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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on March 20, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
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. Claims 1-20 are directed to a method and apparatus for determining a health metric using a computational algorithm, which is an abstract idea. Claims 1-20 do not include additional elements that integrate the exception into a practical application or that are sufficient to amount to significantly more than the judicial exception for the reasons provided below which are in line with the 2014 Interim Guidance on Patent Subject Matter Eligibility (Federal Register, Vol. 79, No. 241, p 74618, December 16, 2014), the July 2015 Update on Subject Matter Eligibility (Federal Register, Vol. 80, No. 146, p. 45429, July 30, 2015), the May 2016 Subject Matter Eligibility Update (Federal Register, Vol. 81, No. 88, p. 27381, May 6, 2016), and the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 4, page 50, January 7, 2019).
The analysis of claim 1 is as follows:
Step 1: Claim 1 is drawn to a process.
Step 2A – Prong One: Claim 1 recites an abstract idea. In particular, claim 1 recites the following limitations:
[A1] – “determining, based on the biological signal, data indicative of one or more biological events, wherein each biological event of the one or more biological events is associated with a weighted factor”;
[B1] – “determining, based on an application of each weighted factor to each biological event, one or more weighted biological events”;
[C1] – “determining, based on the one or more weighted biological events, a health metric”.
These elements [A1]-[C1] of claim 1 are drawn to an abstract idea since they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgment, and opinion and using pen and paper.
Step 2A – Prong Two: Claim 1 recites the following limitations that are beyond the judicial exception:
[A2] – “receiving, by a computing device, a biological signal associated with an individual”.
This element [A2] of claim 1 does not integrate the exception into a practical application of the exception. In particular, the element [A2] is merely an instruction to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f), and recites mere data gathering at a higher level of generality - see MPEP 2106.04(d) and MPEP 2106.05(g).
Step 2B: Claim 1 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the recitation [A1] does not qualify as significantly more because this limitation merely describes the nature of the data and does not incorporate any particular machine as part of the claimed invention. Further, the element [A1] does not qualify as significantly more because this limitation is simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
Claims 2-10 depend from claim 1, and recite the same abstract idea as claim 1. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the algorithm). Each of these claims limitations does not integrate the exception into a practical application.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations of each claim as an ordered combination in conjunction with the claims from which they depend (that is, as a whole) 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, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
The analysis of claim 11 is as follows:
Step 1: Claim 11 is drawn to a machine.
Step 2A – Prong One: Claim 11 recites an abstract idea. In particular, claim 11 recites the following limitations:
[A1] – “determine, based on the biological signal, data indicative of one or more biological events, wherein each biological event of the one or more biological events is associated with a weighted factor”;
[B1] – “determine, based on an application of each weighted factor to each biological event, one or more weighted biological events”;
[C1] – “determine, based on the one or more weighted biological events, a health metric.”
These elements [A1]-[C1] of claim 11 are drawn to an abstract idea since they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgment, and opinion and using pen and paper.
Step 2A – Prong Two: Claim 11 recites the following limitations that are beyond the judicial exception:
[A2] – “one or more processors”;
[B2] – “a memory storing processor-executable instructions”;
[C2] – “determine, based on the biological signal, data indicative of one or more biological events”.
These elements [A2]-[C2] of claim 11 do not integrate the exception into a practical application of the exception. In particular, the elements [A2-C2] are merely instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f). Also, the element [C2] is merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a higher level of generality - see MPEP 2106.04(d) and MPEP 2106.05(g).
Step 2B: Claim 11 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the recitation [C2] does not qualify as significantly more because this limitation merely describes the nature of the data and does not incorporate the any particular machine as part of the claimed invention. Further, the elements [A2-C2] do not qualify as significantly more because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
Claims 12-20 depend from claim 11, and recite the same abstract idea as claim 11. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the algorithm). Each of these claims limitations does not integrate the exception into a practical application.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations of each claim as an ordered combination in conjunction with the claims from which they depend (that is, as a whole) 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, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
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 (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 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 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20200261009 (Everman et al.).
Regarding claim 1, Everman teaches a method comprising:
receiving, by a computing device, a biological signal associated with an individual ([0022] “device 100 includes at least a physiological sensor 116. At least a physiological sensor 116 is configured to detect at least a physiological parameter”; [0023] “At least a physiological parameter may include at least a circulatory and/or hematological parameter, which may include any detectable parameter describing the state of blood vessels such as arteries, veins, or capillaries, any datum describing the rate, volume, pressure, pulse rate, or other state of flow of blood or other fluid through such blood vessels, chemical state of such blood or other fluid, or any other parameter relative to health or current physiological state of user as it pertains to the cardiovascular system.”);
determining, based on the biological signal, data indicative of one or more biological events, wherein each biological event of the one or more biological events is associated with a weighted factor ([0030] “Device 100 may combine two or more physiological parameters to detect a physiological condition and/or physiological alarm condition. For instance, and without limitation, where device 100 is configured to detect hypoxic incapacitation and/or one or more degrees of hypoxemia as described in further detail below, device 100 may perform such determination using a combination of heart rate and blood oxygen saturation, as detected by one or more sensor as described above.”; [0047]; [0062] “Degree of hypoxemia may be determined by relationships between detected factors and/or physiological parameters.”);
determining, based on an application of each weighted factor to each biological event, one or more weighted biological events ([0062] “Degree of hypoxemia may be determined by relationships between detected factors and/or physiological parameters. For instance, and without limitation, a decrease in blood oxygen saturation of 5% by itself may not suffice to trip a threshold based on blood oxygen saturation alone, but a concomitant increase in heart rate or decrease in blood pressure may cause processor 804 to determine that pilot has arrived at a higher or more severe degree of hypoxemia. As a further non-limiting example, one or more factors detected using at least a physiological sensor 116 and/or at least an environmental sensor 124 may cause processor 804 to treat a given hematological or other parameter as indicating a more or less severe degree of hypoxemia.”); and
determining, based on the one or more weighted biological events, a health metric ([0039] “processor 120 may be designed and configured to detect at least a flight condition having a causative association with hypoxemia, measure, using at least a physiological sensor, at least a physiological parameter associated with hypoxemia, and determine, by the processor 120, and based on the at least a physiological parameter, a degree of pilot hypoxemia.”; [0040]; [0062-0063]).
Regarding claim 2, Everman teaches the method of claim 1, wherein the biological signal comprises one or more of a pulse oximetry signal, a CO2 signal, an arterial catheter signal, or a central venous catheter signal ([0023]; [0024] “by directly monitoring the oxygenation of a major branch of the external carotid artery, the measurement of oxygenation to the central nervous system may be more likely to achieve a more accurate indication of oxygen saturation than a peripheral monitor”; [0028] “CO2 saturation levels”; [0032] “measurement of at least a physiological parameter, including without limitation pulse oxygenation and/or pulse rate”; [0041]).
Regarding claim 3, Everman teaches the method of claim 1, wherein the biological event is associated with a hypoxemia event ([0026] “physiological conditions and/or parameters that may be detected by or using at least a physiological sensor 116, such as hypoxemia”; [0030] “where device 100 is configured to detect hypoxic incapacitation and/or one or more degrees of hypoxemia as described in further detail below, device 100 may perform such determination using a combination of heart rate and blood oxygen saturation, as detected by one or more sensor as described above.”; [0040]).
Regarding claim 4, Everman teaches the method of claim 1, wherein determining, based on the biological signal, the data indicative of one or more biological events comprises:
determining, based on a point at which the biological signal crosses from above a threshold to below the threshold, a beginning of each biological event ([0049] “for instance and without limitation, where blood oxygen level drops below a threshold percentage of a baseline level, below an absolute threshold amount, below a certain number of standard deviations, or the like, processor 120 may determine that user is about to lose consciousness or is losing consciousness, and issue an alarm”);
determining, based on a point at which the biological signal crosses from below the threshold to above the threshold, an end of each biological event ([0045] “device 100 may calculate an average level, for one or more parameters of at least a physiological parameter, associated with normal or optimal function, health, or performance of user”; [0055] “Possible indications may be, but are not limited to: imminent unconsciousness, substandard oxygenation, erratic pulse, optimum oxygenation, and/or any other suitable indication”; [0083] “length of period and/or degree of hypoxemia experienced … remain within certain threshold ranges, to increase pilot resistance to hypoxemia and extend such threshold ranges, or the like.”); and
determining, based on the beginning of each biological event and the end of each biological event, the data indicative of one or more biological events ([0083] “length of period and/or degree of hypoxemia experienced”; [0046] “people observing user may note losses of performance or apparent function at times associated with a certain degree of decrease in blood oxygen level or some other physiological parameter”).
Regarding claim 5, Everman teaches the method of claim 4, further comprises determining, for each biological event, one or more of an area under the biological signal or an area above the biological signal below the threshold ([0040] “Detection of a physiological alarm condition may include comparison to two thresholds; for instance, detection that incapacitation and/or loss of consciousness due to hypoxemia is imminent may include detection that a user's heart rate has exceeded one threshold for heart rate and simultaneous or temporally proximal detection that blood oxygen saturation has fallen below a second threshold.”),
wherein determining, based on the application of each weighted factor to each biological event, the one or more weighted biological events comprises determining, based on multiplying the weighted factor of each biological event with one or more of the area under the biological signal or the area above the biological signal below the threshold, the one or more weighted biological events ([0064] “data to be compared to thresholds to test for violations of equipment operation requirements.”; [0065] “Linear regression models may include ridge regression methods, where the function to be minimized includes the least-squares function plus term multiplying the square of each coefficient by a scalar amount to penalize large coefficients. Linear regression models may include least absolute shrinkage and selection operator (LASSO) models, in which ridge regression is combined with multiplying the least-squares term by a factor of 1 divided by double the number of samples.”; [0045] “this may be used, e.g., to generate an alarm indicating that, for instance, a given physiological parameter has recently shifted more than a threshold amount from its average value. Threshold amount may be determined based on amounts by which a typical user may deviate from average amount before experiencing discomfort, loss of function, or loss of consciousness.”).
Regarding claim 6, Everman teaches the method of claim 1, further comprising:
determining at least one biological event associated with a point at which the biological signal is below a threshold ([0040] “detection of the physiological alarm condition further comprises determination that the at least a physiological parameter is falling below a threshold level; as an example, blood oxygen levels below a certain cutoff indicate an imminent loss of consciousness, as may blood pressure below a certain threshold.”; [0049]); and
excluding data indicative the at least one biological event from the data indicative of the one or more biological events ([0044] “reducing the incidence of false alarms, for instance by setting and/or adjusting default threshold levels as described above.”).
Regarding claim 7, Everman teaches the method of claim 1, wherein the weighted factor comprises one or more of a linear value or a non-linear value ([0047] “a linear combination of parameters may is assumed to be associated with a physiological alarm condition, and collected parameter data and associated data describing the physiological alarm condition are evaluated to determine the linear combination by minimizing an error function relating outcomes of the linear combination and the real-world data. Polynomial regression may alternatively assume one or more polynomial functions of parameters and perform a similar minimization process”).
Regarding claim 8, Everman teaches the method of claim 1, further comprising generating the weighted factor based on a machine learning model, wherein the machine learning model is trained based on one or more biological datasets associated with one or more biological attributes ([0047] “relationships between two or more of any of physiological parameters, environmental parameters, and/or user-entered parameters may be determined by one or more machine-learning algorithms.”; [0063] “Machine-learning model, and/or a machine-learning algorithm producing machine-learning model, may be trained and/or iteratively refined using training data. Training data, as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements”).
Regarding claim 9, Everman teaches the method of claim 1, wherein determining, based on the one or more weighted biological events, the health metric comprises:
determining a summation of the one or more weighted biological events ([0065]; [0046] “aggregation may include aggregation of relationships between two or more parameters. For instance, and without limitation, aggregation may calculate a relationship between a first physiological parameter of the at least a physiological parameter and a second physiological parameter of the at least a physiological parameter; this relationship may be calculated, as a non-limiting example, by selecting a first parameter as a parameter associated with a desired state for the user and a second parameter known or suspected to have an effect on the first parameter. For example, first parameter may be blood oxygen level, and second parameter may be blood pressure, such as localized blood pressure in a cranial region; a reduction in cranial blood pressure may be determined to be related to a reduction in cranial blood oxygen level, which in turn may be related to loss of consciousness or other loss of function in user or in a typical user.”); and
determining, based on an application of a normalization factor to the summation of the one or more weighted biological events, the health metric ([0078] “Each vector may be “normalized,” or divided by a “length” attribute, such as a length attribute l as derived using a Pythagorean norm: l=√{square root over (Σi=0nai2)}, where ai is attribute number i of the vector. Scaling and/or normalization may function to make vector comparison independent of absolute quantities of attributes, while preserving any dependency on similarity of attributes; this may, for instance, be advantageous where cases represented in training data are represented by different quantities of samples, which may result in proportionally equivalent vectors with divergent values.”; [0081] “Detection may alternatively or additionally include inputting one or more physiological parameters, one or more environmental parameters, and/or any combination thereof to a machine-learning model, receiving an output from the machine-learning model identifying a violation, and detecting the violation based on the output, including without limitation comparing a quantitative output to a threshold, for instance by comparing an output indicative of a probability of suffering a degraded ability to an upper threshold, an output indicative of a probability of successful performance of an important and/or critical function to a lower threshold indictive of inability to perform adequately, or the like.”; [0062] “A cumulative fatigue model may be generated or applied to determine a degree to which pilot fatigue affects either a current level of hypoxemia or a likely future rate of degradation”).
Regarding claim 10, Everman teaches the method of claim 1, further comprising:
determining, based on the health metric, one or more health risks associated with the individual ([0038] “if user's physiological condition indicates user is experiencing or about to experience physical harm, is losing or is about to lose consciousness, or the like, a physiological alarm condition may exist.”; [0040]); and
causing, based on the one or more health risks, a treatment of the individual ([0043] “how many times the user has to use “anti-G” breathing exercises, or similar activities”; [0055]; [0083] “for instance be to have pilot undergo a particular environmental condition, such as atmospheric oxygen below a set level and/or a series of high-G maneuvers and/or periods, and to attempt and/or practice strategies for avoiding incapacitation. A second instruction may issue, as well; for instance, if pilot is degrading more than expected, a training session may be modified to be less severe or aborted”).
Regarding claim 11, Everman teaches an apparatus comprising:
one or more processors ([0039] “processor 120”); and
a memory storing processor-executable instructions ([0056] “System 800 may include a memory 808, which may be a solid-state memory or the like; memory 808 may be used to record data during test periods, sorties, simulations, and the like, for instance as described above in reference to FIGS. 1-7”) that, when executed by the one or more processors ([0085]), cause the apparatus to:
receive a biological signal associated with an individual ([0022] “device 100 includes at least a physiological sensor 116. At least a physiological sensor 116 is configured to detect at least a physiological parameter”; [0023] “At least a physiological parameter may include at least a circulatory and/or hematological parameter, which may include any detectable parameter describing the state of blood vessels such as arteries, veins, or capillaries, any datum describing the rate, volume, pressure, pulse rate, or other state of flow of blood or other fluid through such blood vessels, chemical state of such blood or other fluid, or any other parameter relative to health or current physiological state of user as it pertains to the cardiovascular system.”);
determine, based on the biological signal, data indicative of one or more biological events, wherein each biological event of the one or more biological events is associated with a weighted factor ([0030] “Device 100 may combine two or more physiological parameters to detect a physiological condition and/or physiological alarm condition. For instance, and without limitation, where device 100 is configured to detect hypoxic incapacitation and/or one or more degrees of hypoxemia as described in further detail below, device 100 may perform such determination using a combination of heart rate and blood oxygen saturation, as detected by one or more sensor as described above.”; [0047]; [0062] “Degree of hypoxemia may be determined by relationships between detected factors and/or physiological parameters.”);
determine, based on an application of each weighted factor to each biological event, one or more weighted biological events ([0062] “Degree of hypoxemia may be determined by relationships between detected factors and/or physiological parameters. For instance, and without limitation, a decrease in blood oxygen saturation of 5% by itself may not suffice to trip a threshold based on blood oxygen saturation alone, but a concomitant increase in heart rate or decrease in blood pressure may cause processor 804 to determine that pilot has arrived at a higher or more severe degree of hypoxemia. As a further non-limiting example, one or more factors detected using at least a physiological sensor 116 and/or at least an environmental sensor 124 may cause processor 804 to treat a given hematological or other parameter as indicating a more or less severe degree of hypoxemia.”); and
determine, based on the one or more weighted biological events, a health metric ([0039] “processor 120 may be designed and configured to detect at least a flight condition having a causative association with hypoxemia, measure, using at least a physiological sensor, at least a physiological parameter associated with hypoxemia, and determine, by the processor 120, and based on the at least a physiological parameter, a degree of pilot hypoxemia.”; [0040]; [0062-0063]).
Regarding claim 12, Everman teaches the apparatus of claim 11, wherein the biological signal comprises one or more of a pulse oximetry signal, a CO2 signal, an arterial catheter signal, or a central venous catheter signal ([0023]; [0024] “by directly monitoring the oxygenation of a major branch of the external carotid artery, the measurement of oxygenation to the central nervous system may be more likely to achieve a more accurate indication of oxygen saturation than a peripheral monitor”; [0028] “CO2 saturation levels”; [0032] “measurement of at least a physiological parameter, including without limitation pulse oxygenation and/or pulse rate”; [0041]).
Regarding claim 13, Everman teaches the apparatus of claim 11, wherein the biological event is associated with a hypoxemia event ([0026] “physiological conditions and/or parameters that may be detected by or using at least a physiological sensor 116, such as hypoxemia”; [0030] “where device 100 is configured to detect hypoxic incapacitation and/or one or more degrees of hypoxemia as described in further detail below, device 100 may perform such determination using a combination of heart rate and blood oxygen saturation, as detected by one or more sensor as described above.”; [0040]).
Regarding claim 14, Everman teaches the apparatus of claim 11, wherein the processor-executable instructions that, when executed by the one or more processors, cause the apparatus to determine, based on the biological signal ([0085]), the data indicative of one or more biological events further cause the apparatus to:
determine, based on a point at which the biological signal crosses from above a threshold to below the threshold, a beginning of each biological event ([0049] “for instance and without limitation, where blood oxygen level drops below a threshold percentage of a baseline level, below an absolute threshold amount, below a certain number of standard deviations, or the like, processor 120 may determine that user is about to lose consciousness or is losing consciousness, and issue an alarm”);
determine, based on a point at which the biological signal crosses from below the threshold to above the threshold, an end of each biological event ([0045] “device 100 may calculate an average level, for one or more parameters of at least a physiological parameter, associated with normal or optimal function, health, or performance of user”; [0055] “Possible indications may be, but are not limited to: imminent unconsciousness, substandard oxygenation, erratic pulse, optimum oxygenation, and/or any other suitable indication”; [0083] “length of period and/or degree of hypoxemia experienced … remain within certain threshold ranges, to increase pilot resistance to hypoxemia and extend such threshold ranges, or the like.”); and
determine, based on the beginning of each biological event and the end of each biological event, the data indicative of one or more biological events ([0083] “length of period and/or degree of hypoxemia experienced”; [0046] “people observing user may note losses of performance or apparent function at times associated with a certain degree of decrease in blood oxygen level or some other physiological parameter”).
Regarding claim 15, Everman teaches the apparatus of claim 14, wherein the processor-executable instructions, when executed by the one or more processors, further cause the apparatus to determine, for each event, one or more of an area under the biological signal or an area above the biological signal below the threshold ([0040] “Detection of a physiological alarm condition may include comparison to two thresholds; for instance, detection that incapacitation and/or loss of consciousness due to hypoxemia is imminent may include detection that a user's heart rate has exceeded one threshold for heart rate and simultaneous or temporally proximal detection that blood oxygen saturation has fallen below a second threshold.”),
wherein the processor-executable instructions that, when executed by the one or more processors, cause the apparatus to determine, based on the application of each weighted factor to each biological event, the one or more weighted biological events, further cause the apparatus to determine, based on multiplying the weighted factor of each biological event with one or more of the area under the biological signal or the area above the biological signal below the threshold, the one or more weighted biological events ([0064] “data to be compared to thresholds to test for violations of equipment operation requirements.”; [0065] “Linear regression models may include ridge regression methods, where the function to be minimized includes the least-squares function plus term multiplying the square of each coefficient by a scalar amount to penalize large coefficients. Linear regression models may include least absolute shrinkage and selection operator (LASSO) models, in which ridge regression is combined with multiplying the least-squares term by a factor of 1 divided by double the number of samples.”; [0045] “this may be used, e.g., to generate an alarm indicating that, for instance, a given physiological parameter has recently shifted more than a threshold amount from its average value. Threshold amount may be determined based on amounts by which a typical user may deviate from average amount before experiencing discomfort, loss of function, or loss of consciousness.”).
Regarding claim 16, Everman teaches the apparatus of claim 11, wherein the processor-executable instructions, when executed by the one or more processors, further cause the apparatus to:
determine at least one biological event associated with a point at which the biological signal is below a threshold ([0040] “detection of the physiological alarm condition further comprises determination that the at least a physiological parameter is falling below a threshold level; as an example, blood oxygen levels below a certain cutoff indicate an imminent loss of consciousness, as may blood pressure below a certain threshold.”; [0049]); and
exclude data indicative of the at least one biological event from the data indicative of the one or more biological events ([0044] “reducing the incidence of false alarms, for instance by setting and/or adjusting default threshold levels as described above.”).
Regarding claim 17, Everman teaches the apparatus of claim 11, wherein the weighted factor comprises one or more of a linear value or a non-linear value ([0047] “a linear combination of parameters may is assumed to be associated with a physiological alarm condition, and collected parameter data and associated data describing the physiological alarm condition are evaluated to determine the linear combination by minimizing an error function relating outcomes of the linear combination and the real-world data. Polynomial regression may alternatively assume one or more polynomial functions of parameters and perform a similar minimization process”).
Regarding claim 18, Everman teaches the apparatus of claim 11, wherein the processor-executable instructions, when executed by the one or more processors, further cause the apparatus to generate the weighted factor based on a machine learning model, wherein the machine learning model is trained based on one or more patient datasets associated with one or more biological attributes ([0047] “relationships between two or more of any of physiological parameters, environmental parameters, and/or user-entered parameters may be determined by one or more machine-learning algorithms.”; [0063] “Machine-learning model, and/or a machine-learning algorithm producing machine-learning model, may be trained and/or iteratively refined using training data. Training data, as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements”).
Regarding claim 19, Everman teaches the apparatus of claim 11, wherein the processor-executable instructions that, when executed by the one or more processors, cause the apparatus to determine based on the one or more weighted biological events, the health metric, further cause the apparatus to: determine a summation of the one or more weighted biological events ([0065]; [0046] “aggregation may include aggregation of relationships between two or more parameters. For instance, and without limitation, aggregation may calculate a relationship between a first physiological parameter of the at least a physiological parameter and a second physiological parameter of the at least a physiological parameter; this relationship may be calculated, as a non-limiting example, by selecting a first parameter as a parameter associated with a desired state for the user and a second parameter known or suspected to have an effect on the first parameter. For example, first parameter may be blood oxygen level, and second parameter may be blood pressure, such as localized blood pressure in a cranial region; a reduction in cranial blood pressure may be determined to be related to a reduction in cranial blood oxygen level, which in turn may be related to loss of consciousness or other loss of function in user or in a typical user.”); and
determine, based on an application of a normalization factor to the summation of the one or more weighted biological events, the health metric ([0078] “Each vector may be “normalized,” or divided by a “length” attribute, such as a length attribute l as derived using a Pythagorean norm: l=√{square root over (Σi=0nai2)}, where ai is attribute number i of the vector. Scaling and/or normalization may function to make vector comparison independent of absolute quantities of attributes, while preserving any dependency on similarity of attributes; this may, for instance, be advantageous where cases represented in training data are represented by different quantities of samples, which may result in proportionally equivalent vectors with divergent values.”; [0081] “Detection may alternatively or additionally include inputting one or more physiological parameters, one or more environmental parameters, and/or any combination thereof to a machine-learning model, receiving an output from the machine-learning model identifying a violation, and detecting the violation based on the output, including without limitation comparing a quantitative output to a threshold, for instance by comparing an output indicative of a probability of suffering a degraded ability to an upper threshold, an output indicative of a probability of successful performance of an important and/or critical function to a lower threshold indictive of inability to perform adequately, or the like.”; [0062] “A cumulative fatigue model may be generated or applied to determine a degree to which pilot fatigue affects either a current level of hypoxemia or a likely future rate of degradation”).
Regarding claim 20, Everman teaches the apparatus of claim 11, wherein the processor-executable instructions, when executed by the one or more processors, further cause the apparatus to:
determine, based on the health metric, one or more health risks associated with the individual ([0038] “if user's physiological condition indicates user is experiencing or about to experience physical harm, is losing or is about to lose consciousness, or the like, a physiological alarm condition may exist.”; [0040]); and
causing, based on the one or more health risks, a treatment of the individual ([0043] “how many times the user has to use “anti-G” breathing exercises, or similar activities”; [0055]; [0083] “for instance be to have pilot undergo a particular environmental condition, such as atmospheric oxygen below a set level and/or a series of high-G maneuvers and/or periods, and to attempt and/or practice strategies for avoiding incapacitation. A second instruction may issue, as well; for instance, if pilot is degrading more than expected, a training session may be modified to be less severe or aborted.”).
Conclusion
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/EVELYN GRACE PARK/Examiner, Art Unit 3791 /TSE CHEN/Supervisory Patent Examiner, Art Unit 3791
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