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 amendment filed November 26, 2025 has been entered. Claims 1-5 and 7-10 remain pending in the application, and claim 6 was cancelled. Applicant’s amendments to the claims have overcome each and every 112, 101, and 102 rejection previously set forth in the Non-Final Office Action mailed October 2, 2025. Applicant’s amendments to the claims necessitate new grounds of rejection, as described in the Response to Arguments and 103 Rejections below.
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 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over US 20180144427 A1 (Ebesu, Takafumi) in view of “Assessment of Fatigue Using Wearable Sensors: A Pilot Study” (Luo et al.)
Regarding claim 1, Ebesu teaches a prediction system comprising:
at least one processor ([0050] “The terminal device 10 is a portable information processing device”) programmed to:
extract a duration time of one or more physical states of a posture or a motion that a person assumes or performs during a performing of a task by analyzing a process of the task of the person ([0033] “The monitoring apparatus 30 analyzes the worker data to determine whether the worker 9 has adopted predetermined posture or done predetermined operation”; [0041]; [0099] “the chronological posture information stored in the condition storage unit 61. In the chronological posture information of each worker 9, the recognized postures and the position information are chronologically recorded with respect to time. For example, bending forward posture is firstly recognized at 9:10:15 (hour:minute:second), and the bending forward posture is lastly recognized at 9:13:18”; Fig. 14B);
acquire a load on a body of the person regarding the one or more physical states ([0033] “when the worker 9 has adopted such predetermined posture, the monitoring apparatus 30 converts the posture into load”; [0100] “The calculated load data of each worker 9 includes the total hours of each posture, and the load and the cumulative load that are calculated based on the posture and its total hours. In other words, how long the worker 9 has taken each posture in total recorded, and the load caused by each posture is recorded. The cumulative load indicates the value of the sum total of the loads of each posture.”); and
predict at least one of a fatigue level or a residual physical strength of the person in the task based on the duration time of the one or more physical states and the load ([0040] “The term “alerted state” indicates a state in which fatigue is predicted to build in the body of a worker. In particular, an alerted state indicates a state when the cumulative load exceeds a threshold.”; [0035]); and
control a display ([0069] “The graphics driver 102 is connected to a liquid crystal display (LCD) 104 through a bus, and monitors the results of processes that are performed by the CPU 101”).
Ebesu does not explicitly teach a plurality of inertial sensors configured to (i) attach to parts of a body of a person, and (ii) acquire data on a load on the body of the person regarding one or more physical states; and
further predict at least one of a subjective fatigue level or a subjective residual physical strength that the person subjectively feels in the task by referring to data of another task including a result of predicting at least one of another fatigue level or another residual physical strength of the person in the another task and at least one of another subjective fatigue level or another subjective residual physical strength that the person has subjectively felt in the another task, and a result of predicting at least one of the fatigue level or the residual physical strength of the person in the task: and
control a display to display the at least one of the subjective fatigue level or the subjective residual physical strength that the person subjectively feels in the task.
However,
a plurality of inertial sensors configured to (i) attach to parts of a body of a person, and (ii) acquire data on a load on the body of the person regarding one or more physical states (Page 60, Introduction: “inertial sensors”; “the use of wearable sensors to assess physical fatigue in healthy cohorts under experimental settings, for example inertial measurement units and heart rate monitors in construction workers”; Page 61, Materials and Methods: “3-axis accelerometer”); and
further predict at least one of a subjective fatigue level or a subjective residual physical strength that the person subjectively feels in the task by referring to data of another task including a result of predicting at least one of another fatigue level or another residual physical strength of the person in the another task and at least one of another subjective fatigue level or another subjective residual physical strength that the person has subjectively felt in the another task, and a result of predicting at least one of the fatigue level or the residual physical strength of the person in the task (Page 61, Introduction: “compare several methods to predict non-pathological fatigue scores from physical activity and vital sign parameters, and further explore behavior and physiology patterns from wearable data and their connection to self-reported fatigue via clustering”; Page 64, Results: “the relationship between subjectively reported non-pathological physical and mental fatigue and objective physiological and behavioral parameters from a number of perspectives: direct correlation, direct classification of PRO scores, anomaly detection (classification of outlying periods of data corresponding to increased fatigue reporting), and unsupervised clustering”; Page 62, Table 1: Activity Class “Type of physical activity: 0 = undefined, 1 = resting, 9 = other, 10 = biking, 11 = running, 12 = walking”); and
display the at least one of the subjective fatigue level or the subjective residual physical strength that the person subjectively feels in the task (Page 66, Cluster Analysis: “one observation and each column to the Z-score normalized values of the 44 features in Xc. For each observation, the corresponding fatigue labels and demographics are also displayed.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the system taught by Ebesu to include inertial sensors and predict at least one of a subjective fatigue level or a subjective residual physical strength. The inertial sensors are described in the Introduction of Luo, and while Ebesu does not explicitly describe inertial sensor, the disclosure teaches “an acceleration acquisition unit 43, an angular speed acquisition unit 44” and inertial sensors use used to gather acceleration and rotational data. One would have been motivated to make this modification because the prediction of non-pathological fatigue can be determined by comparing physiology patterns from wearable data, as suggested by Luo (Page 61, Introduction).
Regarding claim 2, Ebesu teaches the prediction system according to claim l, wherein
the task includes a plurality of sections during which the one or more physical states continue ([0112] “The term “operation” in the posture-operation information indicates a job that the worker 9 does as business operations. The operation consists of one or more postures. For example, in the case of picking operation, the worker 9 may take a bending forward (bowing), squatting, or bending backward posture. In the case of carrying operation, the worker 9 may walk, go up and down the stairs, or push a handcart”), and
the at least one processor predicts at least one of a total fatigue level or a total residual physical strength of the person when the task is ended by calculating the fatigue level caused by the load for each of the sections and accumulating the calculated fatigue levels in all the sections ([0040] “The term “alerted state” indicates a state in which fatigue is predicted to build in the body of a worker. In particular, an alerted state indicates a state when the cumulative load exceeds a threshold.”; [0035]).
Regarding claim 3, Ebesu teaches the prediction system according to claim 2, wherein
the at least one processor detects that the duration time of the one or more physical states for each of the sections is changed with time based on an environment in which the task is performed being changed with time ([0166-0167] “the cumulative load tends to be accumulated rapidly in tough (heavy-load) working environments”), and
the at least one processor predicts at least one of the total fatigue level or the total residual physical strength of the person when the task is ended based on the duration time for each of the sections detected by the at least one processor and the load acquired by the at least one processor ([0040] “The term “alerted state” indicates a state in which fatigue is predicted to build in the body of a worker. In particular, an alerted state indicates a state when the cumulative load exceeds a threshold.”; [0035]).
Regarding claim 4, Ebesu teaches the prediction system according to claim l, wherein
the at least one processor extracts the duration time of the one or more physical states during the performing of the task and a timing when this duration occurs by analyzing the process of the task ([0256] “As the cumulative load is recorded in the present embodiment, the performance can be converted into numbers, and the obtained numbers can be compared with each other among workers. The performance may be, for example, the cumulative load per unit time. It is considered that the worker 9 has done a greater amount of work per unit time as the cumulative load per unit time is heavier.”; Fig. 14B), and
the at least one processor predicts at least one of the fatigue level or the residual physical strength of the person in the task based on the duration time and the timing extracted by the at least one processor and the load acquired by the at least one processor ([0040] “The term “alerted state” indicates a state in which fatigue is predicted to build in the body of a worker. In particular, an alerted state indicates a state when the cumulative load exceeds a threshold.”; [0035]).
Regarding claim 5, Ebesu teaches the prediction system according to claim 1, wherein
the at least one processor acquires loads at a plurality of parts of the body of the person regarding the one or more physical states ([0112] “The term “operation” in the posture-operation information indicates a job that the worker 9 does as business operations. The operation consists of one or more postures. For example, in the case of picking operation, the worker 9 may take a bending forward (bowing), squatting, or bending backward posture. In the case of carrying operation, the worker 9 may walk, go up and down the stairs, or push a handcart.”; [0116] “The posture may be, for example, one of a bending forward posture, a squatting posture, a bending backward posture, walking, going up the stairs, going down the stairs, pushing a handcart, being seated, and having a nap”), and
the at least one processor predicts at least one of fatigue levels or residual physical strengths at the plurality of parts of the person in the task based on the duration time of the one or more physical states extracted by the at least one processor and loads at the plurality of parts acquired by the at least one processor ([0251] “the horizontal axis, the vertical axis on the left, and the vertical axis on the right indicate the time, the work, and the cumulative load, respectively. What kinds of work are done by the worker 9 in what time zone are indicated by arrows 302. Moreover, how the cumulative load 303 increases with respect to time is illustrated in FIG. 14B.”).
Ebesu does not explicitly teach the at least one processor further predicts at least one of subjective fatigue levels or subjective residual physical strengths at the plurality of parts that the person subjectively feels in the task by referring to data of another task including a result of predicting at least one of other fatigue levels or other residual physical strengths at the plurality of parts of the person in the another task and at least one of other subjective fatigue levels or other subjective residual physical strengths at the plurality of parts that the person has subjectively felt in the another task, and a result of predicting at least one of the fatigue levels or the residual physical strengths at the plurality of parts of the person in the task.
However,
Luo teaches the at least one processor further predicts at least one of subjective fatigue levels or subjective residual physical strengths at the plurality of parts that the person subjectively feels in the task by ref erring to data of another task including a result of predicting at least one of other fatigue levels or other residual physical strengths at the plurality of parts of the person in the another task and at least one of other subjective fatigue levels or other subjective residual physical strengths at the plurality of parts that the person has subjectively felt in the another task, and a result of predicting at least one of the fatigue levels or the residual physical strengths at the plurality of parts of the person in the task (Page 61, Materials and Methods: “1. Physical fatigue score (PhF). Question: Physically, today how often did you feel exhausted? Possible answers: never; sometimes; regularly; often; always. 2. Mental fatigue score (MF). Question: Mentally, today how often did you feel exhausted? Same answers as above. 3. Visual analogue scale score (VAS). Question: Describe fatigue on a scale of 1–10, where 1 means you don’t feel tired at all and 10 means the worst tiredness you can imagine”; Page 61, Introduction: “compare several methods to predict non-pathological fatigue scores from physical activity and vital sign parameters, and further explore behavior and physiology patterns from wearable data and their connection to self-reported fatigue via clustering”; Page 64, Results: “the relationship between subjectively reported non-pathological physical and mental fatigue and objective physiological and behavioral parameters from a number of perspectives: direct correlation, direct classification of PRO scores, anomaly detection (classification of outlying periods of data corresponding to increased fatigue reporting), and unsupervised clustering”; Page 62, Table 1: Activity Class “Type of physical activity: 0 = undefined, 1 = resting, 9 = other, 10 = biking, 11 = running, 12 = walking”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the system taught by Ebesu to include inertial sensors and predict at least one of a subjective fatigue level or a subjective residual physical strength. One would have been motivated to make this modification because the prediction of non-pathological fatigue can be determined by comparing physiology patterns from wearable data, as suggested by Luo (Page 61, Introduction).
Regarding claim 7, Ebesu teaches the prediction system according to claim 1, wherein the at least one processor proposes at least one of reduction in a working time or a change in a content of the task when the value of the fatigue level that has been predicted is equal to or larger than a predetermined threshold or the subjective value of the residual physical strength is smaller than a predetermined threshold ([0040] “the monitoring apparatus 30 determines that the worker 9 is in an alerted state, and an alert is sent to at least one of the terminal device 10 and the administrator 8. In FIG. 1, the administrator 8 is notified of such an alert. The administrator 8 monitors an administrator's personal computer (PC) 50 to check which worker 9's cumulative load has exceeded the threshold, and tries to improve the working environment. In particular, the administrator 8 lets the worker 9 to have a rest, or changes the layout of the working environment.”).
Ebesu does not explicitly teach the subjective fatigue level.
However,
Luo teaches the subjective fatigue level (Page 61, Introduction: compare several methods to predict non-pathological fatigue scores from physical activity and vital sign parameters, and further explore behavior and physiology patterns from wearable data and their connection to self-reported fatigue via clustering”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the system taught by Ebesu to include analysis of a subjective fatigue level. One would have been motivated to make this modification because the non-pathological fatigue can correspond to physiology patterns from wearable data, as suggested by Luo (Page 61, Introduction).
Regarding claim 8, Ebesu teaches the prediction system according to claim 5, wherein the at least one processor proposes at least one of reduction in a working time or a change in a content of the task when the value of the fatigue level that has been predicted in at least one of the parts is equal to or larger than a predetermined threshold or the value of the subjective residual physical strength is smaller than a predetermined threshold ([0040] “the monitoring apparatus 30 determines that the worker 9 is in an alerted state, and an alert is sent to at least one of the terminal device 10 and the administrator 8. In FIG. 1, the administrator 8 is notified of such an alert. The administrator 8 monitors an administrator's personal computer (PC) 50 to check which worker 9's cumulative load has exceeded the threshold, and tries to improve the working environment. In particular, the administrator 8 lets the worker 9 to have a rest, or changes the layout of the working environment.”).
Ebesu does not explicitly teach the subjective fatigue level.
However,
Luo teaches the subjective fatigue level (Page 61, Introduction: compare several methods to predict non-pathological fatigue scores from physical activity and vital sign parameters, and further explore behavior and physiology patterns from wearable data and their connection to self-reported fatigue via clustering”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the system taught by Ebesu to include analysis of a subjective fatigue level. One would have been motivated to make this modification because the non-pathological fatigue can correspond to physiology patterns from wearable data, as suggested by Luo (Page 61, Introduction).
Regarding claim 9, Ebesu teaches a method executed by a computer, the method comprising:
extracting a duration time of one or more physical states of a posture or a motion that a person assumes or performs during a performing of a task by analyzing a process of the task of the person ([0033] “The monitoring apparatus 30 analyzes the worker data to determine whether the worker 9 has adopted predetermined posture or done predetermined operation”; [0041]; [0099] “he chronological posture information stored in the condition storage unit 61. In the chronological posture information of each worker 9, the recognized postures and the position information are chronologically recorded with respect to time. For example, bending forward posture is firstly recognized at 9:10:15 (hour:minute:second), and the bending forward posture is lastly recognized at 9:13:18”; Fig. 14B);
receive the data on the load on the body of the person regarding the one or more physical states ([0033] “when the worker 9 has adopted such predetermined posture, the monitoring apparatus 30 converts the posture into load”; [0100] “The calculated load data of each worker 9 includes the total hours of each posture, and the load and the cumulative load that are calculated based on the posture and its total hours. In other words, how long the worker 9 has taken each posture in total recorded, and the load caused by each posture is recorded. The cumulative load indicates the value of the sum total of the loads of each posture.”); and
predicting at least one of a fatigue level or a residual physical strength of the person in the task based on the extracted duration time of the one or more physical states and the acquired load ([0040] “The term “alerted state” indicates a state in which fatigue is predicted to build in the body of a worker. In particular, an alerted state indicates a state when the cumulative load exceeds a threshold.”; [0035]); and
control a display ([0069] “The graphics driver 102 is connected to a liquid crystal display (LCD) 104 through a bus, and monitors the results of processes that are performed by the CPU 101”).
Ebesu does not explicitly teach controlling a plurality of inertial sensors, which are attached to parts of a body of a person, to acquire data on a load on the body of the person regarding one or more physical states; and
further predict at least one of a subjective fatigue level or a subjective residual physical strength that the person subjectively feels in the task by referring to data of another task including a result of predicting at least one of another fatigue level or another residual physical strength of the person in the another task and at least one of another subjective fatigue level or another subjective residual physical strength that the person has subjectively felt in the another task, and a result of predicting at least one of the fatigue level or the residual physical strength of the person in the task: and
control a display to display the at least one of the subjective fatigue level or the subjective residual physical strength that the person subjectively feels in the task.
However,
controlling a plurality of inertial sensors, which are attached to parts of a body of a person, to acquire data on a load on the body of the person regarding one or more physical states (Page 60, Introduction: “inertial sensors”; “the use of wearable sensors to assess physical fatigue in healthy cohorts under experimental settings, for example inertial measurement units and heart rate monitors in construction workers”; Page 61, Materials and Methods: “3-axis accelerometer”); and
further predict at least one of a subjective fatigue level or a subjective residual physical strength that the person subjectively feels in the task by referring to data of another task including a result of predicting at least one of another fatigue level or another residual physical strength of the person in the another task and at least one of another subjective fatigue level or another subjective residual physical strength that the person has subjectively felt in the another task, and a result of predicting at least one of the fatigue level or the residual physical strength of the person in the task (Page 61, Introduction: “compare several methods to predict non-pathological fatigue scores from physical activity and vital sign parameters, and further explore behavior and physiology patterns from wearable data and their connection to self-reported fatigue via clustering”; Page 64, Results: “the relationship between subjectively reported non-pathological physical and mental fatigue and objective physiological and behavioral parameters from a number of perspectives: direct correlation, direct classification of PRO scores, anomaly detection (classification of outlying periods of data corresponding to increased fatigue reporting), and unsupervised clustering”; Page 62, Table 1: Activity Class “Type of physical activity: 0 = undefined, 1 = resting, 9 = other, 10 = biking, 11 = running, 12 = walking”); and
display the at least one of the subjective fatigue level or the subjective residual physical strength that the person subjectively feels in the task (Page 66, Cluster Analysis: “one observation and each column to the Z-score normalized values of the 44 features in Xc. For each observation, the corresponding fatigue labels and demographics are also displayed.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method taught by Ebesu to include inertial sensors and predict at least one of a subjective fatigue level or a subjective residual physical strength. The inertial sensors are described in the Introduction of Luo, and while Ebesu does not explicitly describe inertial sensor, the disclosure teaches “an acceleration acquisition unit 43, an angular speed acquisition unit 44” and inertial sensors use used to gather acceleration and rotational data. One would have been motivated to make this modification because the prediction of non-pathological fatigue can be determined by comparing physiology patterns from wearable data, as suggested by Luo (Page 61, Introduction).
Regarding claim 10, Ebesu teaches a non-transitory computer readable medium storing a program that causes a computer to execute [0059]:
extracting a duration time of one or more physical states of a posture or a motion that a person assumes or performs during a performing of a task by analyzing a process of the task of the person ([0033] “The monitoring apparatus 30 analyzes the worker data to determine whether the worker 9 has adopted predetermined posture or done predetermined operation”; [0041]; [0099] “he chronological posture information stored in the condition storage unit 61. In the chronological posture information of each worker 9, the recognized postures and the position information are chronologically recorded with respect to time. For example, bending forward posture is firstly recognized at 9:10:15 (hour:minute:second), and the bending forward posture is lastly recognized at 9:13:18”; Fig. 14B);
receive the data on the load on the body of the person regarding the one or more physical states ([0033] “when the worker 9 has adopted such predetermined posture, the monitoring apparatus 30 converts the posture into load”; [0100] “The calculated load data of each worker 9 includes the total hours of each posture, and the load and the cumulative load that are calculated based on the posture and its total hours. In other words, how long the worker 9 has taken each posture in total recorded, and the load caused by each posture is recorded. The cumulative load indicates the value of the sum total of the loads of each posture.”); and
predicting at least one of a fatigue level or a residual physical strength of the person in the task based on the extracted duration time of the one or more physical states and the acquired load ([0040] “The term “alerted state” indicates a state in which fatigue is predicted to build in the body of a worker. In particular, an alerted state indicates a state when the cumulative load exceeds a threshold.”; [0035]);
control a display ([0069] “The graphics driver 102 is connected to a liquid crystal display (LCD) 104 through a bus, and monitors the results of processes that are performed by the CPU 101”).
Ebesu does not explicitly teach controlling a plurality of inertial sensors, which are attached to parts of a body of a person, to acquire data on a load on the body of the person regarding one or more physical states; and
further predict at least one of a subjective fatigue level or a subjective residual physical strength that the person subjectively feels in the task by referring to data of another task including a result of predicting at least one of another fatigue level or another residual physical strength of the person in the another task and at least one of another subjective fatigue level or another subjective residual physical strength that the person has subjectively felt in the another task, and a result of predicting at least one of the fatigue level or the residual physical strength of the person in the task: and
control a display to display the at least one of the subjective fatigue level or the subjective residual physical strength that the person subjectively feels in the task.
However,
controlling a plurality of inertial sensors, which are attached to parts of a body of a person, to acquire data on a load on the body of the person regarding one or more physical states (Page 60, Introduction: “inertial sensors”; “the use of wearable sensors to assess physical fatigue in healthy cohorts under experimental settings, for example inertial measurement units and heart rate monitors in construction workers”; Page 61, Materials and Methods: “3-axis accelerometer”); and
further predict at least one of a subjective fatigue level or a subjective residual physical strength that the person subjectively feels in the task by referring to data of another task including a result of predicting at least one of another fatigue level or another residual physical strength of the person in the another task and at least one of another subjective fatigue level or another subjective residual physical strength that the person has subjectively felt in the another task, and a result of predicting at least one of the fatigue level or the residual physical strength of the person in the task (Page 61, Introduction: “compare several methods to predict non-pathological fatigue scores from physical activity and vital sign parameters, and further explore behavior and physiology patterns from wearable data and their connection to self-reported fatigue via clustering”; Page 64, Results: “the relationship between subjectively reported non-pathological physical and mental fatigue and objective physiological and behavioral parameters from a number of perspectives: direct correlation, direct classification of PRO scores, anomaly detection (classification of outlying periods of data corresponding to increased fatigue reporting), and unsupervised clustering”; Page 62, Table 1: Activity Class “Type of physical activity: 0 = undefined, 1 = resting, 9 = other, 10 = biking, 11 = running, 12 = walking”); and
display the at least one of the subjective fatigue level or the subjective residual physical strength that the person subjectively feels in the task (Page 66, Cluster Analysis: “one observation and each column to the Z-score normalized values of the 44 features in Xc. For each observation, the corresponding fatigue labels and demographics are also displayed.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method taught by Ebesu to include inertial sensors and predict at least one of a subjective fatigue level or a subjective residual physical strength. The inertial sensors are described in the Introduction of Luo, and while Ebesu does not explicitly describe inertial sensor, the disclosure teaches “an acceleration acquisition unit 43, an angular speed acquisition unit 44” and inertial sensors use used to gather acceleration and rotational data. One would have been motivated to make this modification because the prediction of non-pathological fatigue can be determined by comparing physiology patterns from wearable data, as suggested by Luo (Page 61, Introduction).
Response to Arguments
Applicant's arguments filed November 26, 2025 have been fully considered. There are new grounds of claim rejections that were necessitated by the claim amendments.
As described above, Ebesu in view of Luo discloses the amended limitations of a plurality of inertial sensors and prediction of a subjective fatigue level or residual physical strength. Claims 2-5 and 7-8 are rejected because the rejections of independent claims 1, 9, and 10 are proper and the prior art teaches or suggests all the features of these claims for the reasons described in the 103 Rejections.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/EVELYN GRACE PARK/Examiner, Art Unit 3791 /TSE W CHEN/Supervisory Patent Examiner, Art Unit 3791