DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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 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.
Special Status
The present application has been accorded “special” status as participating in the Track One program.
Priority
Based on the support in the specification of current application and the priority applications, the priority date of present claims of this application is 09/01/2023.
Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 7/24/26 has been entered.
Response to Amendment / Arguments
The response and amendments, filed 12/22/25, has been entered. Claims 1,4-9,11-14,16-22 are pending upon entry of this Amendment. Applicant’s arguments regarding the prior art rejections of claims have been fully considered but are not persuasive, argument is based on the amendment , however, the amendment doesn’t change the eligibility much except further limit the judicial exception. Also, the rejection is modified to present how amended claim is still obvious over prior art of record as cited in this action.
Claim Objections
Claim 18, comprising in first line should be corrected to “comprising:”
Appropriate action is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1,4-9,11-14,16-20, 22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 cites "generating ... a viscosity profile for the subject comprising at least information of a disease state of the subject. However, the specification does not support creating a viscosity profile comprising information of a disease state of the subject.
The disclosure supports two separate things:
1. Creates a viscosity profile (from measurements)
2. Uses ML techniques and training models to correlate viscosity data → physiological/disease state
But it does not combine them into a single output structure like: “viscosity profile comprising information of a disease state of the subject.”. the disclosure DOES generate a viscosity profile
From the system description: “...the digital data is in turn processed... which can generate one or more viscosity profiles...” viscosity profile = output of measurement/processing stage, also ML uses viscosity data to infer disease/physiological state: “...determining, by a trained algorithm, a correlation between the first data and a first physiological state...” Also: “...the correlation being indicative of a current or future physiological state of the subject.” Therefore, Input → WBV (viscosity data), Output → physiological state (disease-related), training explicitly separates viscosity and disease state, this is the most important paragraph:
“...datasets include: (i) WBV data … and (ii) a dataset including one or more physiological states of the subject...”, Interpretation is viscosity data = input feature, physiological/disease state = label / target. Therefore, ““viscosity profile comprising information of a disease state of the subject.” Is NOT supported, but the specification DOES support generating viscosity profile(s) AND using those (via ML) to predict / correlate physiological or disease.
Claims 9 , 14, 18, and 22 have similar limitation and are rejected for the same reason.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1,4-9,11-14,16-20, 22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites "generating ... a viscosity profile for the subject comprising at least information of a disease state of the subject."
A viscosity profile normally means: viscosity versus shear rate, viscosity versus time, rheological parameters. But a disease state is not normally part of a viscosity profile. It is more like metadata associated with the patient or subject. Therefore, it is not clear what it means. This could be interpreted as: the profile contains WBV/shear information and an annotation about disease state, or the profile itself encodes disease status. A prior art reference that creates a patient viscosity profile and associates it with disease classification could be relevant and teaching the limitation. Other independent claims 9,1 14, 18, 22 have similar limitation and is rejected and interpreted the same.
Remaining claims are rejected because of their dependency to independent claims.
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 1,4-9,11-14,16-22 are rejected under 35 U.S.C. 101 the claimed inventions are directed to an abstract idea without significantly more.
2019 Revised Patent Eligibility Guidance (PEG): Step 1:
Claims 18-20 are directed to a directed to a system (i.e., a machine) and claims 1,4-9,11-14,16-17, 21-22 are directed to a method (i.e., a process). Accordingly, claims 1,4-9,11-14,16-22 are all within at least one of the four statutory categories.
2019 PEG: Step 2A - Prong One:
Regarding Prong One of Step 2A of the 2019 PEG, the claim limitations are to be analyzed to determine whether they recite subject matter that falls within one of the following groupings of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Representative independent claims 1, 9, 14, 18, 21 includes limitations that recite an abstract idea. The Examiner submits that the following underlined limitations constitute and directed toward at least one abstract idea, and the limitations given in bold are additional elements.
More specifically, independent claim 1 recites:
A method, comprising:
generating, by a system comprising at least a viscometer, n records from a subject, n being a positive integer, and
an n-th record comprising at least an n-th whole blood viscosity (WBV) that is collected at an n-th time and at a shear rate of from about 1/s to about 1000/s;
determining, by a trained algorithm that receives the n records, an indication of an inflammatory response of the subject based at least in part on the WBV and shear rate from one or more of the n records, wherein an n-th record further comprises a clinical information of the subject, the clinical information comprising any one or more of age, gender, medical history, surgical history, social history, a pending medical condition, or a medication; and
generating based on the indication of the inflammatory response, the WBV, and shear rate from the n record, a viscosity profile for the subject comprising at least information of a disease state of the subject [ the examiner finds all foregoing underlined limitations are mental process, certain methods of organizing human activity or math steps of generating a profile].
Claim 9:
A method, comprising:
receiving, at a trained algorithm, first data including a first whole blood viscosity (WBV) of a subject collected, by a system comprising at least a viscometer, at a first time and at a shear rate of from about 1/s to about 1000/s, the first data further comprising a clinical information of the subject, the clinical information representing any one or more of age, gender, medical history, surgical history, social history, a pending medical condition, or a medication; and determining, by the trained algorithm, a correlation between the first data and a first physiological state, the correlation being indicative of an inflammatory response of the subject, or (2) receiving, at the trained algorithm, second data including a first WBV of a subject collected at a second time and at a shear rate of from about 1/to about 1000/s, and determining, by the trained algorithm, a correlation between the first data and the second data, the correlation being indicative of a current or future inflammatory response of the subject; and
generating a viscosity profile for the subject comprising information of s disease state of the subject [the examiner finds all foregoing underlined limitations are abstract for the same reason as cited above].
Claim 14:
A method, comprising:
receiving, at a trained algorithm, at least (i) a first data including to a first whole blood viscosity (WBV) of a subject collected at a first time, by a system comprising at least a viscometer, and at a shear rate of from about 1/s to about 1000/s, the first data further including a characteristic of the subject, the characteristic comprising any one or more of age, gender, medical history,
surgical history, social history, a pending medical condition, or a medication, and (ii) a second data including a second WBV of a subject collected at a second time and at a shear rate of from about 1/s to about 1000/s;
determining, by the trained algorithm, a correlation between the first data and the second data, wherein the correlation is indicative of a current or future inflammatory response of the subject; and
generating a viscosity profile for the subject comprising information of a disease state of the subject [ the examiner finds all foregoing underlined limitations are abstract for the same reason as cited above].
Claim 18:
A system, comprising a viscometer,
the viscometer configured to receive a whole blood sample from a subject and determine a whole blood viscosity (WBV) of the sample at one or more shear rates of from 1/s to 1000/s; a processing stage,(1) the processing stage configured to implement a trained algorithm that receives at least a first data from the subject, the first data including a WBV collected at a first time and at a shear rate of from about 1/s to about 1000/s ,the first data further including a characteristic of the subject, the characteristic being other than WBV, and the characteristic comprising
any one or more of age, gender, medical history, surgical history, social history, a pending medical condition, or a medication, the trained algorithm configured to determine a correlation between the first data and a first physiological state, the correlation being indicative of a current or future inflammatory response of the subject, or (2) the processing stage configured to implement a trained algorithm that receives (i) a first data from a subject, the first data including a WBV collected at a first time and at a shear rate of from about 1/s to about 1000/s and (ii) a second data from the subject, the second data including a WBV collected at a second time and at a shear rate of from about 1/s to about 1000/s, the trained algorithm configured to determine a correlation between the first data and the second data, wherein the correlation is indicative of a disease state of the subject [ the examiner finds all foregoing underlined limitations are all underlined limitations are mental process, certain methods of organizing human activity or math steps of determining a correlation].
Claim 21:
A method of training an algorithm for detecting a current or future physiological state of a subject, the method comprising:
inputting a plurality of datasets for each of a plurality of subjects into a model function, wherein the plurality of datasets include:(i) a first dataset including a first whole blood viscosity (WBV) of a subject collected at a first time and at a shear rate of from about 1/s to about 1000/s, ,wherein the first dataset further includes a characteristic of the subject, the characteristic comprising any one or more of age, gender, medical history, surgical history, social history, a pending medical condition, or a medication;(ii) a second dataset including a second WBV of a subject collected at a second time and at a shear rate of from about 1/s to about 1000/s; and (iii) a third dataset including at least one or more physiological states indicative of a disease state of the subject; and calculating weighted values for each data type of the first dataset and the second dataset via the model function; and generating a trained model by assigning the weighted values to the data type of the first dataset and the second dataset [ the examiner finds all foregoing underlined limitations are all underlined limitations are mental process, certain methods of organizing human activity or math steps of calculating weighted values].
Claim 22:
A method, comprising:
generating, by a system comprising at least a viscometer, n records from a subject, n being a positive integer, and an n-th record comprising at least an n-th whole blood viscosity (WBV) that is collected at an n-th time and at a shear rate of from about 1/s to about 1000/s, the n-th record comprising an average between a viscosity curve collected at a positive pressure and a viscosity curve collected at a negative pressure;
determining, by a trained algorithm that receives the n records, at least one of (1) a relation between at least two of the n records and (2) an indication of an inflammatory response of the subject based at least in part on the WBV and shear rate from one or more of the n records; and generating a viscosity profile for the subject comprising information of a disease state of the subject [the examiner finds all foregoing underlined limitations are abstract for the same reason as cited above in claim 1, also math step of averaging which is Abstract].
Furthermore, the dependent claims include limitations that merely further define the abstract idea (and thus fail to make the abstract idea any less abstract) or fail to integrate the abstract idea into a practical application as set forth below.
2019 PEG: Step 2A - Prong Two:
Regarding Prong Two of Step 2A of the 2019 PEG, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the “abstract idea”), physical measurements and physical component such as viscometer are additional that do not integrate the above-noted abstract idea into a practical application, because it is not that these components (viscometer) have a practical application, the analysis is , and a known measurement (viscosity) does not provide an practical application to the abstract and mental steps. If the search for the inventive concept is the measurement and claimed structure, then it is a practical application of the abstract and mental steps, but here it is not the case, as the system has a known structure.
generating, by a system comprising at least a viscometer, n records from a subject, an n-th record comprising at least an n-th whole blood viscosity (WBV) that is collected at an n-th time and at a shear rate of from about 1/s to about 1000/s, by a system comprising at least a viscometer
The examiner finds that each of the following additional elements merely recites the words “apply it” (or an equivalent) with the abstract idea, or merely includes mere data gathering:
by a trained algorithm that receives the n records
Using a trained algorithm to determine the relation is mere instructions to apply an exception using AI – see MPEP 2106.05(f). The claims recite using the idea of a solution but don’t recite how this is accomplished. It is broad and appears to use an algorithm to perform an existing process.
Step 2B: Does the Claim Recite Additional Elements That Amount to Significantly More Than the Abstract Idea?
The examiner finds that the additional elements do not amount to significantly more than the abstract idea for the same reasons discussed above with respect to the conclusion that the additional elements do not integrate the abstract idea into a practical application. Limitations are well-understood, routine, conventional activity (“WURC”).
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 1, 4-9, 11-14, 16-22 are rejected under 35 U.S.C. 103 as being unpatentable over “ “Sean”, (Farrington, Sean, et al. "Physiology-based parameterization of human blood steady shear rheology via machine learning: a hemostatistics contribution." Rheologica Acta 62.10 (2023): 491-506., LEE, KR 20160135121 A, and ATAROT, WO 2023058013 A1.
Claim 1
Sean in figs.1-10 teaches:
A method, comprising:
generating, n records (figs.2-3 and 9 different data of shear viscosity in different shear rate) from a subject (e.g., Donor I fig.2, or any of Donors A to U in fig.9),
n being a positive integer (for each donor there are n data in different shear rates and number of data is a positive integer),
and an n-th record comprising at least an n-th whole blood viscosity (WBV) that is collected at a shear rate of from about 1/s to about 1000/s (page 493 col.1: using the Casson equation to fit whole blood data, each data on fig.2 is correlated to WBV on figs.1 and 9 that meet the limitation for each donor);
determining, by a trained algorithm (e.g., page 497 col.1, table 3) that receives the n records (for each donor), patients physiological parameters based at least in part on the WBV (viscosity e.g., fig.4,6) and shear rate (e.g., shown on fig.4) from one or more of the n records (for each donor there are n data in different shear rates); wherein an n-th record further comprises a clinical information of the subject, the clinical information comprising any one or more of age, gender (e.g., page 494, col.2 first para), and
generating, based on, the WBV, and shear rate from the n records, a viscosity profile for the subject (measured WBV values correspond to donor blood samples, with each rheological behavior, training models learn a viscosity profile to predict rheological behavior from selected donors).
Sean does not disclose:
by a system comprising at least a viscometer (although Sean uses Horner data which are obtained from published measurements).
at an n-th time and determining, an indication of an inflammatory response of the subject,
generating, based on the indication of the inflammatory response, a viscosity profile for the subject comprising at least information of a disease state of the subject.
Regarding limitation 1
In the similar field of endeavor, LEE in Figs.1-4e teaches a viscometer (100) .
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use LEE‘s viscometer for Sean‘s method and generating Sean’s n records from Sean’s subject. One of ordinary skill in the art knows the generated data from measurement and the data from literature can be compared and equivalent would have been motivated to make this modification in order to generate the data and update them with experimental data, besides, based on MPEP 2143 (C), courts have ruled that Use of known technique (viscometer of LEE) to improve similar devices (and method of Sean) in the same way is within the purview of a skilled artisan. See KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421,82 USPQ2d 1385, 1395-97 (2007).
Regarding limitations 2:
In the similar field of endeavor, ATAROT teaches a method (using system 100), comprising:
determining, by a trained algorithm (e.g.,¶0019: using ML for patient condition assessment, ¶0020: indicating inflammatory status, ¶0026: analysis is performed by ML, ¶0028:160A/160B) that receives the n records at an n-th time (data continuously is gathered signals from 210 and 220 from patient as shown in figs.1A-C plus clinical lab, patient medical history, hemorheology associated with blood flow, e.g., ¶000429-00431,¶00463-00465,00466-00467), an indication of an inflammatory response of the subject (e.g., ¶0362, 00466-00467) from one or more of the n records (signals of fig.3B from PPG for each patient are response of the blood flow that is correlated to shear rate and viscosity given by fig.7 so that the pressure measured by sensors 220B are related to the same correlation for non-Newtonian viscosity of whole blood: e.g., ¶00440, also records over time e.g., ¶00429-00431,00463-00465,00466-00467), wherein an n-th record further comprises a clinical information of the subject, the clinical information comprising any one or more of age, gender, medical history, (e.g., ¶0135 ¶0281, ¶0436¶0453); generating, based on the indication of the inflammatory response, generating, based on the indication of the inflammatory response, a profile for the subject comprising at least information of a disease state of the subject (e.g., 100A/100B/(¶0019: using ML for patient condition assessment, ¶0020: indicating inflammatory status, ¶0026: analysis is performed by ML, ¶0028).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use ATAROT‘s training algorithms for the modified Sean with LEE‘s method and an n-th record comprising at least an n-th whole blood viscosity (WBV) that is collected at an n-th time, determining, by a trained algorithm that receives the modified Sean’s n records an indication of an inflammatory response of the subject based at least in part on the modified Sean’s WBV and shear rate from one or more of the n records, generating, based on the modified Sean’s indication of the inflammatory response, the WBV, and shear rate from the n records, a viscosity profile for the subject comprising at least information of a disease state of the subject. One of ordinary skill in the art would know training models can extract physiological information from blood rheology (as emphasized by Sean), ATAROT teaches applying ML-derived blood-related signals to inflammatory disease monitoring and prediction, Sean and ATAROT both seek to derive disease-state information from blood-related physiological characteristics using machine learning. Sean blood rheology has diagnosis implications (at least see pages 492,494,504 on Cardiovascular disease and rheology and using training model techniques for diagnosis) and ATAROT teaches ML learning models for diagnosis of one of these disease that is inflammatory response of patients, One of ordinary skill in the art would have been motivated for this modification to provide a clinically diagnostic result therefore improving patient monitoring and early intervention for inflammatory disease (e.g., ¶001 of ATAROT), besides, based on MPEP 2143 (D), courts have ruled that applying a known technique (ML models of ATAROT) to a known product (the modified Sean’s system and training models to relate blood rheology to patient’s diagnosis) to yield predictable results (ATAROT’s diagnosis of inflammatory response) is within the purview of a skilled artisan. See KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421,82 USPQ2d 1385, 1395-97 (2007).
Claim 4
Sean in view of LEE and ATAROT teaches the method of claim 1, Sean further teaches wherein an n-th record and an (n+1)-th record each include respective clinical information (e.g., tables 1 and 2 for Hematocrit % that are correlated to n-th and (n+1)-th records given in fig.2 and made by ML models to generate table 1 and 2).
Claim 5
Sean in view of LEE and ATAROT teaches the method of claim 1, Sean further teaches wherein the clinical information of the n-th record is representative of that clinical information of the subject at the n-th time and wherein the clinical information of the (n+1)th record is representative of that clinical information of the subject at the (n+1)th time (e.g., tables 1 and 2 for Hematocrit % that are correlated to n-th and (n+1)-th records given in fig.2 and made by ML models to generate table 1 and 2).
Claim 6
Sean in view of LEE and ATAROT teaches the method of claim 1, Sean further teaches wherein the relation is indicative of a current or future physiological state of the subject (e.g., figs. 4 and 8).
Claim 7
Sean in view of LEE and ATAROT teaches the method of claim 1, ATAROT further teaches wherein the determining comprises any one or more of (1) comparing a data of an n-th record to a threshold, and (2) comparing a difference between a data of an n-th record and a corresponding data of an (n+1)-th record to a threshold (e.g., ¶0080¶0133). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use ATAROT‘s threshold for the modified Sean‘s inflammatory response. One of ordinary skill in the art would know this as a part of models to check inflammatory status (e.g., ¶0047 of ATAROT) have been motivated to make this modification in order to compare the inflammatory status with a baseline (e.g.,¶0047).
Claim 9
Sean in figs.1-10 teaches:
A method, comprising:
receiving, at a trained algorithm (algorithms cited in e.g., Conclusion for viscosity in page 497 col.1/table 3), first data including a first whole blood viscosity (WBV, viscosity in Casson viscosity: page 493 col.1: using the Casson equation to fit whole blood data, each data on fig.2 is correlated to WBV on figs.1 and 9 that meet the limitation for each donor) of a subject (any of donors in fig.9), at a first time (each data in each shear rate is interpreted as first time) and at a shear rate of from about 1/s to about 1000/s (e.g., any of fig.9 similar to fig.1 for each donor); the first data further comprising a clinical information of the subject, the clinical information representing any one or more of age, gender (e.g., page 494 col.2 first para); (1) determining, by the trained algorithm, a correlation between the first data and a first state (the ML and training models correlate the WBV to state of donors e.g., fig.6), and generating a viscosity profile for the subject (see e.g. , figs. 1-6 and tables).
Sean does not disclose:
collected, by a system comprising at least a viscometer (although Sean uses Horner data which are obtained from published measurements).
a correlation between the first data and a first physiological state, the correlation being indicative of an inflammatory response of the subject and generating a viscosity profile for the subject comprising information of a disease state of the subject.
Regarding limitation 1
In the similar field of endeavor, LEE in Figs.1-4e teaches a viscometer (100) .
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use LEE‘s viscometer for Sean‘s method and first data collected, by the modified Sean’s viscometer. One of ordinary skill in the art knows the generated data from measurement and the data from literature can be compared and equivalent would have been motivated to make this modification in order to generate the data and update them with experimental data, besides, based on MPEP 2143 (C), courts have ruled that Use of known technique (viscometer of LEE) to improve similar devices (and method of Sean) in the same way is within the purview of a skilled artisan. See KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421,82 USPQ2d 1385, 1395-97 (2007).
Regarding limitation 2
In the similar field of endeavor, ATAROT teaches a method (using system 100), comprising:
determining, by the trained algorithm (e.g., ¶00429-00431,00463-00465,00466-00467), a correlation between the first data and a first physiological state (e.g., ¶00277,00278,00402,00419-00420), the correlation being indicative of an inflammatory response of the subject (e.g., ¶0281,0429-00431,00466,00467,00421-00424) and generating a viscosity profile (viscosity info as input e.g.,¶0041,00097,00153,00221,0280¶00460 blood viscosity as listed predicted used inputs ) for the subject comprising information of a disease state of the subject (e.g., ¶00280,00460).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use ATAROT‘s training algorithms for the modified Sean with LEE‘s method and indication of an inflammatory response, determining, by the trained algorithm, a correlation between the first data and a first physiological state, the correlation being indicative of an inflammatory response of the subject and generating a viscosity profile for the subject comprising information of a disease state of the subject. One of ordinary skill in the art would know training models can extract physiological information from blood rheology, ATAROT teaches applying ML-derived blood-related signals to inflammatory disease monitoring and prediction, Sean and ATAROT both seek to derive disease-state information from blood-related physiological characteristics using machine learning . Sean blood rheology has diagnosis implications and ATAROT teaches ML learning models for diagnosis of one of these disease that is inflammatory response of patients, One of ordinary skill in the art would have been motivated for this modification to provide a clinically diagnostic result therefore improving patient monitoring and early intervention for inflammatory disease (e.g., ¶001 of ATAROT), besides, based on MPEP 2143 (D), courts have ruled that applying a known technique (ML models of ATAROT) to a known product (the modified Sean’s system) to yield predictable results (ATAROT’s diagnosis of inflammatory response) is within the purview of a skilled artisan. See KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421,82 USPQ2d 1385, 1395-97 (2007).
Claim 12
Sean in view of LEE and ATAROT teaches the method of claim 9, Sean further teaches wherein the second data further comprises a clinical information of the subject, the clinical information being other than WBV, and the clinical information optionally comprising any one or more of age (page 497 col.1 2nd para and col.2 effect of aging and physiology on viscosity of blood).
Claim 13
Sean in view of LEE and ATAROT teaches the method of claim 9, Sean further teaches comprising training the algorithm (training machine learning algorithm e.g., page 497 col.1).
Claim 14
Sean in figs.1-10 teaches:
A method, comprising: receiving, at a trained algorithm (e.g., page 497 col.1 and table 3), at least (i) a first data including to a first whole blood viscosity (WBV) (blood viscosity given in this work e.g., in fig.4a from Casson are whole blood viscosity see e.g., page 493 col.1 first para) of a subject (Donors A to U shown via fig.9) collected at a first time (fig.9 data are for any of shear rates), and at a shear rate of from about 1/s to about 1000/s (as shown in any of figs.9 or fig.1) and (ii) a second data including a second WBV of a subject collected at a shear rate of from about 1/s to about 1000/s (any of data for another on e.g., fig.1 or any of shown in fig.9); the first data further comprising a clinical information of the subject, the clinical information representing any one or more of age, gender (e.g., page 494 col.2 first para); determining, by the trained algorithm (e.g., table 3), a correlation between the first data and the second data (correlation as given by Eq.1), wherein the correlation is indicative of a current or future response of the subject (current on fig.4 and future on fig.10), generating a viscosity profile for the subject (at least figures and tables and cited info regarding parametrizing wbv of donors based on their information)
Sean does not disclose:
collected, by a system comprising at least a viscometer (although Sean uses Horner data which are obtained from published measurements).
collected at a second time and , wherein the correlation is indicative of a current or future inflammatory response of the subject; and generating a viscosity profile for the subject comprising information of a disease state of the subject.
Regarding limitation 1
In the similar field of endeavor, LEE in Figs.1-4e teaches a viscometer (100) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use LEE‘s viscometer for Sean‘s method and a fist data collected, by a system comprising at least the modified Sean’s viscometer. One of ordinary skill in the art knows the generated data from measurement and the data from literature can be compared and equivalent would have been motivated to make this modification in order to generate the data and update them with experimental data, besides, based on MPEP 2143 (C), courts have ruled that Use of known technique (viscometer of LEE) to improve similar devices (and method of Sean) in the same way is within the purview of a skilled artisan. See KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421,82 USPQ2d 1385, 1395-97 (2007).
Regarding limitation 2
In the similar field of endeavor, ATAROT teaches a method (using system 100), comprising: a second data including a record of a subject collected at a second time (medical history over different time are as input to the model e.g., ¶0023,00281,00424); determining, by the trained algorithm (e.g., ¶0019¶00429-00431,00463-00465,00466-00467), a correlation between the first data and the second data (e.g., ¶00277,00278,00402,00419-00420), wherein the correlation is indicative of a current or future inflammatory response of the subject (e.g., ¶0281,0429-00431,00466,00467,00421-00424); and generating a viscosity profile for the subject comprising information of a disease state of the subject (viscosity info as input e.g.,¶0041,00097,00153,00221,0280¶00460 blood viscosity as listed predicted used inputs ) for the subject comprising information of a disease state of the subject (e.g., ¶00280,00460).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use ATAROT‘s training algorithms for the modified Sean with LEE‘s method and indication of an inflammatory response, surgical history, social history, a pending medical condition, or a medication, and (ii) a second data including a second WBV of a subject collected at a second time and at a shear rate of from about 1/s to about 1000/s; determining, by the trained algorithm, a correlation between the first data and the second data, wherein the correlation is indicative of a current or future inflammatory response of the subject; and generating a viscosity profile for the subject comprising information of a disease state of the subject. One of ordinary skill in the art would know training models can extract physiological information from blood rheology, ATAROT teaches applying ML-derived blood-related signals to inflammatory disease monitoring and prediction, Sean and ATAROT both seek to derive disease-state information from blood-related physiological characteristics using machine learning. Sean blood rheology has diagnosis implications and ATAROT teaches ML learning models for diagnosis of one of these disease that is inflammatory response of patients, One of ordinary skill in the art would have been motivated for this modification to provide a clinically diagnostic result therefore improving patient monitoring and early intervention for inflammatory disease (e.g., ¶001 of ATAROT), besides, based on MPEP 2143 (D), courts have ruled that applying a known technique (ML models of ATAROT) to a known product (the modified Sean’s system) to yield predictable results (ATAROT’s diagnosis of inflammatory response) is within the purview of a skilled artisan. See KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421,82 USPQ2d 1385, 1395-97 (2007).
Claim 16
Sean in view of LEE and ATAROT teaches the method of claim 14, Sean further teaches the second data further includes a characteristic of the subject, the characteristic being other than WBV, and the characteristic optionally comprising any one or more of age (page 497 col.1 2nd para and col.2 effect of aging and physiology on viscosity of blood).
Claim 18
Sean in figs.1-10 teaches:
A system, comprising
determine a whole blood viscosity (WBV) of the sample at one or more shear rates of from 1/s to 1000/s (fig.9 for patients A to U); a processing stage (not shown but processor for processing and preparing results),(1) the processing stage configured to implement a trained algorithm (e.g., page 497 col.1 /table 3) that receives at least a first data (viscosity in any of fig.9) from the subject (any donor in fig.9), the first data including a WBV collected at a first time (WBV in any of shear rate) and at a shear rate of from about 1/s to about 1000/s (better shown in fig.1 that is same as any of fig.9), the first data further comprising a characteristic of the subject, the characteristic being other than WBV, representing any one or more of age, gender (e.g., page 494 col.2 first para); the trained algorithm (e.g., given in table 3) configured to determine a correlation between the first data and a first (e.g., MCH level in figs.4 and 8), the correlation being indicative of a current (figs.4 and 8,fig.10 and page 504 col.1 2nd para and page 504 col.2 first para: clinical diagnostics being healthy or cardiovascular disease) response of the subject (clinical diagnostics being healthy or cardiovascular disease.
Sean does not disclose:
a viscometer, the viscometer configured to receive a whole blood sample from a subject (although Sean uses Horner data which are obtained from published measurements).
the trained algorithm configured to determine a correlation between the first data and a first physiological state, the correlation being indicative of a current or future inflammatory response of the subject.
Regarding limitation 1
In the similar field of endeavor, LEE in Figs.1-4e teaches a viscometer (100) .
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use LEE‘s viscometer for Sean‘s method configured to receive a whole blood sample from the modified Sean’ subject. One of ordinary skill in the art knows the generated data from measurement and the data from literature can be compared and equivalent would have been motivated to make this modification in order to generate the data and update them with experimental data, besides, based on MPEP 2143 (C), courts have ruled that Use of known technique (viscometer of LEE) to improve similar devices (and method of Sean) in the same way is within the purview of a skilled artisan. See KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421,82 USPQ2d 1385, 1395-97 (2007).
Regarding limitation 2
In the similar field of endeavor, ATAROT teaches a method (using system 100), comprising: 2- the trained algorithm configured to determine a correlation between the first data and a first physiological state (e.g., ¶00277,00278,00402,00419-00420), the correlation being indicative of a current or future inflammatory response of the subject (e.g., ¶00280,00460).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use ATAROT‘s training algorithms for the modified Sean with LEE‘s method and indication of an inflammatory response, and 1- the trained algorithm configured to determine a correlation between the first data and a first physiological state, the correlation being indicative of a current or future inflammatory response of the subject. One of ordinary skill in the art would know training models can extract physiological information from blood rheology, ATAROT teaches applying ML-derived blood-related signals to inflammatory disease monitoring and prediction, Sean and ATAROT both seek to derive disease-state information from blood-related physiological characteristics using machine learning. Sean blood rheology has diagnosis implications and ATAROT teaches ML learning models for diagnosis of one of these disease that is inflammatory response of patients, One of ordinary skill in the art would have been motivated for this modification to provide a clinically diagnostic result therefore improving patient monitoring and early intervention for inflammatory disease (e.g., ¶001 of ATAROT), besides, based on MPEP 2143 (D), courts have ruled that applying a known technique (ML models of ATAROT) to a known product (the modified Sean’s system) to yield predictable results (ATAROT’s diagnosis of inflammatory response) is within the purview of a skilled artisan. See KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421,82 USPQ2d 1385, 1395-97 (2007).
Claim 21
Sean in figs.1-10 teaches:
A method of training an algorithm (e.g., page497 col.1: training various algorithms, table 3) for detecting a current or future physiological state of a subject (donors A to U in fig.9 and physiological states shown in figs. 4 and 8 and future clinical diagnosis in page 504 col.1 2nd para to col.2 first col), the method comprising:
inputting a plurality of datasets (Horner data on page 497 col.1) for each of a plurality of subjects (donors A to U in fig.9) into a model function (Casson equation Eq.(1) and fitted in fig.1 and fig.9), wherein the plurality of datasets include:(i) a first dataset including a first whole blood viscosity (WBV) of a subject (e.g., donor A in fig.9 for WBV) collected at a first time and at a shear rate of from about 1/s to about 1000/s (e.g., fig.9 for donor A in one of shear rates), wherein the first dataset further includes a characteristic of the subject, the characteristic being any one or more of age, gender (e.g., page 494 col.2 first para); the trained algorithm (e.g., given in table 3);(ii) a second dataset including a second WBV of a subject (e.g., fig.9 for donor B) and at a shear rate of from about 1/s to about 1000/s (e.g., any of shear rates in fig.9 for donor B) ; and (iii) a third dataset including (e.g., Hematorcite % for these donors in tables 1 and 2 and figs. 4 and 8) ; and
calculating weighted values for each data type of the first dataset and the second dataset via the model function (calculating weighted values are inherently by ML techniques, training models1); and
generating a trained model by assigning the weighted values to the data type of the first dataset and the second dataset (this is inherently by ML techniques and algorithms).
Sean does not disclose:
by a system comprising at least a viscometer (although Sean uses Horner data which are obtained from published measurements).
2- collected at a second time, a third dataset including at least one or more physiological states indicative of a disease state of the subject.
Regarding limitation 1
In the similar field of endeavor, LEE in Figs.1-4e teaches a viscometer (100) .
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use LEE‘s viscometer for Sean‘s method. One of ordinary skill in the art knows the generated data from measurement and the data from literature can be compared and equivalent would have been motivated to make this modification in order to generate the data and update them with experimental data, besides, based on MPEP 2143 (C), courts have ruled that Use of known technique (viscometer of LEE) to improve similar devices (and method of Sean) in the same way is within the purview of a skilled artisan. See KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421,82 USPQ2d 1385, 1395-97 (2007).
Regarding limitation 2
In the similar field of endeavor, ATAROT teaches a method (using system 100), comprising: a second dataset including a second data of a subject collected at a second time (e.g., ¶00277,00278,00402,00419-00420), and a third dataset including at least one or more physiological states indicative of a disease state of the subject (e.g., ¶00280,00460).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use ATAROT‘s training algorithms for the modified Sean with LEE‘s method and indication of an inflammatory response, a second dataset including a second WBV of a subject collected at a second time and at a shear rate of from about 1/s to about 1000/s; and a third dataset including at least one or more physiological states indicative of a disease state of the subject. One of ordinary skill in the art would know training models can extract physiological information from blood rheology, ATAROT teaches applying ML-derived blood-related signals to inflammatory disease monitoring and prediction, Sean and ATAROT both seek to derive disease-state information from blood-related physiological characteristics using machine learning. Sean blood rheology has diagnosis implications and ATAROT teaches ML learning models for diagnosis of one of these disease that is inflammatory response of patients, One of ordinary skill in the art would have been motivated for this modification to provide a clinically diagnostic result therefore improving patient monitoring and early intervention for inflammatory disease (e.g., ¶001 of ATAROT), besides, based on MPEP 2143 (D), courts have ruled that applying a known technique (ML models of ATAROT) to a known product (the modified Sean’s system) to yield predictable results (ATAROT’s diagnosis of inflammatory response) is within the purview of a skilled artisan. See KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421,82 USPQ2d 1385, 1395-97 (2007).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over “Sean”, (Farrington, Sean, et al. "Physiology-based parameterization of human blood steady shear rheology via machine learning: a hemostatistics contribution." Rheologica Acta 62.10 (2023): 491-506., LEE, KR 20160135121 A, and ATAROT, WO 2023058013 A1 further in view of US 20180011116 A1, “Chapman”.
Claim 8
Sean in view of LEE and ATAROT teaches the method of claim 1, but the combination does not specifically teach wherein an n-th time and an (n+1)-th time are separated by less than about 12 hours, optionally separated by less than about 6 hours.
In the similar field of endeavor, Chapman teaches wherein an n-th time and an (n+1)-th time are separated by less than about 12 hours, optionally separated by less than about 6 hours (¶0158).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Chapman’s12 or 6 hours separation between n-th time and an (n+1)-th time of method of Sean combined with LEE and ATAROT. One of ordinary skill in the art would know this time frame for patients in emergency status of immense bleeding and have been motivated to make this modification in order to identifying a patient as likely to have an onset of massive hemorrhage (at least Abstract of Chapman).
Claims 11 and 17 are rejected under 35 U.S.C. 103 as being unpatentable “Sean”, (Farrington, Sean, et al. "Physiology-based parameterization of human blood steady shear rheology via machine learning: a hemostatistics contribution." Rheologica Acta 62.10 (2023): 491-506., LEE, KR 20160135121 A, and ATAROT, WO 2023058013 A1 further in view of US 20230218165 A1, “Minamide”.
Claim 11
Sean in view of LEE and ATAROT teaches the method of claim 1, but the combination does not specifically teach wherein the physiological state is a septic state.(Although ATAROT teaches that the ML models are based on infection related diseases and related data (e.g.,¶00328))
In the similar field of endeavor, Minamide teaches wherein the physiological state is a septic state (¶0051).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use septic state as physiological state of the modified Sean combined with ATAROT as taught by Minamide. One of ordinary skill in the art would know information representing a state related to sepsis and an infectious disease are related to the circulatory system of the patient have been motivated to make this modification in order to early diagnosis of circulatory system disorders.
Claim 17
Sean in view of LEE and ATAROT teaches the method of claim 1, but the combination does not specifically teach wherein the physiological state is a septic state.
In the similar field of endeavor, Minamide teaches wherein the physiological state is a septic state (¶0051).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use septic state as physiological state of Liao combined with Sean as taught by Minamide. One of ordinary skill in the art would know information representing a state related to sepsis and an infectious disease are related to the circulatory system of the patient have been motivated to make this modification in order to early diagnosis of circulatory system disorders.
Claims 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over “ “Sean”, (Farrington, Sean, et al. "Physiology-based parameterization of human blood steady shear rheology via machine learning: a hemostatistics contribution." Rheologica Acta 62.10 (2023): 491-506.) in view of “Liao”, LEE, KR 20160135121 A and “ATAROT,” WO 2023058013 A1, further in view of “Kron”2, US 4858127 A.
Claim 19
Sean combined with LEE and ATAROT teaches the system of claim 18, but the combination does not specifically teach wherein the viscometer comprises a pressure transducer in fluid communication with a volume configured to receive a whole blood sample of a subject.
In the similar field of endeavor, Kron in Figs.3-5 teaches a viscometer comprises a pressure transducer (45) in fluid communication with a volume configured to receive a whole blood sample of a subject (blood in chamber 42).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Kron’s pressure transducer in fluid communication with a volume configured to receive a whole blood sample of the modified Sean’s subject. One of ordinary skill in the art would know these devices as well-known techniques and have been motivated to make this modification in order to obtain the reliable blood rheology information as input data for AI models.
Claim 20
Sean combined with LEE ATAROT Kron teaches the system of claim 19, Kron further teaches wherein the viscometer stage is configured to collect a pressure vs. time curve of a whole blood sample within 60 seconds (results is pressure time curve as shown in fig.4 e.g., C.3 L.34-35 also Fig.4 that is a typical chart of measured pressure vs. time for any of viscometers disclosed by Kron) so as to generate at least a first set of pressure vs. time data (e.g., C.7 L.43-48 e.g. Fig.4 curves A/B/C/D/E/F), for the same reason and motivation cited for claim 19. Examiner notes that Kron does not explicitly teach within 60 seconds range. Nonetheless, the skilled artisan would know too that this time range would affect the viscosity measurement based on variations of pressure. the specific claimed range, absent any criticality, is only considered to be the “optimum” disclosed by Kron that a person having ordinary skill in the art would have been able to determine using routine experimentation, and neither non-obvious nor unexpected results, i.e. results which are different in kind and not in degree from the results of the prior art, will be obtained as long as the range of 60 second is used, as already suggested by Kron.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over “ “Sean”, (Farrington, Sean, et al. "Physiology-based parameterization of human blood steady shear rheology via machine learning: a hemostatistics contribution." Rheologica Acta 62.10 (2023): 491-506., in view of Becker, WO2006066565A1, and ATAROT, WO 2023058013 A1.
Claim 22
Sean in figs.1-10 teaches:
A method, comprising:
generating, n records (figs.2-3 and 9 different data of shear viscosity in different shear rate) from a subject (e.g., Donor I fig.2, or any of Donors A to U in fig.9),
n being a positive integer (for each donor there are n data in different shear rates and number of data is a positive integer),
and an n-th record comprising at least an n-th whole blood viscosity (WBV) that is collected at a shear rate of from about 1/s to about 1000/s (page 493 col.1: using the Casson equation to fit whole blood data, each data on fig.2 is correlated to WBV on figs.1 and 9 that meet the limitation for each donor);
determining, by a trained algorithm (e.g., page 497 col.1, table 3) that receives the n records (for each donor), at least (1) a relation between at least two of the n records (e.g.,fig.2 that is correlation between two of n record fit on Casson Eq.1); and generating a viscosity profile for the subject (see e.g. , figs. 1-6 and tables).
Sean does not disclose:
by a system comprising at least a viscometer (although Sean uses Horner data which are obtained from published measurements), the n-th record comprising an average between a viscosity curve collected at a positive pressure and a viscosity curve collected at a negative pressure;
collected at an n-th time , generating a viscosity profile for the subject comprising information of a disease state of the subject.
Regarding limitation 1
In the similar field of endeavor, Becker in e.g., figs.1-3 teaches a system comprising at least a viscometer, comprising an average between a viscosity curve collected at a positive pressure and a viscosity curve collected at a negative pressure (at least e.g., fig.2 and related citation to the figure teaches measuring viscosity from viscosity curve collected at a positive pressure and a viscosity curve collected at a negative pressure and at least claims 9 and 10 teach the viscosity is determined by average between overpressure measurements and under pressure measurements for both Newtonian and non-Newtonian fluids such as whole blood)3.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Becker‘s viscometer for Sean‘s method and generating Sean’s n records from Sean’s subject, the modified Sean’s n-th record comprising an average between a viscosity curve collected at a positive pressure and a viscosity curve collected at a negative pressure. One of ordinary skill in the art knows the generated data from measurement and the data from literature can be compared and equivalent would have been motivated to make this modification in order to generate the data and update them with experimental data, and making averaging eliminates the errors and improve accuracy; besides, based on MPEP 2143 (C), courts have ruled that Use of known technique (viscometer of Becker) to improve similar devices (system and method of Sean) in the same way is within the purview of a skilled artisan. See KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421,82 USPQ2d 1385, 1395-97 (2007).
Regarding limitation 2
In the similar field of endeavor, ATAROT teaches a method (using system 100), comprising: collected input data at an n-th time (medical history over different time are as input to the model e.g., ¶0023,00281,00424), generating a viscosity profile for the subject comprising information of a disease state of the subject (viscosity info as input e.g.,¶0041,00097,00153,00221,0280¶00460 blood viscosity and inflammatory response as listed predicted used inputs also see ¶00280,00460 ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use ATAROT‘s training algorithms for the modified Sean with LEE‘s method and indication of an inflammatory response, and whole blood viscosity (WBV) that is collected at an n-th time and generating a viscosity profile for the subject comprising information of a disease state of the subject. One of ordinary skill in the art would know training models can extract physiological information from blood rheology, ATAROT teaches applying ML-derived blood-related signals to inflammatory disease monitoring and prediction, Sean and ATAROT both seek to derive disease-state information from blood-related physiological characteristics using machine learning. Sean blood rheology has diagnosis implications and ATAROT teaches ML learning models for diagnosis of one of these disease that is inflammatory response of patients, One of ordinary skill in the art would have been motivated for this modification to provide a clinically diagnostic result therefore improving patient monitoring and early intervention for inflammatory disease (e.g., ¶001 of ATAROT), besides, based on MPEP 2143 (D), courts have ruled that applying a known technique (ML models of ATAROT) to a known product (the modified Sean’s system) to yield predictable results (ATAROT’s diagnosis of inflammatory response) is within the purview of a skilled artisan. See KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421,82 USPQ2d 1385, 1395-97 (2007).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Fatemeh E. Nia whose telephone number is (469)295-9187. The examiner can normally be reached 9:00 am to 4:00 pm.
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/FATEMEH ESFANDIARI NIA/Examiner, Art Unit 2855
1 Note there are no details of these training models and weights that are broadly cited in the disclosure
2 prior art of record
3 See e.g., underlined portions on pages 1-4 on the English translation provided by the office.
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