DETAILED ACTION
This action is made in response to the amendments/remarks filed on January 14, 2026. This action is made final.
Claims 1-20 are pending. Claim 1 has been amended. Claim 1 is the sole independent claim
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s argument with respect to the prior art rejection has been fully considered but is moot in light of the new grounds of rejection.
Applicant’s arguments with respect to the 101 rejection has been fully considered but is not persuasive.
As a first matter, Applicant argues the claims are not directed toward “managing personal behavior”. However, the examiner respectfully disagrees.
MPEP 2106. 04(a)(2)(II) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The Examiner submits that the identified claim elements represent a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow to diagnosis a kidney condition based on EIT data. Analyzing data to diagnosis a patient is a human activity and furthermore healthcare itself is inherently represents the organization of human activity. Applicant has not pointed to anything in the claims that fall outside of this characterization. Because the claim elements fall under a series of rules or instructions that a person or persons would follow to diagnose a kidney state, the claimed invention is directed to an abstract idea.
Applicant further argues the “processing an electrical impedance tomography (EIT) data set” is an additional element that constitute meaningful limitations and the claimed invention provides a specific improvement to the functioning of an EIT computer system. However, the examiner respectfully disagrees.
MPEP 2106.04(d)(1) states that a practical application may be present where the claimed invention improves another technology. See also MPEP 2106.05(a)(II). Applicant’s claim is confined to a general-purpose computer (see Specification Fig. 10, page 21-22) and does not recite “another technology.” Because no other technology is recited in the claim, the claim cannot improve another technology (see, e.g., MPEP 2106.05(I)(A)(i) describing an example of an improvement to another technology where the abstract idea implemented on a computer improved the claimed additional element of a rubber molding machine). As such, these additional elements are not improved through implementation of the abstract idea and a practical application is not present.
Insomuch as Applicant is alleging EIT data and/or systems is a “another technology”, the examiner respectfully disagrees. As previously stated, Applicant’s claims are confined to a general-purpose computer and does not recite “another technology”. The claimed invention is using a computer as a tool and any purported improvement, at best, is an improvement to the abstract idea of kidney disease diagnosis. As there is no improvement to a computer, technology, or technical field, there is no practical application.
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 1-11 and 13-20 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-12 and 14-21 of copending Application No. 18/726,191(hereinafter ‘191). Although the claims at issue are not identical, they are not patentably distinct from each other.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Present Application
Co-Pending Application (‘191)
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
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.
21. The computer-implemented method of claim 1, wherein the tissue or organ comprises a lung, a kidney, a liver, or a heart
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.
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.
3. The computer-implemented method of claim 2, wherein the determining comprises: determining, based on at least the one or more determined kidney-related conductivity characteristics, a value associated with an estimated glomerular filtration rate of the subject, e.g., the estimated glomerular filtration rate of the subject.
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.
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.
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.
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.
6. The computer-implemented method of claim 5, wherein the machine learning based processing model comprises a regression model.
5. The computer-implemented method of claim 4, wherein the machine learning based processing model comprises a regression model.
7. The computer-implemented method of claim 6, wherein the regression model comprises a linear regression model, such as a Lasso model.
6.) The computer-implemented method of claim 4, wherein the machine learning based processing model comprises a classification model.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
As can be seen from the table above, claims 1-13 and 14-21 of the ‘191 application contains all the limitations of claim 1-12 and 14-20 of the present application but for the tissue or organ being “kidney”. Notably, dependent claim 21 of the ‘191 states the tissue or organ can be a kidney. Furthermore, Groenendaal teaches the health state of a kidney (e.g., see “summary and prospects” of Groenendaal). Accordingly, it would have been obvious to use the EIT data set for determining kidney-related conditions for diagnosing early stated of kidney disease in a non-invasive manner (e.g., see Abstract of Groenendaal).
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 1-20 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.
Claims 1-20 recite a method of diagnosing a disease, which is within the statutory category of a process.
Claims are eligible for patent protection under § 101 if they are in one of the four statutory categories and not directed to a judicial exception to patentability. Alice Corp. v. CLS Bank Int'l, 573 U.S. ___ (2014). Claims 1-20, each considered as a whole and as an ordered combination, are 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:
The bolded limitations of:
Claim 1
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.
as presently drafted, under the broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions). For example, the claim encompasses a person following rules or instructions to process and analyze data in the manner described in the abstract idea such as analyzing EIT data to determine kidney-related conductivity characteristics and a health state/condition of the patient’s kidney. The examiner further notes that “methods of organizing human activity” includes a person’s interaction with a computer (see October 2019 Update: Subject Matter Eligibility at Pg. 5). If the claim limitation, under its broadest reasonable interpretation, covers managing persona behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally, 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 of a patient’s kidney 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 Fig. 10, page 21-22 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 2-20 merely recites an additional determinations made based on additional received data, the placement of the electrodes, step of repeating the measuring and analysis steps, and/or processing the data across different frequencies and the analysis thereof, which covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions).
Claims 4-7, 16, and 20 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 § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 4-8, 13-15 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Groenendaal et al. (“Wearable Bioimpedance Monitoring: Viewpoint for Application in Chronic Conditions”; hereinafter Haick) and in further view of Tsai et al. (“Association of Fluid Overload with Kidney Disease Progression in Advanced CKD: A Prospective Cohort Study”; hereinafter Tsai).
As to claim 1, Groenendaal teaches A method (e.g., see Abstract), 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 (e.g., Pages 3, 4 teaching the use of electrical impedance tomography wherein electrodes are placed on the surface of the skin to ensure electrical contact with the particular tissue, including the kidney, see also abstract, page 8 wherein the bioimpedance is measured for those with end-stage kidney disease and disease progression);
extracting conductivity characteristics of one or more kidney-related conductivity characteristics of the subject using the kidney-related component (e.g., see page 2-3 discussion of basic principle of bioimpedance is to assess the electrical properties of a tissue); 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 (e.g., see page 8 wherein disease progression monitoring and management, including those with kidney disease are monitored using bioimpedance).
While Groenendaal teaches a method, Groenendaal fails to explicitly teach a computer-implemented method. However, in the same field of endeavor of monitoring patient disease progression, Tsai teaches a computer-implemented method (e.g., see page 70, Statistical analysis teaches a computer for analyzing the data). Accordingly, it would have been obvious to modify Groenendaal in view of Tsai before the effective date of the present application with a reasonable expectation of success. One would have been motivated to make the modification in order to simply complex data tasks.
As to claim 2, the rejection of claim 1 is incorporated. Groenendaal-Tsai further teaches 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 (e.g., see page 8 of Groenendaal and page 69-70 of Tsai wherein based on the measurement, a patient’s kidney disease progression can be assessed).
As to claim 4, the rejection of claim 1 is incorporated. Groenendaal-Tsai further teaches 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 (e.g., table 1 of Tsai teaching collecting various demographic data in addition to the measure bioimpedance. See also “introduction” of Groenendaal and “statistical analysis” of Tsai and teaching the use of artificial intelligence and machine learning models).
As to claim 5, the rejection of claim 1 is incorporated. Haick-Kao further teaches 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 (e.g., table 1 of Tsai teaching collecting various demographic data in addition to the measure bioimpedance data. See also “introduction” of Groenendaal and “statistical analysis” of Tsai and teaching the use of artificial intelligence and machine learning models for determining disease progression. See also “measurement of hydration and body composition” of Tsia wherein the results are compared to a healthy cohort)
As to claim 6, the rejection of claim 5 is incorporated. Tsai further teaches wherein the machine learning based processing model comprises a regression model (e.g., see “statistical analysis” of Tsai wherein the machine learning includes regression models).
As to claim 7, the rejection of claim 6 is incorporated. Tsai further teaches wherein the regression model comprises a linear regression model, such as a Lasso model (e.g., see “statistical analysis” of Tsai wherein the machine learning includes regression models. Notably, the “Lasso model” is merely recited as an exemplary model and not required in the claims. Nonetheless, it is noted that a lasso model is a known regression model and it would have been obvious as a simple substitution. See KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007); and MPEP 2143).
As to claim 8, the rejection of claim 7 is incorporated. Tsai further teaches 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 (e.g., see table 1 of Tsai wherein the collected demographic data includes age, gender, body mass index of the patient).
As to claim 13, the rejection of claim 1 is incorporated. Groenendaal further teaches 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 a plurality of frequencies (e.g., see “introduction”, “basic principle of bioimpedance”, and “EIT Measurements” teaching collecting measurements from EIT placed on the surface of the skin to ensure electrical contact with the desired tissue, including exciting the electrodes to capture a responsive signal and repeating the process to obtain a plurality of datasets at different moments in time and different frequencies).
As to claim 14, the rejection of claim 13 is incorporated. Groendaal teaches
processing the EIT data set to obtain a processed EIT data set with a plurality of processed EITH 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 (e.g., see “introduction”, “basic principle of bioimpedance”, and “EIT Measurements” teaching collecting measurements from EIT to obtain a plurality of datasets at different moments in time and different frequencies to determine electrical properties of the tissue).
As to claim 15, the rejection of claim 14 is incorporated. Groenendaal further teaches wherein the processing of the EIT data set comprises: filtering and/or smoothing each of the plurality of EIT data subsets (e.g., see “challenges and limitations of wearable bioimpedance” teaching data correction such as filtering to remove unwanted measurements).
As to claim 19, the rejection of claim 13 is incorporated. Groenendaal further teaches determine one or more kidney-related components and performing a conductivity characteristics extraction operation using the kidney-related component (e.g., see page 2-3 discussion of basic principle of bioimpedance is to assess the electrical properties of a tissue, including kidney disease).
However, Groenendaal fails to teach 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.
However, Tsai teaches teach 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 (e.g., see “introduction”, “basic principle of bioimpedance”, and “EIT Measurements” teaching collecting measurements from EIT to obtain a plurality of datasets at different moments in time and different frequencies to determine electrical properties of the tissue).
Claim(s) 3 and 9-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Groenendaal and Tsai, as applied above, and in further view of Trapero Martin et al. (USPPN: 2021/0228134; hereinafter Trapero Martin).
As to claim 3, the rejection of claim 2 is incorporated. While Tsai teaches determining a value associated with an estimated glomerular filtration rate of the subject (e.g., see Study Participants of Tsai), Tsai fails to the eGFR based on at least the one or more determined kidney-related conductivity characteristics.
However, in the same field of endeavor of patient monitoring, Trapero Martin teaches wherein the determining comprises: determining, based on at least the one or more determined kidney-related conductivity characteristics, a value associated with an estimated glomerular filtration rate of the subject, e.g., the estimated glomerular filtration rate of the subject (e.g., see [0060] teaching determining estimated glomerular filtration rate based on a bioimpedance data).
Accordingly, it would have been obvious to modify Groenendaal-Tsai in view of Trapero Martin before the effective date of the present application with a reasonable expectation of success. One would have been motivated to make the modification in order to aid in the prevention and early detection of various medical problems (e.g., see [0004] of Trapero Martin).
As to claim 9, the rejection of claim 8 is incorporated. While Tsai teaches determining a value associated with an estimated glomerular filtration rate of the subject (e.g., see Study Participants of Tsai), Tsai fails to the 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.
However, in the same field of endeavor of patient monitoring, Trapero Martin teaches 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 (e.g., see [0060] teaching determining estimated glomerular filtration rate based on a bioimpedance data).
Accordingly, it would have been obvious to modify Groenendaal-Tsai in view of Trapero Martin before the effective date of the present application with a reasonable expectation of success. One would have been motivated to make the modification in order to aid in the prevention and early detection of various medical problems (e.g., see [0004] of Trapero Martin).
As to claim 10, the rejection of claim 9 is incorporated. Groenendaal-Tsai further teaches 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 (e.g., see “summary and prospects” of Groenendaal and ”measurement of hydration and body composition” and “Discussion” of Tsia wherein the data is compared to a control sample to determine whether the patient has kidney disease).
As to claim 11, the rejection of claim 10 is incorporated. Groenendaal-Tsai further teaches wherein the determining further comprises: classifying, based on the comparing, a stage or a severity of the kidney disease (e.g., see “slowly evolving parameters” of Groenendaal and “Discussion” of Tsai wherein disease progression can be determined).
As to claim 12, the rejection of claim 11 is incorporated. Groenendaal-Tsai further teaches wherein the kidney disease is a chronic kidney disease (e.g., see “slowly evolving parameters” of Groenendaal and “Discussion” of Tsai wherein the disease is chronic kidney disease or end stage kidney disease).
Claim(s) 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Groenendaal and Tsai, as applied above, and in further view of Kao et al. (USPPN: 2015/0002168; hereinafter Kao).
As to claim 16, the rejection of claim 15 is incorporated. Groenendaal fails to teach 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.
However, in the same field of endeavor of patient health analysis, Kao teaches 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 (e.g., see [0046], [0047], [0050], [0052] wherein the quality of the measurements of the electrodes are checked for calibration errors or user operator errors and the data outlier data is removed). Accordingly, it would have been obvious to modify Groenendaal-Tsai in view of Kao with a reasonable expectation of success. One would have been motivated to make the modification as a simple substitution of one known type of non-invasive image technique, such as the generic impedance sensor taught in Haick, with the specific electrical impedance tomography taught in Kao to yield the predictable results of providing a non-invasive conductivity property of an object (See KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007); MPEP 2143; and [0002] of Kao).
As to claim 17, the rejection of claim 16 is incorporated. Groenendaal-Tsai-Kao further teaches 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 (e.g., see “introduction”, “basic principle of bioimpedance”, and “EIT Measurements” of Groenendaal teaching collecting measurements from EIT to obtain a plurality of datasets at different moments in time and different frequencies to determine electrical properties of the tissue. See also [0022], [0039], [0048]-[0049] wherein the EIT may be applied at different frequencies to generate multiple datasets at multiple different frequencies for analysis.). Accordingly, it would have been obvious to modify Groenendaal-Tsai in view of Kao with a reasonable expectation of success. One would have been motivated to make the modification as a simple substitution of one known type of non-invasive image technique, such as the generic impedance sensor taught in Groenendaal, with the specific electrical impedance tomography taught in Kao to yield the predictable results of providing a non-invasive conductivity property of an object (See KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007); MPEP 2143; and [0002] of Kao)).
As to claim 18, the rejection of claim 17 is incorporated. Groenendaal-Tsai-Kao further teaches wherein the reference EIT data subset comprises at least one of the plurality of processed EIT data subsets (e.g., see “introduction”, “basic principle of bioimpedance”, and “EIT Measurements” of Groenendaal teaching collecting measurements from EIT to obtain a plurality of datasets at different moments in time and different frequencies to determine electrical properties of the tissue. See also rejection above citing Kao for the processing of EIT data).
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Groenendaal and Tsai, as applied above, and in further view of Haick et al. (USPPN: 2013/0236981; hereinafter Haick).
As to claim 20, the rejection of claim 19 is incorporated. While Groenendaal teaches reduction techniques for data and frequency difference EIT data sets (e.g., see “challenges and limitations of wearable bioimpedance” and rejection above), Groenendaal-Tsai fail to explicitly teach performing a dimensionality reduction operation.
However, in the same field of endeavor of patient monitoring, Groenendaal-Haick teaches 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 (e.g., see [0057], [0084] of Haick teaching various techniques for the data analysis including linear discriminant analysis, principal component analysis, etc., (i.e., dimensionality reduction operation). See rejection above of Groenendaal teaching frequency difference EIT data sets).
Accordingly, it would have been obvious to modify Groenendaal-Tsai in view of Haick before the effective date of the application with a reasonable expectation of success. One would have been motivated to make the modification in order to transform data from a high-dimensional space into a low dimensional space while preserving meaningful properties.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/STELLA HIGGS/Primary Examiner, Art Unit 3681