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 .
DETAILED ACTION
Status of the Application
Claims 6-10, 22-25, and 35-45 are currently pending in this case and have been examined and addressed below. This communication is a Final Rejection in response to the Amendment to the Claims and Remarks filed on 02/16/2026.
Claims 1-5, 11-21, 26-34 are currently canceled and not considered at this time.
Claims 6-8, 22 are currently amended.
Claims 35-45 are newly added.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 22-25 and 41-45 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 22 recites the limitation "said herpesvirus vaccine" in line 17. There is insufficient antecedent basis for this limitation in the claim.
As per Claims 22-25 and 41-45, the claims depend on Claim 22 and do not remedy the indefiniteness issues of Claim 22. As dependent claims inherit the deficiencies of the claims they depend on, they are also rejected.
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 6-10 and 35-40 are rejected because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 6-10 and 35-40 fall within the statutory category of a process.
Step 2A, Prong One
As per Claim 6, the limitations of detecting in a bodily fluid sample from the pregnant subject at least two anti-herpesvirus antibody features; classifying the pregnant subject as having a high risk for vertical transmission; and providing a therapeutic intervention to the pregnant subject to reduce risk of vertical transmission, under its broadest reasonable interpretation, covers managing personal behaviors or personal interactions between people as these are behaviors that a physician carries out in the course of treating a pregnant subject. The physician carries out the activities of obtaining patient data, analyzing the data, and making a recommendation for intervention to a patient in the normal activities of patient care. Activities performed by a physician in patient care including personal interactions and personal behaviors fall into the “certain methods of organizing human activity” grouping of abstract ideas. The claim also includes the limitations of applying a machine learning algorithm to the anti-herpesvirus antibody features, wherein the machine learning algorithm has importance measures assigned to the anti-herpesvirus antibody features based on data from a plurality of maternal samples and wherein the machine learning algorithm assigns an importance measure to one or more anti-herpesvirus antibody features, which covers mathematical calculations, relationships, formulas or equations. Applying data to a machine learning algorithm to determine an output (in this case a classification of risk for vertical transmission) is a mathematical calculation/relationship which falls into the abstract grouping of mathematical concepts.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application because the additional elements and combination of additional elements do not impose meaningful limits on the judicial exception. In particular, the claims recite the additional element of using a machine learning model to apply the concepts of the abstract idea in the claims, which is recited at a high-level of generality such that it amounts to mere instructions to apply the exception. As per MPEP 2106.05(f), a claim that recites only the idea of a solution or outcome and fails to recite details of how a solution to a problem is accomplished and use of computers as a tool to perform existing processes such as a commonplace mathematical algorithm applied on a general purpose computer, has been found by the courts to amount to mere instructions to apply the exception and does not integrate the abstract idea into a practical application or provide significantly more. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Because the additional elements do not impose meaningful limitations on the judicial exception, the claim is directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with the respect to integration of the abstract idea into a practical application, the additional element of using a machine learning model to apply the concepts of the abstract idea in the claims amounts to no more than mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. 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 the computer or improves another technology. The claims do not amount to significantly more than the underlying abstract idea.
Dependent Claims
Dependent Claims 7-10 and 35-40 add further limitations which are also directed to an abstract idea. Claims 7-8 and 35-36 include limitations which further specify or limit the elements of the independent claims, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claim 6. Claim 9 includes administering an antiviral therapy to the pregnant subject which falls into the abstract idea of certain methods of organizing human activity. The claim does not recite a particular therapy to the pregnant subject, as an antiviral therapy can be many different particular therapies. Claim 10 recites that the therapy is a monoclonal or polyclonal anti-herpesvirus antibody, but this is also not particular as it could be either a monoclonal or a polyclonal anti-herpesvirus antibody, so this has not limited the claim to the particular treatment that is administered to the subject. Claim 37 recites that providing the therapeutic intervention comprises administering an antiviral therapy to the subject, wherein the antiviral therapy is selected from the group consisting of nucleoside inhibitor, a DNA terminase complex inhibitor, a DNA polymerase inhibitor, and an anti-herpesvirus antibody. This “treatment” does not integrate the abstract idea into a practical application because the treatment is not particular, as per MPEP 2106.04(d)(2). The claim depends on Claim 6 in which the claim recites classifying the subject as having a high risk for vertical transmission using the machine learning model and providing a therapeutic intervention to the pregnant subject to reduce risk of vertical transmission. The claim does not distinguish which pregnant patients are administered the intervention because it does not limit this to those that are determined to be of high risk. Therefore, this can be considered to be merely extra-solution activity because it does not depend on the results of the abstract idea. Additionally, it is not particular because it can be one of any of the antiviral therapies recited in Claim 37, which does not integrate the abstract idea into a practical application. Examiner suggests further specifying in Claim 6 such that the therapeutic intervention is provided to pregnant subjects which have been classified as having a high risk and not broadly to provide the intervention to all pregnant subjects and additionally to specify in Claim 37 which antiviral therapy is being administered. Claims 38-40 further specify or limit Claim 37 and are directed to an abstract idea for the same reasoning. Claims 38-40 are not necessarily required depending on which antiviral therapy is selected in Claim 37. For example, if the antiviral therapy selected from the group in Claim 37 is anti-herpesvirus antibody, then these claims are not given patentable weight. Because the additional elements do not impose meaningful limitations on the judicial exception and the additional elements are well-understood, routine and conventional functionalities in the art, the claims are directed to an abstract idea and are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 6-10 and 35 are rejected under 35 U.S.C. 103 as being unpatentable over Michon (US 2017/0323078 A1), in view of Kaneko (Masatoki Kaneko, Junsuke Muraoka, Kazumi Kusumoto and Toshio Minematsu; Low Maternal Immunoglobulin G Avidity and Single Parity as Adverse Implications of Human Cytomegalovirus Vertical Transmission in Pregnant Women with Immunoglobulin M Positivity; 9 May 2021; Viruses 2021, 13, 866), in view of Gorthi et al. (Aparna Gorthi, Celine Firtion and Jithendra Vepa; Automated risk assessment tool for pregnancy care; 6 September 2009; 31st Annual International Conference of the IEEE EMBS; Pp. 6222-6225), hereinafter Gorthi.
As per Claim 6, Michon discloses a method for treating a pregnant subject having a primary herpesvirus infection to reduce risk of vertical transmission to the subject's offspring ([0739-0740] diagnostic testing for herpes simplex virus type 2, [1638-1640] providing diagnostic tests to pregnant women for immune status including CMV), said method comprising:
(a) detecting in a bodily fluid sample from the pregnant subject at least two anti-herpesvirus antibody features ([0080] assay panels for an individual which represent quantity of antibodies per unit of blood or bodily fluid, [0093] diagnostic data obtained includes gene products such as antibodies, cytokines) selected from the group consisting of:
i. isotype ([0150] tests collected including IgA, IgG, IgM antibodies, see Figs. 6),
ii. subclass,
iii. Fc receptor binding capacity ([0144] tests collected including FcyR2a receptor, see Fig. 6 FcR2A),
iv. viral neutralization, and
v. effector function (e.g., including phagocytosis by monocytes (ADCP) and/or by neutrophils (ADNP), complement deposition (ADCD), antibody dependent cellular cytotoxicity (ADCC)) (Examiner notes that only two of the above are required);
(b) applying a machine learning algorithm to the at least two anti-herpesvirus antibody features ([0860] neural network includes immune status data as input and output data about onset of disease; [0277-0286] data includes antibody data collected such as antibody to CMV as an indicator of poor prognosis, i.e. high risk, and antibody data values indicating improved prognosis, i.e. low risk);
(c) using the machine learning algorithm to classify the subject ([0860] training data for neural network includes immune status data as input and output data about onset of disease; [0277-0286] data includes antibody data collected such as antibody to CMV as an indicator of poor prognosis, i.e. high risk, and antibody data values indicating improved prognosis, i.e. low risk); and
(d) providing a therapeutic intervention to the pregnant subject to reduce risk of vertical transmission ([0753-0754] determining recommendation for vaccine administration to a pregnant woman).
However, Michon may not explicitly disclose the following which is taught by Kaneko: classifying pregnant subjects as high risk for vertical transmission (Page 2 fourth paragraph identifying pregnant women, i.e. classifying, with high risk of vertical transmission).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of classifying a subject as high risk for vertical transmission from Kaneko with the use of machine learning to predict a herpesvirus risk using antibody data for a person from Michon in order to establish preventive strategies for vertical transmission of CMV (Kaneko Page 1 Abstract).
However, Michon and Kaneko may not explicitly disclose the following which is taught by Gorthi: wherein the machine learning algorithm has importance measures assigned to the at least two anti- herpesvirus antibody features based on data from a plurality of maternal samples and wherein the machine learning algorithm assigns an importance measure to the at least two anti-herpesvirus antibody features (Page 6222, Abstract machine learning approach for determining risk category of a subject including using the importance of specific parameters, Page 6224 III. Results classification model includes importance of each parameter)
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of assigning importance measures to the antibody features of the machine learning model from Gorthi with the use of machine learning to predict a herpesvirus risk using antibody data for a person from Michon and Kaneko in order to provide an accurate model for predicting an outcome (Gorthi Page 6225 Col. 1).
As per Claim 7, Michon, Kaneko, and Gorthi discloses the limitations of Claim 6. Michon also teaches the herpesvirus is cytomegalovirus (CMV) and the set of anti-herpesvirus antibody features is derived from antibodies that specifically recognize a CMV surface or structural protein and/or CMV glycoprotein B (gB), a CMV pentamer complex, or a CMV tegument protein ([0212] cytokines are a group of secreted proteins that bind cell-surface receptors, [0150]/[0160] tests to measure immunity to STDs include cytokines).
As per Claim 8, Michon, Kaneko, and Gorthi discloses the limitations of Claim 6. Kaneko also teaches the importance measure assigned to the one or more anti-herpesvirus antibody features of step (b) is greater than the importance measure assigned to avidity of anti-herpesvirus IgG antibodies (Page 6 Table 5 Factors of the model include IgG avidity during pregnancy which has a lower p-value, i.e. importance measure, than the other antibody features).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of determining the order of importance measures from Kaneko with the use of machine learning to predict a herpesvirus risk using antibody data for a person in order to establish preventive strategies for vertical transmission of CMV (Kaneko Page 1 Abstract).
As per Claim 9, Michon, Kaneko, and Gorthi discloses the limitations of Claim 6. Michon also teaches administering an antiviral therapy to the pregnant subject ([0753-0754] determining recommendation for vaccine administration to a pregnant woman).
As per Claim 10, Michon, Kaneko, and Gorthi discloses the limitations of Claim 9. Michon also teaches the antiviral therapy is a monoclonal or polyclonal anti-herpesvirus antibody ([0385] using monoclonal antibodies for generating immune response and inhibit proliferation in vitro).
As per Claim 35, Michon, Kaneko, and Gorthi discloses the limitations of Claim 6. Michon also teaches detecting in a bodily fluid sample from the pregnant subject at least three anti-herpesvirus antibody features ([0080] assay panels for an individual which represent quantity of antibodies per unit of blood or bodily fluid, [0093] diagnostic data obtained includes gene products such as antibodies, cytokines) selected from the group consisting of:
i. isotype ([0150] tests collected including IgA, IgG, IgM antibodies, see Figs. 6),
ii. subclass,
iii. Fc receptor binding capacity ([0144] tests collected including FcyR2a receptor, see Fig. 6 FcR2A),
iv. viral neutralization, and
v. effector function (e.g., including phagocytosis by monocytes (ADCP) and/or by neutrophils (ADNP), complement deposition (ADCD), antibody dependent cellular cytotoxicity (ADCC)) (Examiner notes that only three of the above are required).
Claims 37-40 are rejected under 35 U.S.C. 103 as being unpatentable over Michon (US 2017/0323078 A1), in view of Kaneko (Masatoki Kaneko, Junsuke Muraoka, Kazumi Kusumoto and Toshio Minematsu; Low Maternal Immunoglobulin G Avidity and Single Parity as Adverse Implications of Human Cytomegalovirus Vertical Transmission in Pregnant Women with Immunoglobulin M Positivity; 9 May 2021; Viruses 2021, 13, 866), in view of Gorthi (Aparna Gorthi, Celine Firtion and Jithendra Vepa; Automated risk assessment tool for pregnancy care; 6 September 2009; 31st Annual International Conference of the IEEE EMBS; Pp. 6222-6225), in view of Faller (US 2018/0185345 A1), hereinafter Faller.
As per Claim 37, Michon, Kaneko, and Gorthi discloses the limitations of Claim 6. Michon also teaches administering an antiviral therapy to the subject ([0753-0754] determining recommendation for vaccine administration to a pregnant woman).
However, Michon, Kaneko, and Gorthi may not explicitly disclose the following which is taught by Faller: wherein the antiviral therapy is selected from the group consisting of a nucleoside inhibitor, a DNA terminase complex inhibitor, a DNA polymerase inhibitor, and an anti-herpesvirus antibody ([0011] anti-viral agent is nucleoside analog, [0065] antiviral agents including polymerase inhibitors; Examiner notes that only one of the above group is required).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of an anti-viral including nucleoside inhibitor or polymerase inhibitor from Faller with the use of machine learning to predict a herpesvirus risk using antibody data for a person and administration of antiviral therapy to the person from Michon, Kaneko, and Gorthi in order to treat herpesvirus to reduce risk of exacerbation of autoimmune or inflammatory conditions (Faller [0002]).
As per Claim 38, Michon, Kaneko, Gorthi, and Faller discloses the limitations of Claim 37. However, Michon, Kaneko, and Gorthi may not explicitly disclose the following which is taught by Faller: the nucleoside inhibitor is ganciclovir, valganciclovir, or valacyclovir ([0011]/[0068] anti-viral agent is nucleoside analog such as ganciclovir valganciclovir).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of an anti-viral including specific nucleoside inhibitor from Faller with the use of machine learning to predict a herpesvirus risk using antibody data for a person and administration of antiviral therapy to the person from Michon, Kaneko, and Gorthi in order to treat herpesvirus to reduce risk of exacerbation of autoimmune or inflammatory conditions (Faller [0002]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of an anti-viral including nucleoside inhibitor from Faller with the use of machine learning to predict a herpesvirus risk using antibody data for a person and administration of antiviral therapy to the person in order to treat herpesvirus to reduce risk of exacerbation of autoimmune or inflammatory conditions (Faller [0002]).
As per Claim 39, Michon, Kaneko, Gorthi, and Faller discloses the limitations of Claim 37. However, Michon, Kaneko, and Gorthi may not explicitly disclose the following which is taught by Faller: the DNA terminase complex inhibitor is letemovir (as the DNA terminase complex inhibitor is not selected from the group in Claim 47, this claim does not have patentable weight as unselected in the group).
As per Claim 40, Michon, Kaneko, Gorthi, and Faller discloses the limitations of Claim 37. However, Michon, Kaneko, and Gorthi may not explicitly disclose the following which is taught by Faller: the DNA polymerase inhibitor is foscarnet ([0068] anti-viral agent is includes foscarnet).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of an anti-viral including polymerase inhibitor from Faller with the use of machine learning to predict a herpesvirus risk using antibody data for a person and administration of antiviral therapy to the person from Michon, Kaneko, and Gorthi in order to treat herpesvirus to reduce risk of exacerbation of autoimmune or inflammatory conditions (Faller [0002]).
Claims 22-23, 41 are rejected under 35 U.S.C. 103 as being unpatentable over Michon (US 2017/0323078 A1), in view of Arav-Boger et al. (Ravit Arav-Boger, Yuval S. Boger, Charles B. Foster and Zvi Boger; The Use of Artificial Neural Networks in Prediction of Congenital CMV Outcome from Sequence Data; 2008; Bioinformatics and Biology Insights; 2 281-289), hereinafter Arav-Boger.
As per Claim 22, Michon discloses a method for eliciting an immune response against a herpesvirus in a human subject having a primary herpesvirus infection ([0739-0740] diagnostic testing for herpes simplex virus type 2, [1638-1640] providing diagnostic tests to pregnant women for immune status including CMV), said method comprising:
(a) detecting in a bodily fluid sample from the human subject at least two anti-herpesvirus antibody features ([0080] assay panels for an individual which represent quantity of antibodies per unit of blood or bodily fluid, [0093] diagnostic data obtained includes gene products such as antibodies, cytokines) selected from the group consisting of:
i. isotype ([0150] tests collected including IgA, IgG, IgM antibodies, see Figs. 6),
ii. subclass,
iii. Fc receptor binding capacity ([0144] tests collected including FcyR2a receptor, see Fig. 6 FcR2A),
iv. viral neutralization, and
v. effector function (Examiner notes that only two of the above are required);
(d) determining whether the human subject is a suitable candidate for receiving said herpesvirus vaccine based on the assigned herpesvirus classification ([1703] individual is classified as immunocompetent or immune-deficient based on immunoscore, i.e. result of model; where immunocompetent can receive vaccination and immune-deficient would not get a vaccination); and
(e) responsive to determining that the human subject is suitable, administering said herpesvirus vaccine to the human subject ([0080] recommendations include individual obtaining a vaccine/ administering prophylactic therapies, [0753-0754] determining recommendation for vaccine administration to a pregnant woman).
However, Michon may not explicitly disclose the following which is taught by Arav-Boger: (b) generating an input vector that includes data indicative of the anti-herpesvirus antibody features of the human subject (Page 282 Col. 2 Construction of inputs to the ANN – combining the collected patient data into one vector for each of the samples; Page 283 ANN output - vectors used to predict congenital CMV infection);
(c) applying the input vector to a trained neural network algorithm that is configured to generate an assigned herpesvirus classification to the human subject, wherein the assigned herpesvirus classification is one of a plurality of potential herpesvirus classifications of the neural network algorithm (Page 283 ANN output - vectors used to predict congenital CMV infection based on the input values from the vectors, Page 286 Table 1 shows symptomatic or asymptomatic outcome for each sample input, Table 2 prediction of outcome of the subject as either asymptomatic or symptomatic of CMV/herpesvirus classification).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of generating an input vector from antibody features for use in the machine learning model from Arav-Boger with the use of machine learning to predict a herpesvirus risk using antibody data for a person from Michon in order to use an artificial neural network for classifying a person because artificial neural networks accurately and efficiently analyze sequence of data from large cohorts (Arav-Boger Page 281 Abstract).
As per Claim 23, Michon and Arav-Boger discloses the limitations of Claim 22. Arav-Boger also teaches the plurality of potential herpesvirus classifications includes a negative subject, a positive non-transmitting subject, and positive transmitting subject (Page 282 Materials and Methods/Samples subjects, in this case the neonates are classified as symptomatic or non-symptomatic, Page 283 ANN output ANN generates an output of symptomatic or asymptomatic outcome, Training methods the output of the ANN determines the classification of the subject as symptomatic or asymptomatic, Examiner interprets that if the neonate is symptomatic/asymptomatic that is a determination of transmission/non-transmission from the mother).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of a machine learning model which classifies person for whether they transmit to another subject from Arav-Boger with the use of machine learning to predict a herpesvirus risk using antibody data for a person from Michon in order to use an artificial neural network for classifying a person because artificial neural networks accurately and efficiently analyze sequence of data from large cohorts (Arav-Boger Page 281 Abstract).
As per Claim 41, Michon and Arav-Boger discloses the limitations of Claim 22. Michon also teaches detecting in a bodily fluid sample from the pregnant subject at least three anti-herpesvirus antibody features ([0080] assay panels for an individual which represent quantity of antibodies per unit of blood or bodily fluid, [0093] diagnostic data obtained includes gene products such as antibodies, cytokines) selected from the group consisting of:
i. isotype ([0150] tests collected including IgA, IgG, IgM antibodies, see Figs. 6),
ii. subclass,
iii. Fc receptor binding capacity ([0144] tests collected including FcyR2a receptor, see Fig. 6 FcR2A),
iv. viral neutralization, and
v. effector function (e.g., including phagocytosis by monocytes (ADCP) and/or by neutrophils (ADNP), complement deposition (ADCD), antibody dependent cellular cytotoxicity (ADCC)) (Examiner notes that only three of the above are required).
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Michon (US 2017/0323078 A1), in view of Arav-Boger (Ravit Arav-Boger, Yuval S. Boger, Charles B. Foster and Zvi Boger; The Use of Artificial Neural Networks in Prediction of Congenital CMV Outcome from Sequence Data; 2008; Bioinformatics and Biology Insights; 2 281-289), in view of Gao (US 2020/0279157 A1).
As per Claim 24, Michon and Arav-Boger discloses the limitations of Claim 22. Michon and Arav-Boger may not explicitly disclose the following which is taught by Gao: neural network algorithm is a convolutional neural network algorithm (Abstract convolutional neural network classifier, [0174] convolutional neural network used for classification based on high-dimensional data).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of a machine learning model which is a convolutional neural network from Gao with the use of machine learning to predict a herpesvirus risk using antibody data for a person from Michon and Arav-Boger in order to process raw input data of multiple parameters which are processed and weights are applied to detect patterns in the data which predict the output of the model (Gao [0037]).
Claim 25 is rejected under 35 U.S.C. 103 as being unpatentable over Michon (US 2017/0323078 A1), in view of Arav-Boger (Ravit Arav-Boger, Yuval S. Boger, Charles B. Foster and Zvi Boger; The Use of Artificial Neural Networks in Prediction of Congenital CMV Outcome from Sequence Data; 2008; Bioinformatics and Biology Insights; 2 281-289), in view of Gorthi et al. (Aparna Gorthi, Celine Firtion and Jithendra Vepa; Automated risk assessment tool for pregnancy care; 6 September 2009; 31st Annual International Conference of the IEEE EMBS; Pp. 6222-6225), hereinafter Gorthi.
As per Claim 25, Michon and Arav-Boger discloses the limitations of Claim 22. Michon and Arav-Boger may not explicitly disclose the following which is taught by Gorthi: the input vector is generated to further include a biophysical profile of the human subject (Page 6225 Figure 4 fetal parameters, i.e. biophysical profile, data is used as parameters for the risk prediction, Col. 1 includes that fetal heart rate variability is a parameter for the model).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of the input to the machine learning model including a biophysical profile from Gorthi with the use of machine learning to predict a herpesvirus risk using antibody data for a person from Michon and Arav-Boger in order to provide an accurate model for predicting a clinical outcome (Gorthi Page 6225 Col. 1).
Claim 43 is rejected under 35 U.S.C. 103 as being unpatentable over Michon (US 2017/0323078 A1), in view of Arav-Boger (Ravit Arav-Boger, Yuval S. Boger, Charles B. Foster and Zvi Boger; The Use of Artificial Neural Networks in Prediction of Congenital CMV Outcome from Sequence Data; 2008; Bioinformatics and Biology Insights; 2 281-289), in view of Faller (US 2018/0185345 A1).
As per Claim 43, Michon and Arav-Boger discloses the limitations of Claim 22. Michon and Arav-Boger may not explicitly disclose the following which is taught by Faller: said herpesvirus vaccine comprises at least one CMV antigen or a nucleic acid encoding at least one CMV antigen ([0074] administering an additional agent with the vaccine, such as a vaccine comprising an antigen, [0036] treatment is for herpesvirus such as cytomegalovirus CMV).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of an herpesvirus vaccine including a CMV antigen from Faller with the use of machine learning to predict a herpesvirus risk using antibody data for a person and administration of antiviral therapy to the person from Michon, Arav-Boger in order to treat herpesvirus to reduce risk of exacerbation of autoimmune or inflammatory conditions (Faller [0002]).
Claim 44 is rejected under 35 U.S.C. 103 as being unpatentable over Michon (US 2017/0323078 A1), in view of Arav-Boger (Ravit Arav-Boger, Yuval S. Boger, Charles B. Foster and Zvi Boger; The Use of Artificial Neural Networks in Prediction of Congenital CMV Outcome from Sequence Data; 2008; Bioinformatics and Biology Insights; 2 281-289), in view of Faller (US 2018/0185345 A1), in view of Biswas et al. (US 2021/0128717 A1), hereinafter Biswas.
As per Claim 44, Michon, Arav-Boger, and Faller discloses the limitations of Claim 43. Michon, Arav-Boger, and Faller may not explicitly disclose the following which is taught by Biswas: the at least one CMV antigen is a CMV glycoprotein B (gB) antigen, a CMV pentamer complex antigen, a CMV tegument protein antigen, or a combination thereof ([0022] protein components of the vaccine can include pentameric complex and CMV gB glycoprotein, Examiner notes that only one of the group is required).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of an herpesvirus vaccine including a CMV antigen with protein components from Biswas with the use of machine learning to predict a herpesvirus risk using antibody data for a person and administration of antiviral therapy to the person from Michon, Arav-Boger, and Faller in order to prevent neonatal developmental disabilities from HCMV (Biswas [0005]).
Claim 45 is rejected under 35 U.S.C. 103 as being unpatentable over Michon (US 2017/0323078 A1), in view of Arav-Boger (Ravit Arav-Boger, Yuval S. Boger, Charles B. Foster and Zvi Boger; The Use of Artificial Neural Networks in Prediction of Congenital CMV Outcome from Sequence Data; 2008; Bioinformatics and Biology Insights; 2 281-289), in view of Biswas et al. (US 2021/0128717 A1), hereinafter Biswas.
As per Claim 45, Michon and Arav-Boger discloses the limitations of Claim 22. Michon and Arav-Boger may not explicitly disclose the following which is taught by Biswas: said herpesvirus vaccine comprises a multivalent vaccine comprising a gB antigen and a pentamer antigen or nucleic acids encoding such antigens ([0022] protein components of the vaccine can include pentameric complex and CMV gB glycoprotein).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of an herpesvirus vaccine including a CMV antigen with protein components from Biswas with the use of machine learning to predict a herpesvirus risk using antibody data for a person and administration of antiviral therapy to the person from Michon, Arav-Boger, and Faller in order to prevent neonatal developmental disabilities from HCMV (Biswas [0005]).
Eligible Subject Matter
Examiner notes that Claims 22-25 and 41-45 are eligible subject matter because Claim 22 recites administering a herpesvirus vaccine to the human subject based on the human subject being determined to be a suitable candidate. This is similar to MPEP 2106.04(d)(2) in which the step of “vaccinating a second group of domestic cats in accordance with the lowest-risk vaccination schedule." Was found to apply the exception by using the result of the analysis of the abstract idea to determine the subjects which are to receive the vaccination and type of vaccination. Therefore, this integrates the abstract idea into a practical application. Claims 23-25 and 41-45 depend on Claim 22 and therefore include the same limitations which integrate the abstract idea into a practical application.
Response to Arguments
Applicant’s arguments, see Pages 7-12, “Rejections Under 35 U.S.C. §101”, filed 02/16/2026 with respect to claims 1-10, 22-25, and 27-34 have been fully considered.
With regard to independent claim 6:
Applicant argues that the claim integrates the abstract idea into a practical application because the claim recites detecting in a bodily fluid sample from the pregnant subject at least two anti-herpesvirus antibody features, and providing a therapeutic intervention to the pregnant subject to reduce risk of vertical transmission. Examiner respectfully disagrees. With respect to detecting in a bodily fluid sample at least two anti-herpesvirus antibody features, Examiner notes that this is part of diagnosing a patient which is activity performed by a physician when examining a patient. The claim does not specify how the antibody features are detected other than there is some interaction with a bodily fluid sample. Therefore, the claim can be performed by a physician during the course of diagnosing or examining a patient. With respect to the providing a therapeutic intervention to the pregnant subject to reduce risk of vertical transmission, Examiner notes that this is recited broadly such that it can be merely telling a pregnant subject what intervention has been recommended. This is a routine part of physician and patient interaction and therefore falls into the abstract grouping of certain methods of organizing human activity. This limitation does not actually recite any administration of a treatment or prophylaxis and additionally does not provide a particular treatment or prophylaxis. Therefore, claim 6 remains directed to an abstract idea.
Applicant argues that claim 6 recites a method of treatment for a specific patient to achieve a specific outcome. Examiner respectfully disagrees. As stated above, the claim recites providing a therapeutic intervention to the pregnant subject, which is recited broadly such that it can be merely telling a pregnant subject what intervention has been recommended. This limitation does not actually recite any administration of a treatment or prophylaxis and additionally does not provide a particular treatment or prophylaxis.
Applicant argues that claim 6 amounts to significantly more than the abstract idea because the claim recites detecting in a bodily fluid sample from the pregnant subject at least two anti-herpesvirus antibody features, applying a machine learning algorithm to the antibody features, using the machine learning algorithm to classify the subject as having a high risk for transmission, and providing a therapeutic intervention to the pregnant subject, which are discrete recitations that form a non-conventional and non-generic ordered combination of steps. Examiner respectfully disagrees. The order of the steps that make up the abstract idea itself are still directed to the abstract idea. No matter how much of an advance in the field the claims recite, the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the nonabstract application realm. An advance of that nature is ineligible for patenting.
With regard to independent Claim 22:
Applicant argues similar to the arguments related to Claim 6. Unlike Claim 6, Claim 22 recites determining whether the human subject is a suitable candidate for receiving herpesvirus vaccine based on the assigned classification, and responsive to determining that the human subject is suitable, administering said herpesvirus vaccine to the human subject. These elements integrate the abstract idea into a practical application because they use the results of the data analysis of the abstract idea (determining the subject is a suitable candidate for receiving herpesvirus vaccine) and based on the analysis, actively recite the administration of a particular treatment for a particular group of patients or condition (herpesvirus vaccine). Therefore, the 101 rejection for claims 22-25 and 41-45 has been withdrawn.
Applicant’s arguments, see Pages 12-14, “Rejections Under 35 U.S.C. §103”, filed 02/16/2026 with respect to claims 1-10, 22-25, and 27-34 have been fully considered but they are not persuasive.
Applicant argues that the combination of references does not teach the limitations of Claims 6 and 22. Examiner respectfully disagrees. The arguments related to the FcRIIa receptor are moot as the claim only requires two of the features and Michon also teaches isotype and effector function. Applicant argues that the references do not teach the subject having a herpesvirus infection. Examiner respectfully disagrees. This is taught in Michon as cited in the rejection above where Michon discloses testing a pregnant subject for disease to determine risk for the passing to the newborn. Therefore, the rejections are maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/EVANGELINE BARR/Primary Examiner, Art Unit 3682
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