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 .
Response to Arguments
Applicant’s arguments submitted in the Pre-Appeal filed 6/18/26, with respect to the rejection(s) of claim(s) 1 [and similarly for claims 12 and 13] have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Kalafatis as discussed below.
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.
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because a “computer program” is considered “software per se” [MPEP 2106.03].
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Information acquisition unit, Estimation processing unit, Learning processing unit, Biological information estimation unit: [0041] “Note that the first acquisition unit31, the second acquisition unit32, the learning processing unit35, the estimation processing unit37, and the like described above function at the processors 301 of the yGT estimation device30 when operating” with the associated algorithms/functions of linear regression, logistic regression, random forest, neural network, SMOTE, one-hot encoding, normalization / transformation, PCA, and ensemble voting.
Estimation model storage unit and Training data storage unit: [0041] “The memory 302 temporarily stores a computer program and a calculation result of the computer program. The storage 303 stores a computer program configured to execute processing by the yGT estimation device 30. The storage 303 may be any computer-readable storage and may be, for example, various kinds of recording media such as a magnetic disk, an optical disk, a random-access memory, a flash memory, and a read-only memory.”
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 6 and associated dependent claims is/are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The disclosure indicates the coefficient to be 0.61 without indication of which correlation coefficient [Pearson? Spearman?...] over which sample set [same-day measurements?...] with what logarithm base? It’s also noted that the claim stipulates the coefficient to be also equal to 0.6 but there does not seem to be any support for such in the disclosure.
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 1, 6 and associated dependent claims is/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.
Regarding claim 1, “wherein, by executing the instructions, the at least one processor is configured to perform functions comprising” seems incomplete as the list that follows consists of functional units.
Regarding claim 6, the claimed “a coefficient of correlation between a logarithm of the yGT estimated value and a logarithm of the yGT measured value is equal to or larger than 0.6” is result-oriented which appears open-ended without clear boundaries that can be established by how the number is derived as discussed above.
Claim Rejections - 35 USC § 103
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, and 9-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Kodama” (WO 2020203728 A1) in view of “Kalafatis” (US 20180322958 A1).
With regards to claim 1, KODAMA discloses a gamma-glutamyl transpeptidase (yGT) estimation device [health information providing system 50, user/information processing terminal 20, control unit 505; Fig. 3, 4, 8; Description of Embodiments, describing the biochemical test value estimation unit 55 that estimates biochemical test values including “γ-GT (γ-GTP)”] comprising:
a memory storing instructions; and at least one processor configured to execute the instructions [“user terminal 20 is a portable terminal provided with a computer capable of executing an application program, an internal storage”; “control unit 505 controls each part… according to the health information providing program”];
a training data storage unit configured to store a training data set, wherein the training data set includes non-invasive biological information and a blood-measured yGT measured value of a subject [e.g., storage unit 504; “learning model obtained by learning a large number of sets of biological information and body composition information and biochemical test values as teacher data”];
a learning processing unit configured to generate a yGT estimation model by machine learning based on the training data set [“learning model generated by learning using a decision tree or a neural network”], wherein the learning processing unit provides labels indicating existence of the yGT risk to the training data set based on the blood-measured yGT measured value [health risk evaluation unit 56; “uses biological information and body composition information as explanatory variables and biochemical test values as objective variables”; “health risk evaluation unit 56 evaluates the health risk based on the biochemical test values”];
an information acquisition unit configured to acquire attribute information and non-invasive biological information of a predetermined user [height/age/gender acquisition unit 51, a weight acquisition unit 52, and a BI acquisition unit 53];
an estimation model storage unit configured to store the yGT estimation model [“various information used for those programs (For example, a learning model described later) is also stored”]; and
an estimation processing unit configured to calculate a yGT risk estimated value of the predetermined user by inputting the attribute information and the non-invasive biological information of the predetermined user into the yGT estimation model and outputting, from the yGT estimation model, the yGT risk estimated value of the predetermined user [biochemical test value estimation unit 55 and health risk evaluation unit 56; “estimates each biochemical test value by substituting the biological information and body composition information required… into the regression formula”; “health risk evaluation unit 56 evaluates the health risk based on the biochemical test values estimated”].
However, KODAMA does not explicitly disclose wherein, when a difference between the numbers of pieces of data with the yGT risk and data without the yGT risk among the labels is equal to or larger than a predetermined value, the learning processing unit increases the number of pieces of sample data in the training data set to reduce the difference.
KALAFATIS, also directed towards applying machine learning and data mining techniques to biological and medical data to predict health conditions and risks, discloses:
providing labels indicating existence of a risk to training data based on measured values [0178+: “This file may either be transformed into a new dataset 700 which contains T/F values instead of frequencies. If a frequency exceeds a threshold value T1 then ‘True’ is inserted otherwise ‘False’ is inserted for every cell of the frequencies file”; 0248+: “the calculated frequency table 655 is converted into a new table (T/F Table) where frequencies are replaced by ‘T’ for ‘True’ and ‘F’ for ‘False’. If any cell on the frequency table is larger than a given threshold frequency T3 (for example, T3=2%) then the cell frequency value is replaced with a ‘T’, otherwise it is replaced with an ‘F’”; converting frequency/measured data into True/False labels (i.e., labels indicating existence or non-existence of a condition) based on threshold values – i.e., teaches providing binary risk/no-risk labels to data based on whether measured values exceed a predetermined threshold];
when a difference between the numbers of pieces of data with the risk and data without the risk among the labels is equal to or larger than a predetermined value, increasing the number of pieces of sample data in the training data set to reduce the difference: [0260: “The resulting symptoms need to be balanced so as to come up with unbiased results. For example, in order for the algorithm to be able to learn efficiently the problem at hand, if the algorithm has 1000 cases of Symptom=TRUE but only 15 cases of Symptom=FALSE then it will be very hard to identify which features differentiate Symptom vs. no-Symptom. For this reason additional asymptomatic data should be used or should be created by any technique known in prior art”; 0263: “oversample using the Random minority over-sampling with replacement, Synthetic Minority Oversampling TEchnique (SMOTE), bSMOTE(1 & 2)—Borderline SMOTE of types 1 and 2, SVM SMOTE—Support Vectors SMOTE, ADAptive SYNthetic (ADASYN) sampling approach for imbalanced learning”; i.e., teaches that when the difference between the numbers of True (risk) and False (no-risk) data is large (e.g., 1000 vs. 15), the system increases the number of minority class sample data to reduce this difference].
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 KODAMA with the teachings of KALAFATIS -- providing risk labels to the training data based on measured γGT values and balancing the labeled training data through oversampling when class imbalance exists would enhance the base device of KODAMA by improving its ability to accurately classify users into risk categories rather than merely providing a continuous estimated value, and by ensuring that the machine learning model is trained on balanced data so that the model does not exhibit bias toward the majority class [e.g., non-risk subjects], thereby improving the sensitivity and overall accuracy of γGT risk estimation.
With regards to claim 2, KODAMA discloses the yGT estimation device according to claim 1, wherein the attribute information includes any one or a combination of age and sex, and wherein the non-invasive biological information includes any one or a combination of BMI, blood pressure, pulse wave data, electrocardiogram data, and biological impedance (pg 3 [8]- pg 4[1] “The height / age / gender acquisition unit 51 acquires the height, age, and gender information, which is the user's biological information, by receiving the user's operation input in the input unit 501. The weight acquisition unit 52 acquires the weight, which is the biometric information of the user, by measuring the weight of the user with the weight measurement unit 502. The BI acquisition unit 53 measures the bioelectrical impedance of the whole body and each body part, which is the biometric information of the user.” Pg 4 [10] – pg 5 [1] “The body composition acquisition unit 54 … Acquires body composition information such as fat mass, basal metabolic rate, bone mass, body water content, BMI (Body Mass Index)”).
With regards to claim 4, KODAMA discloses the yGT estimation device according to claim 1, wherein the training data set includes attribute information, the non-invasive biological information, and the blood-measured yGT measured value of the subject (pg 5 [9] “The biochemical test value estimation unit 55 uses the multiple regression equation obtained by performing multiple regression analysis on a large number of sets of biometric information and body composition information and biochemical test values, and uses the biochemical test value and body composition information. Estimate the biochemical test value from. This multiple regression equation uses biological information and body composition information as explanatory variables and biochemical test values as objective variables. In addition, this multiple regression equation can be said to be a learning model obtained by learning a large number of sets of biological information and body composition information and biochemical test values as teacher data.”).
With regards to claim 9, KODAMA discloses the yGT estimation device according to claim 1, wherein the learning processing unit generates a first yGT risk estimation model and a second yGT risk estimation model by machine learning based on each of training data sets of different kinds (pg 5 [9] “a learning model obtained by learning a large number of sets of biological information and body composition information and biochemical test values as teacher data. The learning model is not limited to the multiple regression equation, and may be, for example, a learning model generated by learning using a decision tree or a neural network. Further, for age and gender, for example, different multiple regression equations may be prepared and used for each age and gender without using them as explanatory variables.”), and wherein the estimation processing unit calculates the yGT risk estimated value of the predetermined user by using the first yGT risk estimation model and the second yGT risk estimation model (pg 6 [3] “The health risk evaluation unit 56 evaluates the health risk based on the biochemical test values estimated by the biochemical test value estimation unit 55 [based on the first and second estimation models varying by age and gender]. For this purpose, the health risk evaluation unit 56 stores a table that defines the relationship between the range of biochemical test values and the health risk. The health risk evaluation unit 56 extracts the health risk evaluation corresponding to the biochemical test value estimated by the biochemical test value estimation unit 55 by referring to the table.”).
With regards to claim 10, KODAMA discloses the yGT estimation device according to claim 1, further comprising a biological information estimation unit configured to estimate at least one piece or more of biological information among BMI and biological impedance included in the biological information, wherein the information acquisition unit acquires, as biological information of the predetermined user, the biological information estimated by the biological information estimation unit (Komada: Fig. 3, the weight measuring unit 502 and the BI (bioelectric impedance) measuring unit 503 are provided in the measuring device 10; Pg 4 [10] – pg 5 [1] “The body composition acquisition unit 54 uses biometric information including height, age, gender acquired by the height / age / gender acquisition unit 51, weight acquired by the weight acquisition unit 52, and bioelectric impedance acquired by the BI acquisition unit 53. The user's body composition information is acquired by the calculated calculation… Acquires body composition information such as fat mass, basal metabolic rate, bone mass, body water content, BMI (Body Mass Index)”).
With regards to claim 11, KODAMA discloses a non-invasive yGT estimation system (Kodama: health information providing system 50) comprising: the yGT estimation device according to claim 1 (Kodama: user/information processing terminal 20, control unit 505; Fig. 3, Fig. 4); and a biological information measurement device configured to measure non-invasive biological information (Kodama: measuring device 10, Fig. 2).
With regards to claim 12, KODAMA in view of KALAFATIS discloses a gamma-glutamyl transpeptidase (yGT) estimation method comprising the steps of storing a training data set, generating a yGT estimation model by providing labels, increasing the number of pieces of sample data to reduce the difference when labels are imbalanced, performing machine learning, and calculating a yGT risk estimated value. The limitations of this method claim are identical in scope to the apparatus limitations of claim 1. Therefore, the combination of KODAMA and KALAFATIS discloses this claim for the same reasons set forth above in the rejection of claim 1.
With regards to claim 13, KODAMA in view of KALAFATIS discloses a computer program configured to cause a computer to execute the steps of storing a training data set, generating a yGT estimation model by providing labels, increasing the number of pieces of sample data to reduce the difference when labels are imbalanced, performing machine learning, and calculating a yGT risk estimated value. The limitations of this computer program claim are identical in scope to the apparatus limitations of claim 1. Therefore, the combination of KODAMA and KALAFATIS discloses this claim for the same reasons set forth above in the rejection of claim 1.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Kodama” (WO 2020203728 A1) in view of “Kalafatis” (US 20180322958 A1) and in further view of “Gude” (https://doi.org/10.1016/j.cca.2009.06.034).
Regarding claim 5, the combination of Kodama/Kalafatis discloses the yGT estimation device according to claim 4. However, Kodama fails to disclose oxygen saturation. Gude teaches the relation between serum GGT levels and markers of nocturnal hypoxemia.
Gude discloses, wherein the non-invasive biological information further includes oxygen saturation (SpO2) (Gude: pg 68, 2.6 Nocturnal pulse oximetry “The recording of SpO2 was performed at the patient's home using a Criticare 504 DX oximeter (CSI, Wankeska, WI, USA) with a finger probe, with sampling at a frequency of 0.2 Hz (one sample every 4 s).” pg 67, Abstract, “Serum GGT levels were associated negatively and independently with average arterial oxygen saturation during sleep (P= 0.001). Conclusions: Serum concentrations of GGT are associated with nocturnal arterial oxygen desaturations”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the combination of Kodama/Kalafatis include oxygen saturation as non-invasive biological information as disclosed in Gude because nocturnal hypoxemia should be taken into account when interpreting serum levels of GGT, independently of alcohol consumption, obesity, and metabolic syndrome (Gude pg 71 [4]).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Kodama” (WO 2020203728 A1) in view of “Kalafatis” (US 20180322958 A1) and in further view of “Hanna” (CA 2380243 A1).
Regarding claim 6, the combination of Kodama/Kalafatis discloses the yGT estimation device according to claim 4. However, Komada fails to disclose a coefficient of correlation. Hanna teaches a method and apparatus for non-invasively determining the concentration of an analyte in a biological sample.
The combination of Komada/Hanna discloses wherein a coefficient of correlation between a logarithm of the yGT estimated value and a logarithm of the yGT measured value is equal to or larger than 0.6 (Komada: pg 5 [2-3] “the biochemical test value refers to any test item of the biochemical test in the health examination…. γ-GT (γ-GTP);” Hanna: pg 48 line 25 - pg 49 line 15 “Classical linear regression was employed to correlate a model to comprising reflectance measurement at each single sampling distance at three wavelengths with the fit values of reference glucose concentrations... where, Log[R(~,)] represents the natural logarithm of reflectance at wavelength ~, (nm). The models yielded a correlation coefficient of 0.98 and a standard error of calibration of 8.9 mg/dL…. in FIG. 9, where the calculated glucose values are plotted against the reference glucose values [rather, calculated yGT values per Komada against reference yGT values].”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the combination of Kodama/Kalafatis include the coefficient of correlation as disclosed in Hanna to result in the best performance, as is indicated by optimal statistical parameters, such as the highest correlation coefficient and the lowest standard error of estimation (Hanna pg 38 lines 6-10).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kodama (WO 2020203728 A1) in view of Kalafatis (US 20180322958 A1 and in further view of Quinn (US 20210022660 A1), Huiku (US 20100081942 A1), Barnacka (US 20210045647 A1), and Brunswick (US 20100274113 A1).
Regarding claim 14, the combination of Kodama/Kalafatis discloses the yGT estimation device according to claim 1, wherein the attribute information of the predetermined user includes age and sex (Komada: Pg 4 [10] “The body composition acquisition unit 54 uses biometric information including height, age, gender acquired by the height / age / gender acquisition unit 51”), and the non-invasive biological information includes weight (Komada: Pg 4 [10] “weight acquired by the weight acquisition unit 52”), biological impedance (Komada: Pg 4 [10] “bioelectric impedance acquired by the BI acquisition unit 53”), lean body weight, body fat amount, muscle mass, total moisture content (Komada: Pg 4 [10] – pg 5 [1] “The body composition acquisition unit 54 applies the biological information to a predetermined regression equation and performs a calculation to calculate the fat ratio, fat mass, defatted fat mass, muscle mass, visceral fat mass, visceral fat level, visceral fat area, and subcutaneous. Acquires body composition information such as fat mass, basal metabolic rate, bone mass, body water content, BMI (Body Mass Index), intracellular fluid volume, and extracellular fluid volume.”), and hand-to-hand electric conductivity (Komada: Fig. 2; Pg 2 [6] “The heel of the left foot is in contact with, the finger of the right hand is in contact with the electrode 161R for energization, the palm of the right hand is in contact with the electrode 162R for measurement, and the finger of the left hand is in contact with the electrode 161L for energization. The palm of the left hand comes into contact with the electrode 162L.”).
However, the combination of Kodama/Kalafatis fails to disclose non-invasive biological information including circulating blood amount, elasticity index, ejection fraction, cardiac output, forehead-path electric conductivity, blood pressure, average arterial blood pressure, diastolic blood pressure, pulse wave data, electrocardiogram data, heart rate, and standard deviation of the RR interval.
Quinn teaches systems and methods for collecting and analyzing vital sign information to predict a likelihood of a subject having a disease or disorder. Quinn discloses the non-invasive biological information includes blood pressure, average arterial blood pressure, diastolic blood pressure, pulse wave data, electrocardiogram data, heart rate, and standard deviation of the RR interval ([0007] “In some embodiments, the ECG sensor comprises one or more ECG electrodes.” [0008] “In some embodiments, the plurality of vital sign measurements comprises one or more measurements selected from the group consisting of heart rate, heart rate variability, blood pressure (e.g., systolic and diastolic);” [0048] “vital sign data may include heart rate, heart rate variability, blood pressure, respiratory rate, blood oxygen concentration (e.g., by pulse oximetry)”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the combination of Kodama/Kalafatis include blood pressure, average arterial blood pressure, diastolic blood pressure, pulse wave data, electrocardiogram data, heart rate, and standard deviation of the RR interval as disclosed in Quinn to serve as training datasets that help in detecting or predicting an adverse health condition (e.g., deterioration of the patient's state, occurrence or recurrence of a disease or disorder, or occurrence of a complication) in the subject over the period of time (Quinn [0048, 0058]).
However, the combination of Kodama/Kalafatis/Quinn fails to disclose circulating blood amount, elasticity index, ejection fraction, cardiac output, and forehead-path electric conductivity.
Huiku teaches a method and apparatus for monitoring fluid balance status of a subject. Huiku discloses the non-invasive biological information includes cardiac output and circulating blood amount ([0053] “Especially the respiratory sinus arrhythmia, RSA, the respiratory variation of the heart rate, or more accurately of the heart beat-to-beat interval, is indicative of the cardiac output (CO)” [0055] “Perfusion index, PI, or the plethysmographic pulse amplitude in a finger (measured through a pulse oximeter), is still an alternative physiological parameter that may indicate the volemia status of a patient.” [0026] “the indicator unit 13 may calculate quantitative blood volume estimates”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the combination of Kodama/Kalafatis/Quinn include cardiac output and circulating blood amount as disclosed in Huiku to give an assessment of the volemia status of the patient and/or suggest the correct fluid therapy form for effective fluid management that improves patient outcome and reduces hospital costs in acute care (Huiku [0004, 0026]).
Barnacka teaches a non-invasive system and method for cardiovascular monitoring and reporting. Barnacka discloses the non-invasive biological information includes elasticity index and ejection fraction ([0168] “The CV function measurements include various measurements that the CV monitoring system 10A calculates from the raw CV signals 101 and/or stacked CV signals 101S of the detected information 952. These measurements include … an elasticity index 412;” [0184] “For calculating the AVO time 202, the echo system is typically used. The CV monitoring system 10 also calculates the AVO time 202, and additionally calculates the LVET 208, which is used as noninvasive measure of cardiovascular health. The CV monitoring system 10 can also use the LVET 208 to determine various other measurements such as the stroke volume (SV) 210, a left ventricle ejection fraction, and also to identify general aortic and ventricle functioning, in examples.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the combination of Kodama/Kalaftis/Quinn/Huiku include elasticity index and ejection fraction as disclosed in Barnacka to detect previously undiagnosed cardiovascular disease when the individuals may not be experiencing traditional symptoms or discomfort (Barnacka [0028]).
Brunswick teaches an electrophysiological analysis system, in particular for detecting pathological states. Brunswick discloses the non-invasive biological information includes forehead-path electric conductivity ([0034] “The system includes two electrodes for left and right frontal lobes, two electrodes for left and right hands and two electrodes for left and right feet.” [0035] “The switching circuit is capable of connecting, to the voltage source, electrode pairs consisting of the left forehead electrode and the right forehead electrode, the right forehead electrode and the left forehead electrode, the left hand electrode and the right hand electrode, the right hand electrode and the left hand electrode, the left foot electrode and the right foot electrode and the right foot electrode and the left foot electrode.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the combination of Kodama/Kalfatis/Quinn/Huiku/Barnacka include forehead-path electric conductivity as disclosed in Brunswick to provide a simple-to-use, non-invasive diagnostic system having degrees of specificity and sensitivity which are equivalent to laboratory tests and which enables certain diseases, certain pathological predispositions or certain organ dysfunctions to be detected with improved reliability and with a broader range of possibilities (Brunswick [0009]).
Conclusion
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/TSE CHEN/ Supervisory Patent Examiner, Art Unit 3791
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