DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . It is responsive to the submission dated 09/08/2026. Claims 1-8, and 14-28 are presented for examination, of which, claims 1, 14 and 21 are independent claims.
Response to Arguments/Amendments
2. Applicant’s arguments, see pages 8-9 of Applicant’s Remarks, filed 0/08/2026, with respect to the objections of claims 1 and 21 and the indefiniteness rejections under 35 USC 112(b) of the claims has been reviewed and are accepted to overcome the claim objections and each and every rejection under § 112(b) previously set forth in the Non-Final Office Action. The amendments to the claims are sufficient to overcome the informalities of the previous claims. Thus, the rejections of Claims 1-8 and 14-2 under § 112(b) are withdrawn.
3. Applicant’s arguments, see pages 9-13 of Applicant’s Remarks, filed 09/08/2026, with respect to the Patent Subject Matter Eligibility Rejections under 35 USC 101 of the claims have been fully considered and are persuasive. The rejections of these claims have been withdrawn since claims 1, 14 and 21 as amended, recite a text input that automatically generates output, and then annotating a reply message. These are not an observation, evaluation, judgment, or opinion. Thus, claims 1, 14 and 21 do not recite a mental process. In addition, claims 1, 14, and 21 similarly recite receiving or storing "a user stress profile" and "causing modified output, of the content item, based on the detected stress event" (claims 1 and 21) or "causing, based on the determined stress events in the content item, modified output of the content item" (claim 14). These are improvements in the technology of content delivery.
4. Applicant's Amendments, see pages 14-16 of Applicant’s Remarks, filed 09/08/2026, with respect to the anticipation rejections under 35 USC 102(a1) of the claims have been fully considered and are persuasive. The rejections of these claims have been withdrawn since the amendments to the claims are sufficient to overcome the prior art rejections. Thus, the rejections of Claims 1-8 and 14-28 under § 35 USC 102(a1)) are withdrawn.
Claim Rejections - 35 USC § 102
5. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
6. Claims 1-8, and 14-28 are rejected under 35 U.S.C. 102(a)(a2) as being anticipated by Pasley et al. (US 20240164672).
Considering claim 1, Pasley discloses a method comprising: receiving, by a computing device (10, fig. 1), a stress event schedule indicating portions of a content item in which stress events occur (e.g., Pasley discloses: providing devices and methods that assess physiological data of a user and the context of an experience of the user to predict a stress level (e.g., predicting when a user is experiencing stress), and provide a feedback mechanism based on the stress level of the user (e.g., a notification to the user).See para. 3 and also para. 79. In addition, Pasley discloses: the context of the experience determined includes an activity of the user based on a user's schedule. See paras. 19 and 71),
wherein the stress events comprise types of events in the content item predicted to induce a stress symptom in a viewer of the content item (e.g., Pasley discloses: Different feedback mechanisms can be presented based on the user's stress levels and the type of stress. For example, a feedback mechanism may provide simple notifications, recommend meditation, or create a relaxing VR space to encourage a reduced stress level. See para. 3. Physiological data may be used to determine stress level. For example, some implementations may identify that the user's eye characteristics (e.g., blink rate, stable gaze direction, saccade amplitude/velocity, and/or pupil radius), electrodermal activity, heart rate, and/or movement, correspond to a “calm” stress level rather than a “distressed” stress level. See para. 4. In addition, Pasley discloses: a machine learning model may be used to determine stress levels and/or type of stress level of the user during a portion of the experience based on the obtained physiological data and the context of the experience. For example, one or more physiological characteristics may be determined, aggregated, and used to classify the user's stress level using statistical or machine learning techniques. In some implementations, the response may be compared with the user's own prior responses or typical user stress levels to similar content of a similar experience and/or similar environment attributes. See para. 72. See also para. 74); and
a user stress profile indicating a type of stress trigger associated with a user, wherein the type of stress trigger comprises a type of event identified as predicted to induce a stress symptom in the user (e.g., providing a notification based on an identified stress level (e.g., notifying the user they are exhibiting higher than normal stress levels during a stressful experience). See para. 6. Based on the obtained physiological data, the techniques described herein can determine a user's stress level 3 (e.g., calm, eustress, distress, etc.) and a type of stress (e.g., physical, cognitive, social, etc.) during the experience (e.g., a learning experience). See para. 7. In addition, Pasley discloses determining the context of the experience may involve determining a scene understanding or scene knowledge at a particular location that a user experiences is more or less likely to lead to a stressful state (e.g., based on past stressful experiences that occurred there). Determining a scene understanding or scene knowledge of an experience may involve monitoring level characteristics of a scene that can evoke stress. For example, loud noises, looming sounds, bright flashes of light, sirens, rumbling sounds, and the like, may be monitored and analyzed as part of the scene understanding or scene knowledge. Additionally, scene knowledge may provide information that a particular activity or content might be troubling or stressful… such as threatening stimuli (e.g., an aggressive dog), harm done to a loved one, perceived physical danger to the user (e.g., an oncoming car), online bullying, being berated in person, and the like. See para. 70. See also para. 74);
causing, based on a comparison of the stress event schedule and the type of stress trigger associated with the user, and during output of the content item to the user,
output of a message indicating that a stress triggering scene has been detected in the content item (e.g., Pasley discloses: After a segment of time after the user's physiological data is analyzed (e.g., by a physiological data instruction set) and the context data of the content 302 and/or environment 304 is analyzed (e.g., by a context instruction set), content presentation instant 330 is presented to the user with a feedback mechanism 334 because the stress level assessment was that the user was may have exhibited higher stress levels than desired. For example, a user is exhibiting a high stress level while at work and the feedback mechanism indicates as such to the user and may provide the user with some alternatives for calming. See para. 50. In addition, Pasley discloses assessing the second stress level of the user based on the physiological response of the user to the feedback mechanism, and determining whether the feedback mechanism reduced stress of the user by comparing the second stress level to the first stress level. See para. 16); and
modified output, of the content item, based on the detected stress event (e.g., Pasley discloses: providing a notification to the user based on the stress level. In some aspects, the method further includes customizing content included in the experience based on the stress level of the user. See para. 14. In addition, Pasley discloses Based on the physiological data and associated physiological response, the techniques can provide feedback to the user that the current stress level differs from an intended stress level of the experience and adjust content or feedback mechanisms corresponding to the experience. See para. 9. See also paras. 85-86).
As per claim 2, Pasley discloses causing output of an interface indicating a plurality of types of stress trigger events (e.g., Pasley discloses: a user has an experience in which the user perceives a real-world environment while holding, wearing, or being proximate to an electronic device that includes one or more sensors that obtain physiological data that is indicative of the user's stress level. In another example, a user has an experience in which the user perceives content displayed by an electronic device while the same or another electronic obtains physiological data (e.g., pupil data, EEG data, etc.) to assess the user's stress level. In another example, a user has an experience in which the user holds, wears, or is proximate to an electronic device that provides a series of visual instructions that guide the experience. For example, the instructions may instruct the user to have particular stress levels during particular time segments of the experience, e.g., instructing the user to focus on his or her attention to a particular portion of the educational video, etc. See para. 36. Para. 46 of Pasley discloses that the user may be presented with a list of content experiences for the user to select via GUI. See para. 41. See also para. 74 for display of stress trigger and stress events).
As per claim 3, Pasley discloses causing output of an interface indicating a plurality of available responses to stress trigger events (see paras. 36 and 74 of Pasley and claim 2 rejections above); and receiving user selection of one or more of the responses to stress trigger events (e.g., Pasley discloses: content for the experience can be adjusted corresponding to the experience based on the stress level differing from an intended stress level for the experience. For example, content may be adjusted by an experience developer to improve recorded content for a subsequent use for the user or other users. See para. 86. After the user interacts with the experience changing a feedback mechanism the technique informs the physiological response of the user in subsequent interactions with the experience or a particular segment of the experience. See para. 87).
As per claim 4, Pasley discloses the modified output comprises: automatically replacing the detected stress event with calming content comprising content predicted to mitigate a stress symptom (e.g., Pasley discloses Some implementations improve user experiences by providing stress level assessments that minimize or avoid interrupting or disturbing user experiences, for example, without significantly interrupting a user's attention or ability to perform a task. In one aspect, a process described herein determines that a user is in a “high stress” state and help the user calm down, e.g., based on detecting physiology corresponding to stressed state, the device may provide relaxing content. See paras. 6 and 60);
automatically skipping the detected stress event; automatically replacing the detected stress event with a textual summary of the stress event; or automatically replacing the detected stress with information for assisting the user in mitigating a stress symptom associated with the stress event (e.g., Pasley discloses: providing the feedback mechanism includes providing a graphical indicator or sound configured to change the stress level to a second stress level corresponding to stress exhibited by the user (e.g., as detected by the physiological data and/or context data) in the task during the portion of the experience. …. In some implementations, the method 500 further includes adjusting content corresponding to the experience based on the stress level (e.g., customized to the stress level of the user). For example, content recommendation for a content developer can be provided based on determining stress levels during the presented experience and changes of the experience or content presented therein. For example, the user may focus well when particular types of content are provided. In some implementations, the method 500 may further include identifying content based on similarity of the content to the experience, and providing a recommendation of the content to the user based on determining that the user has the stress level during the experience (e.g., mind wandering). In some implementations, the method 500 may further include customizing content included in the experience based on the stress level of the user (e.g., breaking the content into smaller pieces). See paras. 84-87).
Claim 5 contains features that correspond in scope with the limitations recited in claim 4. Claim 5 is therefore subject to rejections under the same rationale as claim 4. In addition, Pasley discloses generating the user stress profile by causing output of an interface that includes selectable options (e.g., Para. 46 of Pasley discloses that the user may be presented with a list of content experiences for the user to select via GUI. See paras. 41 and 53. See also para. 74 for display of stress trigger and stress events).
As per claim 6, Pasley discloses receiving user input indicating: a type of stress event to be avoided (e.g., Pasley the system accounts for a user's preferences, goals, and/or history in providing feedback. For example, based on a user identified goals of improving fitness, feedback to the user may encourage a user to exercise or meditate rather than consuming unhealthy food during stressful times. See para. 53); and
a filtering threshold for the type of stress event to be avoided (e.g., Pasley discloses acquires and presents content to a user, analyzes the content and/or the environment for context data, obtains physiological data associated with the user during presentation of the content, assesses a stress level of the user based on the physiological data of the user and the context data, and provides a feedback mechanism based on the stress level (e.g., a notification/alert based on a high stress threshold and/or a low stress threshold). See paras. 55 and 73).
As per claim 7, Pasley discloses the modified output comprises output, after the detected stress event is output to the user, of information indicating assisting the user in mitigating a stress symptom associated with the stress event. See paras. 60 and 84-87.
As per claim 8, Pasley discloses the modified output is based on information indicating different actions for different values of a type of stress trigger event. See paras. 9, 50-55 and 73-76.
As per claim 14, Pasley discloses a method comprising: causing, by a computing device (10, fig. 1), output of a listing of a plurality of different types of stress events (e.g., Pasley discloses: when presented with a list of content experiences (e.g., meditation environments), by a device 10, the user 25 may select an experience without requiring the user 25 to complete a physical button press. See paras. 46 and 49-50),
wherein each of the different types of stress events comprises a type of event predicted to induce a stress symptom in a viewer of the content item (e.g., Pasley discloses: Different feedback mechanisms can be presented based on the user's stress levels and the type of stress. For example, a feedback mechanism may provide simple notifications, recommend meditation, or create a relaxing VR space to encourage a reduced stress level. See para. 3. Physiological data may be used to determine stress level. For example, some implementations may identify that the user's eye characteristics (e.g., blink rate, stable gaze direction, saccade amplitude/velocity, and/or pupil radius), electrodermal activity, heart rate, and/or movement, correspond to a “calm” stress level rather than a “distressed” stress level. See para. 4. In addition, Pasley discloses: a machine learning model may be used to determine stress levels and/or type of stress level of the user during a portion of the experience based on the obtained physiological data and the context of the experience. For example, one or more physiological characteristics may be determined, aggregated, and used to classify the user's stress level using statistical or machine learning techniques. In some implementations, the response may be compared with the user's own prior responses or typical user stress levels to similar content of a similar experience and/or similar environment attributes. See para. 72. See also paras. 74-79); and
receiving user input indicating that a user wishes to avoid one or more types of stress events selected from the listing (e.g., Pasley discloses: customization of the experience could be controlled by the user. For example, a user could select the experience he or she desires, such as he or she can choose the ambience, background scene, music, etc. Additionally, the user could alter the threshold of providing the feedback mechanism. For example, the user can customize the sensitivity of triggering the feedback mechanism based on prior experience of a session. For example, a user may desire to not have as many feedback notifications and allow some mind wandering (e.g., eye position deviations) before a notification is triggered. Thus, particular experiences can be customized on triggering a threshold when higher criteria is met. For example, some experiences, such as an education experience, a user may not want to be bothered during a study session if he or she is briefly staring off task or mind wandering by briefly looking towards a different area for a moment (e.g., less than 30 seconds) to contemplate what he or she just read. However, the student/reader would want to be given a notification if he or she is mind wandering for a longer period (e.g., longer than or equal to 30 seconds) by providing a feedback mechanism such as an auditory notification (e.g., “wake up”). See paras. 90-93. See also para. 97);
storing, for the user, a user stress profile based on the user input (e.g., Pasley discloses: obtaining and storing physiological data and other user information on the user’s device. See para. 65. In addition, Pasley discloses the techniques can direct a user to select a button on the screen and record the user's physiological data to identify a pattern associated with the user's stress level, wherein the pattern associated with the physiological response of the user is stored in a user profile associated with the user and the user profile can be updated or recalibrated at any time in the future. See paras. 93);
receiving user input indicating a content item selected for output (e.g., Pasley discloses: a user could select the experience he or she desires, and the user can customize the sensitivity of triggering the feedback mechanism based on prior stress level experiences of a session. See paras. 93. In addition, Pasley discloses: the techniques can identify a particular object within the content presented on the display 15 of the device 10 and change a state of the visual characteristic 30 associated with the overall content experience responsively to a spoken verbal command received from the user 25 in combination with the identified stress level of the user 25. For example, a particular object within the content may be an icon associated with a software application, and the user may gaze at the icon, say the word “select” to choose the application, and a highlighting effect may be applied to the icon. The techniques can then use further physiological data (e.g., pupillary data 40) in response to the visual characteristic 30 (e.g., a feedback mechanism) to further identify a stress level of the user 25 as a confirmation of the user's verbal command. See para. 97. See also para. 115);
determining, based on comparing the selected content item with the one or more types of stress events indicated by the user stress profile, stress events in the content item (e.g., Pasley discloses: Pasley discloses displaying a stress level graph 340 provided for use of comparing the detected level of stress of the user and the performance level associated with that stress. See para. 50. Pasley further discloses one or more physiological characteristics of the user may be determined, aggregated, and used to classify the user's stress level using statistical or machine learning techniques. In some implementations, the response may be compared with the user's own prior responses or typical user stress levels to similar content of a similar experience and/or similar environment attributes. See para. 72. Pasley further teaches: the techniques can change a state of the visual characteristic 30 associated with the overall content experience responsively to a spoken verbal command received from the user 25 in combination with the identified stress level of the user 25. For example, a particular object within the content may be an icon associated with a software application, and the user may gaze at the icon, say the word “select” to choose the application, and a highlighting effect may be applied to the icon. The techniques can then use further physiological data (e.g., pupillary data 40) in response to the visual characteristic 30 (e.g., a feedback mechanism) to further identify a stress level of the user 25 as a confirmation of the user's verbal command. In some implementations, the techniques can identify a given interactive item responsive to the direction of the user's gaze, and to manipulate the given interactive item responsively to physiological data (e.g., variability of the gaze characteristics). The techniques can then confirm the direction of the user's gaze based on further identifying stress levels of a user with physiological data in response to interactions with the experience. See para. 97. See also paras. 83-87); and
causing, based on the determined stress events in the content item, modified output of the content item (e.g., Pasley discloses: After a segment of time after the user's physiological data is analyzed and the context data of the content 302 and/or environment 304 is analyzed, content presentation instant 330 is presented to the user with a feedback mechanism 334 because the stress level assessment was that the user may have exhibited higher stress levels than desired. For example, a user is exhibiting a high stress level while at work and the feedback mechanism indicates as such to the user and may provide the user with some alternatives for calming. The processes described herein may provide a feedback mechanism to alert the user of this current performance metric and offer suggestions on ways to increase the stress level, and thus in turn, increase performance. See para. 50. The feedback mechanism 334 may include a visual presentation. For example, an icon may appear, or a text box may appear instructing the user to pay attention. See para. 51. Alternatively, Pasley discloses: a context analysis may be generated, to determine what content the user is focusing on that is creating an increase (or decrease) in stress level. The method may further include identifying the portion of the experience associated with the stress level and provide a recommendation of similar content or portions of the content to the user. See para. 77. The determined stress level could be used to provide feedback to the user via the feedback mechanism which may aid the user and/or provide statistics to the user to help improve the content of the experience. See para. 78).
As per claim 15, Pasley discloses generating the user stress profile by causing output of a selectable option for automatically skipping a detected stress event (e.g., Pasley discloses the device 10 may use the physiological data to measure one or both of a user's physiological response to the visual characteristic 30 or the user's intention to interact with content 20. For example, when presented with a list of content experiences, by a device 10, the user 25 may select an experience without requiring the user 25 to complete a physical button press. See para. 46. In some implementations, the techniques can remove an interactive item or content based on the identified user’s interest or intent, in accordance to pre-stored feedback mechanisms associated with the user’s stress level. See para. 97).
As per claim 16, Pasley discloses generating the user stress profile by causing output of a selectable option for automatically replacing a detected stress triggering event with a textual summary of the stress event (e.g., Pasley discloses: a context analysis may be generated, to determine what content the user is focusing on that is creating an increase (or decrease) in stress level. The method may further include identifying the portion of the experience associated with the stress level and provide a recommendation of similar content or portions of the content to the user. The determined stress level could be used to provide feedback to the user via the feedback mechanism which may aid the user and/or provide statistics to the user to help improve the content of the experience. See paras. 77-78. In addition, when presented with a list of content experiences, by a device 10, the user 25 may select an experience without requiring the user 25 to complete a physical button press. See para. 46. The process may detect a level of stress and provide a feedback mechanism to alert the user of this current performance metric and offer suggestions on ways to increase the stress level. The feedback mechanism 334 may include a visual presentation. For example, an icon, or a text box may appear instructing the user to pay attention. These visual and/or auditory cues can help direct the user to particular feedback mechanisms that can aid the user in coping with different stress levels to increase his or her performance levels. See paras. 50-51).
As per claim 17, Newman discloses generating the user stress profile by causing output of a selectable option for automatically replacing a detected stress triggering event with calming content, comprising content predicted to mitigate a stress symptom. See paras. 6, 50-51, 60-61 and 97.
As per claim 18, Newman discloses generating the user stress profile by causing output of a selectable option for automatically replacing a detected stress triggering event with information for assisting the user in mitigating a stress symptom associated with the stress event (e.g., Pasley discloses: providing the feedback mechanism includes providing a graphical indicator or sound configured to change the stress level to a second stress level corresponding to stress exhibited by the user (e.g., as detected by the physiological data and/or context data) in the task during the portion of the experience. …. In some implementations, the method 500 further includes adjusting content corresponding to the experience based on the stress level (e.g., customized to the stress level of the user). For example, content recommendation for a content developer can be provided based on determining stress levels during the presented experience and changes of the experience or content presented therein. For example, the user may focus well when particular types of content are provided. In some implementations, the method 500 may further include identifying content based on similarity of the content to the experience, and providing a recommendation of the content to the user based on determining that the user has the stress level during the experience (e.g., mind wandering). In some implementations, the method 500 may further include customizing content included in the experience based on the stress level of the user (e.g., breaking the content into smaller pieces). See paras. 84-87).
As per claim 19, Pasley discloses output of a prompt for a filtering threshold to be used with comparing the one or more types of stress events to stress events in content items item (e.g., Pasley discloses: Pasley discloses displaying a stress level graph 340 provided for use of comparing the detected level of stress of the user and the performance level associated with that stress. See para. 50. Pasley further discloses one or more physiological characteristics of the user may be determined, aggregated, and used to classify the user's stress level using statistical or machine learning techniques. In some implementations, the response may be compared with the user's own prior responses or typical user stress levels to similar content of a similar experience and/or similar environment attributes. See para. 72. Pasley further teaches: the techniques can change a state of the visual characteristic 30 associated with the overall content experience responsively to a spoken verbal command received from the user 25 in combination with the identified stress level of the user 25. For example, a particular object within the content may be an icon associated with a software application, and the user may gaze at the icon, say the word “select” to choose the application, and a highlighting effect may be applied to the icon. The techniques can then use further physiological data (e.g., pupillary data 40) in response to the visual characteristic 30 (e.g., a feedback mechanism) to further identify a stress level of the user 25 as a confirmation of the user's verbal command. In some implementations, the techniques can identify a given interactive item responsive to the direction of the user's gaze, and to manipulate the given interactive item responsively to physiological data (e.g., variability of the gaze characteristics). The techniques can then confirm the direction of the user's gaze based on further identifying stress levels of a user with physiological data in response to interactions with the experience. See para. 97. See also paras. 83-87).
As per claim 20, Pasley discloses the causing of the modified output is based on weighting different trigger indicators differently for a first type of stress event (e.g., Pasley discloses: determining that the user has a particular threshold of stress (e.g., high, low, etc.) includes determining a level of stress as a sliding scale. For example, the system could determine a level of stress as a stress barometer that can be customized based on the type of content shown during the user experience. In some implementations, stress level may be determined using statistical or machine learning-based classification techniques. For example, determining that the user has a stress level includes using a machine learning model trained using ground truth data that includes self-assessments in which users labelled portions of experiences with stress level labels. … After each “stress event”, each subject could be prompted at or after a particular stress event in the video content to enter his or her stress level. See paras. 73-74. In some implementations, one or more pupillary or EEG characteristics may be determined, aggregated, and used to classify the user's stress level using statistical or machine learning techniques. In some implementations, the physiological data is classified based on comparing the variability of the physiological data to a threshold. For example, if the baseline for a user's EEG data is determined during an initial segment of time (e.g., 30-60 seconds), and during a subsequent segment of time following an auditory stimulus (e.g., 5 seconds) the EEG data deviates more than +/−10% from the EEG baseline during the subsequent segment of time, than the techniques described herein could classify the user as transitioned away from the high stress level and entered a second lower stress level. Similarly, the heart rate data and/or EDA data may also be classified based on comparing the variability of the heart rate data and/or EDA data to a particular threshold. See para. 75.)
The subject-matters of claim 21 correspond in terms of an apparatus to those of independent method claim 1. The features of claim 21 are substantially the same as those of claim 1; except the invention category. Accordingly, the same reasonings applied for the rejections of claim 1 also apply to claim 21. In addition, Pasley discloses one or more processors (602); and a memory (620) storing instructions (640) that are executed by the one or more processors. See fig. 6 and paras. 98-99.
Claim 22 is rejected under the same rationale as claim 2.
Claim 23 is rejected under the same rationale as claim 3.
Claim 24 is rejected under the same rationale as claim 4.
Claim 25 is rejected under the same rationale as claim 5.
Claim 26 is rejected under the same rationale as claim 6.
Claim 27 is rejected under the same rationale as claim 7
Claim 28 is rejected under the same rationale as claim 8.
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hurwitz et al. (US 20220384034) discloses a local and private model can be used to analyze textual, visual, and auditory content on a user's local device to provide notifications about events which are stressful beyond a predetermined threshold. The model can be trained by a combination of health sensor data and an analysis of the content. Multiple models or techniques can be used to analyze the content and vectorize the content. A vector based approach can be used to classify future content as stressful when meeting the predetermined threshold.
8. 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.
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to WESNER SAJOUS whose telephone number is (571) 272-7791. The examiner can normally be reached on M-F 10:00 TO 7:30 (ET).
Examiner interviews are available via telephone 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 or email the Examiner directly at wesner.sajous@uspto.gov.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Said Broome can be reached on 571-272-2931. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. 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. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/WESNER SAJOUS/Primary Examiner, Art Unit 2612
WS
10/13/2026
Prosecution Insights