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
Claim 10 objected to because of the following informalities: claim 10 recites the limitation “determine that an analyte measurement” should read “determine that the analyte measurement”. 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.
Claim 10 is 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.
The term “too” in claim 10 is a relative term which renders the claim indefinite. The term “too” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The limitation “too inaccurate” has been rendered indefinite by the use of the term “too” because it is unclear what constitutes “too inaccurate”. The examiner is interpreting the limitation as inaccurate analyte measurement.
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.
Claim(s) 1-8, 13, 15-17, and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hanna et al (NPL: “Wearable flexible body matched electromagnetic sensors for personalized non-invasive glucose monitoring”).
Regarding claim 1, Hanna teaches a non-invasive analyte sensor system, comprising (page 2; we developed a fully non-invasive continuous glucose monitoring system that leverages electromagnetic and radio frequency (RF) technology in sensors with topologies that mimic the vasculature anatomy of a human leg):
an antenna that is configured to detect an electromagnetic wave in a radio or microwave frequency range that results from transmission of a transmit signal in the radio or microwave frequency range into a target containing at least one analyte (pages 2-3; The EM sensors are designed with topologies that mimic the vessels corresponding to their respective body locations. For example, the EM sensor integrated in a sock is designed with a topology that mimics the leg vessels, while adopting a quasi-antenna-array form factor (Fig. 2). The proposed sensors operate at multiple frequencies ranging between 0.5 and 4 GHz. The EM waves at this frequency range can effectively penetrate human tissues, which allows, due to the optimized sensor topologies, an enhanced exposure of the blood stream to the EM signals. More specifically, changes in the scattering parameters (magnitude and phase) of the reflected and\or transmitted waves are associated with the glucose fluctuations in the medium under test as detailed in supplementary Notes 2–4.);
a receive circuit that is electrically connectable to the antenna to convert the electromagnetic wave into one or more signals representing the at least one analyte (figure 1, page 3; A signal processing module to process the output of the multiple integrated sensors along with a regression model to convert the readings into absolute glucose levels. The data collected includes the reflection coefficients from the glucose sensors along with readings from the environmental/physiological sensors. The data is processed and integrated into a multivariate Gaussian Processes regression model27 to predict the absolute glucose levels as shown in Fig. 1A.); and
a motion sensor (page 3; we designed a sensing module comprising: a skin temperature sensor28, a skin conductance response (SCR) sensor29, an environmental temperature and humidity sensor30 and a motion sensor31. Each sensor provides non-invasive and continuous monitoring of these different perturbing factors resulting in a holistic glucose sensing system).
Regarding claim 2, Hanna teaches the non-invasive analyte sensor system of claim 1, wherein the motion sensor comprises an accelerometer, a gyroscope, or an inertial measurement unit (supplementary notes, page 8; Motion could affect the EM-sensor to skin contact, hence to take into consideration these movements effect on the S-parameters readings, we added a FLORA Accelerometer sensor41 to the proposed system).
Regarding claim 3, Hanna teaches the non-invasive analyte sensor system of claim 1, wherein the at least one analyte comprises glucose, alcohol, white blood cells, oxygen, or luteinizing hormone (page 3; The EM sensors are designed to target different body locations simultaneously. Electromagnetic waves are transmitted to the body where the reflected and the transmitted waves are impacted by the underlying tissues. More specifically, changes in the scattering parameters (magnitude and phase) of the reflected and\or transmitted waves are associated with the glucose fluctuations in the medium under test as detailed in supplementary Notes 2–4. By monitoring and analyzing these changes, variations in blood glucose levels can be detected).
Regarding claim 4, Hanna teaches the non-invasive analyte sensor system of claim 1, further comprising a housing configured to contain the motion sensor (figure 1, page 2; Principle of a multi-sensing, multi-location approach. (A), i, the system is composed of two sensors targeting two on-body locations. Additional environmental and physiological sensors are added to the system to calibrate the temperature, humidity, sweat, and motion effects. ii, all these sensors are worn within the sock apparatus. The examiner notes that the sock apparatus contains the motion sensor and the antenna).
Regarding claim 5, Hanna teaches the non-invasive analyte sensor system of claim 4, wherein the motion sensor is configured to sense motion of the housing relative to the target (pages 2 and 7 and supplementary notes page 8; To test and compensate for the possible influence of perturbing factors such as temperature, humidity, skin sweat as well as body movements, on the glucose sensors’ readings, we designed a sensing module comprising: a skin temperature sensor28, a skin conductance response (SCR) sensor29, an environmental temperature and humidity sensor30 and a motion sensor31. Signals from the hand and EM leg sensors along with the integrated environmental sensors were monitored, while varying the blood glucose levels, during a two hour OGTT for everyone. One of the study goals is to check the effect of the ambient temperature, humidity, skin temperature, galvanic skin response and, movement on the RF-signal and to compensate for these perturbations. These perturbing factors include ambient temperature and humidity, skin temperature, skin conductance which is mainly affected by the sweat, and motion which could affect the sensor skin contact. As a result, any noninvasive glucose monitoring system based on EM technology must also take into consideration the different perturbing factors. The examiner notes that the motion sensor sense the movement of the body and the movement of the sensor relative to the body.).
Regarding claim 6, Hanna teaches the non-invasive analyte sensor system of claim 4, wherein the motion sensor is configured to sense motion of the housing together with the target (pages 2 and 7 and supplementary notes page 8; To test and compensate for the possible influence of perturbing factors such as temperature, humidity, skin sweat as well as body movements, on the glucose sensors’ readings, we designed a sensing module comprising: a skin temperature sensor28, a skin conductance response (SCR) sensor29, an environmental temperature and humidity sensor30 and a motion sensor31. Signals from the hand and EM leg sensors along with the integrated environmental sensors were monitored, while varying the blood glucose levels, during a two hour OGTT for everyone. One of the study goals is to check the effect of the ambient temperature, humidity, skin temperature, galvanic skin response and, movement on the RF-signal and to compensate for these perturbations. These perturbing factors include ambient temperature and humidity, skin temperature, skin conductance which is mainly affected by the sweat, and motion which could affect the sensor skin contact. As a result, any noninvasive glucose monitoring system based on EM technology must also take into consideration the different perturbing factors. The examiner notes that the motion sensor sense the movement of the body and the movement of the sensor relative to the body.).
Regarding claim 7, Hanna teaches the non-invasive analyte sensor system of claim 1, further comprising a controller configured to process analyte data contained in the one or more signals representing the at least one analyte in view of motion data obtained by the motion sensor (page 3; An environmental and physiological sensing module, which calibrates out the potential perturbing factors in real time. To test and compensate for the possible influence of perturbing factors such as temperature, humidity, skin sweat as well as body movements, on the glucose sensors’ readings, we designed a sensing module comprising: a skin temperature sensor28, a skin conductance response (SCR) sensor29, an environmental temperature and humidity sensor30 and a motion sensor31. Each sensor provides non-invasive and continuous monitoring of these different perturbing factors resulting in a holistic glucose sensing system. The data collected includes the reflection coefficients from the glucose sensors along with readings from the environmental/physiological sensors. The data is processed and integrated into a multivariate Gaussian Processes regression model27 to predict the absolute glucose levels as shown in Fig. 1A. More details about the data processing are presented in supplementary Note 8–9. The examiner notes that the analyte signals obtained from the EM sensor is process continuously with data obtained from motion sensor).
Regarding claim 8, Hanna teaches the non-invasive analyte sensor system of claim 7, wherein the controller is configured to process the analyte data to filter analyte data obtained at a time of movement based on the motion data at the time of movement (page 3; To test and compensate for the possible influence of perturbing factors such as temperature, humidity, skin sweat as well as body movements, on the glucose sensors’ readings, we designed a sensing module comprising: a skin temperature sensor28, a skin conductance response (SCR) sensor29, an environmental temperature and humidity sensor30 and a motion sensor31. Each sensor provides non-invasive and continuous monitoring of these different perturbing factors resulting in a holistic glucose sensing system. The data collected includes the reflection coefficients from the glucose sensors along with readings from the environmental/physiological sensors. The data is processed and integrated into a multivariate Gaussian Processes regression model27 to predict the absolute glucose levels as shown in Fig. 1A. More details about the data processing are presented in supplementary Note 8–9. An environmental and physiological sensing module, which calibrates out the potential perturbing factors in real time. The data collected includes the reflection coefficients from the glucose sensors along with readings from the environmental/physiological sensors. The data is processed and integrated into a multivariate Gaussian Processes regression model27 to predict the absolute glucose levels. The examiner notes that the motion perturbation is compensated for in real time (at the time of occurrence) since all data are processed together in real time).
Regarding claim 13, Hanna teaches a non-invasive analyte sensor system, comprising (page 2; we developed a fully non-invasive continuous glucose monitoring system that leverages electromagnetic and radio frequency (RF) technology in sensors with topologies that mimic the vasculature anatomy of a human leg):
an antenna that is configured to detect an electromagnetic wave in a radio or microwave frequency range that results from transmission of a transmit signal in the radio or microwave frequency range into a target containing at least one analyte (pages 2-3; The EM sensors are designed with topologies that mimic the vessels corresponding to their respective body locations. For example, the EM sensor integrated in a sock is designed with a topology that mimics the leg vessels, while adopting a quasi-antenna-array form factor (Fig. 2). The proposed sensors operate at multiple frequencies ranging between 0.5 and 4 GHz. The EM waves at this frequency range can effectively penetrate human tissues, which allows, due to the optimized sensor topologies, an enhanced exposure of the blood stream to the EM signals. More specifically, changes in the scattering parameters (magnitude and phase) of the reflected and\or transmitted waves are associated with the glucose fluctuations in the medium under test as detailed in supplementary Notes 2–4.);
a receive circuit that is electrically connectable to the antenna to convert the electromagnetic wave into one or more signals representing the at least one analyte (figure 1, page 3; A signal processing module to process the output of the multiple integrated sensors along with a regression model to convert the readings into absolute glucose levels. The data collected includes the reflection coefficients from the glucose sensors along with readings from the environmental/physiological sensors. The data is processed and integrated into a multivariate Gaussian Processes regression model27 to predict the absolute glucose levels as shown in Fig. 1A.); and
a temperature sensor (page 3; we designed a sensing module comprising: a skin temperature sensor28, a skin conductance response (SCR) sensor29, an environmental temperature and humidity sensor30 and a motion sensor31. Each sensor provides non-invasive and continuous monitoring of these different perturbing factors resulting in a holistic glucose sensing system).
Regarding claim 15, Hanna teaches the non-invasive analyte sensor system of claim 13, wherein the temperature sensor is configured to sense a temperature of the target (page 3; To test and compensate for the possible influence of perturbing factors such as temperature, humidity, skin sweat as well as body movements, on the glucose sensors’ readings, we designed a sensing module comprising: a skin temperature sensor28, a skin conductance response (SCR) sensor29, an environmental temperature and humidity sensor30 and a motion sensor31. Each sensor provides non-invasive and continuous monitoring of these different perturbing factors resulting in a holistic glucose sensing system).
Regarding claim 16, Hanna teaches the non-invasive analyte sensor system of claim 13, wherein the temperature sensor is configured to sense a temperature of the at least one analyte (page 3; To test and compensate for the possible influence of perturbing factors such as temperature, humidity, skin sweat as well as body movements, on the glucose sensors’ readings, we designed a sensing module comprising: a skin temperature sensor28, a skin conductance response (SCR) sensor29, an environmental temperature and humidity sensor30 and a motion sensor31. Each sensor provides non-invasive and continuous monitoring of these different perturbing factors resulting in a holistic glucose sensing system. The examiner notes that the skin temperature sensor measures the temperature of the skin/body of the user. Because analyte (glucose) resides within the body at a body temperature, the temperature sensed by the skin temperature reasonably corresponds to the temperature of the analyte (glucose).).
Regarding claim 17, Hanna teaches the non-invasive analyte sensor system of claim 13, wherein the temperature sensor is configured to sense an ambient temperature (pages 2-3; (page 3; To test and compensate for the possible influence of perturbing factors such as temperature, humidity, skin sweat as well as body movements, on the glucose sensors’ readings, we designed a sensing module comprising: a skin temperature sensor28, a skin conductance response (SCR) sensor29, an environmental temperature and humidity sensor30 and a motion sensor31. Each sensor provides non-invasive and continuous monitoring of these different perturbing factors resulting in a holistic glucose sensing system.).
Regarding claim 19, Hanna teaches the non-invasive analyte sensor system of claim 13, further comprising a controller configured to process the one or more signals representing the at least one analyte based on temperature data obtained by the temperature sensor (page 3; An environmental and physiological sensing module, which calibrates out the potential perturbing factors in real time. To test and compensate for the possible influence of perturbing factors such as temperature, humidity, skin sweat as well as body movements, on the glucose sensors’ readings, we designed a sensing module comprising: a skin temperature sensor28, a skin conductance response (SCR) sensor29, an environmental temperature and humidity sensor30 and a motion sensor31. Each sensor provides non-invasive and continuous monitoring of these different perturbing factors resulting in a holistic glucose sensing system. The data collected includes the reflection coefficients from the glucose sensors along with readings from the environmental/physiological sensors. The data is processed and integrated into a multivariate Gaussian Processes regression model27 to predict the absolute glucose levels as shown in Fig. 1A. More details about the data processing are presented in supplementary Note 8–9. The examiner notes that the analyte signals obtained from the EM sensor is process continuously with data obtained from temperature sensor).
Regarding claim 20, Hanna teaches the non-invasive analyte sensor system of claim 19, wherein the controller is configured to process the one or more signals representing the at least one analyte based on the temperature data obtained by the temperature sensor to account for temperature-related signal drift (pages 3 and 7; . To test and compensate for the possible influence of perturbing factors such as temperature, humidity, skin sweat as well as body movements, on the glucose sensors’ readings, we designed a sensing module comprising: a skin temperature sensor28, a skin conductance response (SCR) sensor29, an environmental temperature and humidity sensor30 and a motion sensor31. Each sensor provides non-invasive and continuous monitoring of these different perturbing factors resulting in a holistic glucose sensing system. The data collected includes the reflection coefficients from the glucose sensors along with readings from the environmental/physiological sensors. The data is processed and integrated into a multivariate Gaussian Processes regression model27 to predict the absolute glucose levels as shown in Fig. 1A. More details about the data processing are presented in supplementary Note 8–9. The examiner notes that the analyte signals obtained from the EM sensor are processed in real time with the temperature data obtained from the temperature sensor to compensate for temperature perturbation).
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.
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.
Claim(s) 9-12 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Hanna et al (NPL: “Wearable flexible body matched electromagnetic sensors for personalized non-invasive glucose monitoring”) in the view of Sung et al. (US 2022/0369949).
Regarding claim 9, Hanna teaches the non-invasive analyte sensor system of claim 7, however fails to explicitly teach wherein the controller is configured to process the analyte data to determine that an analyte measurement should not be utilized based on the motion data.
Sung, in the same field of endeavor, teaches wherein the controller is configured to process the analyte data to determine that an analyte measurement should not be utilized based on the motion data (para. 0070; The obtained activity information may be used to compensate for a concentration of an analyte measured by the sensor unit 110 or to determine the validity of the measured concentration of the analyte. For example, when a change in a value of activity information is equal to or greater than a threshold, the MCU 361 may request the sensor unit 110 to measure a concentration of an analyte again. For another example, when a change in a value of activity information is equal to or greater than the threshold, the MCU 361 may compensate for a measured change characteristic. The examiner notes that the system determine the validity of the analyte measurement based on motion data and determine if the analyte is not accurate then remeasure the analyte).
It would have been obvious to one in the ordinary skill in the art before the effective filling date of the claimed invention to have modified system of Hanna to incorporate the teachings of Sung to provide a step of determining that an analyte measurement should not be utilized based on the motion data. This modification will increase the validity of the measurements and helps to determine whether to repeat the measurements to provide a more accurate measurement as disclosed in Sung para. 0070.
Regarding claim 10, Hanna teaches the non-invasive analyte sensor system of claim 9, however fails to explicitly teach wherein the controller is configured to determine that an analyte measurement should not be utilized if it is determined that the analyte measurement is too inaccurate based on the motion data.
Sung, in the same field of endeavor, teaches wherein the controller is configured to determine that an analyte measurement should not be utilized if it is determined that the analyte measurement is too inaccurate based on the motion data (para. 0070; The obtained activity information may be used to compensate for a concentration of an analyte measured by the sensor unit 110 or to determine the validity of the measured concentration of the analyte. For example, when a change in a value of activity information is equal to or greater than a threshold, the MCU 361 may request the sensor unit 110 to measure a concentration of an analyte again. For another example, when a change in a value of activity information is equal to or greater than the threshold, the MCU 361 may compensate for a measured change characteristic. The examiner notes that the system determine the validity of the analyte measurement based on motion data and determine if the analyte is not accurate then discard the obtained measurements and remeasure the analyte).
It would have been obvious to one in the ordinary skill in the art before the effective filling date of the claimed invention to have modified system of Hanna to incorporate the teachings of Sung to provide a step of determining that an analyte measurement should not be utilized if it is determined that the analyte measurement is too inaccurate based on the motion data. This modification will increase the validity of the measurements and helps to determine whether to repeat the measurements to provide a more accurate measurement as disclosed in Sung para. 0070.
Regarding claim 11, Hanna teaches the non-invasive analyte sensor system of claim 1, however fails to explicitly teach a controller configured to utilize motion data obtained by the motion sensor to trigger an analyte measurement using the antenna.
Sung, in the same field of endeavor, teaches a controller configured to utilize motion data obtained by the motion sensor to trigger an analyte measurement using the antenna (para. 0070; The obtained activity information may be used to compensate for a concentration of an analyte measured by the sensor unit 110 or to determine the validity of the measured concentration of the analyte. For example, when a change in a value of activity information is equal to or greater than a threshold, the MCU 361 may request the sensor unit 110 to measure a concentration of an analyte again. For another example, when a change in a value of activity information is equal to or greater than the threshold, the MCU 361 may compensate for a measured change characteristic. The examiner notes that the system determine the validity of the analyte measurement based on motion data and determine if the analyte is not accurate then discard the obtained measurements and trigger the sensor to repeat the measurement).
It would have been obvious to one in the ordinary skill in the art before the effective filling date of the claimed invention to have modified system of Hanna to incorporate the teachings of Sung to provide a step of utilizing motion data obtained by the motion sensor to trigger an analyte measurement using the antenna. This modification will increase the validity of the measurements and helps to determine whether activity information obtained from the motion sensor is above a motion threshold to trigger the sensor to repeat the measurements to provide a more accurate measurement as disclosed in Sung para. 0070.
Regarding claim 12, Hanna teaches the non-invasive analyte sensor system of claim 1, however fails to explicitly teach a controller configured to utilize motion data obtained by the motion sensor to prevent an analyte measurement using the antenna.
Sung, in the same field of endeavor, teaches a controller configured to utilize motion data obtained by the motion sensor to prevent an analyte measurement using the antenna (para. 0070; The obtained activity information may be used to compensate for a concentration of an analyte measured by the sensor unit 110 or to determine the validity of the measured concentration of the analyte. For example, when a change in a value of activity information is equal to or greater than a threshold, the MCU 361 may request the sensor unit 110 to measure a concentration of an analyte again. For another example, when a change in a value of activity information is equal to or greater than the threshold, the MCU 361 may compensate for a measured change characteristic. The examiner notes that the system determine the validity of the analyte measurement based on motion data and determine if the analyte is accurate then the system stops the measurement and outputs the measurement result).
It would have been obvious to one in the ordinary skill in the art before the effective filling date of the claimed invention to have modified system of Hanna to incorporate the teachings of Sung to provide a step of utilizing motion data obtained by the motion sensor to prevent an analyte measurement using the antenna. This modification will increase the validity of the measurements and helps to determine whether activity information obtained from the motion sensor is within a motion threshold to stop the sensor from repeating the measurements as disclosed in Sung para. 0070.
Regarding claim 14, Hanna teaches the non-invasive analyte sensor system of claim 13, however fails to explicitly teach wherein the temperature sensor is configured to sense a temperature of one or more of the antenna and the receive circuit.
Sung, in the same field of endeavor, teaches wherein the temperature sensor is configured to sense a temperature of one or more of the antenna and the receive circuit (paras. 0022 and 0055; the system may further include a temperature sensor configured to measure a temperature around the sensor unit. The sensor unit may incorporate the measured temperature into the change in the resonant frequency or the measured change characteristic. The temperature sensor 150 may be implemented to measure a temperature around the system 100 for detecting a concentration of an analyte.).
It would have been obvious to one in the ordinary skill in the art before the effective filling date of the claimed invention to have modified system of Hanna to incorporate the teachings of Sung to provide a temperature sensor to sense a temperature of one or more of the antenna and the receive circuit. This modification will increase the validity of the measurements and helps to calibrate an original sensor frequency outputted by the sensor unit based on a temperature, and may calculate a rate of change in an analyte level in sensing data (i.e., a change in the oscillation frequency (or resonant frequency) based on a change in capacitance) measured based on the calibrated original sensor frequency. Substantially, the processing unit may determine a change or a rate of change in an analyte by incorporating a temperature, measured by the temperature sensor, into a change in the resonant frequency or a change characteristic measured with respect to the analyte as disclosed in Sung para. 0055.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Hanna et al (NPL: “Wearable flexible body matched electromagnetic sensors for personalized non-invasive glucose monitoring”) in the view of Connor et al. (US 2017/0164878).
Regarding claim 18, Hanna teaches the non-invasive analyte sensor system of claim 13, however fails to explicitly teach a controller configured to adjust operation of the antenna based on temperature data obtained by the temperature sensor.
Connor, in the same field of endeavor, teaches a controller configured to adjust operation of the antenna based on temperature data obtained by the temperature sensor (paras. 0303-0304; the one or more parameters of a resonator in a wearable glucose-monitoring microwave sensor can be automatically adjusted based on one or more physiological and environment factors selected from the group consisting of: body motion or configuration; body moisture level and/or ambient humidity; body temperature and/or ambient temperature; food consumption; exercise; geographic location; and ambient electromagnetic activity.).
It would have been obvious to one in the ordinary skill in the art before the effective filling date of the claimed invention to have modified system of Hanna to incorporate the teachings of Connor to provide a step of adjusting operation of the antenna based on temperature data. This modification will increase the accuracy of the measurement as disclosed in Connor para. 0302. Additionally, temperature effects the resonant behavior and measurement accuracy of the sensor, therefore, adjusting the operational parameter of the sensor would reduce thermal drift and improve performance.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZAINAB M ALDARRAJI whose telephone number is (571)272-8726. The examiner can normally be reached Monday-Thursday7AM-5PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Carey Michael can be reached at (571) 270-7235. 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.
/ZAINAB MOHAMMED ALDARRAJI/ Patent Examiner, Art Unit 3797