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 .
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 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.
Claims 23-40 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ong et al. (US 2011/0224565; hereinafter “Ong”).
Regarding claim 23, Ong discloses a system for triaging sepsis patients, comprising: memory (e.g. ¶¶ 59); at least one processor (e.g. ¶¶ 59); and a prediction module comprising a predictive model, wherein the memory stores instructions that, when executed by the processor(s), cause the processor(s) to: receive electrocardiogram data (ECG data) comprising data of a plurality of heart beats from a patient (e.g. ¶¶ 168); determine a value for each of a plurality of heart rate variability (HRV) metrics and each of a plurality of heart rate n-variability (HRnV) metrics from the ECG data (e.g. ¶¶ 199-203); transform the heart rate n-variability metrics into respective vector representations (e.g. ¶¶ 87-90); determine one or more kernel metrics based on a combination of at least two vector representations (e.g. ¶¶ 247-248); apply the predictive model to the values and the kernel metrics to determine a sepsis risk category for the patient from a plurality of sepsis risk categories (e.g. ¶¶ 151-153, 139 – where sepsis risk is a consideration).
Regarding claim 32, Ong discloses a method for triaging sepsis patients, comprising: receiving electrocardiogram data (ECG data) comprising data of a plurality of heart beats from a patient (e.g. ¶¶ 168); determining a value for each of a plurality of heart rate variability (HRV) metrics and each of a plurality of heart rate n-variability (HRnV) metrics from the ECG data (e.g. ¶¶ 199-203); transforming the heart rate n-variability metrics into respective vector representations (e.g. ¶¶ 87-90); determining one or more kernel metrics based on a combination of at least two vector representations (e.g. ¶¶ 247-248); applying a predictive model to the values and the kernel metrics to determine a sepsis risk category for the patient from a plurality of sepsis risk categories (e.g. ¶¶ 151-153, 139 – where sepsis risk is a consideration).
Regarding claims 24 and 33, Ong discloses the kernel metrics are determined using any one of: cosine similarity function, polynomial kernel function, sigmoid kernel function, RBF kernel function, Laplacian kernel function or Chi-squared kernel function (e.g. ¶¶ 240).
Regarding claims 25 and 34, Ong discloses the processor(s) are further configured to receive data, the data comprising at least one of patient demographic data and clinical data, and the processor(s) applies the predictive model to the values to determine a sepsis risk category for the patient by applying the predictive model to the patient data (e.g. ¶¶ 151-153, 139 – where sepsis risk is a consideration).
Regarding claims 26 and 35, Ong discloses at least one of the HRnV parameters is NNxn, where: N is a number of conventional RR intervals (RRIs) combined to form a single RR n-interval (RRnI), N «N where N is a total number of RRIs in the ECG data; 1snsN; x is an absolute variation multiple; and NNxn is a number of times an absolute difference between successive RRnls exceeds xn milliseconds (e.g. ¶¶ 200-217).
Regarding claims 28 and 37, Ong discloses x is 50 (e.g. ¶¶ 211).
Regarding claims 27 and 36, Ong discloses at least one of the HRnV parameters is pNNxn, where: N is a number of conventional RR intervals combined to form a single RR n- interval (RRnI), N «N where N is a total number of RRIs in the ECG data;1snsN;x is an absolute variation multiple; andpNNxn is a number of times an absolute difference between successive RRnls exceeds xn milliseconds, expressed as a proportion of N (e.g. ¶¶ 210-217).
Regarding claims 29 and 38, Ong discloses the predictive model is a machine learning model trained on past data from a pool of patients, to identify patterns in the values corresponding to the sepsis risk categories (e.g. ¶¶ 222, 255-258, etc.).
Regarding claims 30 and 39, Ong discloses the HRV parameters and/or HRnV parameters comprise at least one of a time domain parameter, a frequency domain parameter, a Poincare parameter, a deviation parameter, an entropy parameter, and a detrended fluctuation analysis (DFA) parameter (e.g. ¶¶ 162).
Regarding claims 31 and 40, Ong discloses the HRV parameters and/or HRnV parameters comprise one or more parameters from Table 1 (e.g. ¶¶ 314-315).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael D’Abreu whose telephone number is (571) 270-3816. The examiner can normally be reached on 7AM-4PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Hamaoui can be reached at (571) 270-5625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL J D'ABREU/Primary Examiner, Art Unit 3796
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