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 .
Response to Amendment
In response to amendments, filed January 20, 2026, claims 1, 9, and 12-13 have been amended. Claims 3 and 7-8 have been cancelled. Claim 14 has been added. Claims 1-2, 4-6, and 9-14 are pending.
Response to Arguments
Applicant’s arguments, see Remarks, filed January 20, 2026, with respect to the objection of the specification and 35 USC 112(f) claim interpretation have been fully considered and are persuasive in view of the amendments. The objection to the specification and interpretations under 35 USC 112(f) have been withdrawn.
Applicant's arguments with respect to the rejections under 35 USC 112(b) have been fully considered and are persuasive in view of the amendments. While the previous rejections under 35 USC 112(b) have been withdrawn, new rejections under 35 USC 112(b) have been issued in view of the amendments.
Applicant's arguments with respect to the rejections under 35 USC 101 have been fully considered and are persuasive in view of the amendments. The rejections under 35 USC 101 have been withdrawn.
Applicant's arguments with respect to the prior art rejections have been fully considered but they are not persuasive. Regarding applicant’s argument that Galloway fails to disclose “increasing the number of pieces of sample data in the training data set to reduce the difference,” Examiner respectfully disagrees. In [0015], Galloway describes a significant number of samples with a certain label may be inaccurately labeled when the prevalence of that certain label in the training data and the error rate in the associated measurement for that label are of a similar order of magnitude. Then in [0027], Galloway describes continuing to update the weight matrices of the machine learning model with additional training data “until the model converges,” or in other words, the final output of the model better approximates the correct label and, therefore, the prevalence of the certain label with a significant error rate has been reduced and is closer to the prevalence of the other label(s).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-11 and 14 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, “the yGT estimation model” recited on line 10 of the claim lacks sufficient antecedent basis, while on line 20 of claim 1, it is unclear whether “a yGT estimation model” is the same or different from “the yGT estimation model” of line 10. By virtue of dependency, claims 2-11 and 14 are also rejected.
Regarding claim 11, it is unclear whether “non-invasive biological information” is the same or different from the “non-invasive biological information”s of claim 1.
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 Galloway (US 20180233227 A1).
Regarding claim 1, Kodama teaches a gamma-glutamyl transpeptidase (yGT) estimation device (health information providing system 50, user/information processing terminal 20, control unit 505; Fig. 3, Fig. 4) comprising:
a memory storing instructions; and at least one processor configured to execute the instructions (Pg 2 [8] “The user terminal 20 is a portable terminal provided with a computer capable of executing an application program, an internal storage such as a flash memory;” storage unit 504, control unit 505); wherein, by executing the instructions, the at least one processor is configured to perform functions comprising:
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; a learning processing unit configured to generate the yGT estimation model by machine learning based on the training data set (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. 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.”; 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);” pg 7 [6] “The biochemical test value estimation unit 55 adjusts the learning model based on the user's biochemical test value (measured value) acquired by actual measurement and the biochemical test value (reference estimated value) estimated using the learning model.”),
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 (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. 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.” Fig. 8; Pg 6 [6] “The output unit 58 displays the biochemical test value estimated by the biochemical test value estimation unit 5[5], and also displays the health risk evaluation obtained by the health risk evaluation unit 56 and the health advice obtained by the health advice determination unit 57. Display as health information. The biochemical test value, the health risk assessment, and the health advice may be displayed by switching, or only one of them may be displayed.”).
However, Komada fails to disclose a difference between the quantity of the yGT risk and without yGT risk labels.
Galloway teaches a machine learning model trained to determine an indication of a level of analyte based on physiological data. The combination of Komada/Galloway discloses and 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 (Galloway: [0015] “In particular, errors in the training data may cause reduced accuracy of a trained machine learning model, or may make it difficult to accurately assess the performance of a given model during or after the training process. This problem may be particularly pronounced when the distribution of analyte [yGT per Komada] measurements is non-uniform, as is the case for many. For example, errors may be more significant to the training of a machine learning model if there are few samples with a particular label. Notably, if the prevalence of a certain label [yGT health risk per Komada] in the training data and the error rate in the associated measurement for that label are of a similar order of magnitude, a significant number of samples with that label may be inaccurately labeled.” [0027] “The output or prediction is compared to the label (e.g., known blood analyte concentration) for the training data, the machine learning model may be updated, e.g., weight matrices may be updated using back propagation so that the final output of the model better approximates the correct label, or known data, during a next processing stage. The process is continued with additional training data or repeated with the same training data until the model converges.”).
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 device of Kodama include a difference between the quantity of the yGT risk and without yGT risk labels and obtaining additional training data as disclosed in Galloway to reduce inaccurately labeled samples that can reduce the effectiveness of training the machine learning model and the overall accuracy of the model as applied to samples of subjects (Galloway [0015]).
The combination of Kodama/Galloway discloses:
an information acquisition unit configured to acquire attribute information and non-invasive biological information of a predetermined user (Komada: height / age / gender acquisition unit 51, weight acquisition unit 52, bioelectrical impedance acquisition unit 53, body composition acquisition unit 54);
an estimation model storage unit configured to store a yGT estimation model; 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 (Komada: storage unit 504; pg 5 [3] “γ-GT (γ-GTP)”; 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.” Pg 5 [2] “The biochemical test value estimation unit 55 includes 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. And the biochemical test value [yGT] is estimated based on the body composition information calculated by the body composition acquisition unit 54.” 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.” Pg 6 [6] “The output unit 58 displays the biochemical test value estimated by the biochemical test value estimation unit 5[5], and also displays the health risk evaluation obtained by the health risk evaluation unit 56.”).
Regarding claim 2, the combination of Kodama/Galloway 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 and biological impedance (Komada: 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)”).
Regarding claim 4, the combination of Kodama/Galloway 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 a subject (Komada: 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.”).
Regarding claim 9, the combination of Kodama/Galloway 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 (Komada: 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 (Komada: 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.”).
Regarding claim 10, the combination of Kodama/Galloway 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)”).
Regarding claim 11, the combination of Kodama/Galloway 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).
Regarding claim 12, Kodama teaches a gamma-glutamyl transpeptidase (yGT) estimation method ([Abstract] “a health information providing program, whereby a biochemical test value can be estimated, or health risk can be evaluated or advice for maintenance or restoration of health can be given, by a simpler method without use of a blood test or the like”) comprising: a step of storing a training data set including attribute information, non-invasive biological information, and a blood-measured yGT measured value of a subject; a step of generating a yGT estimation model (storage unit 504; 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)”; Pg 5 [9] “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.”) by:
providing labels indicating an existence of the yGT risk to the training data set based on the blood-measured yGT measured value (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.” 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. 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.” Fig. 8; Pg 6 [6] “The output unit 58 displays the biochemical test value estimated by the biochemical test value estimation unit 5[5], and also displays the health risk evaluation obtained by the health risk evaluation unit 56”).
However, Komada fails to disclose a difference between the quantity of the yGT risk and without yGT risk labels.
The combination of Komada/Galloway discloses 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, increasing the number of pieces of sample data in the training data set to reduce the difference (Galloway: [0015] “In particular, errors in the training data may cause reduced accuracy of a trained machine learning model, or may make it difficult to accurately assess the performance of a given model during or after the training process. This problem may be particularly pronounced when the distribution of analyte [yGT per Komada] measurements is non-uniform, as is the case for many. For example, errors may be more significant to the training of a machine learning model if there are few samples with a particular label. Notably, if the prevalence of a certain label [yGT health risk per Komada] in the training data and the error rate in the associated measurement for that label are of a similar order of magnitude, a significant number of samples with that label may be inaccurately labeled.” [0027] “The output or prediction is compared to the label (e.g., known blood analyte concentration) for the training data, the machine learning model may be updated, e.g., weight matrices may be updated using back propagation so that the final output of the model better approximates the correct label, or known data, during a next processing stage. The process is continued with additional training data or repeated with the same training data until the model converges.”).
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 device of Kodama include a difference between the quantity of the yGT risk and without yGT risk labels and obtaining additional training data as disclosed in Galloway to reduce inaccurately labeled samples that can reduce the effectiveness of training the machine learning model and the overall accuracy of the model as applied to samples of subjects (Galloway [0015]).
The combination of Kodama/Galloway discloses:
and machine learning based on the training data set (Kodama: 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. … 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.”);
and a step of calculating a yGT risk estimated value of a predetermined user by inputting attribute information and 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 (Komada: pg 5 [3] “γ-GT (γ-GTP)”; 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.” Pg 5 [2] “The biochemical test value estimation unit 55 includes 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. And the biochemical test value [yGT] is estimated based on the body composition information calculated by the body composition acquisition unit 54.” 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.” Pg 6 [6] “The output unit 58 displays the biochemical test value estimated by the biochemical test value estimation unit 5[5], and also displays the health risk evaluation obtained by the health risk evaluation unit 56.”).
Regarding claim 13, Kodama teaches a computer program (pg 2 [8] “The user terminal 20 is a portable terminal provided with a computer capable of executing an application program”) configured to cause a computer to execute: a step of storing a training data set including attribute information, non-invasive biological information, and a blood-measured gamma-glutamyl transpeptidase (yGT) measured value of a subject; a step of generating a yGT estimation model (storage unit 504; 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)”; Pg 5 [9] “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.”) by:
providing labels indicating an existence of the yGT risk to the training data set based on the blood-measured yGT measured value (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.” 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. 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.” Fig. 8; Pg 6 [6] “The output unit 58 displays the biochemical test value estimated by the biochemical test value estimation unit 5[5], and also displays the health risk evaluation obtained by the health risk evaluation unit 56”).
However, Komada fails to disclose a difference between the quantity of the yGT risk and without yGT risk labels.
The combination of Komada/Galloway discloses 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, increasing the number of pieces of sample data in the training data set to reduce the difference (Galloway: [0015] “In particular, errors in the training data may cause reduced accuracy of a trained machine learning model, or may make it difficult to accurately assess the performance of a given model during or after the training process. This problem may be particularly pronounced when the distribution of analyte [yGT per Komada] measurements is non-uniform, as is the case for many. For example, errors may be more significant to the training of a machine learning model if there are few samples with a particular label. Notably, if the prevalence of a certain label [yGT health risk per Komada] in the training data and the error rate in the associated measurement for that label are of a similar order of magnitude, a significant number of samples with that label may be inaccurately labeled.” [0027] “The output or prediction is compared to the label (e.g., known blood analyte concentration) for the training data, the machine learning model may be updated, e.g., weight matrices may be updated using back propagation so that the final output of the model better approximates the correct label, or known data, during a next processing stage. The process is continued with additional training data or repeated with the same training data until the model converges.”).
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 device of Kodama include a difference between the quantity of the yGT risk and without yGT risk labels and obtaining additional training data as disclosed in Galloway to reduce inaccurately labeled samples that can reduce the effectiveness of training the machine learning model and the overall accuracy of the model as applied to samples of subjects (Galloway [0015]).
The combination of Kodama/Galloway discloses:
and machine learning based on the training data set (Kodama: 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. … 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.”);
and a step of calculating a yGT risk estimated value of a predetermined user by inputting attribute information and 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 (Komada: pg 5 [3] “γ-GT (γ-GTP)”; 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.” Pg 5 [2] “The biochemical test value estimation unit 55 includes 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. And the biochemical test value [yGT] is estimated based on the body composition information calculated by the body composition acquisition unit 54.” 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.” Pg 6 [6] “The output unit 58 displays the biochemical test value estimated by the biochemical test value estimation unit 5[5], and also displays the health risk evaluation obtained by the health risk evaluation unit 56.”).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kodama (WO 2020203728 A1) in view of Galloway (US 20180233227 A1) and in further view of Gude (https://doi.org/10.1016/j.cca.2009.06.034).
Regarding claim 5, the combination of Kodama/Galloway 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/Galloway 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 Galloway (US 20180233227 A1) and in further view of Hanna (CA 2380243 A1).
Regarding claim 6, the combination of Kodama/Galloway 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/Galloway 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 Galloway (US 20180233227 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/Galloway 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/Galloway 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/Galloway 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/Galloway/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/Galloway/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/Galloway/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/Galloway/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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/M.H./Examiner, Art Unit 3791
/DEVIN B HENSON/Primary Examiner, Art Unit 3791