3DETAILED 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
Rejections under 35 USC 102/103
Applicant’s arguments, see Remarks filed 05/05/2026, with respect to the rejection of claims 1-20 under 35 USC 102/103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration as necessitated by the amended limitations, which introduced new scope to the claimed invention, a new ground of rejection is made over Chen et al (US 20150297104 A1) in view of Burnam et al (WO 2022139971 A1).
Rejections under 35 USC 101
Applicant's arguments filed 05/05/2026 with respect to the rejection of claims 1-20 under 35 USC 101 have been fully considered but they are not persuasive.
Applicant argues that the claimed invention is directed to significantly more than an abstract idea because the claimed invention requires specific signal transformation operations that require signal processing hardware and cannot be performed in the human mind. Applicant also argues that the claimed invention is integrated into practical application because the claimed invention provides a specific technological improvement in non-invasive SNS measurement accuracy, and that unlike generic data analysis, amended claim 1 recites a concrete signal-processing pipeline tied to physiology.
Examiner respectfully disagrees and argues that the claimed invention is not directed to significantly more than an abstract idea. Examiner maintains that the steps of receiving and processing data is directed to extra-solution activity, which does not amount to significantly more than the abstract idea. Examiner concedes that in light of the amended claims, the claimed invention is no longer entirely directed to an abstract idea that may be practically performed in the human mind. The claimed invention as amended includes limitations that are now more accurately reflected as an abstract idea in the form of a mathematical concept, most specifically organizing information and manipulating information through mathematical correlations. Examiner also maintains that the connection of an abstract idea to a specific field, in this case non-invasive SNS measurement accuracy, is not enough to amount to significantly more than the abstract idea of organizing information and manipulating information through mathematical correlations.
With this in consideration, the rejection of the claimed invention under 35 USC 101 is maintained. The rejection under 35 USC 101 detailed below has been modified to reflect the amended limitations.
Information Disclosure Statement
The Information Disclosure Statement (IDS) filed 05/09/2025 has been considered by the Examiner.
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.
Claims 1-4, 6, 9, 12, 14, and 17-29 rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1 and 12 recite a machine and claim 17 recites a method.
Step 2A, Prong 1
Claims 1, 12, and 17 recite the limitations of providing a sympathetic nervous system (SNS) response measurement signal based on filtered physiological signals in order to initiate an activity. These steps, given their broadest reasonable interpretation can be characterized as an abstract idea in the form of a mathematical relationship, namely organizing information and manipulating information through mathematical correlations and/or a mental process in the form of an observation, analysis, and/or judgement.
For example, the limitation recited in step (ii) to ‘apply a bandpass filter to the ECG signals to extract skin sympathetic nerve activity (SKNA) signals’ is a mathematical function because filtering performed by a processor is merely an algorithm that uses a transfer function to remove data points/information from a signal.
The limitation recited in step (iii) ‘rectify the SKNA signals and apply a moving average filter to generate integrated SKNA (iSKNA) signals’ is a mathematical function because it comprises an algorithm based rectification, which is another mathematical transformation step, and averaging of a signal, which is another mathematical concept.
The limitation recited in step (vii) ‘apply a signal-quality gating operation prior to at least one of the bandpass filtering and feature extraction’ is a mathematical function performed by a processor using an algorithm to process the signals based on a quality criteria.
The limitations recited in steps
(v) ‘extract features from the iSKNA signals comprising at least one of amplitude, variance, or duration of high-amplitude intervals;’
(vi) ‘compare extracted features of baseline and stimulated periods to generate an SNS response measurement;’ and
(viii) ‘classify the SNS response using a trained machine-learning model based on the extracted features’ are directed to mental processes because a person is capable of, looking at a signal and identifying at least the features of amplitude and duration and noting them down, making a comparison of an extracted feature value against a baseline value, and making a mental classification of the SNS response based on the extracted feature values.
Using mathematical operations/relationships to organize measured signals using a bandpass filter and performing subsequent mathematical operations/mental processes to characterize/classify the signals is identified as an abstract idea.
Step 2A, Prong 2
Claims 1, 12, and 17 do not provide any additional elements which integrate the abstract idea into a practical application.
Claims 1, 12, and 17 include the additional elements of an electrode for measuring ECG signals, a processing unit comprising an algorithm programmed by machine learning, transmitting a control signal based on the classification of the SNS response (step ix), and an activity device.
The limitation of receiving at least one of ECG signals from the electrode is pre-solution activity of data collection in the form of performing clinical tests to obtain input for an equation, in this case gathering ECG signals as input for the algorithm in order to extract skin sympathetic nerve activity signals to determine a sympathetic nervous system response. See MPEP 2106.05(g), In re Grams, 888 F.2d 835.
The processing unit comprising an algorithm programmed by machine learning is generally claimed such that it amounts to generic computer implementation of the abstract idea.
Transmitting a control signal based on the classification of the SNS response is generic computer implementation of a communication interface and may be regarded as extra-solution activity in the form of necessary data outputting for the use of the recited judicial exception. See MPEP 2106.05(g), Mayo, 566 U.S. at 79, 101.
The activity device is post-solution activity which does not amount to an inventive concept as it is merely outputting the conclusion of the abstract idea as performed. See MPEP 2106.05(g).
Therefore, the additional elements do not amount to integrating the abstract idea into practical application.
Step 2B
Claims 1, 12, and 17 do not include any additional elements that amount to significantly more than the abstract idea.
See the analysis of the additional elements including receiving an ECG signal from an electrode, a processing unit comprising an algorithm programmed by machine learning, transmitting a control signal based on the classification of the SNS response (step ix), and an activity device above in Step 2A, Prong 2. Additionally, the additional elements of the claimed apparatus (electrodes, a processor, control signal transmission, and an activity device) can be held to be well-understood, routine, and conventional in the art, and they are recited with a high level of generality which does not amount to significantly more than the abstract idea itself.
Claims 2-4, and 18-20 further limit the extra-solution activity regarding the activity device.
Claims 6, 13-16, and 21-29 further limit the extra solution activity of data gathering.
Claim 9 further defines the abstract idea.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 6, 9, 12, 14, 17-19, 21, 22, 24, 26, 27, and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al (US 20150297104 A1) in view of Burnam et al (WO 2022139971 A1, with reference to the US publication US 20240299754 A1 for citation purposes).
Regarding claim 1, Chen teaches an apparatus (100) for influencing performance of a physical activity based on a sympathetic nervous system (SNS) response to a stimulus, the apparatus comprising:
an electrode (104) configured to contact a person undergoing the stimulus;
a processing unit (112) and configured to:
(i) receive electrocardiogram (ECG) signals from the electrode (see [0050]; the electrodes are arranged in a configuration for monitoring both the nerve activity and monitoring an ECG, Fig. 3; 304 sample electrical signals received from cutaneous or sub-cutaneous electrodes);
(ii) apply a bandpass filter to the ECG signals to extract skin sympathetic nerve activity (SKNA) signals (see [0069], Fig. 3; 308 apply high-pass filter to signal samples to identify nerve activity signal, [0065]; it can also be appreciated that the nerve activity is referred to as NeuroElectrocardiogram or NECG because the electrical signals identified in the neurons that innervate the skin provide information about the activity of the heart);
(iii) rectify the SKNA signals and apply a moving average filter to generate integrated SKNA (iSKNA) signals (see [0075]; system 100 integrates the identified NECG signals over time);
(iv) segment the iSKNA signals into time windows corresponding to baseline and stimulated periods (see [0073-0074], Fig. 3; 324 identify baseline in NECG activity including average levels of activity in nerves that innervate the skin)
(v) extract features from the iSKNA signals comprising at least one of amplitude, variance, or duration of high-amplitude intervals (see [0073]; the NECG baseline includes the average amplitude and expected variation in the sympathetic nerves for the subject);
(vi) compare extracted features of baseline and stimulated periods to generate an SNS response measurement (see [0074]; if either or both of the NECG or ECG activity deviate from the baseline by greater than a predetermined threshold a signal is generated, Fig. 3; step 328);
(vii) apply a signal-quality gating operation prior to at least one of the bandpass filtering and feature extraction (see [0060]; the lower-frequency cutoff of the high-pass filter can be adjusted based on the characteristics of different subjects to enable identification of the electrical signals in the nerves that innervate the skin while attenuating the electrical signals from muscles and other sources of electrical noise in the subject);
(viii) classify the SNS response based on the extracted features (see [0074]; if either or both of the NECG or ECG activity deviate from the baseline by greater than a predetermined threshold a signal is generated, Fig. 3; step 328); and
(ix) transmit a control signal based on the classified SNS response for influencing performance of the physical activity in response to the SNS response measurement (see Fig. 3, 332, [0074]; signal processor 112 generates a signal in response to an identified change in NECG activity); and
an activity device (alarm 128, electrical stimulation device 132, and/or medicine delivery device 136) configured to influence performance of the physical activity in response to the control signal (see 0074]; a signal is generated in response to an identified change in NECG activity to activate the alarm 128, activate the electrical stimulation device 132, or deliver medicine with the medicine delivery device 136);
wherein the receive, apply a bandpass filter, rectify, segment, extract, compare, apply a motion-artifact reduction, classify, and transmit are performed in real time (see [0075] process 300 can be used for continuous recording of nerve activity over a prolonged period of time).
Chen is silent regarding wherein the processing unit comprises an algorithm programmed by machine-learning and wherein the SNS responses is classified using a trained machine-learning model based on extracted features.
Burnam teaches a system for non-invasive monitoring of autonomic nerve activity, that incorporates the teaching of Chen and builds upon it to use a machine learning model to mediate the system in order to therapeutically treat a subject by
sensing sympathetic nerve activity;
communication the sensed sympathetic nerve activity to a processor;
using machine learning in the processor to identify input data sets correlated to a physiological end point in the subject by processing the input data input sets to experientially optimize an algorithmically defined physiological goal defined in output data sets by the machine learning (Burnam [0016]).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Chen’s system for monitoring sympathetic nerve activity with Burnam’s machine-learning implementation. One of ordinary skill in the art would have been motivated to make this modification in order to provide advantages including continuous, rapid, real-time data acquisition and processing, the ability to dynamically vary band-pass filtering to sample a wider dynamic spectrum of nerve activity, and the possibility of acquiring and simultaneously analyzing more than one nervous system activity marker (Burnam [0015]).
Regarding claim 2, Chen in view of Burnam teaches the apparatus according to claim 1, wherein the SNS response is due to pain (see Chen [0066-0067]; NECG signals may correspond to a hypoglycemic condition in the subject which corresponds to hunger, trembling of hands or legs, palpitations, anxiety, pallor, sweating in the subject) and the activity device comprises a medication injector (see [0067]; medication delivery device 132 may be an insulin pump).
Regarding claim 3, Chen in view of Burnam teaches the apparatus according to claim 1, wherein the activity device comprises a display disposed in a medical care environment (visual output device 120) and
configured to receive the activity signal, wherein the activity signal comprises an amount of medication to be administered to the person (see Chen [0067]; the NECG signal that monitors abnormal nerve activity can be used to identify clinical conditions that require an adjustment to the level of insulin infusion in the insulin pump).
Regarding claim 6, Chen in view of Burnam teaches the apparatus according to claim 1, wherein the processing unit is further configured to receive baseline ECG signals indicative of no stimulus being applied to the person (see Chen [0073], Fig. 3; at step 324 the monitoring system identified baseline of ECG activity including an average heart rate and an expected variation in times between heart beats).
Regarding claim 9, Chen in view of Burnam teaches the apparatus according to claim 1. Chen is silent regarding wherein the processing unit is further configured to train the algorithm using extracted features from training iSKNA signals.
Burnam teaches wherein the processing unit is further configured to train the algorithm using extracted features from training iSKNA signal (see Burnam [0048]; the input sympathetic nerve activity and ECG data inputs are used to train the machine learning model).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Chen’s system for monitoring sympathetic nerve activity with Burnam’s machine-learning implementation using sympathetic nerve activity signals to train the machine learning model. One of ordinary skill in the art would have been motivated to make this modification in order to provide advantages including continuous, rapid, real-time data acquisition and processing, the ability to dynamically vary band-pass filtering to sample a wider dynamic spectrum of nerve activity, and the possibility of acquiring and simultaneously analyzing more than one nervous system activity marker (Burnam [0015]).
Regarding claim 12, Chen teaches a non-transitory computer-readable medium storing instructions that, when executed by a processor (112), cause the processor to perform a method for influencing performance of a physical activity, the method comprising:
receiving electrocardiogram (ECG) signals from an electrode (104) configured to contact a person undergoing a stimulus (see [0050]; the electrodes 104 are arranged in a configuration for monitoring both the nerve activity and monitoring an ECG, Fig. 3; 304 sample electrical signals received from cutaneous or sub-cutaneous electrodes);
applying a bandpass filter to the ECG signals to extract skin sympathetic nerve activity (SKNA) signals (see [0069], Fig. 3; 308 apply high-pass filter to signal samples to identify nerve activity signal, [0065]; it can also be appreciated that the nerve activity is referred to as NeuroElectrocardiogram or NECG because the electrical signals identified in the neurons that innervate the skin provide information about the activity of the heart);
rectifying the SKNA signals and apply a moving average filter to generate integrated SKNA (iSKNA) signals (see [0075]; system 100 integrates the identified NECG signals over time);
segmenting the iSKNA signals into time windows corresponding to baseline and stimulated periods (see [0073-0074], Fig. 3; 324 identify baseline in NECG activity including average levels of activity in nerves that innervate the skin)
extracting features from the iSKNA signals comprising at least one of amplitude, variance, or duration of high-amplitude intervals (see [0073]; the NECG baseline includes the average amplitude and expected variation in the sympathetic nerves for the subject);
comparing extracted features of baseline and stimulated periods to generate an SNS response measurement (see [0074]; if either or both of the NECG or ECG activity deviate from the baseline by greater than a predetermined threshold a signal is generated, Fig. 3; step 328);
applying a signal-quality gating operation prior to at least one of the bandpass filtering and feature extraction (see [0060]; the lower-frequency cutoff of the high-pass filter can be adjusted based on the characteristics of different subjects to enable identification of the electrical signals in the nerves that innervate the skin while attenuating the electrical signals from muscles and other sources of electrical noise in the subject);
classifying the SNS response based on the extracted features (see [0074]; if either or both of the NECG or ECG activity deviate from the baseline by greater than a predetermined threshold a signal is generated, Fig. 3; step 328); and
transmitting a control signal (see Fig. 3, 332, [0074]; signal processor 112 generates a signal in response to an identified change in NECG activity) based on the classified SNS response to an activity device (alarm 128, electrical stimulation device 132, and/or medicine delivery device 136) configured to influence performance of the physical activity (see [0074]; a signal is generated in response to an identified change in NECG activity to activate the alarm 128, activate the electrical stimulation device 132, or deliver medicine with the medicine delivery device 136);
wherein the receiving, applying a bandpass filter, rectifying, segmenting, extracting, comparing, applying a motion-artifact reduction, classifying, and transmitting are performed in real time (see [0075] process 300 can be used for continuous recording of nerve activity over a prolonged period of time).
Chen is silent regarding wherein the SNS responses is classified using a trained machine-learning model based on extracted features.
Burnam teaches a system for non-invasive monitoring of autonomic nerve activity, that incorporates the teaching of Chen and builds upon it to use a machine learning model to mediate the system in order to therapeutically treat a subject by
sensing sympathetic nerve activity;
communication the sensed sympathetic nerve activity to a processor;
using machine learning in the processor to identify input data sets correlated to a physiological end point in the subject by processing the input data input sets to experientially optimize an algorithmically defined physiological goal defined in output data sets by the machine learning (Burnam [0016]).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Chen’s method for monitoring sympathetic nerve activity with Burnam’s machine-learning implementation. One of ordinary skill in the art would have been motivated to make this modification in order to provide advantages including continuous, rapid, real-time data acquisition and processing, the ability to dynamically vary band-pass filtering to sample a wider dynamic spectrum of nerve activity, and the possibility of acquiring and simultaneously analyzing more than one nervous system activity marker (Burnam [0015]).
Regarding claim 14, Chen in view of Burnam teaches the non-transitory computer-readable medium according to claim 12, wherein the method performed by the processor further comprises receiving baseline ECG signals indicative of no stimulus being applied to the person (see Chen [0073], Fig. 3; at step 324 the monitoring system identified baseline of ECG activity including an average heart rate and an expected variation in times between heart beats).
Regarding claim 17, Chen teaches a method for influencing performance of a physical activity based on a sympathetic nervous system (SNS) response to a stimulus, the method comprising:
receiving electrocardiogram (ECG) signals from an electrode (104) configured to contact a person undergoing a stimulus (see [0050]; the electrodes 104 are arranged in a configuration for monitoring both the nerve activity and monitoring an ECG, Fig. 3; 304 sample electrical signals received from cutaneous or sub-cutaneous electrodes);
applying a bandpass filter to the ECG signals to extract skin sympathetic nerve activity (SKNA) signals (see [0069], Fig. 3; 308 apply high-pass filter to signal samples to identify nerve activity signal, [0065]; it can also be appreciated that the nerve activity is referred to as NeuroElectrocardiogram or NECG because the electrical signals identified in the neurons that innervate the skin provide information about the activity of the heart);
rectifying the SKNA signals and apply a moving average filter to generate integrated SKNA (iSKNA) signals (see [0075]; system 100 integrates the identified NECG signals over time);
segmenting the iSKNA signals into time windows corresponding to baseline and stimulated periods (see [0073-0074], Fig. 3; 324 identify baseline in NECG activity including average levels of activity in nerves that innervate the skin)
extracting features from the iSKNA signals comprising at least one of amplitude, variance, or duration of high-amplitude intervals (see [0073]; the NECG baseline includes the average amplitude and expected variation in the sympathetic nerves for the subject);
comparing extracted features of baseline and stimulated periods to generate an SNS response measurement (see [0074]; if either or both of the NECG or ECG activity deviate from the baseline by greater than a predetermined threshold a signal is generated, Fig. 3; step 328);
applying a signal-quality gating operation prior to at least one of the bandpass filtering and feature extraction (see [0060]; the lower-frequency cutoff of the high-pass filter can be adjusted based on the characteristics of different subjects to enable identification of the electrical signals in the nerves that innervate the skin while attenuating the electrical signals from muscles and other sources of electrical noise in the subject);
classifying the SNS response based on the extracted features (see [0074]; if either or both of the NECG or ECG activity deviate from the baseline by greater than a predetermined threshold a signal is generated, Fig. 3; step 328); and
transmitting a control signal (see Fig. 3, 332, [0074]; signal processor 112 generates a signal in response to an identified change in NECG activity) based on the classified SNS response to an activity device (alarm 128, electrical stimulation device 132, and/or medicine delivery device 136) configured to influence performance of the physical activity (see [0074]; a signal is generated in response to an identified change in NECG activity to activate the alarm 128, activate the electrical stimulation device 132, or deliver medicine with the medicine delivery device 136);
wherein the receiving, applying a bandpass filter, rectifying, segmenting, extracting, comparing, applying a motion-artifact reduction, classifying, and transmitting are performed in real time (see [0075] process 300 can be used for continuous recording of nerve activity over a prolonged period of time) by a processing unit (112).
Chen is silent regarding wherein the SNS responses is classified using a trained machine-learning model based on extracted features.
Burnam teaches a system for non-invasive monitoring of autonomic nerve activity, that incorporates the teaching of Chen and builds upon it to use a machine learning model to mediate the system in order to therapeutically treat a subject by
sensing sympathetic nerve activity;
communication the sensed sympathetic nerve activity to a processor;
using machine learning in the processor to identify input data sets correlated to a physiological end point in the subject by processing the input data input sets to experientially optimize an algorithmically defined physiological goal defined in output data sets by the machine learning (Burnam [0016]).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Chen’s method for monitoring sympathetic nerve activity with Burnam’s machine-learning implementation. One of ordinary skill in the art would have been motivated to make this modification in order to provide advantages including continuous, rapid, real-time data acquisition and processing, the ability to dynamically vary band-pass filtering to sample a wider dynamic spectrum of nerve activity, and the possibility of acquiring and simultaneously analyzing more than one nervous system activity marker (Burnam [0015]).
Regarding claim 18, Chen in view of Burnam teaches the method according to claim 17, wherein the physical activity comprises administering an amount of medication to the person in contact with the electrode (see [0074]; processor 112 generates a signal to deliver medicine with the medicine delivert device 136).
Regarding claim 19, Chen in view of Burnam teaches the method according to claim 18, wherein administering comprises injecting using a medication injector (see [0067]; [0067]; medicine delivery device may include an insulin pump).
Regarding claim 21, Chen in view of Burnam teaches the apparatus according to claim 1, wherein the electrode is part of a wearable device configured to place the electrode at the wrist or chest of the person (see Fig. 15 which pictures the electrodes 1504, 1508, and 1512 positioned on the chest of the user).
Regarding claim 22, Chen in view of Burnam teaches the apparatus according to claim 1, wherein the SKNA signals are derived non-invasively from the ECG signals (see Fig. 3, [0068-0070]; concurrent monitoring of both ECG and nerve activity in the electrical signals received from the subject via cutaneous electrodes).
Regarding claim 23, Chen in view of Burnam teaches the apparatus according to claim 1, wherein the moving average filter uses a time window between 50ms and 100ms (see [0118]; SNA signals may be filtered and then integrated at 100ms intervals, [0075]; the system may be used for continuous recording of nerve activity where the NECG signals are integrated over time).
Chen is silent regarding wherein the moving average is calculated with an overlap between 25ms and 75ms. However, it can be appreciated that when the general conditions of the claim are disclosed by the prior art, optimization of the parameters through routine experimentation does not patentably distinguish the claimed invention from the prior art device. In this case, Chen discloses wherein the signals are integrated over time using a moving average, so finding the optimal time window would simply be optimization of the conditions through routine experimentation.
Regarding claim 24, Chen in view of Burnam teaches the apparatus according to claim 1, wherein the signal-quality gating operation comprises rejecting segments of the ECG signals exceeding a motion threshold (see [0143]; signal processing software may automatically eliminate noise such as that generated by muscle contraction, electrical appliances, body motion, respiration, and radiofrequency signals).
Regarding claim 26, Chen in view of Burnam teaches the apparatus according to claim 1, wherein the SNS response measurement is determined as a difference between averaged feature values of the baseline period and the stimulus period (see [0074]; if either or both of the NECG or ECG activity deviate from the baseline by greater than a predetermined threshold a signal is generated, Fig. 3 step 328).
Regarding step 27, Chen in view of Burnam teaches the apparatus according to claim 1, wherein the amplitude of the features extracted from the iSKNA signals comprises peak amplitude (see Fig. 4, [0073]; NECG baseline includes the average amplitude).
Regarding claim 29, Chen in view of Burnam teaches the apparatus according to claim 1, wherein the electrode and the processing unit provide feedback control of the activity device (see [0066]; monitored nerve activity may also be desirable for providing guidance while performing a procedure, and also for determining an effectiveness of a treatment after delivery with reference to a difference in the identified nerve activity, [0067]; NECG signals can be used to identify clinical conditions that require an adjustment to the level of insulin infusion in the insulin pump).
Claims 4 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al (US 20150297104 A1) in view of Burnam et al (WO 2022139971 A1, with reference to the US publication US 20240299754 A1 for citation purposes) and Angelini et al (US 20070283953 A1).
Regarding claims 4 and 20, Chen in view of Burnam teaches the apparatus according to claim 1 and the method according to claim 17. They are silent regarding wherein the SNS response is due to oxygen-toxicity and
the activity device is a submersible alert device configured to emit energy to alert a diver to oxygen-toxicity going forward.
Angelini teaches an apparatus for a diving computer (10) which displays information to a diver including information regarding an SNS response (see Angelini [0039]; heart rate monitor may be used as input for calculations for the dive) which may be due to oxygen-toxicity (see Angelini [0032]; oxygen toxicity alarms), wherein the diving computer is considered to be an activity device and the activity device is a submersible alert device (see Angelini [0035]; diving computer 10 having a waterproof housing 12) configured to emit energy to alert a diver to oxygen-toxicity going forward (see Angelini [0032]; oxygen toxicity alarm).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Chen’s system for measuring a sympathetic nervous system response for use to monitor the safety of a diver as disclosed by Angelini. One of ordinary skill in the art would have been motivated to make this modification in order to remotely monitor and alert the user to a change in the sympathetic nervous system which would result in a necessary action to avoid any major complications.
Claims 25 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al (US 20150297104 A1), hereinafter referred to as Chen ‘104, in view of Burnam et al (WO 2022139971 A1, with reference to the US publication US 20240299754 A1 for citation purposes) and Chen et al (US 20170215752 A1), hereinafter referred to as Chen ‘752.
Regarding Claim 25, Chen ‘104 in view of Burnam teaches the apparatus according to claim 1 wherein the baseline period corresponds to a resting state of the person and the stimulation period corresponds to the person experiencing a stimulation that causes the sympathetic nerve activity to deviate from the baseline (Chen ‘104 [0073-0074]). Chen is silent regarding wherein the stimulation is an externally applied stimulus.
Chen ‘752 teaches a system for non-invasively monitoring and controlling sympathetic nerve activity wherein a baseline skin nerve activity level may be determined while a person is at rest/not experiencing external stimulus (Chen ‘752 [0069]); and
the skin nerve activity level may be determined during a stimulation period corresponding to the person experiencing an external stimulus (Chen ‘752 [0070]; nerve activity following delivery of the electrical stimulation may be monitored).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Chen ‘104’s method for determining skin nerve activity while the user is at rest and experiencing a stimulation that causes the nerve activity to deviate from the baseline with Chen ‘752’s system which delivers electrical stimulation and monitors the skin nerve activity as a result of the stimulation. One of ordinary skill in the art would have been motivated to make this modification in order to control the sympathetic nerve activity using the electrical stimulation pulses and determine the effects by monitoring the resulting skin nerve activity (Chen ‘752 [0068-0070]).
Regarding claim 28, Chen ‘104 in view of Burnam teaches the apparatus according to claim 1, wherein a bandpass filter is used to derive the skin sympathetic nerve activity signals. Chen is silent regarding wherein the bandpass filter has a range of 500 to 1000 Hz.
Chen ‘752 teaches wherein the signal is filtered using a bandpass filter in order to extract skin sympathetic nerve activity signals wherein the bandpass filter has a range of 500 to 1000 Hz (see Chen ‘752 [0051]; the high-pass filter cutoff frequency may be between 150 Hz and 700 Hz).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Chen ‘104’s filter cutoff frequency with Chen ‘752’s filter cutoff frequency. One of ordinary skill in the art would have been motivated to make this modification because Chen ‘104 uses a frequency of 150 Hz which may allow for some muscle noise to remain in the signal, and Chen ‘752’s cutoff frequency around 700 Hz would be more specific to nerve activity and filter out all muscle noise (Chen ‘752 [0051]).
Conclusion
THIS ACTION IS MADE FINAL. 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 ALISHA J SIRCAR whose telephone number is (571)272-0450. The examiner can normally be reached Monday - Thursday 9-6:30, Friday 9-5:30 CT.
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/A.J.S./Examiner, Art Unit 3792
/ALLEN PORTER/Primary Examiner, Art Unit 3796
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