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 .
Priority
Acknowledgment is made of applicant’s claim for priority to US Provisional Application No. 63/324,323, filed on 3/28/2022. As such the effective filing date of claims 1-20 is 3/28/2022.
Claim Status
Claims 1-20 are pending.
Claims 1-20 are rejected.
Drawings
Color photographs and color drawings are not accepted in utility applications unless a petition filed under 37 CFR 1.84(a)(2) is granted. Any such petition must be accompanied by the appropriate fee set forth in 37 CFR 1.17(h), one set of color drawings or color photographs, as appropriate, if submitted via the USPTO patent electronic filing system or three sets of color drawings or color photographs, as appropriate, if not submitted via the via USPTO patent electronic filing system, and, unless already present, an amendment to include the following language as the first paragraph of the brief description of the drawings section of the specification:
The patent or application file contains at least one drawing executed in color. Specifically, Figures 3A, 3B, 4A, 4B, 5A, 5B, 5E, 6A, 6B, 6C, 9A, 9B, 10A, and 10B. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
Color photographs will be accepted if the conditions for accepting color drawings and black and white photographs have been satisfied. See 37 CFR 1.84(b)(2).
Specification
The use of the term Owren-Koller, STA-Unicalibrator, STA-System Control, STA-Coag Control, STA-Deficient, STA-Deficient V, ThermoFisher, Fluoroskan, MATLAB, which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claims recite a method, system, and CRM for administering blood products with a personalized concentration of coagulation factors. The judicial exception is not integrated into a practical application because while claims 1-20 attempt to integrated the exception into a practical application, said application is either generically recited computer elements that do not add a meaningful limitation to the abstract idea or it is insignificant extra solution activity and merely implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements only store and retrieve information in memory as well as perform basic calculations that are known to be well understood, routine and conventional computer functions as recognized by the decisions listed in MPEP § 2106.05(d).
Framework with which to Analyze Subject Matter Eligibility:
Step 1: Are the claims directed to a category of stator subject matter (a process, machine, manufacture, or composition of matter)? [see MPEP § 2106.03]
Claims are directed to statutory subject matter, specifically a method (Claims 1-7), a system (8-14), and a CRM (Claims 15-20)
Step 2A Prong One: Do the claims recite a judicially recognized exception, i.e., an abstract idea, a law of nature, or a natural phenomenon? [see MPEP § 2106.04(a)]
The claims herein recite abstract ideas, specifically mental processes and mathematical concepts.
With respect to the Step 2A Prong One evaluation, the instant claims are found herein to recite abstract ideas that fall into the grouping of mental processes and mathematical concepts.
Claims 1, 8, and 15: Generating a clotting prediction, determining coagulation factor concentrations, iteratively generating a new clotting prediction, and iteratively determining additional coagulation factor concentrations are processes of comparing/contrasting, and calculating information that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Claims 2, 9, and 16: The clotting prediction comprising the measurements specified is merely further limiting the data itself, which is an abstract idea, specifically a mental process.
Claims 3, 10, and 17: The clotting prediction being based on a third-order linear dynamics system model having five unconstrained parameters is merely further limiting the data itself, which is an abstract idea, specifically a mental process. The clotting prediction being based on a third-order linear dynamics system model having five unconstrained parameters is a verbal articulation of a mathematical process which is an abstract idea, specifically a mathematical concept.
Claim 4, 11, and 18: Inputting the measured concentrations into a predictive model, executing the predictive thrombin dynamics model, and predicting a CAT trajectory are processes of identifying, selecting, and calculating information that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Claim 5, 12, and 19: The measured blood factors comprise the specified concentrations is merely further limiting the data itself, which is an abstract idea, specifically a mental process.
Claim 7: The coagulation factor concentrations moving the subject toward normal equilibrium values of the subject is merely defining an existing relationship between set values and a specific “normal” human state, and as such is a naturally occurring principle, specifically a natural law.
Step 2A Prong Two: If the claims recite a judicial exception under prong one, then is the judicial exception integrated into a practical application? [see MPEP § 2106.04(d) and MPEP § 2106.05(a)-(c) & (e)-(h)]
Because the claims do recite judicial exceptions, direction under Step 2A Prong Two provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application.
The following claims recite the following additional elements in the form of nonabstract elements:
Claims 1, 8, and 15: A system, processor, computing device, memory, program instructions, and non-transitory computer readable medium are generic and nonspecific computer elements that do not improve the functioning of any computer or technology described herein [See MPEP § 2106.04(d)(1) and MPEP 2106.05(d)]. Obtaining measured coagulation factor concentrations, and outputting a recommended set of coagulation factor concentrations are insignificant extra solution activities, specifically mere data gathering and necessary data outputting (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989), PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis), Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential), and Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55) [See MPEP § 2106.05(g)].
Claim 6: The factor concentrations being obtained from a blood coagulation sensor is an insignificant extra solution activity, specifically mere data gathering (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989), PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis), Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential), and Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55) [See MPEP § 2106.05(g)].
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive
concept? [see MPEP § 2106.05]
Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept.
The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are generic, conventional or nonspecific. These additional elements include:
The additional elements of a system, processor, computing device, memory, program instructions, and non-transitory computer readable medium are generic and nonspecific elements of a computer that are well-understood, routine and conventional within the art and therefore do not improve the functioning of any computer or technology described therein (See Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values), and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) [See MPEP § 2106.05(d)(II)]. Therefore, taken both individually and as a whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
The additional elements of obtaining measured coagulation factor concentrations, the factor concentrations being obtained from a blood coagulation sensor (Conventional: Specification Paragraph [0073] – Conventional Apparatus: TA Compact Max® benchtop coagulation analyzer), and outputting a recommended set of coagulation factor concentrations are insignificant extra solution activities, specifically mere data gathering and necessary data outputting (See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering)) [See MPEP § 2106.05(g)]. Therefore, taken both individually and as whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
Therefore, claims 1-20, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(I) as being anticipated by Ghetmiri et al. (npj Systems Biology and Applications (2021) 1-17).
Claim 1 is directed to a method of administering blood products with a personalized concentration.
Claim 8 is directed to a system of administering blood products with a personalized concentration.
Claim 15 is directed to a CRM of administering blood products with a personalized concentration.
Ghetmiri et al. teaches in the abstract “we develop a Goal-oriented Coagulation Management (GCM) algorithm, a personalized and automated ordered sequence of operations to compute and specify coagulation factor concentrations that rectify clotting. This novel GCM algorithm also integrates new control-oriented advancements that we make in this work: an improvement of a prior thrombin dynamics model that captures the coagulation process to control, a use of rapidly measurable concentrations to help predict patient state, and an accounting of patient-specific effects and limitations when adding coagulation factors to remedy coagulopathy”, on page 4, column 1, paragraph 1 “As we also show, a treatment algorithm that leverages such an improved model can provide frequent, personalized, and dynamic recommendations based on sample clotting predictions, to move a trauma patient’s coagulation state toward a desired recovery trajectory”, page 2, column 2, paragraph 2 “The original contributions of this article are as follows: 1. We provide significant evidence from patient data that there is substantial merit to administering coagulation factors to treat trauma patients. Trauma patient survival 24 h after hospital admission occurs if and only if coagulation factor concentrations equilibrate at a normal value, either from inadvertent plasma-based modulation or from innate compensation. 2. We develop a Goal-oriented Coagulation Management (GCM) algorithm, a personalized and automated ordered sequence of operations to compute and specify coagulation factor concentrations that rectify clotting. For this algorithm, we: (a) substantially improve a recent black-box process dynamics model by harnessing more data, and we then validate our improvements in silico on a separate dataset (Fig. 1a “plant”); (b) use rapidly-measurable coagulation factor concentrations and this updated model to predict individual clotting dynamics (Fig. 1a “sensors”); (c) confirm that administering coagulation factor concentrations accurately changes clotting as described by our improved dynamics model, noting saturating behavior for excessive coagulation factor concentration administration that motivates keeping levels between generallyaccepted normal limits when modulated in a treatment scheme (Fig. 1a “actuators”); and (d) propose a novel ordering in which to tune coagulation factor concentrations to satisfy a clotting improvement goal (Fig. 1a “controller”). 3. We validate the GCM algorithm’s guidance in silico on a separate dataset for the critical first 24 h of care. We show superior performance over clinical practice in attaining normal coagulation factor concentrations and normal clotting profiles simultaneously”, on page 11, column 2, paragraph 4 “An iterative approach permits quicker model updates, greater personalization, and a responsiveness to uncertainties, all of which will improve patient outcomes”, and on page 7, column 1, paragraph 5 “The process is iterated five times for five unique divisions (folds) of the original dataset. The mean model output properties of these five iterations for the combined dataset of 20 normal samples and 40 trauma patient samples (datasets 4 and 5) are reported in Fig. 5d”, on page 9, column 2, paragraph 3 “First, the GCM algorithm is guaranteed to converge to a set of personalized coagulation factor concentration recommendations”, and on page 15, column 2, paragraph 2 “The MATLAB controller GCM algorithm and underlying parameterized model is available at…The algorithm code is available for personal use”, reading on a system comprising: a processor of a computing device; a memory in communication with the processor, the memory storing program instructions, the processor operative with the program instructions to perform the operations of, a non-transitory computer-readable medium comprising program instructions that, when executed by at least one computing device, direct the at least one computing device to, and a method for administering blood products having a personalized concentration of coagulation factors comprising: obtaining, by a computing device, measured coagulation factor concentrations from a blood sample of a subject; generating, by the computing device, a clotting prediction for the subject based on the measured blood factor concentrations of the subject; determining, by the computing device, one or more coagulation factor concentrations to be administered to the subject based on the clotting prediction; iteratively generating, by the computing device, a new clotting prediction for the subject based on the determined coagulation factors; iteratively determining, by the computing device, additional coagulation factor concentrations to be administered to the subject based on the new clotting prediction until the subject's coagulation factor concentrations are predicted to equilibrate at a predefined normal range; and outputting, by the computing device, a recommended set of coagulation factor concentrations to be administered to the subject based on the determined coagulation factor concentrations.
Claim 2 is directed to the method of claim 1 but further specifies that the prediction comprise a Calibrated Automated Thrombogram (CAT).
Claim 9 is directed to the system of claim 8 but further specifies that the prediction comprise a Calibrated Automated Thrombogram (CAT).
Claim 16 is directed to the CRM of claim 16 but further specifies that the prediction comprise a Calibrated Automated Thrombogram (CAT).
Ghetmiri et al. teaches on page 4, column 1, paragraph 1 “Models that mathematically predict the concentration time-history of thrombin from patient plasma sample coagulation factor concentrations, and that thereby capture the dynamics of the coagulation system process while simultaneously replacing the CAT assay, can be useful in controller development”, on page 6, column 2, paragraph 2 “Since there is merit to administering coagulation factors, the next question is how to administer them to personalize trauma patient treatment. Predictions of effect are first required. Menezes et al. proposed a third-order linear dynamical systems model to rapidly predict CAT trajectories from quickly-measured coagulation factor concentrations. While this model has satisfactory prediction capability, we hypothesized that an embedded constraint limits its prediction accuracy. We investigated whether model improvement was possible without changing model structure, by just adding a single degree-of-freedom parameter to remove this underlying constraint”, reading on wherein the clotting prediction comprises a predicted Calibrated Automated Thrombogram (CAT) trajectory from the measured coagulation factor concentrations.
Claim 3 is directed to the method of claim 1 but further specifies that the prediction is based on a third-order linear dynamics system model having five unconstrained parameters.
Claim 10 is directed to the system of claim 8 but further specifies that the prediction is based on a third-order linear dynamics system model having five unconstrained parameters.
Claim 17 is directed to the CRM of claim 15 but further specifies that the prediction is based on a third-order linear dynamics system model having five unconstrained parameters.
Ghetmiri et al. teaches on page 6, column 2, paragraph 2 “Since there is merit to administering coagulation factors, the next question is how to administer them to personalize trauma patient treatment. Predictions of effect are first required. Menezes et al. proposed a third-order linear dynamical systems model to rapidly predict CAT trajectories from quickly-measured coagulation factor concentrations. While this model has satisfactory prediction capability, we hypothesized that an embedded constraint limits its prediction accuracy. We investigated whether model improvement was possible without changing model structure, by just adding a single degree-of-freedom parameter to remove this underlying constraint…where K0, K1, K2, Kn, and Kd are five patient-specific model parameters (the prior model48 used four parameters with its fifth parameter constrained; the models are mathematically-equivalent), Y(s) is the predicted output thrombin concentration time-history in the frequency domain, and U(s) is a 5 pM impulse input tissue factor (TF) concentration in the frequency domain”, reading on wherein the clotting prediction is based on a third- order linear dynamics system model having five unconstrained parameters.
Claim 4 is directed to the method of claim 1 but further specifies inputting blood factor concentrations, and predicting a CAT trajectory.
Claim 11 is directed to the system of claim 8 but further specifies inputting blood factor concentrations, and predicting a CAT trajectory.
Claim 18 is directed to the CRM of claim 15 but further specifies inputting blood factor concentrations, and predicting a CAT trajectory.
Ghetmiri et al. teaches on page 7, column 1, paragraph 3 “We included the initial PC concentration with the initial concentrations of factors II, V, VII, VIII, IX, X, and antithrombin (ATIII), creating new linear regressions for the five parameters via the same greedy method, the matching pursuit algorithm, as previously”, on page 4, column 1, paragraph 1 “Models that mathematically predict the concentration time-history of thrombin from patient plasma sample coagulation factor concentrations, and that thereby capture the dynamics of the coagulation system process while simultaneously replacing the CAT assay, can be useful in controller development”, on page 6, column 2, paragraph 2 “Since there is merit to administering coagulation factors, the next question is how to administer them to personalize trauma patient treatment. Predictions of effect are first required. Menezes et al. proposed a third-order linear dynamical systems model to rapidly predict CAT trajectories from quickly-measured coagulation factor concentrations. While this model has satisfactory prediction capability, we hypothesized that an embedded constraint limits its prediction accuracy. We investigated whether model improvement was possible without changing model structure, by just adding a single degree-of-freedom parameter to remove this underlying constraint”, reading on further comprising: inputting, by the computing device, the measured blood factor concentrations of the blood sample of the subject into a predictive thrombin dynamics model; executing, by the computing device, the predictive thrombin dynamics model; and predicting, by the computing device using the predictive thrombin dynamics model, a Calibrated Automated Thrombogram (CAT) trajectory for the subject.
Claim 5 is directed to the method of claim 1 but further specifies the composition of blood factor concentrations.
Claim 12 is directed to the system of claim 8 but further specifies the composition of blood factor concentrations.
Claim 19 is directed to the CRM of claim 15 but further specifies the composition of blood factor concentrations.
Ghetmiri et al. teaches in Figure 4 “Trauma patient coagulation factor concentration time history over the first 24 h a. Mean ± one standard deviation of the concentrations of coagulation factors (CFs) during the first 24 h after hospital admission, for factors II, V, VII, VIII, IX, X, ATIII, and protein C of 252 trauma patients”, reading on wherein the measured blood factor concentrations that are input in the predictive thrombin dynamics model comprise initial concentrations of protein C and factors II, V, VII, VIII, IX, X, and antithrombin (ATIII).
Claim 6 is directed to the method of claim 1 but further specifies the concentrations are obtained from a blood coagulation sensor.
Claim 13 is directed to the system of claim 8 but further specifies the concentrations are obtained from a blood coagulation sensor.
Claim 20 is directed to the CRM of claim 15 but further specifies the concentrations are obtained from a blood coagulation sensor.
Ghetmiri et al. teaches on page 2, column 1, paragraph 2 “We seek a dynamic, goal-oriented, model-based, rapid trauma patient treatment strategy that follows the control architecture in Fig. 1a, comprising sensors, actuators, process dynamics, and a controller that uses sensed measurements of coagulation factor concentrations to actuate clotting dynamics by manipulating these concentrations”, in Figure 1, “By using sensors and coagulation assays, coagulation factor concentrations in the blood sample can be quickly quantified”, and page 13, column 2, paragraph 2 “Plasma sample thrombin expression experimental data was obtained using the ThermoFisher Fluoroskan Microplate Fluorometer with Calibrated Automated Thrombogram software”, reading on wherein the measured blood factor concentrations are obtained from a blood coagulation sensor that measures blood factor concentrations of the blood sample.
Claim 7 is directed to the method of claim 1 but further specifies that the recommended set of concentrations move the subject toward equilibrium values of the subject.
Ghetmiri et al. teaches on page 4, column 2, paragraph 3 “Specifically, coagulation factor concentrations move toward an equilibrium concentration that is representative of homeostasis”, reading on wherein the recommended set of coagulation factor concentrations move the coagulation factor concentration values of the subject toward normal equilibrium values of the subject.
Conclusion
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/K.N.A./Examiner, Art Unit 1687
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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