DETAILED ACTION
Applicant’s arguments, filed on 04/07/2026, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed on 04/07/2026, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1, 3-6, 8, and 14-19 are the current claims hereby under examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, the frequency-difference curve from claim 19 must be shown or the feature(s) canceled from the claim(s). No new matter should be entered.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 1 and 14 are objected to because of the following informalities:
In claim 1, line 13, “comprising” should read “comprises”
In claim 14, line 8, a semicolon should be inserted after “to”
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 17 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.
Regarding claim 17, the claim recites the limitation “a 16-electrode belt” in line 2. It is unclear if this limitation is meant to refer to the belt of electrode from claim 16, line 2, or a different, second belt. If it is meant to refer to the belt of electrode from claim 16, it needs to refer back to it. If it is meant to refer to a different second belt, it needs to be distinguished from the belt of electrode from claim 16. For purposes of examination, it is being interpreted as referring to the belt of electrode from claim 16.
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.
Claims 1, 3, and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Kobayashi (JP 2020195677) in view of De Limon (US 20180177430) and Santos (US 20210027461). Citations to JP 2020195677 will refer to the English Machine Translation that accompanies this Office Action.
Regarding independent claim 1, Kobayashi teaches a computer-implemented method for liver health assessment ([0001]: “The present disclosure relates to a biometric information processing method”), comprising:
receiving Electrical Impedance Tomography (EIT) voltage data associated with a liver of a subject ([0018]: “The processing device 1 may be a dedicated device for processing the EIT data transmitted from each EIT measuring device”; [0027]: “the abdominal EIT measurement device 10c is attached to the abdomen of a subject K, such as a patient, and is configured to acquire abdominal EIT data indicating at least the kidneys and liver present in the abdomen of the subject K”), the EIT voltage data being collected from a wearable device ([0018]: “The processing device 1 may be a dedicated device for processing the EIT data transmitted from each EIT measuring device. The processing device 1 may also be a personal computer, a workstation, a smartphone, a tablet, or a wearable device (e.g., AR glasses) attached to the body (e.g., arm, head, etc.) of a medical professional.”).
However, Kobayashi is silent on whether multiple frequencies are used for the EIT data.
De Limon discloses methods for analyzing a region of a body by electrical impedance. Specifically, De Limon teaches wherein the EIT data comprises multi-frequency EIT voltage data, ([0132]: “Multi-frequency EIT (MFEIT) has been shown to be particularly helpful for biological imaging using systems. For example, EIT measurements using the bipolar arrays of electrodes described herein may be used by injection of current at multiple frequencies through an array of skin/scalp bipolar electrodes”; [0047]: “electrode pairs of the plurality of pairs of bipolar electrodes and measuring voltages at the bipolar electrode pairs”). Kobayashi and De Limon are analogous art as they are both in the same field of endeavor and are related to methods used to analyze regions of a body using EIT techniques.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to use the multi-frequency EIT voltage data from De Limon into the method from Kobayashi as Kobayashi is silent on the frequencies, and De Limon discloses a suitable type of EIT data in an analogous method that provides more information for analysis.
The Kobayashi/De Limon combination teaches the steps of processing the multi-frequency EIT voltage data to determine a health condition of the liver of the subject (Kobayashi, [0002]: “According to EIT, the impedance between a pair of electrodes among a plurality of electrodes arranged around a measurement object is measured based on the AC current and AC voltage flowing between the pair of electrodes. In this way, by measuring a plurality of impedances between a plurality of pairs of electrodes, it is possible to calculate the impedance value of each of a plurality of minute regions (pixels) that make up the measurement object through numerical analysis. Furthermore, by visualizing the EIT data indicating the impedance value of each pixel, it becomes possible to visually grasp the state of the patient's measurement target (particularly, the organs)”; [0081]: “by visually checking the heart, liver, and kidneys displayed on the organ display screen G1, medical personnel can intuitively determine whether or not sufficient blood is being supplied from the heart to the vital organs, the liver and kidneys”. The determination of whether sufficient blood is being supplied to the liver is the health condition of the liver that is determined.), by:
performing an image reconstruction operation to generate conductivity change images, wherein the image reconstruction operation processes the multi-frequency EIT voltage data with reference to a pre-defined abdomen shape prior (Kobayashi, [0030]: “the control unit 2 generates image data showing the organ display screen G1 (see Figure 4), and then displays the organ display screen G1 on the display unit 5 based on the generated image data “; [0011]: “the bioinformation processing device generates image data showing an organ display screen on which at least one organ of the subject is displayed … updates the image data based on the impedance change value so that the visual appearance of the organ displayed on the organ display screen is changed”; [0027]: “The abdominal EIT data includes the impedance value of each pixel that constitutes the abdominal cross section”. The image data is updated so that the appearance of the organ and uses abdominal EIT data from the abdominal cross section, therefore using reference to a pre-defined abdomen shape.).
However, the Kobayashi/De Limon combination does not teach performing a post-processing operation on the conductivity change images.
Santos teaches systems for determining fluid and tissue volume estimations using electrical tomography. Specifically, Santos teaches the step of performing a post-processing operation on the conductivity change images, wherein the post-processing operation comprising: segmenting liver regions from the conductivity change images with reference to a liver shape prior to produce segmented images ([0099]: “segmenting the enhanced impedance image may include utilizing a mean contour of a segmented object (e.g., organ) within the one or more priors (e.g., CT-scans) to select a region of interest of as a region of the segmented object”; [0006]: “segmenting the enhanced impedance image to identify one or more tissues depicted within the enhance impedance image, selecting a region of interest within the enhanced impedance image, determining a relationship parameter that relates electrical properties represented within the region of interest of the enhanced impedance image with one or more properties of the region of interest, and estimating a fluid volume within the region of interest based at least partially on the relationship parameter and the enhanced impedance image”); and determining a spatial average of a change in conductivity within the segmented images ([0090]: “regional averages and conditional covariances may be determined, that the regional averages and conditional covariances may be utilized to determine (e.g., calculate) corrected impedances of the initial impedance image”. The regional average is the spatial average in the segmented images.), and using machine learning for processing ([0092]: “based on a comparison between the electrical property distributions represented in the initial impedance image and the reference reconstructed images, a relationship function (e.g., linear function or non-linear function) may be determined and utilized to adjust the electrical properties within a region of interest of the initial impedance image. In some instances, various machine learning models may be utilized within the process of enhancing the initial impedance images. For instance, enhancing the initial impedance image may include machine learning and/or deep learning techniques that include providing training corpora to a matching learning algorithm or neural network to train a machine to aid or perform enhancing the initial impedance image or portions of the initial impedance image”). Kobayashi, De Limon, and Santos are analogous art as they are all in the same field of endeavor and are related to methods used to analyze regions of a body using EIT techniques.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the additional image processing from Santos into the Kobayashi/De Limon combination as it allows the method to perform additional analysis that can be used as additional data in the estimation of the health condition of the user, which can provide a more comprehensive and accurate analysis. Additionally, it would have been obvious to use machine learning for analysis as it allows for a quick, dynamic analysis technique.
The Kobayashi/De Limon/Santos combination teaches the step of using a trained machine learning processing model to determine the health condition of the liver based on the determined spatial average of the change in conductivity (Kobayashi, [0081]: “by visually checking the heart, liver, and kidneys displayed on the organ display screen G1, medical personnel can intuitively determine whether or not sufficient blood is being supplied from the heart to the vital organs, the liver and kidneys”. The determination of whether sufficient blood is being supplied to the liver is the health condition of the liver that is determined; Santos, [0092]: “based on a comparison between the electrical property distributions represented in the initial impedance image and the reference reconstructed images, a relationship function (e.g., linear function or non-linear function) may be determined and utilized to adjust the electrical properties within a region of interest of the initial impedance image. In some instances, various machine learning models may be utilized within the process of enhancing the initial impedance images. For instance, enhancing the initial impedance image may include machine learning and/or deep learning techniques that include providing training corpora to a matching learning algorithm or neural network to train a machine to aid or perform enhancing the initial impedance image or portions of the initial impedance image”).
Regarding claim 3, the Kobayashi/De Limon/Santos combination teaches the computer-implemented method of claim 1, wherein the health condition of the liver comprises a property associated with a liver biomarker of the subject (Kobayashi, [0081]: “by visually checking the heart, liver, and kidneys displayed on the organ display screen G1, medical personnel can intuitively determine whether or not sufficient blood is being supplied from the heart to the vital organs, the liver and kidneys”. The determination of whether sufficient blood is being supplied to the liver is the health condition of the liver that is determined, and oxygen is the biomarker being monitored. The property is the determination of sufficient amounts of oxygen.).
Regarding claim 15, the Kobayashi/De Limon/Santos combination teaches a non-transitory computer-readable medium comprising instructions which, when executed by one or more processors, causes the one or more processors to perform the computer-implemented method for liver health assessment of claim 1 (Kobayashi, [0010]: “a computer-readable storage medium storing the biometric information processing program may be provided.”).
Regarding claim 16, the Kobayashi/De Limon/Santos combination teaches the computer-implemented method of claim 1.
However, the Kobayashi/De Limon/Santos combination is silent on the type of wearable device used.
Santos teaches wherein the wearable device comprises a belt of electrode ([0046]: “The EIT system 300 may include an electrode belt”; [0009]: “an electrical property tomography system showing a plurality of electrodes positioned around a region of interest of a patient”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to use the wearable device type from Santos into the Kobayashi/De Limon/Santos combination as the combination is silent on the type of wearable device used and Santos discloses a suitable wearable device in an analogous device.
The Kobayashi/De Limon/Santos combination teaches wherein collecting the multi-frequency EIT voltage data comprises positioning the belt of electrode to target an upper abdominal region of the subject indicating a bottom boundary of a ribcage (Kobayashi, [0027]: “the abdominal EIT measurement device 10c is attached to the abdomen of a subject K, such as a patient, and is configured to acquire abdominal EIT data indicating at least the kidneys and liver present in the abdomen of the subject K”. The wearable device is placed on the abdomen, which starts at the bottom boundary of a ribcage, therefore teaching on this limitation).
Regarding independent claim 14, Kobayashi teaches a system for liver health assessment ([0001]: “The present disclosure relates to a biometric information processing method, a biometric information processing program, and a biometric information processing device”), comprising one or more processors ([0011]: “A biometric information processing device according to one aspect of the present disclosure includes: a processor”) arranged to:
receive Electrical Impedance Tomography (EIT) voltage data associated with a liver of a subject ([0018]: “The processing device 1 may be a dedicated device for processing the EIT data transmitted from each EIT measuring device”; [0027]: “the abdominal EIT measurement device 10c is attached to the abdomen of a subject K, such as a patient, and is configured to acquire abdominal EIT data indicating at least the kidneys and liver present in the abdomen of the subject K”), the EIT voltage data being collected from a wearable device ([0018]: “The processing device 1 may be a dedicated device for processing the EIT data transmitted from each EIT measuring device. The processing device 1 may also be a personal computer, a workstation, a smartphone, a tablet, or a wearable device (e.g., AR glasses) attached to the body (e.g., arm, head, etc.) of a medical professional.”).
However, Kobayashi is silent on whether multiple frequencies are used for the EIT data.
De Limon discloses methods for analyzing a region of a body by electrical impedance. Specifically, De Limon teaches wherein the EIT data comprises multi-frequency EIT voltage data, ([0132]: “Multi-frequency EIT (MFEIT) has been shown to be particularly helpful for biological imaging using systems. For example, EIT measurements using the bipolar arrays of electrodes described herein may be used by injection of current at multiple frequencies through an array of skin/scalp bipolar electrodes”; [0047]: “electrode pairs of the plurality of pairs of bipolar electrodes and measuring voltages at the bipolar electrode pairs”). Kobayashi and De Limon are analogous art as they are both in the same field of endeavor and are related to methods used to analyze regions of a body using EIT techniques.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to use the multi-frequency EIT voltage data from De Limon into the method from Kobayashi as Kobayashi is silent on the frequencies, and De Limon discloses a suitable type of EIT data in an analogous method that provides more information for analysis.
The Kobayashi/De Limon combination teaches the steps of processing the multi-frequency EIT voltage data to determine a health condition of the liver of the subject (Kobayashi, [0002]: “According to EIT, the impedance between a pair of electrodes among a plurality of electrodes arranged around a measurement object is measured based on the AC current and AC voltage flowing between the pair of electrodes. In this way, by measuring a plurality of impedances between a plurality of pairs of electrodes, it is possible to calculate the impedance value of each of a plurality of minute regions (pixels) that make up the measurement object through numerical analysis. Furthermore, by visualizing the EIT data indicating the impedance value of each pixel, it becomes possible to visually grasp the state of the patient's measurement target (particularly, the organs)”; [0081]: “by visually checking the heart, liver, and kidneys displayed on the organ display screen G1, medical personnel can intuitively determine whether or not sufficient blood is being supplied from the heart to the vital organs, the liver and kidneys”. The determination of whether sufficient blood is being supplied to the liver is the health condition of the liver that is determined.), by:
performing an image reconstruction operation to generate conductivity change images, wherein the image reconstruction operation processes the multi- frequency EIT voltage data with reference to a pre-defined abdomen shape prior (Kobayashi, [0030]: “the control unit 2 generates image data showing the organ display screen G1 (see Figure 4), and then displays the organ display screen G1 on the display unit 5 based on the generated image data “; [0011]: “the bioinformation processing device generates image data showing an organ display screen on which at least one organ of the subject is displayed … updates the image data based on the impedance change value so that the visual appearance of the organ displayed on the organ display screen is changed”; [0027]: “The abdominal EIT data includes the impedance value of each pixel that constitutes the abdominal cross section”. The image data is updated so that the appearance of the organ and uses abdominal EIT data from the abdominal cross section, therefore using reference to a pre-defined abdomen shape.).
However, the Kobayashi/De Limon combination does not teach performing a post-processing operation on the conductivity change images.
Santos teaches systems for determining fluid and tissue volume estimations using electrical tomography. Specifically, Santos teaches the step of performing a post-processing operation on the conductivity change images, wherein the post-processing operation comprising: segmenting liver regions from the conductivity change images with reference to a liver shape prior to produce segmented images ([0099]: “segmenting the enhanced impedance image may include utilizing a mean contour of a segmented object (e.g., organ) within the one or more priors (e.g., CT-scans) to select a region of interest of as a region of the segmented object”; [0006]: “segmenting the enhanced impedance image to identify one or more tissues depicted within the enhance impedance image, selecting a region of interest within the enhanced impedance image, determining a relationship parameter that relates electrical properties represented within the region of interest of the enhanced impedance image with one or more properties of the region of interest, and estimating a fluid volume within the region of interest based at least partially on the relationship parameter and the enhanced impedance image”); and determining a spatial average of a change in conductivity within the segmented images ([0090]: “regional averages and conditional covariances may be determined, that the regional averages and conditional covariances may be utilized to determine (e.g., calculate) corrected impedances of the initial impedance image”. The regional average is the spatial average in the segmented images.), and using machine learning for processing ([0092]: “based on a comparison between the electrical property distributions represented in the initial impedance image and the reference reconstructed images, a relationship function (e.g., linear function or non-linear function) may be determined and utilized to adjust the electrical properties within a region of interest of the initial impedance image. In some instances, various machine learning models may be utilized within the process of enhancing the initial impedance images. For instance, enhancing the initial impedance image may include machine learning and/or deep learning techniques that include providing training corpora to a matching learning algorithm or neural network to train a machine to aid or perform enhancing the initial impedance image or portions of the initial impedance image”). Kobayashi, De Limon, and Santos are analogous art as they are all in the same field of endeavor and are related to methods used to analyze regions of a body using EIT techniques.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the additional image processing from Santos into the Kobayashi/De Limon combination as it allows the method to perform additional analysis that can be used as additional data in the estimation of the health condition of the user, which can provide a more comprehensive and accurate analysis. Additionally, it would have been obvious to use machine learning for analysis as it allows for a quick, dynamic analysis technique.
The Kobayashi/De Limon/Santos combination teaches the use of a trained machine learning processing model to determine the health condition of the liver based on the determined spatial average of the change in conductivity (Kobayashi, [0081]: “by visually checking the heart, liver, and kidneys displayed on the organ display screen G1, medical personnel can intuitively determine whether or not sufficient blood is being supplied from the heart to the vital organs, the liver and kidneys”. The determination of whether sufficient blood is being supplied to the liver is the health condition of the liver that is determined; Santos, [0092]: “based on a comparison between the electrical property distributions represented in the initial impedance image and the reference reconstructed images, a relationship function (e.g., linear function or non-linear function) may be determined and utilized to adjust the electrical properties within a region of interest of the initial impedance image. In some instances, various machine learning models may be utilized within the process of enhancing the initial impedance images. For instance, enhancing the initial impedance image may include machine learning and/or deep learning techniques that include providing training corpora to a matching learning algorithm or neural network to train a machine to aid or perform enhancing the initial impedance image or portions of the initial impedance image”).
Claims 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over the Kobayashi/De Limon/Santos as applied to claim 1 above, and further in view of Sandrin (US 20220015737).
Regarding claim 4, the Kobayashi/De Limon/Santos combination teaches the computer-implemented method of claim 1.
However, the Kobayashi/De Limon/Santos combination does not teach wherein using the a trained machine learning processing model comprises determining a controlled attenuation parameter (CAP) value of the subject.
Sandrin discloses a method for determining a health condition of the liver of a subject. Specifically, Sandrin teaches the step of determining a controlled attenuation parameter (CAP) value of the subject ([0251]: “a CAP value is determined”). Kobayashi, De Limon, Santos, and Sandrin are analogous arts as they are all in the same field of endeavor and are all related to methods to determine health conditions of a body part of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to determine the CAP value from Sandrin into the Kobayashi/De Limon/Santos combination as it allows the method to determine the CAP, which is an important parameter for analysis of the liver and can provide a more comprehensive analysis.
Regarding claim 5, the Kobayashi/De Limon/Santos/Sandrin combination teaches the computer-implemented method of claim 4.
However, the Kobayashi/De Limon/Santos combination is silent on the type of machine learning model used.
Santos teaches wherein the trained machine learning processing model comprises a linear regression model or a non-linear regression model ([0092]: “the machine-learning models may include a quadratic regression analysis, a logistic regression analysis, a support vector machine, a Gaussian process regression, ensemble models, or any other regression analysis. Furthermore, in yet further embodiments, the machine-learning models may include decision tree learning, regression trees, boosted trees, gradient boosted tree, multilayer perceptron, one-vs-rest, Naïve Bayes, k-nearest neighbor, association rule learning, a neural network, deep learning, pattern recognition, or any other type of machine-learning”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to use the machine learning types from Santos into the Kobayashi/De Limon/Santos combination as the combination is silent on the type of machine learning model used, and Santos discloses suitable machine learning models in an analogous device.
Claims 6, 8, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over the Kobayashi/De Limon/Santos/Sandrin as applied to claim 5 above, and further in view of Lee (“The relationship between visceral obesity and hepatic steatosis measured by controlled attenuation parameter”), Blomqvist (“A feasibility study of altered spatial distribution of losses introduced by eddy currents in body composition analysis”), and Staynor (“DXA reference values and anthropometric screening for visceral obesity in Western Australian adults”).
Regarding claim 6, the Kobayashi/De Limon/Santos/Sandrin combination teaches the computer-implemented method of claim 5.
However, the Kobayashi/De Limon/Santos/Sandrin combination is silent on how the CAP value is calculated.
Lee discloses variables related to a controlled attenuation parameter. Specifically, Lee teaches wherein the regression model determines the controlled attenuation parameter (CAP) value of the subject based on visceral fat area (Page 1: “Visceral fat area (VFA) was significantly related to hepatic steatosis assessed by CAP”; Page 2: “Our data demonstrated that VFA and TG is significantly related to hepatic steatosis assessed by CAP”). Kobayashi, De Limon, Santos, and Lee are analogous arts as they are all in the same field of endeavor and are all related to methods used to determine health conditions of a user and calculations used for determining different parameters.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the evaluation of CAP from Lee into the Kobayashi/De Limon/Santos/Sandrin combination as the combination is silent on how the CAP is determined, and Lee discloses a suitable way to determine CAP in an analogous art.
However, the Kobayashi/De Limon/Santos/Sandrin/Lee combination does not teach the methods to determine the visceral fat area.
Blomqvist and Staynor disclose variables that can be used to determine visceral fat. Specifically, Blomqvist and Staynor teach wherein the visceral fat area is based on the determined spatial average of the change in conductivity (Blomqvist, Page 2: “Tomographic imaging is an accurate and reliable method to measure the visceral fat area (VFA)”. Tomographic imaging includes a determined spatial average of the change in conductivity and is used to determine the visceral fat area.) and an anthropometric variable of the subject (Staynor, Page 2, “Visceral adipose tissue (VAT) is a fat depot located within the abdominal cavity, in close proximity to the internal organs1. VAT is a significant predictor of incident cardiovascular disease, type 2 diabetes, cancer, and mortality. Compared with subcutaneous fat, VAT secretes free fatty acids and adipocytokines (inflammatory markers), with direct access to the liver through the portal vein1. Within the liver, these biomarkers promote insulin resistance, liver fat accumulation”; Page 1, “waist circumference (WC), waist-hip ratio, and waist-height ratio (WHtR) had ‘high’ correlations with VAT mass. In women, only WHtR was ‘highly’ correlated with VAT mass.”. VAT mass is related to visceral fat area, which can be determined from the weight circumference over height measure, which is the anthropometric variable of the subject.). Kobayashi, De Limon, Santos, Blomqvist, and Staynor are analogous arts as they are all in the same field of endeavor and are all related to methods used to determine health conditions of a user and calculations used for determining different parameters.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the determination of visceral fat area from Blomquist and Staynor into the determination of CAP from the Kobayashi/De Limon/Santos/Sandrin/Lee combination as the combination is silent on how the visceral fat area is determined, and Blomqvist and Staynor disclose suitable variables to include in this determination.
Regarding claim 8, the Kobayashi/De Limon/Santos/Sandrin/Lee/Blomqvist/Staynor combination teaches the computer-implemented method of claim 6, wherein the anthropometric variable comprises a waist circumference over height measure (Staynor, Page 2, “Visceral adipose tissue (VAT) is a fat depot located within the abdominal cavity, in close proximity to the internal organs1. VAT is a significant predictor of incident cardiovascular disease, type 2 diabetes, cancer, and mortality. Compared with subcutaneous fat, VAT secretes free fatty acids and adipocytokines (inflammatory markers), with direct access to the liver through the portal vein1. Within the liver, these biomarkers promote insulin resistance, liver fat accumulation”; Page 1, “waist circumference (WC), waist-hip ratio, and waist-height ratio (WHtR) had ‘high’ correlations with VAT mass. In women, only WHtR was ‘highly’ correlated with VAT mass.”).
Regarding claim 18, the Kobayashi/De Limon/Santos/Sandrin/Lee/Blomqvist/Staynor combination teaches the computer-implemented method of claim 6, wherein processing the multi-frequency EIT voltage data further comprises: extracting multiple order statistics, including the determined spatial average of the change in conductivity, from the segmented images; and concatenating the extracted multiple order statistics with the anthropometric variable to form a 1D feature vector, wherein the 1D feature vector is input into the trained machine learning processing model (Kobayashi, [0088]: “An estimate of the vector x.sub.i may be determined from y.sub.i by using the prior statistics and relation between the set of images x.sub.i and y.sub.i”; [0089]: “By reordering equation (6), a first-order correction for the initial impedance image (σ.sub.reconst..sup.2D) from the reconstruction method may be determined. The correction may consider the prior distribution of a population and the forward problem model.”; [0092]: “various machine learning models may be utilized within the process of enhancing the initial impedance images”; Staynor, Page 1, “waist circumference (WC), waist-hip ratio, and waist-height ratio (WHtR) had ‘high’ correlations with VAT mass. In women, only WHtR was ‘highly’ correlated with VAT mass.” Including the anthropometric variable into the statistical analysis would have been obvious, as the ratios are highly correlated to VAT mass, which has an effect on the measurements and therefore should be incorporated in the analysis.).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over the Kobayashi/De Limon/Santos as applied to claim 16 above, and further in view of Garber (US 20190038173).
Regarding claim 17, the Kobayashi/De Limon/Santos combination teaches the computer-implemented method of claim 16.
However, the Kobayashi/De Limon/Santos combination is silent on the amount of electrodes on the electrode belt.
Garber discloses a device and method for determination of difference parameters on the basis of EIT data. Specifically, Garber teaches wherein the wearable device comprises a 16-electrode belt. Kobayashi, De Limon, Santos, and Garber are analogous art as they are all in the same field of endeavor and are related to methods used to analyze regions of a body using EIT techniques.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the 16 electrode belt from Garber into the Kobayashi/De Limon/Santos combination as the combination is silent on the amount of electrodes, and Garber discloses a suitable number of electrodes in an analogous device.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over the Kobayashi/De Limon/Santos as applied to claim 1 above, and further in view of Nikon (“Spectral Imaging and Linear Unmixing”) and Thomas (“Taylor Series Approximation”).
Regarding claim 19, the Kobayashi/De Limon/Santos combination teaches the computer-implemented method of claim 1.
However, the Kobayashi/De Limon/Santos combination is silent on the calculations performed in the image reconstruction.
Nikon and Thomas teach wherein performing the image reconstruction operation to generate the conductivity change images comprises extracting information from a frequency-difference curve by applying a spectral unmixing method (Nikon, Pages 15-16: “The most useful mathematical approaches to this category of image analysis have been termed Principle Component Analysis (PCA), Supervised Classification Analysis (SCA), Multivariate Curve Resolution (MCR), and Linear Unmixing (LU). These algorithms are based on the assumption that the measured signal from each wavelength (or color) is linearly proportional to the percentage or concentration of that wavelength in the specimen”), wherein a shape of a measured voltage over frequency is approximated by a Taylor expansion indicating that the change in conductivity with respect to frequency is a linear combination of a shape of conductivity changes over frequency (Thomas, Pages 1-2). Kobayashi, Nikon, and Thomas are analogous art as they are directed to solving a similar problem and are related to relevant calculations.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the calculations from Nikon and Thomas into the Kobayashi/De Limon/Santos combination as the combination is silent on the calculations used, and Nikon and Thomas disclose suitable calculations in analogous arts.
Response to Arguments
All of applicant’s argument regarding the rejections and objections previously set forth have been fully considered and are persuasive unless directly addressed subsequently.
The applicant has amended the claims to overcome the objections and 112(b) rejections, however the amendments have introduced new objections and 112(b) rejections.
Applicant’s arguments with respect to claims 1, 3-6, 8, and 14-19 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/E.K.M./Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791
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