DETAILED ACTION
This action is pursuant to claims filed on 5/28/2025. Claims 1-15 are pending. A first action on the merits of claims 1-15 is as follows.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: reference character “100” in Figure 1 does not appear in the specification. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 1 and 14 are objected to because of the following informalities:
In claim 1, the acronym “EIT” must be spelled out when it is initially introduced in the claim
In claim 14, the acronym “EIT” must be spelled out when it is initially introduced in the claim
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 5-8, and 10-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 2, the claim recites the limitation “which may be a pair of multi-frequency EIT voltage data”. The use of “may be” creates confusion as to whether the limitation of “a pair of multi-frequency EIT voltage data” is required by the claimed invention, or is an optional limitation. It is unclear what structure is required due to the use of “may be” in the claim, therefore the claim is indefinite. For purposes of examination, the limitation of “a pair of multi-frequency EIT voltage data” will not be required to teach on this limitation.
Regarding claim 5, the claim recites the limitation “which may be a linear regression model or a non-linear regression model”. The use of “may be” creates confusion as to whether the limitation of “a linear regression model or a non-linear regression model” is required by the claimed invention, or is an optional limitation. It is unclear what structure is required due to the use of “may be” in the claim, therefore the claim is indefinite. For purposes of examination, the limitation of “a linear regression model or a non-linear regression model” will not be required to teach on this limitation. Claims 6-8 are also rejected due to their dependency on claim 5.
Regarding claim 7, the claim recites the limitation “the change in conductivity”. There is insufficient antecedent basis for this limitation in the claim. Additionally, it is unclear if this limitation is referring to the conductivity measure from claim 6, or a different conductivity. If it is referring to the conductivity measure from claim 6, it needs to refer back to it. If it is referring to a different conductivity, it needs to be distinguished. For purposes of examination, it is being interpreted as referring to the conductivity measure from claim 6. Claim 8 is also rejected due to its dependency on claim 7.
Regarding claim 10, the claim recites the limitation “wherein the image reconstruction operation comprises: determining change in conductivity images based on processing the EIT data”. It is unclear when the processing of the EIT data occurs, since in claim 9, which claim 10 is dependent on, says that the image reconstruction operation occurs prior to processing the EIT data, yet claim 10 states that the image reconstruction operation includes processing the EIT data. This apparent contradiction fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, the image reconstruction operation being performed either prior to or during the processing of the EIT data will teach on this limitation. Claims 11-13 are also rejected due to their dependency on claim 10.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Under the two-step 101 analysis, the claims fail to satisfy the criteria for subject matter eligibility.
Regarding Step 1, claims 1-15 are all within at least one of the four statutory categories.
Claim 1 and its dependent claims disclose a method (process).
Claim 14 discloses a system (machine).
Regarding Step 2A, Prong One, the independent claims 1 and 14 recite an abstract idea. In particular, the claims generally recite the following:
receiving EIT data associated with a liver of a subject (claim 1);
processing the EIT data to determine a health condition of the liver of the subject (claim 1);
receiving multi-frequency EIT voltage data associated with a liver of a subject (claim 14);
process the multi-frequency EIT voltage data using a trained machine learning processing model to determine a property associated with a liver biomarker of the subject, so as to determine a health condition of the liver of the subject (claim 14).
These elements recited in claims 1 and 14 are drawn to abstract ideas since they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgement, and opinion and using pen and paper.
Receiving EIT data associated with a liver of a subject is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art could reasonably receive the EIT data on a piece of paper. There is nothing to suggest an undue level of complexity in receiving EIT data associated with a liver of a subject.
Processing the EIT data to determine a health condition of the liver of the subject is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art could reasonably process the EIT data to determine a health condition mentally based on evaluation, mathematical concepts, and judgement. These techniques are based on algorithms, calculations, evaluation, and judgement, which can be performed by hand or mentally. The mathematics of processing the EIT data is not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas. There is nothing to suggest an undue level of complexity in processing the EIT data to determine a health condition of the liver of the subject.
Receiving multi-frequency EIT voltage data associated with a liver of a subject is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art could reasonably receive the EIT data on a piece of paper. There is nothing to suggest an undue level of complexity in receiving multi-frequency EIT voltage data associated with a liver of a subject .
Process the multi-frequency EIT voltage data using a trained machine learning processing model to determine a property associated with a liver biomarker of the subject, so as to determine a health condition of the liver of the subject is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art could reasonably process the EIT data to determine a health condition mentally based on evaluation, mathematical concepts, and judgement. These techniques are based on algorithms, calculations, evaluation, and judgement, which can be performed by hand or mentally. The mathematics of processing the EIT data is not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas. There is nothing to suggest an undue level of complexity in process the multi-frequency EIT voltage data using a trained machine learning processing model to determine a property associated with a liver biomarker of the subject, so as to determine a health condition of the liver of the subject.
Regarding Step 2A, Prong Two, claims 1 and 14 do not recite additional elements that integrate the exception into a practical application. Therefore, the claims are directed to the abstract idea. The additional elements merely:
Recite the words “apply it” or an equivalent with the judicial exception, or include instructions to implement the abstract idea on a computer, or merely use the computer as a tool to perform the abstract idea (e.g., “a computer-implemented method” (claim 1), “one or more processors” (claim 14)).
As a whole, the additional elements merely serve to gather information to be used by the abstract idea, while generically implementing it on a computer. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. The processing performed remains in the abstract realm, i.e., the result is not used for a treatment. No improvement to the technology is evident. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Regarding Step 2B, claims 1 and 14 do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the same reasons as described above.
The element of a computer implemented method in claim 1 and one or more processors in claim 14 does not qualify as significantly more because this limitation is simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above judicial exception. Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Regarding the dependent claims, claims 2-13 and 15 depend on claim 1. The dependent claims merely further define the abstract idea or are additional data output that is well-understood, routine, and previously known in the industry.
For example, the following are dependent claims reciting abstract ideas and can be performed in the human mind:
(Claim 2): “wherein the EIT data comprises multi-frequency EIT voltage data, which may be a pair of multi-frequency EIT voltage data” further describes the abstract idea since it further defines the type of data being used;
(Claim 3): “processing the EIT data using a trained machine learning processing model to determine a property associated with a liver biomarker of the subject” further defines the abstract idea since it can be performed mentally or with the aid of pen and paper. These techniques are based on algorithms and calculations and mathematical principles, which can be performed by hand. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas;
(Claim 4): “processing the EIT data using a trained machine learning processing model to determine a controlled attenuation parameter (CAP) value of the subject” further defines the abstract idea since it can be performed mentally or with the aid of pen and paper. These techniques are based on algorithms and calculations and mathematical principles, which can be performed by hand. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas;
(Claim 5): “wherein the trained machine learning processing model comprises a regression model, which may be a linear regression model or a non-linear regression model” further defines the abstract idea since it can be performed mentally or with the aid of pen and paper. These techniques are based on algorithms and calculations and mathematical principles, which can be performed by hand. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas;
(Claim 6): “wherein the regression model determines the controlled attenuation parameter (CAP) value of the subject based on a conductivity measure of the subject as determined from the EIT data and an anthropometric variable of the subject.” further defines the abstract idea since it can be performed mentally or with the aid of pen and paper. These techniques are based on algorithms and calculations and mathematical principles, which can be performed by hand. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas;
(Claim 7): “wherein the conductivity measure comprises a spatial average of the change in conductivity” further describes the abstract idea since it further defines the type of data being used;
(Claim 8): “wherein the anthropometric variable comprises a waist circumference over height measure” further describes the abstract idea since it further defines the type of data being used;
(Claim 9): “performing an image reconstruction operation prior to processing the EIT data using the trained machine learning processing model” further defines the abstract idea since it can be performed mentally or with the aid of pen and paper. These techniques are based on algorithms and calculations and mathematical principles, which can be performed by hand. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas;
(Claim 10): “wherein the image reconstruction operation comprises: determining change in conductivity images based on processing the EIT data with reference to abdomen shape prior or reference data” further defines the abstract idea since it can be performed mentally or with the aid of pen and paper. These techniques are based on algorithms and calculations and mathematical principles, which can be performed by hand. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas;
(Claim 11): “performing a post-processing operation after the image reconstruction operation and prior to processing the EIT data using the trained machine learning processing model” further defines the abstract idea since it can be performed mentally or with the aid of pen and paper. These techniques are based on algorithms and calculations and mathematical principles, which can be performed by hand. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas;
(Claim 12): “segmenting liver regions from the change in conductivity images; and determining a spatial average of the change in conductivity” further defines the abstract idea since it can be performed mentally or with the aid of pen and paper. These techniques are based on algorithms and calculations and mathematical principles, which can be performed by hand. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas;
(Claim 13): “segmenting the liver regions from the change in conductivity images with reference to a liver shape prior or reference data” further defines the abstract idea since it can be performed mentally or with the aid of pen and paper. These techniques are based on algorithms and calculations and mathematical principles, which can be performed by hand. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas;
(Claim 15): “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” is merely implementation using computer components or computer-media that implement that does not integrate the exception into a practical application and does not amount to significantly more than the above judicial exception.
The dependent claims do not recite significantly more than the abstract ideas. Therefore, claims 1-15 are rejected as being directed to non-statutory subject matter.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 and 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kobayashi (JP 2020195677). 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 ([0010]: “a biometric information processing program for causing a computer to execute the biometric information processing method may be provided”), comprising:
receiving EIT data associated with a liver of a subject ([0013]: “the bioinformation processing device: acquires first EIT data indicating at least one organ of the subject from at least one EIT measurement device”; [0030]: “The medical staff may be able to select the organ to be measured via the input operation unit 6 . For example, when the brain, lungs, heart, and liver are selected as the organs to be measured”); and
processing the EIT data to determine a health condition of the liver of the subject ([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”).
Regarding claim 15, Kobayashi 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 ([0010]: “a computer-readable storage medium storing the biometric information processing program may be provided.”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Kobayashi as applied to claim 1 above, and further in view of De Limon (WO 2016205872).
Regarding claim 2, Kobayashi teaches the computer-implemented method of claim 1, wherein the EIT data comprises EIT voltage data, which may be a pair of EIT voltage data ([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”).
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, which may be a pair of 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 arts as they are both 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.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Kobayashi as applied to claim 1 above, and further in view of Garff (US 20210219913).
Regarding claim 3, Kobayashi teaches the computer-implemented method of claim 1,
wherein the processing comprises: processing the EIT data to determine a property associated with a liver biomarker of the subject ([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”; [0045]: “the control unit 2 calculates the impedance change value ΔZ=|Z−Zr| of each organ based on the reference impedance value Zr of each organ and the impedance value Z of each organ”).
However, Kobayashi does not teach using a trained machine learning processing model.
Garff teaches a method for measuring tissue. Specifically, Garff teaches using a trained machine learning processing model ([0077]: “The artificial intelligence/machine learning algorithms 118 may comprise various types of machine learning algorithms”). Kobayashi and Garff are analogous arts as they are both 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 machine learning model from Garff into the method from Kobayashi as it allows the method to process the measured information quickly and allows for a fast dynamic analysis.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Kobayashi in further view of De Limon and Garff.
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 EIT voltage data associated with a liver of a subject ([0013]: “the bioinformation processing device: acquires first EIT data indicating at least one organ of the subject from at least one EIT measurement device”; [0030]: “The medical staff may be able to select the organ to be measured via the input operation unit 6 . For example, when the brain, lungs, heart, and liver are selected as the organs to be measured”).
However, Kobayashi is silent on whether multiple frequencies are used for the EIT data.
De Limon teaches 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”).
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 system from Kobayashi as Kobayashi is silent on the frequencies, and De Limon discloses a suitable type of EIT data in an analogous system that provides more information for analysis.
The Kobayashi/De Limon combination teaches process the multi-frequency EIT voltage data to determine a property associated with a liver biomarker of the subject, so as to determine a health condition of the liver of the subject ([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”; [0045]: “the control unit 2 calculates the impedance change value ΔZ=|Z−Zr| of each organ based on the reference impedance value Zr of each organ and the impedance value Z of each organ”).
However, the Kobayashi/De Limon combination does not teach using a trained machine learning processing model.
Garff teaches using a trained machine learning processing model ([0077]: “The artificial intelligence/machine learning algorithms 118 may comprise various types of machine learning algorithms”).
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 model from Garff into the system from the Kobayashi/De Limon combination as it allows the method to process the measured information quickly and allows for a fast dynamic analysis.
Claims 4-5 and 9-13 are rejected under 35 U.S.C. 103 as being unpatentable over Kobayashi as applied to claim 1 above, and further in view of Garff and Sandrin (US 20220015737).
Regarding claim 4, Kobayashi teaches the computer-implemented method of claim 1.
However, Kobayashi does not teach wherein the processing comprises: processing the EIT data using a trained machine learning processing model to determine a controlled attenuation parameter (CAP) value of the subject.
Garff teaches wherein the processing comprises: processing the EIT data using a trained machine learning processing model ([0077]: “The artificial intelligence/machine learning algorithms 118 may comprise various types of machine learning algorithms”).
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 model from Garff into the method from Kobayashi as it allows the method to process the measured information quickly and allows for a fast dynamic analysis.
However, the Kobayashi/Garff combination does not teach determine 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 determine a controlled attenuation parameter (CAP) value of the subject ([0251]: “a CAP value is determined”). Kobayashi, Garff, and Sandrin are analogous arts as they 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/Garff 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/Garff/Sandrin combination teaches the computer-implemented method of claim 4, wherein the trained machine learning processing model comprises a regression model, which may be a linear regression model or a non-linear regression model (Garff, [0077]: “The artificial intelligence/machine learning algorithms 118 may comprise various types of machine learning algorithms such as supervised machine learning algorithms (e.g., nearest neighbor, naïve bayes, decision trees, linear regression, support vector machines, neural networks, etc.), unsupervised machine learning algorithms (e.g., k-means clustering, association rules, etc.), semi-supervised machine learning algorithms, and/or reinforcement machine learning algorithms (e.g., Q-learning, temporal difference, deep adversarial networks, etc.)”).
Regarding claim 9, the Kobayashi/Garff/Sandrin combination teaches the computer-implemented method of claim 4, wherein the processing further comprises: performing an image reconstruction operation prior to processing the EIT data using the trained machine learning processing model (Kobayashi, [0007]: “A biological information processing method according to one aspect of the present disclosure includes the steps of: generating image data showing an organ display screen on which at least one organ of a subject is displayed”).
Regarding claim 10, the Kobayashi/Garff/Sandrin combination teaches the computer-implemented method of claim 9, wherein the image reconstruction operation comprises: determining change in conductivity images based on processing the EIT data with reference to abdomen shape prior or reference data (Kobayashi, [0032]: “The control unit 2 identifies the reference impedance values Zr of the kidney and liver from the abdominal EIT data”; [0045]: “the control unit 2 calculates the impedance change value ΔZ=|Z−Zr| of each organ based on the reference impedance value Zr of each organ and the impedance value Z of each organ”).
Regarding claim 11, the Kobayashi/Garff/Sandrin combination teaches the computer-implemented method of claim 10, wherein the processing further comprises: performing a post-processing operation after the image reconstruction operation and prior to processing the EIT data using the trained machine learning processing model (Kobayashi, [0046]: “the control unit 2 determines whether or not it is necessary to update the organ display screen G1 (see FIG. 4) based on the impedance change value ΔZ of each organ. If the determination result in step S8 is YES, the control unit 2 updates the organ display screen G1 based on the impedance change value ΔZ of each organ”).
Regarding claim 12, the Kobayashi/Garff/Sandrin combination teaches the computer-implemented method of claim 11, wherein the post-processing operation comprises: segmenting liver regions from the change in conductivity images (Kobayashi, [0002]: “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”); and determining a spatial average of the change in conductivity (Kobayashi, [0034]: “the control unit 2 may determine the average value of multiple impedance values”).
Regarding claim 13, the Kobayashi/Garff/Sandrin combination teaches the computer-implemented method of claim 12, wherein segmenting the liver regions comprises: segmenting the liver regions from the change in conductivity images with reference to a liver shape prior or reference data (Kobayashi, [0002]: “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”; [0032]: “The control unit 2 identifies the reference impedance values Zr of the kidney and liver from the abdominal EIT data”; [0045]: “the control unit 2 calculates the impedance change value ΔZ=|Z−Zr| of each organ based on the reference impedance value Zr of each organ and the impedance value Z of each organ”).
Claims 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over the Kobayashi/Garff/Sandrin combination as applied to claim 5 above, and further in view of Castellani (“Relation between the conductivity, the ultrasonic attenuation, and nonlinear σ-model composite operators at the Anderson transition”) and Staynor (“DXA reference values and anthropometric screening for visceral obesity in Western Australian adults”).
Regarding claim 6, the Kobayashi/Garff/Sandrin combination teaches the computer-implemented method of claim 5.
However, the Kobayashi/Garff/Sandrin combination is silent on how the CAP value is calculated.
Castellani discloses determining a relationship between ultrasonic attenuation and conductivity. Specifically, Castellani teaches wherein the regression model determines the controlled attenuation parameter (CAP) value of the subject based on a conductivity measure of the subject (Pages 1-3).
Additionally, Staynor discloses using anthropometric screening for analysis in health conditions. Specifically, Staynor teaches an anthropometric variable of the subject (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.”). Castellani, Staynor, Kobayashi, Garff, and Sandrin are analogous arts as they 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 calculations from Castellani and Staynor into the determination of CAP from the Kobayashi/Garff/Sandrin combination as the combination is silent on the calculations used, and Castellani teaches a relation between the attenuation parameter and conduction, which allows the method to determine the CAP value which is an important parameter for determining liver health. Additionally, the inclusion of the anthropometric variable from Staynor allows the method to include important parameters that can impact the state of the liver, which can allow for a more accurate, comprehensive analysis.
Regarding claim 7, the Kobayashi/Garff/Sandrin/Castellani/Staynor combination teaches the computer-implemented method of claim 6, wherein the conductivity measure comprises a spatial average of the change in conductivity (Kobayashi. [0034]: “the control unit 2 may determine the average value of multiple impedance values”).
Regarding claim 8, the Kobayashi/Garff/Sandrin/Castellani/Staynor combination teaches the computer-implemented method of claim 7, 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.”).
Conclusion
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/E.K.M./Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791