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 August April 22, 2025 has been entered. Claims 1, 14, 22, 28, 30, 37, 41, 44, 47-48, 54, 58 and 65-69 remain pending in the application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Section 33(a) of the America Invents Act reads as follows:
Notwithstanding any other provision of law, no patent may issue on a claim directed to or encompassing a human organism.
Claims 1, 14, 22, 28, 30, 37, 41, 44, 47-48, 54, 58 and 65-69 are rejected under 35 U.S.C. 101 and section 33(a) of the America Invents Act as being directed to or encompassing a human organism. See also Animals - Patentability, 1077 Off. Gaz. Pat. Office 24 (April 21, 1987) (indicating that human organisms are excluded from the scope of patentable subject matter under 35 U.S.C. 101). Claim 1 recites, “for each of the plurality of users, a signal module and a wearable device destined to be worn on a wrist of the user”. Firstly, “for the plurality of users” is not needed and it just adds to confusion of whether that is positively being claimed as part of the system. And “destined to be worn on a wrist of the user” is reciting the human body. It is interpreted to read, “a signal model and a wearable device configured to be worn on the wrist of a user…” and the first limitation of claim 1 should be amended as such. Claims 66-68 recite the same issues. Dependent claims are rejected based on their dependency to a rejected claim.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1, 14, 22, 28, 30, 37, 41, 44, 47-48, 54, 58 and 65-69 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation, “wherein the signal module comprises a controlling processor including a triggering processor comprising a firmware portion or a hardware portion configured to initiate and end a pulsatility measurement according to a trigger parameter such that the pulsatility sensing unit measures the plurality of pulsatility signals at the user’s wrist during a predetermined measurement time period” it is unclear how the pulsatility measurement is started and stopped according to a trigger parameter, but then also the pulsatility measurement is during a predetermined time period. It is unclear how it can be both predetermined and only initiate from a threshold movement.
Claims 66-68 have the same issues.
Claim 1 recites, ““… a motion sensor representative of a user’s movement…”. There is insufficient antecedent basis for this limitation in the claim. It is unclear if it is the same user. The suggest language in the 101 rejection to amend to, “a signal model and a wearable device configured to be worn on the wrist of a user…” and claiming the entirety of the sensors on a single user, and then later in the claim discussing the blood pressure calculated based on a plurality would clarify the claim. In other words, describing the sensors and devices as singular on a single user, a single wrist, measuring a single users data…. Then claiming that the sensor/measurements are done for a plurality of users each having the sensors but the remote server is calculating based on each of them. Claims 67 and 68 recite the same issues.
Claim 1 recites in the new amendments, “initiate and end a pulsatility measurement”. There is insufficient antecedent basis for this limitation in the claim. The claim recites that the optical sensors are configured to measure pulsatility signals. It is unclear if the trigger parameter is triggering the same measurements or different measurements, are “a pulsatility measurement” and “pulsatility signals” the same. They are interpreted to be the same, but the claim language does not read that. Claims 67 and 68 recite the same issues.
Claim 69 recites the limitation, “the motion signal is further configured to calculate an intense exercise time period” it is unclear how a signal is configured to calculate a time period rather than the period being calculated based on the signal.
Dependent claims are rejected based on their dependency to a rejected base claim.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 nonobviousness.
Claims 1, 14, 22, 28, 30, 37, 41, 44, 47-48, 54, 58 and 65-69 are rejected under 35 U.S.C. 103 as being unpatentable over Bhushan et al. (US 2018/0358119 A1) (“Bhushan”) in view of Pantelopoulos (US 2017/0209055 A1)("Pantelopoulos").
Regarding claim 1, Bhushan discloses A system for determining a blood pressure (BP) of one or a plurality of users, the system comprising (Abstract and entire document, see at least [0011] discussing blood pressure and [0107] discussing one or multiple users, see also FIG. 10),
for each of the plurality of users, a signal module and a wearable device destined to be worn on a wrist of the user (FIG. 1-3 and associated paragraphs, see at least [0122], “FIG. 1 shows a schematic layout of the Wearable Device (115) and shows a plurality of sensors including an electrical sensor (101), accelerometer(s) (102), PPG sensor (103), Temperature sensor (104) that record data, and send the data to a microcontroller or microprocessor or another computing device (105) on the Wearable.” Wherein the MCU 105 is interpreted as a signal module, the smartphone/smartwatch is also interpreted as a signal module. See further see [0046], “In various embodiments, the Wearable device includes a reflective Photoplethysmograph (PPG) module attached to the underside of the device, and in direct visual contact with the skin on the chest/wrist/forehead or other location where the device adheres.”);
wherein the wearable device comprises a pulsatility sensing unit including an optical measuring sensor configured to measure pulsatility signals at the user’s wrist when the device is worn, and a motion sensor measuring a motion signal representative of a user’s movement (FIG. 1-3, “PPG sensor 103”, see also [0046], “In various embodiments, the Wearable device includes a reflective Photoplethysmograph (PPG) module attached to the underside of the device, and in direct visual contact with the skin on the chest/wrist/forehead or other location where the device adheres.” See also [0032], [0043], [0122] discussing accelerometers, motion sensors);
wherein the signal module comprises a controlling processor including a triggering processor comprising a firmware portion or a hardware portion configured to initiate and end a pulsatility measurement according to a trigger parameter such that the pulsatility sensing unit measures the plurality of pulsatility signals at the user’s wrist during a predetermined measurement time period ([0126], “In Step 3, the User or the User sends a command from the smart phone to the Wearable devices, to record physiological data from the User's body. In Step 4, physiological data is recorded by the Wearables from sensors on the device. In Step 5, the data collected is processed by the MCU on the Wearable device.” The processor triggers measurements to occur according to a user command by the processor acting as a trigger parameter and measuring during a time period see also [0113], measuring both before and during exercise);
wherein the signal module further comprises a processing processor comprising a firmware portion or a hardware portion being configured for processing the plurality of pulsatility signals to obtain pulsatility signal data for the user (FIG. 1-3, MCU 105 and [0126], “In Step 5, the data collected is processed by the MCU on the Wearable device. In Step 6, this processed data is stored on a memory chip on the device, or sent to the smart phone using some wireless communication protocol.”);
wherein the system further comprises an external service module, comprising one or a plurality of remote servers or computers and remote from the wearable device and from the signal module (FIG. 1-3 and [0126], “In Step 7, this data is also sent from the Wearable or smart phone to a secure storage location on the Cloud. In Step 8, the data is optionally further processed, and other insights are derived by processes running on the web server, as described elsewhere herein.”);
the signal module further comprising a communication processor including a long range communication link, wherein the communication processor comprises a firmware portion or a hardware portion configured to remotely transmit said pulsatility signal data from the signal module to the external service module via the long range communication link ([0123], “The computing device (105) is configured to process this data as described elsewhere herein, and then send the data and/or results of the processing for transmission to the BLE chip (106). The wireless transmission module or BLE chip (106) then sends this data to the BLE antenna (107), which then communicates the data to other devices.”);
wherein the external service module includes a database storage system configured for storing in a database the transmitted pulsatility signal data for each user of the plurality of users (FIG. 1-3 and [0126], “In Step 7, this data is also sent from the Wearable or smart phone to a secure storage location on the Cloud. In Step 8, the data is optionally further processed, and other insights are derived by processes running on the web server, as described elsewhere herein.”);
wherein the database storage system is further configured to store in the database the plurality of transmitted pulsatility signal data obtained from the wearable device of each user of the plurality of users ([0107], “In various embodiments, the data stored on the web for multiple Users, is used in Machine learning algorithms such as a convolutional neural networks and/or Bayesian Classifiers and/or support vector machines,” multiple users data is stored);
wherein the external service module further includes a calculating processor configured to calculate a BP value for each user of the plurality of users, based on the transmitted pulsatility signal data stored in the database (FIG. 1-3 and 10 and see [0126], “In Step 8, the data is optionally further processed, and other insights are derived by processes running on the web server, as described elsewhere herein.” See further [0011] and [0120 – 0121] discussing blood pressure and other values that are calculated based on each users pulse signal data); and
wherein the calculating processor is further configured to calculate the BP value for each user of the plurality of users based on the plurality of pulsatility signal data stored in the database, or for a subset of said plurality of pulsatility signal data comprising more than one pulsatility signal data (FIG. 1-3 and 10 and see [0126], “In Step 8, the data is optionally further processed, and other insights are derived by processes running on the web server, as described elsewhere herein.” See further [0011] and [0120 – 0121] discussing blood pressure and other values that are calculated based on each users pulse signal data. See further [0107], “In various embodiments, the data stored on the web for multiple Users, is used in Machine learning algorithms such as a convolutional neural networks and/or Bayesian Classifiers and/or support vector machines, to distinguish between healthy and pathological conditions of the User in question, by using the stored and annotated data as a training set” multiple users data is used as a training set. Which is further used to estimate BP and the other health values discussed above); and
wherein the system further comprises a display interface configured for displaying the calculated BP value ([0012] “display” and fig. 1-3, the smart devices described disclose displays, see also [0106], “This data can then be accessed by doctors or caregivers or the User themselves, using a web application, and historical data for each patient can be viewed and analysed.”).
Bhushan fails to disclose wherein the trigger parameter comprises an activity level of the user calculated from the motion signal, the triggering processor controlling the pulsatility sensing unit such that the pulsatility measurement is initiated once a resting interval exceeds a threshold duration after activity, or stopped otherwise;
However, in the same field of endeavor, Pantelopoulos teaches wherein the trigger parameter comprises an activity level of the user calculated from the motion signal, the triggering processor controlling the pulsatility sensing unit such that the pulsatility measurement is initiated once a resting interval exceeds a threshold duration after activity, or stopped otherwise ([0024], “In some implementations, the one or more conditions include one or more of the following: a motion level of the user being below a motion threshold, an activity of the user being a specific activity type, a body temperature of the user meeting a criterion, noise in previously obtained pulse waveform data being above a noise threshold, a force indicative of a tightness between the biometric monitoring device and the user meeting a criterion, historical activity data meeting a past activity criterion,” see also [0044] and [0228]. The trigger parameter being an activity or motion level of a user being below a threshold, while the user is at rest to reduce noise, see also [0250], “Here, in one embodiment, heart rate as a function of speed may be “plotted” for the user, or the data may be broken down into different levels including, but not limited to, sleeping, resting, sedentary, moderately active, active, and highly active.”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the system as taught by Bhushan to include wherein the trigger parameter comprises an activity level of the user calculated from the motion signal, the triggering processor controlling the pulsatility sensing unit such that the pulsatility measurement is initiated once a resting interval exceeds a threshold duration after activity, or stopped otherwise; as taught by Pantelopoulos to allow convenient and accurate measurements ([0005], “Therefore, there are needs for devices and methods that allow convenient, noninvasive, and accurate measuring of arterial stiffness.” And reducing noise, [0024], [0098], [0044], [0224]).
Regarding claim 14, Bhushan discloses The system according to claim 1, wherein the trigger parameter comprises a user-related behavioral information ([0126], “In Step 3, the User or the User sends a command from the smart phone to the Wearable devices, to record physiological data from the User's body. In Step 4, physiological data is recorded by the Wearables from sensors on the device. In Step 5, the data collected is processed by the MCU on the Wearable device.” The processor triggers measurements to occur according to a user command by the processor acting as a trigger parameter and measuring during a time period).
Regarding claim 22, Bhushan discloses The system according to claim 1, wherein the processing processor is configured to perform a pre-processing step on the measured pulsatility signals at the user’s wrist to obtain said plurality of pulsatility signals (see at least [0110 - 0111] discussing filters and artifact removal); and
wherein said pre-processing step comprises a lossless compression of the measured pulsatility signals at the user’s wrist, and executing an ensemble averaging algorithm on the measured pulsatility signals at the user’s wrist ([0056 – 0058] discussing ensemble averaging see also [0115] discussing wavelet transformation and peak interpolation techniques).
Regarding claim 28, Bhushan discloses The system according to claim 1, wherein the signal module is remote from the wearable device and the signal module cooperates with the wearable device via a first short range communication link ([0123], “The computing device (105) is configured to process this data as described elsewhere herein, and then send the data and/or results of the processing for transmission to the BLE chip (106). The wireless transmission module or BLE chip (106) then sends this data to the BLE antenna (107), which then communicates the data to other devices.” And [0124 – [0125], “FIG. 1 also shows that in certain instances, on the Wearable device includes a memory chip (110). Memory chip (110) is configured to store data received from the sensors (101, 102, 103, 104), and/or the output of microprocessor (105). The stored data may later be transmitted to another device.” And [0126], “In Step 6, this processed data is stored on a memory chip on the device, or sent to the smart phone using some wireless communication protocol. In Step 7, this data is also sent from the Wearable or smart phone to a secure storage location on the Cloud.” Both short range communication is disclosed); and
wherein the signal module comprises a first short range data buffer adapted to store the measured pulsatility signals at the user’s wrist ([0122 – 0126] each of the wearable, the smartphone, the gateway, and the cloud have storage capability); and/or
wherein the wearable device comprises a long range data buffer adapted to store the pulsatility signal data ([0122 – 0126] each of the wearable, the smartphone, the gateway, and the cloud have storage capability).
Regarding claim 30, Bhushan discloses The system according to claim 1, wherein the communication processor is comprised in a portable gateway device (FIG. 1-3, “gateway 118”, see at least para. [0126] discussing communication between wearable, gateway and external device).
Regarding claim 37, Bhushan discloses The system according to claim 1, wherein the database storage system is configured for storing in the database any one of: the pulsatility signal data for each of the plurality of users, the trigger parameter for each pulsatility signal data, or the triggering input for each pulsatility signal data (FIG. 1-3 and [0126], “In Step 7, this data is also sent from the Wearable or smart phone to a secure storage location on the Cloud. In Step 8, the data is optionally further processed, and other insights are derived by processes running on the web server, as described elsewhere herein.” The pulse data is sent and stored as well as any of the other sensor data.).
Regarding claim 41, Bhushan discloses The system according to claim 1, configured for inputting a user-specific information for each of the plurality of users ([0117] discussing user specific information and see also [0122 – 0126] and fig. 1-3, gateway or smartphone smartwatch are all understood to have user interfaces for inputs).
Regarding claim 44, Bhushan discloses The system according to claim 41, wherein said user-specific information comprises one or a plurality of reference BP measurements, each measured independently from to the measurement performed with the pulsatility sensing unit; or wherein said one or a plurality of reference BP measurements is measured simultaneously with the measurement performed with the pulsatility sensing unit ([0107], “In various embodiments, the data stored on the web for multiple Users, is used in Machine learning algorithms such as a convolutional neural networks and/or Bayesian Classifiers and/or support vector machines, to distinguish between healthy and pathological conditions of the User in question, by using the stored and annotated data as a training set, and applying the classification algorithms on the User's data in real-time on the MCU of the wearable device or the gateway device, while it is connected to the wearable device over Bluetooth.”).
Regarding claim 47, Bhushan discloses The system according to claim 41, wherein the database storage system is configured for storing said user-specific information in the database (FIG. 1-3 and [0126], “In Step 7, this data is also sent from the Wearable or smart phone to a secure storage location on the Cloud. In Step 8, the data is optionally further processed, and other insights are derived by processes running on the web server, as described elsewhere herein.” The pulse data is sent and stored as well as any of the other sensor data.).
Regarding claim 48, Bhushan discloses The system according to claim 41, wherein the communication processor is comprised in a portable gateway device (FIG. 1-3, “gateway 118”, see at least para. [0126] discussing communication between wearable, gateway and external device); and wherein the portable gateway device is configured for introducing said user-specific information in the database ([0117] discussing user specific information and see also [0122 – 0126] and fig. 1-3, gateway or smartphone smartwatch are all understood to have user interfaces for inputs).
Regarding claim 54, Bhushan discloses The system according to claim 41, wherein the calculating processor is configured for calculating the BP value by further using said user-specific information ([0107], “In various embodiments, the data stored on the web for multiple Users, is used in Machine learning algorithms such as a convolutional neural networks and/or Bayesian Classifiers and/or support vector machines, to distinguish between healthy and pathological conditions of the User in question, by using the stored and annotated data as a training set, and applying the classification algorithms on the User's data in real-time on the MCU of the wearable device or the gateway device, while it is connected to the wearable device over Bluetooth.” All of the pulse wave data and user information is a part of the model for the calculations).
Regarding claim 58, Bhushan discloses The system according to claim 1, wherein the calculating processor is further configured for calculating other physiological parameters including any one of: systolic BP, diastolic BP or mean arterial pressure, pulse pressure, central pulse wave velocity, peripheral pulse wave velocity, arterial stiffness, aortic pulse transit time, augmentation index, stroke volume, stroke volume variations, pulse pressure variations, cardiac output, systemic vascular resistance, venous pressure, systemic hemodynamic parameters, pulmonary hemodynamic parameters, cerebral hemodynamic parameters, heart rate, heart rate variability, inter-beat intervals, arrhythmias detection, ejection duration, SpO2, SpHb, SpMet, SpCO, respiratory rate, tidal volume, apnea detection, sleep quality, sleep scoring, sleep analysis, bed time, sleep duration, rem sleep time, light sleep time, deep sleep time, time to get up, time to sleep, sleep efficiency, minutes awake after sleep onset, snoring duration, stress indexes, and general cardiovascular or health indexes ([0121] describing SBP, DBP and other health information).
Regarding claim 65, Bhushan discloses The system according to claim 41, wherein the calculating processor is configured for calculating the BP value according to a calculating technique, based on the pulsatility signal data, or the trigger parameter for each pulsatility signal data, or the triggering input for each pulsatility signal data, or the user-specific information, stored in the database of the database storage system from the plurality of users; wherein the calculating processor is configured for calculating the BP value and for training the calculating technique ([0107], “In various embodiments, the data stored on the web for multiple Users, is used in Machine learning algorithms such as a convolutional neural networks and/or Bayesian Classifiers and/or support vector machines, to distinguish between healthy and pathological conditions of the User in question, by using the stored and annotated data as a training set, and applying the classification algorithms on the User's data in real-time on the MCU of the wearable device or the gateway device, while it is connected to the wearable device over Bluetooth.” All of the pulse wave data and user information is a part of the model for the calculations).
Claim 66 substantially recites the same elements as claim 1. The same rejections apply as applying to one or a plurality of users and the claimed trigger parameter.
Claim 67 substantially recites the same elements as claim 1 but includes a user specific information such as claimed in claims 41, 47, 48 and 54. The same rejections apply.
Claim 68 substantially recites the same elements as claim 1 but includes a user specific information such as claimed in claims 41, 47, 48 and 54. The same rejections apply.
Regarding claim 69, Bhushan as modified discloses The system according to claim 1, Bhushan fails to disclose wherein the motion signal is further configured to calculate an intense exercise time period, the intense exercise time period corresponding to the user performing an activity with an intensity being above a given intensity level; and wherein the triggering processor controls the pulsatility sensing unit such that the pulsatility measurement is initiated once the resting interval exceeds the threshold duration after activity and predetermined resting period after said intense exercise.
However, in the same field of endeavor, Pantelopoulos teaches wherein the motion signal is further configured to calculate an intense exercise time period, the intense exercise time period corresponding to the user performing an activity with an intensity being above a given intensity level; and wherein the triggering processor controls the pulsatility sensing unit such that the pulsatility measurement is initiated once the resting interval exceeds the threshold duration after activity and predetermined resting period after said intense exercise ([0024], “In some implementations, the one or more conditions include one or more of the following: a motion level of the user being below a motion threshold, an activity of the user being a specific activity type, a body temperature of the user meeting a criterion, noise in previously obtained pulse waveform data being above a noise threshold, a force indicative of a tightness between the biometric monitoring device and the user meeting a criterion, historical activity data meeting a past activity criterion,” see also [0044] and [0228]. The trigger parameter being an activity or motion level of a user being below a threshold, while the user is at rest to reduce noise, see also [0250], “Here, in one embodiment, heart rate as a function of speed may be “plotted” for the user, or the data may be broken down into different levels including, but not limited to, sleeping, resting, sedentary, moderately active, active, and highly active.” Additionally discussing monitoring when above and activity threshold);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the system as taught by Bhushan to include wherein the trigger parameter comprises an activity level of the user calculated from the motion signal, the triggering processor controlling the pulsatility sensing unit such that the pulsatility measurement is initiated once a resting interval exceeds a threshold duration after activity, or stopped otherwise; as taught by Pantelopoulos to allow convenient and accurate measurements ([0005], “Therefore, there are needs for devices and methods that allow convenient, noninvasive, and accurate measuring of arterial stiffness.” And reducing noise, [0024], [0098], [0044], [0224]).
Response to Arguments
Applicant’s arguments with respect to the art related rejections have been considered but are moot because the new ground of rejection does not rely solely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. With respect to the arguments regarding Bhushan, the amendments required a new reference. However, it is noted that while Bhushan is directed mainly towards taking measurements during activity, it also disclose monitoring while at rest. See Bhushan para. [0113], measuring both before and during exercise. The arguments on Page 20 argue that Bhushan fails to disclose estimating the blood pressure directly from only the wrist ppg measurements. However, Bhushan discloses calculating blood pressure from optical sensor data, both on the chest mounted optical sensors and from the wrist mounted optical sensors. Bhushan further discloses that the discusses sensors are attached to either the wrist or the chest, it is a choice or configurable location for placement and is not limited to the chest, see [0046], “In various embodiments, the Wearable device includes a reflective Photoplethysmograph (PPG) module attached to the underside of the device, and in direct visual contact with the skin on the chest/wrist/forehead or other location where the device adheres.” Thus, the arguments regarding chest mounted versus wrist mounted devices are not persuasive. The new rejection and newly cited reference specifically teaches the new limitations recited and a modification to more accurately measure during the rest periods.
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 JOSEPH A TOMBERS whose telephone number is (571)272-6851. The examiner can normally be reached on M-TH 7:00-16:00, F 7:00-11:00(Eastern).
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/J.A.T./Examiner, Art Unit 3791
/TSE W CHEN/Supervisory Patent Examiner, Art Unit 3791