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 .
Notice of Reply
This communication is responsive to the amendment(s) and/or argument(s) filed 2/20/26. The previous ground(s) of objection and/or rejection is/are withdrawn. The following new and/or reiterated ground(s) of rejection is/are set forth hereinbelow.
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.
(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) 1-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lee et al. (10/26/23 IDS NPL citation 3, “LEE SOOJEONG ET AL: "On Using Maximum a Posteriori Probability Based on a Bayesian Model for Oscillometric Blood Pressure Estimation" SENSORS, vol. 13, no. 10,10 October 2013, pages 13609-13623.”).
For claim 1, Lee discloses a computer-implemented method of determining blood pressure of a subject, the method comprising inter alia:
receiving an envelope data set for the subject (Sections 1-2) (Figs 1-2), the envelope data set comprising pairs of static cuff pressure and cuff pressure oscillation amplitude measured by a cuff pressure sensor during inflation and/or deflation of a cuff placed around a body part of the subject (Sections 1-2) (Figs 1-2);
using a set of probability density functions, PDFs, to determine, for a plurality of pairs of systolic blood pressure and diastolic blood pressure values, respective probabilities of obtaining the measurements in the envelope data set for different pairs of values of the systolic blood pressure and diastolic blood pressure (Section 2, PDF) (Fig 4), and combining the respective probabilities for the measurements in the enveloped data set by summing logarithms of the respective probabilities to obtain a likelihood for the envelope data set for that pair of systolic blood pressure and diastolic blood pressure values (Section 2.2) (Fig 4);
determining the systolic blood pressure and the diastolic blood pressure for the subject as the pair of values of the systolic blood pressure and diastolic blood pressure that provides the highest likelihood for the envelope data set (Fig 4), and outputting the determined systolic blood pressure and diastolic blood pressure to a user interface or patient record (Section 2.2, patient record data saved) (Fig 4).
For claim 2, Lee discloses the method as claimed in claim 1, wherein the step of using the set of PDFs comprises determining respective probabilities for obtaining the measurements in the envelope data set for all possible pairs of values of the systolic blood pressure and diastolic blood pressure (Section 2).
For claim 3, Lee discloses the method as claimed in claim 1, wherein the step of using the set of PDFs and determining the systolic blood pressure and diastolic blood pressure comprises using an iterative optimization algorithm to determine the highest likelihood for the envelope data set (Fig 4).
For claim 4, Lee discloses the method in claim 1, wherein the step of using the set of PDFs comprises:
determining a blood flow scaling factor based on the one or more highest measurements of blood flow in the envelope data set (Section 2, NPB); and
using the blood flow scaling factor to align blood flow values in the set of PDFs and the measurements of blood flow in the envelope data set (Section 2) (Fig 4).
For claim 5, Lee discloses the method in claim 4, wherein the step of determining the blood flow scaling factor comprises determining the blood flow scaling factor based on a function or median average of a plurality of the highest measurements of blood flow in the envelope data set (Section 2).
For claim 6, Lee discloses the method in claim 1, wherein the method further comprises:
generating the set of PDFs from a prior distribution relating to systolic blood pressure and diastolic blood pressure (Section 2) (Fig 4).
For claim 7, Lee discloses the method in claim 6, wherein the prior distribution comprises, or is based on, one or more of the following: information on physiologically possible values of systolic blood pressure and diastolic blood pressure (Section 2); information on physiologically possible differences in systolic blood pressure and diastolic blood pressure values (Section 2); one or more previous values of systolic blood pressure and diastolic blood pressure for the subject (Section 2); one or more characteristics of the subject (Section 2); one or more characteristics of a sensor used to measure the blood flow (Section 2); and one or more characteristics of a device used to apply restriction to the body part (Section 2).
For claim 8, Lee discloses a computer program product comprising a non-transitory computer readable medium having computer readable code embodied therein, the computer readable code being configured such that, on execution by a suitable computer or processing unit, the computer or processing unit is caused to perform the method of claim 1 (Section 2) (Fig 4).
For claim 9, Lee discloses an apparatus configured to determine blood pressure of a subject, the apparatus comprising inter alia a processor that is configured to:
receive an envelope data set for the subject (Sections 1-2) (Figs 1-2), the envelope data set comprising pairs of static cuff pressure and cuff pressure oscillation amplitude measured by a cuff pressure sensor during inflation and/or deflation of a cuff placed around a body part of the subject (Sections 1-2) (Figs 1-2);
use a set of probability density functions, PDFs, to determine, for a plurality of pairs of systolic blood pressure and diastolic blood pressure values, respective probabilities of obtaining the measurements in the envelope data set for different pairs of values of the systolic blood pressure and diastolic blood pressure (Section 2, PDF) (Fig 4), and combining the respective probabilities for the measurements in the enveloped data set by summing logarithms of the respective probabilities to obtain a likelihood for the envelope data set for that pair of systolic blood pressure and diastolic blood pressure values (Section 2.2) (Fig 4) (Equations 8-16); and
determine the systolic blood pressure and the diastolic blood pressure for the subject as the pair of systolic blood pressure and diastolic blood pressure values that provides the highest likelihood for the envelope data set (Fig 4), and output the determined systolic blood pressure and diastolic blood pressure to a user interface or patient record (Section 2.2, patient record data saved) (Fig 4).
For claim 10, Lee discloses the apparatus as claimed in claim 9, wherein the processor is configured to use the set of PDFs by determining respective probabilities for obtaining the measurements in the envelope data set for all possible pairs of values of the systolic blood pressure and diastolic blood pressure (Section 2) (Fig 4).
For claim 11, Lee discloses the apparatus as claimed in claim 9, wherein the processor is configured to use the set of PDFs and determine the systolic blood pressure and diastolic blood pressure by using an iterative optimization algorithm to determine the highest likelihood for the envelope data set (Section 2) (Fig 4).
For claim 12, Lee discloses the apparatus as claimed in claim 9, wherein the processor is configured to use the set of PDFs by:
determining a blood flow scaling factor based on the one or more highest measurements of blood flow in the envelope data set (Section 2, NPB) (Fig 4); and
using the blood flow scaling factor to align blood flow values in the set of PDFs and the measurements of blood flow in the envelope data set (Section 2) (Fig 4).
For claim 13, Lee discloses the apparatus as claimed in claim 12, wherein the processor is configured to determine the blood flow scaling factor based on a function or median average of a plurality of the highest measurements of blood flow in the envelope data set (Section 2) (Fig 4).
For claim 14, Lee discloses the apparatus as claimed in claim 9, wherein the processor is further configured to:
generate the set of PDFs from a prior distribution relating to systolic blood pressure and diastolic blood pressure (Section 2) (Fig 4).
For claim 15, Lee discloses the apparatus as claimed in claim 9, wherein the prior distribution comprises, or is based on, one or more of the following: information on physiologically possible values of systolic blood pressure and diastolic blood pressure (Section 2); information on physiologically possible differences in systolic blood pressure and diastolic blood pressure values (Section 2); one or more previous values of systolic blood pressure and diastolic blood pressure for the subject (Section 2); one or more characteristics of the subject (Section 2); one or more characteristics of a sensor used to measure the blood flow (Section 2); and one or more characteristics of a device used to apply restriction to the body part (Section 2).
Response to Arguments
Applicant’s arguments, see pages 7-12, filed 2/20/26, with respect to the amended claims overcoming the 101 rejection have been fully considered and are persuasive. The previous 101 rejection of the claims has been withdrawn.
Applicant's arguments, see pages 13-14, filed 2/20/26, with respect to the anticipatory rejection of the amended claims under Lee (as set forth hereinabove) have been fully considered but they are not persuasive.
Applicant argues:
The amendment to claims 1 and 9 introduces additional, concrete limitations directed to
(i) how the envelope is formed from cuff-pressure data,
(ii) how likelihoods are computed across the entire envelope, and
(iii) how the resulting systolic and diastolic blood pressures are integrated into a
monitoring environment.
These limitations are not identified anywhere in the Office Action's mapping to Lee and, to the best of Applicant's understanding, are not taught by Lee at all. Indeed, Examiner's mapping to Lee does not identify, and does not purport to identify, any passage in Lee that:
(i) Treats the entire oscillometric envelope as a set of samples from a family of PDFs
parameterized directly by systolic and diastolic blood pressure values, and computes, for each candidate pair, a joint likelihood by combining the respective probabilities for the measurements via a sum of their logarithms; or
(ii) Determines the systolic and diastolic blood pressures as the pair that provides the highest such likelihood for the envelope data set and then outputs those determined values to a user interface or patient record.
Rather, Examiner references Section 2 and Figures 1-4 of Lee at a high level to assert that Lee uses a Bayesian model to estimate blood pressure from oscillometric signals. (Previous Action, pp. 7-11). A reference to general Bayesian processing does not, however, disclose the specific combination of envelope-wide probability computation and log-sum combination as claimed, nor does it disclose the claimed integration into a physiological monitoring device with user-interface/record output.
Moreover, with respect to claim 9 in particular, the Previous Action's mapping does not identify Lee as disclosing a processor configured to treat the cuff pressure sensor as both the pressure sensor and the blood-flow sensor, generating a cuff-oscillation envelope "comprising pairs of static cuff pressure and cuff pressure oscillation amplitude" during inflation and/or deflation, as now expressly recited. The cited passages from Lee in the Office Action are silent on these specific apparatus-level configurations and operations.
The Examiner respectfully disagrees and notes the following in response:
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “how the envelope is formed”, “computed across the entire envelope”, “resulting systolic and diastolic blood pressures are integrated into a monitoring environment”, “treats the entire oscillometric envelope as a set of samples from a family of PDFs”, “a family of PDFs parameterized directly by systolic and diastolic blood pressure values”, “parameterized directly by systolic and diastolic blood pressure values”, “computes, for each candidate pair, a joint likelihood by combining the respective probabilities for the measurements via a sum of their logarithms, “envelope-wide probability computation and log-sum combination”, “integration into a physiological monitoring device with user-interface/record output”, “a processor configured to treat the cuff pressure sensor as both the pressure sensor and the blood-flow sensor”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
In response to applicant’s argument that Lee fails to disclose determining “the systolic and diastolic blood pressures as the pair that provides the highest such likelihood for the envelope data set and then outputs those determined values to a user interface or patient record” and/or “comprising pairs of static cuff pressure and cuff pressure oscillation amplitude”, the Examiner respectfully and notes that Lee explicitly states and shows inter alia the following (emphasis added):
Section 1:
“Oscillometric measurements have recently gained popularity and are used in blood pressure (BP) monitors, which are now readily available on the market [1–9]. Although vendors of oscillometric BP monitors rarely disclose their algorithms, and the determination of oscillometric systolic blood pressure (SBP) and diastolic blood pressure (DBP) values has been declared controversial [1], the maximum amplitude algorithm (MAA) is one of the most popular algorithms for estimation of arterial blood pressure (ABP) using the oscillometric measurement [2–4]. The MAA approximates the mean blood pressure as the cuff pressure (CP) at which the maximum oscillation in amplitude occurs and then linearly relates the SBP and DBP to this mean pressure using heuristically obtained ratios [2,3,5]. These ratios are utilized to determine the time points at which the cuff pressure (CP) coincides with the systolic and diastolic pressures, respectively”
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Section 2.2:
“As the number of measurements increases with respect to one subject, the probability density function (PDF) of brs i;j and brd i;j becomes normal and more concentrated about bri_ (shown in Figure 3) as observed using a nonparametric bootstrap (NPB) technique [16] and confirmed through a normality test as presented in [17]. The NPB method is most useful technique where we do not know the sampling distribution. As the distribution of the pseudo SBP ratios using the NBP approximates the distribution of the original SBP five ratios, we use to check normality of the SBP ratio’s distribution [17]. Specifically, the distribution of the pseudo SBP ratio in Figure 3a are obtained from each individual subject employing the NPB algorithms because we have only five measurements for each subject. A similar procedure is followed for the pseudo DBP ratio as shown in Figure 3b. The fundamental concept of the NPB technique is to offer a large number of independent bootstrap ratios by resampling the original five ratios r = (r1; r2; :::; rm) of m measurements at random from a unknown probability distribution F.”
“The following step by step procedure is used to estimate the SBP and DBP ratios using the Bayesian approach as shown Figure 4.
(1) As the first step, the ranges of the systolic and diastolic ratios used in the proposed method are initially found experimentally [2,3] and the PP is defined as shown in Equations (7) and (8).
(2) The SBP and DBP estimates are obtained using both the MA value and the fixed a priori ratios of SBP and DBP.
(3) The reference SBPR and DBPR are obtained using the reference auscultatory measurement, which itself is obtained using the cuff pressure, reference auscultatory measurement, and maximum amplitude for each subject.
(4) The a priori likelihoods are obtained as shown in Equation (11) to Equation (12).
(5) The calculation of POP is performed to determine the final ratio of SBP and DBP in Equations (9) and (10).
(6) The SBPR and DBPR that produced the maximum a posteriori probability in Equation (17) are taken as the best ratio for the measurement. As the estimation of the SBPR and DBPR is based on the a priori probability and the likelihood function, the final ratios brs i;j are presented as the maximum a posteriori probability. Brs i;j = arg max cr(l)s(i;j) p(cr(l)s(i;j)jbys(i;j)) (17)
Similarly, the ratio for the DBP, brd i;j can also be obtained. Using these ratios, the SBP and DBP estimates are obtained. In the method above, each measurement will produce one SBPR and DBPR.”
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 Jeffrey G. Hoekstra whose telephone number is (571)272-7232. The examiner can normally be reached Monday through Thursday from 5am-3pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles A. Marmor II can be reached at (571)272-4730. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Jeffrey G. Hoekstra
Primary Examiner
Art Unit 3791
/JEFFREY G. HOEKSTRA/ Primary Examiner, Art Unit 3791
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