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 .
Information Disclosure Statement
The Information Disclosure Statement (IDS) filed 05/09/2025 has been considered by the Examiner.
Claim Objections
Claim 12 objected to because of the following informalities: ‘signals form the electrode’ in line 5 should read ‘signals from the electrode’. Appropriate correction is required.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 12-16 and 18-20 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 12 recites the limitation "the electrode" in line 5 and “the activity” in line 9. There is insufficient antecedent basis for this limitation in the claim.
Due to their dependence on a rejected parent claim, claims 13-16 and 17-20 are also rejected under 35 USC 112.
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 1-20 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 physiological signals in order to initiate an activity. These steps, given their broadest reasonable interpretation, can be practically performed in the human mind and are thereby considered to be directed to an abstract idea/mental process. A person of ordinary skill in the art could determine an SNS response based upon skin sympathetic nerve activity signals and/or electrodermal activity signals and then perform an activity based upon the determined SNS response measurement signal.
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 and/or EDA signals, a processing unit comprising an algorithm programmed by machine learning, and an activity device.
The limitation of receiving at least one of ECG signals or EDA 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 and/or EDA signals as input for the algorithm in order 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.
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 at least one ECG/EDA signal from an electrode, a processing unit comprising an algorithm programmed by machine learning, and an activity device above in Step 2A, Prong 2. Additionally, the additional elements of the claimed apparatus (electrodes, a processor, 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 5-8, 10, 11, 13-16 further limit the extra solution activity of data gathering.
Claim 9 further defines the abstract idea.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 5, 6, 8-14, and 16-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Subramanian et al (US 20220323002 A1).
Regarding claim 1, Subramanian teaches an apparatus (108) for performing an activity based on a sympathetic nervous system (SNS) response to a stimulus, the apparatus comprising:
an electrode configured to contact a person undergoing the stimulus (see [0054], [0057], [0059]; heart rate sensor 102 such as an ECG and EDA sensor 104 which may be electrodes);
a processing unit (106) comprising an algorithm programmed by machine-learning (see [0062]; processor 106 uses regression, state-state models, neural networks, and other statistical frameworks) and configured to:
(i) receive at least one of electrocardiogram (ECG) signals or electrodermal activity (EDA) signals from the electrode (see [0062]; the HR sensor 102 and EDA sensor 104 send data to the processor 106 in real time);
(ii) analyze at least one of skin sympathetic nerve activity (SKNA) signals derived from the ECG signals or the EDA signals to measure the SNS response and provide an SNS response measurement (see [0066]; the point process models for HRV and EDA are applied to the ECG data 208 and EDA data 210 to yield point process indices 220 which are used to yield a probability of perceiving nociception); and
(iii) transmit an SNS response measurement signal to initiate the activity in response to the SNS response measurement (see [0063]; the processor can display an indication of the resulting probability of the patient's perception of nociception to an anesthesiologist); and
an activity device (112) configured to perform the activity in response to the SNS response measurement signal (see [0064]; the anesthesiologist can respond to this indication by adjusting the type and dosages of the drugs 112 administered to the patient).
Regarding claim 2, Subramanian teaches the apparatus according to claim 1, wherein the SNS response is due to pain (see [0063]; the processor 106 can display the probability that the patient is perceiving nociception) and the activity device comprises a medication injector (see [0065]; administer a combination of drugs 216 which may include ketamine, dexmedetomidine, lidocaine, etc., it can be appreciated that the previously listed drugs are known to be administered intravenously when a patient is under anesthesia).
Regarding claim 3, Subramanian teaches the apparatus according to claim 1, wherein the activity device (108) comprises a display disposed in a medical care environment and configured to receive the activity signal (see [0060]; display 108 showing a probability of a patient perceiving nociception expressed as a value between 0 and 1),
wherein the activity signal comprises an amount of medication to be administered to the person (see [0077-0078]; in a scenario, the probability of perception of nociception has been under 0.5 for 10 minutes, which indicates a stable and low nociceptive state, so the clinician does not administer any drugs; in another scenario, the probability of perception of nociception has increased from 0.5 to 0.7, which indicates an increased nociception, so the clinician administers a bolus dose of an opioid; it can be appreciated that the activity signal comprising the probability of perception of nociception corresponds to an amount of medication to be administered, and the amount may be zero).
Regarding claim 5, Subramanian teaches the apparatus according to claim 1, wherein the processing unit is further configured to extract a phasic component from the EDA signals (see [0087]; the processor extracts pulses from the phasic component of the EDA data).
Regarding claim 6, Subramanian teaches the apparatus according to claim 5, wherein the processing unit is further configured to receive at least one of baseline ECG signals or baseline EDA signals indicative of no stimulus being applied to the person (see [0113]; continuous ECG, respiration, and EDA data were collected in subjects breathing at various rates including a baseline period before the start of the task).
Regarding claim 8, Subramanian teaches the apparatus according to claim 6, wherein the processing unit (106) is further configured to:
to process the extract a phasic component from the baseline EDA signals (see [0085]; the processor isolates and removed the tonic component of the data to leave only the phasic component);
extract features from the phasic component of the baseline EDA signals (see Fig. 4, [0087]; the processor extracts pulses from the phasic component of the EDA by identifying peaks based on prominence); and
compare the extracted features corresponding to the baseline EDA signals to corresponding features of the stimulated EDA signals to measure the SNS response (see [0096-0097]; model fits dynamic autoregressive coefficients that encode history dependence, and a goodness-of-fit analysis can be performed to assess how well the fitted model corresponds to the data).
Regarding claim 9, Subramanian teaches the apparatus according to claim 1, wherein the processing unit is further configured to train the algorithm using at least one of extracted features from training iSKNA signals or extracted features from training EDA signals. See [0129-0140], Tables 2-4, where the process of training/validating the multimodal model is described, including table 3 which denotes the division of collected data into training and validation sets. See also, paragraphs [0148-0151].
Regarding claim 10, Subramanian teaches the apparatus according to claim 1, wherein the processing unit is configured to measure the SNS response in real time (see [0062]; the HR sensor 102 and EDA sensor 104 send data to the processor 106 in real time where the data is transformed into point process data 212 and used to compute the probability of the patient’s perception of nociception 216 in real time or with a lag of at most a few seconds).
Regarding claim 11, Subramanian teaches the apparatus according to claim 1, wherein the processing unit is further configured to apply a motion artifact reduction algorithm to the ECG signals before extracting the SKNA to reduce the impact of subject movement on the SNS response measurement (see [0058]; peaks of a pulse pressure wave correspond to R waves in the ECG but with a delay, so they can be useful to ‘fill in’ ECG data if there are artifacts in the ECG data).
Regarding claim 12, Subramanian teaches a non-transitory computer-readable medium (120) storing instructions that,
when executed by a processor (106),
cause the processor to perform a method for measuring a sympathetic nervous system (SNS) response to a stimulus (see [0054]; a system for tracking nociception in a patient using a multimodal metric), the method comprising:
receiving at least one of electrocardiogram (ECG) signals (208) or electrodermal activity (EDA) signals (210) from the electrode (see [0062]; the HR sensor 102 and EDA sensor 104 send data to the processor 106 in real time);
analyzing at least one of skin sympathetic nerve activity (SKNA) signals derived from the ECG signals or the EDA signals to measure the SNS response and provide an SNS response measurement (see [0066]; the point process models for HRV and EDA are applied to the ECG data 208 and EDA data 210 to yield point process indices 220 which are used to yield a probability of perceiving nociception); and
transmitting an SNS response measurement signal to initiate the activity in response to the SNS response measurement (see [0063]; the processor can display an indication of the resulting probability of the patient's perception of nociception to an anesthesiologist).
Regarding claim 13, Subramanian teaches the non-transitory computer-readable medium according to claim 12, wherein the method performed by the processor further comprises extracting a phasic component from the EDA signals (see [0087]; the processor extracts pulses from the phasic component of the EDA data).
Regarding claim 14, Subramanian teaches the non-transitory computer-readable medium according to claim 13, wherein the method performed by the processor further comprises receiving at least one of baseline ECG signals or baseline EDA signals indicative of no stimulus being applied to the person (see [0113]; continuous ECG, respiration, and EDA data were collected in subjects breathing at various rates including a baseline period before the start of the task).
Regarding claim 16, Subramanian teaches the on-transitory computer-readable medium according to claim 14, wherein the method performed by the processor further comprises
extracting a phasic component from the baseline EDA signals (see [0085]; the processor isolates and removed the tonic component of the data to leave only the phasic component);
extracting features from the phasic component of the baseline EDA signals (see Fig. 4, [0087]; the processor extracts pulses from the phasic component of the EDA by identifying peaks based on prominence); and
comparing the extracted features corresponding to the baseline EDA signals to corresponding features of the stimulated EDA signals to measure the SNS response (see [0096-0097]; model fits dynamic autoregressive coefficients that encode history dependence, and a goodness-of-fit analysis can be performed to assess how well the fitted model corresponds to the data).
Regarding claim 17, Subramanian teaches a method for performing an activity based on a sympathetic nervous system (SNS) response to a stimulus, the method comprising:
receiving at least one of electrocardiogram (ECG) signals (208) or electrodermal activity (EDA) signals (210) from an electrode (see [0054], [0057], [0059]; heart rate sensor 102 such as an ECG and EDA sensor 104 which may be electrodes);
analyzing at least one of skin sympathetic nerve activity (SKNA) signals derived from the ECG signals or the EDA signals to measure the SNS response and provide an SNS response measurement (see [0066]; the point process models for HRV and EDA are applied to the ECG data 208 and EDA data 210 to yield point process indices 220 which are used to yield a probability of perceiving nociception);
transmitting an SNS response measurement signal to initiate the activity in response to the SNS response measurement (see [0063]; the processor can display an indication of the resulting probability of the patient's perception of nociception to an anesthesiologist); and
performing the activity using an activity device (112) in response to the SNS response measurement signal (see [0064]; the anesthesiologist can respond to this indication by adjusting the type and dosages of the drugs 112 administered to the patient).
Regarding claim 18, Subramanian teaches the method according to claim 14, wherein administering comprises injecting using a medication injector (see [0065]; administer a combination of drugs 216 which may include ketamine, dexmedetomidine, lidocaine, etc., it can be appreciated that the previously listed drugs are known to be administered intravenously when a patient is under anesthesia).
Claim(s) 12 and 17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chen et al (US 20190374121 A1).
Regarding claim 12, Chen teaches a non-transitory computer-readable medium storing instructions (120) that,
when executed by a processor (102),
cause the processor to perform a method (300) for measuring a sympathetic nervous system (SNS) response to a stimulus (see [0012]; a method for estimating a sympathetic nerve activity), the method comprising:
receiving electrodermal activity (EDA) signals form the electrode (see Fig. 3, [0065-0066]; step 302 sample electrical signals from electrodes where three or more electrodes may be placed on the subject in a cutaneous configuration);
analyzing at least one of the EDA signals (see Fig. 3, [0067]; step 306 skin nerve activity is identified using high-frequency signals that pass through the high-pass filter) to measure the SNS response and provide an SNS response measurement (see Fig. 3, [0068]; step 308 estimate sympathetic nerve activity using the identified skin nerve activity); and
transmitting an SNS response measurement signal to initiate the activity in response to the SNS response measurement (see Fig. 3, [0068]; step 310 generate an electrical stimulation configured to achieve a desired therapeutic effect based on the estimated sympathetic nerve activity).
Regarding claim 17, Chen teaches a method (300) for performing an activity based on a sympathetic nervous system (SNS) response to a stimulus, the method comprising:
receiving at electrodermal activity (EDA) signals from an electrode (see Fig. 3, [0065-0066]; step 302 sample electrical signals from electrodes where three or more electrodes may be placed on the subject in a cutaneous configuration);
analyzing at least one of the EDA signals (see Fig. 3, [0067]; step 306 skin nerve activity is identified using high-frequency signals that pass through the high-pass filter) to measure the SNS response and provide an SNS response measurement (see Fig. 3, [0068]; step 308 estimate sympathetic nerve activity using the identified skin nerve activity); and
transmitting an SNS response measurement signal to initiate the activity in response to the SNS response measurement (see Fig. 3, [0068]; step 310 generate an electrical stimulation configured to achieve a desired therapeutic effect based on the estimated sympathetic nerve activity); and
performing the activity using an activity device in response to the SNS response measurement signal (see Fig. 3; step 312 deliver the electrical stimulation).
Regarding claim 18, Subramanian teaches the method according to claim 13, further comprising administering an amount of medication to a person in contact with the electrode (see [0064]; the anesthesiologist can respond to an indication of the probability of a patient’s perception to nociception by adjusting the type and dosage of the drugs 112 administered to the patient).
Regarding claim 19, Subramanian teaches the method according to claim 14, wherein administering comprises injecting using a medication injector.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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.
Claim(s) 4 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Subramanian et al (US 20220323002 A1) in view of Angelini et al (US 20070283953 A1).
Regarding claims 4 and 20, Subramanian teaches the apparatus according to claim 1 and the method according to claim 13. Subramanian is 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 Subramanian’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.
Claim(s) 13, 14, 16, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al (US 20190374121 A1) in view of Osorio (US 20120298105 A1).
Regarding claim 13, Chen teaches the non-transitory computer-readable medium according to claim 12. Chen is silent regarding wherein the method performed by the processor further comprises extracting a phasic component from the EDA signals.
Osorio teaches a method for detecting a condition of a patient based on a body signal where the condition may be a migraine and the body signal may be an electrodermal activity signal which is used to indicate sympathetic nerve activity (Osorio [0035]); wherein the method comprises extracting a phasic component from the EDA signals (see Osorio [0029]; tonic or phasic components of skin conductance such as its meal level, derivate of the mean, number of fluctuations/unit time which express sympathetic skin nerve activity may be measured).
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 the method of measuring electrodermal activity as taught by Chen with the phasic electrodermal measurements taught by Osorio. One of ordinary skill in the art would have been motivated to make this modification in order to express the sympathetic skin nerve activity as a result of the number of fluctuations per time unit which can then be used to determine the sympathetic nervous system response (Osorio [0029], [0035]).
Regarding claim 14, Chen and Osorio teach the non-transitory computer-readable medium according to claim 13. Chen further teaches wherein the method performed by the processor further comprises receiving baseline EDA signals indicative of no stimulus being applied to the person (see Chen [0053]; the processor 102 may identify a baseline nerve activity, such as skin nerve activity).
Regarding claim 16, Chen and Osorio teach the non-transitory computer-readable medium according to claim 14. Chen further teaches the method further comprising extracting features from the baseline EDA signal (see Chen [0053], [0071]; baseline nerve activity is determined and features including average signal amplitude or signal variation are determined) and comparing the extracted features corresponding to the baseline EDA signal to corresponding features of the stimulated EDA signals to measure the SNS response (see Chen [0063-0064]; effectiveness of the electrical stimulation may be determined by analyzing changes in nerve activity as compared to a baseline.
Chen is silent regarding extracting features from a phasic component from the baseline EDA signal.
Osorio teaches extracting a phasic component from an EDA signal and extracting features from the phasic component (see Osorio [0029]; phasic components of skin conductance and its multiple possible mathematical transformations such as mean level, fluctuations/unit time, and amplitude of the fluctuations may be measured).
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 the establishment of baseline EDA signals by extracting features as disclosed by Chen with features of a phasic component of the EDA signal as taught by Osorio. One of ordinary skill in the art would have been motivated to make this modification in order to establish a baseline that may characterize a normal EDA signal over time for comparison to an EDA signal following a treatment.
Regarding claim 18, Chen and Osorio teach the method according to claim 13. Chen teaches administering an electrical stimulation configured to achieve a desired therapeutic effect (Chen [0068-0069]), but is silent regarding the method further comprising administering an amount of medication to a person in contact with the electrode.
Osorio teaches the method further comprising administering an amount of medication to a person in contact with the electrode (see Osorio [0040]; once the condition has been detected the method can further comprise enabling at least one action including raising the concentration of a medication through the administration of supplemental doses).
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 the action step of the method taught by Chen for determining a SNS response with the medication administration as taught by Osorio. One of ordinary skill in the art would have been motivated to make this modification in order to administer a therapeutic treatment in the form of medication once a condition has been detected in a user (Osorio [0040-0042]).
Allowable Subject Matter
Claims 7 and 15 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claims 7 and 15, Subramanian teaches measuring an ECG signal to determine a sympathetic nervous system response. However, Subramanian is silent regarding extracting baseline SKNA; calculating baseline rectified SKNA signals (iSKNA) from the extracted baseline SKNA; extracting baseline features from the iSKNA signals; and comparing the extracted baseline features to corresponding features of the stimulated ECG signals to measure the SNS response.
Conclusion
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
/Benjamin J Klein/Supervisory Patent Examiner, Art Unit 3792