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 .
Claim Objections
Claims 1, 14, and 20 are objected to because of the following informalities:
Claim 1 recites “comprising the steps” in line 2, but should read “comprising steps”
Claim 14 recites “comprising the steps” in line 2, but should read “comprising steps”
Claim 14 recites “receiving signal data from a non-invasive blood monitor” in line 3, but should read “receiving signal data from the non-invasive blood monitor”
Claim 14 recites “estimate a blood analyte concentration” in line 6, but should read “estimate the blood analyte concentration”
Claim 20 recites “using a trained model to estimate a blood analyte concentration” in lines 1-2, but should read “using the trained model to estimate the blood analyte concentration”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “signal data” in line 7. It is unclear as to whether this limitation is referring to the data of the previously recited “non-invasive signal”, or a separate element.
Claim 11 recites “an estimate of a blood analyte condition” in line 2. It is unclear as to whether this limitation is referring to the previously recited “blood analyte condition” that was estimated in Claim 1, or a separate element.
Claim 14 recites “signal data” in line 3. It is unclear as to whether this limitation is referring to the “data” previously recited in line 1, or a separate element.
Claim 15 recites “a signal at two distinct electromagnetic frequencies” in lines 2-3. It is unclear as to whether this limitation is referring to the previously recited “the signal data includes data associated with at least two distinct electromagnetic frequencies” of Claim 14, or a separate element.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Each of Claims 1-20 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 1
Claims 1-20 recite a series of steps or acts for estimating blood analytes non-invasively and estimating a blood analyte concentration using data from a non-invasive blood monitor. Thus, the claims are directed to a process, which is one of the statutory categories of invention.
Step 2A, Prong 1
Each of Claims 1-20 recites at least one step or instruction for estimating blood analyte data, which is grouped as a mental process under the 2019 PEG. Both independent claims 1 and 14 recite abstract ideas in the form of mental processes. If a claim, under its broadest reasonable interpretation, covers performance in the mind but for the recitation of generic computer components, then it is still in the mental processes category unless the claim cannot practically be performed in the mind, see Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318 (Fed. Cir. 2016). Estimating blood analyte data are assessments that may be performed by a human. This applies for all claims dependent on claims 1 and 14. Accordingly, each of Claims 1-20 recites an abstract idea.
Specifically, Claim 1 recites the abstract idea of: “training a model of a relationship between a blood analyte and a non-invasive signal, wherein the model uses a feature comprising at least two distinct electromagnetic radiation wavelengths, and wherein the model comprises a Beer-Lambert inversion model” and “using the trained model to estimate a blood analyte condition associated with the blood analyte”.
Specifically, Claim 14 recites the abstract idea of: “using a trained model to estimate a blood analyte concentration, wherein the trained model comprises a Beer-Lambert inversion model”.
The above claim limitations constitute an abstract idea that is part of the Mathematical Concepts and/or Mental Processes group identified in the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019.
“A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words ….” October 2019 Update: Subject Matter Eligibility, II. A. i. “[T]here are instances where a formula or equation is written in text format that should also be considered as falling within this grouping.” Id. at II. A. ii. “[A] claim does not have to recite the word “calculating” in order to be considered a mathematical calculation.” Id. at II. A. iii. See for example, SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65 (Fed. Cir. 2018). Thus, the claimed steps of training and estimating recite a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations).
Examiner further notes that the step of receiving signal data is not a part of the abstract idea, as it is considered data-gathering, which is categorized as insignificant extra-solution activity. Further, dependent Claims 2-13 and 15-20 merely include limitations that either further define the abstract idea (and thus don’t make the abstract idea any less abstract) or amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they’re merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
Accordingly, as indicated above, each of the above-identified claims recites an abstract idea.
Step 2A, Prong 2
The above-identified abstract idea in each of independent Claims 1 and 14 (and their respective dependent claims) is not integrated into a practical application under 2019 PEG because the additional elements, either alone or in combination, generally link the use of the above-identified abstract idea to a particular technological environment or field of use. More specifically, the additional element of: “a non-invasive blood monitor” in independent claims 1 and 14 is a generically recited computer element which does not improve the functioning of a computer, or any other technology or technical field, and ultimately serves as a data-gathering element. Nor do these above-identified additional element(s) serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified additional elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract idea identified above in independent Claims 1 and 14 (and their respective dependent claims) is not integrated into a practical application under 2019 PEG.
Moreover, the above-identified abstract idea is not integrated into a practical application under 2019 PEG because the claimed method merely implements the above-identified abstract idea (e.g., mental process/mathematical concept) using rules (e.g., computer instructions) executed by a computer (although no computer or processor is claimed, a computer/processor is implied). In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in independent Claims 1 and 14 (and their respective dependent claims) is not integrated into a practical application under the 2019 PEG.
Accordingly, independent Claims 1 and 14 (and their respective dependent claims) are each directed to an abstract idea under 2019 PEG.
Step 2B
None of Claims 1-20 include additional elements that are sufficient to amount to significantly more than the abstract idea for at least the following reasons.
These claims require the additional element(s) of: “a non-invasive blood monitor” in independent claims 1 and 14. The above-identified additional element serves as a data-gathering element. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, Versata Dev. Group, Inc. v. SAP Am., Inc. , 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Those in the relevant field of art would recognize the above-identified additional elements as being well-understood, routine, and conventional means for data-gathering and computing, as demonstrated by
Applicant’s specification (e.g. paragraphs [0040]) which discloses that the non-invasive blood monitor comprises generic components that are configured to perform the generic data-gathering functions of a physiological sensor that are well-understood, routine, and conventional activities previously known to the pertinent industry;
Applicant’s specification (e.g. paragraphs [0150-0152]) which discloses that the implied computer/processor comprise generic computer components that are configured to perform the generic computer functions (e.g. training and estimating) that are well-understood, routine, and conventional activities previously known to the pertinent industry;
The cited prior art and non-patent literature of record in the application.
Examiner notes that there is no computer or processor specifically being claimed, although a computer/processor is implied. Thus, the implied computer/processor is reasonably construed as a generic computing device. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear, from the claims themselves and the specification, that these limitations require no improved computer resources, just already available computers, with their already available basic functions, to use as tools in executing the claimed process.
Furthermore, Applicant’s specification does not describe any special programming or algorithms required for the implied computer/processor. This lack of disclosure is acceptable under 35 U.S.C. §112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the computer arts. By omitting any specialized programming or algorithms, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the computer industry or arts. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional elements because it describes these additional elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a) (see Berkheimer memo from April 19, 2018, (III)(A)(1) on page 3). Adding hardware that performs “‘well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible (TLI Communications).
The recitation of the above-identified additional limitations in Claims 1-20 amounts to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement.
Here, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. v. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
For at least the above reasons, the method of Claims 1-20 is directed to applying an abstract idea as identified above on a general purpose computer without (i) improving the performance of the computer itself, or (ii) providing a technical solution to a problem in a technical field. None of Claims 1-20 provides meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in independent Claims 1 and 14 (and their dependent claims) do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment. That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. When viewed as whole, the above-identified additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, Claims 1-20 merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself (as in Bascom and Enfish), or (ii) provide a technical solution to a problem in a technical field (as in DDR).
Therefore, none of the Claims 1-20 amounts to significantly more than the abstract idea itself. Accordingly, Claims 1-20 are not patent eligible and rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 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 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Newberry et al (U.S. Publication No. 2021/0137464; cited by Applicant).
Regarding Claim 1, Newberry discloses a method for estimating blood analytes non-invasively (System and method for obtaining health data using photoplethysmography; Abstract; One or more of the embodiments of the biosensor 100 described herein is configured to detect a concentration level of one or more substances within blood flow using photoplethysmography (PPG) techniques; [0069]; method 300; [0084]; Figures 2-3), the method comprising the steps of:
training a model (neural network; [0222-0230]) of a relationship between a blood analyte and a non-invasive signal, wherein the model uses a feature comprising at least two distinct electromagnetic radiation wavelengths (a first predetermined wavelength and at a second predetermined wavelength; [0084]), and wherein the model comprises a Beer-Lambert inversion model (One or more of the embodiments of the biosensor 100 described herein is configured to detect a concentration level of one or more substances within blood flow using photoplethysmography (PPG) techniques…the biosensor 100 may thus determine the concentration of various substances in arterial blood flow from the Beer-Lambert principles using the spectral responses of at least two different wavelengths. FIG. 3 illustrates a logical flow diagram of an embodiment of a method 300 for determining concentration level of a substance in blood flow using Beer-Lambert principles. The biosensor 100 transmits light at a first predetermined wavelength and at a second predetermined wavelength. The biosensor 100 detects the light (reflected from the skin or transmitted through the skin) and determines the spectral response at the first wavelength at 302 and at the second wavelength at 304; [0069-0084]; [0112-0115]; Figures 2-3, 7, 13, 21);
receiving signal data from a non-invasive blood monitor using the at least two distinct electromagnetic radiation wavelengths (biosensor 100; [0057-0070]; [0084]; [0226]; Figure 2-3, 7, 20); and
using the trained model to estimate a blood analyte condition associated with the blood analyte (To determine a concentration level of the substance, a calibration table or database is used that associates the obtained R value to a concentration level of the substance at 720; [0111-0112]; One or more types of artificial neural networks (a.k.a. machine learning algorithms) may be implemented herein to determine health data from PPG signals; [0222-0223]; The input vector is processed by a processing device executing a neural network (aka machine learning algorithm). The processing device executes the machine learning algorithm with the input vector and determines health data at 2010. The health data includes one or more of heart rate, respiration rate, blood pressure, oxygen saturation level, NO level, liver enzyme level, Glucose level, Blood alcohol level, blood type, sepsis risk factor, infection risk factor, cancer, virus detection, creatinine level or electrolyte level. The health data may be generated as an output fixed length vector; [0227]; Figure 7, 20).
Regarding Claim 2, Newberry discloses wherein the signal comprises a pulsatile signal (When the heart pumps blood to the body and the lungs during systole, the amount of blood that reaches the capillaries in the skin surface increases, resulting in more light absorption. The blood then travels back to the heart through the venous network, leading to a decrease of blood volume in the capillaries and less light absorption. The measured PPG waveform therefore comprises a pulsatile (often called “AC”) physiological waveform that reflects cardiac synchronous changes in the blood volume with each heartbeat, which is superimposed on a much larger slowly varying quasi-static (“DC”) baseline; [0018]).
Regarding Claim 3, Newberry discloses wherein the pulsatile signal comprises a heartbeat (When the heart pumps blood to the body and the lungs during systole, the amount of blood that reaches the capillaries in the skin surface increases, resulting in more light absorption. The blood then travels back to the heart through the venous network, leading to a decrease of blood volume in the capillaries and less light absorption. The measured PPG waveform therefore comprises a pulsatile (often called “AC”) physiological waveform that reflects cardiac synchronous changes in the blood volume with each heartbeat, which is superimposed on a much larger slowly varying quasi-static (“DC”) baseline; [0018]).
Regarding Claim 4, Newberry discloses wherein the feature comprises use of signal values taken at a time of systole and a time of diastole of the heartbeat for two independent wavelengths of the at least two distinct electromagnetic radiation wavelengths (The spectral responses are obtained around the plurality of wavelengths, including at least a first wavelength and a second wavelength at 502…the systolic and diastolic points of the spectral response are then determined…a peak detection algorithm is applied to determine the systolic and diastolic points at 506. If not detected concurrently, the systolic and diastolic points of the spectral response for each of the wavelengths may be aligned or may be aligned with systolic and diastolic points of a pressure pulse waveform or cardiac cycle; [0095-0097]; Figures 4-5).
Regarding Claim 5, Newberry discloses wherein the signal values comprise a maximum value and a minimum value taken during a single heartbeat (At a peak of blood flow or volume, the reflected light IL 414 is at a minimum due to absorption by the pulsating blood, non-pulsating blood, other tissue, etc. At a minimum of blood flow or volume during the cardiac cycle, the Incident or reflected light IH 416 is at a maximum due to lack of absorption from the pulsating blood volume; [0089]; The relative contributions of the AC and DC components are obtained IAC+DC and IAC. A peak detection algorithm is applied to determine the systolic and diastolic points at 506. If not detected concurrently, the systolic and diastolic points of the spectral response for each of the wavelengths may be aligned or may be aligned with systolic and diastolic points of a pressure pulse waveform or cardiac cycle; [0097]; Figures 4-5).
Regarding Claim 6, Newberry discloses wherein the blood analyte condition comprises a concentration of the blood analyte (One or more of the embodiments of the biosensor 100 described herein is configured to detect a concentration level of one or more substances within blood flow using photoplethysmography (PPG) techniques. For example, the biosensor 100 may detect nitric oxide (NO) concentration levels and correlate the NO concentration level to a blood glucose level. The biosensor 100 may also detect oxygen saturation (SaO2 or SpO2) levels in blood flow; [0069]).
Regarding Claim 7, Newberry discloses wherein the blood analyte comprises glucose (One or more of the embodiments of the biosensor 100 described herein is configured to detect a concentration level of one or more substances within blood flow using photoplethysmography (PPG) techniques. For example, the biosensor 100 may detect nitric oxide (NO) concentration levels and correlate the NO concentration level to a blood glucose level; [0069]).
Regarding Claim 8, Newberry discloses wherein the blood analyte comprises oxygen (One or more of the embodiments of the biosensor 100 described herein is configured to detect a concentration level of one or more substances within blood flow using photoplethysmography (PPG) techniques. For example, the biosensor 100 may detect nitric oxide (NO) concentration levels and correlate the NO concentration level to a blood glucose level. The biosensor 100 may also detect oxygen saturation (SaO2 or SpO2) levels in blood flow; [0069]).
Regarding Claim 9, Newberry discloses wherein the model is configured to estimate a tissue-dependent DC offset component of the signal data (The pulse oximeter filters the absorbance of the pulsatile fraction of the blood, i.e. that due to arterial blood (AC components), from the constant absorbance by nonpulsatile venous or capillary blood and other tissue pigments (DC components), to eliminate the effect of tissue absorbance to measure the oxygen saturation of arterial blood. Such PPG techniques are heretofore been limited to determining oxygen saturation; [0017]; FIG. 7 illustrates a logical flow diagram of an exemplary method 700 to determine levels of a substance using the spectral responses at a plurality of wavelengths in more detail…the IDC component is thus isolated from the spectral signal at 708; [0109]; Figure 7).
Regarding Claim 10, Newberry discloses processing the signal data to extract the tissue-dependent DC offset component ([0134-0135]; FIG. 21 illustrates a schematic block diagram of an embodiment of a neural network processing device 2100. The neural network processing device 2100 obtains or generates an input vector 2102. In this embodiment, the input vector includes an AC component IAC of one or more PPG signals at different wavelengths. For example, a PPG signal may be processed to isolate or filter an AC component IAC from a DC component; [0229]; Figure 21).
Regarding Claim 11, Newberry discloses evaluating the processed signal data to provide an estimate of a blood analyte condition using only a frequency component corresponding to a heart rate pulse of a user of the non-invasive blood monitor and a non-pulsatile blood component corresponding to light reflected from within a blood vessel from the signal data ([0017-0018]; [0134-0135]; the input vector 2102 may additionally or alternatively include other PPG input data; [0232]; Figure 21).
Regarding Claim 12, Newberry discloses wherein the Beer-Lambert model comprises a linear model ([0083]; An example for calculating the concentration of a substance over multiple wavelengths may be performed using a linear function, such as is illustrated herein below; [0103-0107]; [0116-0121]).
Regarding Claim 13, Newberry discloses wherein the Beer-Lambert model comprises a non-linear model ([0125]; The training is performed using defined set of rules also known as the learning algorithm. Machine learning techniques include ridge linear regression, a multilayer perceptron neural network, support vector machines and random forests. For example, a gradient descent training algorithm is used in case of supervised training model. In case, the actual output is different from target output, the difference or error is determined. The gradient descent algorithm changes the weights of the network in such a manner to minimize this error; [0223-0225]; [0232]; [0236]; Figures 21 and 23).
Regarding Claim 14, Newberry discloses a method for estimating a blood analyte concentration using data from a non-invasive blood monitor (System and method for obtaining health data using photoplethysmography; Abstract; One or more of the embodiments of the biosensor 100 described herein is configured to detect a concentration level of one or more substances within blood flow using photoplethysmography (PPG) techniques; [0069]; method 300; [0084]; Figures 2-3), the method comprising the steps of:
receiving signal data from a non-invasive blood monitor (biosensor 100; [0057-0070]; [0084]; [0226]; Figure 2-3, 7, 20), wherein the signal data includes data associated with at least two distinct electromagnetic frequencies (a first predetermined wavelength and at a second predetermined wavelength; [0084]); and
using a trained model (neural network; [0222-0230]) to estimate a blood analyte concentration (To determine a concentration level of the substance, a calibration table or database is used that associates the obtained R value to a concentration level of the substance at 720; [0111-0112]; One or more types of artificial neural networks (a.k.a. machine learning algorithms) may be implemented herein to determine health data from PPG signals; [0222-0223]; The input vector is processed by a processing device executing a neural network (aka machine learning algorithm). The processing device executes the machine learning algorithm with the input vector and determines health data at 2010. The health data includes one or more of heart rate, respiration rate, blood pressure, oxygen saturation level, NO level, liver enzyme level, Glucose level, Blood alcohol level, blood type, sepsis risk factor, infection risk factor, cancer, virus detection, creatinine level or electrolyte level. The health data may be generated as an output fixed length vector; [0227]; Figure 7, 20), wherein the trained model comprises a Beer-Lambert inversion model (One or more of the embodiments of the biosensor 100 described herein is configured to detect a concentration level of one or more substances within blood flow using photoplethysmography (PPG) techniques…the biosensor 100 may thus determine the concentration of various substances in arterial blood flow from the Beer-Lambert principles using the spectral responses of at least two different wavelengths. FIG. 3 illustrates a logical flow diagram of an embodiment of a method 300 for determining concentration level of a substance in blood flow using Beer-Lambert principles. The biosensor 100 transmits light at a first predetermined wavelength and at a second predetermined wavelength. The biosensor 100 detects the light (reflected from the skin or transmitted through the skin) and determines the spectral response at the first wavelength at 302 and at the second wavelength at 304; [0069-0084]; [0112-0115]; Figures 2-3, 7, 13, 21).
Regarding Claim 15, Newberry discloses wherein the trained model uses a feature using an equation comprising maximum and minimum values of a signal at two distinct electromagnetic frequencies (At a peak of blood flow or volume, the reflected light IL 414 is at a minimum due to absorption by the pulsating blood, non-pulsating blood, other tissue, etc. At a minimum of blood flow or volume during the cardiac cycle, the Incident or reflected light IH 416 is at a maximum due to lack of absorption from the pulsating blood volume; [0089]; The relative contributions of the AC and DC components are obtained IAC+DC and IAC. A peak detection algorithm is applied to determine the systolic and diastolic points at 506. If not detected concurrently, the systolic and diastolic points of the spectral response for each of the wavelengths may be aligned or may be aligned with systolic and diastolic points of a pressure pulse waveform or cardiac cycle; [0096-0101]; [0229-0230]; [0234]; Figures 4-5 and 21).
Regarding Claim 16, Newberry discloses wherein the equation comprises values of the signal at a time of systole and a time of diastole of a heartbeat for the two distinct electromagnetic frequencies (The relative contributions of the AC and DC components are obtained IAC+DC and IAC. A peak detection algorithm is applied to determine the systolic and diastolic points at 506. If not detected concurrently, the systolic and diastolic points of the spectral response for each of the wavelengths may be aligned or may be aligned with systolic and diastolic points of a pressure pulse waveform or cardiac cycle; [0096-0101]; Figure 5).
Regarding Claim 17, Newberry discloses wherein the maximum value corresponds to the time of diastole and the minimum value corresponds to the time of systole (The relative contributions of the AC and DC components are obtained IAC+DC and IAC. A peak detection algorithm is applied to determine the systolic and diastolic points at 506. If not detected concurrently, the systolic and diastolic points of the spectral response for each of the wavelengths may be aligned or may be aligned with systolic and diastolic points of a pressure pulse waveform or cardiac cycle; [0089]; The relative contributions of the AC and DC components are obtained IAC+DC and IAC. A peak detection algorithm is applied to determine the systolic and diastolic points at 506. If not detected concurrently, the systolic and diastolic points of the spectral response for each of the wavelengths may be aligned or may be aligned with systolic and diastolic points of a pressure pulse waveform or cardiac cycle; [0096-0101]).
Regarding Claim 18, Newberry discloses wherein the signal data comprises: a frequency component corresponding to a heart rate pulse of a user of the non-invasive blood monitor (One current non-invasive method is known for measuring the oxygen saturation of blood using pulse oximeters. Pulse oximeters detect oxygen saturation of hemoglobin by using, e.g., spectrophotometry to determine spectral absorbencies and determining concentration levels of oxygen based on Beer-Lambert law principles. In addition, pulse oximetry may use photoplethysmography (PPG) methods for the assessment of oxygen saturation in pulsatile arterial blood flow. The subject's skin at a ‘measurement location’ is illuminated with two distinct wavelengths of light and the relative absorbance at each of the wavelengths is determined. For example, a wavelength in the visible red spectrum (for example, at 660 nm) has an extinction coefficient of hemoglobin that exceeds the extinction coefficient of oxihemoglobin. At a wavelength in the near infrared spectrum (for example, at 940 nm), the extinction coefficient of oxihemoglobin exceeds the extinction coefficient of hemoglobin; [0017-0018]; [0062]); a non-pulsatile blood component corresponding to light reflected from within a blood vessel (The pulse oximeter filters the absorbance of the pulsatile fraction of the blood, i.e. that due to arterial blood (AC components), from the constant absorbance by nonpulsatile venous or capillary blood and other tissue pigments (DC components), to eliminate the effect of tissue absorbance to measure the oxygen saturation of arterial blood. Such PPG techniques are heretofore been limited to determining oxygen saturation; [0017-0018]); and a tissue-dependent DC offset component (The pulse oximeter filters the absorbance of the pulsatile fraction of the blood, i.e. that due to arterial blood (AC components), from the constant absorbance by nonpulsatile venous or capillary blood and other tissue pigments (DC components), to eliminate the effect of tissue absorbance to measure the oxygen saturation of arterial blood…the measured PPG waveform therefore comprises a pulsatile (often called “AC”) physiological waveform that reflects cardiac synchronous changes in the blood volume with each heartbeat, which is superimposed on a much larger slowly varying quasi-static (“DC”) baseline; [0017-0018]).
Regarding Claim 19, Newberry discloses extracting the tissue-dependent DC offset component from the signal data ([0134-0135]; FIG. 21 illustrates a schematic block diagram of an embodiment of a neural network processing device 2100. The neural network processing device 2100 obtains or generates an input vector 2102. In this embodiment, the input vector includes an AC component IAC of one or more PPG signals at different wavelengths. For example, a PPG signal may be processed to isolate or filter an AC component IAC from a DC component; [0229]; Figure 21).
Regarding Claim 20, Newberry discloses wherein the step of using a trained model to estimate a blood analyte concentration comprises estimating the blood analyte concentration using only the frequency component and the non-pulsatile blood component ([0017-0018]; [0134-0135]; the input vector 2102 may additionally or alternatively include other PPG input data; [0232]; Figure 21).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHANEL J YOON whose telephone number is (571) 272-2695. The examiner can normally be reached on Monday-Friday 9:00AM-5:00PM.
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/CHANEL J YOON/Examiner, Art Unit 3791
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