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 .
Status of Claims
This Final Office Action is in response to the Amendment and Remarks filed 02/06/2026. Claims 9, 10, 13 and 14 are amended. Claim 17 is new. Claims 9-14 and 17 are pending and considered herein.
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 9-14 and 17 are rejected under 35 U.S.C. §101 because they recite an abstract idea without significantly more.
Claim 9 recites, wherein the abstract idea is not emboldened:
A biometric information computing system for outputting information relating to a feature of blood carbon dioxide of a user comprising: a first processor, comprising a strain sensor or a pulse wave sensor, that is configured to obtain evaluation data based on a pulse wave of the user; a database that stores classification information generated using a plurality of training data, the training data being a pair of input data based on a preliminarily obtained training pulse wave and reference data including the feature of blood carbon dioxide associated with the input data, wherein the classification information includes a plurality of pieces of attribute-based classification information calculated using the training data different from one another, wherein the attribute-based classification information comprises classifications based on different attributes of gender, age, and health condition; and a second processor that is configured to refer to the database, generate an evaluation result including the feature of blood carbon dioxide for the evaluation data, wherein the first processor is configured to obtain data corresponding to a velocity pulse wave based on the pulse wave as the evaluation data; and data corresponding to an acceleration pulse wave based on the pulse wave as a preliminary evaluation data, different from the evaluation data; refer to the preliminary evaluation data, based on the preliminary evaluation data, select first classification information among the plurality of pieces of attribute-based classification information; refer to the first classification information and, based on the first classification information, generate the evaluation result for the evaluation data; and a display configured to display the evaluation result.
The claimed invention is broadly directed to the abstract idea of collecting patient information, analyzing the information, and determining an evaluation result related to the patient based on the analyses.
The limitations “to obtain evaluation data based on a pulse wave of the user; generated using a plurality of training data, the training data being a pair of input data based on a preliminarily obtained training pulse wave and reference data including the feature of blood carbon dioxide associated with the input data, wherein the classification information includes a plurality of pieces of attribute-based classification information calculated using the training data different from one another, wherein the attribute-based classification information comprises classifications based on different attributes of gender, age, and health condition; and generate an evaluation result including the feature of blood carbon dioxide for the evaluation data, to obtain data corresponding to a velocity pulse wave based on the pulse wave as the evaluation data; and data corresponding to an acceleration pulse wave based on the pulse wave as a preliminary evaluation data, different from the evaluation data; refer to the preliminary evaluation data, based on the preliminary evaluation data, select first classification information among the plurality of pieces of attribute-based classification information; refer to the first classification information and, based on the first classification information, generate the evaluation result for the evaluation data,” as drafted, is a process that, under its broadest reasonable interpretation, is an abstract idea that covers performance of the limitation as certain methods of organizing human activity. For example, but for the generic recitation of a “computer system,” “first and second processors,” “database,” “strain sensor or a pulse wave sensor,” and “display,” analyzing patient pulse wave data and determining relevant information based on the analyses, in the context of this claim, is an abstract idea that covers performance of the limitation as organizing human activity including following rules or instructions. These recited limitations fall within certain methods of organizing human activity grouping of abstract ideas because the limitations allowing access to a (user) patient pulse wave data that is analyzed and an evaluation result is generated based on the analyses. This is a method of managing interactions between people. Under its broadest reasonable interpretation, the limitations are categorized as methods of organizing human activity, specifically associated with managing personal behavior or relationships or interactions between people including a patient and clinician. Therefore, the limitation falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a). The mere nominal recitation of a generic “computer system,” “database,” “strain sensor or a pulse wave sensor,” “display” and computer “units” does not remove the claims from the method of organizing human interactions grouping. Thus, the claims recite an abstract idea.
Under the broadest reasonable interpretation, the claims can also be considered a mental process where the additional elements of the processors, database, display and strain and pulse wave sensors would comprise tools that are leveraged to perform the abstract idea, and add insignificant extra-solution activity to the abstract idea. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components or comprise an insignificant extra-solution activity, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of being implemented by a generic “computer system,” “first and second processor,” “database,” “strain sensor or a pulse wave sensor,” and “display” for the sending and receiving and calculation of information related to a pulse wave of a patient/user. The devices in these steps are recited at a high-level of generality (i.e., as a generic processor/server/storage/display performing a generic computer function of receiving inputs, analyzing the inputs, and displaying or sending selected information, or as mathematical concepts) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, alone or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The limitations appear to monopolize the abstract idea of patient analysis and general diagnostic techniques between a physician and her patient. Furthermore, there is no clear improvement to the underlying computer technology in the claim. The claim is thus directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the strain sensor or a pulse wave sensor amount to insignificant extra-solution activity while the generic computer system, processors, database and display amounts to no more than mere instructions to apply the exception using a computer component. Mere instructions to apply an exception using a generic computer component and those that add insignificant extra-solution activity cannot provide an inventive concept. Therefore, when considering the additional elements alone, and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible.
The dependent claims do not remedy the deficiencies of the independent claims with respect to patent eligible subject matter. The dependent claims further limit the abstract idea and do not overcome the rejection under 35 U.S.C. §101. Claim 10 further describes the reference data, classification information and a trained model, which is recited at a high level of generality such that it amounts no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the data/information and trained model does not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Claim 11 details training data and health condition result and further limits the abstract idea. Claim 12 describes a regression model and further limits the abstract idea. Claim 13 further details the obtaining and generating unit and limits the abstract idea. Claim 14 further describes the generating unit and database, which is recited at a high level of generality such that it amounts no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the generating means and database do not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Claim 17 details a trained regression model, which is recited at a high level of generality such that it amounts no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the trained regression model does not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 9-14 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2016/0242700 A1 to Ferber et al., hereinafter “Ferber,” in view of U.S. 2021/0052218 A1 to Quinn et al., hereinafter “Quinn,” in view of U.S. 2017/0135591 A1 to Ishizawa et al., hereinafter “Ishizawa,” in view of U.S. 2014/0278285 A1 to Marmarelis et al., hereinafter “Marmarelis” and further in view of U.S. 2018/0008175 A1 to Ishizawa et al., hereinafter “Ishizawa ‘175.”
Regarding claim 9, Ferber discloses A biometric information computing system for outputting information relating to a feature of blood carbon dioxide of a user comprising: a first processor that is configured to obtain evaluation data based on a pulse wave of the user (See Ferber at least at Abstract (The blood pressure calculation system includes a wave selection module configured to identify subsets of waves of the signals, a feature extraction module configured to generate sets of feature vectors form the subsets of waves, and a blood pressure processing module configured to calculate an arterial blood pressure value based on the sets of feature vectors”); Paras. [0013]-[0014], [0196]-[0204], [0223]-[0228]; Claim 8; Figs. 1, 2, 14); a database that stores classification information generated using a plurality of training data (See id. at least at Paras. [0242]-[0254]; Figs. 14, 15, 16, 17).
Ferber may not specifically describe but Quinn teaches the training data being a pair of input data based on a preliminarily obtained training pulse wave and reference data including the feature of blood carbon dioxide associated with the input data (See Quinn at least at Paras. [0006]-[0016], [0022]-[0023], [0028]-[0029], [0055], [0061]-[0072] (“Examples of vital sign data may include heart rate, heart rate variability, blood pressure, respiratory rate, blood oxygen concentration (e.g., by pulse oximetry), carbon dioxide concentration in respiratory gases, a hormone level, sweat analysis, blood glucose, body temperature, impedance (e.g., bioimpedance), conductivity, capacitance, resistivity, electromyography, galvanic skin response, neurological signals (e.g., electroencephalography), and immunology markers. The data may be measured, collected, and/or recorded in real-time (e.g., by using suitable biosensors and/or mechanical sensors), and may be transmitted continuously […] Training datasets may be generated from, for example, one or more cohorts of patients having common clinical characteristics (features) and clinical outcomes (labels). Training datasets may comprise a set of features and labels corresponding to the features. Features may correspond to algorithm inputs comprising patient demographic information derived from electronic medical records (EMR) and medical observations. Features may comprise clinical characteristics such as, for example, certain ranges or categories of vital sign measurements, such as heart rate, heart rate variability, blood pressure (e.g., systolic and diastolic), respiratory rate, blood oxygen concentration (SpO.sub.2), carbon dioxide concentration in respiratory gases”); Figs. 2, 9); wherein the classification information includes a plurality of pieces of attribute-based classification information calculated using the training data different from one another (See Quinn at least at Paras. [0006]-[0016], [0022]-[0023], [0028]-[0029], [0055], [0061]-[0074] (“Training datasets may be generated from, for example, one or more cohorts of patients having common clinical characteristics (features) and clinical outcomes (labels). Training datasets may comprise a set of features and labels corresponding to the features. Features may correspond to algorithm inputs comprising patient demographic information derived from electronic medical records (EMR) and medical observations.”); Figs. 2, 9); wherein the attribute-based classification information comprises classifications based on different attributes of gender, age, and health condition (See id. at least at Abstract; Paras. [0052]-[0055] (age and gender), [0058]-[0061] (health conditions) [0249]-[0252]; Figs. 10-12).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ferber to incorporate the teachings of Quinn and provide relevant classification and training data and based on at least a feature of blood carbon dioxide for a patient. Quinn is directed to systems for collecting and analyzing vital sign information to predict the likelihood a patient has a disease or disorder. See Quinn at Abstract. Incorporating the vital sign analysis, trained machine learning and predictions as in Quinn with the non-invasive blood pressure measurement techniques and analysis as in Ferber would thereby increase the functionality and effectiveness of implementing the claimed biometric information computing system.
Ferber and Quinn may not specifically describe but Ishizawa teaches a first processor comprising a strain sensor or a pulse wave sensor (See Ishizawa at least at Abstract (“A blood pressure measurement device (1) is provided with a pulse wave measurement unit (20) for measuring the pulse wave of a subject using an FBG sensor (10), and a blood pressure value calculation unit (30) for calculating a blood pressure value from waveform date of the measured pulse wave.”); Paras [0008]-[0011], [0015]-[0016] (pulse wave measurement unit); Figs. 1, 2) wherein the first processor is configured to obtain data corresponding to an acceleration pulse wave based on the pulse wave as a preliminary evaluation data, different from the evaluation data (See id. at least at Paras. [0008]-[0016] (“[A] calibration model that represents the correlation between measured waveform data of a measured acceleration pulse wave and measured blood pressure values measured at individual measurement time points of the measured waveform data, and the calibration model is used to estimate blood pressure values of a subject at the time of the acceleration pulse wave measurement, from the waveform data of the acceleration pulse wave measured from the subject.”), [0074]-[0084] (“[I]n preliminary measurement (steps ST1 to ST5), the acceleration pulse wave and blood pressure value are simultaneously measured as many times as is needed, and a calibration model is constructed, which represents the correlation between measured waveform data of the measured acceleration pulse waves.”); Figs. 6-11, 25-35); a second processor that is configured to refer to the database and generate an evaluation result [0008]-[0016], [0074]-[0080], [0115]-[0119] (“[T]he pulse wave waveforms of different subjects were measured using an FBG sensor, a calibration model was constructed using the PLS regression analysis method, and the correlation was examined between estimated blood pressure values estimated from the measured waveform data of the pulse waves, and the actual measured blood pressure values measured with an automatic blood pressure gauge.”); Figs. 1-11, 15-24); refer to the first classification information and, based on the first classification information, generate the evaluation result for the evaluation data (See id. at least at Paras. [0008]-[0012], [0024]-[0025], [0040]-[0045], [0074]-[0080] (“[I]n main measurement (steps ST6 and ST7), during which the actual blood pressure measurement is performed, the acceleration pulse wave of the subject is measured using the FBG sensor 10, and the blood pressure value calculation unit 30 uses the calibration model to predict (estimate) the blood pressure value of the subject at the time of acceleration pulse wave measurement from the obtained waveform data.”) and a display configured to display the evaluation result (See id. at least at Paras. [0071], [0115]-[0119]; Figs. 1, 3, 4, 11).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ferber and Quinn to incorporate the teachings of Ishizawa and provide data related to a velocity pulse wave and acceleration pulse wave. Ishizawa is directed to a blood pressure estimation method and evaluation thereof. See Ishizawa at Abstract. Incorporating the blood pressure evaluation data as in Ishizawa with the vital sign analysis, trained machine learning and predictions as in Quinn and the non-invasive blood pressure measurement techniques and analysis as in Ferber would benefit the claimed blood measurements and analyses by improving evaluation accuracy and accessibility.
Ferber, Quinn and Ishizawa may not specifically describe but Marmarelis teaches a blood feature including the feature of blood carbon dioxide for the evaluation data (See Marmarelis at least at Abstract; Paras. [0010]-[0013] (“The invention generally relates to a method for computer-aided quantitative diagnosis of cerebrovascular, neurovascular and neurodegenerative diseases via a vasomotor reactivity index (VMRI) which is acquired via computations based on an advanced mathematical and computational model of the dynamic nonlinear relationships among beat-to-beat time-series measurements of mean cerebral blood flow velocity, mean arterial blood pressure and blood CO2 tension (represented by the surrogate measurement of end-tidal CO2) obtained by non-invasive means in human subjects”), [0019]-[0023], [0031]-[0032]; Claim 8; Figs. 1-6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ferber, Quinn and Ishizawa to incorporate the teachings of Marmarelis and provide generating an evaluation based on at least a feature of blood carbon dioxide for a patient. Marmarelis relates to quantitative diagnoses using blood flow velocity and end-tidal CO2 analyses. See Marmarelis at Abstract. Incorporating the blood pressure and CO2 analysis in Marmarelis with the blood pressure evaluation data and devices as in Ishizawa, the vital sign analysis, trained machine learning and predictions as in Quinn and the non-invasive blood pressure measurement techniques and analysis as in Ferber would further benefit the analysis of blood-based diagnoses and evaluations with information related to blood CO2.
The references may not specifically describe but Ishizawa ‘175 teaches to refer to the preliminary evaluation data and, based on the preliminary evaluation data, select first classification information among the plurality of pieces of attribute-based classification information (See Ishizawa ‘175 at least at Abstract; Paras. [0009] (“[A]n acceleration pulse wave of a test subject is measured, and blood glucose level information of the test subject is extracted from waveform information of the measured acceleration pulse wave, on the basis of a correlation between the blood glucose level measured by an invasive measurement method and the simultaneously-measured acceleration pulse wave.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ferber, Quinn, Marmarelis and Ishizawa to incorporate the teachings of Ishizawa ‘175 and provide pulse wave sensors and strain sensors. Ishizawa ‘175 is directed to a non-invasive blood glucose measurement and device. Incorporating the blood glucose measurement devices as in Ishizawa ‘175 with the blood pressure evaluation data as in Ishizawa, the vital sign analysis, trained machine learning and predictions as in Quinn, the blood pressure and CO2 analysis in Marmarelis and the non-invasive blood pressure measurement techniques and analysis as in Ferber would improve the correlation of classification information with evaluation data and pulse wave information.
The references may not specifically describe but Winter teaches a processor configured to obtain data corresponding to a velocity pulse wave based on the pulse wave as the evaluation data (See Winter at least at Paras. [0019], [0023]-[0025]; Figs. 1, 2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ferber, Quinn, Marmarelis, Ishizawa and Ishizawa ‘175 to incorporate the teachings of Winter and provide velocity pulse wave data. Winter is directed to a health monitoring device. Incorporating the health monitoring device of Winter with the blood glucose measurement devices as in Ishizawa ‘175, the blood pressure evaluation data as in Ishizawa, the vital sign analysis, trained machine learning and predictions as in Quinn with the blood pressure and CO2 analysis in Marmarelis and the non-invasive blood pressure measurement techniques and analysis as in Ferber would thereby increase the functionality and accuracy of implementing the claimed blood pressure estimation and pulse wave analysis.
Regarding claim 10, Ferber as modified by Quinn, Marmarelis, Ishizawa, Ishizawa ‘175 and Winter teaches all the limitations of claim 9, and Ferber further discloses wherein the reference data includes state information indicating a health condition of an examinee from whom the training pulse wave has been measured, the classification information includes a trained model generated by machine learning using a plurality of the training data, (See Ferber at least at Abstract; Paras. [0245], [0267], [0273]-[0277]; [0307]-[0310]; Figs. 1, 2, 14). While Quinn teaches the second processor is further configured to: refer to the trained model and generate the evaluation result with a health condition result included, the health condition result indicating a health condition of the user associated with the evaluation data (See Quinn at least at Paras. [0006]-[0016], [0022]-[0023], [0028]-[0029], [0055], [0061], [0071]-[0072]; Figs. 2, 9).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ferber, Marmarelis, Ishizawa, Winter and Ishizawa ‘175 to incorporate the teachings of Quinn and provide generating a health result. Quinn is directed to machine learning health predictions. Incorporating the trained machine learning and predictions and vital signa analysis as in Quinn with the health monitoring device of Winter, the blood glucose measurement devices as in Ishizawa ‘175, the blood pressure evaluation data as in Ishizawa, the blood pressure and CO2 analysis in Marmarelis and the non-invasive blood pressure measurement techniques and analysis as in Ferber would thereby increase the functionality and effectiveness of implementing the claimed biometric information computing system.
Regarding claim 11, Ferber as modified by Quinn, Marmarelis, Ishizawa, Ishizawa ‘175 and Winter teaches all the limitations of claim 10, and Quinn further teaches wherein the plurality of training data includes only the input data and the reference data from when the examinee is healthy, and the health condition result indicates that the health condition of the user associated with the evaluation data is healthy or a state other than healthy (See Quinn at least at Paras. [0015]-[0017], [0020]-[0023], [0045], [0056]-[0057], [0064]-[0066], [0072]-[0074], [0077]; Figs. 2, 9).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ferber, Marmarelis, Ishizawa, Winter and Ishizawa ‘175 to incorporate the teachings of Quinn and provide generating a health result. Quinn is directed to machine learning health predictions. Incorporating the trained machine learning and predictions and vital signa analysis as in Quinn with the health monitoring device of Winter, the blood glucose measurement devices as in Ishizawa ‘175, the blood pressure evaluation data as in Ishizawa, the blood pressure and CO2 analysis in Marmarelis and the non-invasive blood pressure measurement techniques and analysis as in Ferber would thereby increase the functionality and effectiveness of implementing the claimed biometric information computing system and pulse wave analyses.
Regarding claim 12, Ferber as modified by Quinn, Marmarelis, Ishizawa, Ishizawa ‘175 and Winter teaches all the limitations of claim 9, while Ishizawa further teaches wherein the classification information is a calibration model obtained using a PLS regression analysis with the input data as an explanatory variable and the reference data as an objective variable (See Ishizawa at least at Paras. [0011]-[0016], [0083]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ferber, Marmarelis, Quinn, Winter and Ishizawa ‘175 to incorporate the teachings of Ishizawa and provide a calibration model and regression model analyses. Ishizawa is directed to blood pressure evaluation techniques. Incorporating the blood pressure evaluation data as in Ishizawa with the trained machine learning and predictions and vital signa analysis as in Quinn, the health monitoring device of Winter, the blood glucose measurement devices as in Ishizawa ‘175, the blood pressure and CO2 analysis in Marmarelis and the non-invasive blood pressure measurement techniques and analysis as in Ferber would thereby increase the functionality and effectiveness of implementing the claimed biometric information computing system.
Regarding claim 13, Ferber as modified by Quinn, Marmarelis, Ishizawa, Ishizawa ‘175 and Winter teaches all the limitations of claim 9, and Quinn further teaches wherein the second processor is further configured to refer to the database and calculate biometric information of the user for the preliminary evaluation data (See Quinn at least at Paras. [0006]-[0016], [0022]-[0023], [0028]-[0029], [0055], [0061], [0071]-[0074]; Figs. 2, 9).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ferber, Marmarelis, Ishizawa, Winter and Ishizawa ‘175 to incorporate the teachings of Quinn and provide calculating certain biometric information. Quinn is directed to machine learning health predictions. Incorporating the trained machine learning and predictions and vital signa analysis as in Quinn with the health monitoring device of Winter, the blood glucose measurement devices as in Ishizawa ‘175, the blood pressure evaluation data as in Ishizawa, the blood pressure and CO2 analysis in Marmarelis and the non-invasive blood pressure measurement techniques and analysis as in Ferber would thereby increase the functionality and effectiveness of implementing the claimed biometric information computing system.
Regarding claim 14, Ferber as modified by Quinn, Marmarelis, Ishizawa, Ishizawa ‘175 and Winter teaches all the limitations of claim 13, and Quinn further teaches wherein the database is further configured to store preliminary classification information generated using a plurality of preliminary training data, the preliminary training data being a pair of preliminary input data based on the training pulse wave and preliminary reference data including the biometric information associated with the preliminary input data (See Quinn at least at Paras. [0006]-[0016], [0022]-[0023], [0028]-[0029], [0055], [0061], [0071]-[0074]; Figs. 2, 9). While Ishizawa further teaches second processor is further configured to refer to the preliminary classification information and calculate the biometric information for the preliminary evaluation data. (See Ishizawa at least at Paras. [0074]-[0084]; Figs. 1A-B).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ferber, Marmarelis, Winter and Ishizawa ‘175 to incorporate the teachings of Quinn and Ishizawa and provide generating a health result and classifications. Quinn is directed to machine learning health predictions. Ishizawa is directed to blood pressure evaluation techniques. Incorporating the blood pressure evaluation data as in Ishizawa with the trained machine learning and predictions and vital signa analysis as in Quinn, the health monitoring device of Winter, the blood glucose measurement devices as in Ishizawa ‘175, the blood pressure evaluation data as in Ishizawa, the blood pressure and CO2 analysis in Marmarelis and the non-invasive blood pressure measurement techniques and analysis as in Ferber would thereby increase the functionality and effectiveness of implementing the claimed biometric information computing system.
Regarding claim 17, Ferber as modified by Quinn, Marmarelis, Ishizawa, Ishizawa ‘175 and Winter teaches all the limitations of claim 9 and Ishizawa further teaches wherein the classification information includes a trained regression or calibration model generated from a plurality of training pulse waves (See Ishizawa at least at Abstract (“[A] calibration model representing the correlation between measured waveform date of a previously measured pulse wave, and a measured blood pressure value measured by an automatic blood pressure gauge at each measurement point in time of the measured waveform date to estimate the blood pressure value of the subject from the measured waveform date of the pulse wave.”) Paras. [0078]-[0084] (“[A] regression analysis is performed and a calibration model is constructed (step ST4: PLS regression analysis, step ST5: calibration model construction). A PSL regression analysis method can be used as the regression analysis method.”)) the training pulse waves being obtained using a same type of sensor, sensor generation conditions, and preprocessing conditions as those used for obtaining the evaluation data (See id. at least at Paras. [0024]-[0025], [0034], [0077]-[0087]).
While Marmarelis teaches corresponding reference data including a feature of blood carbon dioxide (See Marmarelis at least at Paras. [0010]-]0016], [0026]; Claim 9; Figs. 1-6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ferber, Quinn, Winter and Ishizawa ‘175 to incorporate the teachings of Marmarelis and Ishizawa and provide generating a health result and classifications. Marmarelis relates to quantitative diagnoses using blood flow velocity and end-tidal CO2 analyses. Ishizawa is directed to blood pressure evaluation techniques. Incorporating the blood pressure evaluation data as in Ishizawa with the blood pressure and CO2 analysis in Marmarelis, the trained machine learning and predictions and vital signa analysis as in Quinn, the health monitoring device of Winter, the blood glucose measurement devices as in Ishizawa ‘175, the blood pressure evaluation data as in Ishizawa and the non-invasive blood pressure measurement techniques and analysis as in Ferber would thereby increase the functionality and effectiveness of implementing the claimed biometric information computing system.
Response to Arguments
Applicant’s remarks filed February 6, 2026 have been fully considered, but they are not persuasive. The following explains why:
Applicant’s arguments pertaining to prior art rejections are not persuasive. The claims have been addressed with regard to the 35 U.S.C. §103 rejection discussed above. The arguments pertaining to prior art references of the Applicant’s Remarks at Pages 8-11 are not persuasive as indicated in the updated rejection discussed above. The Examiner notes that Winter is relied on for teaching a processor that is configured to obtain data corresponding to a velocity pulse wave based on the pulse wave as the evaluation data (See Winter at least at Paras. [0019], [0023]-[0025]; Figs. 1, 2), and not Ishizawa or Sobol as indicated in the arguments. Furthermore, Ishizawa teaches data corresponding to an acceleration pulse wave based on the pulse wave as a preliminary evaluation data, different from the evaluation data at least at Paras. [0008]-[0016] (“[A] calibration model that represents the correlation between measured waveform data of a measured acceleration pulse wave and measured blood pressure values measured at individual measurement time points of the measured waveform data, and the calibration model is used to estimate blood pressure values of a subject at the time of the acceleration pulse wave measurement, from the waveform data of the acceleration pulse wave measured from the subject.”).
Furthermore, at pages 10-11, the arguments relating to Sobol are moot in light of reference to Ishizawa. See the updated rejection under 35 U.S.C. §103, discussed above. As such, it is submitted that the cited prior art, including those identified by Applicant, in the same field of endeavor, i.e., techniques for patient data analysis including analysis of blood data or other patient biometrics, teaches and/or suggests all of the limitations of the pending claims under a broad and reasonable interpretation thereof.
Applicant’s arguments pertaining to subject matter eligibility are not persuasive. The claims have been addressed with regard to the updated 35 U.S.C. §101 rejection discussed above, and considered under relevant sections of the MPEP and guidance from Director Squires in December of 2025. The arguments at pages 6-7 of Applicant’s Remarks are not persuasive. At pages 6-7 the Examiner disagrees that there is not an abstract idea, that there is any practical application thereof or there is a technological improvement in the claims. The Examiner disagrees there is significantly more than the abstract idea. Here the trained data and various computing units act as a computer tool used to employ the abstract idea. That it may be tedious or laborious to perform analyses in the mind or manually is not of consequence in the eligibility analysis. The current claims are included as a mental process(es) for the judicial exception as well as organizing human activity. A physician would use the claimed additional elements as insignificant extra-solution activity to perform the abstract idea of organizing human activity, as discussed. The first and second processors, database, display and pulse wave sensor or strain sensor in the claims are recited at a high level, and amount to applying the exception using a generic computer (See e.g. Updated PEG Example 47, claim 2, where the “detecting” and “analyzing” were mental processes, and “using the trained ANN” amounted to generic computer implementation). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. For at least these reasons and those stated above, the claims are not patent eligible.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM T. MONTICELLO whose telephone number is (313)446-4871. The examiner can normally be reached M-Th; 08:30-18:30 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FONYA LONG can be reached at (571) 270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/WILLIAM T. MONTICELLO/Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682
Prosecution Insights