DETAILED ACTION
The following is a Final Office action in response to applicant’s amendment and remarks filed on 02/26/2026. Claims 1, 11, 13, and 17 have been amended. Claims 1-20 are currently pending and have been considered as follows.
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
In view of the amendment to the claims, the objection to Claims 1 and 13 is withdrawn.
Applicant’s arguments and filing of the terminal disclaimer on 02/26/2026 have obviated the nonstatutory double patenting rejections of Claims 1-7, 9, 10, and 17-20.
Applicant’s arguments in the remarks on pages 9-10, filed 02/26/2026, with respect to the 35 U.S.C. 101 rejection of Claims 1-20 have been fully considered and are persuasive. The 35 U.S.C. 101 rejection of Claims 1-20 has been withdrawn.
Applicant’s amendment of independent Claims 1, 11, and 17 with newly added limitations “wherein the one or more health determinations are derived, by execution of the program instructions, from differences identified between first and second sensor observation-derived representations of the person, the first sensor observation-derived representation corresponding to a first physiological state of the person and the second sensor observation-derived representation corresponding to a second physiological state of the person” has changed the scope of the claimed invention. Therefore, applicant’s arguments on pages 10-12 of the remarks filed 02/26/2026 have been fully considered but are moot because the amendment necessitates new ground(s) of rejection where applicant’s arguments do not apply to the updated reference(s) for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 1-7 and 9-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Basu et al. (US 20180116600 A1, hereinafter Basu).
As to Amended Claim 1:
Basu discloses a health metrics monitoring system (e.g. Basu “a broad set of features that can be obtained non-invasively from unobtrusive wearable sensors and other obtainable information is described. This approach not only provides a more practical solution to ambulatory BP monitoring, but also, given the unobtrusiveness of the required sensing, it opens the door to new types of treatment that involve continuous monitoring” [0013]), comprising: a plurality of health metrics monitors comprising program instructions stored in a memory of the health metrics monitoring system (e.g. Basu “In wearable sensing device 200, computing device 10 is situated below display device 18 and operatively coupled to the display device 18, along with loudspeaker 216, communication suite 220, and the various sensors. The computing device 10 includes a data-storage machine 244 to hold data and instructions, and a logic machine 248 to execute the instructions” [0020]) and configured to execute the program instructions to:
receive information associated with health metrics of a person from one or more resources, wherein the one or more resources comprise at least one of a computing device, a bicorder, a sensor, programming code, or health metric related data (e.g. Basu “FIG. 1A shows a pair of contact sensor modules 276A and 276B that contact the wearer's skin when wearable sensing device 200 is worn. The contact sensor modules may include independent or cooperating sensor elements to provide a plurality of sensory functions. For example, the contact sensor modules may provide an electrical resistance and/or capacitance sensory function, which measures the electrical resistance and/or capacitance of the wearer's skin. At least one contact sensor module is an electrocardiograph 54 configured to detect a pulse electrical signal 56 when the user touches the wearable sensing device 200 with the hand not wearing the device 200 to form a complete circuit that includes the wearable sensing device 200 and the user's heart. In some examples, a contact sensor module may also provide measurement of the wearer's skin temperature. At least one contact sensor module is a pulse pressure sensor 50 configured to detect a pulse pressure wave signal 52” [0025]; [0027]; “The static data may include calibration data, which may contain measurements both from the device 200 and a conventional blood pressure measurement (via auscultatory method, oscillometric cuff” [0030]); and
generate at least one report regarding one or more health determinations (e.g. Basu “The machine learning model 40 may also convey the blood pressure estimate 60 for display to a clinician. The blood pressure estimate 60 may be conveyed to the clinician as a direct presentation including the systolic and diastolic blood pressure. It may also be conveyed to the clinician in the form of alerts or continuously varying risk scores and risk score predictions” [0068]; [0069]; “The machine learning model 40 may make automatic or recommended adjustments to medication doses based on the blood pressure estimate 60. The blood pressure estimate 60 may also be used to recommend scheduling of inpatient or outpatient visits” [0072]) based, at least in part, on the information received from the one or more resources and health-related tells associated with a corresponding population of people (e.g. Basu “The data with which the machine learning model 40 makes the blood pressure estimate 60 includes a subject data set 42. The subject data set 42 may include contextual data 44, time-varying features 46, and blood pressure measurements 48” [0027]; “The machine learning model 40 also makes the blood pressure estimate 60 based on a cohort data set 62. The cohort data set 62 includes subject-specific contextual data 64, time-varying features 66, and blood pressure measurements” [0028]; combining static data and dynamic data measurements for the generated blood pressure estimate [0029]; cohort of patients [0068]), wherein the one or more health determinations are derived, by execution of the program instructions, from differences identified between first and second sensor observation-derived representations of the person (e.g. Basu “The wearable sensing device 200 may detect a variety of conditions under which the patient's blood pressure is likely to change. Based on detecting such conditions, the machine learning model 40 may compare the blood pressure of the patient to the blood pressure that the patient had previously under similar conditions” [0038]; “Variation in the blood pressure values for the calibration data can also be achieved via pharmacological intervention, i.e., measurements can be taken before and after (e.g., at 10 minute intervals) taking a dose of blood pressure-lowering medication” [0033]; [0041]), the first sensor observation-derived representation corresponding to a first physiological state of the person (e.g. Basu “a large fraction of people exhibit a phenomena known as “nighttime dipping,” in which the blood pressure drops significantly during the night. The wearable sensing device 200 may detect a time of day and input the time of day into the machine learning model 40. Based on the inputs of the sensors of the wearable sensing device 200” [0039]) and the second sensor observation-derived representation corresponding to a second physiological state of the person (e.g. Basu “The wearable sensing device 200 may also detect physical activity of the patient. Physical activity that changes heart rate affects blood pressure differently than other factors; thus it is important both to estimate the level of physical activity over time and to model the ways in which it can affect blood pressure. The wearable sensing device 200 may detect that the patient is engaging in physical activity by, for example, detecting a signature reading from an accelerometer 232 in conjunction with an increase in heart rate” [0040]; [0041]; “The wearable sensing device 200 may use physiological data, along with clinical data and patient-reported symptom data, not only to predict outcomes, but also to change the rate and timing at which physiological measurements and symptom reports are taken” [0065]).
As to Claim 2:
Basu discloses the health metrics monitoring system of claim 1, wherein the sensor comprises at least one of: an internal sensor; an external sensor; a wearable sensor (e.g. Basu “FIGS. 1A and 1B, aspects of an example computing device 10 in the form of a wearable sensing device 200 will now be described. In this example wearable sensing device 200 is band-shaped with fastening componentry 212A and 212B arranged at both ends of the device” [0014]; [0015]); or a sensor that is in an observable proximity of people who are subjects of sensor observations.
As to Claim 3:
Basu discloses the health metrics monitoring system of claim 1, wherein the bicorder includes at least one of: a virtual device; a physical device; or a combination of a physical device and a virtual device (e.g. Basu “In wearable sensing device 200, computing device 10 is situated below display device 18 and operatively coupled to the display device 18, along with loudspeaker 216, communication suite 220, and the various sensors. The computing device 10 includes a data-storage machine 244 to hold data and instructions, and a logic machine 248 to execute the instructions” [0020]).
As to Claim 4:
Basu discloses the health metrics monitoring system of claim 1, wherein, to generate the at least one report regarding one or more health determinations based, at least in part, on the information received from the one or more resources and health-related tells associated with a corresponding population of people (e.g. Basu “The data with which the machine learning model 40 makes the blood pressure estimate 60 includes a subject data set 42. The subject data set 42 may include contextual data 44, time-varying features 46, and blood pressure measurements 48” [0027]; “The machine learning model 40 also makes the blood pressure estimate 60 based on a cohort data set 62. The cohort data set 62 includes subject-specific contextual data 64, time-varying features 66, and blood pressure measurements” [0028]; combining static data and dynamic data measurements for the generated blood pressure estimate [0029]; cohort of patients [0068]), the program instructions are further executable to: select or derive data that are included in concise datasets (e.g. Basu “a method for estimating blood pressure is provided, comprising training a machine learning model on a cohort data set. The cohort data set may include subject-specific contextual data, time-varying features, and blood pressure measurements for a plurality of subjects” [0005]; [0013]; “The data with which the machine learning model 40 makes the blood pressure estimate 60 includes a subject data set 42. The subject data set 42 may include contextual data 44, time-varying features 46, and blood pressure measurements 48” [0027]).
As to Claim 5:
Basu discloses the health metrics monitoring system of claim 4, wherein the concise datasets include selected sensor data, wherein selected sensor data include informational representations that were selected from sensor observation datasets (e.g. Basu [0025]; “The machine learning model 40 may also determine a variety of pressure wave morphology metrics based on the pulse pressure wave signal 52. The pulse pressure sensor 50 may be configured to detect the pressure wave morphology of the pulse pressure wave signal 52, including pressure wave morphology metrics selected from a group consisting of augmentation index, maximum systolic slope, systolic rise time, ejection time, dicrotic notch height, dicrotic notch time, pulse pressure, reflected wave arrival time, and heart rate” [0037]).
As to Claim 6:
Basu discloses the health metrics monitoring system of claim 5, wherein the concise datasets includes derived data, wherein derived data include informational representations that were derived from processing informational representations that were selected from sensor observation datasets or derived data (e.g. Basu “The machine learning model 40 may also determine a variety of pressure wave morphology metrics based on the pulse pressure wave signal 52. The pulse pressure sensor 50 may be configured to detect the pressure wave morphology of the pulse pressure wave signal 52, including pressure wave morphology metrics selected from a group consisting of augmentation index, maximum systolic slope, systolic rise time, ejection time, dicrotic notch height, dicrotic notch time, pulse pressure, reflected wave arrival time, and heart rate” [0037]; “Medium- to long-term use of the wearable sensing device 200 can show a patient's overall level of physical activity and physical activity patterns, which can provide another set of contextual factors for blood pressure” [0040]; “the wearable sensing device 200 may use recent data from the accelerometer 232 and gyroscope 236 to determine whether the patient is sitting, lying, standing, or walking” [0042]; [0109]).
As to Claim 7:
Basu discloses the health metrics monitoring system of claim 1, wherein, to generate the at least one report regarding one or more health determinations based, at least in part, on the information received from the one or more resources and health-related tells associated with a corresponding population of people (e.g. Basu [0027]; [0028]; [0029]; “The machine learning model 40 may also convey the blood pressure estimate 60 for display to a clinician. The blood pressure estimate 60 may be conveyed to the clinician as a direct presentation including the systolic and diastolic blood pressure. It may also be conveyed to the clinician in the form of alerts or continuously varying risk scores and risk score predictions” [0068]; [0069]; “The machine learning model 40 may make automatic or recommended adjustments to medication doses based on the blood pressure estimate 60. The blood pressure estimate 60 may also be used to recommend scheduling of inpatient or outpatient visits” [0072]), the program instructions are further executable to: determine differences between sensor observation-derived representations captured at a first time when blood flow from a heartbeat is at or near a highest level and at a second time when blood flow from a heartbeat is at a lower level (e.g. Basu “Measuring blood pressure (BP) involves determining a systolic (SBP) and diastolic (DBP) value; representing the peak and minimum values of blood pressure in the artery, respectively. The clinical standard of attaining these measurements involves a stethoscope and a sphygmomanometer (inflatable cuff with pressure gauge) and listening for the changes in blood flow as the artery is completely occluded, partially occluded, and unoccluded; this is known as the auscultatory method” [0003]; [0037]).
As to Claim 9:
Basu discloses the health metrics monitoring system of claim 1, wherein, to generate the at least one report regarding one or more health determinations based, at least in part, on the information received from the one or more resources and health-related tells associated with a corresponding population of people (e.g. Basu [0027]; [0028]; [0029]; “The machine learning model 40 may also convey the blood pressure estimate 60 for display to a clinician. The blood pressure estimate 60 may be conveyed to the clinician as a direct presentation including the systolic and diastolic blood pressure. It may also be conveyed to the clinician in the form of alerts or continuously varying risk scores and risk score predictions” [0068]; [0069]; “The machine learning model 40 may make automatic or recommended adjustments to medication doses based on the blood pressure estimate 60. The blood pressure estimate 60 may also be used to recommend scheduling of inpatient or outpatient visits” [0072]), the program instructions are further executable to:
locate analytically rich aspects, characteristics, or features of or from sensor observations or sensor observation-derived representations of people via utilization of measure points (e.g. Basu “The static data may include calibration data, which may contain measurements both from the device 200 and a conventional blood pressure measurement (via auscultatory method, oscillometric cuff, etc.). The calibration data can be used to modify the blood pressure estimate 60 using the machine learning model 40” [0030]; “The pulse pressure sensor 50 is configured to detect a pulse pressure wave. Upon detecting a pulse pressure wave, the pulse pressure sensor 50 inputs a pulse pressure wave signal 52 into the processor 12” [0034]; “The processor 12 may also be configured to receive input from an electrocardiograph (EKG) 54. The EKG 54 may be configured to detect a pulse electrical signal 56. Upon detecting a pulse electrical signal 56, the EKG 54 may input the pulse electrical signal into the processor 12” [0035]); and assign appropriate informational representations to the measure points (e.g. Basu “Based on at least the pulse pressure wave signal 52 and the pulse electrical signal 56, the machine learning model 40 may determine a pulse arrival time 58. The pulse arrival time 58 is the time difference between the time at which a heartbeat is detected in the pulse electrical signal 56 and the time at which the same heartbeat is detected in the pulse pressure wave signal 52” [0036]; “The machine learning model 40 may also determine a variety of pressure wave morphology metrics based on the pulse pressure wave signal 52. The pulse pressure sensor 50 may be configured to detect the pressure wave morphology of the pulse pressure wave signal 52, including pressure wave morphology metrics selected from a group consisting of augmentation index, maximum systolic slope, systolic rise time, ejection time, dicrotic notch height, dicrotic notch time, pulse pressure, reflected wave arrival time, and heart rate” [0037]).
As to Claim 10:
Basu discloses the health metrics monitoring system of claim 1, wherein the program instructions are further executable to:
recognize previously determined aspects, characteristics, or features (e.g. Basu “The method may include receiving contextual data for a specific subject, wherein the contextual data includes medical history data of the subject.” [0005]; [0027]);
assign informational representations regarding the recognized aspects, characteristics, or features (e.g. Basu “The subject data set 42 may include contextual data 44, time-varying features 46, and blood pressure measurements 48. The contextual data 44 includes medical history data of the subject. The subject contextual data 44 of the subject may include information from the subject's medical history data selected from the group consisting of demographic information, comorbidities, past and present medications, vital signs, laboratory test results, recent weight change, echocardiogram results, cardiovascular disease history, smoking history, and past and present pregnancy” [0027]; “subjects of a particular gender, weight, or medical history may have a different characteristic set of parameters” [0056]; [0074]); and
utilize the informational representations to make one or more determinations regarding the person's health (e.g. Basu “knowing a patient's heart rate, activity state (including recent activity levels), physical pose, time and type of last medication dose, or even static information from their health records (previous smoking history, recent significant weight loss or gain, pregnancy, etc.) can greatly augment the predictive power of a BP model” [0013]; “the machine learning model 40 may be a time-series model using features from both the current timestep and history” [0045]; “The machine learning model 40 may also convey the blood pressure estimate 60 for display to a clinician. The blood pressure estimate 60 may be conveyed to the clinician as a direct presentation including the systolic and diastolic blood pressure. It may also be conveyed to the clinician in the form of alerts or continuously varying risk scores and risk score predictions” [0068]; [0069]; “The machine learning model 40 may make automatic or recommended adjustments to medication doses based on the blood pressure estimate 60. The blood pressure estimate 60 may also be used to recommend scheduling of inpatient or outpatient visits” [0072]; [0090]; [0109]).
As to Amended Claim 11:
Basu discloses a non-transitory computer readable memory medium storing instructions (e.g. Basu “The computing device 10 includes a data-storage machine 244 to hold data and instructions, and a logic machine 248 to execute the instructions” [0020]; “Non-volatile storage device 512 includes one or more physical devices configured to hold instructions executable by the logic processors to implement the methods and processes described herein” [0083]) executable to:
receive information associated with health metrics of a person from one or more resources, wherein the one or more resources comprise at least one of a computing device, a bicorder, a sensor, programming code, or health metric related data (e.g. Basu “FIG. 1A shows a pair of contact sensor modules 276A and 276B that contact the wearer's skin when wearable sensing device 200 is worn. The contact sensor modules may include independent or cooperating sensor elements to provide a plurality of sensory functions. For example, the contact sensor modules may provide an electrical resistance and/or capacitance sensory function, which measures the electrical resistance and/or capacitance of the wearer's skin. At least one contact sensor module is an electrocardiograph 54 configured to detect a pulse electrical signal 56 when the user touches the wearable sensing device 200 with the hand not wearing the device 200 to form a complete circuit that includes the wearable sensing device 200 and the user's heart. In some examples, a contact sensor module may also provide measurement of the wearer's skin temperature. At least one contact sensor module is a pulse pressure sensor 50 configured to detect a pulse pressure wave signal 52” [0025]; [0027]; “The static data may include calibration data, which may contain measurements both from the device 200 and a conventional blood pressure measurement (via auscultatory method, oscillometric cuff” [0030]); and
generate at least one report regarding one or more health determinations (e.g. Basu “The machine learning model 40 may also convey the blood pressure estimate 60 for display to a clinician. The blood pressure estimate 60 may be conveyed to the clinician as a direct presentation including the systolic and diastolic blood pressure. It may also be conveyed to the clinician in the form of alerts or continuously varying risk scores and risk score predictions” [0068]; [0069]; “The machine learning model 40 may make automatic or recommended adjustments to medication doses based on the blood pressure estimate 60. The blood pressure estimate 60 may also be used to recommend scheduling of inpatient or outpatient visits” [0072]) based, at least in part, on the information received from the one or more resources and health-related tells associated with a corresponding population of people (e.g. Basu “The data with which the machine learning model 40 makes the blood pressure estimate 60 includes a subject data set 42. The subject data set 42 may include contextual data 44, time-varying features 46, and blood pressure measurements 48” [0027]; “The machine learning model 40 also makes the blood pressure estimate 60 based on a cohort data set 62. The cohort data set 62 includes subject-specific contextual data 64, time-varying features 66, and blood pressure measurements” [0028]; combining static data and dynamic data measurements for the generated blood pressure estimate [0029]; cohort of patients [0068]), wherein the one or more health determinations are derived, by execution of the program instructions, from differences identified between first and second sensor observation-derived representations of the person (e.g. Basu “The wearable sensing device 200 may detect a variety of conditions under which the patient's blood pressure is likely to change. Based on detecting such conditions, the machine learning model 40 may compare the blood pressure of the patient to the blood pressure that the patient had previously under similar conditions” [0038]; “Variation in the blood pressure values for the calibration data can also be achieved via pharmacological intervention, i.e., measurements can be taken before and after (e.g., at 10 minute intervals) taking a dose of blood pressure-lowering medication” [0033]; [0041]), the first sensor observation-derived representation corresponding to a first physiological state of the person (e.g. Basu “a large fraction of people exhibit a phenomena known as “nighttime dipping,” in which the blood pressure drops significantly during the night. The wearable sensing device 200 may detect a time of day and input the time of day into the machine learning model 40. Based on the inputs of the sensors of the wearable sensing device 200” [0039]) and the second sensor observation-derived representation corresponding to a second physiological state of the person (e.g. Basu “The wearable sensing device 200 may also detect physical activity of the patient. Physical activity that changes heart rate affects blood pressure differently than other factors; thus it is important both to estimate the level of physical activity over time and to model the ways in which it can affect blood pressure. The wearable sensing device 200 may detect that the patient is engaging in physical activity by, for example, detecting a signature reading from an accelerometer 232 in conjunction with an increase in heart rate” [0040]; [0041]; “The wearable sensing device 200 may use physiological data, along with clinical data and patient-reported symptom data, not only to predict outcomes, but also to change the rate and timing at which physiological measurements and symptom reports are taken” [0065]).
As to Claim 12:
Basu discloses the non-transitory computer readable memory medium of claim 11, wherein the instructions are further executable to:
recognize analytically rich aspects, characteristics, or features (e.g. Basu “The method may include receiving contextual data for a specific subject, wherein the contextual data includes medical history data of the subject.” [0005]; [0027]);
assign informational representations regarding the analytically rich aspects, characteristics, or features (e.g. Basu “The subject data set 42 may include contextual data 44, time-varying features 46, and blood pressure measurements 48. The contextual data 44 includes medical history data of the subject. The subject contextual data 44 of the subject may include information from the subject's medical history data selected from the group consisting of demographic information, comorbidities, past and present medications, vital signs, laboratory test results, recent weight change, echocardiogram results, cardiovascular disease history, smoking history, and past and present pregnancy” [0027]; “subjects of a particular gender, weight, or medical history may have a different characteristic set of parameters” [0056]; [0074]); and
utilize the informational representations to make one or more determinations regarding the person's health (e.g. Basu “knowing a patient's heart rate, activity state (including recent activity levels), physical pose, time and type of last medication dose, or even static information from their health records (previous smoking history, recent significant weight loss or gain, pregnancy, etc.) can greatly augment the predictive power of a BP model” [0013]; “the machine learning model 40 may be a time-series model using features from both the current timestep and history” [0045]; “The machine learning model 40 may also convey the blood pressure estimate 60 for display to a clinician. The blood pressure estimate 60 may be conveyed to the clinician as a direct presentation including the systolic and diastolic blood pressure. It may also be conveyed to the clinician in the form of alerts or continuously varying risk scores and risk score predictions” [0068]; [0069]; “The machine learning model 40 may make automatic or recommended adjustments to medication doses based on the blood pressure estimate 60. The blood pressure estimate 60 may also be used to recommend scheduling of inpatient or outpatient visits” [0072]; [0090]; [0109]).
As to Claim 13:
Basu discloses the non-transitory computer readable memory medium of claim 12, wherein, to recognize analytically rich aspects, characteristics, or features, the instructions are further executable to: utilize one or more measure points to recognize or locate one or more analytically rich aspects, characteristics, or features (e.g. Basu “The static data may include calibration data, which may contain measurements both from the device 200 and a conventional blood pressure measurement (via auscultatory method, oscillometric cuff, etc.). The calibration data can be used to modify the blood pressure estimate 60 using the machine learning model 40” [0030]; “The pulse pressure sensor 50 is configured to detect a pulse pressure wave. Upon detecting a pulse pressure wave, the pulse pressure sensor 50 inputs a pulse pressure wave signal 52 into the processor 12” [0034]; “The processor 12 may also be configured to receive input from an electrocardiograph (EKG) 54. The EKG 54 may be configured to detect a pulse electrical signal 56. Upon detecting a pulse electrical signal 56, the EKG 54 may input the pulse electrical signal into the processor 12” [0035]).
As to Claim 14:
Basu discloses the non-transitory computer readable memory medium of claim 11, wherein the instructions are further executable to:
recognize previously determined aspects, characteristics, or features (e.g. Basu “The method may include receiving contextual data for a specific subject, wherein the contextual data includes medical history data of the subject.” [0005]; [0027]);
recognize analytically rich aspects, characteristics, or features (e.g. Basu “static data (measured in the clinician's office, obtained from medical records, or entered by the patient) and dynamic data (measured by the wearable sensing device 200) into a blood pressure estimate 60 using the machine learning model 40” [0029]; [0030]; make use of dynamic features [0044]; [0045]); and
assign first informational representations regarding the previously determined aspects, characteristics, or features and second informational representations regarding the analytically rich aspects, characteristics, or features (e.g. Basu “The subject data set 42 may include contextual data 44, time-varying features 46, and blood pressure measurements 48. The contextual data 44 includes medical history data of the subject. The subject contextual data 44 of the subject may include information from the subject's medical history data selected from the group consisting of demographic information, comorbidities, past and present medications, vital signs, laboratory test results, recent weight change, echocardiogram results, cardiovascular disease history, smoking history, and past and present pregnancy” [0027]; [0030]-[0033]; “subjects of a particular gender, weight, or medical history may have a different characteristic set of parameters” [0056]; [0074]).
As to Claim 15:
Basu discloses the non-transitory computer readable memory medium of claim 14, wherein the instructions are further executable to:
match first informational representations to second informational representations (e.g. Basu “Variation in the blood pressure values for the calibration data can also be achieved via pharmacological intervention, i.e., measurements can be taken before and after (e.g., at 10 minute intervals) taking a dose of blood pressure-lowering medication” [0033]);
compare the matched first informational representations with the second informational representations (e.g. Basu “The wearable sensing device 200 may detect a variety of conditions under which the patient's blood pressure is likely to change. Based on detecting such conditions, the machine learning model 40 may compare the blood pressure of the patient to the blood pressure that the patient had previously under similar conditions” [0038]); and
provide one or more conclusions or observations based on the comparison (e.g. Basu “The machine learning model 40 may use a variety of machine learning techniques. Machine learning techniques used by the machine learning model 40 to generate the blood pressure estimate” [0043]; “The machine learning model 40 may also convey the blood pressure estimate 60 for display to a clinician. The blood pressure estimate 60 may be conveyed to the clinician as a direct presentation including the systolic and diastolic blood pressure. It may also be conveyed to the clinician in the form of alerts or continuously varying risk scores and risk score predictions” [0068]; [0069]; “The machine learning model 40 may make automatic or recommended adjustments to medication doses based on the blood pressure estimate 60. The blood pressure estimate 60 may also be used to recommend scheduling of inpatient or outpatient visits” [0072]).
As to Claim 16:
Basu discloses the non-transitory computer readable memory medium of claim 11, wherein the instructions are further executable to identify one or more health-related tells regarding the person's health (e.g. Basu “The machine learning model 40 may also convey the blood pressure estimate 60 for display to a clinician. The blood pressure estimate 60 may be conveyed to the clinician as a direct presentation including the systolic and diastolic blood pressure. It may also be conveyed to the clinician in the form of alerts or continuously varying risk scores and risk score predictions” [0068]; [0069]; “The machine learning model 40 may make automatic or recommended adjustments to medication doses based on the blood pressure estimate 60. The blood pressure estimate 60 may also be used to recommend scheduling of inpatient or outpatient visits” [0072]).
As to Amended Claim 17:
Basu discloses a system (e.g. Basu “a broad set of features that can be obtained non-invasively from unobtrusive wearable sensors and other obtainable information is described. This approach not only provides a more practical solution to ambulatory BP monitoring, but also, given the unobtrusiveness of the required sensing, it opens the door to new types of treatment that involve continuous monitoring” [0013]), comprising:
one or more universal health metrics monitors, wherein said universal health metrics monitors comprise program instructions stored in a memory (e.g. Basu “In wearable sensing device 200, computing device 10 is situated below display device 18 and operatively coupled to the display device 18, along with loudspeaker 216, communication suite 220, and the various sensors. The computing device 10 includes a data-storage machine 244 to hold data and instructions, and a logic machine 248 to execute the instructions” [0020]; “Non-volatile storage device 512 includes one or more physical devices configured to hold instructions executable by the logic processors to implement the methods and processes described herein” [0083]) and executable to:
receive information associated with health metrics of a person from one or more resources, wherein the one or more resources comprise at least one of a computing device, a bicorder, a sensor, programming code, or health metric related data (e.g. Basu “FIG. 1A shows a pair of contact sensor modules 276A and 276B that contact the wearer's skin when wearable sensing device 200 is worn. The contact sensor modules may include independent or cooperating sensor elements to provide a plurality of sensory functions. For example, the contact sensor modules may provide an electrical resistance and/or capacitance sensory function, which measures the electrical resistance and/or capacitance of the wearer's skin. At least one contact sensor module is an electrocardiograph 54 configured to detect a pulse electrical signal 56 when the user touches the wearable sensing device 200 with the hand not wearing the device 200 to form a complete circuit that includes the wearable sensing device 200 and the user's heart. In some examples, a contact sensor module may also provide measurement of the wearer's skin temperature. At least one contact sensor module is a pulse pressure sensor 50 configured to detect a pulse pressure wave signal 52” [0025]; [0027]; “The static data may include calibration data, which may contain measurements both from the device 200 and a conventional blood pressure measurement (via auscultatory method, oscillometric cuff” [0030]); and
generate at least one report regarding one or more health determinations (e.g. Basu “The machine learning model 40 may also convey the blood pressure estimate 60 for display to a clinician. The blood pressure estimate 60 may be conveyed to the clinician as a direct presentation including the systolic and diastolic blood pressure. It may also be conveyed to the clinician in the form of alerts or continuously varying risk scores and risk score predictions” [0068]; [0069]; “The machine learning model 40 may make automatic or recommended adjustments to medication doses based on the blood pressure estimate 60. The blood pressure estimate 60 may also be used to recommend scheduling of inpatient or outpatient visits” [0072]) based, at least in part, on the information received from the one or more resources and health-related tells associated with a corresponding population of people (e.g. Basu “The data with which the machine learning model 40 makes the blood pressure estimate 60 includes a subject data set 42. The subject data set 42 may include contextual data 44, time-varying features 46, and blood pressure measurements 48” [0027]; “The machine learning model 40 also makes the blood pressure estimate 60 based on a cohort data set 62. The cohort data set 62 includes subject-specific contextual data 64, time-varying features 66, and blood pressure measurements” [0028]; combining static data and dynamic data measurements for the generated blood pressure estimate [0029]; cohort of patients [0068]), wherein the one or more health determinations are derived, by execution of the program instructions, from differences identified between first and second sensor observation-derived representations of the person (e.g. Basu “The wearable sensing device 200 may detect a variety of conditions under which the patient's blood pressure is likely to change. Based on detecting such conditions, the machine learning model 40 may compare the blood pressure of the patient to the blood pressure that the patient had previously under similar conditions” [0038]; “Variation in the blood pressure values for the calibration data can also be achieved via pharmacological intervention, i.e., measurements can be taken before and after (e.g., at 10 minute intervals) taking a dose of blood pressure-lowering medication” [0033]; [0041]), the first sensor observation-derived representation corresponding to a first physiological state of the person (e.g. Basu “a large fraction of people exhibit a phenomena known as “nighttime dipping,” in which the blood pressure drops significantly during the night. The wearable sensing device 200 may detect a time of day and input the time of day into the machine learning model 40. Based on the inputs of the sensors of the wearable sensing device 200” [0039]) and the second sensor observation-derived representation corresponding to a second physiological state of the person (e.g. Basu “The wearable sensing device 200 may also detect physical activity of the patient. Physical activity that changes heart rate affects blood pressure differently than other factors; thus it is important both to estimate the level of physical activity over time and to model the ways in which it can affect blood pressure. The wearable sensing device 200 may detect that the patient is engaging in physical activity by, for example, detecting a signature reading from an accelerometer 232 in conjunction with an increase in heart rate” [0040]; [0041]; “The wearable sensing device 200 may use physiological data, along with clinical data and patient-reported symptom data, not only to predict outcomes, but also to change the rate and timing at which physiological measurements and symptom reports are taken” [0065]).
As to Claim 18:
Basu discloses the system of claim 17, wherein, to generate the at least one report regarding one or more health determinations based, at least in part, on the information received from the one or more resources and health-related tells associated with a corresponding population of people (e.g. Basu [0027]; [0028]; [0029]; “The machine learning model 40 may also convey the blood pressure estimate 60 for display to a clinician. The blood pressure estimate 60 may be conveyed to the clinician as a direct presentation including the systolic and diastolic blood pressure. It may also be conveyed to the clinician in the form of alerts or continuously varying risk scores and risk score predictions” [0068]; [0069]; “The machine learning model 40 may make automatic or recommended adjustments to medication doses based on the blood pressure estimate 60. The blood pressure estimate 60 may also be used to recommend scheduling of inpatient or outpatient visits” [0072]), the program instructions are further executable to: determine differences between sensor observation-derived representations captured at a first time when blood flow from a heartbeat is at or near a highest level and at a second time when blood flow from a heartbeat is at a lower level (e.g. Basu “Measuring blood pressure (BP) involves determining a systolic (SBP) and diastolic (DBP) value; representing the peak and minimum values of blood pressure in the artery, respectively. The clinical standard of attaining these measurements involves a stethoscope and a sphygmomanometer (inflatable cuff with pressure gauge) and listening for the changes in blood flow as the artery is completely occluded, partially occluded, and unoccluded; this is known as the auscultatory method” [0003]; [0037]).
As to Claim 19:
Basu discloses the system of claim 17, wherein the health-related tells comprise one or more of analytically rich aspects, characteristics, or features of or from sensor observation-derived representations that are used to make one or more selected health determinations (e.g. Basu “The machine learning model 40 also makes the blood pressure estimate 60 based on a cohort data set 62. The cohort data set 62 includes subject-specific contextual data 64, time-varying features 66, and blood pressure measurements” [0028]; combining static data and dynamic data measurements for the generated blood pressure estimate [0029]; cohort of patients [0068]; “The machine learning model 40 may also convey the blood pressure estimate 60 for display to a clinician. The blood pressure estimate 60 may be conveyed to the clinician as a direct presentation including the systolic and diastolic blood pressure. It may also be conveyed to the clinician in the form of alerts or continuously varying risk scores and risk score predictions” [0068]; [0069]; “The machine learning model 40 may make automatic or recommended adjustments to medication doses based on the blood pressure estimate 60. The blood pressure estimate 60 may also be used to recommend scheduling of inpatient or outpatient visits” [0072]).
As to Claim 20:
Basu discloses the system of claim 17, wherein the program instructions are further executable to identify one or more health-related tells regarding the person's health (e.g. Basu “The machine learning model 40 may also convey the blood pressure estimate 60 for display to a clinician. The blood pressure estimate 60 may be conveyed to the clinician as a direct presentation including the systolic and diastolic blood pressure. It may also be conveyed to the clinician in the form of alerts or continuously varying risk scores and risk score predictions” [0068]; [0069]; “The machine learning model 40 may make automatic or recommended adjustments to medication doses based on the blood pressure estimate 60. The blood pressure estimate 60 may also be used to recommend scheduling of inpatient or outpatient visits” [0072]).
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 8 is rejected under 35 U.S.C. 103 as being unpatentable over Basu in view of Alphonse et al. (US 20060103850 A1, hereinafter Alphonse).
As to Claim 8:
Basu discloses the health metrics monitoring system of claim 1, wherein, to generate the at least one report regarding one or more health determinations based, at least in part, on the information received from the one or more resources and health-related tells associated with a corresponding population of people (e.g. Basu [0027]; [0028]; [0029]; “The machine learning model 40 may also convey the blood pressure estimate 60 for display to a clinician. The blood pressure estimate 60 may be conveyed to the clinician as a direct presentation including the systolic and diastolic blood pressure. It may also be conveyed to the clinician in the form of alerts or continuously varying risk scores and risk score predictions” [0068]; [0069]; “The machine learning model 40 may make automatic or recommended adjustments to medication doses based on the blood pressure estimate 60. The blood pressure estimate 60 may also be used to recommend scheduling of inpatient or outpatient visits” [0072]), but does not specifically disclose:
determine a location or orientation of a tumor on the person based, at least in part, on the information received from the one or more resources and health-related tells associated with the corresponding population of people.
However, the analogous art Alphonse does disclose determine a location or orientation of a tumor on the person based, at least in part, on the information received from the one or more resources and health-related tells associated with the corresponding population of people (e.g. Alphonse “The scans from each waveguide in the device may be digitized and compared to each other or a population of normal artery scans and used to diagnose the presence or absence, state, extent, or location of a lesion such as… a tumor” [0101]). Basu and Alphonse are analogous art because they are from the same field of endeavor in sensors for medical diagnostics.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, having the teachings of Basu and Alphonse before him or her, to modify the disclosure of Basu with the teachings of Alphonse to include determine a location or orientation of a tumor on the person based, at least in part, on the information received from the one or more resources and health-related tells associated with the corresponding population of people as claimed. The suggestion/motivation for doing so would have been to quickly determine differences, common features, and provide diagnosis (Alphonse [0021]). Therefore, it would have been obvious to combine Basu and Alphonse to obtain the invention as specified in the instant claim(s).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Gevins (US 20080167571 A1)
Greenhut et al. (US 20110105927 A1)
Weiner et al. (US 20110213218 A1)
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kenneth Chang whose telephone number is (571)270-7530. The examiner can normally be reached Monday - Friday 9:30am-5:30pm 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, Taghi Arani can be reached at 571-272-3787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KENNETH W CHANG/Primary Examiner, Art Unit 2438
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