a device associated with the user to display a user interface with a trendline for the aggregated health score overlayed on the baseline health score and a user interface element, the trendline for the aggregated health score including the value of the aggregated health score generated for the current time and a set of values of the aggregated health score generated by the machine-learning model over a defined time period;
causing, by the content presentation module, the device associated with the user to update the user interface with the ranked list of items for inclusion into a cart of the user via an interaction with the user interface;
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 6/26/2026 have been fully considered but they are not persuasive as set forth below.
With respect to the remarks directed to 35 USC 101, the remarks are not persuasive.
While the claims now recite applying the machine learning model to actively infer consumption information from items that are not ordered using seasonality attributes, as was stated in the Recentive decision (see page 15), "Stated differently, patents may be directed to abstract ideas where they disclose the use of an “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." In the instant application, the machine learning inferring consumptions is using machine learning at its already available basic functions. While machine learning may require a computer system, the machine learning and the computer system are recited at a high-level generality. The recitation of more specific data does not necessarily integrate the judicial exception into a practical application.
With respect to the amendment directed to ranking the items in a dynamic manner, the examiner asserts that this step was not characterized as a mental process and the machine learning was given its weight as an additional element. However, the steps does fall into part of the abstract idea of methods of organizing human activity being carried out by the additional element (machine learning) recited at a high level of generality. Further, the interface is still recited at a high level of generality, merely the data displayed on the interface is more specific, but this data is part of the abstract idea. The updating of the information on the interface and the received inputs to the interface, again are part of the abstract idea and mere output of data through a computer tool.
With respect to the computer components, the examiner reiterates the discussion above as to the generic nature of these computer components. Further, the modules now recited are again mere computer tools implementing the abstract idea. The interrelationship of the inferential operation, the user interface being dynamically updated and the specific components of the system does not alone or in combination overcome the rejection under 35 USC 101. The more specifically recited elements of the computer that make up the architecture system do not amount to more than the specific computer components implementing the abstract idea.
With respect to the characterization of the machine learning model, the examiner maintains, as discussed above, the machine learning model is being used as a generic tool to implement the abstract idea. The additional streams of data as recited, still amount to being part of the abstract idea. Machine learning models receiving inputs which are processed as part of their already available basic functions. There does not appear to be a more technical implementation of this process in the instant application that amounts to improving the computer or another technology.
With respect to the specification's discussion on the alleged improvement, the examiner asserts that this improvement lies in the abstract idea (determine what constitutes a healthy diet for a heterogeneous employee population scale). This is not a technical solution to a technical problem. The remarks directed to Ex Parte Desjardins, are not found to be persuasive. Unlike in Desjardins which recited an improvement to the machine learning technology itself, the instant application improves the data, not the machine learning technology.
With respect to the machine learning model being maintained as a generic computer function, the examiner here reiterates the discussion above. Further, the examiner does not find the machine learning itself (the operation) to be improved. At most, the details added about the machine learning to the claims may improve the data and output, however, part of the abstract idea. The level of detail in the claims and the specification for the demographic input channel is found to detail part of the abstract idea and not to recited an improved operation to the machine learning technology, as was concluded in Desjardins.
For at least these reasons the rejection is maintained.
Relevant Art Not Cited
Granger US 20200200416 scores the environment of a building (i.e. a business) based on a plurality of factors (i.e. health scores) and provides recommendations for improvements.
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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
VICTORIA E. FRUNZI
Primary Examiner
Art Unit TC 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 7/14/2026
Prosecution Insights