DETAILED ACTION
Claims 1-24 are pending and hereby under examination.
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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: “445” in paragraph 0058. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: “405”, “442”, “444”, and “514”. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“energy detecting element” first recited in claim 1;
“biosensing device” first recited in claim 1; and
“trained machine learning model” first recited in claim 1;
The identified structure for the corresponding claim limitations are as follows:
"energy detecting element" is identified as “the sensor(s) 450 may include one or more optical sensors configured to emit light via one or more light sources and/or detect reflected or refracted light via one or more light detectors” (Paragraph 0058).
“biosensing device” is identified as “The biosensing device features an operating system that may include or be configured to process logic deployed within a housing that is attached to a wearer through an adhesive for example. The wearable biosensing device includes an electronics assembly, a power assembly, and a sensing assembly positioned between the electronics assembly and the power assembly” (Paragraph 0024) and “biosensing data may include raw signals, constructed indexes, and/or metrics obtained or determined by wearable biosensing device” (Paragraph 0025).
“trained machine learning model” is identified as “training and deploying either a regression or classification model, the input vector may include features from optical sensor signals obtained using a plurality of light wavelengths (where an optical sensor signal refers to a recorded waveform itself). These optical features may include time-domain features such as the signal amplitude, period, and/or location within the period of features associated with the cardiac cycle. Additional features may include prior predictions of patient health metrics, such as blood hemoglobin, and demographic information, such as patient skin pigmentation, age, height, and weight” (Paragraph 0084) and “In such an example, a classification model may be trained and configured to provide a prediction of a patient's serum potassium status” (Paragraph 0083).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 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 person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 5-10, 13-18, and 21-24 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Newberry (US 20210259640 – cited by Applicant).
Regarding claim 1, Newberry teaches a computerized method comprising:
capturing a first energy measurement reading by an energy detecting element of an optical sensor (Paragraph 0109), wherein the optical sensor is a component of a biosensing device (Fig. 2, biosensor 100 and PPG circuit 110), and wherein the biosensing device is disposed on a skin surface of the patient (Paragraph 0109);
performing a feature extraction on the first energy measurement reading resulting in a feature vector representative of volumetric variations in blood flow of the patient (Paragraph 0109, “A processing circuit integrated in the biosensor or in communication with the biosensor processes the PPG signals to obtain a user's vitals, concentrations of substances in blood flow and/or other health information”);
deploying a trained machine learning model configured to take the feature vector as input (Paragraph 0191-0193, wherein an artificial neural network is implemented to determine health data from PPG signals, such as a concentration level of health data) and determine a serum potassium level classification of the patient at a time corresponding to when the first energy measurement reading was captured (Paragraphs 0412, 0415, and 0420, wherein an electrolyte concentration level includes potassium); and
generating a graphic user interface (GUI) that displays the serum potassium level classification (Fig. 1, display 116).
Regarding claim 2, Newberry further teaches wherein the optical sensor further includes a light source configured to emit light, wherein the first energy measurement is reflected or refracted light that corresponds to the light emitted from the light source, and wherein reflection or refraction of the light occurs as the light is traveling to or through a vessel or homogeneously perfused tissue site (Paragraphs 0109-0110).
Regarding claim 5, Newberry further teaches, prior to deployment of the machine learning model, capturing, by an accelerometer component of the biosensing device, accelerometer reading indicative of movement of the patient at the time that the first energy measurement reading was captured (Fig. 1, motion sensor 120); and
determining whether the accelerometer reading satisfies a motion threshold comparison (Paragraph 0114, “The biosensor 100 may also include a motion sensor 114 configured to detect motion of the biosensor 100 or patient … an acceptable tolerance for a PPG signal quality indicator may be set. When a motion level exceeds a threshold”).
Regarding claim 6, Newberry further teaches when the accelerometer reading satisfies the motion threshold comparison, continuing with analysis of the first energy measurement reading; and
when the accelerometer reading does not satisfy the motion threshold comparison, rejecting the first energy measurement reading and excluding the first energy measurement reading from analysis by the machine learning model (Paragraph 0114, When a motion level exceeds a threshold, then the PPG data may be ignored to avoid measurement errors”).
Regarding claim 7, Newberry further teaches wherein the feature vector includes features extracted from a PPG waveform generated from the reflected or refracted light, wherein the features include one or more of amplitude (Paragraph 0177), area (Paragraph 0179, systolic/diastolic area, pulse pressure area), width (Paragraph 0179, pulse width), maximum or minimum slope (Paragraph 0179, first or second derivative of the PPG waveform), or location of other fiducial points (Paragraph 0179, systolic/diastolic peaks, dicrotic notch; Fig. 9B) related to events during the cardiac cycle.
Regarding claim 8, Newberry further teaches wherein the trained machine learning model is trained through a training process that includes:
a forward pass that includes passing historical data through a machine learning algorithm, wherein the historical data includes features and target values of serum potassium (Paragraph 0370, “ A regression module neural network may be trained using one or more learning vectors with similar types of input parameters and known outputs as described further hereinabove”; Paragraph 0393),
a loss calculation operation that includes determining a difference between predicted values and the target values (Paragraph 0394, “In case the actual output is different from target output, the difference or error is determined”),
a backward propagation step during which includes computing how each parameter contributed to an error in a prediction determined in the forward pass (Paragraph 0192, “Other learning algorithms include back propagation”), and
a parameter revision step that includes revising of initial internal variables such that the trained machine learning model is configured to determine a serum potassium level (Paragraph 0192, The neural network learns by adjusting its parameters, weights and thresholds iteratively to yield desired output”), and wherein the serum potassium level is classified according to one or more threshold comparisons (Paragraph 0370, A classifier neural network may be applied to the one or more parameters to determine a glucose level or other health data. The glucose level may be expressed as within one or more ranges, such as normal, above normal, below normal, etc. (Region 1, 2, 3, 4 of glucose range—normal, below or above; Examiner notes that while Newberry only explicitly refers glucose ranges in this step, Newberry discusses “other health data” to include potassium as discussed above. One of ordinary skill would be able to classify not just glucose levels, but potassium levels, too. Thus, Newberry reads on this limitation).
Regarding claim 9, Newberry teaches a computing device, comprising:
a processor (Fig. 1, processing circuit 102); and
a non-transitory computer-readable medium having stored thereon instructions that, when executed by the processor, cause the processor to perform operations (Paragraph 0111, “the memory device 104 may include one or more non-transitory processor readable memories that store instructions which when executed by the one or more processing circuits 102, causes the one or more processing circuits 102 to perform one or more functions described herein”) including:
capturing a first energy measurement reading by an energy detecting element of an optical sensor (Paragraph 0109), wherein the optical sensor is a component of a biosensing device (Fig. 2, biosensor 100 and PPG circuit 110), and wherein the biosensing device is disposed on a skin surface of the patient (Paragraph 0109);
performing a feature extraction on the first energy measurement reading resulting in a feature vector representative of volumetric variations in blood flow of the patient (Paragraph 0109, “A processing circuit integrated in the biosensor or in communication with the biosensor processes the PPG signals to obtain a user's vitals, concentrations of substances in blood flow and/or other health information”);
deploying a trained machine learning model configured to take the feature vector as input (Paragraph 0191-0193, wherein an artificial neural network is implemented to determine health data from PPG signals, such as a concentration level of health data) and determine a serum potassium level classification of the patient at a time corresponding to when the first energy measurement reading was captured (Paragraphs 0412, 0415, and 0420, wherein an electrolyte concentration level includes potassium); and
generating a graphic user interface (GUI) that displays the serum potassium level classification (Fig. 1, display 116).
Regarding claim 10, Newberry further teaches wherein the optical sensor further includes a light source configured to emit light, wherein the first energy measurement is reflected or refracted light that corresponds to the light emitted from the light source, and wherein reflection or refraction of the light occurs as the light is traveling to or through a vessel or homogeneously perfused tissue site (Paragraphs 0109-0110).
Regarding claim 13, Newberry further teaches, prior to deployment of the machine learning model, capturing, by an accelerometer component of the biosensing device, accelerometer reading indicative of movement of the patient at the time that the first energy measurement reading was captured (Fig. 1, motion sensor 120); and
determining whether the accelerometer reading satisfies a motion threshold comparison (Paragraph 0114, “The biosensor 100 may also include a motion sensor 114 configured to detect motion of the biosensor 100 or patient … an acceptable tolerance for a PPG signal quality indicator may be set. When a motion level exceeds a threshold”).
Regarding claim 14, Newberry further teaches when the accelerometer reading satisfies the motion threshold comparison, continuing with analysis of the first energy measurement reading; and
when the accelerometer reading does not satisfy the motion threshold comparison, rejecting the first energy measurement reading and excluding the first energy measurement reading from analysis by the machine learning model (Paragraph 0114, When a motion level exceeds a threshold, then the PPG data may be ignored to avoid measurement errors”).
Regarding claim 15, Newberry further teaches wherein the feature vector includes features extracted from a PPG waveform generated from the reflected or refracted light, wherein the features include one or more of amplitude (Paragraph 0177), area (Paragraph 0179, systolic/diastolic area, pulse pressure area), width (Paragraph 0179, pulse width), maximum or minimum slope (Paragraph 0179, first or second derivative of the PPG waveform), or location of other fiducial points (Paragraph 0179, systolic/diastolic peaks, dicrotic notch; Fig. 9B) related to events during the cardiac cycle.
Regarding claim 16, Newberry further teaches wherein the trained machine learning model is trained through a training process that includes:
a forward pass that includes passing historical data through a machine learning algorithm, wherein the historical data includes features and target values of serum potassium (Paragraph 0370, “ A regression module neural network may be trained using one or more learning vectors with similar types of input parameters and known outputs as described further hereinabove”; Paragraph 0393),
a loss calculation operation that includes determining a difference between predicted values and the target values (Paragraph 0394, “In case the actual output is different from target output, the difference or error is determined”),
a backward propagation step during which includes computing how each parameter contributed to an error in a prediction determined in the forward pass (Paragraph 0192, “Other learning algorithms include back propagation”), and
a parameter revision step that includes revising of initial internal variables such that the trained machine learning model is configured to determine a serum potassium level (Paragraph 0192, The neural network learns by adjusting its parameters, weights and thresholds iteratively to yield desired output”), and wherein the serum potassium level is classified according to one or more threshold comparisons (Paragraph 0370, A classifier neural network may be applied to the one or more parameters to determine a glucose level or other health data. The glucose level may be expressed as within one or more ranges, such as normal, above normal, below normal, etc. (Region 1, 2, 3, 4 of glucose range—normal, below or above; Examiner notes that while Newberry only explicitly refers glucose ranges in this step, Newberry discusses “other health data” to include potassium as discussed above. One of ordinary skill would be able to classify not just glucose levels, but potassium levels, too. Thus, Newberry reads on this limitation).
Regarding claim 17, Newberry teaches a non-transitory computer-readable medium having stored thereon instructions that, when executed by one or more processors, cause the one or more processors to perform operations including:
capturing a first energy measurement reading by an energy detecting element of an optical sensor (Paragraph 0109), wherein the optical sensor is a component of a biosensing device (Fig. 2, biosensor 100 and PPG circuit 110), and wherein the biosensing device is disposed on a skin surface of the patient (Paragraph 0109);
performing a feature extraction on the first energy measurement reading resulting in a feature vector representative of volumetric variations in blood flow of the patient (Paragraph 0109, “A processing circuit integrated in the biosensor or in communication with the biosensor processes the PPG signals to obtain a user's vitals, concentrations of substances in blood flow and/or other health information”);
deploying a trained machine learning model configured to take the feature vector as input (Paragraph 0191-0193, wherein an artificial neural network is implemented to determine health data from PPG signals, such as a concentration level of health data) and determine a serum potassium level classification of the patient at a time corresponding to when the first energy measurement reading was captured (Paragraphs 0412, 0415, and 0420, wherein an electrolyte concentration level includes potassium); and
generating a graphic user interface (GUI) that displays the serum potassium level classification (Fig. 1, display 116).
Regarding claim 18, Newberry further teaches wherein the optical sensor further includes a light source configured to emit light, wherein the first energy measurement is reflected or refracted light that corresponds to the light emitted from the light source, and wherein reflection or refraction of the light occurs as the light is traveling to or through a vessel or homogeneously perfused tissue site (Paragraphs 0109-0110).
Regarding claim 21, Newberry further teaches, prior to deployment of the machine learning model, capturing, by an accelerometer component of the biosensing device, accelerometer reading indicative of movement of the patient at the time that the first energy measurement reading was captured (Fig. 1, motion sensor 120); and
determining whether the accelerometer reading satisfies a motion threshold comparison (Paragraph 0114, “The biosensor 100 may also include a motion sensor 114 configured to detect motion of the biosensor 100 or patient … an acceptable tolerance for a PPG signal quality indicator may be set. When a motion level exceeds a threshold”).
Regarding claim 22, Newberry further teaches when the accelerometer reading satisfies the motion threshold comparison, continuing with analysis of the first energy measurement reading; and
when the accelerometer reading does not satisfy the motion threshold comparison, rejecting the first energy measurement reading and excluding the first energy measurement reading from analysis by the machine learning model (Paragraph 0114, When a motion level exceeds a threshold, then the PPG data may be ignored to avoid measurement errors”).
Regarding claim 23, Newberry further teaches wherein the feature vector includes features extracted from a PPG waveform generated from the reflected or refracted light, wherein the features include one or more of amplitude (Paragraph 0177), area (Paragraph 0179, systolic/diastolic area, pulse pressure area), width (Paragraph 0179, pulse width), maximum or minimum slope (Paragraph 0179, first or second derivative of the PPG waveform), or location of other fiducial points (Paragraph 0179, systolic/diastolic peaks, dicrotic notch; Fig. 9B) related to events during the cardiac cycle.
Regarding claim 24, Newberry further teaches wherein the trained machine learning model is trained through a training process that includes:
a forward pass that includes passing historical data through a machine learning algorithm, wherein the historical data includes features and target values of serum potassium (Paragraph 0370, “ A regression module neural network may be trained using one or more learning vectors with similar types of input parameters and known outputs as described further hereinabove”; Paragraph 0393),
a loss calculation operation that includes determining a difference between predicted values and the target values (Paragraph 0394, “In case the actual output is different from target output, the difference or error is determined”),
a backward propagation step during which includes computing how each parameter contributed to an error in a prediction determined in the forward pass (Paragraph 0192, “Other learning algorithms include back propagation”), and
a parameter revision step that includes revising of initial internal variables such that the trained machine learning model is configured to determine a serum potassium level (Paragraph 0192, The neural network learns by adjusting its parameters, weights and thresholds iteratively to yield desired output”), and wherein the serum potassium level is classified according to one or more threshold comparisons (Paragraph 0370, A classifier neural network may be applied to the one or more parameters to determine a glucose level or other health data. The glucose level may be expressed as within one or more ranges, such as normal, above normal, below normal, etc. (Region 1, 2, 3, 4 of glucose range—normal, below or above; Examiner notes that while Newberry only explicitly refers glucose ranges in this step, Newberry discusses “other health data” to include potassium as discussed above. One of ordinary skill would be able to classify not just glucose levels, but potassium levels, too. Thus, Newberry reads on this limitation).
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.
The factual inquiries 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 3-4, 11-12, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Newberry as applied to claims 1, 9, and 17 above, and further in view of Johnson (US 20230263434).
Regarding claim 3, Newberry discloses, prior to deployment of the machine learning model, capturing, by a temperature sensor of the optical sensor (Fig. 1, temperature sensor 114), a skin temperature reading of the patient at the time that the first energy measurement reading was captured (Paragraph 0367, determining temperature during PPG signal readings); and
While Newberry discusses measuring temperature and setting thresholds / excluding data that don’t meet a certain threshold as discussed above, Newberry fails to link setting a threshold to the temperature measurement.
However, Johnson teaches a method for monitoring potassium in the blood (Abstract), wherein body temperature levels can affect measured potassium levels (Paragraphs 0213 and 0216-0220). Thus, one of ordinary skill in the art would be motivated to set a threshold against a body temperature measurement for correct potassium readings as a higher body temperature (and a patient experiencing hypothermia) may induce lower potassium levels (Paragraphs 0217 and 0219). 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 modified Newberry to incorporate the teachings of Johnson to set a threshold for temperature measurements to ensure accurate potassium readings.
Regarding claim 4, While Newberry discusses measuring temperature and setting thresholds / excluding data that don’t meet a certain threshold as discussed above, Newberry fails to explicitly disclose excluding temperature data.
However, Johnson teaches a method for monitoring potassium in the blood (Abstract), wherein body temperature levels can affect measured potassium levels (Paragraphs 0213 and 0216-0220). Thus, one of ordinary skill in the art would be motivated to set a threshold against a body temperature measurement, and exclude data outside of the threshold, for correct potassium readings as a higher body temperature (and a patient experiencing hypothermia) may induce lower potassium levels (Paragraphs 0217 and 0219).
Regarding claim 11, Newberry discloses, prior to deployment of the machine learning model, capturing, by a temperature sensor of the optical sensor (Fig. 1, temperature sensor 114), a skin temperature reading of the patient at the time that the first energy measurement reading was captured (Paragraph 0367, determining temperature during PPG signal readings); and
While Newberry discusses measuring temperature and setting thresholds / excluding data that don’t meet a certain threshold as discussed above, Newberry fails to link setting a threshold to the temperature measurement.
However, Johnson teaches a method for monitoring potassium in the blood (Abstract), wherein body temperature levels can affect measured potassium levels (Paragraphs 0213 and 0216-0220). Thus, one of ordinary skill in the art would be motivated to set a threshold against a body temperature measurement for correct potassium readings as a higher body temperature (and a patient experiencing hypothermia) may induce lower potassium levels (Paragraphs 0217 and 0219). 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 modified Newberry to incorporate the teachings of Johnson to set a threshold for temperature measurements to ensure accurate potassium readings.
Regarding claim 12, While Newberry discusses measuring temperature and setting thresholds / excluding data that don’t meet a certain threshold as discussed above, Newberry fails to explicitly disclose excluding temperature data.
However, Johnson teaches a method for monitoring potassium in the blood (Abstract), wherein body temperature levels can affect measured potassium levels (Paragraphs 0213 and 0216-0220). Thus, one of ordinary skill in the art would be motivated to set a threshold against a body temperature measurement, and exclude data outside of the threshold, for correct potassium readings as a higher body temperature (and a patient experiencing hypothermia) may induce lower potassium levels (Paragraphs 0217 and 0219).
Regarding claim 19, Newberry discloses, prior to deployment of the machine learning model, capturing, by a temperature sensor of the optical sensor (Fig. 1, temperature sensor 114), a skin temperature reading of the patient at the time that the first energy measurement reading was captured (Paragraph 0367, determining temperature during PPG signal readings); and
While Newberry discusses measuring temperature and setting thresholds / excluding data that don’t meet a certain threshold as discussed above, Newberry fails to link setting a threshold to the temperature measurement.
However, Johnson teaches a method for monitoring potassium in the blood (Abstract), wherein body temperature levels can affect measured potassium levels (Paragraphs 0213 and 0216-0220). Thus, one of ordinary skill in the art would be motivated to set a threshold against a body temperature measurement for correct potassium readings as a higher body temperature (and a patient experiencing hypothermia) may induce lower potassium levels (Paragraphs 0217 and 0219). 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 modified Newberry to incorporate the teachings of Johnson to set a threshold for temperature measurements to ensure accurate potassium readings.
Regarding claim 20, While Newberry discusses measuring temperature and setting thresholds / excluding data that don’t meet a certain threshold as discussed above, Newberry fails to explicitly disclose excluding temperature data.
However, Johnson teaches a method for monitoring potassium in the blood (Abstract), wherein body temperature levels can affect measured potassium levels (Paragraphs 0213 and 0216-0220). Thus, one of ordinary skill in the art would be motivated to set a threshold against a body temperature measurement, and exclude data outside of the threshold, for correct potassium readings as a higher body temperature (and a patient experiencing hypothermia) may induce lower potassium levels (Paragraphs 0217 and 0219).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NOAH MICHAEL HEALY whose telephone number is (703)756-5534. The examiner can normally be reached Monday - Friday 8:30am - 5:30pm ET.
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/NOAH M HEALY/Examiner, Art Unit 3791
/JASON M SIMS/Supervisory Patent Examiner, Art Unit 3791