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 .
Response to Amendment
The proposed reply filed on 07/06/2026 has been entered.
Claims 1, 8, and 15 have been amended and no claims have been added and/or canceled.
In light of applicant’s amendment/argument, previous claim rejections 35 USC 103, with respect to claims 1-20, have been withdrawn.
Claims 1-20 are pending with claims 1, 8, and 15 as independent claims.
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 1-2, 4-9, 11-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dey et al. (US 2019/0197698, published 6/27/2019, hereinafter as Dey) in view of Chien et al. (US 2020/0041159, published 12/31/2020, hereinafter as Chien) in view of Silverstein et al. (US 2022/0012666, published 1/13/2022, hereinafter as Silverstein).
Claim 1. A computer-implemented method for recommending an optimal break for a user, the computer-implemented method comprising:
capturing different activities performed by the user by capturing activity data for the user from an environment using a capture device, wherein the captured activity data comprises a combination of images, video, audio, and data associated with the different activities performed by the user and captured by the capture device; Dey discloses in [0025] “Cognitive states are defined as functions of measures of a user's total behavior collected over some period of time from at least one personal information collector (e.g., including musculoskeletal gestures, speech gestures, eye movements, internal physiological changes, measured by imaging circuits, microphones, physiological and kinematic sensors in a high dimensional measurement space, etc.) within a lower dimensional feature space.” And in [0030-0033 and 0050] “The gaze tracking circuit 102A and the emotional and facial expression tracking circuit 102B track the cognitive state of the employee in real-time as well as store the tracked cognitive state over periods of times for a given document (i.e., according to the document and difficulty of the document) in the cognitive state database 130… tracks a cognitive state (e.g., based on eye gaze, facial, and/or emotional expressions) of the employee as the employee is working on the document.” (emphasis added) examiner note: during a user viewing a document (environment), cognitive data such as eye gaze, emotional, and facial expression may be captured as “activity data” indicates the current cognitive state of the user. The use of microphone/speech gestures may be to capture audio data and the use of imaging circuits may be to capture video and/or image data,
obtaining prior activity data related to the user, wherein the prior activity data includes the captured activity data that further comprises prior activities performed by the user; Dey discloses in [0030-0035 and 0050] “The deviation detecting circuit 103 compares a current cognitive state of the employee as measured by the cognitive state tracking circuit 102 with the stored profiles for the predetermined amount of time of the employees cognitive state in the cognitive state database 130 to detect a deviation between the current cognitive state and the cognitive state profile so as to determine whether the employee may require a short break, a long-break, or the like.” (emphasis added) examiner note: the stored cognitive state in database 130 provide prior activity data for the user and the identified preferred break types may be “short break” and “long break” depending on the result of the deviation between the current cognitive state and past cognitive state of the user,
training a machine learning model to generate and display a break recommendation for the user based on a specific activity performed by the user, wherein the training further comprises training the machine learning model to associate and predict a preferred break type with the specific activity based on the prior activity data and [received user input based on user responses], wherein the machine learning model is trained based on training data including the prior activity data and information about the user, and wherein training the machine learning model to associate and predict the preferred break type further comprises training the machine learning model to detect a pattern in the prior activity data and identify at least one type of break preferred by the user and corresponding to the specific activity performed by the user based on the detected pattern; Dey discloses in [0031-0035] “the eye tracking circuit 102A learns an eye gaze profile of a given employee with respect to a given document type and objective difficulty level of the document, for short time window slots (such as 10-minute slots) and longer time window slots (such as 5-day slots) to store in the cognitive state database 130 to be compared to as described later… the emotional and facial expression tracking circuit 102B learns emotional/facial ease profile of a given employee with respect to a given document type and objective difficulty level of the document, for short time window slots (such as ten-minute slots) and longer time window slots (such as five-day slots) to store in the cognitive state database 130 to be compared to as described later… the cognitive state database 130 includes learned data for the employee's usual eye gaze smoothness and facial/emotional ease when the employee is working on documents, and benchmarks that with respect to the difficulty of text. The learned data is bootstrapped over a predetermined period of time (i.e., potentially in a supervised setting, but extensible to an unsupervised model also) for different sliding window sizes (i.e., short windows such as ten-minute windows with a one-minute slide and longer windows such as five-day window with a one-day slide… The deviation detecting circuit 103 compares a current cognitive state of the employee as measured by the cognitive state tracking circuit 102 with the stored profiles for the predetermined amount of time of the employees cognitive state in the cognitive state database 130 to detect a deviation between the current cognitive state and the cognitive state profile so as to determine whether the employee may require a short break, a long-break, or the like. In other words, the deviation detection circuit 102 compares the characteristic parameters of each recently-completed window to a few windows prior.” And in [0042 and 0053] “the eye gaze tracker 315 and the emotion and facial expression tracker 325 track the current cognitive state of the employee (e.g., the cognitive state tracking circuit 102) and the eye gaze smoothness profile learner 302 and the emotion/facial expression ease profile learner 303 (e.g., the cognitive state tracking circuit 102) learns the typical cognitive state of the employee and stores the same… the invention can monitor the employee's eye gaze movement and facial/emotional expressions over time, as they work with documents on digital devices in their workplace and learn their usual eye gaze smoothness and facial/emotional ease when they read text documents, and benchmark that with respect to the difficulty of text. This is bootstrapped over some period of time for different sliding window sizes (e.g., short such as ten-minute windows with a one-minute slide and long such as five-day windows with a one-day slide) to thereby create a profile for each employee for eye gaze smoothness and facial emotional ease with respect to document type and difficulty level.” (emphasis added) examiner note: learning an eye gaze and emotional/facial ease profile of a given employee with respect to a given document type indicate training the eye tracking circuit 102A and the emotional and facial expression tracking circuit 102B using cognitive state stored (prior activity) in cognitive state database 130 to determine (predict) whether the employee may require a break type such as short break, long break, etc. Learning an eye gaze profile and emotional/facial ease profile of a given employee, with respect to a given document type and objective difficulty level of the document (may indicate specific activity associated with the document type and the level of difficulty of the document using difficulty measuring circuit 101 as indicated in [0027 and 0041]), may indicate detecting patterns in order to predict a break type for that employee. The machine learning model, here, may be trained (indicated by learning) on labeled data as in supervised bootstrapping model or trained on unlabeled data in an unsupervised bootstrapping model.
determining, by the trained machine learning model, that the user needs a break from a current activity based on the activity data, wherein the trained machine learning model further comprises deep learning and computer vision techniques identifying eye movement and gaze information associated with the user to determine that the user needs the break from the current activity; Dey discloses in [0030-0037 and 0050] “the eye tracking circuit 102A monitors the eye gaze movement of an employee as they read document(s) of various types. Also, the eye tracking circuit 102A learns an eye gaze profile of a given employee with respect to a given document type and objective difficulty level of the document, for short time window slots (such as 10-minute slots) and longer time window slots (such as 5-day slots) to store in the cognitive state database 130 to be compared to as described later… The deviation detecting circuit 103 compares a current cognitive state of the employee as measured by the cognitive state tracking circuit 102 with the stored profiles for the predetermined amount of time of the employees cognitive state in the cognitive state database 130 to detect a deviation between the current cognitive state and the cognitive state profile so as to determine whether the employee may require a short break, a long-break, or the like… The recommending circuit 104 sends a recommendation to the employee for the employee to take a break based on a size of the deviation between the cognitive state profile and the current cognitive state of the employee.” (emphasis added).
generating, by the trained machine learning model, the break recommendation for the user based on the current activity, wherein generating the break recommendation further comprises the trained machine learning model applying the preferred break type to the current activity; Dey discloses in [0030-0037 and 0050] “The recommending circuit 104 sends a recommendation to the employee for the employee to take a break based on a size of the deviation between the cognitive state profile and the current cognitive state of the employee.” (emphasis added) examiner note: “the recommendation circuit 104 sends a recommendation to the employee” indicates the break recommendation has been generated,
Dey does not explicitly disclose
received user input based on user responses; generating at least one of path information or contact information associated with a recommended activity. However, Chien, in an analogous art, discloses in [0078-0080] “Using the smart phone, Abel inputs personal activity objectives to be identified by activity recommendation program 300. Abel's personal activity objectives include preferences to run outdoors twice during the week and participate in weightlifting indoors once during the week. Abel also inputs a preference to participate in physical activities during the morning. Using the activity profile developed by activity recommendation program 300, the weather forecast for the week, and Abel's personal activity objectives, activity recommendation program 300 determines activity schedules. Activity recommendation program 300 generates an interactive list of activity schedules for Abel, as depicted in FIGS. 4A and 4B… As depicted in FIG. 4B, activity recommendation program 300 displays route options for the running 3 miles activity (activity option display 414). Activity recommendation program 300 displays the running routes for the three route options on a map, as depicted by map display 422.” (emphasis added) examiner note: the user (Abel) may provide input about preferred activities as shown in fig. 4A. Also, the activity recommendation program 300 may recommend activity 414 and generate route option depicted by map display 422 as shown in fig. 4B.
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 Dey with the teaching of Chien because “employees work for a construction company where the primary tasks for the employees are painting, bricklaying, welding, and wiring. Each employee may be tracked by an activity tracking device… activity recommendation program 300 schedules employee Baker to paint on Monday, brick lay on Tuesday, weld on Wednesday, and wire on Thursday, due to the varying air temperature, chance of rain, and humidity throughout the week.” Chien [0085], and
Dey does not explicitly disclose displaying the break recommendation including the preferred break type. However, Silverstein, in an analogous art, discloses in [0050] “When the user's average code output begins to decrease, as determined by lack of code commits, the integrated recommendation engine 252 pushes a notification to user that the user may benefit from a walk right (distraction).” (emphasis added) examiner note: the display of recommended break type may be implemented via push notification.
determining whether an actual break is taken by the user in response to the break recommendation by tracking, using at least one of motion sensors or device global positioning satellite (GPS) data, user movements, and determining an effectiveness of the actual break; Silverstein, further, teaches in [0013 and 0031] “the location information is determined through the use of a Geographical Positioning System (GPS) satellite 132. In these embodiments, a handheld computer or mobile telephone 116, or other device, uses signals transmitted by the GPS satellite 132 to generate location information, which in turn is provided via the network 106 to the knowledge manager system 102 for processing… If user's efficiency falls beneath a threshold of statistical significance during a period, such as a workday, a recommendation can be made as to an activity/break (distraction) that has a high average post-activity output score on each cluster. Based on information, such as metadata and determined effects on productivity, recommendation as to distractions or activity can be adjusted. The effects can be tracked after a user is notified of a given activity/activity type to verify the increase/decrease of performance on a given cluster… The cognitive processing engine 114 can capture length of time activity and post recommendation performance, which can be stored in data store 308 as metadata and fed back to and used by cognitive processing engine 114 to optimize a recommended alternative activity and length of time. The computing system 314 detects an optimum alternative activity with respect to time and a current undertaken activity. A learning loop is created by the metadata being fed back the cognitive processing engine 114.”
And in [0043 and 0049-0050] “the captured category, length of time of activity and post recommendation performance by iterative cognitive improvement engine 116 is stored as metadata to be used for feedback for optimum activity recommendation to cognitive processing engine 114. At step 420, the process 400 ends… a determination is made if the user 302 accepted the improvement, such as detecting biometric data from user devices 304 and/or IoT and data gathering devices 306, such as a detection of the user 302 biking, walking, etc. after a recommendation to exercise. In certain implementations, metadata for certain distractions are captured and stored temporarily for a given time period. As a user 302 returns to an activity or work related task from which they were recommended to replace with a distraction, change in performance metrics can be captured stored for the particular user 302 as historical data… The recommendation module 252 and information gathering module 254 (i.e., system 300) captures the user's average productivity throughout the day and uses a combination of the user's phone and smart watch (i.e., wearables) to capture break activities by detecting activity patterns, biometric data, geolocation data, and other integrated sources. Some days the user's watch captures the user going on walks as determined by a routine of walking along the office and average heartbeat increase. After going on this walk, recommendation module 252 and information gathering module 254 (i.e., system 300) detects an aggregate increase in code output. When the user's average code output begins to decrease, as determined by lack of code commits, the integrated recommendation engine 252 pushes a notification to user that the user may benefit from a walk right (distraction). After the user goes on the walk, data is re-captured to further reinforce the iterative cognitive improvement engine 116. As the user works on different activities and goes on different breaks or takes different distractions throughout the work week, the iterative cognitive improvement engine 116 captures the performance increase with regards to each method and the cognitive processing engine 114 provides tailored recommendations based on the user's schedule and current activities.” (emphasis added) examiner note: after recommending a beak to the user, the user may be tracked, via handheld device, to gather information (walking, biking, etc. as motion information) as metadata that indicates performance increase as effectiveness of the actual break, and
updating and adjusting the trained machine learning model based on the determination of whether the actual break is taken, an identification of the actual break that is performed, and the effectiveness of the actual break as training feedback data to predict the preferred break type and determine a necessity of the break. Silverstein, also, teaches in [0031 and 0042] “The cognitive processing engine 114 can capture length of time activity and post recommendation performance, which can be stored in data store 308 as metadata and fed back to and used by cognitive processing engine 114 to optimize a recommended alternative activity and length of time. The computing system 314 detects an optimum alternative activity with respect to time and a current undertaken activity. A learning loop is created by the metadata being fed back the cognitive processing engine 114… a determination is made if the user 302 accepted the improvement, such as detecting biometric data from user devices 304 and/or IoT and data gathering devices 306, such as a detection of the user 302 biking, walking, etc. after a recommendation to exercise. In certain implementations, metadata for certain distractions are captured and stored temporarily for a given time period. As a user 302 returns to an activity or work related task from which they were recommended to replace with a distraction, change in performance metrics can be captured stored for the particular user 302 as historical data.” And in [0049] “the captured category, length of time of activity and post recommendation performance by iterative cognitive improvement engine 116 is stored as metadata to be used for feedback for optimum activity recommendation to cognitive processing engine 114.” (emphasis added) examiner note: the metadata (taken break such as walking, biking, etc.) may be utilized, to optimize (update) the cognitive processing engine (machine learning model) to provide accurate break prediction.
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 Dey with the teaching of Silverstein because “As the user works on different activities and goes on different breaks or takes different distractions throughout the work week, the iterative cognitive improvement engine 116. captures the performance increase with regards to each method and the cognitive processing engine 114 provides tailored recommendations based on the user's schedule and current activities.” Silverstein [0050].
Claims 2, 9, and 16. The rejection of the computer-implemented method of claim 1 is incorporated, further comprising:
Dey does not explicitly disclose
monitoring user interactions with the break recommendation; Dey discloses in [0039-0040] “data gathering devices 306 to continuously track users 302 performance and interaction with the various platforms of user devices 304 and IoT and data gathering devices 306 to determine users 302 focus and productivity. Focus and productivity can be metric based, such as writing a certain number of lines of code for a software developer. Focus and productivity can be biometric associated such as eye-focus (e.g., how long is user 302 looking at a screen)… a level “T” is determinative of an action for remediation or change in an activity of user 302. The threshold “T” can include time factors, for example, a user 302 that is inactive for five minutes is not given a feedback, while user 302 being inactive for an hour is given a feedback.” (emphasis added) examiner note: user interactions may be tracked to determine user productivity, and
updating the break recommendation based on the user interactions. Dey discloses in [0031] “The cognitive processing engine 114 can capture length of time activity and post recommendation performance, which can be stored in data store 308 as metadata and fed back to and used by cognitive processing engine 114 to optimize a recommended alternative activity and length of time. The computing system 314 detects an optimum alternative activity with respect to time and a current undertaken activity. A learning loop is created by the metadata being fed back the cognitive processing engine 114.” And in [0049] “the captured category, length of time of activity and post recommendation performance by iterative cognitive improvement engine 116 is stored as metadata to be used for feedback for optimum activity recommendation to cognitive processing engine 114.” (emphasis added) examiner note: the metadata feedback may be to update the cognitive processing engine to optimize the break recommendation.
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 Dey with the teaching of Silverstein because “As the user works on different activities and goes on different breaks or takes different distractions throughout the work week, the iterative cognitive improvement engine 116. captures the performance increase with regards to each method and the cognitive processing engine 114 provides tailored recommendations based on the user's schedule and current activities.” Silverstein [0050].
Claims 4, 11, and 18. The rejection of the computer-implemented method of claim 1 is incorporated, wherein the capture device comprises an eye tracking device and the determining that the user needs the break from the current activity comprises identifying the eye movement of the user while performing the current activity in the activity data. Dey discloses in [0006] “a system can recommend short or long breaks from work to employees by measuring and/or monitoring on a continuous/frequent basis a cognitive state of employees working on computers, mobiles, digital devices, or the like, with cameras, while they work on their devices, using eye gaze tracking and facial/emotional expression tracking, and suggest that the employee take short breaks (e.g., a few minutes every hour) and/or long holidays/vacation (e.g., a few days every year) in the event of significant-enough fall of eye gaze movement smoothness and/or facial/emotional ease from the usual.” (emphasis added).
Claims 5, 12, and 19. The rejection of the computer-implemented method of claim 1 is incorporated, wherein the predicting the preferred break type for the user utilizes the trained machine learning model that predicts a type of break from ongoing tasks based on user activity. Dey discloses in [0006] “a system can recommend short or long breaks from work to employees by measuring and/or monitoring on a continuous/frequent basis a cognitive state of employees working on computers, mobiles, digital devices, or the like, with cameras, while they work on their devices, using eye gaze tracking and facial/emotional expression tracking, and suggest that the employee take short breaks (e.g., a few minutes every hour) and/or long holidays/vacation (e.g., a few days every year) in the event of significant-enough fall of eye gaze movement smoothness and/or facial/emotional ease from the usual.” And in [0042] “the eye gaze tracker 315 and the emotion and facial expression tracker 325 track the current cognitive state of the employee (e.g., the cognitive state tracking circuit 102) and the eye gaze smoothness profile learner 302 and the emotion/facial expression ease profile learner 303 (e.g., the cognitive state tracking circuit 102) learns the typical cognitive state of the employee and stores the same.” And in [0045-0046] “The short break detector 305 detects the last time that the employee took a short break as stored in the short work break recorder for employee 350a and the short break recommender 306 (e.g., the recommending circuit 104) recommends the employee take a short break when the current cognitive state of the employee deviates by a predetermined amount from the employee profile… the vacation detector 308 detects that the employee has not taken a vacation in several months and the vacation recommender 307 can make a recommendation that the employee take an extended vacation if, for example, the module 304 detects the employee is exhibited an exacerbated expression every few hours.” (emphasis added).
Claims 6, 13, and 20. The rejection of the computer-implemented method of claim 1 is incorporated, wherein the generating the break recommendation for the user utilizes the trained machine learning model that applies the preferred break type for the user to an identified activity in the activity data. Dey discloses in [0045-0046] “The short break detector 305 detects the last time that the employee took a short break as stored in the short work break recorder for employee 350a and the short break recommender 306 (e.g., the recommending circuit 104) recommends the employee take a short break when the current cognitive state of the employee deviates by a predetermined amount from the employee profile… the vacation detector 308 detects that the employee has not taken a vacation in several months and the vacation recommender 307 can make a recommendation that the employee take an extended vacation if, for example, the module 304 detects the employee is exhibited an exacerbated expression every few hours.” (emphasis added).
Claims 7, 14. The rejection of the computer-implemented method of claim 1 is incorporated, further comprising:
determining that a group of users includes the user; Dey discloses in [0035] “The deviation detecting circuit 103 compares a current cognitive state of the employee as measured by the cognitive state tracking circuit 102 with the stored profiles for the predetermined amount of time of the employees cognitive state in the cognitive state database 130 to detect a deviation between the current cognitive state and the cognitive state profile so as to determine whether the employee may require a short break, a long-break, or the like. In other words, the deviation detection circuit 102 compares the characteristic parameters of each recently-completed window to a few windows prior.” (emphasis added), and
transmitting the break recommendation to one or more devices associated with the group of users. Dey discloses in [0036] “The recommending circuit 104 sends a recommendation to the employee for the employee to take a break based on a size of the deviation between the cognitive state profile and the current cognitive state of the employee.” (emphasis added).
Claim 8. The claim is directed towards a computer system, for recommending an optimal break for a user, to implement the steps of the method of claim 1. Therefore, the claim is similarly rejected as claim 1. Further, Dey discloses one or more processors, one or more computer-readable memories, and one or more computer-readable storage media; in [0076-0079] “computer system/server 12 in cloud computing node 10 is shown in the form of a general-purpose computing circuit. The components of computer system/server 12 may include, but are not limited to, one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including system memory 28 to processor 16… System memory 28 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and/or cache memory 32.” (emphasis added).
Claim 15. The claim is directed towards a computer program product, comprising one or more computer-readable storage media, to implement the method of claim 1. Therefore, the claim is similarly rejected as claim 1.
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Dey, Chien, and Silverstein as applied to claim 1 above, and further in view of Aimone et al. (US 2014/0347265, published 11/27/2014, hereinafter as Aimone).
Claims 3, 10, and 17. The rejection of the computer-implemented method of claim 1 is incorporated, Dey does not explicitly disclose wherein the displaying the break recommendation to the user further comprises adding visual cues relating to the break recommendation to a display in an augmented reality (AR) device associated with the user. However, Aimone, in an analogous art, discloses in [0179] “a line of workers on a factory line may be doing a set of repeated tasks. Each worker wears a wearable computing device of the present invention while they conduct their work… Glass can display the error using Augmented Reality where the part of the work that is in error is highlighted in color, as represented by the augmented reality encircling of an error detected as shown in FIG. 47. The device may optionally prompt the user to fix the error in a particular way. Also schematics, drawings and or assembly instructions may be quickly called in the worker's wearable computing device display to inform the work. The worker's supervisor can monitor the performance of the workers and suggest interventions or rewards tied to the level of error free work. Also the design of new manufacturing steps can be evaluated to determine which set of steps leads to the lowest number of ERN and therefore mistakes. In addition other brain states of the workers can be monitored for stress, fatigue, drowsiness that can impact their performance. Interventions such as suggested break times, change in the type of music, an office stretch break may be recommended.” (emphasis added) examiner note: the error may be a visual cue indicating worker’s stress and/or fatigue that lead recommending a break.
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 Dey and Silverstein with the teaching of Aimone because “Interventions such as suggested break times, change in the type of music, an office stretch break may be recommended” when “An error-related negativity (ERN), is an event related potential that occurs in the brain of person when they commit an error”, occurs in order to lower the number of ERN and therefore mistakes. Aimone [0178-0179].
Response to Arguments
Applicant’s arguments with respect to claims 1, 8, and 15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Argument: Applicant argues “Dey in view of Silverstein nowhere specifically teaches or describes associating and predicting "wherein the training further comprises training the machine learning model to associate and predict a preferred break type with the specific activity based on the prior activity data and received user input based user responses"… the references fail to specifically teach the requirement from the newly amended independent claims including wherein generating the break recommendation further comprises the trained machine learning model applying the preferred break type to the current activity and generating at least one of path information or contact information associated with a recommended activity".”
Response: as can be seen above, Applicant’s argument appears to be based on amendment to the independent claims 1, 8, and 15. Accordingly, new cited reference has found to address the amendment as detailed above in claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
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 AHAMED I NAZAR whose telephone number is (571)270-3174. The examiner can normally be reached 10 am to 7 pm Mon-Fri.
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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.
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/AHAMED I NAZAR/Examiner, Art Unit 2178 09/15/2026
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178
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