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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 8-15, 19-22, and 24-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claims
Step 1 analysis:
Claims 1 and 24 are drawn to a method (i.e., process), and Claim 12 is drawn to a system, which are all within the four statutory categories. (Step 1 – Yes, the claim falls into one of the statutory categories).
Step 2A analysis – Prong One:
Claim 1 recites:
A method for evaluating a treatment using one or more model parameters associated with a quantitative systems pharmacology (QSP) model, the method comprising:
receiving input data corresponding to a treatment;
sending the input data into a trained machine learning model, the trained machine learning model approximating at least a portion of the QSP model; and
generating, via the trained machine learning model, a set of values for a set of treatment effect parameters associated with the QSP model, wherein the set of values are configured to be used to evaluate the treatment on a subject.
The series of steps as recited above describes managing personal behavior or relationships or interactions between people including following rules or instructions, and therefore fall within the scope of certain methods of organizing human activity. Fundamentally, the method is that of a person evaluating the treatment of another person, such as a doctor evaluating the treatment of their patient, which encompasses a person interacting with another individual including following rules or instructions. Accordingly, the claim recites an abstract idea of managing interactions between people.
The series of steps as recited above also falls within the “mental processes” grouping of abstract ideas, and describes concepts that can be performed in the human mind through observation, evaluation, judgement, and opinion. Evaluating a treatment and generating a set of values for a set of treatment effect parameters can all be performed in the human mind, with or without the use of a physical aid. Therefore, the claim recites an abstract idea of a mental process.
Claim 12 recites/describes nearly identical steps as claim 1 (and therefore also recites limitations that fall within this subject matter grouping of abstract ideas), and this claim is therefore determined to recite an abstract idea under the same analysis.
Claim 24 recites:
A method for evaluating a treatment using one or more model parameters associated with a quantitative systems pharmacology (QSP) model, the method comprising:
generating simulated dose response data and simulated treatment effect data for the treatment;
training an encoder and a decoder of a machine learning model using the simulated dose response data and the simulated treatment effect data to minimize a difference between the simulated dose response data input to the encoder and reconstructed treatment effect data output from the decoder;
receiving dose response data corresponding to the treatment;
sending the dose response data to the trained encoder;
generating, via the trained encoder, a set of values for a set of treatment effect parameters associated with the QSP model; and
generating a final output based on an evaluation of the treatment on a subject using the set of values for the set of treatment effect parameters.
The series of steps as recited above describes managing personal behavior or relationships or interactions between people including following rules or instructions, and therefore fall within the scope of certain methods of organizing human activity. Fundamentally, the method is that of a person evaluating the treatment of another person, such as a doctor evaluating the treatment of their patient, which encompasses a person interacting with another individual including following rules or instructions. Accordingly, the claim recites an abstract idea of managing interactions between people.
The series of steps as recited above also falls within the “mental processes” grouping of abstract ideas, and describes concepts that can be performed in the human mind through observation, evaluation, judgement, and opinion. Evaluating a treatment, generating a set of values for a set of treatment effect parameters, and generating an output based on an evaluation of the treatment on a subject can all be performed in the human mind, with or without the use of a physical aid. Therefore, the claim recites an abstract idea of a mental process.
Claim 24 also falls within the “mathematical concepts” grouping of abstract ideas. The concept of training an encoder and a decoder falls within the abstract idea of mathematical concepts because it recites steps of computing neural network parameters using a series of mathematical calculations (See Example 47, claim 2). Therefore, Claim 24 recites an abstract idea of a mathematical concept.
Step 2A analysis – Prong 2:
This judicial exception is not integrated into a practical application. Specifically, independent claims 1, 12, and 24 recite the following additional elements beyond the abstract idea: a machine learning model, a quantitative systems pharmacology model, at least one data processor, at least one memory, an encoder, and a decoder. These limitations are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. The limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Specifically, machine learning model 114 includes a deep learning model or system that includes any number of deep learning models and/or algorithms. A deep learning system may be implemented using, but is not limited to, one or more neural networks. For example, the deep learning system may include a neural network system comprised of any number of or combination of neural networks. For example, a neural network system may include one or more neural networks, each of which includes or more neural networks itself. (See Applicant’s specification [0073]). the “encoder” is a type of neural network that learns to efficiently encode data into a vector of parameters having a number of dimensions. A "decoder" is a type of neural network that learns to efficiently decode a vector of parameters (e.g., one or more QSP model parameters such as, for example, treatment effect parameters) having a number of dimensions (e.g., a number of preselected dimensions) into output data (e.g., dose response data) (See specification [0058]).
The limitations “receiving input data corresponding to a treatment”, “sending the input data”, “receiving dose response data”, and “sending the dose response data” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
The additional elements do not show an improvement to the functioning of a computer or to any other technology, rather the additional elements perform general computing functions and do not indicate how the particular combination improves any technology or provides a technical solution to a technical problem. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, Claims 1, 12, and 24 are directed to an abstract idea without practical application. (Step 2A – Prong 2: No, the additional elements are not integrated into a practical application).
Step 2B analysis:
As discussed above in “Step 2A analysis – Prong 2”, the identified additional elements in Independent Claims 1, 12, and 24 are equivalent to adding the words “apply it” on a generic computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
Additional elements “receiving input data corresponding to a treatment”, “sending the input data”, “receiving dose response data”, and “sending the dose response data” were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and outputting. However, a conclusion that an additional element is insignificant extra-solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g).
For the role of a computer in a computer implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of “well- understood, routine, [and] conventional activities previously known to the industry.” Further, “the mere recitation of a generic computer cannot transform a patent ineligible abstract idea into a patent-eligible invention.”
The applicant’s specification discloses: machine learning model 114 includes a deep learning model or system that includes any number of deep learning models and/or algorithms. A deep learning system may be implemented using, but is not limited to, one or more neural networks. For example, the deep learning system may include a neural network system comprised of any number of or combination of neural networks. For example, a neural network system may include one or more neural networks, each of which includes or more neural networks itself. (See Applicant’s specification [0073]). the “encoder” is a type of neural network that learns to efficiently encode data into a vector of parameters having a number of dimensions. A "decoder" is a type of neural network that learns to efficiently decode a vector of parameters (e.g., one or more QSP model parameters such as, for example, treatment effect parameters) having a number of dimensions (e.g., a number of preselected dimensions) into output data (e.g., dose response data) (See specification [0058]).
Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. Here, the claim limitations are similar to receiving and sending information over a network (Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OJP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); See MPEP 2106.05(d)(ll)(i)).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the steps for treatment evaluation amount to no more than using computer related devices to implement the abstract idea.
The use of a computer or processor to merely automate or implement the abstract idea cannot provide significantly more than the abstract idea itself. (See MPEP 2106.05(f) where mere instructions to apply an exception does not render an abstract idea patent eligible). There is no indication that the additional limitations alone or in combination improves the functioning of a computer or any other technology, improves another technology or technical field, or effects a transformation or reduction of a particular article to a different state or thing. Therefore, the claims are not patent eligible. The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claims amount to significantly more than the abstract idea identified above (Step 2B: Independent claims - NO).
Dependent Claims
Dependent Claims 2-4, 8-13, 19-22, and 25-26 are directed towards elements used to describe the treatment, treatment effects, input data, and response data. These elements include the treatment including a molecule, predicting an effect of the treatment, receiving dose response data, generating an Emax or EC50 value, and training the machine learning model. These elements describe managing personal behavior or relationships or interactions between people including following rules or instructions, and therefore fall within the same scope of certain methods of organizing human activity as the independent claims.
The elements as recited above also falls within the same “mental processes” grouping of abstract ideas as the independent claims, and describes concepts that can be performed in the human mind through observation, evaluation, judgement, and opinion. Determining whether a user is eligible for benefits and determining that a request is in a non-standardized format are all tasks that can be performed in the human mind. Therefore, the dependent claims recite an abstract idea of a mental process. The training element as recited above also falls within the same “mathematical calculations” abstract idea as independent claim 24.
The additional elements do not show an improvement to the functioning of a computer or to
any other technology, rather the additional elements perform general computing functions and do not
indicate how the particular combination improves any technology or provides a technical solution to a
technical problem. Accordingly, these additional elements, when considered separately and as an
ordered combination, do not integrate the abstract idea into a practical application because they do not
impose any meaningful limits on practicing the abstract idea. Therefore, the dependent claims are
directed to an abstract idea without practical application. (Step 2A – Prong 2: No, the additional
elements are not integrated into a practical application).
The use of a computer or processor to merely automate or implement the abstract idea cannot
provide significantly more than the abstract idea itself. (See MPEP 2106.05(f) where mere instructions to apply an exception does not render an abstract idea patent eligible). There is no indication that the
additional limitations alone or in combination improves the functioning of a computer or any other
technology, improves another technology or technical field, or effects a transformation or reduction of a
particular article to a different state or thing. Therefore, the claims are not patent eligible.
The Examiner has therefore determined that no additional element, or combination of
additional claims elements is/are sufficient to ensure the claims amount to significantly more than the
abstract idea identified above.
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.
Claims 1-4, 8-10, 12-15, and 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over Hall et al. (WO 2019/095017) (hereinafter Hall) in view of Thiel et al., Using quantitative systems pharmacology to evaluate the drug efficacy of COX-2 and 5-LOX inhibitors in therapeutic situations (Hereinafter Thiel).
Regarding Claim 1, Hall teaches the following:
A method for evaluating a treatment ([0001] method for predicting the effectiveness of cancer therapy in a subject), the method comprising:
receiving input data corresponding to a treatment ([0009] the method includes obtaining subject data indicative of a sequence of a nucleic acid molecule from the subject, which relates to a cancer therapy);
sending the input data into a trained machine learning model, the trained machine learning model approximating at least a portion of the [QSP] model ([0009], [0016]: apply the plurality of metrics to at least one computational model to determine a therapy indicator indicative of a predicted responsiveness to cancer therapy. The data is used for calculating at least one computational model, the at least one computational model being used for generating a therapy indicator for use in assessing responsiveness to cancer therapy for a subject); and
generating, via the trained machine learning model, a set of values for a set of treatment effect parameters associated with the [QSP] model, wherein the set of values are configured to be used to evaluate the treatment on a subject ([0009], [0012]: generating a therapy indicator for use in assessing responsiveness to cancer therapy for a subject using a computational model. The computational model generates a range of one to over 200 metrics for use in assessing the responsiveness).
However, Hall does not disclose the following which is met by Thiel:
using one or more model parameters associated with a quantitative systems pharmacology (QSP) model (See Thiel Fig. 1: overview of the QSP approach model which uses parameters associated with the QSP including pretreatment data and EC50 values).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method of evaluating a treatment as taught by Hall with the quantitative pharmacology (QSP) model as taught by Thiel because using the QSP approach allows a quantitative description of drug-induced modulations on biological systems, which provides the opportunity to gain insights into the dynamics of modulated biological processes induced by drug-target binding and optimize dosing schedules (See Thiel Pg. 2, par. 3 and Pg. 8, Col. 2, par. 4).
Regarding Claim 2, the combination of Hall and Thiel teach the method of claim 1, and Hall further discloses:
The method of claim 1, wherein the trained machine learning model comprises a neural network ([00252]: The nature of the model and the training performed can be of any suitable form: decision tree learning, random forest, logistic regression, correlation rule learning, artificial neural networks, deep learning, recursive logic programming, support vectors).
Regarding Claim 3, the combination of Hall and Thiel teach the method of claim 1, and Hall further discloses:
The method of claim 1, wherein the treatment includes a molecule, wherein the molecule is at least one of a micro molecule having a molecular weight of less than 1000 Daltons ([00305]-[00310]: The therapies include drugs (i.e., small molecules), for example, non-targeted chemotherapy. This includes, but is not limited to, alkylating agents such as altretamine, busulfan, carboplatin, carmustine, chlorambucil, cisplatin, cyclophosphamide, dacarbazine, lomustine, melphalan, oxaliplatin, temozolomide and thiotepa; antimetabolites such as 5-fluorouracil (5-FU), 6-mercaptopurine (6-MP), aapecitabine (Xeloda®), aytarabine (Ara-C®), floxuridine, fludarabine, gemcitabine (Gemzar®), hydroxyurea, methotrexate, and pemetrexed (Alimta®); anti-tumor antibiotics such as anthracyclines (e.g. daunorubicin, doxorubicin (Adriamycin®), epirubicin and idarubici), actinomycin-D, bleomycin, mitomycin-C and mitoxantrone; topoisomerase inhibitors, topotecan, irinotecan (CPT-11), etoposide (VP-16), teniposide and mitoxantrone; mitotic inhibitors such as docetaxel, estramustine, ixabepilone, paclitaxel, vinblastine, vincristine, vinorelbine; and corticosteroids such as prednisone, methylprednisolone (Solumedrol®) and dexamethasone (Decadron®), all of which have a molecular weight of less than 1000 Daltons) and a macro molecule having a molecular weight of greater than or equal to 1000 Daltons ([00310]-[00311]: The therapies may include proteins (including antibodies) and targeted therapies, such as drugs (e.g. tyrosine kinase inhibitors) and monoclonal antibodies (including chimeric, humanized or fully human antibodies, whether naked or conjugated with a toxic moiety) specific for ABL, Anaplastic lymphoma kinase (ALK), Beta-1,4 N-acetylgalactosaminyltransferase 1 (B4GALNT1), B-cell activating factor (BAFF), B-Raf, Bruton's tyrosine kinase (BTK), CD19, CD20, CD27, CD30, CD38, CD52, CD137 cytotoxic T-Lymphocyte associated protein 4 (CTLA-4), epidermal growth factor receptor (EGFR), FMS-like tyrosine kinase-3 (FLT3), histone deacetylase (HDAC), human epidermal growth factor receptor 2 (HER-2), isocitrate dehydrogenase 1 (IDH1), IDH2, interleukin 1 beta (IL-113), IL-6, IL-6R, c-KIT, MEK, MET, mTOR, Poly (ADP-ribose) polymerase (PARP), programmed cell death protein 1 (PD-1), Nectin-4, platelet-derived growth factor receptors a (PDGFRa), PDGFRl3, programmed death-ligand 1 (PD-L1), phosphatidylinositol-3-kinase delta (PI3Ko), receptor activator of nuclear factor kappa-B ligand (RANKL), RET, ROS1, signaling lymphocytic activation molecule F7 (SLAMF7), vascular endothelial growth factor (VEGF), VEGF receptor (VEGFR) and VEGFR2.).
Regarding Claim 4, the combination of Hall and Thiel teaches the method of claim 1, and Hall further discloses:
The method of claim 1, further comprising:
predicting an effect of the treatment on the subject ([0001]: predicting the effectiveness of cancer therapy in a subject) using the set of values for the set of treatment effect parameters ([0009] apply the plurality of metrics to at least one computational model to determine a therapy indicator indicative of a predicted responsiveness to cancer therapy).
Regarding Claim 8, the combination of Hall and Thiel teaches the method of claim 1, and Thiel further discloses:
The method of claim 1, wherein receiving the input data comprises:
receiving dose response data, wherein the dose response data includes a dose response of a plurality of cell types to the treatment (Thiel Abstract and Pg. 8, Col. 2, Par. 5: A quantitative analysis of dose-response relationships. The response data is predicted in venous blood cells, as well as liver cells (hepatocytes)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method of evaluating a treatment as taught by Hall with the dose response data of a plurality of cell types, as taught by Thiel, because the QSP approach that utilizes PBPK modeling is applicable for drugs with diverse sites of action such as the heart or the kidney assuming, though, an adequate cellular network model including the pharmacological target that is mostly responsible for the respective mode of action, providing a benefit of using the model (See Thiel Pg. 9, par. 3).
Regarding Claim 9, the combination of Hall and Thiel teaches the method of claim 1, and Hall further teaches:
The method of claim 1, wherein generating, via the trained machine learning model, the set of values for the treatment effect parameters ([0009], [0012]: generating a therapy indicator for use in assessing responsiveness to cancer therapy for a subject using a computational model. The computational model generates a range of one to over 200 metrics for use in assessing the responsiveness) comprises:
However, Hall does not teach the following that is met by Thiel:
generating, [via the trained machine learning model], at least one of an Emax value or an EC50 value for each of a plurality of cell types (Fig. 1a and Pg. 10, Col. 2, par. 2: generate an EC50 value for each cell type based on literature values).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method of evaluating a treatment as taught by Hall with the generation of an EC50 value for the cell types because the QSP approach that utilizes PBPK modeling is applicable for drugs with diverse sites of action such as the heart or the kidney assuming, though, an adequate cellular network model including the pharmacological target that is mostly responsible for the respective mode of action, providing a benefit of using the model. By generating the EC50 value, a more accurate model for treatment evaluation can be made (See Thiel Pg. 9, par. 3 and Pg. 10, Col. 2, par. 2).
Regarding Claim 10, the combination of Hall and Thiel teaches the method of claim 1, and Hall further discloses:
The method of claim 1, further comprising:
training the machine learning model ([0015] use the plurality of reference metrics and known responsiveness for a number of reference subjects to train at least one computational model).
Regarding Claim 12, Hall teaches the following:
A system ([0001] system for predicting the effectiveness of cancer therapy in a subject) comprising:
at least one data processor ([00213] a processing system includes at least one microprocessor); and
at least one memory storing instructions, which when executed by the at least one data processor, result in operations ([00213] The microprocessor can execute instructions in the form of applications software stored in the memory to allow the methods of the present disclosure to be performed) comprising:
receiving input data corresponding to a treatment ([0009] the system includes obtaining subject data indicative of a sequence of a nucleic acid molecule from the subject, which relates to a cancer therapy);
sending the input data into a trained machine learning model, the trained machine learning model approximating at least a portion of a [quantitative systems pharmacology (QSP)] model ([0009], [0016]: apply the plurality of metrics to at least one computational model to determine a therapy indicator indicative of a predicted responsiveness to cancer therapy. The data is used for calculating at least one computational model, the at least one computational model being used for generating a therapy indicator for use in assessing responsiveness to cancer therapy for a subject); and
generating, via the trained machine learning model, a set of values for a set of treatment effect parameters associated with the [QSP] model, wherein the set of values are configured to be used to evaluate the treatment on a subject ([0009], [0012]: generating a therapy indicator for use in assessing responsiveness to cancer therapy for a subject using a computational model. The computational model generates a range of one to over 200 metrics for use in assessing the responsiveness).
However, Hall does not disclose the following which is met by Thiel:
a quantitative systems pharmacology (QSP) model ((See Thiel Fig. 1: overview of the QSP approach model which uses parameters associated with the QSP including pretreatment data and EC50 values)
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method of evaluating a treatment as taught by Hall with the quantitative pharmacology (QSP) model as taught by Thiel because using the QSP approach allows a quantitative description of drug-induced modulations on biological systems, which provides the opportunity to gain insights into the dynamics of modulated biological processes induced by drug-target binding and optimize dosing schedules (See Thiel Pg. 2, par. 3 and Pg. 8, Col. 2, par. 4).
Regarding Claim 13, the combination of Hall and Thiel teach the system of claim 12, and Hall further discloses:
The system of claim 12, wherein the trained machine learning model comprises a neural network ([00252]: The nature of the model and the training performed can be of any suitable form: decision tree learning, random forest, logistic regression, correlation rule learning, artificial neural networks, deep learning, recursive logic programming, support vectors).
Regarding Claim 14, the combination of Hall and Thiel teach the system of claim 12, and Hall further discloses:
The system of claim 12, wherein the treatment includes a molecule, wherein the molecule is at least one of a micro molecule having a molecular weight of less than 1000 Daltons ([00305]-[00310]: The therapies include drugs (i.e., small molecules), for example, non-targeted chemotherapy. This includes, but is not limited to, alkylating agents such as altretamine, busulfan, carboplatin, carmustine, chlorambucil, cisplatin, cyclophosphamide, dacarbazine, lomustine, melphalan, oxaliplatin, temozolomide and thiotepa; antimetabolites such as 5-fluorouracil (5-FU), 6-mercaptopurine (6-MP), aapecitabine (Xeloda®), aytarabine (Ara-C®), floxuridine, fludarabine, gemcitabine (Gemzar®), hydroxyurea, methotrexate, and pemetrexed (Alimta®); anti-tumor antibiotics such as anthracyclines (e.g. daunorubicin, doxorubicin (Adriamycin®), epirubicin and idarubici), actinomycin-D, bleomycin, mitomycin-C and mitoxantrone; topoisomerase inhibitors, topotecan, irinotecan (CPT-11), etoposide (VP-16), teniposide and mitoxantrone; mitotic inhibitors such as docetaxel, estramustine, ixabepilone, paclitaxel, vinblastine, vincristine, vinorelbine; and corticosteroids such as prednisone, methylprednisolone (Solumedrol®) and dexamethasone (Decadron®), all of which have a molecular weight of less than 1000 Daltons) and a macro molecule having a molecular weight of greater than or equal to 1000 Daltons ([00310]-[00311]: The therapies may include proteins (including antibodies) and targeted therapies, such as drugs (e.g. tyrosine kinase inhibitors) and monoclonal antibodies (including chimeric, humanized or fully human antibodies, whether naked or conjugated with a toxic moiety) specific for ABL, Anaplastic lymphoma kinase (ALK), Beta-1,4 N-acetylgalactosaminyltransferase 1 (B4GALNT1), B-cell activating factor (BAFF), B-Raf, Bruton's tyrosine kinase (BTK), CD19, CD20, CD27, CD30, CD38, CD52, CD137 cytotoxic T-Lymphocyte associated protein 4 (CTLA-4), epidermal growth factor receptor (EGFR), FMS-like tyrosine kinase-3 (FLT3), histone deacetylase (HDAC), human epidermal growth factor receptor 2 (HER-2), isocitrate dehydrogenase 1 (IDH1), IDH2, interleukin 1 beta (IL-113), IL-6, IL-6R, c-KIT, MEK, MET, mTOR, Poly (ADP-ribose) polymerase (PARP), programmed cell death protein 1 (PD-1), Nectin-4, platelet-derived growth factor receptors a (PDGFRa), PDGFRl3, programmed death-ligand 1 (PD-L1), phosphatidylinositol-3-kinase delta (PI3Ko), receptor activator of nuclear factor kappa-B ligand (RANKL), RET, ROS1, signaling lymphocytic activation molecule F7 (SLAMF7), vascular endothelial growth factor (VEGF), VEGF receptor (VEGFR) and VEGFR2.).
Regarding Claim 15, the combination of Hall and Thiel teach the system of claim 12, and Hall further discloses:
The system of claim 12, wherein the operations further comprise:
predicting an effect of the treatment on the subject ([0001]: predicting the effectiveness of cancer therapy in a subject) using the set of values for the set of treatment effect parameters ([0009] apply the plurality of metrics to at least one computational model to determine a therapy indicator indicative of a predicted responsiveness to cancer therapy).
Regarding Claim 19, the combination of Hall and Thiel teaches the system of claim 12, and Thiel further discloses:
The system of claim 12, wherein receiving the input data comprises:
receiving dose response data, wherein the dose response data includes a dose response of a plurality of cell types to the treatment (Thiel Abstract and Pg. 8, Col. 2, Par. 5: A quantitative analysis of dose-response relationships. The response data is predicted in venous blood cells, as well as liver cells (hepatocytes)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method of evaluating a treatment as taught by Hall with the dose response data of a plurality of cell types, as taught by Thiel, because the QSP approach that utilizes PBPK modeling is applicable for drugs with diverse sites of action such as the heart or the kidney assuming, though, an adequate cellular network model including the pharmacological target that is mostly responsible for the respective mode of action, providing a benefit of using the model (See Thiel Pg. 9, par. 3).
Regarding Claim 20, the combination of Hall and Thiel teaches the system of claim 12, and Hall further discloses:
The system of claim 12, wherein generating, via the trained machine learning model, the set of values for the treatment effect parameters ([0009], [0012]: generating a therapy indicator for use in assessing responsiveness to cancer therapy for a subject using a computational model. The computational model generates a range of one to over 200 metrics for use in assessing the responsiveness) comprises:
However, Hall does not disclose the following that is met by Thiel:
generating, [via the trained machine learning model], at least one of an Emax value or an EC50 value for each of a plurality of cell types (Fig. 1a and Pg. 10, Col. 2, par. 2: generate an EC50 value for each cell type based on literature values).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method of evaluating a treatment as taught by Hall with the generation of an EC50 value for the cell types because the QSP approach that utilizes PBPK modeling is applicable for drugs with diverse sites of action such as the heart or the kidney assuming, though, an adequate cellular network model including the pharmacological target that is mostly responsible for the respective mode of action, providing a benefit of using the model. By generating the EC50 value, a more accurate model for treatment evaluation can be made (See Thiel Pg. 9, par. 3 and Pg. 10, Col. 2, par. 2).
Regarding Claim 21, the combination of Hall and Thiel teaches the system of claim 12, and Hall further discloses:
The system of claim 12, wherein the operations further comprise:
training the machine learning model ([0015] use the plurality of reference metrics and known responsiveness for a number of reference subjects to train at least one computational model).
Relevant Prior Art of Record Not Currently Being Applied
The relevant art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Derbalah et al., A Framework for Simplification of Quantitative Systems Pharmacology Models in Clinical Pharmacology, describes methods of simplifying QSP models, including utilizing artificial intelligence to approximate most of the model.
Goswami et al. (US 11,075,010 B1) describes a pharmacology model optimization method associated with drug dosage of medications.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXIS K VAN DUZER whose telephone number is (571)270-5832. The examiner can normally be reached Monday thru Thursday 8-5 CT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marc Jimenez can be reached at (571) 272-4530. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A.K.V./Examiner, Art Unit 3681
/MARC Q JIMENEZ/Supervisory Patent Examiner, Art Unit 3681