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 .
Response to Amendment
The IDSes have been considered.
In light of the amendments, the previous 112(f) claim interpretation has been withdrawn.
In light of the amendments, the previous 112(b) rejection has been withdrawn.
In light of the amendments, the claims are rejected under 35 U.S.C. 101.
In light of the amendments, the claims are rejected under 35 U.S.C. 103.
Notice to Applicant
In the amendment dated 12/15/2025, the following has occurred: claims 1, 5-6, 9-11, and 16 have been amended; claims 2-3, 12-13, and 17 have been canceled; claims 4, 7-8, and 14-15 remain unchanged; and claims 18-23 have been added.
Claims 1, 4-11, 14-16, and 18-23 are pending.
Effective Filing Date: 02/24/2022
Response to Arguments
Claim Interpretation:
Applicant amended the claims to overcome the previous claim interpretation. Examiner withdraws this interpretation.
35 U.S.C. 112(b) Rejections:
Applicant amended the claims to overcome this previous claim rejection. Examiner withdraws this rejection.
35 U.S.C. 101 Rejections:
Applicant argues that the amended claims overcome the 101 because the claim limitations cannot be practically performed within the human mind or with the aid of a pen and paper. The abstract idea of the present claims however is directed to certain methods of organizing human activity, not mental processes.
Applicant further states that there is an improvement to predicting a presence of a disease in a subject. This improvement however seems to be a biproduct of an application of a trained machine learning model with an abstract idea.
35 U.S.C. 103 Rejections:
Applicant argues that previous claims 2 and 3 are not taught using the previous rejection, and the subject matter of those claims were rolled into the independent claims. Examiner however respectfully disagrees that the updated independent claims are not taught using the previous references. For example, the “requirement scenario” is broad enough to encompass anything associated with a provider and also anything that involves a determination of further testing needed. The sensitivity of the model is also based on this. Therefore, the model would have to be based on a need to require further testing. The Kushwah et al. reference teaches of outputting suggestions for further testing, and it also outputs a determined confidence level. A determined confidence level with an additional suggestion for further testing meets these current independent claim limitations.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 08/20/2025 and 01/05/2026 were received. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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, 4-11, 14-16, and 18-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1, 4-10, 16, and 18-20 are drawn to systems and claim 11, 14-15, and 21-23 are drawn to a method, each of which is within the four statutory categories. Claims 1, 4-11, 14-16, and 18-23 are further directed to an abstract idea on the grounds set out in detail below. As discussed below, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea (Step 1: YES).
Step 2A:
Prong One:
Claim 1 recites a system for predicting a presence of a disease in a subject, the system comprising:
a) one or more processors, and
b) a medical database coupled to the one or more processors, the medical database comprising patient data, wherein the one or more processor are configured to:
1) receive a plurality of parameters associated with the subject, wherein the plurality of parameters is obtained from a complete blood count/differential test performed on the subject;
2) determine a threshold for sensitivity and/or specificity associated with c) a trained machine learning model based on a requirement scenario associated with a healthcare provider, wherein the requirement scenario comprises a reduction of infectious disease testing burden or reverse transcriptase polymerase chain reaction (RT-PCR) testing burden, a substitution of infectious disease testing burden, substitution of RT-PCR testing, a determination of a need for testing of subjects, or a combination thereof;
3) predict, using the trained machine learning model, the presence of the disease in the subject based on a specific threshold of the trained machine learning model and the plurality of parameters associated with the subject; and
4) output the prediction on d) an output unit.
Claim 1 recites, in part, performing the steps of 1) receive a plurality of parameters associated with the subject, wherein the plurality of parameters is obtained from a complete blood count/differential test performed on the subject, 2) determine a threshold for sensitivity and/or specificity associated with a model based on a requirement scenario associated with a healthcare provider, wherein the requirement scenario comprises a reduction of infectious disease testing burden or reverse transcriptase polymerase chain reaction (RT-PCR) testing burden, a substitution of infectious disease testing burden, substitution of RT-PCR testing, a determination of a need for testing of subjects, or a combination thereof, 3) predict, using the model, the presence of the disease in the subject based on a specific threshold of the trained machine learning model and the plurality of parameters associated with the subject, and 4) output the prediction. These steps correspond to Certain Methods of Organizing Human Activity, more particularly, managing personal behavior or relationships or interactions between people (including following rules or instructions). For example, the claim describes how one can make a prediction on a subject’s data. Independent claims 11 and 16 recite similar limitations and are also directed to an abstract idea under the same analysis.
Depending claims 4-10, 14-15, and 18-23 include all of the limitations of claims 1 and 11, and therefore likewise incorporate the above described abstract idea. Depending claim 9 adds the additional step of “using the trained machine learning model, predict a need for hospitalization of the subject based on the plurality of the parameters associated with the subject”; claim 10 adds the additional step of “predict occurrence of long COVID-19 in the subject”; claim 15 adds the additional steps of “predicting a need for hospitalization of the subject based on the plurality of the parameters associated with the subject, using the trained machine learning model” and “predicting an occurrence of long COVID-19 in the subject”; claims 18 and 21 add the additional step of “display the prediction”. Additionally, the limitations of depending claims 2-8, 12-14, 19-20, and 23 further specify elements from the claims from which they depend on without adding any additional steps. Furthermore, claim 22 adds a treatment step but it is not particular enough (both the affliction and the treatment, see: MPEP 2106.04(d)(2)). These additional limitations only further serve to limit the abstract idea. Thus, depending claims 4-10, 14-15, and 18-23 are nonetheless directed towards fundamentally the same abstract idea as independent claims 1 and 11 (Step 2A (Prong One): YES).
Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of – using a) one or more processors, b) a medical database coupled to the one or more processors, the medical database comprising patient data, c) a trained machine learning model, and d) an output unit (when considered a computing element) to perform the claimed steps.
The a) one or more processors, b) a medical database coupled to the one or more processors, the medical database comprising patient data, c) a trained machine learning model, and d) an output unit in these steps are recited at a high-level of generality (i.e., as generic components performing generic computer functions) such that they amount to no more than mere instructions to apply the exception using generic computer components (see: Applicant’s specification where there is a lack of description of anything but what may be considered as generic computing components, see MPEP 2106.05(f)).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea (Step 2A (Prong Two): NO).
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a) one or more processors, b) a medical database coupled to the one or more processors, the medical database comprising patient data, c) a trained machine learning model, and d) an output unit to perform the claimed steps amounts to no more than insignificant extra-solution activity in the form of WURC activity (well-understood, routine, and conventional activity), a general linking to a particular technological field, or mere instructions to apply the exception using a generic computer component that does not offer “significantly more” than the abstract idea itself because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of any computer itself, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment. It should be noted that the claims do not include additional elements that amount to significantly more than the judicial exception because the Specification recites mere generic computer components, as discussed above that are being used to apply certain mental steps, certain method steps of organizing human activity, or certain mathematical steps. Specifically, MPEP 2106.05(f) recites that the following limitations are not significantly more:
Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)).
The current invention outputs a predication utilizing a) one or more processors, b) a medical database, c) a trained machine learning model, and d) an output unit, thus these computing components are adding the words “apply it” with mere instructions to implement the abstract idea on a computer and using machine learning.
Mere instructions to apply an exception using generic computer components in the form of WURC activity cannot provide an inventive concept. The claims are not patent eligible (Step 2B: NO).
Claims 1, 4-11, 14-16, and 18-23 are therefore rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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, 4-5, 7-11, 14, 16, and 18-23 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2021/0327562 to Kushwah et al. in view of U.S. 2021/0131938 to Vacca et al.
As per claim 1, Kushwah et al. teaches a system for predicting a presence of a disease in a subject, the system comprising:
--one or more processors; (see: paragraph [0054] where there is a processor)
--a medical database coupled to the one or more processors, the medical database comprising patient data, (see: paragraph [0054] where there is a data storage which comprises of patient data of infectious disease of an individual) wherein the one or more processors are configured to:
--receive a plurality of parameters associated with the subject; (see: paragraph [0070] where there is reception of parameters from a subject)
--determine a threshold for sensitivity and/or specificity associated with a trained machine learning model based on a requirement scenario associated with a healthcare provider, (see: paragraph [0087] where there is a determination of a confidence level associated with infection likelihood satisfies a threshold. Also see: paragraphs [0079] and [0081] where there are suggestions. There is a determination of a threshold for sensitivity associated with a model here and it is based on a requirement scenario associated with a provider (this is broad and can describe anything associated with a provider and anything also associated with determining a threshold/confidence level here. This includes the system suggesting further testing related to the individual in combination with a confidence level being provided)) wherein the requirement scenario comprises a reduction of infectious disease testing burden or reverse transcriptase polymerase chain reaction (RT-PCR) testing burden, a substitution of infectious disease testing burden, substitution of RT-PCR testing, a determination of a need for testing of subjects, or a combination thereof; (see: paragraphs [0079] and [0081] where there is a suggestion of performing additional tests (a need for tests) if the selected analytical model produces a positive result for a probability of an infectious disease. The requirement scenario here is broad and can describe anything associated with a provider and anything also associated with determining a threshold/confidence level here. This includes the system suggesting further testing related to the individual in combination with a confidence level being provided).
--predict, using the trained machine learning model, the presence of the disease in the subject based on a specific threshold of the trained machine learning model and the plurality of parameters associated with the subject; (see: paragraphs [0020], [0066], and [0070] where there is a determination of a presence of a disease in the subject based on a threshold of the model which uses the parameters to assess infectious diseases. Once the threshold is met, a determination/prediction of an infectious disease is made) and
--output the prediction on an output unit (see: paragraph [0025] where an output result is being output by a system).
Kushwah et al. may not further, specifically teach:
--wherein the plurality of parameters is obtained from a complete blood count/differential test performed on the subject.
Vacca et al. teaches:
--wherein the plurality of parameters is obtained from a complete blood count/differential test performed on the subject (see: paragraph [0102] where there is a WBC differential assay used to obtain blood parameter information).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have wherein the plurality of parameters is obtained from a complete blood count/differential test performed on the subject as taught by Vacca et al. in the system as taught by Kushwah et al. with the motivation(s) of providing valuable insight into the condition of a patient (see: paragraph [0001] of Vacca et al.).
As per claim 4, Kushwah et al. and Vacca et al. in combination teaches the system of claim 1, see discussion of claim 1. Kushwah et al. further teaches wherein the plurality of parameters associated with the subject comprises red blood cells (RBC) parameters, platelet parameters, and white blood cells (WBC) parameters (see: paragraph [0093] and paragraph [0161] where there are RBC parameters, platelet parameters (eosinophils and basophiles), and WBC parameters).
As per claim 5, Kushwah et al. and Vacca et al. in combination teaches the system of claim 4, see discussion of claim 4. Vacca et al. further teaches wherein the RBC parameters comprise hemoglobin level, hematocrit, RBC size, hemoglobin level in individual RBC, mean corpuscular volume, mean corpuscular hemoglobin concentration, normal RBCs, number of RBCs with hemoglobin concentrations ≥ 28g/dL and ≤ 41g/dL, number of RBCs with hemoglobin volumes ≥ 60fL and ≤120fL, hemoglobin distribution width, RBC mean corpuscular volume, RBC volume distribution width, hemoglobin content distribution width, mean of hemoglobin content, or combinations thereof (see: paragraph [0066] where there is an MCV).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 1, and incorporated herein.
As per claim 7, Kushwah et al. and Vacca et al. in combination teaches the system of claim 5, see discussion of claim 5. Vacca et al. further teaches wherein the WBC parameters further comprise cell hemoglobin concentration mean, number of RBCs with hemoglobin concentrations greater than 41 g/dL, number of RBCs with hemoglobin concentrations less than 28g/dL, or combinations thereof (see: paragraphs [0022], [0051], [0066], and [0069] where there is a hemoglobin mean).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 1, and incorporated herein.
As per claim 8, Kushwah et al. and Vacca et al. in combination teaches the system of claim 1, see discussion of claim 1. Kushwah et al. further teaches wherein the trained machine learning model is preselected (see: paragraph [0078] where there is a preselected model).
As per claim 9, Kushwah et al. and Vacca et al. in combination teaches the system of claim 1, see discussion of claim 1. Kushwah et al. further teaches wherein the one or more processors are further configured to, using the trained machine learning model, predict a need for stratification/severity or hospitalization of the subject based on the plurality of the parameters associated with the subject (see: paragraph [0097] where there is prediction of a severity of the infectious disease for the subject).
As per claim 10, Kushwah et al. and Vacca et al. in combination teaches the system of claim 1, see discussion of claim 1. Kushwah et al. further teaches wherein the one or more processors are further configured to predict occurrence of long COVID-19 in the subject (see: paragraph [0182] where there is a module that predicts an infection level of the subject, such as for COVID).
As per claim 11, claim 11 is similar to claim 1 and is therefore rejected in a similar manner.
As per claim 14, Kushwah et al. and Vacca et al. in combination teaches the method of claim 11, see discussion of claim 11. Kushwah et al. further teaches wherein the disease is COVID-19 (see: paragraph [0065] where the disease is COVID-19).
As per claim 16, claim 16 is similar to claim 1 and is therefore rejected in a similar manner. Kushwah et al. further teaches a system or a component of the system comprising a non-transitory computer-readable medium with instructions encoded thereon (see: paragraph [0099] where there are instructions and memory).
As per claim 18, Kushwah et al. and Vacca et al. in combination teaches the system of claim 1, see discussion of claim 1. Kushwah et al. further teaches wherein the output unit is a display, (see: paragraph [0102] where there is a display) and
--wherein the display is configured to display the prediction (see: paragraph [0125] where there is outputting of a prediction).
As per claim 19, Kushwah et al. and Vacca et al. in combination teaches the system of claim 1, see discussion of claim 1. Kushwah et al. further teaches wherein the trained machine learning model has been trained using hematology data associated with a plurality of subjects (see: paragraph [0174] where there is blood data captured by the thermal camera and paragraph [0175] where there is training of the system using the data captured by the thermal camera).
As per claim 20, Kushwah et al. and Vacca et al. in combination teaches the system of claim 1, see discussion of claim 1. Kushwah et al. further teaches wherein the disease is COVID-19 (see: paragraph [0096] where there is COVID-19 as the disease).
As per claim 21, Kushwah et al. and Vacca et al. in combination teaches the method of claim 11, see discussion of claim 11. Kushwah et al. further teaches wherein the output unit is a display, (see: paragraph [0102] where there is a display) and
--wherein the display displays the prediction (see: paragraph [0125] where there is outputting of a prediction).
As per claim 22, Kushwah et al. and Vacca et al. in combination teaches the method of claim 11, see discussion of claim 11. Kushwah et al. further teaches:
--treating the subject based on the prediction (see: paragraph [0184] where there is an infectious disease detection system which can be used for medical treatment of the individuals that are infected with the disease).
As per claim 23, Kushwah et al. and Vacca et al. in combination teaches the method of claim 11, see discussion of claim 11. Kushwah et al. further teaches wherein the trained machine learning model is trained using hematology data associated with a plurality of subjects (see: paragraph [0174] where there is blood data captured by the thermal camera and paragraph [0175] where there is training of the system using the data captured by the thermal camera).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2021/0327562 to Kushwah et al. in view of U.S. 2021/0131938 to Vacca et al. as applied to claim 1, and further in view of U.S. 2017/0147768 to Ma et al.
As per claim 6, Kushwah et al. and Vacca et al. in combination teaches the system of claim 4, see discussion of claim 4. The combination may not further, specifically teach wherein the WBC parameters comprise WBC type, WBC percentage, ratio of neutrophils to lymphocytes, ratio of large unstained cells to lymphocytes, number of WBCs indicating pseudobasophilia, WBC maturity parameters, or combinations thereof.
Ma et al. teaches:
--wherein the WBC parameters comprise WBC type, WBC percentage, ratio of neutrophils to lymphocytes, ratio of large unstained cells to lymphocytes, number of WBCs indicating pseudobasophilia, WBC maturity parameters, or combinations thereof (see: paragraph [0042] where there a WBC percentage, etc.).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have wherein the WBC parameters comprise WBC type, WBC percentage, ratio of neutrophils to lymphocytes, ratio of large unstained cells to lymphocytes, number of WBCs indicating pseudobasophilia, WBC maturity parameters, or combinations thereof as taught by Ma et al. in the method as taught by Kushwah et al. and Vacca et al. in combination with the motivation(s) of being other types of blood parameters (see: paragraph [0289] of Ma et al.).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2021/0327562 to Kushwah et al. in view of U.S. 2021/0131938 to Vacca et al. as applied to claim 11, and further in view of U.S. 2020/0253562 to Newberry et al.
As per claim 15, Kushwah et al. and Vacca et al. in combination teaches the method of claim 11, see discussion of claim 11. Kushwah et al. further teaches:
--predicting an occurrence of long COVID-19 in the subject (see: paragraph [0182] where there is a module that predicts an infection level of the subject, such as for COVID).
Kushwah et al. and Vacca et al. in combination may not further, specifically teach:
--predicting a need for hospitalization of the subject based on the plurality of the parameters associated with the subject, using the trained machine learning model.
Newberry et al. teaches:
--predicting a need for hospitalization of the subject based on the plurality of the parameters associated with the subject, using the trained machine learning model (see: paragraphs [0329] and [0330] where there is prediction of a need for hospitalization based on the subject’s parameters).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to predict a need for hospitalization of the subject based on the plurality of the parameters associated with the subject, using the trained machine learning model as taught by Newberry et al. in the method as taught by Kushwah et al. and Vacca et al. in combination with the motivation(s) of assessing a patient’s biometrics in order to determine a solution (see: paragraph [0289] of Newberry et al.).
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 Steven G.S. Sanghera whose telephone number is (571)272-6873. The examiner can normally be reached M-F 7:30-5:00 (alternating Fri).
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/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684