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 Objections
Claims 6, 25 are objected to because of the following informalities:
Claim 6: “the first coherence are based on a single band of frequencies” should be “the first coherence is based on a single band of frequencies”.
Claim 25: “the stored instructions when executed cause the system controller to determine the presence or the absence of the confounding factor further cause the system controller to determine…” should be “… and further cause the system controller to determine …”
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 21, 39 and associated dependent claims are 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 21 recites the limitation "the target sensing device". There is insufficient antecedent basis for this limitation in the claim. It is not clear if the target sensing device is referring to the previous “third sensing device” or another device.
Regarding claim 39, the dependency is on cancelled claim 36.
Claim Rejections - 35 USC § 103
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 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(s) 1-2,4-6,10,13,15,17,21,23-26,30-31,35 and 37-39 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thewissen, “Measuring Near-Infrared Spectroscopy Derived Cerebral Autoregulation in Neonates: From Research Tool Toward Bedside Multimodal Monitoring” in view of Caldwell, “Modelling confounding effects from extracerebral contamination and systemic factors on functional near-infrared spectroscopy”.
Regarding claim 1, Thewissen discloses a method for determining a target physiologic parameter of a subject [Introduction: “the ability of the human body to maintain cerebral blood flow (CBF) in a wide range of perfusion pressures, can be calculated by describing the relation between arterial blood pressure (ABP) and cerebral oxygen saturation measured by near-infrared spectroscopy (NIRS)”; “Maintaining adequate brain tissue oxygenation…is one of the major goals”], comprising:
sensing a subject with a first sensing device configured to sense a first physiologic parameter, the first sensing device producing first physiologic data signals representative of the first physiologic parameter during a period of time [Clinical Framework; Figure 3; monitoring producing continuous arterial blood pressure signals; Table 1: sample frequencies ranging from 100Hz to 0.03Hz for ABP data acquisition over measurement durations ranging from minutes to 72 hours; Introduction: “To study dynamic flow-pressure CAR, the continuous measurement of changes in CPP, and thus ABP, is mandatory”];
sensing the subject with a second sensing device configured to sense a second physiologic parameter, the second sensing device producing second physiologic data signals representative of the second physiologic parameter during the period of time [Clinical Framework; Figure 3; pulse oximetry monitoring of arterial oxygen saturation (SaO2) as part of the multimodal monitoring setup; Preprocessing: correction for SaO2; “Arterial oxygen saturation (SaO2) has a major influence on NIRS derived cerebral oxygenation”; Table 1: various studies acquiring SaO2 concurrently with other signals];
sensing the subject with a third sensing device configured to sense a target physiologic parameter, the third sensing device producing target physiologic data signals representative of the target physiologic parameter during the period of time [Clinical Framework; Figure 3; NIRS monitoring measuring “cerebral oxygen saturation” and related parameters including “rTHb, relative total tissue hemoglobin”; Introduction: “With NIRS, cerebral oxygen saturation and cerebral fractional tissue oxygen extraction (cFTOE) can be measured”; Table 1: studies using HVx defined as “moving COR between MAP and rTHb” with NIRS instrument];
determining a presence or an absence of a confounding factor that taints a determination of the target physiologic parameter, the determination using the first physiologic data signals, the second physiologic data signals, and the target physiologic data signals [Preprocessing: correction for SaO2, “Arterial oxygen saturation (SaO2) has a major influence on NIRS derived cerebral oxygenation, leading to a hypoxic (low oxygen content) cerebral desaturation but not necessarily an ischemic (low blood flow) cerebral desaturation”; “De Smet et al. proposed the use of partial coherence (PACOH) in order to correct for variations in SaO2 on the NIRS signals”; “Caicedo et al. proposed the use of oblique sub-space projections (ObSP)…ObSP makes use of sub-space system identification that uses input-output observations of the system in order to produce a mathematical model that can explain the measured output…ObSP is able to decouple the linked dynamics between the different underlying subsystems in order to decompose the observed output in terms of the partial contributions of each input variable”];
advancing the first physiologic data signals, the second physiologic data signals, and the target physiologic data signals produced in the absence of the confounding factor for further processing, and setting aside the first physiologic data signals, the second physiologic data signals, and the target physiologic data signals produced in the presence of the confounding factor [Preprocessing: correction for SaO2, “Most prevalent is the exclusion of data with variability in SaO2 larger than 5%. Therefore, conclusions are based on patients during stable SaO2”; Preprocessing: Artifact Removal, “Once artifacts have been detected, they can be corrected by linear interpolation or simply eliminated for further analysis”]; and
determining a value of the target physiologic parameter using the first physiologic data signals, the second physiologic data signals, and the target physiologic data signals produced in the absence of the confounding factor [Mathematical Models: “All of these methodologies try to quantify the relationship between ABP and CBF”; Table 1: calculation of CAR indices such as HVx, COx, COH, TF gain using ABP and NIRS signals during periods of stable SaO2; Discussion: “De Smet et al…correction was applied…this set of signals can be used to define scores for the assessment of the coupling between systemic and brain hemodynamic variables. In essence a correction for SaO2 is provided by eliminating its contribution in the observed NIRS signal, which makes the residual component suited for the assessment of CAR even when changes in SaO2 are present”].
However, Thewissen does not explicitly disclose that the step of determining the presence or absence of the confounding factor expressly uses the first physiologic data signals [ABP], the second physiologic data signals [SaO2], and the target physiologic data signals [NIRS/rTHb] together in a unified confounding determination step. While Thewissen teaches confounding correction using SaO2 and discusses partial coherence and ObSP methods that analyze multiple signals, the reference does not explicitly detail a unified methodology wherein all three signal types are simultaneously evaluated to determine confounding presence.
Caldwell, also directed towards non-invasive physiological monitoring using functional near-infrared spectroscopy to determine cerebral hemodynamic parameters and address the problem of confounding factors [e.g., systemic blood pressure changes, oxygen saturation variations, extracerebral contamination] that taint NIRS-derived measurements, discloses determining a presence or an absence of a confounding factor that taints a determination of the target physiologic parameter, the determination using first physiologic data signals [blood pressure], second physiologic data signals [oxygen saturation], and target physiologic data signals [NIRS cerebral hemoglobin signals] together [Section 3.2: “Numerical optimisation was performed to find combinations of input changes that could generate false positive (FP) outputs—resembling Fig. 5A despite the absence of genuine functional activation—and false negative (FN) outputs—resembling the scenarios of Fig. 5B despite an actual increase in metabolic demand”; Fig.5: simulated false positives and false negatives arising from systemic contamination by evaluating Pa, SaO2, and NIRS Hb signals together; Fig. 6: simultaneous variation of PaCO2 and blood pressure to classify responses as true or false; Abstract: “changes in systemic physiological parameters such as blood pressure and concentration of CO2 can also affect regional blood flow and may confound haemodynamics-based neuroimaging…It is therefore important to record the major potential confounders in the course of fNIRS experiments. Our model may then allow the observed behaviour to be attributed among the potential causes and hence reduce identification errors”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Thewissen with the teachings of Caldwell – using multiple physiological signals [e.g., blood pressure, oxygen saturation, and NIRS target signals] together in a unified confounding determination step, as taught by Caldwell, would enhance the base method of Thewissen by improving its ability to reliably and accurately distinguish between genuine cerebral hemodynamic responses and confounding artifacts from systemic physiological changes, since Caldwell explicitly demonstrates that analyzing multiple signals simultaneously allows attribution of observed behavior among potential causes and reduces identification errors [Caldwell, Abstract].
Regarding claim 21, Thewissen discloses a system for determining a target physiologic parameter of a subject [Fig. 3: “Clinical framework to study cerebral flow-pressure autoregulation status using multimodal monitoring”], comprising:
a first sensing device configured to sense a first physiologic parameter continuously during a period of time, and to produce first physiologic data signals representative of the first physiologic parameter during the period of time [Fig. 3C: e.g., ABP monitor];
a second sensing device configured to sense a second physiologic parameter continuously during the period of time, and to produce second physiologic data signals representative of the second physiologic parameter during the period of time [Fig. 3C: e.g., SaO2 monitor];
a third sensing device configured to sense a target physiologic parameter continuously during the period of time, and to produce target physiologic data signals representative of the target physiologic parameter during the period of time [Fig. 3C: e.g., NIRS monitor];
a system controller in communication with the first sensing device, the second sensing device, and the target sensing device, the system controller including at least one processor and a memory device configured to store instructions, the stored instructions when executed cause the system controller to [Fig. 3: multimodal monitoring system and data processing pipeline which implicitly requires a system controller/processor to execute the mathematical models]:
a) determine a presence or an absence of a confounding factor that taints a determination of the target physiologic parameter, the determination using the first physiologic data signals, the second physiologic data signals, and the target physiologic data signals [Page 5: artifact detection and SaO2 variability exclusion];
b) advance the first physiologic data signals, the second physiologic data signals, and the target physiologic data signals produced in the absence of the confounding factor for further processing, and set aside the first physiologic data signals, the second physiologic data signals, and the target physiologic data signals produced in the presence of the confounding factor [Page 5: “exclusion of data”, “simply eliminated”]; and
c) determine a value of the target physiologic parameter using the first physiologic data signals, the second physiologic data signals, and the target physiologic data signals produced in the absence of the confounding factor [Fig. 3; E].
However, Thewissen does not explicitly disclose determining the presence or absence of the confounding factor by simultaneously evaluating the first, second, and target physiologic data signals to identify false responses.
Caldwell discloses determining the presence or absence of a confounding factor [false positives/negatives] using the first physiologic data signals [blood pressure], the second physiologic data signals [SaO2], and the target physiologic data signals [NIRS] [Fig. 5: simulated false positives and false negatives arising from systemic contamination by evaluating Pa, SaO2, and NIRS Hb signals together].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Thewissen with the teachings of Caldwell for the same reasons and predictable results as set forth above in the analysis of claim 1.
Regarding claim 2, Thewissen discloses wherein the signals produced in the presence of the confounding factor are not used in the step of determining the value of the target physiologic parameter [Page 5: “simply eliminated for further analysis”].
Regarding claims 4 and 24, Thewissen and Caldwell disclose wherein the step of determining the presence or the absence of the confounding factor uses a frequency domain methodology [Thewissen, Mathematical Models -- Frequency Domain: “Frequency domain analysis explores the relation between 2 signals in specific frequency bands. A major advantage is that it considers the fact that CAR may be composed of responses with different temporal properties”; Preprocessing -- Correction for SaO2, “De Smet et al. proposed the use of partial coherence (PACOH) in order to correct for variations in SaO2 on the NIRS signals” – i.e., partial coherence is a frequency domain method] [Caldwell, Page 95: “frequency domain multi-distance method… to compensate for confounding”].
Regarding claims 5 and 25, Thewissen discloses determining a first coherence between the first physiologic data signals and the target physiologic data signals, and a second coherence between the second physiologic data signals and the target physiologic data signals [Page 5: “De Smet et al. proposed the use of partial coherence (PACOH) in order to correct for variations in SaO2 on the NIRS signals” -- computing the coherence between ABP/NIRS and SaO2/NIRS to partial out the confounding effect].
Regarding claims 6 and 26, Thewissen discloses wherein the first coherence is based on a single band of frequencies. [Table 1: coherence calculations in specific single bands such as the “0.003-0.04 Hz band”].
Regarding claims 10 and 30, Caldwell discloses determining a first trend of the first physiologic parameter, a second trend of the second physiologic parameter, and a third trend of the target physiologic parameter, and comparing the first trend, the second trend, and the third trend relative to one another [Fig. 4-5: comparing time courses/trends of Pa, SaO2, and NIRS Hb signals to identify confounding factors].
Regarding claim 13, Thewissen discloses wherein the step of determining the presence or the absence of the confounding factor uses a correlation methodology [Page 12: “Among the temporal analysis of CAR, correlation (COR) and linear regression are most commonly used”].
Regarding claims 15 and 35, Thewissen discloses wherein the steps are performed on a continuous basis during the period of time [Page 17, “continuous bedside CAR measurement”; Fig. 3 caption: “collected continuously in a time-stamped method”].
Regarding claims 17 and 37, Thewissen discloses wherein the target physiologic parameter is relative total hemoglobin concentration per volume of tissue (rTHb) of tissue sensed [Table 1, listing multiple studies utilizing “rTHb” as the target parameter].
Regarding claim 23, Thewissen discloses determining the presence or the absence of the confounding factor using a comparison of processed signals [Figure 3D: preprocessing data by down sampling and filtering before comparison and analysis].
Regarding claim 31, Thewissen discloses using one or more polarity filters configured to evaluate the first trend, the second trend, and the third trend [Thewissen, Figure 4 caption: “A large and positive COR coefficient indicates impaired CAR, while a small or negative COR coefficient indicates intact CAR” which constitutes evaluating the polarity of the trends].
Regarding claim 38, Thewissen discloses wherein the third sensing device is a near infrared spectroscopy (NIRS) tissue oximeter [Page 3: “NIRS reflects the effect of changes in ABP… cerebral oxygen saturation… can be measured”; Table 1: NIRS instruments used to measure rTHb including “INVOS 5100”, “NIRO 300,” “NIRO 500,” “Foresight”].
Regarding claim 39, Thewissen discloses wherein the first physiologic parameter relates to a blood pressure of the subject, and the first sensing device is a blood pressure sensing device. [Figure 3C: ABP monitoring].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. WO 2016164891 discloses optical sensing with continuous physiologic monitoring utilizing well-known conventional processing elements.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tse Chen whose telephone number is (571)272-3672. The examiner can normally be reached M-F 7-3 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jonathan Moffat can be reached at 571-272-4390. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/TSE CHEN/Supervisory Patent Examiner, Art Unit 3791
Prosecution Insights