Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 1 and 11 are objected to because of the following informalities:
Claim limitation “EIT” in claim 1 should be spelled out in its entirety as “Electrical Impedance Tomography” when first introduced.
Claim limitation “obtain” is suggested to be amended to “obtained” in claim 11.
Appropriate correction is required.
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 1-21 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.
MPEP 2106 Step 2A – Prong 1:
Claim 1
A computer-implemented method, comprising: processing a EIT data set of a subject to determine one or more conductivity characteristics associated with a tissue or organ of the subject; and determining, based on at least the one or more determined conductivity characteristics, a health state or condition of the tissue or organ of the subject.
As presently drafted, under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper for determining the health state or condition of a patient’s tissue or organ based on EIT data. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the "Mental Process" grouping of abstract ideas. A person would readily be able to perform this process either mentally or with the assistance of pen and paper. See MPEP § 2106.04(a)(2).
MPEP 2106 Step 2A – Prong 2:
This judicial exception is not integrated into a practical application because there are no meaningful limitations that transform the exception into a patent eligible application. The additional elements merely amount to instructions to apply the exception using generic computer components (“computer”—all recited at a high level of generality). Although they have and execute instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The claims only manipulate abstract data elements into another form. They do not set forth improvements to another technological field or the functioning of the computer itself and instead use computer elements as tools in a conventional way to improve the functioning of the abstract idea identified above. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken 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. None of the additional elements recited "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment,' that is, implementation via computers." Alice Corp., slip op. at 16 (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)).
At the levels of abstraction described above, the claims do not readily lend themselves to a finding that they are directed to a nonabstract idea. Therefore, the analysis proceeds to step 2B. See BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1349 (Fed. Cir. 2016) ("The Enfish claims, understood in light of their specific limitations, were unambiguously directed to an improvement in computer capabilities. Here, in contrast, the claims and their specific limitations do not readily lend themselves to a step-one finding that they are directed to a nonabstract idea. We therefore defer our consideration of the specific claim limitations’ narrowing effect for step two.") (citations omitted).
MPEP 2106 Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reasons as presented in Step 2A Prong 2. Moreover, the additional elements recited are known and conventional generic computing elements (“computer”—see Specification page 26-27, under Example systems describing the various components as general purpose, common, standard, known to one of ordinary skill, and at a high level of generality, and in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy the statutory disclosure requirements). Therefore, these additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept that amounts to significantly more. See MPEP 2106.05(f).
The Federal Circuit has recognized that "an invocation of already-available computers that are not themselves plausibly asserted to be an advance, for use in carrying out improved mathematical calculations, amounts to a recitation of what is 'well-understood, routine, [and] conventional.'" SAP Am., Inc. v. InvestPic, LLC, 890 F.3d 1016, 1023 (Fed. Cir. 2018) (alteration in original) (citing Mayo v. Prometheus, 566 U.S. 66, 73 (2012)). Apart from the instructions to implement the abstract idea, they only serve to perform well-understood functions (e.g., receiving, translating, and displaying data—see Specification above as well as Alice Corp.; Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307 (Fed. Cir. 2016); and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015) covering the well-known nature of these computer functions).
Dependent Claims
The limitations of dependent but for those addressed below merely set forth further refinements of the abstract idea without changing the analysis already presented. Claims 2-21 merely recites insignificant extra solution activity used to obtain data to make the determination of the subject’s tissue or organ health or condition.
Claims 3-6, 15, and 18-19 further refine the abstract idea described in the independent claim and merely recite further using a machine learning model or a specific model. The use of the model provides nothing more than mere instructions to implement the abstract idea, supra. July 2024 Subject Matter Eligibility Examples, Example 47, Claim 2, discussion of item (c) at Pgs. 7-9. Accordingly, the use of the AI engine provides nothing more than mere instructions to implement an abstract idea on a generic computer (“apply it”). See MPEP 2106.05(f). MPEP 2106.05(f); July 2024 Subject Matter Eligibility Examples, Example 47, Claim 2, discussion of items (d) and (e) at Pgs. 8-9. These additional elements are considered to “apply it” under both the practical application and significantly more analysis, as detailed in the analysis above.
Claim Rejections - 35 USC § 102
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 1 and 2 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kao et al. (US 20150002168 A1), hereinafter Kao.
Regarding claim 1, Kao discloses a computer-implemented method, comprising: processing a EIT data set of a subject to determine one or more conductivity characteristics associated with a tissue or organ of the subject [the soft-field tomography system 10 may be an EIT or EIS system that is configured to estimate the electrical properties (e.g., conductivity and/or permittivity) inside a body or object using measurements obtained on the surface of the body or object (i.e., non-invasively), see in ¶ 0024]; and determining, based on at least the one or more determined conductivity characteristics, a health state or condition of the tissue or organ of the subject [in EIS/EIT systems, an estimate of the distribution of electrical conductivities of probed internal structures is made and utilized to reconstruct the conductivity and/or permittivity of the materials within the probed area or volume… visual representations of the estimates are then formed and may be utilized, for example, by a medical practitioner to identify clinically relevant information about the object or subject, see in ¶ 0002; in accordance with some presently disclosed embodiments, one or more hydration changes between one or more depths or layers of tissues within the body may be estimated using the electrical properties measured by the system 10, see in ¶ 0024].
Regarding claim 2, Kao discloses the computer-implemented method of claim 1, wherein the determining comprises: determining, based on at least the one or more determined conductivity characteristics, whether the subject has a disease associated with the tissue or organ [presently disclosed embodiments may offer advantages, for example, but not limited to edema monitoring medical applications in which it may be desirable to detect, monitor, track, and/or trend peripheral edema (e.g., in subjects with congestive heart failure, chronic kidney disease, hypertension, etc.), see in ¶ 0020], and optionally: further classifying a stage or a severity of the disease associated with the tissue or organ.
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.
Claims 3-21 are rejected under 35 U.S.C. 103 as being unpatentable over Kao (US 20150002168 A1) in view of Haick et al. (US 20130236981 A1), hereinafter Haick.
Regarding claim 3, Kao discloses the computer-implemented method of claim 1.
Kao fails to disclose wherein the determining comprises: processing, at least, the one or more determined conductivity characteristics of the subject and one or more anthropometric characteristics of the subject, using a machine learning based processing model, to determine a quantitative or qualitative parameter associated with the health state or condition of the tissue or organ of the subject.
However, Haick discloses wherein the determining comprises: processing, at least, the one or more determined conductivity characteristics of the subject and one or more anthropometric characteristics of the subject, using a machine learning based processing model, to determine a quantitative or qualitative parameter associated with the health state or condition of the tissue or organ of the subject [machine learning for analyzing of conductivity data for determining stages of kidney disease, taking into account patient’s age, weight, gender, etc, see in ¶ 0034, ¶ 0052, ¶ 0054, ¶ 0058-¶ 0059, and ¶ 0077; see also Table 1].
Kao and Haick are both analogous to the claimed invention because they are in the same field of non-invasive evaluation of organ/tissue health. Examiner notes that Haick does not utilize EIT and instead applies to chemical sensing instead to evaluate the targeted organ health. However, Examiner argues that, despite differences in technology, Haick’s disclosed invention is still applicable to incorporate with Kao’s disclosed invention, as they both are involved in the process of non-invasive diagnostics and have overlapping applications. Therefore, it would have been obvious to someone of ordinary skill in the art before the filing date of the claimed invention to have modified Kao to incorporate the teachings of Haick and include that determining comprises: processing, at least, the one or more determined conductivity characteristics of the subject and one or more anthropometric characteristics of the subject, using a machine learning based processing model, to determine a quantitative or qualitative parameter associated with the health state or condition of the tissue or organ of the subject, in order to efficiently evaluate parameters for diagnostic purposes.
Regarding claim 4, Kao, as modified, discloses the computer-implemented method of claim 1.
Kao fails to disclose wherein the determining comprises: processing, using a machine learning based processing model,(i) the one or more determined conductivity characteristics of the subject,(ii) one or more anthropometric characteristics of the subject, and(iii) one or more determined conductivity characteristics of one or more reference subjects and/or of a group containing the subject and the one or more reference subjects, to determine a quantitative or qualitative parameter associated with the health state or condition of the tissue or organ of the subject.
However, Haick discloses wherein the determining comprises: processing, using a machine learning based processing model,(i) the one or more determined conductivity characteristics of the subject,(ii) one or more anthropometric characteristics of the subject, and(iii) one or more determined conductivity characteristics of one or more reference subjects and/or of a group containing the subject and the one or more reference subjects, to determine a quantitative or qualitative parameter associated with the health state or condition of the tissue or organ of the subject [machine learning for analyzing of conductivity data for determining stages of kidney disease, taking into account patient’s age, weight, gender, etc. and comparing to a control sample, see in ¶ 0034, ¶ 0052, ¶ 0054, ¶ 0058-¶ 0059, and ¶ 0077; see also Table 1].
Kao and Haick are both analogous to the claimed invention because they are in the same field of non-invasive evaluation of organ/tissue health. Therefore, it would have been obvious to someone of ordinary skill in the art before the filing date of the claimed invention to have modified Kao to incorporate the teachings of Haick and include that determining comprises: processing, using a machine learning based processing model,(i) the one or more determined conductivity characteristics of the subject,(ii) one or more anthropometric characteristics of the subject, and(iii) one or more determined conductivity characteristics of one or more reference subjects and/or of a group containing the subject and the one or more reference subjects, to determine a quantitative or qualitative parameter associated with the health state or condition of the tissue or organ of the subject in order to efficiently evaluate parameters for diagnostic purposes.
Regarding claim 5, Kao, as modified, discloses the computer-implemented method of claim 3.
Kao fails to disclose wherein the machine learning based processing model comprises a regression model.
However, Haick discloses wherein the machine learning based processing model comprises a regression model [machine learning includes regression models, see in ¶ 0054].
Kao and Haick are both analogous to the claimed invention because they are in the same field of non-invasive evaluation of organ/tissue health. Therefore, it would have been obvious to someone of ordinary skill in the art before the filing date of the claimed invention to have modified Kao to incorporate the teachings of Haick and include that the machine learning based processing model comprises a regression model for prediction of a disease state.
Regarding claim 6, Kao, as modified, discloses the computer-implemented method of claim 3.
Kao fails to disclose wherein the machine learning based processing model comprises a classification model.
However, Haick discloses wherein the machine learning based processing model comprises a classification model [machine learning includes classification models like SVM, see in ¶ 0054].
Kao and Haick are both analogous to the claimed invention because they are in the same field of non-invasive evaluation of organ/tissue health. Therefore, it would have been obvious to someone of ordinary skill in the art before the filing date of the claimed invention to have modified Kao to incorporate the teachings of Haick and include that the machine learning based processing model comprises a classification model for utilizing machine learning for diagnosing a disease state using the collected data values.
Regarding claim 7, Kao, as modified, discloses the computer-implemented method of claim 6.
Kao fails to disclose wherein the one or more anthropometric characteristics comprise, or are related to, one or more of: age of the subject, weight of the subject, height of the subject, waist circumference of the subject, waist-over-height ratio of the subject, body mass index (BMI) of the subject, gender of the subject, and race of the subject.
However, Haick discloses wherein the one or more anthropometric characteristics comprise, or are related to, one or more of: age of the subject, weight of the subject, height of the subject, waist circumference of the subject, waist-over-height ratio of the subject, body mass index (BMI) of the subject, gender of the subject, and race of the subject [body weight and age, see ¶ 0077 and Table 1].
Kao and Haick are both analogous to the claimed invention because they are in the same field of non-invasive evaluation of organ/tissue health. Therefore, it would have been obvious to someone of ordinary skill in the art before the filing date of the claimed invention to have modified Kao to incorporate the teachings of Haick and include that the one or more anthropometric characteristics comprise, or are related to, one or more of: age of the subject, weight of the subject, height of the subject, waist circumference of the subject, waist-over-height ratio of the subject, body mass index (BMI) of the subject, gender of the subject, and race of the subject in order to incorporate a specific form of general body measurement when running the machine learning algorithm.
Regarding claim 8, Kao, as modified, discloses the computer-implemented method of claim 7.
Kao fails to disclose wherein the quantitative or qualitative parameter associated with the health state or condition of the tissue or organ of the subject comprises: a value associated with an estimated performance of the tissue or organ of the subject.
However, Haick discloses wherein the quantitative or qualitative parameter associated with the health state or condition of the tissue or organ of the subject comprises: a value associated with an estimated performance of the tissue or organ of the subject [based on the analyzed signal, including change in conductivity, a patient can be diagnosed as having kidney disease, including using a glomerular filtration rate of the patient, see in ¶ 0034, ¶ 0052, and ¶ 0058-¶ 0059; see also Table 1].
Kao and Haick are both analogous to the claimed invention because they are in the same field of non-invasive evaluation of organ/tissue health. Therefore, it would have been obvious to someone of ordinary skill in the art before the filing date of the claimed invention to have modified Kao to incorporate the teachings of Haick and include that the quantitative or qualitative parameter associated with the health state or condition of the tissue or organ of the subject comprises: a value associated with an estimated performance of the tissue or organ of the subject, as performance value is an obvious indicator of the tissue/organ’s health state.
Regarding claim 9, Kao, as modified, discloses the computer-implemented method of claim 8.
Kao fails to disclose wherein the determining further comprises: comparing the quantitative or qualitative parameter associated with the health state or condition of the tissue or organ of the subject with reference parameter data to determine whether the subject has a disease associated with the tissue or organ.
However, Haick discloses wherein the determining further comprises: comparing the quantitative or qualitative parameter associated with the health state or condition of the tissue or organ of the subject with reference parameter data to determine whether the subject has a disease associated with the tissue or organ [data is compared to a control sample to determine whether the patient has kidney disease, see in ¶ 0059].
Kao and Haick are both analogous to the claimed invention because they are in the same field of non-invasive evaluation of organ/tissue health. Therefore, it would have been obvious to someone of ordinary skill in the art before the filing date of the claimed invention to have modified Kao to incorporate the teachings of Haick and include that the determining further comprises: comparing the quantitative or qualitative parameter associated with the health state or condition of the tissue or organ of the subject with reference parameter data to determine whether the subject has a disease associated with the tissue or organ, as comparing a tested value with a reference value is standard to determine an abnormal/disease state.
Regarding claim 10, Kao, as modified, discloses the computer-implemented method of claim 9.
Kao fails to disclose wherein the determining further comprises: classifying, based on the comparing, a stage or a severity of the disease associated with the tissue or organ.
However, Haick discloses wherein the determining further comprises: classifying, based on the comparing, a stage or a severity of the disease associated with the tissue or organ [severity/stage of the disease can be identified, see in ¶ 0059].
Kao and Haick are both analogous to the claimed invention because they are in the same field of non-invasive evaluation of organ/tissue health. Therefore, it would have been obvious to someone of ordinary skill in the art before the filing date of the claimed invention to have modified Kao to incorporate the teachings of Haick and include that determining further comprises: classifying, based on the comparing, a stage or a severity of the disease associated with the tissue or organ, as disease stage and severity are standard information obtained for diagnostic systems.
Regarding claim 11, Kao, as modified, discloses the computer-implemented method of claim 10, wherein the EIT data set contains EIT data [¶ 0023-¶ 0024]; wherein the EIT data is obtained from a region of the subject containing the tissue or organ [the use of electrical impedance tomography to determine conductivity of an object, wherein the electrodes are placed on the surface of the interrogation region of the object/subject, see in ¶ 0002 and ¶ 0024], wherein the signals are provided at a frequency, and wherein the steps are repeated for a plurality of frequencies; and wherein the EIT data set comprises a plurality of EIT data subsets each associated with a respective one of the plurality of frequencies [EIT may be applied at different frequencies to generate multiple datasets at multiple frequencies, see in ¶ 0022, ¶ 0039, and ¶ 0048-¶ 0049].
Kao fails to explicitly disclose wherein the EIT data set is obtained by (a) providing excitation signals to the subject via electrodes attached to the region of the subject, (b) measuring responsive signals received via the electrodes as a result of the providing of the excitation signals.
However, Haick discloses wherein the data set is obtained by (a) providing excitation signals to the subject via electrodes attached to the region of the subject,(b) measuring responsive signals received via the electrodes as a result of the providing of the excitation signals [collecting measurements from impedance sensors via impedance sensors including exciting the electrodes to capture a responsive signal and repeating the process to obtain a plurality of datasets, see in ¶ 0051, ¶ 0081, and ¶ 0083].
Kao and Haick are both analogous to the claimed invention because they are in the same field of non-invasive evaluation of organ/tissue health. Therefore, it would have been obvious to someone of ordinary skill in the art before the filing date of the claimed invention to have modified Kao to incorporate the teachings of Haick and include that the data set is obtained by (a) providing excitation signals to the subject via electrodes attached to the region of the subject, (b) measuring responsive signals received via the electrodes as a result of the providing of the excitation signals, in order to selectively measure data from the targeted tissue/organ for further diagnosing.
Regarding claim 12, Kao, as modified, discloses computer-implemented method of claim 11, wherein the processing comprises:(i) processing the EIT data set to obtain a frequency difference EIT data set, the frequency difference EIT data set includes a plurality of frequency difference EIT data subsets;(ii) performing a group source separation operation using the frequency difference EIT data set and one or more reference frequency difference EIT data sets of corresponding one or more reference subjects to determine component of the frequency difference EIT data set related to the tissue or organ and component of each of the one or more reference frequency difference EIT data sets related to the tissue or organ; and (iii) performing a conductivity characteristics extraction operation using the component of the frequency difference EIT data set related to the tissue or organ and optionally the component of each of the one or more reference frequency difference EIT data sets related to the tissue or organ to determine at least the one or more conductivity characteristics of the subject [EIT may be applied at different frequencies to generate multiple datasets at multiple different frequencies for analysis, see in ¶ 0022, ¶ 0039, ¶ 0048, and ¶ 0049].
Regarding claim 13, Kao, as modified, discloses the computer-implemented method of claim 12, wherein the processing further comprises: pre-processing the EIT data set before the processing in (i) so that the EIT data set processed in (i) is a pre-processed EIT data set [data is normalized and/or outlier data is removed (i.e. filter/smoothed), see in ¶ 0050 and ¶ 0052].
Regarding claim 14, Kao, as modified, discloses the computer-implemented method of claim 13, wherein the pre- processing of the EIT data set comprises: filtering and/or smoothing each of the plurality of EIT data subsets [data is normalized and/or outlier data is removed (i.e. filter/smoothed), see in ¶ 0050 and ¶ 0052].
Regarding claim 15, Kao, as modified, discloses the computer-implemented method of claim 14, wherein the pre-processing of the EIT data set comprises: processing the EIT data set using a classifier model to determine respective performance of each of the plurality of electrodes, the performance being associated with quality of responsive signals or data obtained from the respective electrode; and preventing the responsive signals or data obtained via any one or more of the plurality of electrodes determined to have insufficient performance from being included in the processed EIT data set [quality of the measurements of the electrodes are checked for calibration errors or user operator errors and the data outlier data is removed, see in ¶ 0046, ¶ 0047, ¶ 0050, and ¶ 0052].
Regarding claim 16, Kao, as modified, discloses the computer-implemented method of claim 15.
Kao fails to disclose wherein the processing of the EIT data set in (ii) comprises: determining, for each respective one or more of the plurality of processed EIT data subsets, respective difference between the respective processed EIT data subset and a reference EIT data subset, so as to obtain the plurality of frequency difference EIT data subsets each associated with a respective one of a difference between the respective processed EIT data subset and a reference EIT data subset.
However, Haick discloses wherein the processing of the EIT data set in (ii) comprises: determining, for each respective one or more of the plurality of processed EIT data subsets, respective difference between the respective processed EIT data subset and a reference EIT data subset, so as to obtain the plurality of frequency difference EIT data subsets each associated with a respective one of a difference between the respective processed EIT data subset and a reference EIT data subset [signal data or a plurality of data signals, including conductivity parameters, are analyzed and compared to control sample for diagnosis kidney disease, see in ¶ 0052 and ¶ 0058-¶ 0059; Examiner notes that frequency difference data subsets were disclosed in Haick modified by Kao in the claim rejection for claim 12].
Kao and Haick are both analogous to the claimed invention because they are in the same field of non-invasive evaluation of organ/tissue health. Therefore, it would have been obvious to someone of ordinary skill in the art before the filing date of the claimed invention to have modified Kao to incorporate the teachings of Haick and include that the processing of the EIT data set in (ii) comprises: determining, for each respective one or more of the plurality of processed EIT data subsets, respective difference between the respective processed EIT data subset and a reference EIT data subset, so as to obtain the plurality of frequency difference EIT data subsets each associated with a respective one of a difference between the respective processed EIT data subset and a reference EIT data subset, as comparing between tested values and reference values are standard in diagnostic systems.
Regarding claim 17, Kao, as modified, discloses the computer-implemented method of claim 16.
Kao fails to disclose wherein the reference EIT data subset comprises at least one of the plurality of processed EIT data subsets.
However, Haick discloses wherein the reference EIT data subset comprises at least one of the plurality of processed EIT data subsets [a plurality of signals as the control samples, see in ¶ 0058 and ¶ 0062].
Kao and Haick are both analogous to the claimed invention because they are in the same field of non-invasive evaluation of organ/tissue health. Therefore, it would have been obvious to someone of ordinary skill in the art before the filing date of the claimed invention to have modified Kao to incorporate the teachings of Haick and include that the reference EIT data subset comprises at least one of the plurality of processed EIT data subsets in order to ensure that the reference values are normalized and appropriately processed for comparison and further analysis.
Regarding claim 18, Kao, as modified, discloses the computer-implemented method of claim 17.
Kao fails to disclose wherein the performing of the group source separation operation comprises: performing a dimensionality reduction operation on the frequency difference EIT data set and one or more reference frequency difference EIT data sets of corresponding one or more reference subjects.
However, Haick discloses wherein the performing of the group source separation operation comprises: performing a dimensionality reduction operation on the frequency difference EIT data set and one or more reference frequency difference EIT data sets of corresponding one or more reference subjects [various techniques for the data analysis including linear discriminant analysis, principal component analysis, etc., (i.e., dimensionality reduction operation), see in ¶ 0057 and ¶ 0084].
Kao and Haick are both analogous to the claimed invention because they are in the same field of non-invasive evaluation of organ/tissue health. Therefore, it would have been obvious to someone of ordinary skill in the art before the filing date of the claimed invention to have modified Kao to incorporate the teachings of Haick and include that the performing of the group source separation operation comprises: performing a dimensionality reduction operation on the frequency difference EIT data set and one or more reference frequency difference EIT data sets of corresponding one or more reference subjects in order to simplify the dataset and improve model performance.
Regarding claim 19, Kao, as modified, discloses the computer-implemented method of claim 18.
Kao fails to disclose wherein the performing of the conductivity characteristics extraction operation comprises: determining, using the component of the frequency difference EIT data set related to the tissue or organ, the one or more conductivity characteristics of the subject.
However, Haick discloses wherein the performing of the conductivity characteristics extraction operation comprises: determining, using the component of the frequency difference EIT data set related to the tissue or organ, the one or more conductivity characteristics of the subject [various techniques for the data analysis including linear discriminant analysis, principal component analysis, etc., (i.e., dimensionality reduction operation), see in ¶ 0057 and ¶ 0084].
Kao and Haick are both analogous to the claimed invention because they are in the same field of non-invasive evaluation of organ/tissue health. Therefore, it would have been obvious to someone of ordinary skill in the art before the filing date of the claimed invention to have modified Kao to incorporate the teachings of Haick and include that the performing of the conductivity characteristics extraction operation comprises: determining, using the component of the frequency difference EIT data set related to the tissue or organ, the one or more conductivity characteristics of the subject in order to specifically extract relevant EIT data features for the model to work analyze.
Regarding claim 20, Kao, as modified, discloses the computer-implemented method of claim 19, wherein the performing of the conductivity characteristics extraction operation comprises: determining, using the component of the frequency difference EIT data set related to the tissue or organ and respective component of each of the one or more reference frequency difference EIT data sets related to the tissue or organ, one or more conductivity characteristics of a group containing the subject and the one or more reference subjects; and wherein the determining of the health state or condition of the tissue or organ of the subject is further based on the one or more conductivity characteristics of the group [EIT may be applied at different frequencies to generate multiple datasets at multiple different frequencies for analysis, see in ¶ 0022, ¶ 0039, and ¶ 0048].
Regarding claim 21, Kao, as modified, discloses the computer-implemented method of claim 1.
Kao fails to disclose wherein the tissue or organ comprises a lung, a kidney, a liver, or a heart.
However, Haick discloses wherein the tissue or organ comprises a lung, a kidney, a liver, or a heart [the present invention provides a method of diagnosing, staging or monitoring chronic kidney disease in a subject, see in ¶ 0024].
Kao and Haick are both analogous to the claimed invention because they are in the same field of non-invasive evaluation of organ/tissue health. Therefore, it would have been obvious to someone of ordinary skill in the art before the filing date of the claimed invention to have modified Kao to incorporate the teachings of Haick and include that the tissue or organ comprises a lung, a kidney, a liver, or a heart as example organs to target for determining tissue/organ health state.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-12 and 14-21 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2, 4-11 and 13-20 of co-pending Application No. 18/832,299 (hereinafter ‘299). Although the claims at issue are not identical, they are not patentably distinct from each other because the claimed inventions are functionally identical.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
18/726,191 (Present Application)
‘299 (Co-Pending Application)
1. A computer-implemented method, comprising: processing a EIT data set of a subject to determine one or more conductivity characteristics associated with a tissue or organ of the subject; and determining, based on at least the one or more determined conductivity characteristics, a health state or condition of the tissue or organ of the subject.
1. A computer-implemented method, comprising: processing an electrical impedance tomography (EIT) data set obtained from electrodes attached to an abdominal region of a subject to determine a kidney-related component of the subject; extracting conductivity characteristics of one or more kidney-related conductivity characteristics of the subject using the kidney-related component; and determining, based on at least the one or more determined kidney-related conductivity characteristics, a health state or condition of the at least one kidney of the subject.
2. The computer-implemented method of claim 1, wherein the determining comprises: determining, based on at least the one or more determined conductivity characteristics, whether the subject has a disease associated with the tissue or organ, and optionally: further classifying a stage or a severity of the disease associated with the tissue or organ.
2. The computer-implemented method of claim 1, wherein the determining comprises: determining, based on at least the one or more determined kidney-related conductivity characteristics, whether the subject has a kidney disease, and optionally: further classifying a stage or a severity of the kidney disease.
3. The computer-implemented method of claim 1, wherein the determining comprises: processing, at least, the one or more determined conductivity characteristics of the subject and one or more anthropometric characteristics of the subject, using a machine learning based processing model, to determine a quantitative or qualitative parameter associated with the health state or condition of the tissue or organ of the subject.
4. The computer-implemented method of claim 1, wherein the determining comprises: processing, at least, the one or more determined kidney-related conductivity characteristics of the subject and one or more anthropometric characteristics of the subject, using a machine learning based processing model, to determine a quantitative or qualitative parameter associated with the health state or condition of the at least one kidney of the subject.
4. The computer-implemented method of claim 1, wherein the determining comprises: processing, using a machine learning based processing model,(i) the one or more determined conductivity characteristics of the subject,(ii) one or more anthropometric characteristics of the subject, and(iii) one or more determined conductivity characteristics of one or more reference subjects and/or of a group containing the subject and the one or more reference subjects, to determine a quantitative or qualitative parameter associated with the health state or condition of the tissue or organ of the subject.
5. The computer-implemented method of claim 1, wherein the determining comprises: processing, using a machine learning based processing model, (i) the one or more determined kidney-related conductivity characteristics of the subject, (ii) one or more anthropometric characteristics of the subject, and (iii) one or more determined kidney-related conductivity characteristics of one or more reference subjects and/or one or more determined kidney-related conductivity characteristics of a group containing the subject and the one or more reference subjects, to determine a quantitative or qualitative parameter associated with the health state or condition of the at least one kidney of the subject.
5. The computer-implemented method of claim 4, wherein the machine learning based processing model comprises a regression model.
6. The computer-implemented method of claim 5, wherein the machine learning based processing model comprises a regression model.
6.) The computer-implemented method of claim 4, wherein the machine learning based processing model comprises a classification model.
7. The computer-implemented method of claim 6, wherein the regression model comprises a linear regression model, such as a Lasso model.
7. The computer-implemented method claim 6, wherein the one or more anthropometric characteristics comprise, or are related to, one or more of: age of the subject, weight of the subject, height of the subject, waist circumference of the subject, waist-over-height ratio of the subject, body mass index (BMI) of the subject, gender of the subject, and race of the subject.
8. The computer-implemented method of claim 7, wherein the one or more anthropometric characteristics comprise, or are related to, one or more of: age of the subject, weight of the subject, height of the subject, and waist circumference of the subject.
8. The computer-implemented method of claim 7, wherein the quantitative or qualitative parameter associated with the health state or condition of the tissue or organ of the subject comprises: a value associated with an estimated performance of the tissue or organ of the subject.
9. The computer-implemented method of claim 8, wherein the quantitative or qualitative parameter associated with the health state or condition of the at least one kidney of the subject comprises: a value associated with an estimated GFR of the subject, e.g., an estimated GFR score of the subject.
9. The computer-implemented method of claim 8, wherein the determining further comprises: comparing the quantitative or qualitative parameter associated with the health state or condition of the tissue or organ of the subject with reference parameter data to determine whether the subject has a disease associated with the tissue or organ.
10. The computer-implemented method of claim 9, wherein the determining further comprises: comparing the quantitative or qualitative parameter associated with the health state or condition of the at least one kidney of the subject with reference parameter data to determine whether the subject has a kidney disease.
10. The computer-implemented method of claim 9, wherein the determining further comprises: classifying, based on the comparing, a stage or a severity of the disease associated with the tissue or organ.
11. The computer-implemented method of claim 10, wherein the determining further comprises: classifying, based on the comparing, a stage or a severity of the kidney disease.
11. The computer-implemented method of any claim10,wherein the EIT data set contains EIT data obtain from a region of the subject containing the tissue or organ; wherein the EIT data set is obtained by (a) providing excitation signals at a frequency to the subject via electrodes attached to the region of the subject,(b) measuring responsive signals received via the electrodes as a result of the providing of the excitation signals, and (c) repeating steps (a) and (b) for a plurality of frequencies; and wherein the EIT data set comprises a plurality of EIT data subsets each associated with a respective one of the plurality of frequencies.
13. The computer-implemented method of claim 1, wherein the EIT data set contains EIT data obtain from an abdominal region of the subject; wherein the EIT data set is obtained by(a) providing excitation signals at a frequency to the subject via electrodes attached to the abdominal region of the subject, (b) measuring responsive signals received via the electrodes as a result of the providing of the excitation signals, and (c) repeating steps (a) and (b) for a plurality of frequencies; and wherein the EIT data set comprises a plurality of EIT data subsets each associated with a respective one of the plurality of frequencies.
12. The computer-implemented method of claim 11, wherein the processing comprises:(i) processing the EIT data set to obtain a frequency difference EIT data set, the frequency difference EIT data set includes a plurality of frequency difference EIT data subsets;(ii) performing a group source separation operation using the frequency difference EIT data set and one or more reference frequency difference EIT data sets of corresponding one or more reference subjects to determine component of the frequency difference EIT data set related to the tissue or organ and component of each of the one or more reference frequency difference EIT data sets related to the tissue or organ; and(iii) performing a conductivity characteristics extraction operation using the component of the frequency difference EIT data set related to the tissue or organ and optionally the component of each of the one or more reference frequency difference EIT data sets related to the tissue or organ to determine at least the one or more conductivity characteristics of the subject.
14. The computer-implemented method of claim 13, wherein the processing comprises: processing the EIT data set to obtain a processed EIT data set with a plurality of processed EIT data subsets; processing the processed EIT data set to obtain a frequency difference EIT data set, the frequency difference EIT data set includes a plurality of frequency difference EIT data subsets; performing a group source separation operation using the frequency difference EIT data set and one or more reference frequency difference EIT data sets of corresponding one or more reference subjects to determine kidney-related component of the frequency difference EIT data set and kidney- related component of each of the one or more reference frequency difference EIT data sets; and performing a conductivity characteristics extraction operation using the kidney-related component of the frequency difference EIT data set and optionally the kidney-related component of each of the one or more reference frequency difference EIT data sets to determine at least the one or more kidney-related conductivity characteristics of the subject.
14. The computer-implemented method of claim 13, wherein the pre- processing of the EIT data set comprises: filtering and/or smoothing each of the plurality of EIT data subsets.
15. The computer-implemented method of claim 14, wherein the processing of the EIT data set comprises: filtering and/or smoothing each of the plurality of EIT data subsets.
15. The computer-implemented method of claim 14, wherein the pre-processing of the EIT data set comprises: processing the EIT data set using a classifier model to determine respective performance of each of the plurality of electrodes, the performance being associated with quality of responsive signals or data obtained from the respective electrode; and preventing the responsive signals or data obtained via any one or more of the plurality of electrodes determined to have insufficient performance from being included in the processed EIT data set.
16. The computer-implemented method of claim 15, wherein the processing of the EIT data set comprises: processing the EIT data set using a classifier model to determine respective performance of each of the plurality of electrodes, the performance being associated with quality of responsive signals or data obtained from the respective electrode; and preventing the responsive signals or data obtained via any one or more of the plurality of electrodes determined to have insufficient performance from being included in the processed EIT data set.
16. The computer-implemented method of claim15, wherein the processing of the EIT data set in (ii) comprises: determining, for each respective one or more of the plurality of processed EIT data subsets, respective difference between the respective processed EIT data subset and a reference EIT data subset, so as to obtain the plurality of frequency difference EIT data subsets each associated with a respective one of a difference between the respective processed EIT data subset and a reference EIT data subset.
17. The computer-implemented method of claim 16, wherein the processing of the processed EIT data set comprises: determining, for each respective one or more of the plurality of processed EIT data subsets, respective difference between the respective processed EIT data subset and a reference EIT data subset, so as to obtain the plurality of frequency difference EIT data subsets each associated with a respective one of a difference between the respective processed EIT data subset and a reference EIT data subset.
17. The computer-implemented method of claim 16, wherein the reference EIT data subset comprises at least one of the plurality of processed EIT data subsets.
18. The computer-implemented method of claim 17, wherein the reference EIT data subset comprises at least one of the plurality of processed EIT data subsets.
18. The computer-implemented method of claim 17, wherein the performing of the group source separation operation comprises: performing a dimensionality reduction operation on the frequency difference EIT data set and one or more reference frequency difference EIT data sets of corresponding one or more reference subjects.
20. The computer-implemented method of claim 19, wherein the performing of the group source separation operation comprises: performing a dimensionality reduction operation on the frequency difference EIT data set and one or more reference frequency difference EIT data sets of corresponding one or more reference subjects to determine kidney-related component of the frequency difference EIT data set and respective kidney-related component of each of the one or more reference frequency difference EIT data sets.
19. The computer-implemented method of claim18, wherein the performing of the conductivity characteristics extraction operation comprises: determining, using the component of the frequency difference EIT data set related to the tissue or organ, the one or more conductivity characteristics of the subject.
20. The computer-implemented method of claim 19, wherein the performing of the group source separation operation comprises: performing a dimensionality reduction operation on the frequency difference EIT data set and one or more reference frequency difference EIT data sets of corresponding one or more reference subjects to determine kidney-related component of the frequency difference EIT data set and respective kidney-related component of each of the one or more reference frequency difference EIT data sets.
20. The computer-implemented method of claim19, wherein the performing of the conductivity characteristics extraction operation comprises: determining, using the component of the frequency difference EIT data set related to the tissue or organ and respective component of each of the one or more reference frequency difference EIT data sets related to the tissue or organ, one or more conductivity characteristics of a group containing the subject and the one or more reference subjects; and wherein the determining of the health state or condition of the tissue or organ of the subject is further based on the one or more conductivity characteristics of the group.
19. The computer-implemented method of claim 13, wherein the processing comprises: processing the EIT data set to obtain a frequency difference EIT data set, the frequency difference EIT data set includes a plurality of frequency difference EIT data subsets; performing a group source separation operation using the frequency difference EIT data set and one or more reference frequency difference EIT data sets of corresponding one or more reference subjects to determine kidney-related component of the frequency difference EIT data set and kidney- related component of each of the one or more reference frequency difference EIT data sets; and performing a conductivity characteristics extraction operation using the kidney-related component of the frequency difference EIT data set and optionally the kidney-related component of each of the one or more reference frequency difference EIT data sets to determine at least the one or more kidney-related conductivity characteristics of the subject.
21. The computer-implemented method of claim 1, wherein the tissue or organ comprises a lung, a kidney, a liver, or a heart.
1. A computer-implemented method, comprising: processing an electrical impedance tomography (EIT) data set obtained from electrodes attached to an abdominal region of a subject to determine a kidney-related component of the subject; extracting conductivity characteristics of one or more kidney-related conductivity characteristics of the subject using the kidney-related component; and determining, based on at least the one or more determined kidney-related conductivity characteristics, a health state or condition of the at least one kidney of the subject.
Claim 13 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 14 of co-pending Application No. ‘299. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are made obvious by the cited co-pending Application.
Regarding claim 13, ‘299 discloses the computer-implemented method of claim 12 but does not explicitly disclose that the processing further comprises: pre-processing the EIT data set before the processing in (i) so that the EIT data set processed in (i) is a pre-processed EIT data set.
However, Examiner notes that pre-processing data is a standard and necessary step in data processing. As such, it would be obvious to one of ordinary skill in the art to recognize that pre-processing steps are included in all data processing systems
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HY KHANH DOAN whose telephone number is (703)756-5434. The examiner can normally be reached Monday - Friday 8:00 a.m. - 5 p.m..
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, Robert Chen can be reached at (571) 272-3672. 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.
/HY KHANH DOAN/Examiner, Art Unit 3791 /TSE CHEN/Supervisory Patent Examiner, Art Unit 3791
Prosecution Insights