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 .
Priority
The present application claims priority to US Provisional Application no. 63/568,484 filed 22 March 2024. Upon review, the stated provisional application lacks any support of disclosure as required under 35 USC 112(a) for the limitations of the present invention. Accordingly, the earliest effective filing data afforded to the present invention is that of 24 March 2025 which is the filing date under 35 USC 111(a).
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.
Claim 9 recites the limitation "complementary data" in line 3. There is insufficient antecedent basis for this limitation in the claim. Accordingly, it is unclear what previously recited data is intended to correspond to “the complementary data.” Clarification is respectfully requested.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, and 4-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Poeze et al. (US Publication no. 2021/0007635) in view of Naima (US Publication no. 2016/0183813), further in view of Morokawa et al. (US Publication no. 2004/0012783).
In regard to claim 1, Poeze et al. disclose a portable electronic device for biosignal acquisition using multi-stream spectroscopy comprising (figure 2a): a housing 200A having a chamber (shell components 204 and 206) configured to receive a sample (para 115, shell components 204 and 206 are configured to receive a finger to measuring glucose or other analytes, wherein the tissues and structure of the finger for analyte measurement is considered to be within the BRI of “sample”); (now referring to figure 7a which shows cross section of shell components 204 and 206) a light source array (LEDs 104 formed on emitter shell housing 204/704a) disposed adjacent to the chamber (i.e., the region wherein the finger may be inserted), wherein the light source array 104 is configured to emit light for transmission through the sample 102; a plurality of sensors (photodiodes 106 located in detector shell housing 206/706) configured to detect a plurality of signals from the sample 102 (para 101-104 and 175-179);
a spectrometer sensor (para 74, the spectroscopy used in the sensor can employ visible, infrared and near infrared wavelengths), and
a communications module 116 configured to transmit the plurality of signals (para 109 and 112, shown in figure 1, a network interface 116 which may be a serial port, USB, or wireless interface or other suitable communication device(s) that allows the monitor to communicate and share data with other devices).
Poeze et al. is considered to describe the invention as claimed, however does not teach the plurality of sensors comprising a bioimpedance sensor, and an infrared temperature sensor, for the tunable filter array comprising a plurality of polarizing filters, wherein one or more polarizing filters of the plurality of polarizing filters are oriented perpendicularly to the emitted light and disposed between the light source array and the spectrometer sensor, nor the light source array, the plurality of sensors, the tunable filter array, and the communications module are each disposed within the housing.
Naima is relied on to teach a system and method that provides for non-invasive ambulatory monitoring of a patient’s biosignals in much the same manner as Poeze et al. (para 29, e.g., a finger blood-oxygen saturation monitor). The system 100 of Naima includes various sensors for obtaining various physiologic metrics including heart rate (HR), heart rate variability (HRV); activity levels; respiration rate (RR); pitting edema (e.g. peripheral edema), etc. (para 28). The sensors include a temperature sensor 11 which may be a passive infrared sensor (para 31), a photoplethysomograph (PPG) (para 34), and a bioimpedance spectrometer (para 37-38) to quantify fluid compartmentalization and volumetric changes in blood vessels. Naima also incorporates a wireless data transfer module 104. Figure 1 shows the combination of the light source array 113, the plurality of sensors 101,111, and the communications module 104 are each disposed within a wearable housing 105 (para 33) and figure 7 shows the PPG and bioimpedance circuit located in the same wearable strap (para 65). Moreover, Naima expressly teaches combining the measured sensor data to be used into a clinically-relevant predictor (para 32).
One of ordinary skill in the art would have been motivated to modify the portable spectroscopy device of Poeze et al. to include bioimpedance and infrared temperature sensors because Naima expressly teaches that PPG, bioimpedance, and temperature sensor may be incorporated into a wearable physiological-monitoring system and that a processor may combine the measured data into clinically relevant physiological determinations. The combination would predictably increase the quantity and diversity of physiological information available to the processor and thereby provide a more robust biomarker determination.
Morokawa et al. is directed to a non-invasive blood sugar monitoring apparatus that uses optical light transmittance techniques to measure analyte concentrations. In figure 7, Morokawa et al. describes a configuration that includes a light source 101, a light detectors 107, and a sample placed between the source 101 and detector 107. Additionally, a polarizing filter 102 and polarizing filter 106 are disposed between the light source 101 and 107, wherein the filters 102 and 106 are considered a “plurality” of filters forming an array. The planar polarizers 102 and 106 are disposed transverse to the direction of the propagation of light emitted from light source 101, thereby teaching or at least necessarily implying a polarizing filter oriented perpendicular to the emitted light (para 51-53). The polarization state of the light emitted from the light source 101 and passing through the polarizer 102 is electronically controlled by votlage-controlled liquid crystal optical elements, wherein the polarization angle and/or phase is selectively varied (para 12-15, 47-53). This is considered to teach that the polarizing filters 102/106 of Morokawa et al. are tunable.
It would further have been obvious to modify the invention of Poeze et al. and Naima to include the tunable polarizing filter array to selectively control the polarization of transmitted light to improve the optical measurement used in determining the biomarker.
In regard to claim 2, in Poeze et al. the light source 104 array is configured to emit light at a plurality of wavelengths and pulsating frequencies (para 71).
In regard to claim 4, figure 7a of Poeze et al. shows the chamber is configured to receive the sample 102 between the light source array 104 and the spectrometer sensor 106 but does not show the one or more polarizing filters of the plurality of polarizing filters are arranged in parallel to each other and disposed between the light source array and where the chamber is configured to receive the sample. Morokawa et al. in figure 7 explicitly show this configuration which illustrates light source 101, polarizer 102 106, and sample 103 all arrange parallel to each other such that polarizer 102 is located between source 101 and sample 103. It would have been obvious to one of ordinary skill in the art to modify the arrangement of Poeze et al. to incorporate the polarization control assembly to control the polarization characteristics of the applied light to improve the optical measurement used in determining the biomarker.
In regard to claim 5, figure 7a of Poeze et al. shows the chamber is configured to receive the sample 102 between the light source array 104 and the spectrometer sensor 106 but does not show the one or more polarizing filters of the plurality of polarizing filters are arranged in parallel to each other and disposed between where the chamber is configured to receive the sample and at least one sensor of the plurality of sensors. Morokawa et al. in figure 7 explicitly show this configuration which illustrates light source 101, polarizer 102 106, and sample 103 all arrange parallel to each other such that polarizer 106 is located between sample 103 and sensor/detector 107. It would have been obvious to one of ordinary skill in the art to modify the arrangement of Poeze et al. to incorporate the polarization control assembly to control the polarization characteristics of the applied light to improve the optical measurement used in determining the biomarker.
In regard to claim 6, in Poeze et al., the sample 102 is a peripheral anatomical sample comprising a vascular anatomical segment (para 66, Poeze et al. repeatably use a patient’s finger which is a peripheral anatomical element that has the blood constituents measured through the vascular tissue of that segment.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Poeze et al. (US Publication no. 2021/0007635) in view of Naima (US Publication no. 2016/0183813) and Morokawa et al. (US Publication no. 2004/0012783), further in view of Kumar et al. (US Publication no. 2024/0358292).
In regard to claim 3, Poeze et al. in view of Naima and Morokawa et al. suggest the invention as claimed, however do not teach that the plurality of polarizing filters comprises at least two polarizing filters each having a different polarization state.
Morokawa et al. is relied on to demonstrate the use of polarizing filters 102 and 106 in the configuration as claimed, however does not state that the filters have different polarization states. Kumar et al. describes polarized PPG to improve perfusion signal for advanced biosensing. Kumar et al. utilize at least one polarization filter in a convention PPG sensor (para 24 and 45), wherein the PPG may more than one filter having polarization states that differ such that a first filter may provide vertical polarization, horizontal polarization or diagonal polarization (para 66, 72-74) as well as linearly polarized or circular polarized light (para 116 and 123). It would have been obvious to one of ordinary skill in the art to modify the filters of the Morokawa et al. to have different polarization states since different molecules are sensitive to different types of polarized light, wherein the modification allows for light to be better reflected from or penetrate the biomarker surface in order to improve signal to noise ratios and reliability of the measurement of the intended biomarker.
Claim(s) 7, 12, 13, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Poeze et al. (US Publication no. 2021/0007635) in view of Naima (US Publication no. 2016/0183813), further in view of Zhong et al. (US Publication no. 2018/0271455).
In regard to claims 7 and 12, Poeze et al. disclose a system for biomarker analysis, the system comprising: a device comprising (figure 2a):
a chamber configured to receive a sample 103 (para 115, shell components 204 and 206 are configured to receive a finger to measuring glucose or other analytes, wherein the tissues and structure of the finger for analyte measurement is considered to be within the BRI of “sample”);
a light source array 104 disposed adjacent to the chamber 204 (region configured to hold sample 102), wherein the light source array 104 is configured to emit light for transmission through the sample 102 (figure 7a which shows cross section of shell components 204 and 206; LEDs 104 formed on emitter shell housing 204/704a);
a plurality of sensors configured to detect a plurality of signals from the sample (photodiodes 106 located in detector shell housing 206/706) configured to detect a plurality of signals from the sample 102 (para 101-104 and 175-179), the plurality of sensors comprising a spectrometer sensor (para 74, the spectroscopy used in the sensor can employ visible, infrared and near infrared wavelengths), and
a communications module 116 configured to transmit the plurality of signals (para 109 and 112, shown in figure 1, a network interface 116 which may be a serial port, USB, or wireless interface or other suitable communication device(s) that allows the monitor to communicate and share data with other devices).
Poeze et al. is also considered to comprise features that are similar to computing system in electronic communication with the device, the computing system comprising: one or more memory devices storing executable instructions; and at least one processor configured to execute instructions to perform operations comprising; receiving the plurality of signals from the device; generating biomarker specific features from the plurality of signals (para 109, figure 1 shows a the monitor 109 which is considered to be a computing system in communication with the device, wherein the monitor 109 comprises a signal processor 110 which is the claimed processor, a memory 113 and other storage device 114, a user interface 112, and the network interface 116 which serves as the communication device. Moreover, the system of Poeze et al. is configured to execute instructions to receiving signals from sensor 101 wherein the received signals are processed to generate specific biomarker signals such as pulse rate, hydration, analyte measurements such as oxygen, carbon monoxide, methemoglobin, total hemoglobin, glucose, proteins, glucose, lipids, a percentage thereof (e.g., saturation) (para 6)). Naima includes similar structural elements for generating biomarker signals (e.g., para 28 and 72-73). Additionally, Naima combines the signals measured from the various sensors into a clinically-relevant predictor of a patient state (para 32 and 38). The combined signals (e.g., PPG and bioimpedance) is considered to comprise an ensemble of biomarker determination.
None of the cited references teach the computing system comprising one or more machine learning models and applying the one or more machine learning models to the biomarker specific features to generate a weighted ensemble biomarker determination.
Zhong et al. is directed to a computing system for predicting a patient’s physiological condition, particularly glucose level, using a plurality of different prediction or machine learning models (para 11-13 and 75). Zhong et al. teach determining weighting factors associated with respective prediction models and generating an ensemble prediction as a weighted average of the predicted glucose values produced by different models (para 11-13, 131-132, and 138). The glucose measurement is obtained from a glucose sensor arrangement, and processing the measurements using multiple machine learning models, and combining the output of the models using respective weighting factors to produce a final ensemble glucose prediction.
It would have been obvious to one of ordinary skill in the art to modify the monitor of Poeze et al. as modified in view of Naima to employ machine learning models to produce an ensemble average biomarker determination from the acquired signals since Zhong et al. expressly teaches utilizing the machine learning models using respective weighting factors to generate an ensemble glucose determination. The modification would pertain to the application of a known prediction technique to the physiological and spectroscopic measurements acquired by the combined Poeze et al. and Naima system to provide improved estimates of clinical predictors.
In regard to claim 13, Poeze et al. in view of Naima further teaches obtaining spectrosocopic physiological signals at a plurality of wavelengths and processing the resulting optical signals to determine physiological characteristics. Naima further teaches obtaining PPG data from optical measurements of vascular tissue and obtaining additional information indicative of variability affecting the physiological measurements including motion information for identifying motion artifacts and temperature information associated with physiological measurement (para 31 and 40-43). It would have been obvious to one of ordinary skill in the art to generate spectral data comprising the PPG information with data indicative of system variability data since Naima recognizes that motion and other measurement conditions affect the acquired physiological signals, wherein processing the spectral footprint and system variability allows for distinguishing meaningful physiological variations from measurement variations to improve the reliability of the resulting biomarker determination.
In regard to claim 18, in Poeze et al., the sample 102 is a peripheral anatomical sample comprising a vascular anatomical segment (para 66, Poeze et al. repeatably use a patient’s finger which is a peripheral anatomical element that has the blood constituents measured through the vascular tissue of that segment.
In regard to claim 19, in Poeze et al., the biomarker specific features include at least one of heart rate, heart rate variability (HRV), oxygen saturation, systolic blood pressure, diastolic blood pressure, vascular age estimation, arterial compliance, perfusion index, and respiration rate, and blood glucose (para 6). In Naima, the biomarker specific features include at least one of heart rate, heart rate variability (HRV), oxygen saturation, systolic blood pressure, diastolic blood pressure, vascular age estimation, arterial compliance, perfusion index, and respiration rate, and blood glucose (para 28).
In regard to claim 20, figure 1 of Naima shows the combination of the light source array 113, the plurality of sensors 101,111, and the communications module 104 are each disposed within a wearable housing 105 (para 33) and figure 7 shows the PPG and bioimpedance circuit located in the same wearable strap (para 65). Moreover, Naima expressly teaches combining the measured sensor data to be used into a clinically-relevant predictor (para 32).
Claim(s) 8 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Naima (US Publication no. 2016/0183813) in view of Tomlinson et al. (US Publication no. 2021/0275110), further in view of Zhong et al. (US Publication no. 2018/0271455).
In regard to claim 8, Naima disclose a method performed by at least one processor (para 41, and 72-73, microprocessor 103), the method comprising:
receiving physiological signals for a subject from a device 100 (para 29, e.g., a finger blood-oxygen saturation monitor), the physiological signals including temperature, bioimpedance, and light absorbance measurements (The system 100 of Naima includes various sensors for obtaining various physiologic metrics including heart rate (HR), heart rate variability (HRV); activity levels; respiration rate (RR); pitting edema (e.g. peripheral edema), etc. (para 28). The sensors include a temperature sensor 11 which may be a passive infrared sensor (para 31), a photoplethysomograph (PPG) (para 34), and a bioimpedance spectrometer (para 37-38) to quantify fluid compartmentalization and volumetric changes in blood vessels);
generating a spectral footprint signal from the received physiological signals, the spectral footprint signal including Photoplethysmography (PPG) and system variability data (para 31 and 40-43, Naima further teaches obtaining PPG data from optical measurements of vascular tissue and obtaining additional information indicative of variability affecting the physiological measurements including motion information for identifying motion artifacts and temperature information associated with physiological measurement).
Naima does not teach processing the spectral footprint signal, wherein the processing includes segmenting the PPG data and generating superimposed PPG composite data; extracting features from the composed PPG data to generate biomarker specific features; analyzing, with one or more machine learning models, the biomarker specific features; and generating, based on the analysis, a weighted ensemble biomarker determination.
Tomlinson et al. teach processing PPG data by measurement PPG signals over a plurality of cardiac cycles, segmenting the PPG signal into lengths corresponding to the cardiac-cycle features, comparing the resulting PPG signal segments, and generating a representative composite PGG waveform (para 20, 80-83, 87 and 88). Tomlinson et al. also teaches that the segments may be mathematically combined by averaging, summing, and weighted averaging, or other mathematical approaches and may be sorted into categories or bins before generating the composite waveforms (para 20 and 78). Tomlinson et al. also teaches that generating the composite PPG signal in this manner mitigates signal noise and motion artifacts associated with PPG measurements (para 76). It would have been obvious to one of ordinary skill in the art to process the PPG signals of Naima according to the PPG segmentation and composite signal technique of Tomlinson et al. because Tomlinson et al. expressly teaches that segmenting PPG signals over cardiac cycles and combining the resulting segments into a representative composite waveform mitigates noise and motion artifacts to thereby improve the reliability of the resulting biomarker determination.
Zhong et al. is directed to a computing system for predicting a patient’s physiological condition, particularly glucose level, using a plurality of different prediction or machine learning models (para 11-13 and 75). Zhong et al. teach determining weighting factors associated with respective prediction models and generating an ensemble prediction as a weighted average of the predicted glucose values produced by different models (para 11-13, 131-132, and 138). The glucose measurement is obtained from a glucose sensor arrangement, and processing the measurements using multiple machine learning models, and combining the output of the models using respective weighting factors to produce a final ensemble glucose prediction.
It would have been obvious to one of ordinary skill in the art to modify the monitor of Naima as modified in view of Tomlinson et al. to employ machine learning models to produce an ensemble average biomarker determination from the acquired signals since Zhong et al. expressly teaches utilizing the machine learning models using respective weighting factors to generate an ensemble glucose determination. The modification would pertain to the application of a known prediction technique to the physiological and spectroscopic measurements acquired by the combined Poeze et al. and Naima system to provide improved estimates of clinical predictors.
In regard to claim 10, in Poeze et al., the biomarker specific features include at least one of heart rate, heart rate variability (HRV), oxygen saturation, systolic blood pressure, diastolic blood pressure, vascular age estimation, arterial compliance, perfusion index, and respiration rate, and blood glucose (para 6). In Naima, the biomarker specific features include at least one of heart rate, heart rate variability (HRV), oxygen saturation, systolic blood pressure, diastolic blood pressure, vascular age estimation, arterial compliance, perfusion index, and respiration rate, and blood glucose (para 28).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Naima (US Publication no. 2016/0183813) in view of Tomlinson et al. (US Publication no. 2021/0275110) and Zhong et al. (US Publication no. 2018/0271455), further in view of Genicot et al. (US Publication no. 2020/0305738).
In regard to claim 9, Naima in view of Tomlinson et al. and Zhong et al. suggest the invention as claimed, except for comprising analyzing, with the one or more machine learning models, the biomarker specific features, the physiological signals, the superimposed PPG data, and complementary data, wherein the segmenting and generating superimposed composite data are based on a plurality of wavelengths of the PPG data. Genicot et al. teach obtaining PPG signals at a plurality of optical colors/wavelengths. Figure 1 teaches obtaining PPG for a plurality of colors including red, green, and blue, or cyan, magenta, and yellow (para 107). Genicot et al. further teach segmenting respective PPG signals, identifying good-quality segments, and combining temporally corresponding segments across different colors to generate a multi-color composite PPG signal (para 73 and 112). It would have been obvious to one of ordinary skill in the art to perform the segmentation and generation of the composite PPG data based on a plurality of wavelengths of the PPG data since GEnicot et al. teaches that dynamically combining good-quality PPG segments obtained from different colors produces a composite PPG signal having improved accuracy and reliability.
Claim(s) 14 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Poeze et al. (US Publication no. 2021/0007635) in view of Naima (US Publication no. 2016/0183813) and Zhong et al. (US Publication no. 2018/0271455), further in view of Tomlinson et al. (US Publication no. 2021/0275110).
In regard to claim 14, Poeze et al. in view of Naima and Zhong et al. suggest the invention as claimed, however do not teach the operations further comprise: processing the spectral footprint signal, wherein the processing includes segmenting the PPG data and generating superimposed PPG composite data; and extracting features from the composed PPG data to generate the biomarker specific features. Tomlinson et al. teach processing PPG data by measurement PPG signals over a plurality of cardiac cycles, segmenting the PPG signal into lengths corresponding to the cardiac-cycle features, comparing the resulting PPG signal segments, and generating a representative composite PGG waveform (para 20, 80-83, 87 and 88). Tomlinson et al. also teaches that the segments may be mathematically combined by averaging, summing, and weighted averaging, or other mathematical approaches and may be sorted into categories or bins before generating the composite waveforms (para 20 and 78). Tomlinson et al. also teaches that generating the composite PPG signal in this manner mitigates signal noise and motion artifacts associated with PPG measurements (para 76). It would have been obvious to one of ordinary skill in the art to process the PPG signals of Poeze et al. as modified by Naima according to the PPG segmentation and composite signal technique of Tomlinson et al. because Tomlinson et al. expressly teaches that segmenting PPG signals over cardiac cycles and combining the resulting segments into a representative composite waveform mitigates noise and motion artifacts to thereby improve the reliability of the resulting biomarker determination
In regard to claim 15, Zhong et al. as relied on above teaches that the one or more machine learning models of the computing system comprise classical machine learning models and deep learning models, and wherein applying the one or more machine learning models comprises applying both the classical machine learning models and the deep learning model to at least one of the biomarker specific features, the plurality of signals, the segmented PPG signals and superimposed composite PPG signals, and complementary data (para 61, 122, and 134, e.g., LSTM, ARIMA models, Bayesian techniques, etc). It would have been obvious to one of ordinary skill in the art to employ classical machine learning models and deep learning models in the spectroscopy/PPG derived biomarker determination systems of Poeze et al. and Naima since Zhong et al. expressly teaches using known machine learning techniques to analyze glucose biomarker information to improve biomarker determinations and predictions.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Poeze et al. (US Publication no. 2021/0007635) in view of Naima (US Publication no. 2016/0183813), Zhong et al. (US Publication no. 2018/0271455), and Tomlinson et al. (US Publication no. 2021/0275110), further in view of Morokawa et al. (US Publication no. 2004/0012783).
In regard to claim 16, Poeze et al. in view of Naima, Zhong et al., and Tomlinson et al. suggest the invention as claimed except for the tunable filter array having a plurality of polarizing filters. Morokawa et al. is directed to a non-invasive blood sugar monitoring apparatus that uses optical light transmittance techniques to measure analyte concentrations. In figure 7, Morokawa et al. describes a configuration that includes a light source 101, a light detectors 107, and a sample placed between the source 101 and detector 107. Additionally, a polarizing filter 102 and polarizing filter 106 are disposed between the light source 101 and 107, wherein the filters 102 and 106 are considered a “plurality” of filters forming an array. The planar polarizers 102 and 106 are disposed transverse to the direction of the propagation of light emitted from light source 101, thereby teaching or at least necessarily implying a polarizing filter oriented perpendicular to the emitted light (para 51-53). The polarization state of the light emitted from the light source 101 and passing through the polarizer 102 is electronically controlled by votlage-controlled liquid crystal optical elements, wherein the polarization angle and/or phase is selectively varied (para 12-15, 47-53). This is considered to teach that the polarizing filters 102/106 of Morokawa et al. are tunable.
It would further have been obvious to modify the invention of Poeze et al. and Naima to include the tunable polarizing filter array to selectively control the polarization of transmitted light to improve the optical measurement used in determining the biomarker.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Poeze et al. (US Publication no. 2021/0007635) in view of Naima (US Publication no. 2016/0183813), Zhong et al. (US Publication no. 2018/0271455), Tomlinson et al. (US Publication no. 2021/0275110), and Morokawa et al. (US Publication no. 2004/0012783), further in view of Kumar et al. (US Publication no. 2024/0358292).
In regard to claim 17, Poeze et al. in view of Naima, Zhong et al., Tomlinson et al., and Morokawa et al. suggest the invention as claimed, however do not teach that the plurality of polarizing filters comprises at least two polarizing filters each having a different polarization state.
Morokawa et al. is relied on to demonstrate the use of polarizing filters 102 and 106 in the configuration as claimed, however does not state that the filters have different polarization states. Kumar et al. describes polarized PPG to improve perfusion signal for advanced biosensing. Kumar et al. utilize at least one polarization filter in a convention PPG sensor (para 24 and 45), wherein the PPG may more than one filter having polarization states that differ such that a first filter may provide vertical polarization, horizontal polarization or diagonal polarization (para 66, 72-74) as well as linearly polarized or circular polarized light (para 116 and 123). It would have been obvious to one of ordinary skill in the art to modify the filters of the Morokawa et al. to have different polarization states since different molecules are sensitive to different types of polarized light, wherein the modification allows for light to be better reflected from or penetrate the biomarker surface in order to improve signal to noise ratios and reliability of the measurement of the intended biomarker.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN T GEDEON whose telephone number is (571)272-3447. The examiner can normally be reached M-F 8:00 am to 5:30 PM ET.
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, David E. Hamaoui can be reached at 571-270-5625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BRIAN T GEDEON/Primary Examiner, Art Unit 3796 20 August 2026
Prosecution Insights