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 .
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.
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.
Claims 1, 3-8, 19, 37, 55, 72, and 89 are rejected under 35 U.S.C. 103 as being unpatentable over John’444 (U.S. Patent 6016444 – previously cited) in view of Scheib'345 (U.S. Patent Publication 20190216345 - cited by applicant).
Regarding Claims 1, John’444 discloses a non-transitory computer-accessible medium having stored thereon computer- executable instructions for providing at least one indication to administer additional anesthesia medication to at least one patient (Column 2 Lines 52-54 - In accordance with the present invention, a Closed Loop Anesthesia Controller (CLAC), including an EEG system and automatic quantitative analysis of the EEG (QEEG) is provided to apply the correct amount of anesthesia during an operation; Column 13 Lines 40-49 - The amount of anesthetic determined to constitute the correction amount will be administered automatically by the CCAC whenever the patient's distance measure deviates 2.5 SD from the set point. For example, the withheld amount of anesthesia may be 1 cc per minute, but the "correction amount" may be greater, i.e., 2 cc per minute. The 2.5 SD is an example of a small perturbation, but further experience may indicate that the criteria (2.5 SD) should be raised or lowered),
receiving electroencephalogram (EEG) information for at least one patient (Column 10 Lines 52-55; simple and rapid analysis of only a few measures at only a few EEG electrodes provides a reliable method to control the application of anesthesia),
determining at least one power spectrum of an alpha band of the at least one patient from the EEG information (Column 10 Lines 61-63 - One preferred measure is EEG power in the selected bands of Alpha 1, Alpha 2, Theta and Delta, and most preferably Alpha 1 and Theta; Column 11 Lines 3-5 - With the application of anesthesia, the EEG output, in the selected bands, rises differentially at the anterior electrodes and the gradient changes; Column 5 Lines 43-49 - A selected fraction of that amount is automatically administered by the CLAC system, as a first approximation, to test if the patient is restored to the set point. The amount required to restore the patient to the set point is the "correction amount" and is retained in system memory and is administered to the patient whenever the patient deviates), and
providing the at least one indication to administer the additional anesthesia medication to the at least one patient (Column 11 Lines 1-8 - A comparison of EEG power in the selected measure at these electrodes defines a gradient from front to back. With the application of anesthesia, the EEG output, in the selected bands, rises differentially at the anterior electrodes and the gradient changes. If that gradient decreases by more than a selected amount, for example, 10% of the selected measure, the patient is tending to be aroused and more anesthetic should be applied; Column 5 Lines 43-49 - A selected fraction of that amount is automatically administered by the CLAC system, as a first approximation, to test if the patient is restored to the set point. The amount required to restore the patient to the set point is the "correction amount" and is retained in system memory and is administered to the patient whenever the patient deviates).
John’444 further discloses obtaining readings occurring during a surgical operation that comprises applying appropriate levels of anesthesia (Column 12 Lines 58-65 - During the surgical operation, the patient is kept on the EEG system of FIG. 1, or re-connected to the system if for some reason there is an interruption. Based on the QEEG trajectory, explained below, the microprocessor controls the anesthesia delivery device 40 (which may be a valve on a gas supply or a syringe on an intravenous infusion device) to increase, maintain constant or decrease the amount of anesthetic delivered to the patient), but fails to disclose detecting a noxious stimulation associated with at least one anesthesia medication to at least one patient. Scheib'345 teaches detecting stress induced by surgical incision and applying appropriate levels of anesthesia to counteract such noxious stimuli (Paragraph [0075] - A common sequence in the practice of anesthesia is to induce anesthesia with enough quantity of anesthetic drugs for the patient to tolerate the high initial stress of endotracheal intubation and surgical incision. This is frequently a high enough level to induce the EEG pattern of burst suppression; Paragraph [0076] - It would be advantageous to have a method to analyze the EEG which would enable the anesthesia provider to reduce the level of agent without potentially harmful reflex responses to surgical stimulation; Paragraph [0104] - If the anesthetic agent is then reduced repeatedly in stages without adding an analgesic agent, it may be possible for the patient to tolerate surgical stimulation at a level of anesthetic agent so low that the patient would be expected to move in response to incision). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of John’444 to include observing changes in EEG caused by noxious stimuli such as stress induced by surgical incision in order to adjust anesthetic levels and reduce harmful reflexes as seen in Scheib'345.
John’444 further fails to disclose determining whether the noxious stimulation is present when a predetermined drop is measured in the at least one power spectrum during an active administration of anesthesia. Scheib'345 teaches observing drops in the alpha power region during noxious stimulation such as surgical incision (Paragraph [0032] - The spectra show a paradoxical response. There are power decreases in alpha and theta and a power increase in delta (0.25-3.5 Hertz) as a response to surgical stimulation). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of John’444 to include observing decreases in the alphas power caused by noxious stimuli such as stress induced by surgical incision in order to properly adjust anesthetic levels and reduce harmful reflexes as seen in Scheib'345 (Paragraph [0076] - It would be advantageous to have a method to analyze the EEG which would enable the anesthesia provider to reduce the level of agent without potentially harmful reflex responses to surgical stimulation).
John’444 further fails to disclose providing the at least one indication to administer additional anesthesia medication to the at least one patient when it is determined that noxious stimulation is present. Scheib'345 teaches administering additional anesthesia to tolerate initial stress to a noxious stimulation such as surgical incision (Paragraph [0075] - A common sequence in the practice of anesthesia is to induce anesthesia with enough quantity of anesthetic drugs for the patient to tolerate the high initial stress of endotracheal intubation and surgical incision. This is frequently a high enough level to induce the EEG pattern of burst suppression). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of John’444 to include providing an indication to administer various levels of anesthesia in order to properly account for noxious stimulation such as surgical incision and reduce harmful reflexes as seen in Scheib'345 (Paragraph [0076] - It would be advantageous to have a method to analyze the EEG which would enable the anesthesia provider to reduce the level of agent without potentially harmful reflex responses to surgical stimulation).
Regarding Claims 3 and 4, John’444 in view of Scheib'345 discloses the computing arrangement outlined in Claim 1 above. John’444 further discloses determining and selecting a predetermined drop of 10% (Column 11 Lines 5-8 - gradient decreases by more than a selected amount, for example, 10% of the selected measure, the patient is tending to be aroused and more anesthetic should be applied).
Regarding Claims 5-8, John’444 in view of Scheib'345 discloses the computing arrangement outlined in Claim 1 above. John’444 further discloses establishing a baseline power spectrum for at least one patient over a time series approximate to the time of a medical procedure performed on a patient (Column 3 Lines 19-24 - After the patient has attained the plane of anesthesia desired by the physician, the CLAC is activated to maintain that desired level. The patient's brain waves are detected and analyzed to form a self-norm ("reference") incorporating a set of QEEG features identified as sensitive to the depth of anesthesia), and
a predetermined drop is based on the baseline power spectrum (Column 10 Lines 47-51; any significant deviation automatically activates the anesthesia supply system. For example, any deviation of the patient from his self-norm by .+-.2.5 S.D. causes an increase, or decrease, in anesthetic delivery).
Regarding Claims 19 and 72, the sections of John’444 cited above in the rejection breakdown of Claim 1 disclose methods comprising the functions set forth in the claim.
Regarding Claims 37 and 89, the sections of John’444 cited above in the rejection breakdown of Claim 1 disclose a system comprising the elements set forth in the claim.
Regarding Claim 55, the sections of John’444 cited above in the rejection breakdown of Claim 1 disclose a system comprising the elements set forth in the claim with the addition of John’444 disclosing microprocessors as non-transitory computers (Column 7 Lines 43-45 - The program 19 with its controlled microprocessor 16 condition the input signals and insure that they are valid biological signals).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over John’444 (U.S. Patent 6016444 – previously cited) in view of Scheib'345 (U.S. Patent Publication 20190216345 - cited by applicant), as applied to Claim 8 above, and further in view of Bibian et. al.’042 (U.S. Patent 11565042 – previously cited).
Regarding Claim 9, John’444 in view of Scheib'345 discloses the computing arrangement outlined in Claim 8 above, but fails to disclose determine when the predetermined drop is achieved based on a rate of a drop in the at least one power spectrum over time. Bibian et. al.’042 teaches determining a rate of change of EEG (Column 26 Lines 59-61 - The median absolute value is a robust measure of the rate of change of EEG, is less sensitive to outliers). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of John’444 in view of Scheib'345 to include computing a rate of change of an EEG in order to have data less sensitive to outliers as seen in Bibian et. al.’042.
Claims 10, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over John’444 (U.S. Patent 6016444 – previously cited) in view of Scheib'345 (U.S. Patent Publication 20190216345 - cited by applicant), as applied to Claim 1 above, and further in view of Koch et. al.’974 (WO Patent Publication 2019238974 – previously cited).
Regarding Claim 10, John’444 in view of Scheib'345 discloses the computing arrangement outlined in Claim 1 above, but fails to disclose determining a predetermined drop using at least one machine learning procedure. Koch et. al.’974 teaches using machine learning to determine a predetermined drop (Paragraph [0014] - whether the determined intraoperative alpha peak frequency is significantly lower than a predetermined reference value can be carried out according to an embodiment variant of the invention via machine learning). It would have been obvious to one of ordinary skill in the art to have modified John’444 in view of Scheib'345 to include a machine learning procedure in order to recognize patterns and learning data as seen in Koch et. al.’974 (Paragraph [0014] - an artificial system learns from examples of a significantly lower intraoperative alpha peak frequency and can generalize these examples after the end of the learning phase, whereby patterns and regularities are recognized in the learning data).
Regarding Claim 16, John’444 in view of Scheib'345 discloses the computing arrangement outlined in Claim 1 above, but fails to disclose determining at least one peak in at least one power spectrum. Koch et. al.’974 teaches determining a peak in at least one power spectrum (Paragraph [0024] - when determining the intraoperative alpha peak frequency of the EEG signal, the frequency is determined at which the power in the power spectrum of the EEG signal is greatest in the alpha band and theta band). It would have been obvious to one of ordinary skill in the art to have modified John’444 in view of Scheib'345 to determine at least one peak in at least one power spectrum in order to enable early intervention as seen in Koch et. al.’974 (Paragraph [0015] – enables early therapeutic intervention).
Regarding Claim 18, John’444 in view of Scheib'345 discloses the computing arrangement outlined in Claim 1 above, but fails to disclose determining at least one spectral property in EEG segments in the EEG information. Koch et. al.’974 teaches determining at least one spectral property in EEG segments in the EEG information (Paragraph [0025] - at least one EEG signal is recorded on the patient's head. It is intended that the power of the alpha band of the EEG signal is determined, whereby the power of the alpha band in the power spectrum of the EEG signal is defined as the integral of the power over all frequencies in the alpha band. The power of the EEG signal is determined across all frequencies in the alpha band). It would have been obvious to one of ordinary skill in the art to have modified John’444 in view of Scheib'345 to include determining a spectral property in EEG segments as stated in Koch et. al.’974 in order to gather more relevant information across an EEG signal.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over John’444 (U.S. Patent 6016444 – previously cited) in view of Scheib'345 (U.S. Patent Publication 20190216345 - cited by applicant), further in view of Koch et. al.’974 (WO Patent Publication 2019238974 - previously cited) as applied to Claim 10 above, further in view of Blomqvist et. al.’288 (EP Patent Application 3476288 - previously cited).
Regarding Claim 11, John’444 in view of Scheib'345 and further in view of Koch et. al.’974 discloses the computing arrangement outlined in Claim 10 above. Koch et. al.’974 further teaches a machine learning procedure comparing predefined changes in the power of an alpha band (Paragraph [0028] - whether an increase in the power of the alpha band from the first power to the second power is below a predefined level can be carried out according to an embodiment of the invention via machine learning), but fails to disclose where a machine learning procedure is a convolutional neural network. Blomqvist et. al.’288 teaches where a machine learning procedure is a convolutional neural network (Paragraph [0008] - The machine learning process may be implemented using fully connected, recurrent or one dimensional convolutional neural network). It would have been obvious to one of ordinary skill in the art to have modified the device of John’444 in view of Scheib'345 comprising the prior art element of the machine learning procedure of Koch et. al.’974 with a known method such as a convolutional neural network as seen in Blomqvist et. al.’288 in order to yield predictable results.
Claims 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over John’444 (U.S. Patent 6016444 - previously cited) in view of Scheib'345 (U.S. Patent Publication 20190216345 - cited by applicant), as applied to Claim 1 above, in view of Kangas et. al.’330 (U.S. Patent 5775330 - previously cited).
Regarding Claims 12 and 15, John’444 in view of Scheib'345 discloses the computing arrangement outlined in Claim 1 above, but fails to disclose determining a first derivative power spectrum based on an alpha band, providing at least one indication to administer additional anesthesia medication to at least one patient based on a predetermined drop in a first derivative power spectrum, or determining a finite difference approximation of a first derivative power spectrum. Kangas et. al.’330 teaches determining a first derivative power spectrum based on an alpha band (Column 6 Lines 42-44 - average the instantaneous first derivative over a number of positions, for example ten positions to obtain a bounded first derivative), providing at least one indication to administer additional or less anesthesia medication to at least one patient based on a predetermined drop in a first derivative power spectrum (Column 6 Lines 44-48 - The bounded first derivative or slope is useful for ascertaining a trend of whether anesthetic depth is increasing or decreasing. Previous methods do not utilize a trend and therefore can under-correct or overcorrect the amount of anesthesia administered to a patient), and utilizing multiple data points in order to determine a first derivative of a power spectrum (Column 10 Lines 58-61 - The bounded first derivatives of FIG. 6 were obtained by averaging ten points behind time (t), averaging ten points ahead of time (t), then computing the slope between the averaged points). It would have been obvious to one of ordinary skill in the art to have modified John’444 in view of Scheib'345 to include taking a derivative of a power spectrum as seen in Kangas et. al.’330 in order to understand a patient’s depth of anesthesia more.
Regarding Claims 13 and 14, John’444 in view of Scheib'345 and further in view of Kangas et. al.’330 discloses the computing arrangement outlined in Claim 12 above, but fails to disclose determining a first derivative power spectrum if a signal strength is below a threshold. Kangas et. al.’330 further teaches determining a first derivative power spectrum if a signal strength shows unexpected, large changes (Column 6 Lines 36-40 - data collection and processing may occur in subsecond intervals that may include sporadic noise which show artificially large changes in predicted anesthetic depth…average the instantaneous first derivative over a number of positions, for example ten positions to obtain a bounded first derivative). It would be obvious to one of ordinary skill in the art that in order to understand if there are large changes in a power spectrum, a baseline or threshold value would need to be inputted by the user. Furthermore, it would be obvious to one of ordinary skill in the art to have modified John’444 in view of Scheib'345 to take derivatives of the data in order to avoid noise in the data as seen by Kangas et. al.’330.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over John’444 (U.S. Patent 6016444 - previously cited) in view of Scheib'345 (U.S. Patent Publication 20190216345 - cited by applicant), further in view of Koch et. al.’974 (WO Patent Publication 2019238974 - previously cited), as applied to Claim 16 above, and further in view of Scheib’047 (U.S. Patent Publication 20110125047 - previously cited).
Regarding Claim 17, John’444 in view of Scheib’345 and further in view of Koch et. al.’974 discloses the computing arrangement outlined in Claim 16 above. Koch et. al.’974 further teaches determining at least one peak in at least one power spectrum, but fails to disclose determining at least one peak using a linear regression procedure. Scheib’047 teaches using a regression procedure to determine at least one peak in at least one power spectrum (Paragraph [0018] - Regression analysis of these parameters revealed that there is a correlation between the alpha peak frequency and concentration of the anesthetic agent; Paragraph [0026] - One of the easiest best-fit approaches is to use a least-squares approach but one of ordinary skill will recognize that there are numerous other data regression schemes that may be used to approximate a line while minimizing error). It would have been obvious for the applicant to try using linear regression as a model of a “regression scheme” based on the above citation from Scheib’047 because of the finite number of identified, predictable solutions, with a reasonable expectation of success that encompasses the different forms of regression analysis. Furthermore, it would have been obvious to one of ordinary skill in the art to have modified the computing arrangement of John’444 in view of Scheib’345 and further in view of Koch et. al.’974 to modify the procedure of determining at least one peak in at least one power spectrum of Koch et. al.’974 by using a regression procedure as seen by Scheib’047 according to known methods that would yield predictable results.
Claims 108 and 109 are rejected under 35 U.S.C. 103 as being unpatentable over John’444 (U.S. Patent 6016444 - previously cited) in view of Scheib'345 (U.S. Patent Publication 20190216345 - cited by applicant), as applied to Claims 1 and 55 above respectively, and further in view of Hight et. al.'2019 (Transient electroencephalographic alpha power loss during maintenance of general anaesthesia).
Regarding Claim 108, John’444 in view of Scheib'345 discloses the computing arrangement outlined in Claim 1 above, but fails to disclose wherein the predetermined drop in the alpha power is a drop in an area under a curve of an alpha oscillatory power. Hight et. al.’2019 teaches utilizing area under a curve (AUC) to observe changes in the alpha power during surgery (Page 640 Paragraph 2 - The final model was well calibrated (P¼0.51 in the Hosmer Lemeshow test,18 using centiles), and had reasonable discrimination; using a probability threshold of 0.5, 76% of patients were classified correctly according to the final model. Receiver operating curves showed a clear deviation from the diagonal, and had an area under the curve of 0.78 (Fig. 4a); Page 640 Paragraph 1 under “Discussion” - The increased occurrence of alpha dropouts during such typically more painful surgery provides some support for the hypothesis that unsuppressed noxious stimuli could be causing a depolarisation of the thalamocortical system, leading to alpha loss; Page 641 Paragraph 1 under “Limitations” - The use of a general linear model assumes the dose-response relationship of alpha power to CeMAC to be linear. This seems to have worked well over the typical range of intraoperative drug concentrations, and has allowed us to tease out the effect of noxious stimuli; but further refining the fitting using a non-monotonic link function, such as a sigmoid or even bi-sigmoidal curve fitting procedure, might better capture this phenomenon). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the computing arrangement of John’444 in view of Scheib'345 to include observing “area under a curve” in an alpha power region in order to account for changes during noxious stimulation as seen in Hight et. al.'2019.
Regarding Claim 109, the sections of John’444 cited above in the rejection breakdown of Claim 108 discloses the system comprising the elements set forth in the claim with the addition of John’444 disclosing microprocessors as non-transitory computers (Column 7 Lines 43-45 - The program 19 with its controlled microprocessor 16 condition the input signals and insure that they are valid biological signals).
Response to Arguments
Applicant's arguments filed 21 July 2026 have been fully considered and they are not entirely persuasive.
Applicant is arguing "intended use" for Claim 12 whenever they state that "the recited subject matter of claim 12 uses the first derivative alpha-band power spectrum to address situations in which the alpha band power is too weak to directly provide useful information on anesthetic depth". However, the applicant has not positively recited “to address situations in which the alpha power band is too weak”. Additionally, the examiner notes that Claim 12 is dependent on a system’s claim and therefore the relied upon prior art would need to merely be capable of performing such actions. As disclosed in Paragraph 7 above comprising a breakdown of the rejection of Claim 12 under 35 U.S.C. 103, the examiner stands by the currently recited paragraphs of Kangas to teach determining a first derivative power spectrum based on an alpha band and providing at least one indication to administer additional or less anesthesia medication to at least one patient based on a predetermined drop in a first derivative power spectrum.
Claims 1, 3-19, 37, 55, 72, 89, and 108-109 are rejected under 35 U.S.C. 103 as necessitated by amendments, as discussed in Paragraphs 3-9 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Cimenser et. al.’2011 (Tracking brain states under general anesthesia by using global coherence analysis) discloses general anesthesia affects on brain states.
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 SARAH ANN WESTFALL whose telephone number is (571) 272-3845. The examiner can normally be reached Monday-Friday 7:30am-4:30pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Robertson can be reached at (571) 272-5001. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SARAH ANN WESTFALL/Examiner, Art Unit 3791
/ETSUB D BERHANU/Primary Examiner, Art Unit 3791
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