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 to Applicant
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on January 15, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 7-11 are objected to because of the following informalities:
Claim 7 recites “a validation steprule”. This limitation should be modified to read “a validation step”.
Claims 8-11 are objected to by virtue of their dependency on claim 7.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Subject Matter Eligibility Criteria – Step 1:
The claims recite subject matter within a statutory category as a process and a machine (claims 1-20). Accordingly, claims 1-20 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria – Step 2A – Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP §2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and /or c) mathematical concepts. MPEP §2106.04(a).
The Examiner identifies method claim 1 as the claim that represents the claimed invention and is similar to system claim 7 and method claim 12.
Claim 1:
A method for patient screening for outcome risk, comprising:
recording raw bispectral electroencephalograph (“BSEEG”) values via a bispectral electroencephalograph handheld device in two distinct channels, the bispectral electroencephalograph handheld device comprising between two and twenty sensors configured to measure the brainwaves of a patient;
partitioning the BSEEG values into windows for data processing;
normalizing the raw BSEEG values to calculate a normalized bispectral electroencephalography ("NBSEEG") score relative to a population of NBSEEG scores of other patients; and
outputting an outcome NBSEEG score,
wherein the data processing comprises a fast Fourier transform function, an analysis step, and a validation step.
These above limitations, not in bold, under their broadest reasonable interpretation, cover performance of the limitation as, mathematical concepts. The claim recites performing data processing, wherein the data processing comprises a fast Fourier transform function, which is a mathematical calculation.
These above limitations, not in bold, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. The claim elements are directed towards a method of recording, normalizing, and outputting scores indicative of “hospital length of stay, discharge disposition, and/or mortality risk” (claim 6), which is assessment of a patient’s health condition. Assessing patient health via electroencephalograph is a human activity that is routinely performed by neurologists for their patients.
Accordingly, the claim recites an abstract idea.
Claims 7 and 12 are abstract for similar reasons.
Subject Matter Eligibility Criteria – Step 2A – Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the idea into a practical application. As noted at MPEP §2106.04 (ID)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional elements beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional elements” while the underlined portions continue to represent the at least one “abstract idea”):
Additional elements in other claims:
a bispectral electroencephalograph handheld device (1,12); device comprising between two and twenty sensors (1,7,12); a handheld system (7); a processor (7); a signal processing device (11); a signal processing module (14); feature analysis module (14)
The handheld device and its constituent parts (handheld system, processor, signal processing device, signal processing module, feature analysis module) are taught at a high level of generality such that the claim elements amounts to no more than mere instructions to apply the exception using any generic component capable of performing the claim limitations. Based on paragraph [0036] of Applicant specification, the Examiner notes that the handheld device appears to encompass general-use computers, such as a smartphone. No specific, technical improvements are being made to handheld device technologies. [0036] recites: “One general aspect includes a system for patient screening, including a handheld screening device including a housing; at least two sensors configured to record one or more brain signals and generate one or more values; a processor and at least one module configured to: perform spectral density analysis on the one or more values and output data presenting an indication of the presence, absence, or likelihood of the subsequent development of mortality. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.” [0089], “The one or more servers/computing devices 42 may represent, for example, any one or more of a server, a computing device such as a server, a personal computer (PC), a laptop, a smart phone, a tablet or the like.”
The bispectral electroencephalograph handheld device and the between two and twenty sensors are also taught at a high level of generality. [0105] recites: “As shown in FIG. 8, in one exemplary implementation of the system 1, a patient 30 is being monitored by sensors 12A, 12B such as by way of the screening device 10 of FIG. 1. In this implementation, the sensors 12 produce one or more signals 80A, 80B from the patient 30.” [0131] recites: “In certain embodiments, the systems and methods may utilize electroencephalogram (EEG) technology that is simplified for an end user.” [0133] further recites: “In certain embodiments, the one or more sensors 12 may be one or more brain sensors, such as, but not limited to, EEG devices, such as one or more EEG leads/electrodes… The one or more signals may be EEG signals. EEG signals may include voltage fluctuations resulting from ionic current within neurons of the patient's brain. In certain embodiments, there may be a plurality of sensors.” No specific, technical improvements are made to electroencephalograms and brain sensors as they are only applied to perform an insignificant extra-solution activity of data gathering.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(IID)(A)(2).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claim 2: This claim recites wherein the NBSEEG score is calculated by: comparing the raw BSEEG values with a BSEEG value population mean; and dividing the result by a BSEEG population standard deviation; which teaches an abstract idea of mathematical concepts by performing a z-score calculation.
Claim 3: This claim recites wherein the outcome NBSEEG score comprises an NBSEEG positive score or NBSEEG negative score; which only serves to further limit the abstract idea of the output score.
Claim 4: This claim recites wherein the outcome NBSEEG score is continuously recalculated and output; which only serves to further limit the abstract idea of the output score.
Claim 5: This claim recites wherein the recording is performed at a primary point of care; which teaches an abstract idea of managing personal behaviors by limiting where a patient can physically be to perform the method steps.
Claims 6, 7, and 20: These claims recite wherein the outcome NBSEEG score is correlated with at least one of hospital length of stay ("LOS"), discharge disposition, and / or mortality risk; which is an abstract idea of certain methods of organizing human activity as assessing a patient condition is a human activity typically performed by medical care professionals for their patients.
Claim 8: This claim recites the system further comprising outputting threshold data; which teaches the system at a high level of generality, such that it is applied to perform the insignificant extra-solution activity of simply outputting data.
Claim 9 and 13: This claim recites the system further comprising comparing the outcome NBSEEG score to a threshold; which teaches an abstract idea of mathematical concepts by comparing a number to a threshold.
Claim 10: This claim recites the system further comprising a signal processing device; which teaches a signal processing device at a high level of generality such that it is only applied to perform the abstract idea of performing a fast Fourier transform.
Claim 14: This claim recites wherein the raw BSEEG values are processed via a signal processing module or feature analysis module in the handheld device; which teaches the handheld device and its module components at a high level of generality, such that they are only applied to perform the abstract idea of processing BSEEG values.
Claim 15: This claim recites wherein the outcome NBSEEG score is categorized as low, medium or high risk by comparison to one or more thresholds; which only serves to further limit the abstract idea of the output score.
Claim 16: This claim recites the method further comprising maintaining a BSEEG population norm value; which teaches an abstract idea of mathematical concepts by maintaining a mean data value.
Claim 17: This claim recites wherein the NBSEEG score is calculated by: comparing the raw BSEEG with the mean of the BSEEG population norm; and dividing the result by the BSEEG population standard deviation; which teaches an abstract idea mathematical concepts by performing a z-score calculation.
Claim 18: This claim recites the method further comprising recording subject outcome; which teaches an abstract idea of certain methods of organizing human activity, as recording patient outcomes is a human activity typically performed by medical staff.
Claim 19: This claim recites wherein the BSEEG population norm is updated to include the raw BSEEG values and subject outcome; which only serves to further limit the abstract idea of the mean value.
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
Amount to elements that have been recognized as activities in particular fields (such as determining the wellness categories of a person based on tested blood, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv); providing a credit offset for the deductible, e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii).
Examiner notes that the bispectral electroencephalograph handheld device and corresponding sensors are known, as evidenced by:
Sarkela (US 20070010755): [0001], “The present invention relates generally to the monitoring of the cerebral state of a subject. The invention finds a typical application in a monitoring process in which the sedation or hypnosis of a patient is assessed by determining a so-called Bispectral Index (BIS.TM.).” [0003], “Electroencephalography (EEG) is a well-established method for assessing brain activity. When measurement electrodes are attached on the skin of the skull surface, the weak biopotential signals generated in the pyramid cells of the cortex may be recorded and analyzed. The EEG has been in wide use for decades in basic research of the neural systems of the brain as well as in the clinical diagnosis of various central nervous system diseases and disorders.” [0062], “The physiological signal(s) obtained from one or more sensors attached to a patient 10 are supplied to an amplifier stage 71, which amplifies the signal(s) before they are sampled and converted into digitized format in an A/D converter 72. ”
Viertio-Oja (US ): [0010], “The biopotential electrical signals are usually obtained by a pair, or plurality of pairs, of electrodes placed on the patient's scalp at locations designated by a recognized protocol and a set, or a plurality of sets or channels, of electrical signals are obtained from the electrodes. These signals are amplified and filtered. The recorded signals comprise an electroencephalogram or EEG.” [0025], “In the form implemented in the product, the parameter, called bispectral index, BIS, consists of the following four subcomponents: SyncFastSlow, BetaRatio, Burst Suppression (BSR), and "QUAZI". The calculation of the subparameter SyncFastSlow utilizes bispectral analysis in the frequency-domain.”
Cromwell (WO 2017096358 A1): [0045], “The disclosed systems, devices and methods relate to non-invasive, point of care diagnostics using fewer than the sixteen-, twenty- or twenty four-lead EEGs found in the prior art. For example, as shown generally in FIG. 1, in certain implementations a 2-lead "bispectral electroencephalography" (BSEEG) screening system 1 is employed, which can be performed with a handheld screening device 10 by applying two leads 12 A, 12B to the forehead of a patient 30 for less than 10 minutes.”
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-6, 8-11, and 13-20, additional limitations which amount to elements that have been recognized as activities in particular fields, claims 2-6, 8-11, and 13-20, e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii); claim claims 2-6, 8-11, and 13-20, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
Claims 1 and 3-15 are rejected under 35 U.S.C. 103 as being unpatentable over Schnetz (US 20190046122) in view of Sigl (Sigl; Jeffrey C.; An introduction to bispectral analysis for the electroencephalogram, November 1994, Journal of Clinical Monitoring).
Regarding claim 1, Schnetz teaches a method for patient screening for outcome risk, comprising:
recording raw bispectral electroencephalography (“BSEEG”) values via a bispectral electroencephalograph handheld device ([0006], “a computer-implemented method is provided, the method comprising obtaining concurrent measures of mean arterial pressure (MAP), bispectral index (BIS),” [0045], “Bispectral index (BIS): A measure for assessing the level of consciousness in a patient undergoing anesthesia. BIS is a derived variable of the EEG that provides a measure of the consistency of phase and power relationships among the various frequencies of the EEG.” [0161], “Cases were included if MAP, BIS, and inhalation anesthetic concentration data were monitored and recorded during the surgery.” [0090], “the disclosed technology can be implemented with other computer system configurations, including hand held devices,…”). The Examiner interprets BIS values to be analogous to BSEEG values.
the bispectral electroencephalograph handheld device comprising between two and twenty sensors configured to measure the brainwaves of a patient ([0095], “the computing system 200 includes one or more sensors for collecting the MAP, MAC, and BIS measurements from the patient during the surgery.” [0045], “BIS is a derived variable of the EEG that provides a measure of the consistency of phase and power relationships among the various frequencies of the EEG.”). Examiner notes that, under the broadest reasonable interpretation, one or more sensors encompasses between two and twenty sensors.
partitioning the BSEEG values into windows for data processing ([0163], “MAP, MAC and BIS raw data were normalized using a Z-score as these variables exist on different scales. Specifically, a Z-score was calculated for each individual measurement relative to the total population of values for that given variable (e.g. MAP) collected from all available cases (16,104). Variables were not consistently measured at the same time point or frequency (e.g. every 1 min vs. 5 min), thus were aggregated using a sliding window (Zeileis and Grothendieck (2005) Journal of Statistical Software, 1-27). For each variable, an average value was calculated over every five measurements starting at the beginning of the monitoring period. The sliding window was performed in non-overlapping, sequential manner. For example, window 1 represents averaged data from the 1.sup.st five measurements, while window 2 represents the next five measurements.”);
normalizing the raw BSEEG values to calculate a normalized bispectral electroencephalography (“NBSEEG”) score relative to a population of NBSEEG scores of other patients ([0088], “the average of the sum of the concurrent MAP, MAC, and BIS measures at each sequential time interval of the surgical procedure of a test patient can be compared with the corresponding values from the reference population of patients, for example to determine a prognosis for the test patient.” [0163], “MAP, MAC and BIS raw data were normalized using a Z-score as these variables exist on different scales.”); and
outputting an outcome NBSEEG score ([0030], “FIG. 14B: 30-day postoperative mortality predicted at individual TVI, MAP, BIS, and MAC z-scores. Grey shading represents 95% confidence interval. MAP=Mean arterial pressure. BIS=Bispectral Index.” [0082], “The determined prognosis (or data representing the determined prognosis) can be, for example, outputted to a user, included in a in a post-surgical report” [0205], “Disclosed herein is an index that combines mean arterial blood pressure (MAP), Bispectral Index (BIS), and minimum alveolar concentration (MAC) data into a single variable, called the Triple Variable Index (TVI), that can be mapped across the intraoperative period.”). The Examiner interprets the Triple Variable Index (TVI), which is a representative variable for normalized bispectral index values, to be analogous to an outputted NBSEEG score. Fig. 14B, below, shows that TVI scores can be generated based only on normalized BIS scores, separate from MAC and MAP scores.
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wherein the data processing comprises an analysis step ([0061], “As in the method described above, the MAP and BIS measures are taken concurrently from sequential time intervals of the surgical procedure, and a test data vector is formed that characterizes the concurrent MAC and BIS measures for the sequential time periods of the surgery of the test patient. Next, a K-means clustering analysis of the test data vector with a plurality of reference data vectors is performed.”)
and a validation step ([0059], “Whether a patient is statistically significant can be determined by using various well known statistic evaluation tools, for example, determination of confidence intervals, p-value determination, cross-validated classification rates and the like.” [0216], “Descriptive statistics were calculated for the patient, procedure, anesthetic (including intraoperative medications), and TVI characteristics. Means were calculated for normally distributed variables and presented with 95% confidence intervals and standard deviation values. Medians were calculated for non-normally distributed variables and presented with 95% confidence intervals and 1.sup.st-3″ quartiles. Ninety-five percent confidence intervals were generated for medians by identifying 0.025, 0.975 percentiles across 10,000 bootstrapped samples. Proportions were presented with 95% confidence intervals except those calculated for the most commonly observed surgical specialties. Variable distributions were compared using boxplots and histograms. For each boxplot, upper and lower whiskers represent maximum and minimum values, respectively. Upper and lower hinges represent 3.sup.rd and 1.sup.st quartiles, respectively. The horizontal, bold line represents the median value. MAP, BIS, MAC and TVI data were plotted across time using a generalized additive model (GAM) with cubic regression splines and Gaussian distributions.”).
Schnetz does not teach wherein the bispectral electroencephalograph handheld device records in two distinct channels and wherein the analysis comprises a transform function and at least one rule.
However, Sigl does teach wherein the bispectral electroencephalograph handheld device records in two distinct channels ([045], “FIG. 1, in certain implementations a 2-lead "bispectral electroencephalography" (BSEEG) screening system 1 is employed, which can be performed with a handheld screening device 10 by applying two leads 12 A, 12B to the forehead of a patient 30 for less than 10 minutes.”) and
wherein the analysis comprises a fast Fourier transform function ([067], “this analysis step can comprise performing a Fast Fourier Transform 100 to create or otherwise compare feature data (as shown in FIG. 6 at 64).”).
Schnetz in view of Sigl are considered analogous to the claimed invention because they are in the field of bispectral analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Schnetz with Sigl for the advantage of “detecting diffuse slowing and, with appropriate signal processing and user interface, may require no special expertise for placement or interpretation, and may be performed with the aid of a simple handheld screening device” (Sigl; [048]).
Regarding claim 3, Schnetz in view of Sigl teaches the method of claim 1. Schnetz further teaches wherein the outcome NBSEEG score comprises an NBSEEG positive score or NBSEEG negative score (Fig. 14B, [0163], “MAP, MAC and BIS raw data were normalized using a Z-score as these variables exist on different scales.”). The Examiner notes that TVI scores can be negative, as indicated in the figure below.
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Regarding claim 4, Schnetz in view of Sigl teaches the method of claim 1. Schnetz further teaches wherein the outcome NBSEEG score is continuously recalculated and updated ([0008], “The representation of the identified cluster indicates whether the test data vector clusters with the cluster of reference data vectors characterizing the relative high, medium, or low concurrent MAP, MAC, and BIS measures. In some embodiments, the representation of the identified cluster is outputted to a user in real time.”). The Examiner interprets continuously recalculating and updating to be functionally analogous to real-time processing.
Regarding claim 5, Schnetz in view of Sigl teaches the method of claim 1. Schnetz further teaches wherein the recording is performed at a primary point of care ([0208], “Surgeries that took place at University of Pittsburgh Medical Center (UPMC) Presbyterian and Montefiore hospitals were evaluated for study inclusion.”).
Regarding claim 6, Schnetz in view of Sigl teaches the method of claim 1. Schnetz further teaches wherein the outcome NBSEEG score is correlated with at least one of hospital length of stay ("LOS"), discharge disposition, and / or mortality risk ([0007], “the prognosis comprises a likelihood of one or more post-surgical outcomes comprising one or more of infection, pain, nausea, vomiting, delirium, post-surgical complications, acute kidney injury, respiratory failure, acute anemia, thrombocytopenia, heart failure, coagulopathy, acidosis, malnutrition, sepsis, shock, acute coronary events (such as myocardial injury and infarction), hospital stay length of greater than average, and death.”).
Regarding claim 7, this claim is rejected for the same reasons as claim 1 above. Schnetz further teaches a handheld system for patient screening for mortality risk ([0090], “the disclosed technology can be implemented with other computer system configurations, including hand held devices,…”), comprising:
a. between two and twenty sensors configured to record one or more brain frequencies ([0094], “data acquisition systems coupled to a plurality of sensors” [0095], “the computing system 200 includes one or more sensors for collecting the MAP, MAC, and BIS measurements from the patient during the surgery”). The Examiner notes that sensors that are for collecting bispectral index measurements would be configured to record one or more brain frequencies. Additionally, under the broadest reasonable interpretation, one or more sensors encompasses between two and twenty sensors.
b. a processor ([0091], “The central processing unit 222 executes computer-executable instructions and can be a real or a virtual processor.”).
Regarding claim 8, this claim is rejected for the same reasons as claim 6 above.
Regarding claim 9, Schnetz in view of Sigl teaches the system of claim 7. Schnetz further teaches the system further comprising outputting threshold data ([0187], “TVI patterns demonstrate unique combinations of disease states… Performing the analysis using lower and higher thresholds of sharing (25% and 35%) revealed similar findings shown in FIG. 6 (FIGS. 13 and 14).”).
Regarding claim 10, Schnetz in view of Sigl teaches the system of claim 7. Schnetz further teaches the system further comprising comparing the outcome NBSEEG score to a threshold ([0021], “FIG. 6: Profiles were grouped according to their TVI pattern and post-surgical mortality... Codes represented by light orange (denoted by the red box) were shared below the chosen threshold (30%).” [0187], “TVI patterns demonstrate unique combinations of disease states... Performing the analysis using lower and higher thresholds of sharing (25% and 35%) revealed similar findings shown in FIG. 6 (FIGS. 13 and 14).”).
Regarding claim 11, Schnetz in view of Sigl teaches the system of claim 7. Schnetz further teaches the system further comprising a signal processing device ([0090], “The disclosed technology can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network.” [0097], “The communication medium conveys information such as computer-executable instructions, compressed graphics information, video, or other data in a modulated data signal.”).
Regarding claim 12, this claim is rejected for the same reasons as claim 1 above. Schnetz further teaches a method ([0090], “This disclosure provides novel methods for integration of heterogeneous patient monitoring data obtained during a surgical procedure on a test patient to provide improved analysis of homeostatic capacity and patient outcome during and following surgery, such as to monitor patient homeostasis during the procedure (for example, to monitor the risk of an intraoperative hypotension (IOH) event) and/or to determine a prognosis for the patient following the procedure.”).
Regarding claim 13, this claim is rejected for the same reasons as claim 10 above.
Regarding claim 14, Schnetz in view of Sigl teaches the method of claim 12. Schnetz further teaches wherein the raw BSEEG values are processed via a signal processing module or feature analysis module in the handheld device ([0061], “At process block 110, MAP, MAC, and BIS measures are obtained by suitable means, for example, from data obtained when monitoring a patient in surgery.” [0064], “At process block 120, a test data vector is formed. The test data vector characterizes the concurrent MAP, BIS, and MAC measures for the sequential time periods of the surgery of the test patient that are obtained at step 110.” [0090], “the disclosed technology can be implemented with other computer system configurations, including hand held devices,…” [0090], “The disclosed technology can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network.” [0100], “The present innovations can be described in the general context of computer-executable instructions, such as those included in program modules, being executed in a computing system on a target real or virtual processor.”). The Examiner notes that the program module that performs the function of characterizing the obtained raw data encompasses processing raw BSEEG values via a signal processing or feature analysis module in the handheld device.
Regarding claim 15, Schnetz in view of Sigl teaches the method of claim 12. Schnetz further teaches wherein the outcome NBSEEG score is categorized as low, medium or high risk by comparison to one or more thresholds ([0008], “The representation of the identified cluster indicates whether the test data vector clusters with the cluster of reference data vectors characterizing the relative high, medium, or low concurrent MAP, MAC, and BIS measures. In some embodiments, the representation of the identified cluster is outputted to a user in real time. In some embodiments, clustering of the test data vector with the cluster of reference data vectors characterizing the relative high concurrent MAP, MAC, and BIS measures indicates a low risk of an intraoperative hypotension event. In some embodiments, clustering of the test data vector with the cluster of reference data vectors characterizing the relative low concurrent MAP, MAC, and BIS measures indicates a high risk of an intraoperative hypotension event.” [0187], “TVI patterns demonstrate unique combinations of disease states... Performing the analysis using lower and higher thresholds of sharing (25% and 35%) revealed similar findings shown in FIG. 6 (FIGS. 13 and 14).”).
Claims 2 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Schnetz (US 20190046122) in view of Sigl (Sigl; Jeffrey C.; An introduction to bispectral analysis for the electroencephalogram, November 1994, Journal of Clinical Monitoring) as evidenced by Bock (Bock; David E., Stats: Modeling the World, 1 January 2009, Pearson, 3rd, pg. 105-107).
Regarding claim 2, Schnetz in view Sigl teaches the method of claim 1. Schnetz further teaches wherein the NBSEEG score is calculated by: comparing the raw BSEEG values with a BSEEG value population mean; and dividing the result by a BSEEG population standard deviation ([0163], “MAP, MAC and BIS raw data were normalized using a Z-score as these variables exist on different scales.”). The Examiner notes that the definition of a Z-score is subtracting a measured value with the mean of the sample and dividing that by the standard deviation of the sample, as evidenced by Bock (Pg. 105, “To standardize a value, we simply subtract the mean performance in that event and then divide this difference by the standard deviation… These values are called standardized values, and are commonly denoted with the letter z. Usually, we just call them z-scores.”). Therefore, normalizing using a z-score encompasses the above claim limitations.
Regarding claim 16, Schnetz in view Sigl teaches the method of claim 12. Schnetz further teaches the system further maintaining a BSEEG population norm (Fig. 14B, [0163], “MAP, MAC and BIS raw data were normalized using a Z-score as these variables exist on different scales. Specifically, a Z-score was calculated for each individual measurement relative to the total population of values for that given variable (e.g. MAP) collected from all available cases (16,104).”). The Examiner notes that [0034] of Applicant Specification describes the norm as a “population norm BSEEG average”. Thus, the Examiner interprets population norm to be equivalent to a population mean. Additionally, because of the mathematical definition of a z-score requires subtraction of a score with the population mean, the norm/mean must be maintained or stored to calculate z-scores for each measurement, as evidenced by Bock (Pg. 105, “To standardize a value, we simply subtract the mean performance in that event and then divide this difference by the standard deviation.”).
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Regarding claim 17, this claim is rejected for the same reasons as claim 2 above.
Regarding claim 18, Schnetz in view Sigl teaches the method of claims 12, 16, and 17. Schnetz further teaches the method further comprising recording subject outcome ([0006], “the method further comprises determining a prognosis of the test patient based on the known physiological state during and/or following the procedure of the reference patients in the cluster including the test data vector” [0067], “The plurality of reference patients can be, for example, one or more of: surgical patients who have undergone similar (or the same) surgical procedure as that of the test patient, surgical patients who have undergone surgery at the same hospital system as that of the test patient, surgical patients who have undergone surgery at the same surgical center as that of the test patient. In some embodiments, the plurality of reference patients is a plurality of surgical patients who have undergone the same surgical procedure at the same surgical center as the test patient.”). The Examiner notes that recording a subject outcome is inherent with utilizing known physiological states of reference patients, as the reference patients are established by recording real patient outcomes.
Claims 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Schnetz (US 20190046122) in view of Sigl (Sigl; Jeffrey C.; An introduction to bispectral analysis for the electroencephalogram, November 1994, Journal of Clinical Monitoring) further in view of Singer (US 20170188932).
Regarding claim 19, Schnetz in view Sigl teaches the method of claims 12 and 16-18. Schnetz wherein the BSEEG population norm is updated to include subject outcome ([0167], “TVI profiles were given a mortality assignment based upon when the patient associated with the profile died. Assignments were mutually exclusive and were defined as (1) death occurring within 30 days, (2) after 30 days and within 1 year, (3) after 1 year and within 2 years, and (4) survived 2 years following surgery. Profiles were separated into groups based on their identified TVI pattern and mortality combinations (e.g. elevated TVI pattern profiles representing 30-day post-surgical mortality). Three TVI patterns and four possible post-surgical outcomes yielded twelve total groups.”). The Examiner interprets giving mortality assignments when a patient dies and defining them within one group out of twelve to encompass updating the population norm subject outcome after the patient dies.
Schnetz in view Sigl does not teach wherein BSEEG population norm is updated to include the raw BSEEG values.
However, Singer does teach wherein BSEEG population norm is updated to include the raw BSEEG values ([0023], “the systems and methods of the present disclosure provide tools for assessing a subject's neurological state based on a database containing brain electrical activity data for numerous subjects, as well as methods and systems for verifying the assessment, updating the database to expand the data included therein...” [0056], “the information related to the neurological state of a subject can take the form of brain electrical information (e.g., EEG) acquired using local device 20.” [0069], “The feature scores may be raw scores or they can be transformed into another mathematical score (e.g., z-score). A composite score, referred to herein as a “summary index score” may be calculated using the raw or transformed feature scores.” [0072], “the summary index score may be subjected to an additional mathematical transform (e.g., conversion to a percentile scale, or the mean/standard deviation of the summary score relative to a normal population”). It would be obvious to one of ordinary skill in the art that updating a database to expand the data that is analyzed would update the population norm/mean with raw data.
Schnetz in view of Sigl further in view of Singer are considered analogous to the claimed invention because they are in the field of bispectral analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Schnetz in view of Sigl with Singer for the advantage of “updat[ing] with data at multiple time points from multiple local devices and/or from multiple subjects” (Singer; [0030]).
Regarding claim 20, this claim is rejected for the same reasons as claim 6 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
System And Method For Guidance Of Anesthesia, Analgesia And Amnesia (US 20040079372) teaches a method for monitoring anesthetization of a patient, includes the steps of removably connecting a plurality of electrodes to the scalp of the patient and administering sufficient anesthesia to the patient so that the patient attains a plane of anesthesia selected by an operator. The brain waves of the patient are then amplified and digitized after the patient has been anesthetized, before beginning the medical procedure, to obtain a first set of digital data. The brain waves of the patient are then amplified and digitized during the medical procedure to provide a second set of digital data and the first and second sets of digital data are analyzed in at least one of a time domain and a frequency domain.
Method For Assessing Brain Function And Portable Automatic Brain Function Assessment Apparatus (US 20070032737) teaches a method and apparatus for performing rapid brain assessment may provide emergency triage to head trauma patients by analyzing a combination of spontaneous and evoked brain potentials. The spontaneous and evoked potentials are analyzed, and the results classified, to present a real-time assessment of a patient's brain, diagnosing any potential abnormalities therein.
Quantitative EEG Method To Identify Individuals At Risk For Adverse Antidepressant Effects (US 20100016751) teaches methods, apparatus, and systems for efficiently and accurately identifying individuals at risk for adverse effects from psychotropic or CNS-active treatment. Changes in a brain activity indicator (e.g. EEG cordance) are used to predict the adverse effects of treatment based on an experimentally derived cutoff value. For example, a reliable biological indicator is provided with high predictive capability for identifying, very early in the course of treatment (e.g. <=48 hours after start of treatment), those individuals who are at greatest risk for worsening suicidality and other adverse effects of antidepressant drugs.
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/D.C./Examiner, Art Unit 3626
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684