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 .
2. This Office Action is sent in response to Applicant's Communication received on October 21, 2025.
Response to Arguments
Applicant’s amendments/arguments filed October 21, 2025, with respect to claims 1-14 rejections under 35 U.S.C. 101 have been fully considered and are persuasive. Accordingly, said claims 1-14 rejections under 35 U.S.C. 101 have been withdrawn.
Applicant’s amendments/arguments filed October 21, 2025, with respect to claims 1-14 rejections under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejections have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of JUNKO as explained below.
Disposition of Claims
Claims 1-14 are pending in this application.
Claims 1-14 are rejected.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1-14 are rejected under 35 U.S.C. 103 as being unpatentable over (YAMAMOTO – US 2019/0202484 A1), in view of (Kernwein – US 9,283,945 B1), further in view of (JUNKO – JP 2019193426 A).
Regarding claim 1, YAMAMOTO discloses:
An adaptively adjusted and accurate parking control method for an “ATO system” (train control device 100 under automatic stop-position control over the train 30 to stop at a target stop position including an on-vehicle ATC device 140: Fig. 1 and [0021-0032, 0036-0057]), comprising the following steps:
S1, monitoring a speed tracking performance (Monitoring thru speed position detector 120: Fig. 1) of a train (train 30: Fig. 1) during each stop process, and determining whether each stop process of the train satisfies an “acceptable stop statistical condition” (Allowable Value: [0069-0082, 0090-0102] “the control-command calculator 104 finds a position on the target deceleration pattern BP which exhibits the same speed as the train-speed estimate, and calculates a deviation of the train position by subtracting the found position from the train-position estimate. It further calculates an allowable value of the positional deviation by multiplying the train-speed estimate by a second certain time (Step S14)” and “As illustrated in FIG. 4, the positional-deviation allowable value is set to the same speed of the target deceleration pattern BP as the train-speed estimate+(speed)×0.5 seconds for the excessive-side (=excessive-side position-deviation allowable value curve dx+) and set to the same−(speed)×1.0 second for the insufficient-side (=insufficient-side position−deviation allowable value curve dx−). Under the conventional speed following control, the positional-deviation allowable value is set to the same speed of the target deceleration pattern BP as the train-speed estimate+1 km/h for the excessive-side (=excessive-side speed-deviation allowable value curve dv+) and the same−2 km/h for the insufficient-side (=insufficient-side speed-deviation allowable value curve dv−). It can be thus seen that the ranges of allowable deviation of the present embodiment and the conventional speed following control substantially match each other”);
S2, updating the result of a stop process satisfying the “acceptable stop statistical condition” (Allowable Value: [0069-0082, 0090-0102]) to a “stop array queue” (Consideration of accuracy of the stop position: [0098-0099 ,0103-0105]: “Specifically, if the positional deviation is 600 cm on the insufficient-side and the positional-deviation allowable value of the insufficient-side is 500 cm, the ratio at which the positional deviation is offset from the positional-deviation allowable value is found by (600−500)/500×100=20%, for example. If the positional deviation is 400 cm on the insufficient-side and the positional-deviation allowable value of the insufficient-side is 500 cm, the positional deviation does not fall outside the positional-deviation allowable value, therefore, the ratio of the positional deviation offset from the positional-deviation allowable value is set to 0%” and “Similarly, if the time deviation is 20 seconds on the insufficient-side and the time-deviation allowable value of the insufficient-side is 15 seconds, the ratio at which the time deviation is offset from the time-deviation allowable value is found by (20−15)/15×100=33%, for example. If the time deviation is 10 seconds on the insufficient-side and the time-deviation allowable value of the insufficient-side is 15 seconds, the time deviation does not fall outside the time-deviation allowable value, therefore, the ratio of the time deviation offset from the time-deviation allowable value is set to 0%”), ;
;
.
Examiner Notes on Claim Limitations Interpretation:
From Present Application Written Specification the limitation “ATO system” is defined as an automatic train operation system.
From Present Application Written Specification Paragraphs [0011, 0046-0049, 0056-0063, 0075, 0078] the limitation “acceptable stop statistical condition” includes:
“Good speed tracking performance” in an electric braking process during the train stop stage. Where the term “Good speed tracking performance” is further defined in Present Application Written Specification Paragraph [0049] as “A reference speed in the electric braking process of the train is set as a target speed; a difference between the target speed and an actual train speed is defined as a speed deviation; a determination standard of the good speed tracking performance in an electric braking process is as follows: the {{{speed deviation satisfies a preset threshold, or the speed deviation exceeds a preset threshold but the speed tracking process converges}}}. The preset threshold for the speed deviation can be set according to actual needs”.
From Present Application Written Specification Paragraphs [0015-0026, 0057] and Figure 5 the limitations “stop array queue” and “statistical feature of every n stop results” are defined as “Preferably, the stop array queue is SSP_Accuracy_Array, and the statistical feature of every n stop results comprises a median offset Offset_Median, a mean offset Offset_Mean and a standard deviation offset Offset_Std” and “Specifically, after the train stops steadily at the platform, if the stop process of the train satisfies the acceptable stop statistical condition, then the stop result will be updated to a stop array queue SSP_Accuracy_Array. As shown in FIG. 5, the stop array SSP_Accuracy_Array stores n stop results, and first-in-first-out. Every n stop is used as a learning period to calculate a statistical feature of every n stop results, i.e., calculating a median offset, a mean offset and a standard deviation offset of every n stop results, the calculation formula thereof is as follows: [0058] Offset_Median=median (SSP_Accuracy_Array) [0059] Offset_Mean=mean (SSP_Accuracy_Array) [0060] Offset_Std=std (SSP_Accuracy_Array) [0061] wherein median, mean, std respectively represent performing median operation, mean operation and standard deviation operation on the stop array SSP_Accuracy_Array; Offset_Median represents a median offset of n stop results, Offset_Mean represents a mean offset of n stop results, and Offset_Std represents a standard deviation offset of n stop results”.
The limitation “parking point offset” is defined as a calculation formula defined in Present Application Written Specification Paragraphs [0016-0025].
But YAMAMOTO does not explicitly and/or specifically meet the following limitations:
using n stops as a learning period to calculate a “statistical feature of every n stop results”, and adaptively calculating a “parking point offset” according to the calculated “statistical feature of every n stop results”; and evaluating, on the basis of the above steps, the every stop result of the train and the stop results within each learning period, and if a preset threshold is exceeded, then clearing the existing parking point offset and restarting a new round of learning process.
wherein a portion of each stop process of the train comprises pneumatic braking; whereby an influence of the pneumatic braking of the train on an accuracy of each stop process is reduced.
However, regarding limitations (A) above, Kernwein discloses/teaches the following:
As is known in probability theory and statistics, the variance of a random variable or distribution is the standard deviation of that variable from its expected value or mean, i.e., variance is the measure of the amount of variation of all the scores for a variable. In a braking distance prediction calculation, it would be preferable for the mean to be the target location, and the variance to be zero, where all stops would be exactly at the target location. However, in the existing braking algorithm or model, there exist more than 35 assumed constants and measured variables, each with a potential inaccuracy or measurement error. In probability theory, the central limit theorem (CLT) indicates conditions under which the mean of a sufficiently large number of independent random variables, each with a finite mean and variance, will be approximately normally distributed. For example, and with respect to railcar weight, an empty 100-ton capacity may weigh 35 tons. By using the above-discussed CLT method, the mean and the variance of the expected braking distance follows a normal distribution. Further, and since the braking distance has a normal distribution, the statistics for normal distributions can be applied and performance-based requirements can be generated ({Column 13, Lines 40-67}).
Similarly, in the on-board implementation, the safety factor may be added incrementally as the curve is built. This allows one single curve to be used with both zero-speed speed targets and non-zero-speed speed targets. In one embodiment, it is recognized that an alert locomotive engineer will instinctively bail off independent brakes if an automatic full-service brake application is applied, and the on-board computer 12 may assume that this will be the case if the number of cars is greater than 8. Whether the engineer does this or not influences the total length of the train stop, but has only a small influence on the variance of all stops conducted with this same assumption. Accordingly, in the design phase, the simulations used to calculate the variance in stopping distance may not include a bail off of the independent brake ({Column 14, Lines 13-33}).
The method includes: (a) for a specified scenario having specified train modeling constants, providing the specified train modeling constants and a plurality of train data inputs into a braking model programmed to determine a predicted braking distance; (b) for a plurality of subsequent specified scenarios having the same specified train modeling constants, modifying a plurality of the train data inputs, and providing the specified train modeling constants and the modified train inputs into the braking model to determine a plurality of subsequent predicted braking distances; (c) determining at least one safety factor based at least in part on the distribution of the predicted braking distance and the subsequent predicted braking distances for the specified scenarios; (d) repeating steps (a)-(c) for a plurality of different specified scenarios; and (e) based at least partially on steps (a)-(d), generating a database populated with a plurality of safety factors selectable base at least partially on (i) at least one operating train constant, and (ii) train speed, track grade, and train weight ({Column 4, Lines 55-67}).
A computer-implemented method for determining multiple safety factors for use in a braking model of at least one train. The method includes: (a) for a specified scenario having specified train modeling constants, providing the specified train modeling constants and multiple train data inputs into a braking model programmed to determine a predicted braking distance; (b) for a plurality of subsequent specified scenarios having the same specified train modeling constants, modifying the train data inputs, and providing the specified train modeling constants and the modified train inputs into the braking model to determine a plurality of subsequent predicted braking distances; (c) determine at least one safety factor based at least in part on the distribution of the predicted braking distance and the subsequent predicted braking distances for the specified scenarios; (d) repeating steps (a)-(c) for a plurality of different specified scenarios; and (e) based at least partially on steps (a)-(d), generating a database populated with a plurality of safety factors selectable based at least partially on (i) at least one operating train constant, and (ii) train speed, track grade, and train weight. As discussed, the operating train constant may be at least one of the following: train type, train total tonnage, number of railcars, position of the locomotive 10, availability of emergency braking, or any combination thereof. In another preferred and non-limiting embodiment, the method includes providing the database to at least one on-board computer 12 of at least one train for use in the selection during train operation of at least one safety factor based at least partially on (i) at least one operating train constant, and (ii) train speed, track grade, and train weight. Further, at least one of the specified train modeling constants may be a parameter representing the use of emergency braking ({Column 12, Lines 48-67} and {Column 13, Lines 1-13}).
It is further noted that Kernwein discloses/teaches the claimed “…restarting a new round of learning process…” as “But after many trials using the Monte Carlo analysis, the shape of the distribution and the standard statistical measures of mean and standard deviation for that set of trials is available. This process of many (e.g., about 1,000) trials is repeated for a wide range of scenarios that cover different train lengths, train types, weight, grade, and speed” in {Column 32, Lines 64-67} and {Column 33, Lines 1-3}.
Further on, regarding limitations (B) above, JUNKO discloses/teaches the following:
The acceleration / deceleration detection unit 205 detects the actual train 10 based on the speed and position detected by the speed position detection unit 101 and the train resistance stored in the characteristic information holding unit 201 during air braking of the train 10. Calculate the estimated value of deceleration. When the train 10 stops after air braking, the parameter adjustment unit 206 estimates the actual deceleration calculated by the characteristic information holding unit 201, the deceleration model and the air-conditioning parameters stored in the characteristic information holding unit 201. The air traffic control parameters stored in the characteristic information holding unit 201 are adjusted based on the difference between the estimated value and the set value indicating the deceleration on the setting of the train 10 at the time corresponding to the estimated value. The control command calculation unit 204 detects the speed and position detected by the speed position detection unit 101, the deceleration model stored in the characteristic information holding unit 201, and the air control during air braking of the train 10 after adjustment of the air control parameter. The braking command is calculated based on the operational parameters and the train resistance ([0079]). By predicting the train behavior, it is possible to stop at the stop target position with higher accuracy. And the parameter adjustment part 206 is based on the average value of the difference (or ratio) of some estimated value and some setting value corresponding to the said some estimated value at the time of the stop after the air control of the train 10. Adjust parameters for air control.
The ATO device 104 executes control for stabilizing the operation of the train 10. For example, when the train 10 approaches the stop station and decelerates, the ATO device 104 predicts the behavior of the train 10 based on a preset deceleration model, and determines the driving / braking control device 14 based on the prediction result. Station stop control for stopping the train 10 at a predetermined position of the stop station is executed by appropriately adjusting the applied braking command. The deceleration model is a model that shows the design value of the deceleration that is actually generated by the braking device 16 in response to a given braking command ([0045]).
Therefore, 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 parking control method of YAMAMOTO incorporating additional controller calculation-unit modules/instructions as taught by Kernwein and JUNKO to improve braking system and/or safety factor that minimizes the chance that the train will stop too great of a distance before the target location.
Regarding claim 2, YAMAMOTO as combined above disclose the adaptively adjusted and accurate parking control method for an ATO according to claim 1, and further on YAMAMOTO as combined above also discloses:
wherein the acceptable stop statistical condition comprises: “good speed tracking performance” (Please see above “Examiner Notes on Claim Limitations Interpretation”) in an electric braking process during the train stop stage, no interference during the train stop stage, and a train stop accuracy satisfying a preset threshold (YAMAMOTO [0021-0032, 0036-0057, 0069-0082, 0090-0102] and Kernwein {Column 4, Lines 55-67; Column 12, Lines 48-67; Column 13, Lines 1-13; Column 13, Lines 40-67; Column 14, Lines 13-33; Column 32, Lines 64-67; Column 33, Lines 1-3}).
Regarding claim 3, YAMAMOTO as combined above disclose the adaptively adjusted and accurate parking control method for an ATO according to claim 2, and further on YAMAMOTO as combined above also discloses:
wherein a determination standard of the “good speed tracking performance” (Please see above “Examiner Notes on Claim Limitations Interpretation”) in an electric braking process during the train stop stage is: a reference speed in the electric braking process of the train is set as a target speed; a difference between the target speed and an actual train speed is defined as a speed deviation; and if the speed deviation satisfies a preset threshold, or if the speed deviation exceeds a preset threshold but the speed tracking process converges, then the “speed tracking performance” in the electric braking process during the train stop stage is “considered to be good” (Please see above “Examiner Notes on Claim Limitations Interpretation”) (YAMAMOTO [0021-0032, 0036-0057, 0069-0082, 0090-0102] and Kernwein {Column 4, Lines 55-67; Column 12, Lines 48-67; Column 13, Lines 1-13; Column 13, Lines 40-67; Column 14, Lines 13-33; Column 32, Lines 64-67; Column 33, Lines 1-3}).
Regarding claim 4, YAMAMOTO as combined above disclose the adaptively adjusted and accurate parking control method for an ATO according to claim 2, and further on YAMAMOTO as combined above also discloses:
wherein interference factors during the train stop stage comprise: non-master core control, non-ATO train control, and platform stopping by taking a non-parking point as the strongest constraint (YAMAMOTO [0021-0032, 0036-0057, 0069-0082, 0090-0102] and Kernwein {Column 4, Lines 55-67; Column 12, Lines 48-67; Column 13, Lines 1-13; Column 13, Lines 40-67; Column 14, Lines 13-33; Column 32, Lines 64-67; Column 33, Lines 1-3}).
Regarding claim 5, YAMAMOTO as combined above disclose the adaptively adjusted and accurate parking control method for an ATO according to claim 1, and further on YAMAMOTO as combined above also discloses:
wherein the acceptable stop statistic condition is also applied to a train real-time stop process; and when a certain train real-time stop process does not satisfy the acceptable stop statistic condition, the stop does not use the method (YAMAMOTO [0021-0032, 0036-0057, 0069-0082, 0090-0102] and Kernwein {Column 4, Lines 55-67; Column 12, Lines 48-67; Column 13, Lines 1-13; Column 13, Lines 40-67; Column 14, Lines 13-33; Column 32, Lines 64-67; Column 33, Lines 1-3}).
Regarding claim 6, YAMAMOTO as combined above disclose the adaptively adjusted and accurate parking control method for an ATO according to claim 1, and further on YAMAMOTO as combined above also discloses:
wherein the stop array queue is SSP_Accuracy_Array, and the statistical feature of every n stop results comprises a median offset Offset_Median, a mean offset Offset_Mean and a standard deviation offset Offset_Std (YAMAMOTO [0021-0032, 0036-0057, 0069-0082, 0090-0102] and Kernwein {Column 4, Lines 55-67; Column 12, Lines 48-67; Column 13, Lines 1-13; Column 13, Lines 40-67; Column 14, Lines 13-33; Column 32, Lines 64-67; Column 33, Lines 1-3}).
Regarding claim 7, YAMAMOTO as combined above disclose the adaptively adjusted and accurate parking control method for an ATO according to claim 6, and further on YAMAMOTO as combined above also discloses:
wherein a calculation formula of the parking point offset SSP_Offset_Adjust is:
SSP_Offset_Adjust+=Adjust_Delta;
wherein, Adjust_Delta is a correction increment of a learning period, the sign+=represents an accumulation operation, and the above formula represents accumulating the correction increment Adjust_Delta of the current learning period on the basis of the last learning period (YAMAMOTO [0021-0032, 0036-0057, 0069-0082, 0090-0102] and Kernwein {Column 4, Lines 55-67; Column 12, Lines 48-67; Column 13, Lines 1-13; Column 13, Lines 40-67; Column 14, Lines 13-33; Column 32, Lines 64-67; Column 33, Lines 1-3}).
Regarding claim 8, YAMAMOTO as combined above disclose the adaptively adjusted and accurate parking control method for an ATO according to claim 7, and further on YAMAMOTO as combined above also discloses:
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wherein a calculation formula of the correction increment Adjust_Delta is as follows:
wherein QUICK_REGION is a preset fast adjustment region, and QUICK_STEP represents a fast adjustment step length adopted when Offset_Median is in the fast adjustment region QUICK_REGION; FINE_REGION is a preset fine adjustment region, and FINE_STEP represents a fine adjustment step length adopted when OffsetMedian is within the fine adjustment region FINE_REGION; SIGN(*) is a sign operation function which returns +1 according to the positivity and negativity of Offset_Median (YAMAMOTO [0021-0032, 0036-0057, 0069-0082, 0090-0102] and Kernwein {Column 4, Lines 55-67; Column 12, Lines 48-67; Column 13, Lines 1-13; Column 13, Lines 40-67; Column 14, Lines 13-33; Column 32, Lines 64-67; Column 33, Lines 1-3}).
Regarding claim 9, YAMAMOTO as combined above disclose the adaptively adjusted and accurate parking control method for an ATO according to claim 7, and further on YAMAMOTO as combined above also discloses:
wherein the parking point offset SSP_Offset_Adjust is constrained with limit values: an upper adjustment limit value and a lower adjustment limit value are set: when a parking point offset SSP_Offset_Adjust acquired after a learning period is greater than the upper adjustment limit value, then the upper adjustment limit value is taken as the parking point offset of the train in the next learning period; and when the parking point offset SSP_Offset_Adjust acquired after a learning period is less than the lower adjustment limit value, then the lower adjustment limit value is taken as the parking point offset of the train in the next learning period (YAMAMOTO [0021-0032, 0036-0057, 0069-0082, 0090-0102] and Kernwein {Column 4, Lines 55-67; Column 12, Lines 48-67; Column 13, Lines 1-13; Column 13, Lines 40-67; Column 14, Lines 13-33; Column 32, Lines 64-67; Column 33, Lines 1-3}).
Regarding claim 10, YAMAMOTO as combined above disclose the adaptively adjusted and accurate parking control method for an ATO according to claim 7, and further on YAMAMOTO as combined above also discloses:
wherein step S4 comprises the following two cases: S41, instantly evaluating a single stop result of the train, and if a train stop characteristic abruptly changes, then clearing the existing parking point offset SSP_Offset_Adjust, and immediately restarting a new round of learning process; and S42, statistically evaluating the stop results of the train in each learning period, and if the n stop results of the train in the learning period do not satisfy a statistical stationary characteristic, then clearing the existing parking point offset SSP_Offset_Adjust, and restarting a new round of learning process (YAMAMOTO [0021-0032, 0036-0057, 0069-0082, 0090-0102] and Kernwein {Column 4, Lines 55-67; Column 12, Lines 48-67; Column 13, Lines 1-13; Column 13, Lines 40-67; Column 14, Lines 13-33; Column 32, Lines 64-67; Column 33, Lines 1-3}).
Regarding claim 11, YAMAMOTO as combined above disclose the adaptively adjusted and accurate parking control method for an ATO according to claim 10, and further on YAMAMOTO as combined above also discloses:
wherein the definition for abrupt changes of train stop characteristic is: a certain stop accuracy of a train having an under-docking characteristic exceeds a preset allowable over-docking distance, or a certain stop accuracy of a train having an over-docking characteristic exceeds a preset allowable under-docking distance (YAMAMOTO [0021-0032, 0036-0057, 0069-0082, 0090-0102] and Kernwein {Column 4, Lines 55-67; Column 12, Lines 48-67; Column 13, Lines 1-13; Column 13, Lines 40-67; Column 14, Lines 13-33; Column 32, Lines 64-67; Column 33, Lines 1-3}).
Regarding claim 12, YAMAMOTO as combined above disclose the adaptively adjusted and accurate parking control method for an ATO according to claim 11, and further on YAMAMOTO as combined above also discloses:
wherein the train having an under-docking characteristic means that the existing parking point offset SSP_Offset_Adjust is greater than zero; and the train having an over-docking characteristic means that the existing parking point offset SSP_Offset_Adjust is less than zero (YAMAMOTO [0021-0032, 0036-0057, 0069-0082, 0090-0102] and Kernwein {Column 4, Lines 55-67; Column 12, Lines 48-67; Column 13, Lines 1-13; Column 13, Lines 40-67; Column 14, Lines 13-33; Column 32, Lines 64-67; Column 33, Lines 1-3}).
Regarding claim 13, YAMAMOTO as combined above disclose the adaptively adjusted and accurate parking control method for an ATO according to claim 10, and further on YAMAMOTO as combined above also discloses:
wherein the conditions for determining whether the train stop satisfies the statistical stationary characteristic are as follows: a difference between the mean offset Offset_Mean and the median offset Offset_Median does not exceed a preset deviation threshold, and the standard deviation offset Offset_Std does not exceed a preset convergence trend threshold (YAMAMOTO [0021-0032, 0036-0057, 0069-0082, 0090-0102] and Kernwein {Column 4, Lines 55-67; Column 12, Lines 48-67; Column 13, Lines 1-13; Column 13, Lines 40-67; Column 14, Lines 13-33; Column 32, Lines 64-67; Column 33, Lines 1-3}).
Regarding claim 14, YAMAMOTO as combined above disclose the adaptively adjusted and accurate parking control method for an ATO according to claim 10, and further on YAMAMOTO as combined above also discloses:
wherein when the number of restarting a learning process due to the abrupt change of the train stop characteristic in a single instant evaluation of the train exceeds a preset abrupt change number threshold, then the learning process is not restarted subsequently, and the present method is no longer used to control parking; and when the number of restarting a learning process due to the reason that the train stop does not satisfy the statistical stationary characteristic during statistical train evaluation exceeds a preset non-stationary number threshold, then the learning process is not restarted subsequently, and the present method is no longer used to control parking (YAMAMOTO [0021-0032, 0036-0057, 0069-0082, 0090-0102] and Kernwein {Column 4, Lines 55-67; Column 12, Lines 48-67; Column 13, Lines 1-13; Column 13, Lines 40-67; Column 14, Lines 13-33; Column 32, Lines 64-67; Column 33, Lines 1-3}).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ruben Picon-Feliciano whose telephone number is (571)-272-4938. The examiner can normally be reached on Monday-Thursday within 11:30 am-7:30 pm ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lindsay M. Low can be reached on (571)272-1196. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RUBEN PICON-FELICIANO/Examiner, Art Unit 3747
/GRANT MOUBRY/Primary Examiner, Art Unit 3747