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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/17/2026 has been entered.
Claim 1 has been amended, claims 6 and 23 have been canceled and no claims have been added.
In light of Applicant’s amendment, previous claim rejections 35 USC 103 have been withdrawn.
Claims 1-5 and 7-22 are pending with claim 1 as independent claim.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 11/21/2025 was filed after the mailing date of the Final rejection on 11/5/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5 and 18-22 are rejected under 35 U.S.C. 103 as being unpatentable over Grayson (US 10,949,432) in view of Nomula (US 2019/0139092).
Claim 1. A method of personalizing digital guidance for use in an underlying computer application, the method comprising the steps of:
identifying an underlying application in which it is desired to provide personalized guidance content recommendations; Grayson teaches in [col. 6, ln 59 to col. 7, ln 55] “the steps in the tax preparation workflow executed by the user prior to invoking a triggering invent of initiating a request to view help content related to a specific step within the tax preparation workflow… After invoking a triggering event (e.g., initiating a request for help content for the application 142), content recommender 146 can present a set of content items determined to be relevant to a user of application 142 based on user activity history within application 142 prior to invocation of the triggering event.” (emphasis added) examiner note: based on activity history, content recommender 142 may identify the tax preparation application as the underlying application,
automatically collecting and structuring usage data of the underlying application at a user level for n days; Grayson teaches in [col. 6, ln 59 to col. 7, ln 55] “User activity information recorded by user activity tracker 144 and stored in user activity repository for a specific user may include information identifying steps in a tax preparation workflow that the user has executed and timestamps at which a user began interacting with each recorded step in the tax preparation workflow.” (emphasis added) examiner note: the user activity (history or n days) information on the tax preparation application may be recorded as usage data,
algorithmically choosing at least two methods from the group consisting of sequence analysis, top popular analysis, repeat usage analysis, similar users analysis and popular analysis, Grayson teaches in [col. 9, ln 62 to col. 10, ln 11] “By using a LSTM RNN, the probabilistic model may be configured to make predictions based on time series data, such as a sequence of events executed within application 142 over a period of time prior to invocation of the triggering event.” And in [claim 8] “adding the user selections of the content items to the user activity history comprises: recording a duration of user interaction with a selected content item; and upon determining that the recorded duration exceeds a threshold interaction time, committing a record of the user interaction with the selected content item to the user activity history.” (emphasis added) examiner note: at least two method have been selected in providing help content to the user. One is sequence analysis indicated by “time series data” and the second is popular analysis indicated by the user interacting with selected content item for at least threshold time value,
wherein the sequence analysis comprises identifying guidance contents that have a high probability of being used one after other, Grayson teaches in [col. 9, ln 1 to col. 10, ln 11] “the j content items viewed by the user may include a first content item that a user interacted with and subsequent content items accessible, directly or indirectly, through links in the first content item to the subsequent content items… By using a LSTM RNN, the probabilistic model may be configured to make predictions based on time series data, such as a sequence of events executed within application 142 over a period of time prior to invocation of the triggering event.” And in [claim 8] “adding the user selections of the content items to the user activity history comprises: recording a duration of user interaction with a selected content item; and upon determining that the recorded duration exceeds a threshold interaction time, committing a record of the user interaction with the selected content item to the user activity history.” (emphasis added) examiner note: the particular content item may be identified based on input sequence of events as a time series, such a time series data input as activity events indicate the use of the content one after other,
wherein the top popular analysis comprises identifying guidance content that is used by more than a threshold proportion of users in a predetermined period of time, Grayson teaches in [col. 8, ln 11-35] “the recorded amount of time a user spends viewing or otherwise interacting with an accessed content item from a presented set of content items may be used to determine whether the recorded interaction should, in fact, be treated as a non-interaction with the accessed content item… To determine whether an interaction is actually a non-interaction and should not be recorded in a vector representative of content the user interacted with after invoking a triggering event, vector generator 132 can compare the recorded interaction time to a threshold value. If the recorded interaction time for a content item is less than the threshold value, the content item can be dropped from the set of content items retrieved from user activity repository. Otherwise, vector generator 132 can determine that a user actually intended to interact with the content item and can include that content item in the set of content items a user interacted with after invoking a triggering event in application 142.” (emphasis added) examiner note: the predetermined period of time may be the threshold value and a user may be the threshold proportion of users such that content items interacted with by at least one user may be recorded as identifiable content item by the predictive model,
Grayson does not explicitly disclose
wherein the repeat usage analysis comprises calculating an affinity of users to reuse content frequently and an affinity of content used by users repeatedly. However, Nomula, in an analogous art, teaches in [0112-0113] “One or more applications showing one or more notifications to the user regarding new deals or upcoming meetings may become more efficient and accurate by using one or more aggregated behavior vectors gathered from multiple sources… once the system 100 has confirmed that the notification is a new notification, the system 100 may concatenate a personal preference (expressed, for example, as a category vector) and one or more demographic group vectors to the notification to ensure that it is a good notification to display to the user. The notification may be scored to evaluate its importance to the user.” (emphasis added) examiner note: the affinity of users to frequently using content items and the affinity of content items being consumed by certain users may be interpreted as user demographic information. For example, if a user profile includes demographic information comprises statistical data such as ethnicity (American) and the content item to be recommended for consumption to the user is “Hamburger”. Based on the ethnicity of the user, the system may recommend “Hamburger sandwich” as content item because users (Americans) consumes Hamburgers and Hamburgers are consumed by Americans,
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Grayson with the teaching of Nomula because “the system 100 may concatenate a personal preference (expressed, for example, as a category vector) and one or more demographic group vectors to the notification to ensure that it is a good notification to display to the user. The notification may be scored to evaluate its importance to the user. In an embodiment, this may be implemented by a simple cosine similarity between the user preference vector and the notification vector. Nomula [0123],
Further, Grayson teaches wherein the similar users analysis comprises measuring a degree of similarity between a plurality of click users and a recommendation user, and then recommending content being used by click users who have a high degree of similarity to the recommendation user, in [col. 5, ln 13-36] “Content that other users of the software application have interacted with and found to be useful in solving a similar problem may be delivered to a user of the application.” And in [col. 16, ln 9-27] “to select the subset of content items, the system can sort the content items into an ordered list based on the predictive scores associated with each content item in the set. Content items with higher predictive scores may be content items that are predicted to be relevant to the user in view of user activity prior to invocation of the training event based on similar content selections by users who have executed similar activities in the application… the system outputs the selected subset of content items to a client device for display.” (emphasis added) examiner note: the segment “event based on similar content selections by users who have executed similar activities in the application” may indicate content used by click users, whereas the segment “content items relevant to the user in view of user activity” may indicate recommendation user, and
wherein the popular analysis comprises identifying content used frequently by other users of the underlying application; Grayson teaches in [col. 20, ln 3-19] “By using predictive models described herein to generate content recommendations based on user activity history with application 142, content recommendation systems can account for complexity of user behavior within application 142 and use activity and content interaction histories of other users to deliver relevant content to a user of application 142.” (emphasis added) examiner note: the phrase “content interaction histories of other users” may indicate content repeatedly interacted with by other users,
automatically combining results from the at least two chosen methods; and automatically generating and presenting personalized content recommendations to the recommendation user within the underlying computer application based upon the combined results; Grayson teaches in [col. 10, ln 59 to col. 11, ln 39] “To customize the generated probabilistic model, the output of the probabilistic model may be converted into a dense vector including probability or predictive scores for each content item that is available in the application and merged with an auxiliary vector including the user attributes. The user information layer in the probabilistic model may be implemented as a final layer of the generated probabilistic model that makes content recommendation predictions based on user activity history within the application 142 and user attributes so that the content recommendations are customized to the user of application 142… content recommendation trainer 134 can additionally use user attributes to personalize the generated probabilistic model and thus personalize the content identified by the generated probabilistic model as relevant to the user. The user attributes may include, for example, a duration of time a user has subscribed to or otherwise used application 142, types of product subscriptions the user owns, a number of external users to whom the user has granted account access to (e.g., a number of external accountants granted access to the user's data in application 142), a number of beneficiaries of the user's access to application 142 (e.g., employees attached to a user's payroll in an accounting application), whether the user is using a trial of paid subscription to application 142, additional features of the application 142 the user is subscribed to, to name a few… During execution of application 142, application 142 can receive a request to initiate a triggering event within application 142 from the user of application 142. As discussed, the triggering event may include, for example, a request for help content in the application 142, a request for information about third-party plugins or features related to a particular component of application 142, or other events that may be defined within application 142 as an event that triggers generation of a recommended set of content to be displayed to a user.” (emphasis added) examiner note: the system utilizes the user activity history, the user attributes, and histories of other users to personalize help content and displayed to the user.
wherein the step of choosing at least two methods from the group comprises choosing the similar users analysis, and the similar users analysis further comprises automatically creating at least one user behavior matrix from the gathered data, wherein the at least one user behavior matrix compares behavior of the representation user in the underlying application to behavior of each of the plurality of click users. Grayson teaches in col. 20, ln 3-19] “the generation of user vectors may be based on navigation between different (non-continuous) pages, different steps of a workflow, or different possible execution paths of the same workflow. Because of the variability in the possible execution paths a user can perform within an application 142, static pre-population of content recommendations based on user context (e.g., where a user is within an application) may be infeasible. By using predictive models described herein to generate content recommendations based on user activity history with application 142, content recommendation systems can account for complexity of user behavior within application 142 and use activity and content interaction histories of other users to deliver relevant content to a user of application 142.” (emphasis added) examiner note: the user behavior table such as table 510A and/or table 520A may be used with use activity and content interaction histories of other users, using similar tables 510/520 of other users, to deliver relevant content to a user application 142. The use of the user activity history and the use activity and content interaction histories of other users may indicate comparing behavior of the user on application 142, as the underlying application, and history behavior of other users. Since the user interaction history represented in tables such as tables 510 and/or 520, other users’ histories may be represented in similar tables.
Claim 2. The rejection of the method of claim 1 is incorporated, wherein the step of algorithmically choosing at least two methods from the group comprises choosing at least three methods from the group; Grayson teaches as indicated above, at least two method have been selected in providing help content to the user. One is sequence analysis indicated by “time series data” and the second is popular analysis indicated by the user interacting with selected content item for at least threshold time value. The third method may be the degree of similarity as indicated in the rejection of claim 1 above.
Claim 3. The rejection of the method of claim 1 is incorporated, wherein the step of algorithmically choosing at least two methods from the group comprises choosing at least four methods from the group; Grayson teaches as indicated above, at least two method have been selected in providing help content to the user. One is sequence analysis indicated by “time series data” and the second is popular analysis indicated by the user interacting with selected content item for at least threshold time value. The third method may be the degree of similarity and the fourth method may be the popular analysis as indicated in the rejection of claim 1 above.
Claim 4. The rejection of the method of claim 1 is incorporated, Grayson does not explicitly disclose wherein the step of algorithmically choosing at least two methods from the group comprises choosing all five methods from the group. However, Nomula teaches the affinity of users to frequently using content items and the affinity of content items being consumed by certain users may be interpreted as user demographic information. For example, if a user profile includes demographic information comprises statistical data such as ethnicity (American) and the content item to be recommended for consumption to the user is “Hamburger”. Based on the ethnicity of the user, the system may recommend “Hamburger sandwich” as content item because users (Americans) consumes Hamburgers and Hamburgers are consumed by Americans.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Grayson with the teaching of Nomula because “the system 100 may concatenate a personal preference (expressed, for example, as a category vector) and one or more demographic group vectors to the notification to ensure that it is a good notification to display to the user. The notification may be scored to evaluate its importance to the user. In an embodiment, this may be implemented by a simple cosine similarity between the user preference vector and the notification vector. Nomula [0123].
Claim 5. The rejection of the method of claim 1 is incorporated, wherein the step of combining results from the at least two methods from the group comprises automatically ruling out a high ranking content recommendation produced by one of the methods and automatically selecting a lower ranking content recommendation from the one method if the high ranking content has already been produced by another of the methods; Grayson teaches in [col. 13, ln 21-42] “content recommender 146 may maintain the sorted list of content items for a user to traverse while attempting to find relevant information from the set of content items. In some embodiments, to reduce the size of the sorted list of content items, content recommender 146 can remove content items associated with low predictive scores from the sorted list. A low predictive score sufficient to remove a content item from the sorted list of content items may include predictive scores below a score relevance threshold value.” And in [col. 17, ln 58 to col. 18, ln 24] “While the activity indicators illustrated in FIG. SA include information about different steps of a workflow accessed during execution of a software application it should be recognized that other information defined as beacons in the application may be tracked and recorded for use in generating content relevance predictions and content recommendations. These beacons may include navigations between different pages and workflows, user data entry into specific data fields in application 142, selections of data in user interface elements displayed in application 142, invocation of various events in application 142 (e.g., to perform a calculation based on data provided by the user to application 142), and other events that may be used to predict content a user may be interested in viewing or otherwise interacting with at a later point in time.” (emphasis added) examiner note: the help recommended contents may be sorted and/or resorted based on relevant to the user interaction within the application page. For example, if the user is inputting information on page one of the application, the help information relevant to the input information in page one would be different for help information relevant to the user in page two, page three, or page four.
Claim 6. The method of claim 1, wherein the step of choosing at least two methods from the group comprises choosing the similar users analysis, and the similar users analysis comprises automatically creating at least one user behavior matrix from the gathered data. Grayson teaches in [col. 17, ln 38 to col. 18, ln 49] “user activity history 510A may include various pieces of information associated with a user's interactions with components of an application. The information may include, for example: user identifiers, activity indicators, and a timestamp associated with the activity indicators.” (emphasis added) examiner note: table 510A may be a user behavior matrix generated based on a user’s interactions (behavior) gathered from components of an application as shown in fig. 5A.
Claim 7. The method of claim 6, wherein the similar users analysis comprises automatically creating a plurality of user behavior matrices from the gathered data. Grayson teaches in [col. 17, ln 38 to col. 18, ln 49] “FIG. 5A illustrates an example of sequential user activity within application 142 and content item interaction data that may be used to generate user vectors for training the probabilistic model. User activity history 510A and content interaction history 520A, as well as other data that may be used to train a content recommender to deliver relevant content to a user in response to invocation of a triggering event, may be stored in user activity repository 150… user activity history 510A may include various pieces of information associated with a user's interactions with components of an application… Content interaction history 520A generally tracks user interaction with content (or links to content) delivered to a user of client device 120 in response to user invocation of a triggering event and a delivery of recommended content in response to invocation of the triggering event.” (emphasis added) examiner note: table 510A and table 520A may be a user behavior matrices generated based on a user’s interactions (behavior) gathered from components of an application as shown in fig. 5A.
Claim 8. The method of claim 7, wherein at least one of the plurality of user behavior matrices comprises a first axis representing users of the underlying application and a second axis representing different pages of the underlying application. Grayson teaches in [col. 17, ln 38 to col. 18, ln 49] “user activity history 510A may include various pieces of information associated with a user's interactions with components of an application. The information may include, for example: user identifiers, activity indicators, and a timestamp associated with the activity indicators… Content interaction history 520A generally tracks user interaction with content (or links to content) delivered to a user of client device 120 in response to user invocation of a triggering event and a delivery of recommended content in response to invocation of the triggering event. While illustrated in this example as articles a user viewed after invoking a triggering event (e.g., articles or other content in a help repository), it should be recognized that the content interaction history 520A recorded for a user may include various types of data relevant to a context in which content recommender 146 executes.” (emphasis added) examiner note: the first column of table 520A may the first axis representing user identifiers and the second column of table 520A may be the second axis representing article identifiers representing different pages of application 142.
Claim 9. The method of claim 8, wherein values in the at least one matrix represent a predetermined measure of each of the users' behavior on the different pages. Grayson teaches in [col. 17, ln 38 to col. 19, ln 15] “the content interaction history 520A may include records of user interaction with content items that are likely to be erroneous records. For example, user interaction with the content item having an article identifier of “6212b7c2,” having an interaction duration of 5 seconds, may be deemed to be a non-interaction with that content item because the interaction duration is less than some defined threshold value. Because the recorded interaction with the content item having an article identifier of “6212b7c2” may not represent an intentional interaction with that article in response to invoking a triggering event, the system need not include information about user interaction with article “6212b7c2” in a user vector. Omitting such interactions from a generated user vector 530A may prevent unintentional interactions with a content item from influencing how a probabilistic model calculates the probability of user interaction with content items for a given list of user activity within an application prior to invocation of a triggering event.” (emphasis added) examiner note: the duration of five seconds may be a predetermined measure (threshold) to determine whether the user interaction has been intentional interaction of not.
Claim 10. The method of claim 9 further comprising using the behavior matrix to automatically perform a user similarity calculation for each pair of the users to obtain a similarity number for each of the pairs of users. Grayson teaches in col. 20, ln 3-19] “the generation of user vectors may be based on navigation between different (non-continuous) pages, different steps of a workflow, or different possible execution paths of the same workflow. Because of the variability in the possible execution paths a user can perform within an application 142, static pre-population of content recommendations based on user context (e.g., where a user is within an application) may be infeasible. By using predictive models described herein to generate content recommendations based on user activity history with application 142, content recommendation systems can account for complexity of user behavior within application 142 and use activity and content interaction histories of other users to deliver relevant content to a user of application 142.” (emphasis added) examiner note: the user behavior table such as table 510A or table 520A may be used with use activity and content interaction histories of other users to deliver relevant content to a user application 142.
Claim 18. The rejection of the method of claim 10 is incorporated, Grayson does not explicitly disclose wherein the user similarity calculations are based on one or more distance metrics selected from a group consisting of Correlation, Euclidean Distance, Manhattan Distance, Minkowski Distance, Hamming Distance and Cosine Similarity. However, Nomula, in an analogous art, teaches in [0101] “while evaluating similarity between two or more users, their similarity may be computed using cosine similarity between two user vectors along with other variables such as conditional probability distances between the users.” (emphasis added).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Grayson with the teaching of Nomula because “the system 100 may concatenate a personal preference (expressed, for example, as a category vector) and one or more demographic group vectors to the notification to ensure that it is a good notification to display to the user. The notification may be scored to evaluate its importance to the user. In an embodiment, this may be implemented by a simple cosine similarity between the user preference vector and the notification vector. Nomula [0123].
Claim 19. The rejection of the method of claim 7 is incorporated, wherein each of the plurality of user behavior matrices is based on a different behavioral dimension. Grayson teaches in [col. 19, ln 16-51] “an event executed prior to the triggering event may be represented as a one-hot vector, where each event in a set of possible events is associated with an index along a vertical dimension, and the event executed at a particular point in time may be assigned a value of “1” at the appropriate index in the one-hot vector. The series of one-hot vectors may be arranged to form a two-dimensional feature vector representing events executed within the application (on the vertical axis, as illustrated) over time (on the horizontal axis, as illustrated).” (emphasis added).
Claim 20. The rejection of the method of claim 19 is incorporated, wherein each of the different behavioral dimensions is selected from the group consisting of page time similarity, content type usage, path taken to close self-help, user maturity, common content usage, and common smart tips usage. Grayson teaches in [col. 18, ln 64 to col. 19, ln 51] “content interaction history 520A may include information identifying the content a user interacted with (e.g., textual content loaded into a content viewing interface of application 142, video content loaded into a video player and played in a content viewing interface of application 142, etc.), a timestamp at which the user initiated interaction with a content item, and a duration of the user's interaction with the content item… additional user information may also be input into the machine learning model to customize the probabilistic model for additional user dimensions, such as a duration of time a user has subscribed to or otherwise used application 142, types of product subscriptions the user owns, a number of external users to whom the user has granted account access to (e.g., a number of external accountants granted access to the user's data in application 142), a number of beneficiaries of the user's access to application 142 (e.g., employees attached to a user's payroll in an accounting application), etc.” (emphasis added).
Claim 21. The rejection of the method of claim 20 is incorporated, wherein the plurality of user behavior matrices comprises at least six user behavior matrices. Grayson teaches in [col. 17, ln 38 to col. 20, ln 53] “FIG. 5A illustrates an example of sequential user activity within application 142 and content item interaction data that may be used to generate user vectors for training the probabilistic model. User activity history 510A and content interaction history 520A, as well as other data that may be used to train a content recommender to deliver relevant content to a user in response to invocation of a triggering event, may be stored in user activity repository 150. The vectors 530A generated from user activity history 510A and content interaction history 520A may be stored in training data repository 160 for use in training probabilistic models used by content recommender 146 to deliver content to a user of application 142… user activity history 510B and content interaction history 520B, as well as other data that may be used to train a content recommender to deliver relevant content to a user in response to invocation of a triggering event, may be stored in user activity repository 150. The vectors 530B generated from user activity history 510B and content interaction history 520B may be stored in training data repository 160 for use in training probabilistic models used by content recommender 146 to deliver content to a user of application 142 in situations where a user can execute steps in a workflow non-sequentially.” (emphasis added) examiner note: at least four matrices, 510A, 510B, 520A, and 52B, and two vectors, 530A and 530B, may be created for user behaviors including user activities and content interacted by the user.
Claim 22. The rejection of the method of claim 21 is incorporated, wherein all six of the behavioral dimensions of the group are utilized. Grayson teaches in [col. 17, ln 38 to col. 20, ln 53] the four matrices, 510A, 510B, 520A, and 52B, and the two vectors, 530A and 530B, may be utilized to deliver content to a user of application 142.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Grayson and Nomula as applied to claim 10 above and further in view of James et al. (US 2015/0201031, published 7/16/2015, hereinafter as James).
Claim 11. The method of claim 10 further comprising Grayson does not explicitly disclose automatically tabulating a consumption count for each of the users and a particular piece of digital guidance content each user has consumed, each of the consumption counts reflecting a number of times a particular user has consumed the particular content. However, James, in an analogous art, teaches in [0028-0030] “The stored people count metrics may be presented as a table, a line graph, a bar graph, a series of pie charts, and any other text based or graph based output… People count may also be beneficial in calculating a visit metric (Visits). Because people count is determined based on a specific individual access to a website, each visit by a specific individual may be counted.” (emphasis added) examiner note: the term “visit” may be interpreted as consumption such that each visit to the website may be counted for each user and the count by each user for each visit may be presented in a graphical table.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Grayson with the teaching of James because “A metric associated with estimated internet traffic may be a count of people visiting a site, (e.g. People Count). People Count may be influenced by factors such as advertising. In an example, a site could drive up its People Count by buying a lot of advertising across the Internet.” James [0027].
Allowable Subject Matter
Claims 12-17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AHAMED I NAZAR whose telephone number is (571)270-3174. The examiner can normally be reached 10 am to 7 pm Mon-Fri.
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, Stephen Hong can be reached at 571-272-4124. 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.
/AHAMED I NAZAR/Examiner, Art Unit 2178 5/2/2026
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178
Prosecution Insights