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.
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:
On pages 9-11 of Remarks, Applicant argues that based on the office's own guidance and with the precedential Appeals Review Panel (ARP) decision in Ex parte Desjardins, Appeal No. 2024- 000567 (PTAB September 26, 2025), the office should withdraw these 101 rejections, because : determining, by a trained algorithm that receives the n records’ is not a mental process. Then, Applicant argues that the claims recite an improvement to technology.
Response:
while determining a relation and/or an indication of inflammatory response are mental processes, using a trained algorithm to make that determination would be an additional element requiring analysis under Step 2A, Prong 2 and Step 2B.
Examiner disagrees that the claims recite an improvement to technology.
Applicant seems to lean on the development and use of viscosity profiles as reciting an improvement to technology. However, generating a viscosity profile appears to be a mental process – see Fig. 6. If the BRI of generating a viscosity profile includes plotting data points, then it’s part of the judicial exception and not an additional element. Per MPEP 2106.05(a), “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.” That said, if generating a viscosity profile is more than just plotting data points, maybe that would be an additional element.
Examiner conclude that Desjardins is distinguishable – if the claim had been recited the specifics of training the machine learning model. In this case, training the algorithm isn’t claimed – only generically using a trained algorithm to determine relationships are claimed. If more specific on how the algorithm is trained or works had been claimed – or what is done with the determination the algorithm makes – had been claimed, then maybe either of those would be a practical application. Examiner has provided a more detailed analysis for 101 rejections in this action, in short the summary of the analysis is:
Step 2A, Prong 1 – determining a relation between at least 2 records, determining an indication of an inflammatory response of the subject, and generating a viscosity profile are all mental processes.
Step 2A, Prong 2 – generating n records from a subject (with the record limitations – the n-th record comprising at least an n-th WBV, and further comprising a clinical information of the subject) is mere data gathering. Using a trained algorithm to determine the relation is mere instructions to apply an exception using AI – see MPEP 2106.05(f). The claim recites using the idea of a solution but doesn’t recite how this is accomplished. It is broad and appears to use an algorithm to perform an existing process.
Step 2B – this is WURC, therefore, the rejection remains.
Therefore, arguments are not persuasive.
On pages 12-14 of Remarks, Applicant is arguing that the discussion on page 497 of Sean relates to the age of the blood sample being tested and does not relate to the age of the subject from whom the blood was taken.
Response:
The MPEP supports using all parts of a prior art reference for teaching limitations, especially in obviousness rejections (35 U.S.C. § 103), by stating references must be read as a whole, including implicit teachings and nonpreferred embodiments, with key sections being MPEP § 2145 (reading references as a whole for obviousness), MPEP § 2123 (relying on broad disclosures, even nonpreferred ones), and MPEP § 2144.01 (implicit teachings). An examiner can use any part of a prior art disclosure, even from a reference that "teaches away," to show anticipation or obviousness, drawing inferences a skilled person would make, but must explain the reasoning:
MPEP § 2145 (Obviousness - Reading References as a Whole): Emphasizes that examiners must consider prior art references as a whole and can combine teachings from multiple references if there's a rationale for that combination.
MPEP § 2123 (Broad Disclosure): States that patents are prior art for everything they contain, including nonpreferred embodiments, and can be relied upon for what they would reasonably suggest to a skilled person.
MPEP § 2144.01 (Implicit Disclosure): Notes that examiners can use not just specific teachings but also reasonable inferences an ordinary skilled person would draw from the reference.
MPEP § 2136.02 (Content of Prior Art): Clarifies that for anticipation (35 U.S.C. § 102(e)), the entire disclosure of a U.S. patent or publication can be used.
In this case, first of all Sean clearly teaches this limitation, Sean uses the data from Horner which are from 17 donor with known gender and age, for example at a minimum Sean discloses: “Each sample in this protocol is physiologically characterized with 25 standard biochemical tests of the blood, gender, and age and [these data] are publicly available (Horner 2020) (Sean: page 494, col.2 last lines of fist para), therefore, Sean clearly teaches the amended claim and limitation: “the clinical information comprising any one or more of age, gender, …” and even if Sean in another embodiment teaches the age of living fluid, still Sean also teaches age and gender as clinical information. Furthermore, it is a common knowledge in the art1 to use age and gender of patients as clinical information to evaluate hormonal shifts, metabolic changes, cell counts , and red blood cell properties which naturally differ between sexes and change with aging, requiring specific age- and gender-specific reference ranges for accurate diagnosis and to understand drug responses, and there is not patentable weight for age and gender being clinical information. Therefore the arguments are not persuasive.
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, 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, 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 a viscosity profile for the subject comprising information indicating the inflammatory response [ 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/sto 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 indicating the inflammatory response [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 indicating the inflammatory response [ 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 current or future inflammatory response 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 an inflammatory response 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 indicating the inflammatory response [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 an n-th time and 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 one of (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 (2) an indication of an response (cardiovascular response (e.g., page 504 col.1 last para) of the subject (e.g., donor N)) of the subject (e.g., donor N) based at least in part on the WBV (viscosity e.g., fig.4) 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).
Sean does not disclose:
by a system comprising at least a viscometer (although Sean uses Horner data which are obtained from published measurements).
indication of an inflammatory response, and generating a viscosity profile for the subject comprising information indicating the inflammatory response .
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 limitation 2
Limitation 2 is not required by the claim language as at least one of them reads on the claim:
But additionally/alternatively:
In the similar field of endeavor, ATAROT teaches a method (using system 100), comprising:
generating, n records from a subject (signals from 210 and 220 from patient as shown in figs.1A-C), n being a positive integer (number of signals), and an n-th record comprising at least related to an n-th whole blood viscosity (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); determining, by a trained algorithm (160A/160B) that receives the n records, at least one of (1) a relation between at least two of the n records (ML models inherently have this relations to train data ) and (2) an indication of an inflammatory response of the subject (patient) based at least in part on the WBV (e.g., ¶00362) from one or more of the n records (ML models 160A/160B are correlating the n data); 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); and generating a viscosity profile for the subject comprising information indicating the inflammatory response (e.g., 100A/100B ).
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 generating the modified Sean’s viscosity profile for the subject comprising information indicating the inflammatory response as taught by ATAROT. One of ordinary skill in the art would know blood flow rheological parameter indicates said inflammatory status (e.g., ¶0045 of ATAROT) have been motivated to make this modification in order to make a non-invasive assessment and treatment of inflammatory conditions and status in patients (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 (LEE’s 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 2
Sean in view of LEE and ATAROT teaches the method of claim 1, Sean further teaches wherein an n-th record further comprises a clinical information of the subject (e.g., Hematocrit% in tables 1 and 2), the clinical information being information other than WBV (e.g., tables 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); and (1) determining, by the trained algorithm (e.g., page 497 col.1 table 3), a correlation between the first data (Casson viscosity) and a first physiological state (Hematocrit % OR fibrinogen concentration in figs.4a, 8a), or (2) receiving, at the trained algorithm (table 3), second data including a first WBV of a subject collected at a second time and at a shear rate (fig.4b or 8b) of from about 1/s to about 1000/s (fig. 1 and 9), and determining, by the trained algorithm (e.g., table 3), a correlation between the first data and the second data, the correlation being indicative of a current or future response (cardiovascular response e.g., page 504 col.1 last para) of the subject (donor).
Sean does not disclose:
collected, by a system comprising at least a viscometer (although Sean uses Horner data which are obtained from published measurements).
The correlation, indication of an inflammatory response, and generating a viscosity profile for the subject comprising information indicating the inflammatory response .
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:
generating, n records from a subject (signals from 210 and 220 from patient as shown in figs.1A-C), n being a positive integer (number of signals), and an n-th record comprising at least related to an n-th whole blood viscosity (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); determining, by a trained algorithm (160A/160B) that receives the n records, at least one of (1) a relation between at least two of the n records (ML models inherently have this relations to train data ) and (2) an indication of an inflammatory response of the subject (patient) based at least in part on the WBV (e.g., ¶00362) from one or more of the n records (ML models 160A/160B are correlating the n data); wherein the first data further comprising a clinical information of the subject, the clinical information representing any one or more of age, gender (e.g., ¶0135 ¶0281, ¶0436¶0453); and generating a viscosity profile for the subject comprising information indicating the inflammatory response (e.g., 100A/100B ).
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 generating the modified Sean’s viscosity profile for the subject comprising information indicating the inflammatory response as taught by ATAROT. One of ordinary skill in the art would know blood flow rheological parameter indicates said inflammatory status (e.g., ¶0045 of ATAROT) have been motivated to make this modification in order to make a non-invasive assessment and treatment of inflammatory conditions and status in patients (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 (LEE’s 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 second time and 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).
Sean does not disclose:
collected, by a system comprising at least a viscometer (although Sean uses Horner data which are obtained from published measurements).
indicative of an inflammatory response, and generating a viscosity profile for the subject comprising information indicating the inflammatory response .
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:
generating, n records from a subject (signals from 210 and 220 from patient as shown in figs.1A-C), n being a positive integer (number of signals), and an n-th record comprising at least related to an n-th whole blood viscosity (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); determining, by a trained algorithm (160A/160B) that receives the n records, at least one of (1) a relation between at least two of the n records (ML models inherently have this relations to train data ) and (2) an indicative of an inflammatory response of the subject (patient) based at least in part on the WBV (e.g., ¶00362) from one or more of the n records (ML models 160A/160B are correlating the n data); wherein the first data further comprising a clinical information of the subject, the clinical information representing any one or more of age, gender (e.g., ¶0135 ¶0281, ¶0436¶0453); and generating a viscosity profile for the subject comprising information indicating the inflammatory response (e.g., 100A/100B ).
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 generating the modified Sean’s viscosity profile for the subject comprising information indicative the inflammatory response as taught by ATAROT. One of ordinary skill in the art would know blood flow rheological parameter indicates said inflammatory status (e.g., ¶0045 of ATAROT) have been motivated to make this modification in order to make a non-invasive assessment and treatment of inflammatory conditions and status in patients (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 (LEE’s 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) or future (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), or(2) the processing stage configured to implement a trained algorithm (e.g., table 3) that receives (i) a first data from a subject (viscosity in first shear rate fig.9 for e.g., donor A), the first data including a WBV collected at a first time (fig.9 viscosity for donor A at first shear rate) and at a shear rate of from about 1/s to about 1000/s (figs. 1 and 9) and (ii) a second data (e.g., second viscosity in fig.9 for donor A) from the subject (e.g., donor A), the second data (second viscosity) including a WBV collected at a second time and at a shear rate of from about 1/s to about 1000/s (any other shear rate in this range but different than first one), the trained algorithm (e.g., table 3) configured to determine a correlation between the first data and the second data, wherein the correlation is indicative of a current or future response of the subject (clinical diagnosis for donors for diseases and disorders such as cardiovascular or stroke or drug: page 504 col.1 and col.2 first para).
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).
indication of an inflammatory response.
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:
generating, n records from a subject (signals from 210 and 220 from patient as shown in figs.1A-C), n being a positive integer (number of signals), and an n-th record comprising at least related to an n-th whole blood viscosity (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); determining, by a trained algorithm (160A/160B) that receives the n records, at least one of (1) a relation between at least two of the n records (ML models inherently have this relations to train data ) and (2) an indication of an inflammatory response of the subject (patient) based at least in part on the WBV (e.g., ¶00362) from one or more of the n records (ML models 160A/160B are correlating the n data); wherein 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., ¶0135 ¶0281, ¶0436¶0453); and generating a viscosity profile for the subject comprising information indicating the inflammatory response (e.g., 100A/100B ).
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 as taught by ATAROT. One of ordinary skill in the art would know blood flow rheological parameter indicates said inflammatory status (e.g., ¶0045 of ATAROT) have been motivated to make this modification in order to make a non-invasive assessment and treatment of inflammatory conditions and status in patients (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 (LEE’s 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) collected at a second time 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 at least one or more physiological states (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 models2); 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- indicative of an 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. 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:
generating, n records from a subject (signals from 210 and 220 from patient as shown in figs.1A-C), n being a positive integer (number of signals), and an n-th record comprising at least related to an n-th whole blood viscosity (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); determining, by a trained algorithm (160A/160B) that receives the n records, at least one of (1) a relation between at least two of the n records (ML models inherently have this relations to train data ) and (2) an indication of an inflammatory response of the subject (patient) based at least in part on the WBV (e.g., ¶00362) from one or more of the n records (ML models 160A/160B are correlating the n data); wherein the first dataset further includes a characteristic of the subject, the characteristic being any one or more of age, gender ( (e.g., ¶0135 ¶0281, ¶0436¶0453); and generating a viscosity profile for the subject comprising information indicating the inflammatory response (e.g., 100A/100B ).
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 as taught by ATAROT. One of ordinary skill in the art would know blood flow rheological parameter indicates said inflammatory status (e.g., ¶0045 of ATAROT) have been motivated to make this modification in order to make a non-invasive assessment and treatment of inflammatory conditions and status in patients (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 (LEE’s 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”3, 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 an n-th time and 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 (2) an indication of an response (cardiovascular response (e.g., page 504 col.1 last para) of the subject (e.g., donor N)) of the subject (e.g., donor N) based at least in part on the WBV (viscosity e.g., fig.4) 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);
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;
indication of an inflammatory response, and generating a viscosity profile for the subject comprising information indicating the inflammatory response .
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)4.
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
Limitation 2 is not required by the claim language as at least one of them reads on the claim:
But additionally/alternatively:
In the similar field of endeavor, ATAROT teaches a method (using system 100), comprising:
generating, n records from a subject (signals from 210 and 220 from patient as shown in figs.1A-C), n being a positive integer (number of signals), and an n-th record comprising at least related to an n-th whole blood viscosity (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); determining, by a trained algorithm (160A/160B) that receives the n records, at least one of (1) a relation between at least two of the n records (ML models inherently have this relations to train data ) and (2) an indication of an inflammatory response of the subject (patient) based at least in part on the WBV (e.g., ¶00362) from one or more of the n records (ML models 160A/160B are correlating the n data); 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); and generating a viscosity profile for the subject comprising information indicating the inflammatory response (e.g., 100A/100B ).
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 Becker‘s method and indication of an inflammatory response, and generating the modified Sean’s viscosity profile for the subject comprising information indicating the inflammatory response as taught by ATAROT. One of ordinary skill in the art would know blood flow rheological parameter indicates said inflammatory status (e.g., ¶0045 of ATAROT) have been motivated to make this modification in order to make a non-invasive assessment and treatment of inflammatory conditions and status in patients (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 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
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.
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 it is certain that this limitation is well known and does not have any novelty nor is inventive. In other words, even if Sean had not been clearly teaching this limitation, it is a common knowledge in the art to use age and gender of patients as clinical information to evaluate hormonal shifts, metabolic changes, cell counts , and red blood cell properties which naturally differ between sexes and change with aging, requiring specific age- and gender-specific reference ranges for accurate diagnosis and to understand drug responses.) see e.g., ¶0135 ¶0281, ¶0436¶0453 of ATTAROT which is prior art of record.
See also the obviousness rejection of this limitation using prior art of record((“Liao”).
2 Note there are no details of these training models and weights that are broadly cited in the disclosure
3 prior art of record
4 See e.g., underlined portions on pages 1-4 on the English translation provided by the office.