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 Arguments
This Office Action is in response to the amendment filed on June 22, 2026. As directed by the amendment, Claims 1-12, 14, 36, 55-58, 63, and 64 have been amended. Claims 1-12, 14, 36, 55-58, 63, and 64 are pending in the instant application.
Regarding the Office Action mailed January 20, 2026:
Applicant’s arguments regarding the 35 USC 103 rejections have been considered but are moot because the new ground of rejection does not rely on any reference and/or interpretation applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. It is noted that Applicant has amended the claims and merely states that such amendments would be allowable despite not specifically arguing the details of the previous 35 USC 103 rejections. In response to the amendments, Examiner has provided a new interpretation of the prior art references in the 35 USC 103 rejections below.
Claim Objections
Claims 2-12, 14, 55-58, 63, and 64 are objected to because of the following informalities:
The phrase “Claim” should be lowercased to “claim” to correct the grammatical error (Claims 2-11, 55-58, and 64, Line 1, and Claims 12, 14, and 63, Line 2).
Appropriate correction is required.
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 (i.e., changing from AIA to pre-AIA ) 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 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 1-3, 6-12, 14, 36, 55-58, 63, and 64 are rejected under 35 U.S.C. 103 as being unpatentable over Wong et al. (US 2017/0157398 A1) in view of Kaemmerer et al. (US 2016/0144186 A1).
Regarding Claim 1, Wong discloses a neurostimulation system (Device and system 10 designed to be worn on wrist or arm, paragraph 0171) for transcutaneously stimulating one or more peripheral nerves of a user (a peripheral nerve stimulator, Abstract; 10 allows customization and optimization of transcutaneous electrical treatment, paragraph 0170), the system comprising: a wearable neurostimulation device (Device and system 10 designed to be worn on wrist or arm, paragraph 0171) comprising one or more electrodes (16, Fig 1D; electrical contacts in band 14 and/or housing 12 transmit stimulation waveform to disposable electrodes 16, paragraph 0171) configured to generate electric stimulation signals (electrodes being spaced on band to deliver electrical stimuli, paragraph 0008); one or more sensors (20, Fig 1E) configured to detect motion signals (motion sensor configured to measure motion, paragraph 0021; detecting tremor characteristics by processing one or more motion sensors, paragraph 0254), wherein the one or more sensors are operably connected to the wearable neurostimulation device (20 is connected to 22 and is inside 12, Fig 1E); and one or more hardware processors (22, Fig 1E; controller or processor 22, paragraph 0172) configured to: receive raw signals in a time domain from the one or more sensors (controller programmed to determine characteristics of tremor based on signal generated by motion sensor, paragraph 0023; may require detecting tremor characteristics by processing one or more motion sensors, paragraph 0254); separate the raw signals into a plurality of frames for each of three axes (a 3 axis gyroscope can be used to measure tremor, each axis is individually windowed, paragraph 0254; multiple sensors and axes are individually windowed and processed to detect tremor characteristics); for each of the plurality of frames: transform the raw signals into a frequency domain (each axis is individually windowed, Fourier transform is applied, paragraph 0254) to generate an amplitude spectra or power spectral density estimate for a respective frame of the plurality of frames from each of the three axes (if a multi-axis accelerometer, gyroscope, or other motion sensor is available, the spectral density can be calculated individually for each axis and then the L2 norm can be found, paragraph 0233); combine the amplitude spectra or power spectral density estimate for the respective frame from each of the three axes into a single combined spectrum by applying a norm for each frequency bin (if there are multiple axes, their spectral densities can be combined, for example, using an L2 norm, paragraph 0238); and determine whether the respective frame is valid by comparing a power in a tremor frequency band of the single combined spectrum to a threshold (peak frequency 2412 in the spectral density curve can then be used to time alternating bursts of stimulation between the nerves, paragraph 0238; calculate the spectral energy in the 4-12 Hz band for a short time signal, if the energy under the curve 2410 is larger than a threshold, therapy can be applied, paragraph 0233; See Figs 24C and 24D; utilization of a threshold is a known technique to remove artifacts in the signal); extract features from the time domain or the single spectrum in the frequency domain (the spectral density can be calculated using a variety of numerical approaches 2408 taking the signal from the time domain to frequency domain, paragraph 0233; from the peaks, the instantaneous frequency of the tremor can be calculated by looking at the difference in time between the two peaks, paragraph 0228; peak frequency in 4-12 Hz range is identified, frequency detected by determining frequency at maximum value in 4-12 Hz range, paragraph 0254; each axis is individually windowed, Fourier transform is applied, magnitude of each axis calculated, square root of sum of squares of axes are calculated as a function of frequency, paragraph 0254); determine rules based on the extracted features (determine various characteristics of tremor and using data as feedback to modify, adjust, and set various stimulation parameters, paragraph 0222; tremor frequency can be measured at all times and then used to update stimulation in real time, paragraph 0259; the “rules” are the same as the stimulation parameters involved as these “rules” would be dependent on the tremor characteristics determined); and determine neurostimulation therapy outcomes based on an application of the determined rules on operational data (determine various characteristics of tremor and using data as feedback to modify, adjust, and set various stimulation parameters, paragraph 0222; tremor frequency can be measured at all times and then used to update stimulation in real time, paragraph 0259; when stimulation is applied to user, the stimulation would inherently determine the therapy outcome in which the user experiences less tremors, more comfort, less pain, etc.).
Wong also discloses the use of predictive adaptation and using predictive algorithms to predict when tremors will increase (paragraphs 0240-0242).
Wong fails to explicitly disclose create a single spectrum by averaging the respective frames determined to be valid; wherein determining the neurostimulation therapy outcomes comprises comparing an examined output calculated by an examined rule with a potential output calculated by a potential rule based on cross-validation accuracy; and wherein both the examined rule and the potential rule are selected from a set of potential rules.
However, Kaemmerer, of the same field of endeavor and reasonably pertinent to the problem of neurostimulation, teaches a method for selecting a combination of electrodes (Abstract) including create a single spectrum by averaging the respective frames determined to be valid (resulting power spectral density (PSD) may be used to calculate the average power in a plurality of frequency bands, mean power in the plurality of bands for one or more of the recordings in a montage may be used to create a feature set for the subsequent analysis, paragraph 0101); wherein determining the neurostimulation therapy outcomes comprises comparing an examined output calculated by an examined rule with a potential output calculated by a potential rule based on cross-validation accuracy; and wherein both the examined rule and the potential rule are selected from a set of potential rules (machine learning algorithms/models may be utilized to enable device to select combination of electrodes for patient, classifier performance may be evaluated based on number of errors produced in a leave-one-out cross-validation scheme, features may be standardized across training observations before being used to train classifier, in order to predict group of new observation, score representing likelihood of being in group is calculated for each of the possible group, and observation is classified as being in group with largest score, paragraph 0103; leave-one-out cross-validation would compare examined output or test set with potential rule or training set to see how accurate or how well the classifier performs; the set of potential rules is merely the series of training sets) since averaging multiple sets of data is known to be used in a feature set and to improve accuracy, and to evaluate the performance based on number of errors produced in cross-validation scheme (paragraph 0103).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize machine learning with cross-validation capabilities to verify and select the most effective stimulation parameters, as taught by Kaemmerer, since averaging multiple sets of data is known to be used in a feature set and to improve accuracy, and to evaluate the performance based on number of errors produced in cross-validation scheme (Kaemmerer: paragraph 0103). It is well-known to average the valid sets of data as a way to improve the accuracy of the device. Additionally, utilizing machine learning to improve a device’s ability to adapt to a patient’s particular needs is well-known in the art. Cross-validation is a well-known type of machine learning in which training sets and test sets are compared to confirm or verify if the performance of the algorithm is correct or effective. Kaemmerer shows that utilizing cross-validation is obvious as it would allow one of ordinary skill in the art to obtain further feedback and ensuring that the device is treating the tremors effectively. It also trains the device to select and choose the most effective combination of electrodes and stimulation parameters for particular tremors. Since Wong already obtains feedback to adjust stimulation parameters, having machine learning would simply further improve upon the existing feedback capabilities. Applicant has not claimed any particular features within the algorithm that are significantly distinct from well-known machine learning methods.
Regarding Claim 2, Wong-Kaemmerer combination teaches the one or more sensors are operably attached to the wearable neurostimulation device (Wong: 20 is within housing 12, Fig 1E; housing 12 containing sensors 20, paragraph 0172).
Regarding Claim 3, Wong-Kaemmerer combination teaches the raw signals relate to tremor activity of the user (Wong: detecting tremor characteristics by processing one or more motion sensors, paragraph 0254).
Regarding Claim 6, Wong-Kaemmerer combination teaches the one or more sensors comprise one or more of a gyroscope, accelerometer, and magnetometer (Wong: motion sensor selected from group consisting of accelerometer, a gyroscope, a magnetometer, and a bend sensor, paragraph 0070).
Regarding Claim 7, Wong-Kaemmerer combination teaches the plurality of frames are non-overlapping frames (Wong: a 3 axis gyroscope can be used to measure tremor, each axis is individually windowed, paragraph 0254; multiple sensors and axes are individually windowed and processed to detect tremor characteristics; the frames would not overlap since the frames or windows are on different axes). It is noted that Applicant has not claimed non-overlapping frames or windows within the same axis.
Regarding Claim 8, Wong-Kaemmerer combination teaches the tremor frequency band is between about 4 Hz and about 12 Hz (Wong: typical tremor frequencies are 4-12 Hz, paragraph 0228; tremor band of 4-12 Hz, paragraph 0260).
Regarding Claim 9, Wong-Kaemmerer combination teaches the norm is an L2 norm (Wong: if there are multiple axes, their spectral densities can be combined, for example, using an L2 norm, paragraph 0238).
Regarding Claim 10, Wong-Kaemmerer combination teaches wherein the features comprise at least one or more of: amplitude (Wong: tremor amplitude, paragraph 0232), bandwidth (Wong: tremor band of 4-12 Hz, paragraph 0260), power (Wong: energy under the curve, paragraph 0233; spectral power at a frequency or spectral energy in the 4-12 Hz band, paragraph 0232), or peak frequency (Wong: peak frequency in 4-12 Hz range, paragraph 0254; peak frequency in spectral density curve, paragraph 0238).
Regarding Claim 11, Wong-Kaemmerer combination teaches the features comprise at least one or more kinematic features (Wong: measuring motion of patient’s arm or wrist during specific task, determining characteristics of tremor, paragraph 0077; specific task is kinetic, paragraph 0078), wherein the one or more kinematic features include regularity, amplitude and shape of the signal of each of the respective frames from the plurality of frames (Wong: Figs 24A-24D showcase the tremors over time including regularity (or period), amplitude, and shape of tremor signal; tremor amplitude and frequency can have daily patterns, paragraph 0240; match certain tremor characteristics including phase, frequency, and amplitude, paragraph 0206; Fig 25B depict disease segmentation separating kinetic tremor characteristics from resting tremor characteristics, paragraph 0244).
Regarding Claim 12, Wong-Kaemmerer combination teaches wherein the features comprise at least one or more of: amplitude or power spectral density ("PSD") at peak tremor frequency (Wong: spectral power at a frequency or spectral energy in the 4-12 Hz band, paragraph 0232; peak frequency in 4-12 Hz range, paragraph 0254; peak frequency in spectral density curve, paragraph 0238), or summed amplitude or PSD in an approximately 4 to 12 Hz band (Wong: magnitude of each axis calculated and square root of sum of squares of the axes calculated as function of frequency, sum spectrum is filtered and peak frequency in 4-12 Hz range is identified, paragraph 0254).
Regarding Claim 14, Wong-Kaemmerer combination teaches the features comprise at least one of displacement (Wong: position and orientation can be determined by integrating accelerometer or gyroscope signals, combining positions in one or more axes, paragraph 0227; gyroscope used to measure tremor from wrist, paragraph 0254; sensors used provide position or displacement data), functional principal component analysis (PCA) (Wong: technique implemented using principal components analysis, paragraph 0244), filtering (Wong: filter may be required to eliminate noise oscillations, paragraph 0228; box car filter or other low pass filter, band pass filter, paragraph 0254), mean (Wong: frequency can be updated sporadically instead of continuously, tremor frequency does not vary dramatically, mean frequency, paragraph 0255; mean frequency capable of being utilized since frequency shifts happen over long periods of time).
Regarding Claim 63, Wong-Kaemmerer combination teaches the neurostimulation therapy outcomes comprise predicting patient tremor severity at a given point (Wong: understanding historical tremor measurements and the time therapy was applied can inform therapy needed on successive days, neural networks, Kalman filters, and other such predictive algorithms can be used to predict when tremor will increase and apply pre-emptive treatment, paragraph 0240).
Regarding Claim 36, Wong discloses a neuromodulation system (Device and system 10 designed to be worn on wrist or arm, paragraph 0171) for modulating one or more nerves of a user (a peripheral nerve stimulator, Abstract; 10 allows customization and optimization of transcutaneous electrical treatment, paragraph 0170), the system comprising: one or more electrodes (16, Fig 1D; electrical contacts in band 14 and/or housing 12 transmit stimulation waveform to disposable electrodes 16, paragraph 0171) configured to generate neuromodulation signals (electrodes being spaced on band to deliver electrical stimuli, paragraph 0008); one or more sensors (20, Fig 1E) configured to detect motion signals (motion sensor configured to measure motion, paragraph 0021; detecting tremor characteristics by processing one or more motion sensors, paragraph 0254); and one or more hardware processors (22, Fig 1E; controller or processor 22, paragraph 0172) configured to: receive raw signals in a time domain from the one or more sensors (controller programmed to determine characteristics of tremor based on signal generated by motion sensor, paragraph 0023; may require detecting tremor characteristics by processing one or more motion sensors, paragraph 0254); separate the raw signals into a plurality of frames for each of three axes (a 3 axis gyroscope can be used to measure tremor, each axis is individually windowed, paragraph 0254; multiple sensors and axes are individually windowed and processed to detect tremor characteristics); for each of the plurality of frames: transform the raw signals into a frequency domain (each axis is individually windowed, Fourier transform is applied, paragraph 0254) to generate an amplitude spectra or power spectral density estimate for a respective frame of the plurality of frames from each of the three axes (if a multi-axis accelerometer, gyroscope, or other motion sensor is available, the spectral density can be calculated individually for each axis and then the L2 norm can be found, paragraph 0233); combine the amplitude spectra or power spectral density estimate for the respective frame from each of the three axes into a single combined spectrum by applying a norm for each frequency bin (if there are multiple axes, their spectral densities can be combined, for example, using an L2 norm, paragraph 0238); and determine whether the respective frame is valid by comparing a power in a tremor frequency band of the single combined spectrum to a threshold (peak frequency 2412 in the spectral density curve can then be used to time alternating bursts of stimulation between the nerves, paragraph 0238; calculate the spectral energy in the 4-12 Hz band for a short time signal, if the energy under the curve 2410 is larger than a threshold, therapy can be applied, paragraph 0233; See Figs 24C and 24D; utilization of a threshold is a known technique to remove artifacts in the signal); extract features from the time domain or the single spectrum in the frequency domain (the spectral density can be calculated using a variety of numerical approaches 2408 taking the signal from the time domain to frequency domain, paragraph 0233; from the peaks, the instantaneous frequency of the tremor can be calculated by looking at the difference in time between the two peaks, paragraph 0228; peak frequency in 4-12 Hz range is identified, frequency detected by determining frequency at maximum value in 4-12 Hz range, paragraph 0254; each axis is individually windowed, Fourier transform is applied, magnitude of each axis calculated, square root of sum of squares of axes are calculated as a function of frequency, paragraph 0254); determine rules based on the extracted features (determine various characteristics of tremor and using data as feedback to modify, adjust, and set various stimulation parameters, paragraph 0222; tremor frequency can be measured at all times and then used to update stimulation in real time, paragraph 0259; the “rules” are the same as the stimulation parameters involved as these “rules” would be dependent on the tremor characteristics determined); and determine neuromodulation therapy outcomes based on an application of the determined rules on operational data (determine various characteristics of tremor and using data as feedback to modify, adjust, and set various stimulation parameters, paragraph 0222; tremor frequency can be measured at all times and then used to update stimulation in real time, paragraph 0259; when stimulation is applied to user, the stimulation would inherently determine the therapy outcome in which the user experiences less tremors, more comfort, less pain, etc.).
Wong also discloses the use of predictive adaptation and using predictive algorithms to predict when tremors will increase (paragraphs 0240-0242).
Wong fails to explicitly disclose create a single spectrum by averaging the respective frames determined to be valid; wherein determining the neurostimulation therapy outcomes comprises comparing an examined output calculated by an examined rule with a potential output calculated by a potential rule based on cross-validation accuracy; and wherein both the examined rule and the potential rule are selected from a set of potential rules.
However, Kaemmerer, of the same field of endeavor and reasonably pertinent to the problem of neurostimulation, teaches a method for selecting a combination of electrodes (Abstract) including create a single spectrum by averaging the respective frames determined to be valid (resulting power spectral density (PSD) may be used to calculate the average power in a plurality of frequency bands, mean power in the plurality of bands for one or more of the recordings in a montage may be used to create a feature set for the subsequent analysis, paragraph 0101); wherein determining the neurostimulation therapy outcomes comprises comparing an examined output calculated by an examined rule with a potential output calculated by a potential rule based on cross-validation accuracy; and wherein both the examined rule and the potential rule are selected from a set of potential rules (machine learning algorithms/models may be utilized to enable device to select combination of electrodes for patient, classifier performance may be evaluated based on number of errors produced in a leave-one-out cross-validation scheme, features may be standardized across training observations before being used to train classifier, in order to predict group of new observation, score representing likelihood of being in group is calculated for each of the possible group, and observation is classified as being in group with largest score, paragraph 0103; leave-one-out cross-validation would compare examined output or test set with potential rule or training set to see how accurate or how well the classifier performs; the set of potential rules is merely the series of training sets) since averaging multiple sets of data is known to be used in a feature set and to improve accuracy, and to evaluate the performance based on number of errors produced in cross-validation scheme (paragraph 0103).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize machine learning with cross-validation capabilities to verify and select the most effective stimulation parameters, as taught by Kaemmerer, since averaging multiple sets of data is known to be used in a feature set and to improve accuracy, and to evaluate the performance based on number of errors produced in cross-validation scheme (Kaemmerer: paragraph 0103). It is well-known to average the valid sets of data as a way to improve the accuracy of the device. Additionally, utilizing machine learning to improve a device’s ability to adapt to a patient’s particular needs is well-known in the art. Cross-validation is a well-known type of machine learning in which training sets and test sets are compared to confirm or verify if the performance of the algorithm is correct or effective. Kaemmerer shows that utilizing cross-validation is obvious as it would allow one of ordinary skill in the art to obtain further feedback and ensuring that the device is treating the tremors effectively. It also trains the device to select and choose the most effective combination of electrodes and stimulation parameters for particular tremors. Since Wong already obtains feedback to adjust stimulation parameters, having machine learning would simply further improve upon the existing feedback capabilities. Applicant has not claimed any particular features within the algorithm that are significantly distinct from well-known machine learning methods.
Regarding Claim 55, Wong-Kaemmerer combination teaches the one or more electrodes and the one or more sensors are operably attached to a wearable device (Wong: 20 is connected to 22 and is inside 12, Fig 1E).
Regarding Claim 56, Wong-Kaemmerer combination teaches the tremor frequency band is between about 4 Hz and about 12 Hz (Wong: typical tremor frequencies are 4-12 Hz, paragraph 0228; tremor band of 4-12 Hz, paragraph 0260).
Regarding Claim 57, Wong-Kaemmerer combination teaches the norm is an L2 norm (Wong: if there are multiple axes, their spectral densities can be combined, for example, using an L2 norm, paragraph 0238).
Regarding Claim 58, Wong-Kaemmerer combination teaches wherein the features comprise at least one or more of: amplitude (Wong: tremor amplitude, paragraph 0232), bandwidth (Wong: tremor band of 4-12 Hz, paragraph 0260), power (Wong: energy under the curve, paragraph 0233; spectral power at a frequency or spectral energy in the 4-12 Hz band, paragraph 0232), peak frequency (Wong: peak frequency in 4-12 Hz range, paragraph 0254; peak frequency in spectral density curve, paragraph 0238).
Regarding Claim 64, Wong-Kaemmerer combination teaches the neurostimulation therapy outcomes comprise predicting patient tremor severity at a given point (Wong: understanding historical tremor measurements and the time therapy was applied can inform therapy needed on successive days, neural networks, Kalman filters, and other such predictive algorithms can be used to predict when tremor will increase and apply pre-emptive treatment, paragraph 0240).
Claims 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Wong et al. (US 2017/0157398 A1) and Kaemmerer et al. (US 2016/0144186 A1) as applied to Claim 1, and in further view of Rosenbluth et al. (US 2015/0321000 A1).
Regarding Claim 4, Wong-Kaemmerer combination teaches the claimed invention of Claim 1. Wong-Kaemmerer combination fail to teach one or more end effectors configured to generate stimulation signals other than electric stimulation signals.
However, Rosenbluth, of the same field of endeavor, teaches a peripheral nerve stimulator can be used to stimulate a peripheral nerve to treat tremor (Abstract) including one or more end effectors configured to generate stimulation signals other than electric stimulation signals (vibrotactile stimulation refers to excitation of proprioceptors by application of biomechanical load to soft tissue and nerves, paragraph 0117; effectors may be mechanical excitation of proprioceptors by means of vibrotactile or haptic sensation, might include force, vibration and/or motion, mechanical effectors include small motors, piezoelectrics, vibrotactile units comprised of mass and effector to move mass such that vibratory stimulus is applied, paragraph 0151) since this is a known way to reduce tremors and is capable of reducing tremors through several methods (paragraph 0153).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add an end effector that generates vibrotactile stimulation, as taught by Rosenbluth, since this is a known way to reduce tremors and is capable of reducing tremors through several methods (Rosenbluth: paragraph 0153). The device already reduces tremors through electrical stimulations. By adding these effectors, the tremors would also be reduced through vibrotactile stimulation. This addition would provide multiple, different avenues in which tremors would be reduced and increase efficacy of treatment.
Regarding Claim 5, Wong-Kaemmerer-Rosenbluth combination teaches the stimulation signals other than the electric stimulation signals are vibrational stimulation signals (Rosenbluth: vibrotactile stimulation refers to excitation of proprioceptors by application of biomechanical load to soft tissue and nerves, paragraph 0117; effectors may be mechanical excitation of proprioceptors by means of vibrotactile or haptic sensation, might include force, vibration and/or motion, mechanical effectors include small motors, piezoelectrics, vibrotactile units comprised of mass and effector to move mass such that vibratory stimulus is applied, paragraph 0151).
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 BRIAN THAI-BINH KHONG whose telephone number is (571)272-1857. The examiner can normally be reached Monday to Thursday 9:00 am-6:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kendra Carter can be reached at (571) 272-9034. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRIAN T KHONG/ Examiner, Art Unit 3785
/KENDRA D CARTER/ Supervisory Patent Examiner, Art Unit 3785
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