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.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 28 January 2026 has been entered.
Claim Rejections - 35 USC § 102
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.
Claims 1, 3-8, 19, 37, 55, 72, and 89 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by John’444 (U.S. Patent 6016444 – previously cited).
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 based on a predetermined drop in the at least one power spectrum (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).
Regarding Claims 19 and 72, the sections of John’444 cited above disclose methods comprising the functions set forth in the claim.
Regarding Claims 37 and 89, the sections of John’444 cited above disclose a system comprising the elements set forth in the claim.
Regarding Claim 55, the sections of John’444 cited above 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).
Regarding Claims 3 and 4, John’444 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 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).
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.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over John’444 (U.S. Patent 6016444 – previously cited) as applied to Claim 8 above, in view of Bibian et. al.’042 (U.S. Patent 11565042).
Regarding Claim 9, John’444 discloses a computing arrangement as stated in Claim 1 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 to include computing a rate of change of a 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) as applied to Claim 1 above, in view of Koch et. al.’974 (WO Patent Publication 2019238974 – previously cited).
Regarding Claim 10, John’444 discloses a computing arrangement as stated 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 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 discloses a computing arrangement stated 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 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 discloses a computing arrangement stated 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 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 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 Koch et. al.’974 discloses a machine learning procedure wherein Koch et. al.’974 teaches (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 combined 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) 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 discloses a computing arrangement stated 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 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 discloses a computing arrangement stated in Claim 1 above, but fails to disclose determining a first derivative power spectrum if a signal strength is below a threshold. Kangas et. al.’330 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 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 Koch et. al.’974 (WO Patent Publication 2019238974 - previously cited) as applied to Claim 16 above, further in view of Scheib’047 (U.S. Patent Publication 20110125047 - previously cited).
Regarding Claim 17, Koch et. al.’974 discloses 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 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.
Response to Arguments
Applicant's arguments filed 28 January 2026 have been fully considered and they are not entirely persuasive.
Applicant's amendments have overcome prior 35 U.S.C. 112(a) rejections.
Applicant's amendments have overcome prior 35 U.S.C. 112(f) interpretations.
Applicant's amendments have overcome prior 35 U.S.C. 112(b) rejections.
Applicant argues that John discloses "gradient" as "a spatial comparison between two different electrodes", but these arguments were found to not be persuasive. The examiner notes that John is comparing EEG readings of electrodes at two different locations on a patient's head, but the "gradient" factor is based on measurements attained at each of those locations. The "gradient" factor is not dependent on the physical separation.
Furthermore, the applicant appears to be arguing that EEG power spectrums are not being observed at each electrode location. If the examiner has interpreted this applicant's argument correctly, the examiner has found these arguments to be not persuasive. The examiner notes that John discloses EEG power spectrums are observed "within" each electrode as well as between other electrodes (Column 3 Lines 38-42 - A comparison is made of the absolute and relative EEG power within each of the two electrodes at selected frequency bands, preferably Theta and Alpha, and the relationships among these spectral measures within and between the set of electrodes; Column 3 Lines 60-65 - This simple system is an application of the general concept of quantification of spectral power measures and their relationships within two (or more) EEG electrodes and evaluating change relative to a clinician-defined state). The examiner would further like to note that the claims are recited in such a manner that do not prohibit or discourage comparing EEG power spectrums of more than one electrode as Claim 1 recites "determining at least one power spectrum" and "based on a predetermined drop in the at least one power spectrum" (emphasis added).
Applicant argues that John "suggests that a need for additional anesthesia is found when the EEG power rises, with the drop in the gradient coming from a different rate of increase of the power at each of the two spatially separated electrodes". However, the examiner does not find this argument to be persuasive. The examiner notes that John does not disclose "suggests that a need for additional anesthesia is found when the EEG power rises" but rather the section that the applicant is referencing of John reads that administering anesthesia to a patient causes a rise in EEG output and whenever the EEG power of the gradient decreases by a certain amount, the need to administer more/additional anesthesia is determined. The examiner has cited an additional section of John that goes into more detail about how administering anesthesia causes a rise in the EEG power spectrum and therefore a drop or decrease in the EEG power spectrum would indicate a subject becoming alert (Column 3 Lines 42-51 - When the patient attains the surgical plane of anesthesia, the cross-spectral matrix will change; the power in each band will change within each electrode and, in addition, the anterior electrode may show greater relative increase in power in some bands than the posterior electrode. The mean and standard deviation of baseline samples or the covariance matrices and of these measures will be used to define a self-norm. If the patient starts to regain consciousness, these changes will begin to reverse).
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 examiner does not read "intended use" into the claim language and therefore the prior art that teaches the recitation "determine a first derivative power spectrum based on the alpha band" still stands.
Claims 1, 3-8, 19, 37, 55, 72, and 89 are rejected under 35 U.S.C. 102, as discussed in Paragraph 4 above.
Claims 9-18 are rejected under 35 U.S.C. 103, as discussed in Paragraphs 5-9 above.
Conclusion
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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/SARAH ANN WESTFALL/Examiner, Art Unit 3791
/ETSUB D BERHANU/Primary Examiner, Art Unit 3791