DETAILED ACTION
Notices to Applicant
This communication is a First Action Non-Final on the merits. Claims 1-27 as filed 05/05/2025, are currently pending and have been considered below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
This present disclosure claims the benefit of U.S. Provisional Application No. 63/643,246, filed on 05/06/2024.
Claim Objections
Claims 1, 2, 23, and 25 are objected to because of the following informalities:
Claim 1, line 2 recites “(a) collecting from the patient values for one or more variables” – the limitation should read “(a) collecting from patient values for one or more variables” or “(a) collecting patient values for one or more variables”.
Claim 2, lines 47-48 recites “(c) administering an antiviral drug to a patient who has a threshold score indicative of a likelihood of a therapeutic response response to anti-viral therapy,” – the limitation should read, “(c) administering an antiviral drug to a patient who has a threshold score indicative of a likelihood of a therapeutic response to anti-viral therapy,”.
Claim 2 informally recites the limitations “immunosuppressive medications,” “anti-cancer medications” and in line 9 “antiviral medications” each in italicized font.
Claim 23 informally recites the limitations “anti-cancer medications” and in line 9 “antiviral medications” each in italicized font.
Claim 25 line 10 informally recite the limitation “(EBVCytomegalovirus (CMV),” with an open ended parenthesis, either unintentionally or without closed parenthesis.
Appropriate correction is required.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-27 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1, lines 11 and 13 each recite the limitation “to a patient,” however, it is unclear as to whether the claim is referring to the same or different patient in each limitation. Further, as a result, it is unclear as to whether the “patient values” of line 2 refer to values for the same or different patients as to that of lines 11 and 13. Independent claim 22 recites the same indefinite language and is rejected for at least the same above rationale as that applied to claim 1.
Claim 1, line 7 recites the element “the values for the one or more variables,” however, there is insufficient antecedent basis for this limitation in the claim. Line 2 recites “the patient values for one or more variables” such that it is unclear as to whether “the values” of line 7 are the same or different as “the patient values” of line 2. Independent claim 22 recites the same indefinite language and is rejected for at least the same above rationale as that applied to claim 1.
Claim 2, lines 3-4 recite the limitation “(a) collecting from patient values for one or more variables selected from the group comprising:” is ambiguous because it is an improper recitation of a Markush grouping by reciting “selected from the group comprising” rather than “consisting,” and further the subsequent listing in lines 5-42 do not provide a conjunction at the end of line 38, such that the claim limitation is indefinite. For purpose of examination, Examiner interprets the claim under its broadest scope as the one or more variables comprising at least one of the listed subsequent variables in lines 5-42.
Claim 2, lines 3-4 recite the limitation “(a) collecting from patient values for one or more variables selected from the group comprising:” however, the claim element of “patient values for one or more variables” is previously recited in claim 1, line 2, such that it is unclear as to whether the claimed elements of dependent claim 2 are the same or different as to that of independent claim 1
Claim 23 lines 3-4 recite the limitation “(a) collecting from patient values for one or more variables selected from the group comprising:” is ambiguous because it is an improper recitation of a Markush grouping by reciting “selected from the group comprising” rather than “consisting,” such that the claim limitation is indefinite. Examiner interprets the claim under its broadest scope as the one or more variables comprising at least one of the listed subsequent variables.
Claim 23 line 13 recites the limitation “a neural network (NN),” however, it is unclear as to whether this element is the same or different element as the initially recite neural network of independent claim 22 such that claim 23 is indefinite.
Claims 24-27 each recite the limitation “the group further comprises,” however, there is insufficient antecedent basis for this limitation in the claim.
Examiner Statement - 35 USC § 101
Claims 1-27 recite patent eligible subject matter under 35 U.S.C. 101. That is, although each of the independent claims 1 and 22, are directed to an abstract idea (Mental Process/Certain Methods of Organizing Human Activity), when viewed in combination the additional elements of the claims integrate the identified abstract idea into a practical application. In particular, the limitations including the additional elements of a neural network (NN) performed on one or more computers to produce scores indicative of likelihoods of a therapeutic response, non-response, or anti-therapeutic response to an anti-viral drug and/or a likelihood of a therapeutic response or non-response to Virus-Specific T-Cells (VSTs) and administering an antiviral drug and/or VSTs to a patient who has a threshold score indicative of response to each therapy, such that when viewed as a whole, provides meaningful limits on the identified abstract idea such that the judicial exception recited in the claim is integrated a practical application beyond the identified abstract idea through a particular treatment for a particular disease. This is analogous to the 2019 USPTO SME Example 43, claims 2-5. Independent claim 22 recites substantially similar limitations such that the additional elements of the claim, when viewed in combination, integrate the judicial exception recited in the claims into a practical application. Accordingly, the claims 1-27 recite patent eligible subject matter
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 1-3, 10-14, 17, 19 and 21-24 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2024/0194340 A1 (hereinafter “Neumann”) in view of U.S. 2018/0358132 A1 (hereinafter “Bagaev et al.”).
RE: Claim 1 Neumann teaches the claimed:
1. A method for treating an immunocompromised patient comprising: (a) collecting from the patient values for one or more variables, comprising selecting prior cancer remission or relapse, prior reaction after transplant, a degree of HLA match, type of viral infection, type of comorbidity or infection, viral load, prior receipt of one or more immunosuppressive medications, prior receipt of one or more anti-cancer medications, or prior receipt of one or more antiviral medications ((Neumann, [0065], [0066], [0078]) (a "condition state label," as used in this disclosure, is an element of data identifying and/or describing a current, incipient, or probable future medical condition affecting a human being; medical condition may include a particular disease, one or more symptoms associated with a syndrome, a syndrome, and/or any other measure of current or future health and/or heathy aging. A condition state label may identify a disease including any condition that impairs the normal function of the human body. A condition state label may identify the absence of a disease or condition; A condition state label may identify an acquired disease such as one that begins at some point during one's lifetime, as opposed to disease already present at birth. For example, a condition state label may identify an acquired disease such as viral cardiomyopathy. A condition state label may identify a congenital disease that may be present at birth such as a baby born with human immune deficiency virus (HIV). computing device may receive previously collected user data to generate condition state training data that includes a plurality of physiological data obtained from user entries and a plurality of correlated user condition state labels obtained from user entries. In an embodiment, one or more biological extraction, one or more elements of user physiological data, and/or one or more condition state label may be stored in a user database));
(b) inputting the values for the one or more variables to a neural network (NN) performed on one or more computers to produce (i) a score indicative of likelihood of a therapeutic response, non-response, or anti-therapeutic response to an anti-viral drug and/or (ii) a score indicative of likelihood of a therapeutic response or non-response to Virus-Specific T-Cells (VSTs) ((Neumann, [0067], [0096]) (machine-learning model may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes; computing device may generate treatment response score using a score machine-learning model. Score machine-learning model may be consistent with any machine-learning model disclosed as part of this disclosure. Score machine-learning model may be trained using score training data. Score training data may be retrieved from machine-learning database. Score training data may include treatment responses correlated to treatment response scores. Score training data may include treatment responses and associated treatment response categories correlated to treatment response scores))
and
(c) […] drug to a patient who has a threshold score indicative of a likelihood of a therapeutic response to […] therapy; and/or […] to a patient who has a threshold score indicative of a likelihood of a therapeutic response to […] therapy ((Neumann, [0080], [0098]) (A "treatment," as used in this disclosure, is any process given, prescribed, and/or recommended for a condition which may be identified using any condition state label 116. A treatment may include a medication including for example any prescription and/or non-prescription medications such as vitamins, herbs, supplements, homeopathic remedies, nutraceuticals, minerals, cosmetics, prescription medications dispensed at a pharmacy, and the like, and can include a meditation program such as for example a meditation practice to be implemented into a user's everyday life routine and may include one or more procedures including for example surgical and/or non-surgical procedures; computing device may generate healthcare notification as a function of comparing treatment response score to treatment response score threshold)).
Neumann fails to explicitly teach, but Bagaev et al. teaches the claimed:
(c) administering an antiviral drug to a patient who has a threshold score indicative of a likelihood of a therapeutic response to anti-viral therapy; and/or administering VSTs to a patient who has a threshold score indicative of a likelihood of a therapeutic response to VST therapy ((Bagaev et al., [0173], [0334], [0363]) (Immunotherapy Portion may include patient specific information as it relates to an immunotherapy. Immunotherapy Portion may provide such information for different immunotherapies, for example, immune checkpoint blockade therapies, anti-cancer vaccine therapies, and T cell therapies; each of the therapies are selected from the group consisting of: surgery, radiation therapy, chemotherapy, immunotherapy, viral therapy, targeted therapy, hormone therapy, transplants, phototherapy, cryotherapy, and hyperthermia. In some embodiments, each of the therapies are selected from immunotherapy and targeted therapy; recommending at least one of the plurality of therapies for the subject based on the determined therapy scores. Some embodiments include ranking the plurality of therapies based on the determined therapy scores. In some embodiments, recommending the at least one of the plurality of therapies comprises: ranking the plurality of therapies based on the determined therapy scores; and recommending at least a threshold number of top-ranked therapies for the subject)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the administering of a recommended determined therapy such as viral therapy, anti-cancer therapies, and T cell therapies as taught by Bagaev et al. within the method and system for customizing treatments as taught by Neumann et al. with the motivation of correctly selecting one or more effective therapies for personalized care of patients for overall wellbeing and survival of the subject (Bagaev et al. [0007]).
RE: Claim 2 Neumann and Bagaev et al. teach the claimed:
2. The method for treating an immunocompromised patient according to claim 1 further comprising: (a) collecting from patient values for one or more variables selected from the group comprising: transplant donor and recipient age and sex, presence or absence of inborn error of immunity, malignant, non-malignant hematology condition, or other primary immunodeficiency, upon original diagnosis, presence or absence of partial or complete cancer remission including no detectable cancer, reduction or growth of a tumor, higher or lower number of cancer cells compared to prior levels, and symptomatic improvement or regression compared to a prior level, prior graft-vs-host reaction after transplant, presence or absence of myeloablative conditioning regiment (MA), reduced intensity conditioning regimen RIC), or no conditioning regiment (NMA), transplant donor type including mismatched related donor, matched related donor, matched unrelated donor, umbilical cord cell transplant, or no donor, a degree of HLA match ranging from 1 to 6 based on the number of major alleles shared, wherein said major alleles include HLA-A, HLA-B, HLA-C and HLA-DR, HLA-DQ and HLA-DP, cellular depletion or ablation of TCRap, CD 19, naive T cells (CD45RA+ T cells) and/or CD34+ T cells, a level of CD8+ or CD8+ T cells or a ratio of CD4+ cells to CD8+ T cells or a higher or lower level compared to a prior level, type of viral infection comprising adenovirus (AdV), Epstien-Barr Virus (EBV), Cytomegalovirus (CMV), Herpes Simplex Virus (HSV), human herpes virus 8, Varicella-Zoster virus, or human papillomarvirus, type of comorbidity or infection caused by an opportunistic virus, bacterium, fungi, or parasite, viral load at a time of infusion of antiviral drug or VST, wherein viral load can be measured in IU/ml by PCR, prior receipt of one or more immunosuppressive medications comprising systemic corticosteroids, Budesonide, Tacrolimus (FK), Cyclosporine (CsA), Mycophenolic acid (MMF), Sirolimus, Anti-thymocyte globulin (ATG), Alemtuzumab (Campath), antivirals including Ganciclovir, Valganciclovir, Foscarnet, Cidofovir, Brincidofovir, or Rituximab,prior receipt of one or more anti-cancer medications comprising azacitidine, doxorubicin, fludarabine, capecitabine, methotrexate, pembrolizumab, cyclophosphamide, clofarabine, fluorouracil, mercaptopurine, altretamine, bendamustine, busulfan, carboplatin, dacarbazine, daunorubicin, floxuridine, gemcitabine, trastuzumab, hydroxyurea, ifosfamine, melphaslan, nivolumab, paclitaxel, or other anticancer or checkpoint inhibitor,prior receipt of one or more antiviral medications comprising oseltamivir, acyclovir, entecavir, peramivir, valacyclovir, amantadine, famciclovir, ribavirin, adefovir, emtrictabine, foscarnet, ganciclovir, lamivudine, telbivudine, zanamivir, zanamivir, baloxavir marboxil, brivudine, cidofovir, laninamivir, sofosbuvir, or tenofovir ((Neumann, [0065], [0066], [0078]) (a "condition state label," as used in this disclosure, is an element of data identifying and/or describing a current, incipient, or probable future medical condition affecting a human being; medical condition may include a particular disease, one or more symptoms associated with a syndrome, a syndrome, and/or any other measure of current or future health and/or heathy aging. A condition state label may identify a disease including any condition that impairs the normal function of the human body. A condition state label may identify the absence of a disease or condition; Progression may indicate a terminal phase, such as a disease where a user will die soon, such as terminal cancer. Progression may indicate an extent of disease such as a localized disease that affects only one part of the body, such as athlete's foot or an eye infection. Progression may indicate disseminated disease that has spread to other areas of the body, such as cancer. Progression may indicate systemic disease, such as a disease that affects the entire body such as influenza or high blood pressure. Progression may indicate diseases that may be classified by involved organ system))
(b) inputting the values for the one or more variables to a neural network (NN) performed on one or more computers to produce (i) a score indicative of likelihood of a therapeutic response, non-response, or anti-therapeutic response to an anti-viral drug and/or (ii) a score indicative of likelihood of a therapeutic response or non-response to Virus-Specific T-Cells (VSTs) ((Neumann, [0067], [0096]) (machine-learning model may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes; computing device may generate treatment response score using a score machine-learning model. Score machine-learning model may be consistent with any machine-learning model disclosed as part of this disclosure. Score machine-learning model may be trained using score training data. Score training data may be retrieved from machine-learning database. Score training data may include treatment responses correlated to treatment response scores. Score training data may include treatment responses and associated treatment response categories correlated to treatment response scores))
and
(c) [..] drug to a patient who has a threshold score indicative of a likelihood of a therapeutic response response to […] therapy; and/or [..] to a patient who has a threshold score indicative of a likelihood of a therapeutic response to […] therapy. ((Neumann, [0080], [0098]) (A "treatment," as used in this disclosure, is any process given, prescribed, and/or recommended for a condition which may be identified using any condition state label 116. A treatment may include a medication including for example any prescription and/or non-prescription medications such as vitamins, herbs, supplements, homeopathic remedies, nutraceuticals, minerals, cosmetics, prescription medications dispensed at a pharmacy, and the like, and can include a meditation program such as for example a meditation practice to be implemented into a user's everyday life routine and may include one or more procedures including for example surgical and/or non-surgical procedures; computing device may generate healthcare notification as a function of comparing treatment response score to treatment response score threshold)).
Neumann fails to explicitly teach, but Bagaev et al. teaches the claimed:
(c) administering an antiviral drug to a patient who has a threshold score indicative of a likelihood of a therapeutic response response to anti-viral therapy; and/or administering VSTs to a patient who has a threshold score indicative of a likelihood of a therapeutic response to VST therapy ((Bagaev et al., [0173], [0334], [0363]) (Immunotherapy Portion may include patient specific information as it relates to an immunotherapy. Immunotherapy Portion may provide such information for different immunotherapies, for example, immune checkpoint blockade therapies, anti-cancer vaccine therapies, and T cell therapies; each of the therapies are selected from the group consisting of: surgery, radiation therapy, chemotherapy, immunotherapy, viral therapy, targeted therapy, hormone therapy, transplants, phototherapy, cryotherapy, and hyperthermia. In some embodiments, each of the therapies are selected from immunotherapy and targeted therapy; recommending at least one of the plurality of therapies for the subject based on the determined therapy scores. Some embodiments include ranking the plurality of therapies based on the determined therapy scores. In some embodiments, recommending the at least one of the plurality of therapies comprises: ranking the plurality of therapies based on the determined therapy scores; and recommending at least a threshold number of top-ranked therapies for the subject)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the administering of a recommended determined therapy such as viral therapy, anti-cancer therapies, and T cell therapies as taught by Bagaev et al. within the method and system for customizing treatments as taught by Neumann et al. with the motivation of correctly selecting one or more effective therapies for personalized care of patients for overall wellbeing and survival of the subject (Bagaev et al. [0007]).
RE: Claim 3 Neumann and Bagaev et al. teach the claimed:
3. The method of claim 1, wherein the immunocompromised patient is infected by an opportunistic virus ((Neumann, [0065]) (condition state label 116 may identify a congenital disease that may be present at birth such as a baby born with human immune deficiency virus (HIV))).
Neumann fails to explicitly teach, but Bagaev et al. teaches the claimed:
and is administered an antiviral drug and/or VST ((Bagaev et al., [0173], [0334], [0363]) (Immunotherapy Portion may include patient specific information as it relates to an immunotherapy. Immunotherapy Portion may provide such information for different immunotherapies, for example, immune checkpoint blockade therapies, anti-cancer vaccine therapies, and T cell therapies; each of the therapies are selected from the group consisting of: surgery, radiation therapy, chemotherapy, immunotherapy, viral therapy, targeted therapy, hormone therapy, transplants, phototherapy, cryotherapy, and hyperthermia. In some embodiments, each of the therapies are selected from immunotherapy and targeted therapy; recommending at least one of the plurality of therapies for the subject based on the determined therapy scores. Some embodiments include ranking the plurality of therapies based on the determined therapy scores. In some embodiments, recommending the at least one of the plurality of therapies comprises: ranking the plurality of therapies based on the determined therapy scores; and recommending at least a threshold number of top-ranked therapies for the subject)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the administering of a recommended determined therapy such as viral therapy, anti-cancer therapies, and T cell therapies as taught by Bagaev et al. within the method and system for customizing treatments as taught by Neumann et al. with the motivation of correctly selecting one or more effective therapies for personalized care of patients for overall wellbeing and survival of the subject (Bagaev et al. [0007]).
RE: Claim 10 Neumann and Bagaev et al. teach the claimed:
10. The method of claim 1, wherein the patient has a primary or secondary immunodeficiency, is infected by an opportunistic virus ((Neumann, [0065]) (condition state label 116 may identify a congenital disease that may be present at birth such as a baby born with human immune deficiency virus (HIV); condition state label may identify an idiopathic disease such as multiple scleroris or diabetes mellitus type 1)).
Neumann fails to explicitly teach, but Bagaev et al. teaches the claimed:
and is administered an antiviral drug and/or VST ((Bagaev et al., [0173], [0334], [0363]) (Immunotherapy Portion may include patient specific information as it relates to an immunotherapy. Immunotherapy Portion may provide such information for different immunotherapies, for example, immune checkpoint blockade therapies, anti-cancer vaccine therapies, and T cell therapies; each of the therapies are selected from the group consisting of: surgery, radiation therapy, chemotherapy, immunotherapy, viral therapy, targeted therapy, hormone therapy, transplants, phototherapy, cryotherapy, and hyperthermia. In some embodiments, each of the therapies are selected from immunotherapy and targeted therapy; recommending at least one of the plurality of therapies for the subject based on the determined therapy scores. Some embodiments include ranking the plurality of therapies based on the determined therapy scores. In some embodiments, recommending the at least one of the plurality of therapies comprises: ranking the plurality of therapies based on the determined therapy scores; and recommending at least a threshold number of top-ranked therapies for the subject)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the administering of a recommended determined therapy such as viral therapy, anti-cancer therapies, and T cell therapies as taught by Bagaev et al. within the method and system for customizing treatments as taught by Neumann et al. with the motivation of correctly selecting one or more effective therapies for personalized care of patients for overall wellbeing and survival of the subject (Bagaev et al. [0007]).
RE: Claim 11 Neumann and Bagaev et al. teach the claimed:
11. The method of claim 1, wherein the patient has a secondary immunodeficiency that comprises infection by HIV, a burn, drug abuse, chemotherapy, radiation therapy, diabetes millitus, malnutrition, or leukemia or other cancer of the immune system, viral hepatitis or other immune complex disease, or multiple myeloma ((Neumann, [0065]) (condition state label 116 may identify a congenital disease that may be present at birth such as a baby born with human immune deficiency virus (HIV); condition state label may identify an idiopathic disease such as multiple scleroris or diabetes mellitus type 1)).
RE: Claim 12 Neumann and Bagaev et al. teach the claimed:
12. The method of claim 1, wherein the NN includes a first NN model and a second NN model cascaded to the first NN model, and inputting the values for the one or more variables to the NN to produce the score includes: inputting the values for the one or more variables to the first NN model to generate synthetic data that are in a larger amount than the values of the one or more variables; and inputting the synthetic data to the second NN model to produce the score ((Neumann, [0067], [0157]) (Data augmentation may be accomplished, without limitation, using interpolation, generation of modified copies of existing entries and/or examples, and/or one or more generative AI processes, for instance using deep neural networks and/or generative adversarial networks; generative processes may be referred to alternatively in this context as "data synthesis" and as creating "synthetic data." Augmentation may include performing one or more transformations on data, such as geometric, color space, affine, brightness, cropping, and/or contrast transformations of images; As a further non-limiting example, a machine-learning model may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of "training" the network, in which elements from a training dataset are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes)).
RE: Claim 13 Neumann and Bagaev et al. teach the claimed:
13. The method of claim 12, wherein the first NN model comprises a generative artificial intelligence (genAI) model, wherein the genAI model comprises a variational autoencoder (VAE) model, a generative adversarial network (GAN) model, or a Gaussian copula synthesizer (GC) model ((Neumann, [0157]) (Data augmentation may be accomplished, without limitation, using interpolation, generation of modified copies of existing entries and/or examples, and/or one or more generative AI processes, for instance using deep neural networks and/or generative adversarial networks; generative processes may be referred to alternatively in this context as "data synthesis" and as creating "synthetic data." Augmentation may include performing one or more transformations on data, such as geometric, color space, affine, brightness, cropping, and/or contrast transformations of images)).
RE: Claim 14 Neumann and Bagaev et al. teach the claimed:
14. The method of claim 12, wherein the second NN model comprises a logistic regression (LR) model, a naive Bayes (NB) model, and/or a support vector machine (SVM) model ((Neuman, [0165]) (Machine-learning algorithms may include support vector machines, including without limitation support vector classification-based regression processes. Machine-learning algorithms may include stochastic gradient descent algorithms, including classification and regression algorithms based on stochastic gradient descent. Machine-learning algorithms may include nearest neighbors algorithms. Machine-learning algorithms may include various forms of latent space regularization such as variational regularization. Machine-learning algorithms may include Gaussian processes such as Gaussian Process Regression. Machine-learning algorithms may include cross-decomposition algorithms, including partial least squares and/or canonical correlation analysis. Machine-learning algorithms may include naive Bayes methods. Machine-learning algorithms may include algorithms based on decision trees, such as decision tree classification or regression algorithms)).
RE: Claim 17 Neumann and Bagaev et al. teach the claimed:
17. The method of claim 1, further comprising: identifying at least one of the variables that contributes most to a predictive ability of the therapeutic approach ((Neumann, [0133]) (A variable ranking having a mathematical expression minimizing the error function may be selected, as representing an optimal expression of relative importance of variables to a system or user)).
RE: Claim 19 Neumann and Bagaev et al. teach the claimed:
19. The method of claim 1, wherein the one or more variables include continuous, binary and/or categorical variables ((Neumann, [0065], [0066], [0078]) (a "condition state label," as used in this disclosure, is an element of data identifying and/or describing a current, incipient, or probable future medical condition affecting a human being; medical condition may include a particular disease, one or more symptoms associated with a syndrome, a syndrome, and/or any other measure of current or future health and/or heathy aging. A condition state label may identify a disease including any condition that impairs the normal function of the human body. A condition state label may identify the absence of a disease or condition; A condition state label may identify an acquired disease such as one that begins at some point during one's lifetime, as opposed to disease already present at birth. For example, a condition state label may identify an acquired disease such as viral cardiomyopathy. A condition state label may identify a congenital disease that may be present at birth such as a baby born with human immune deficiency virus (HIV). computing device may receive previously collected user data to generate condition state training data that includes a plurality of physiological data obtained from user entries and a plurality of correlated user condition state labels obtained from user entries. In an embodiment, one or more biological extraction, one or more elements of user physiological data, and/or one or more condition state label may be stored in a user database)).
RE: Claim 21 Neumann and Bagaev et al. teach the claimed:
21. The method of claim 19, wherein the values of the continuous variables are log normalized ((Neumann, [0145]) (a classifier using a classification algorithm, defined as a processes whereby a computing device and/or any module and/or component operating thereon derives a classifier from training data. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers; As a non-limiting example, training data classifier may classify elements of training data to age groups, biological extraction groups, gender, and treatment preferences)).
RE: Claim 22 Neumann et al. teaches the claimed:
22. A method for treating an immunocompromised patient in need of an anti-cancer medication and/or in need of an anti-viral medication comprising: (a) collecting from patient values for one or more variables, comprising selecting prior cancer remission or relapse, prior reaction after transplant, a degree of HLA match, type of viral infection, type of comorbidity or infection, viral load, prior receipt of one or more immunosuppressive medications, prior receipt of one or more anti-cancer medications, or prior receipt of one or more antiviral medications ((Neumann, [0065], [0066], [0078]) (a "condition state label," as used in this disclosure, is an element of data identifying and/or describing a current, incipient, or probable future medical condition affecting a human being; medical condition may include a particular disease, one or more symptoms associated with a syndrome, a syndrome, and/or any other measure of current or future health and/or heathy aging. A condition state label may identify a disease including any condition that impairs the normal function of the human body. A condition state label may identify the absence of a disease or condition; A condition state label may identify an acquired disease such as one that begins at some point during one's lifetime, as opposed to disease already present at birth. For example, a condition state label may identify an acquired disease such as viral cardiomyopathy. A condition state label may identify a congenital disease that may be present at birth such as a baby born with human immune deficiency virus (HIV). computing device may receive previously collected user data to generate condition state training data that includes a plurality of physiological data obtained from user entries and a plurality of correlated user condition state labels obtained from user entries. In an embodiment, one or more biological extraction, one or more elements of user physiological data, and/or one or more condition state label may be stored in a user database));
(b) inputting the values for the one or more variables to a neural network (NN) performed on one or more computers to produce (i) a score indicative of likelihood of a therapeutic response, non-response, or anti-therapeutic response to an anti-viral drug and/or (ii) a score indicative of likelihood of a therapeutic response or non-response to Virus-Specific T-Cells (VSTs) ((Neumann, [0067], [0096]) (machine-learning model may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes; computing device may generate treatment response score using a score machine-learning model. Score machine-learning model may be consistent with any machine-learning model disclosed as part of this disclosure. Score machine-learning model may be trained using score training data. Score training data may be retrieved from machine-learning database. Score training data may include treatment responses correlated to treatment response scores. Score training data may include treatment responses and associated treatment response categories correlated to treatment response scores)) and
(ci) […] drug to a patient who has a threshold score indicative of a likelihood of a therapeutic response to […] therapy, and/or (c2) […] drug to a patient who has a threshold score indicative of a likelihood of a therapeutic response to […] therapy; and/or (c3) [..] to a patient who has a threshold score indicative of a likelihood of a therapeutic response to […] therapy ((Neumann, [0080], [0098]) (A "treatment," as used in this disclosure, is any process given, prescribed, and/or recommended for a condition which may be identified using any condition state label. A treatment may include a medication including for example any prescription and/or non-prescription medications such as vitamins, herbs, supplements, homeopathic remedies, nutraceuticals, minerals, cosmetics, prescription medications dispensed at a pharmacy, and the like, and can include a meditation program such as for example a meditation practice to be implemented into a user's everyday life routine and may include one or more procedures including for example surgical and/or non-surgical procedures; computing device may generate healthcare notification as a function of comparing treatment response score to treatment response score threshold)).
Neumann fails to explicitly teach, but Bagaev et al. teaches the claimed:
(ci) administering an antiviral drug to a patient who has a threshold score indicative of a likelihood of a therapeutic response to anti-viral therapy, and/or (c2) administering an anti-cancer drug to a patient who has a threshold score indicative of a likelihood of a therapeutic response to anti-cancer therapy; and/or (c3) administering VSTs to a patient who has a threshold score indicative of a likelihood of a therapeutic response to VST therapy ((Bagaev et al., [0173], [0334], [0363]) (Immunotherapy Portion may include patient specific information as it relates to an immunotherapy. Immunotherapy Portion may provide such information for different immunotherapies, for example, immune checkpoint blockade therapies, anti-cancer vaccine therapies, and T cell therapies; each of the therapies are selected from the group consisting of: surgery, radiation therapy, chemotherapy, immunotherapy, viral therapy, targeted therapy, hormone therapy, transplants, phototherapy, cryotherapy, and hyperthermia. In some embodiments, each of the therapies are selected from immunotherapy and targeted therapy; recommending at least one of the plurality of therapies for the subject based on the determined therapy scores. Some embodiments include ranking the plurality of therapies based on the determined therapy scores. In some embodiments, recommending the at least one of the plurality of therapies comprises: ranking the plurality of therapies based on the determined therapy scores; and recommending at least a threshold number of top-ranked therapies for the subject)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the administering of a recommended determined therapy such as viral therapy, anti-cancer therapies, and T cell therapies as taught by Bagaev et al. within the method and system for customizing treatments as taught by Neumann et al. with the motivation of correctly selecting one or more effective therapies for personalized care of patients for overall wellbeing and survival of the subject (Bagaev et al. [0007]).
RE: Claim 23 Neumann and Bagaev et al. teach the claimed:
23. The method of claim 22, further comprising: (a) collecting from the patient values for at least one variable selected from the group comprising: prior receipt of one or more anti-cancer medications comprising azacitidine, doxorubicin, fludarabine, capecitabine, methotrexate, pembrolizumab, cyclophosphamide, clofarabine, fluorouracil, mercaptopurine, altretamine, bendamustine, busulfan, carboplatin, dacarbazine, daunorubicin, floxuridine, gemcitabine, trastuzumab, hydroxyurea, ifosfamine, melphaslan, nivolumab, paclitaxel, or other anticancer or checkpoint inhibitor, or prior receipt of one or more antiviral medications comprising oseltamivir, acyclovir, entecavir, peramivir, valacyclovir, amantadine, famciclovir, ribavirin, adefovir, emtrictabine, foscarnet, ganciclovir, lamivudine, telbivudine, zanamivir, zanamivir, baloxavir marboxil, brivudine, cidofovir, laninamivir, sofosbuvir, or tenofovir ((Neumann, [0065], [0066], [0078]) (a "condition state label," as used in this disclosure, is an element of data identifying and/or describing a current, incipient, or probable future medical condition affecting a human being; medical condition may include a particular disease, one or more symptoms associated with a syndrome, a syndrome, and/or any other measure of current or future health and/or heathy aging. A condition state label may identify a disease including any condition that impairs the normal function of the human body. A condition state label may identify the absence of a disease or condition; Progression may indicate a terminal phase, such as a disease where a user will die soon, such as terminal cancer. Progression may indicate an extent of disease such as a localized disease that affects only one part of the body, such as athlete's foot or an eye infection. Progression may indicate disseminated disease that has spread to other areas of the body, such as cancer. Progression may indicate systemic disease, such as a disease that affects the entire body such as influenza or high blood pressure. Progression may indicate diseases that may be classified by involved organ system))
(b) inputting the values for the one or more variables to a neural network (NN) performed on one or more computers to produce (i) a score indicative of likelihood of response, non-response, or anti-therapeutic response to an anti-viral drug and/or (ii) a score indicative of likelihood of response, non-response, or antitherapeutic response to Virus-Specific T-Cells (VSTs) ((Neumann, [0067], [0096]) (machine-learning model may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes; computing device may generate treatment response score using a score machine-learning model. Score machine-learning model may be consistent with any machine-learning model disclosed as part of this disclosure. Score machine-learning model may be trained using score training data. Score training data may be retrieved from machine-learning database. Score training data may include treatment responses correlated to treatment response scores. Score training data may include treatment responses and associated treatment response categories correlated to treatment response scores))
and
(c) [..] drug to a patient who has a threshold score indicative of a likelihood of a therapeutic response response to […] therapy; and/or [..] to a patient who has a threshold score indicative of a likelihood of a therapeutic response to […] therapy. ((Neumann, [0080], [0098]) (A "treatment," as used in this disclosure, is any process given, prescribed, and/or recommended for a condition which may be identified using any condition state label 116. A treatment may include a medication including for example any prescription and/or non-prescription medications such as vitamins, herbs, supplements, homeopathic remedies, nutraceuticals, minerals, cosmetics, prescription medications dispensed at a pharmacy, and the like, and can include a meditation program such as for example a meditation practice to be implemented into a user's everyday life routine and may include one or more procedures including for example surgical and/or non-surgical procedures; computing device may generate healthcare notification as a function of comparing treatment response score to treatment response score threshold)).
Neumann fails to explicitly teach, but Bagaev et al. teaches the claimed:
(c) administering an antiviral drug to a patient who has a threshold score indicative of a likelihood of a therapeutic response to anti-viral therapy; and/or administering VSTs to a patient who has a threshold score indicative of a likelihood of a therapeutic response to VST therapy ((Bagaev et al., [0173], [0334], [0363]) (Immunotherapy Portion may include patient specific information as it relates to an immunotherapy. Immunotherapy Portion may provide such information for different immunotherapies, for example, immune checkpoint blockade therapies, anti-cancer vaccine therapies, and T cell therapies; each of the therapies are selected from the group consisting of: surgery, radiation therapy, chemotherapy, immunotherapy, viral therapy, targeted therapy, hormone therapy, transplants, phototherapy, cryotherapy, and hyperthermia. In some embodiments, each of the therapies are selected from immunotherapy and targeted therapy; recommending at least one of the plurality of therapies for the subject based on the determined therapy scores. Some embodiments include ranking the plurality of therapies based on the determined therapy scores. In some embodiments, recommending the at least one of the plurality of therapies comprises: ranking the plurality of therapies based on the determined therapy scores; and recommending at least a threshold number of top-ranked therapies for the subject)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the administering of a recommended determined therapy such as viral therapy, anti-cancer therapies, and T cell therapies as taught by Bagaev et al. within the method and system for customizing treatments as taught by Neumann et al. with the motivation of correctly selecting one or more effective therapies for personalized care of patients for overall wellbeing and survival of the subject (Bagaev et al. [0007]).
RE: Claim 24 Neumann and Bagaev et al. teach the claimed
24. The method according to claim 22, wherein the group further comprises: presence or absence of inborn error of immunity, malignant, non-malignant hematology condition, or other diagnosis, upon original diagnosis, presence or absence of partial or complete cancer remission including no detectable cancer, reduction or growth of a tumor, higher or lower number of cancer cells compared to prior levels, and symptomatic improvement or regression compared to a prior level, prior cancer relapse, transplant donor and recipient age and sex, prior graft-vs-host reaction after transplant, or myeloablative conditioning regiment (MA), reduced intensity conditioning regimen RIC), or no conditioning regiment (NMA) , transplant donor type including mismatched related donor, matched related donor, matched unrelated donor, umbilical cord cell transplant, or no donor ((Neumann, [0065], [0066], [0078]) (a "condition state label," as used in this disclosure, is an element of data identifying and/or describing a current, incipient, or probable future medical condition affecting a human being; medical condition may include a particular disease, one or more symptoms associated with a syndrome, a syndrome, and/or any other measure of current or future health and/or heathy aging. A condition state label may identify a disease including any condition that impairs the normal function of the human body. A condition state label may identify the absence of a disease or condition; A condition state label may identify an acquired disease such as one that begins at some point during one's lifetime, as opposed to disease already present at birth. For example, a condition state label may identify an acquired disease such as viral cardiomyopathy. A condition state label may identify a congenital disease that may be present at birth such as a baby born with human immune deficiency virus (HIV). computing device may receive previously collected user data to generate condition state training data that includes a plurality of physiological data obtained from user entries and a plurality of correlated user condition state labels obtained from user entries. In an embodiment, one or more biological extraction, one or more elements of user physiological data, and/or one or more condition state label may be stored in a user database));
Claims 4-9 and 25-27 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2024/0194340 A1 (hereinafter “Neumann”) in view of U.S. 2018/0358132 A1 (hereinafter “Bagaev et al.”) and further in view of Ottaviano, Giorgio et al. “Adoptive T Cell Therapy Strategies for Viral Infections in Patients Receiving Haematopoietic Stem Cell Transplantation.” Cells vol. 8,1 47. 14 Jan. 2019 (hereinafter “Ottaviano et al.”).
RE: Claim 4 Neumann and Bagaev et al. teach the claimed:
4. The method of claim 1.
Neumann and Bagaev et al. fail to explicitly teach, but Ottaviano et al. teaches the claimed:
wherein the immunocompromised patient is infected by cytomegalovirus, Epstein-Barr virus, or Adenovirus; and wherein the patient is administered Ganciclovir, Valganciclovir, Foscarnet, Cidofovir, Brincidofovir, Acyclovir and Rituximab and/or administered VSTs that recognize Cytomegalovirus, Epstein-Barr virus, and/or Adenovirus ((Ottaviano et al., pgs. 2-3) (Routine monitoring of viral reactivation in the post-transplant setting usually includes molecular detection of viral DNA of the three most frequent viruses responsible for refractory infections, namely human adenovirus (AdV), cytomegalovirus (CMV) and Epstein–Barr virus (EBV).; Table 1 Reported incidence of AdV, CMV and EBV post-transplant reactivation in peripheral blood, and disease-specific pharmacological treatment and rate of treatment response in children and adults undergoing haematopoietic stem cell transplantation; Cidofovir, brincidofovir, Gancyclovir, forscarnet, valgancyclovir, Rituximab))).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the treatment of immunocomprimised patients infected by cytomegalovirus, Epstein-Barr virus, or Adenovirus with Ganciclovir, Valganciclovir, Foscarnet, Cidofovir, Brincidofovir, Acyclovir and Rituximab as taught by Ottaviano et al. within the method and system for customizing treatments as taught by Neumann et al. the administering of a recommended determined therapy such as viral therapy, anti-cancer therapies, and T cell therapies as taught by Bagaev et al. with the motivation of controlling viral reactivation in immunosuppressed patients to overcome adverse outcomes following virus-associated disease in patients (Ottaviano et al. pg. 1).
RE: Claim 5 Neumann and Bagaev et al. teach the claimed:
5. The method of claim 1.
Neumann and Bagaev et al. fail to explicitly teach, but Ottaviano et al. teaches the claimed:
wherein the immunocompromised patient has undergone a autograft, an allograft, or a xenograft and is infected by an opportunistic virus and is administered an antiviral drug and/or VST ((Ottaviano et al., pgs. 2-4) (Routine monitoring of viral reactivation in the post-transplant setting usually includes molecular detection of viral DNA of the three most frequent viruses responsible for refractory infections, namely human adenovirus (AdV), cytomegalovirus (CMV) and Epstein–Barr virus (EBV).; Table 1 Reported incidence of AdV, CMV and EBV post-transplant reactivation in peripheral blood, and disease-specific pharmacological treatment and rate of treatment response in children and adults undergoing haematopoietic stem cell transplantation; Cidofovir, brincidofovir, Gancyclovir, forscarnet, valgancyclovir, Rituximab; Use of in vitro or in vivo T cell depletion is universally accepted as a main strategy to avoid graft rejection and reduce graft versus host disease for transplants from unrelated or mismatched family donors.)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the treatment of immunocomprimised patients infected by cytomegalovirus, Epstein-Barr virus, or Adenovirus with Ganciclovir, Valganciclovir, Foscarnet, Cidofovir, Brincidofovir, Acyclovir and Rituximab including following a graft procedure as taught by Ottaviano et al. within the method and system for customizing treatments as taught by Neumann et al. the administering of a recommended determined therapy such as viral therapy, anti-cancer therapies, and T cell therapies as taught by Bagaev et al. with the motivation of controlling viral reactivation in immunosuppressed patients to overcome adverse outcomes following virus-associated disease in patients (Ottaviano et al. pg. 1).
RE: Claim 6 Neumann and Bagaev et al. teach the claimed:
6. The method of claim 1.
Neumann and Bagaev et al. fail to explicitly teach, but Ottaviano et al. teaches the claimed:
wherein the immunocompromised patient has undergone a autograft, an allograft, or a xenograft and is infected by cytomegalovirus, Epstein-Barr virus, or Adenovirus; wherein the patient is administered Ganciclovir, Valganciclovir, Foscarnet, Cidofovir, Brincidofovir, Acyclovir and Rituximab and/or administered VSTs that recognize cytomegalovirus, Epstein-Barr virus, and/or Adenovirus ((Ottaviano et al., pgs. 2-4) (Routine monitoring of viral reactivation in the post-transplant setting usually includes molecular detection of viral DNA of the three most frequent viruses responsible for refractory infections, namely human adenovirus (AdV), cytomegalovirus (CMV) and Epstein–Barr virus (EBV).; Table 1 Reported incidence of AdV, CMV and EBV post-transplant reactivation in peripheral blood, and disease-specific pharmacological treatment and rate of treatment response in children and adults undergoing haematopoietic stem cell transplantation; Cidofovir, brincidofovir, Gancyclovir, forscarnet, valgancyclovir, Rituximab; Use of in vitro or in vivo T cell depletion is universally accepted as a main strategy to avoid graft rejection and reduce graft versus host disease for transplants from unrelated or mismatched family donors.)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the treatment of immunocomprimised patients infected by cytomegalovirus, Epstein-Barr virus, or Adenovirus with Ganciclovir, Valganciclovir, Foscarnet, Cidofovir, Brincidofovir, Acyclovir and Rituximab including following a graft procedure as taught by Ottaviano et al. within the method and system for customizing treatments as taught by Neumann et al. the administering of a recommended determined therapy such as viral therapy, anti-cancer therapies, and T cell therapies as taught by Bagaev et al. with the motivation of controlling viral reactivation in immunosuppressed patients to overcome adverse outcomes following virus-associated disease in patients (Ottaviano et al. pg. 1).
RE: Claim 7 Neumann, Bagaev et al., and Ottaviano et al. teach the claimed:
7. The method of claim 6, wherein the autograft, allograft or xenograft is bone marrow cells or stem cells ((Ottaviano et al., pgs. 4) (Use of in vitro or in vivo T cell depletion is universally accepted as a main strategy to avoid graft rejection and reduce graft versus host disease for transplants from unrelated or mismatched family donors; HLA matching can also impact on the rapidity of lymphocyte recovery post-transplant, and a slower T cell recovery usually occurs after haploidentical transplantation [22]. The stem cell source is also responsible for the speed of lymphocyte recovery. Peripheral blood or bone marrow stem cells generally give a faster increase in the number of T and B lymphocytes, as compared to cord blood)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the treatment of immunocomprimised patients infected by cytomegalovirus, Epstein-Barr virus, or Adenovirus with Ganciclovir, Valganciclovir, Foscarnet, Cidofovir, Brincidofovir, Acyclovir and Rituximab including following a graft procedure as taught by Ottaviano et al. within the method and system for customizing treatments as taught by Neumann et al. the administering of a recommended determined therapy such as viral therapy, anti-cancer therapies, and T cell therapies as taught by Bagaev et al. with the motivation of controlling viral reactivation in immunosuppressed patients to overcome adverse outcomes following virus-associated disease in patients (Ottaviano et al. pg. 1).
RE: Claim 8 Neumann and Bagaev et al. teach the claimed:
8. The method of claim 1,
Neumann and Bagaev et al. fail to explicitly teach, but Ottaviano et al. teaches the claimed:
wherein the immunocompromised patient has undergone a autograft, an allograft, or a xenograft, has been administered an immunosuppressant, and is infected by cytomegalovirus, Epstein-Barr virus, or Adenovirus; wherein the patient is administered Ganciclovir, Valganciclovir, Foscarnet, Cidofovir, Brincidofovir, Acyclovir and Rituximab and/or administered VSTs that recognize cytomegalovirus, Epstein-Barr virus, and/or Adenovirus ((Ottaviano et al., pgs. 2-4) (Routine monitoring of viral reactivation in the post-transplant setting usually includes molecular detection of viral DNA of the three most frequent viruses responsible for refractory infections, namely human adenovirus (AdV), cytomegalovirus (CMV) and Epstein–Barr virus (EBV).; Table 1 Reported incidence of AdV, CMV and EBV post-transplant reactivation in peripheral blood, and disease-specific pharmacological treatment and rate of treatment response in children and adults undergoing haematopoietic stem cell transplantation; Cidofovir, brincidofovir, Gancyclovir, forscarnet, valgancyclovir, Rituximab; Use of in vitro or in vivo T cell depletion is universally accepted as a main strategy to avoid graft rejection and reduce graft versus host disease for transplants from unrelated or mismatched family donors. Anti-thymocyte globulin (ATG), derived from either rabbit or horse sera, specifically targets T cells, while anti-CD52 monoclonal antibody (Alemtuzumab) depletes T and B lymphocytes as well as NK cells)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the treatment of immunocomprimised patients infected by cytomegalovirus, Epstein-Barr virus, or Adenovirus with Ganciclovir, Valganciclovir, Foscarnet, Cidofovir, Brincidofovir, Acyclovir and Rituximab including following a graft procedure as taught by Ottaviano et al. within the method and system for customizing treatments as taught by Neumann et al. the administering of a recommended determined therapy such as viral therapy, anti-cancer therapies, and T cell therapies as taught by Bagaev et al. with the motivation of controlling viral reactivation in immunosuppressed patients to overcome adverse outcomes following virus-associated disease in patients (Ottaviano et al. pg. 1).
RE: Claim 9 Neumann, Bagaev et al., and Ottaviano et al. teach the claimed:
9. The method of claim 8, wherein the immunosuppressant comprises Budesonide GI, Tacrolimus (FK), Mycophenolate mofetil (MMF), Sirolimus, Infliximad, Vedolizumad, Anti-thymocyte globulin (ATG) and Alemtuzumab (Campath) ((Ottaviano et al., pg. 4) ((Anti-thymocyte globulin (ATG), derived from either rabbit or horse sera, specifically targets T cells, while anti-CD52 monoclonal antibody (Alemtuzumab) depletes T and B lymphocytes as well as NK cells)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the immunosuppressant of Anti-thymocyte clobulin as taught by Ottaviano et al. within the method and system for customizing treatments as taught by Neumann et al. the administering of a recommended determined therapy such as viral therapy, anti-cancer therapies, and T cell therapies as taught by Bagaev et al. with the motivation of controlling viral reactivation in immunosuppressed patients to overcome adverse outcomes following virus-associated disease in patients (Ottaviano et al. pg. 1).
RE: Claim 25 Neumann and Bagaev et al. teach the claimed
25. The method according to claim 222
Neumann and Bagaev et al. fail to explicitly teach, but Ottaviano et al. teaches the claimed:
wherein the group further comprises: a degree of HLA match ranging from 1 to 6 based on the number of major alleles shared, wherein said major alleles include HLA-A, HLA-B, HLA-C and HLA-DR, HLA-DQ and HLA- DP, cellular depletion or ablation of TCRap, CD 19, naive T cells (CD45RA+ T cells) and/or CD34+ T cells, a level of CD8+ or CD8+ T cells or a ratio of CD4+ cells to CD8+ T cells or a higher or lower level compared to a prior level, a type of viral infection comprising adenovirus (AdV), Epstien-Barr Virus (EBVCytomegalovirus (CMV), Herpes Simplex Virus (HSV), human herpes virus 8, Varicella- Zoster virus, or human papillomarvirus, or a type of comorbidity or infection caused by an opportunistic bacterium, fungi, or parasite, a viral load at a time of infusion measured in IU/ml by PCR ((Ottaviano et al., pgs. 2-3) (Routine monitoring of viral reactivation in the post-transplant setting usually includes molecular detection of viral DNA of the three most frequent viruses responsible for refractory infections, namely human adenovirus (AdV), cytomegalovirus (CMV) and Epstein–Barr virus (EBV).; Table 1 Reported incidence of AdV, CMV and EBV post-transplant reactivation in peripheral blood, and disease-specific pharmacological treatment and rate of treatment response in children and adults undergoing haematopoietic stem cell transplantation; Cidofovir, brincidofovir, Gancyclovir, forscarnet, valgancyclovir, Rituximab))).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the treatment of immunocomprimised patients infected by cytomegalovirus, Epstein-Barr virus, or Adenovirus with Ganciclovir, Valganciclovir, Foscarnet, Cidofovir, Brincidofovir, Acyclovir and Rituximab as taught by Ottaviano et al. within the method and system for customizing treatments as taught by Neumann et al. the administering of a recommended determined therapy such as viral therapy, anti-cancer therapies, and T cell therapies as taught by Bagaev et al. with the motivation of controlling viral reactivation in immunosuppressed patients to overcome adverse outcomes following virus-associated disease in patients (Ottaviano et al. pg. 1).
RE: Claim 26 Neumann and Bagaev et al. teach the claimed
26. The method according to claim 22,
Neumann and Bagaev et al. fail to explicitly teach, but Ottaviano et al. teaches the claimed:
wherein the variable group further comprises: prior receipt of one or more immunosuppressive medication comprising systemic corticosteroids, Budesonide, Tacrolimus (FK), Cyclosporine (CsA), Mycophenolic acid (MMF), Sirolimus, Anti-thymocyte globulin (ATG), Alemtuzumab (Campath), or prior receipt of one or more antivirals including Ganciclovir, Valganciclovir, Foscarnet, Cidofovir, Brincidofovir, and Rituximab ((Ottaviano et al., pg. 4) ((Anti-thymocyte globulin (ATG), derived from either rabbit or horse sera, specifically targets T cells, while anti-CD52 monoclonal antibody (Alemtuzumab) depletes T and B lymphocytes as well as NK cells)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the immunosuppressant of Anti-thymocyte clobulin as taught by Ottaviano et al. within the method and system for customizing treatments as taught by Neumann et al. the administering of a recommended determined therapy such as viral therapy, anti-cancer therapies, and T cell therapies as taught by Bagaev et al. with the motivation of controlling viral reactivation in immunosuppressed patients to overcome adverse outcomes following virus-associated disease in patients (Ottaviano et al. pg. 1).
RE: Claim 27 Neumann and Bagaev et al. teach the claimed:
27. The method according to claim 22.
Neumann and Bagaev et al. fail to explicitly teach, but Ottaviano et al. teaches the claimed:
wherein the variable group further comprises: prior receipt of one or more immunosuppressive medication comprising systemic corticosteroids, Budesonide, Tacrolimus (FK), Cyclosporine (CsA), Mycophenolic acid (MMF), Sirolimus, Anti-thymocyte globulin (ATG), or Alemtuzumab (Campath), prior receipt of one or more antivirals including Ganciclovir, Valganciclovir, Foscarnet, Cidofovir, Brincidofovir, and Rituximab, prior receipt of one or more anti-cancer medications comprising azacitidine, doxorubicin, fludarabine, capecitabine, methotrexate, pembrolizumab, cyclophosphamide, clofarabine, fluorouracil, mercaptopurine, altretamine, bendamustine, busulfan, carboplatin, dacarbazine, daunorubicin, floxuridine, gemcitabine, trastuzumab, hydroxyurea, ifosfamine, melphaslan, nivolumab, paclitaxel, or other anticancer or checkpoint inhibitor, and prior receipt of one or more antiviral medications comprising oseltamivir, acyclovir, entecavir, peramivir, valacyclovir, amantadine, famciclovir, ribavirin, adefovir, emtrictabine, foscarnet, ganciclovir, lamivudine, telbivudine, zanamivir, zanamivir, baloxavir marboxil, brivudine, cidofovir, laninamivir, sofosbuvir, or tenofovir ((Ottaviano et al., pg. 4) ((Anti-thymocyte globulin (ATG), derived from either rabbit or horse sera, specifically targets T cells, while anti-CD52 monoclonal antibody (Alemtuzumab) depletes T and B lymphocytes as well as NK cells)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the immunosuppressant of Anti-thymocyte clobulin as taught by Ottaviano et al. within the method and system for customizing treatments as taught by Neumann et al. the administering of a recommended determined therapy such as viral therapy, anti-cancer therapies, and T cell therapies as taught by Bagaev et al. with the motivation of controlling viral reactivation in immunosuppressed patients to overcome adverse outcomes following virus-associated disease in patients (Ottaviano et al. pg. 1).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2024/0194340 A1 (hereinafter “Neumann”) in view of U.S. 2018/0358132 A1 (hereinafter “Bagaev et al.”) and further in view of U.S. 2025/0285145 A1 (hereinafter “Rawat et al.”).
RE: Claim 20 Neumann and Bagaev et al. teach the claimed:
20. The method of claim 19,
Neumann and Bagaev et al. fail to explicitly teach, but Rawat et al. teaches the claimed:
wherein the values of the categorical variables are one-hot encoded prior to modeling ((Rawat et al., [0004], [0046]) (For example, the pre-processing engine 220 may create one hot encoding by converting string variables to numeric fields))
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine pre-processing to create one hot encoding for machine learning to generate treatment effect scores from confounding variables as taught by Rawat et al. within the method and system for customizing treatments as taught by Neumann et al. the administering of a recommended determined therapy such as viral therapy, anti-cancer therapies, and T cell therapies as taught by Bagaev et al. with the motivation of determining the impact of a feature for a product (Rawat et al. [0003]).
Claims 15-16 and 18 recite limitations that, in the particular ordered combination as currently claims, are not anticipated or otherwise rendered obvious by the closest prior art of records, such that dependent claims 15-16 and 18 are free of prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2017/0372029 A1 teaches an interface displaying a plurality of previously determined wellness scores for a plurality of patients associated with a characteristic, treatment, and/or diagnosis (Abstract);
US 2021/0313068 A1 teaches generating a probability score for the treatment as a weighted average of a likelihood of success generated by each of the one or more trained machine learning models (Abstract, cl 13); and
US 2022/0367053 A1 teaches systems and methods can quantify the tumor microencironment for diagnosis, prognosis, and therapeutic response prediction (Abstract).
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/A.M.B./Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682
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