DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
In the November 22, 2024 submission, claims 1-20 were presented for consideration and are pending.
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 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 11 and 17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential steps and relationships, such omission amounting to a gap between the steps and relationships used in generating a polygenic score. See MPEP § 2172.01.
Claims 1 and 17 are directed to a method of generating a polygenic score representing the likelihood that a patient will benefit from glycemia treatment. While the claims cite how the score is used, they do not provide further support or detail denoting how the polygenic score is generated. Claim 11 focuses on a system wherein a feature extractor generates a polygenic score representing the likelihood that a patient belongs to a treatment class. As noted in claim 1, the claim describes how the score is used, but does not provide support for generating the score. Regarding the generation of a polygenic score, paragraphs 56-58 of the specification notes the following:
[0056] FIG. 7 illustrates one example of method 700 for assigning a treatment to a patient. At 702, a set of genetic data representing a patient is received. In one implementation, the set of genetic data includes a series of parameters representing a set of single nucleotide polymorphisms (SNPs). At 704, a polygenic score is generated, representing the likelihood that a patient will benefit from intensive glycemia treatment, from the set of genetic data. In one implementation, each SNP in the set of genetic data is represented by an associated weight, and the polygenic score is computed as the sum of the weights of the SNPs associated with the patient.
[0057] At 706, a parameter representing a response of the patient to intensive glycemia treatment is assigned according to the polygenic score. For example, the polygenic score can be provided to a predictive model to assign the parameter. In one implementation, the predictive model can also utilize a set of clinical parameters representing the patient in assigning the parameter representing the expected response. Once the parameter has been assigned, the patient can be provided with intensive glycemia treatment if the parameter indicates that the patient will respond well to the treatment, or with an alternative treatment, such as treating the patient with GLP-1 receptor agonists or SGL T-2 inhibitors, if the parameter indicates that the patient will not respond well to intensive glycemia treatment.
[0058] FIG. 8 illustrates an example of a method 800 for assigning a treatment to a patient. At 802, a set of genetic data representing a patient is received. In one implementation, the set of genetic data includes a series of parameters representing a set of single nucleotide polymorphisms (SNPs), which can include SNPs in Mas1 proto-oncogene (MAS1), Neural EGFL Like 1 (NELL 1), and Supervillin (SVIL), such as rs220721, rs1793004, and rs1270874. At 7804, a polygenic score representing the likelihood that a patient belongs to a treatment class likely to benefit from intensive glycemia treatment given the set of genetic data is generated. In one example, the set of generic data includes parameters representing the presence or absence of each of a plurality of single nucleotide polymorphisms in the patient. Each of the plurality of single nucleotide polymorphisms in the set of genetic data having an associated weight, and the polygenic score is generated by computing the sum of the weights associated with the single nucleotide polymorphisms present in the patient.
In reviewing the specification, generating the score appears to be directed to elements of dependent claims 10 and 15, notably wherein the set of generic data includes parameters representing the presence or absence of each of a plurality of single nucleotide polymorphisms in the patient and each of the plurality of single nucleotide polymorphisms in the set of genetic data having an associated weight, and generating the polygenic score comprises computing the sum of the weights associated with the single nucleotide polymorphisms present in the patient and the predictive model is implemented as a logistic regression model, such that the predictive model generates a weighted sum of the polygenic score and the set of clinical parameters. However, the claims fail to fully cure the above-mentioned omissions
While dependent claims 2-9, 12-14 and 16, and 18-20, provide support to the general methods and system claims noted in independent claims 1, 11 and 17, respectively, the claims fail to cure the aforementioned omissions. Examiner notes that during the examination process, claim language is considered in broad terms, whereby careful consideration is taken to view claim language in light of the specification, and not read details of the specification into the submitted claim language (see MPEP 2111).
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.
Claims 1-10 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Khera et al., "Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations" (published September 2018, hereinafter KHERA), and Mariam et al., “A Type 2 Diabetes Subtype Responsive to ACCORD Intensive Glycemia Treatment” (available January 2020, hereinafter MARIAM), in view of UDLER, "Genetic Risk Scores for Diabetes Diagnosis and Precision Medicine" (published July 2019, hereinafter UDLER).
As per claim 1, KHERA teaches of a method for assigning a treatment to a patient, the method comprising:
receiving a set of genetic data representing a patient (see pg. 1223, col. 1-2: system relies on the use of a patient's genetic data);
generating a polygenic score representing the likelihood that a patient will benefit from intensive glycemia treatment from the set of genetic data (see pg. 1221, col. 1 and 1222, col. 2: based on genetic data, the system generates polygenic scores which can be further analyzed and considered respect of differing form of treatment to further determine risk attributes).
While KHERA focuses on a method for using genetic data to generate a polygenic score
for risk assessments, the art fails to explicitly address the [receiving] set of genetic data including a parameter representing a single nucleotide polymorphism in one of Mas1 proto-oncogene (MAS1), Neural EGFL Like 1 (NELL1), and Supervillin (SVIL), the polygenic score being generated from at least the parameter representing the single nucleotide polymorphism and assigning a parameter representing a response of the patient to intensive glycemia treatment according to the polygenic score.
Like KHERA, MARIAM is directed to a system which evaluates genetic data for treatment and risk assessments for diabetes. MARIAM further performs a genome-wide associated study (GWAS) to identify genetic variants associated with membership in low-risk groups using machine learning to construct polygenic scores (PS) to help predict which patients would benefit from intervention. During evaluation, significant genotyped SNPs were discovered in MAS1, NELL1 and SVIL) (see pg. 1415, section “GWAS”). The cited UDLER also teaches of evaluating genetic data for treatment for diabetes, wherein the reference further focuses on evaluating a patient's response to glycemia treatment based on polygenic scores (see pg. 1509, col. 1 and pg. 1513, col. 1-2).
It would have been obvious to one of ordinary skill in the art before the effective filing
date of the invention, to employ the use of UDLER's method of evaluating glycemia treatment
based on polygenic scores, with MARIAM’s evaluation of genotyped SNP groups and KHERA's method of generating polygenic scores based on genetic data, to further evaluate how genetic factors impact diabetes treatment based on determined polygenic scores and further assigning a personalized treatment plan.
As per claim 2, the combination of KHERA, UDLER and MARIAM teaches all of the limitations noted in the base claim(s) as outlined above, wherein UDLER further teaches of the method of claim 1, wherein assigning the parameter representing the response of the patient to intensive glycemia treatment comprises assigning the parameter representing the response of the patient to intensive glycemia treatment according to the polygenic score and a set of clinical parameters representing the patient (see pg. 1510, col. 2: system evaluates the relationships between glycemic measures respective of clinical parameters (e.g., BMI)).
As per claim 3, the combination of KHERA, UDLER and MARIAM teaches all of the limitations noted in the base claim(s) as outlined above, wherein UDLER further teaches of the method of claim 2, wherein the set of clinical parameters includes a clinical parameter representing a blood pressure of the patient (see pg. 1504, col. 2: baseline clinical factors/parameters (e.g., blood pressure, family history, BMI, etc.) are considered), while MARIAM further teaches of evaluating a patient’s blood pressure determinants (pg. 1411, col. 2-3; and pg. 1415, col. 1)).
As per claim 4, the combination of KHERA, UDLER and MARIAM teaches all of the limitations noted in the base claim(s) as outlined above, wherein MARIAM further teaches of the method of claim 1, wherein the set of genetic data includes a parameter representing a single nucleotide polymorphism in one of Mas1 proto-oncogene (MAS1), Neural EGFL Like 1 (NELL1), and Supervillin (SVIL), the polygenic score being generated from at least the parameter representing the single nucleotide polymorphism (see pg. 1415, sect. “GWAS”: genome-wide associated study (GWAS) identifies genetic variants associated with membership in low-risk groups using machine learning, wherein polygenic scores (PS) constructs are used in predicting which patients would benefit from intervention).
As per claim 5, the combination of KHERA, UDLER and MARIAM teaches all of the limitations noted in the base claim(s) as outlined above, wherein KHERA further teaches of the method of claim 1, wherein assigning the parameter representing the response of the patient to intensive glycemia treatment according to the polygenic score and a set of clinical parameters representing the patient comprises providing each of the polygenic score and the set of clinical parameters to a regression model (see fig. 2, table 2, and pg. 1225, sect. "Polygenic score calculation in the validation dataset" and "Statistical analysis within the testing dataset": regression models are employed in evaluating data sets and providing relationships further defining the relationship between polygenic attributes and clinical parameters).
As per claim 6, the combination of KHERA, UDLER and MARIAM teaches all of the limitations noted in the base claim(s) as outlined above, wherein UDLER further teaches of the method of claim 1, further comprising treating the patient with one of metformin, sulfonylureas, thiazolidinediones, and insulin products if the parameter representing the response of the patient to intensive glycemia treatment indicates that the patient will respond well to intensive glycemia treatment (see pg. 1513, col. 1: method further evaluates the effectiveness of drugs with targeted pathways (e.g., sulfonylureas, thiazolidinediones), and value the treatment response).
As per claim 7, the combination of KHERA, UDLER and MARIAM teaches all of the limitations noted in the base claim(s) as outlined above, wherein MARIAM further teaches of the method of claim 1, further comprising treating the patient with one of GLP-1 receptor agonists and SGLT-2 inhibitors if the parameter representing the response of the patient to intensive glycemia treatment indicates that the patient will not respond well to intensive glycemia treatment (see pg. 1417, col. 3: the introduction of GLP1 and SGLT2 have shown to be beneficial to cardio vascular disease patients).
As per claim 8, the combination of KHERA, UDLER and MARIAM teaches all of the limitations noted in the base claim(s) as outlined above, wherein MARIAM further teaches of the method of claim 1, wherein the set of genetic data includes a parameter representing one of a set of single nucleotide polymorphisms including rs220721, rs1793004, and rs1270874, the polygenic score being generated from at least the parameter representing the one of the set of single nucleotide polymorphisms (see pg. 1410, col. 1; pg. 1415, col. 2 and pg. 1417, col. 1: rs220721 in MAS1 is suggestive of a predicted group with cardio vascular disease presented a decrease risk with intensive versus standard treatment).
As per claim 9, the combination of KHERA, UDLER and MARIAM teaches all of the limitations noted in the base claim(s) as outlined above, wherein MARIAM further teaches of the method of claim 1, wherein the parameter representing the response of the patient to intensive glycemia treatment is a categorical parameter representing one of a plurality of treatment classes, each representing clusters of trajectories of HbA1c values for patients in response to intensive glycemia treatment determined via an unsupervised clustering process (see fig. 1; pg. 1410, col. 1; pg. 1411, col. 2-3; and pg. 1413, col. 3: evaluating HbA1c values and the impact of different clinical subgroups respective of unsupervised clustering).
As per claim 10, the combination of KHERA, UDLER and MARIAM teaches all of the limitations noted in the base claim(s) as outlined above, wherein MARIAM further teaches of the method of claim 1, wherein the set of generic data includes parameters representing the presence or absence of each of a plurality of single nucleotide polymorphisms in the patient and each of the plurality of single nucleotide polymorphisms in the set of genetic data having an associated weight, and generating the polygenic score comprises computing the sum of the weights associated with the single nucleotide polymorphisms present in the patient (see pg. 1413, col. 2: weighted counts are summed to create a final SCT-PS to predict group membership).
As per claim 17, MARIAM teaches of a method for assigning a treatment to a patient, the method comprising:
receiving a set of genetic data representing a patient, the set of generic data including parameters representing the presence or absence of each of a plurality of single nucleotide polymorphisms in the patient and each of the plurality of single nucleotide polymorphisms in the set of genetic data having an associated weight (see pg. 1410, col. 1; pg. 1413, col. 3: employing the use of an inverse-variance weighted method to further evaluate gene-outcome association for CVD outcomes);
generating a polygenic score representing the likelihood that a patient will benefit from intensive glycemia treatment from the set of genetic data, wherein generating the polygenic score comprises computing the sum of the weights associated with the single nucleotide polymorphisms present in the patient (see pg. 1411, col. 1; and pg. 1413, col. 2: weighted counts are summed to create a final SCT-PS to predict group membership), where KHERA further teaches of generating a polygenic score representing the likelihood that a patient will benefit from intensive glycemia treatment from the set of genetic data (see pg. 1221, col. 1 and 1222, col. 2: based on genetic data, the system generates polygenic scores which can be further analyzed and considered respect of differing form of treatment to further determine risk attributes).
The cited UDLER focuses on evaluating genetic data for treatment for diabetes, wherein the reference further focuses on evaluating a patient's response to glycemia treatment based on polygenic scores (see pg. 1509, col. 1 and pg. 1513, col. 1-2).
It would have been obvious to one of ordinary skill in the art before the effective filing
date of the invention, to employ the use of UDLER's method of evaluating glycemia treatment
based on polygenic scores, with MARIAM’s genotyped SNP groups and KHERA's method of generating polygenic scores based on genetic data, to further generate a personalized treatment plan based on how genetic factors impact diabetes, respective of determined polygenic scores.
As per claim 18, the combination of KHERA, UDLER and MARIAM teaches all of the limitations noted in the base claim(s) as outlined above, wherein UDLER further teaches of the method of claim 17, further comprising treating the patient with one of metformin (see pg. 1509, col. 1) , sulfonylureas (see pg. 1513, col. 1), thiazolidinediones (see pg. 1513, col. 1), and insulin products (pg. 1504, col. 1) if the parameter representing the response of the patient to intensive glycemia treatment indicates that the patient will respond well to intensive glycemia treatment (see UDLER-pg. 1504, col. 1; pg. 1509, col. 1; and pg. 1513, col. 1: evaluating how patients respond to different glycemia treatments).
As per claim 19, the combination of KHERA, UDLER and MARIAM teaches all of the limitations noted in the base claim(s) as outlined above, wherein MARIAM further teaches of the method of claim 17, further comprising treating the patient with one of GLP-1 receptor agonists and SGLT-2 inhibitors if the parameter representing the response of the patient to intensive glycemia treatment indicates that the patient will not respond well to intensive glycemia treatment (see pg. 1417, col. 3: the introduction of GLP1 and SGLT2 have shown to be beneficial to cardio vascular disease patients).
As per claim 20, the combination of KHERA, UDLER and MARIAM teaches all of the limitations noted in the base claim(s) as outlined above, wherein MARIAM further teaches of the method of claim 17, the set of genetic data including a parameter representing a single nucleotide polymorphism in one of Mas1 proto-oncogene (MAS1), Neural EGFL Like 1 (NELL1), and Supervillin (SVIL), the polygenic score being generated from at least the parameter representing the single nucleotide polymorphism (see pg. 1415, sect. “GWAS”: genome-wide associated study (GWAS) identifies genetic variants associated with membership in low-risk groups using machine learning, wherein polygenic scores (PS) constructs are used in predicting which patients would benefit from intervention).
Claims 11-16 are rejected under 35 U.S.C. 103 as being unpatentable over KHERA, UDLER and MARIAM in further view of Bondi et al., “Thyroid Dysfuction and Diabetes Mellitus: Two Closely Associated Disorders” (available January 2019, hereinafter BONDI).
As per claim 11, KHERA teaches of a system comprising:
a processor, and a computer readable medium storing executable instructions for assigning a treatment to a patient, the executable instructions comprising: a network interface that receives a set of genetic data representing a patient, is taught by KHERA, wherein the art teaches of software implementation on a computational based [computer] systems which [as commonly used in the art] rely on a processor for processing data, wherein the processing of data is based on stored computer instruction sets (e.g., software programs), which executes operations on the computer platforms, and relies on a user interface as a means for interfacing with user data (see pg. 1225 col. 2, and 1227: use of Hail software package for operation on the system); and
a feature extractor that generates a polygenic score representing the likelihood that a patient belongs to a treatment class likely to benefit from intensive glycemia treatment given the set of genetic data (see pg. 1221, col. 1, 1222, col. 2 and pg. 1223, col. 1-2: system uses a patient's genetic data, wherein based on the genetic data, the system generates polygenic scores which can be further analyzed and considered respect of differing form of treatment to further determine risk attributes).
UDLER further teaches of a system which evaluates genetic data for treatment and risk
assessments for diabetes, wherein model-based analysis of predictive performance is used in
evaluating datasets which further determines a patient's response to glycemia treatment based
on polygenic scores (see pg. 1505, col. 1 and pg. 1509, col. 1 and pg. 1513, col. 1 -2), wherein MARIAM further teaches of a predictive model that assigns a parameter to the patient representing the response of the patient to intensive glycemia treatment according to the polygenic score and a set of clinical parameters representing the patient (see pg. 1411, sect. “Patient Subgrouping” and pg. 1413, sect. “PS and Predictive Model Development”: system employs the use of logistic regression modelling on baseline demographic and medication differences).
BONDI focuses on evaluating the relationship between TSH and diabetes, wherein clinical parameters inclusive of measurements of blood concentration of thyroid stimulating hormone (TSH) for the patient is considered respective of glycemic control and monitoring respective of glycemia treatments (see pg. 796, col. 1; 803, col.1-2; 814, col. 2).
It would have been obvious to one of ordinary skill in the art before the effective filing
date of the invention, to employ the use of KHERA’s and UDLER's system of evaluating genetic data and generating polygenic scores with MARIAM’s use of predictive modeling and BIONDI’s evaluation of TSH analysis, to provide a system for optimally evaluating a patient’s genetic data and generating an intensive glycemia treatment plan in line with a determined polygenic score and clinical patient determinants.
As per claim 12, the combination of KHERA, UDLER and MARIAM teaches all of the limitations noted in the base claim(s) as outlined above, wherein MARIAM further teaches of the system of claim 11, wherein the set of genetic data includes a parameter representing one of a set of single nucleotide polymorphisms including rs220721, rs1793004, and rs1270874, the feature extractor generating the polygenic score from at least the parameter representing the one of the set of single nucleotide polymorphisms (see pg. 1410, col. 1; pg. 1415, col. 2 and pg. 1417, col. 1: rs220721 in MAS1 is suggestive of a predicted group with cardio vascular disease presented a decrease risk with intensive versus standard treatment).
As per claim 13, the combination of KHERA, UDLER, MARIAM and BONDI teaches all of the limitations noted in the base claim(s) as outlined above, wherein BONDI further teaches of the system of claim 11, wherein the set of clinical parameters includes a clinical parameter representing a measurement of blood concentration of thyroid stimulating hormone for the patient (see pg. 796, col. 1; 803, col.1-2; 814, col. 2: evaluating the relationship between TSH and diabetes, respective of glycemia treatments).
As per claim 14, the combination of KHERA, UDLER, MARIAM and BONDI teaches all of the limitations noted in the base claim(s) as outlined above, wherein MARIAM further teaches of the system of claim 11, wherein the set of genetic data includes a parameter representing a single nucleotide polymorphism in one of Mas1 proto-oncogene (MAS1), Neural EGFL Like 1 (NELL1), and Supervillin (SVIL), the feature extractor generating the polygenic score from at least the parameter representing the single nucleotide polymorphism (see pg. 1415, sect. “GWAS”: genome-wide associated study (GWAS) identifies genetic variants associated with membership in low-risk groups using machine learning, wherein polygenic scores (PS) constructs are used in predicting which patients would benefit from intervention).
As per claim 15, the combination of KHERA, UDLER, MARIAM and BONDI teaches all of the limitations noted in the base claim(s) as outlined above, wherein MARIAM further teaches of the system of claim 11, wherein the predictive model is implemented as a logistic regression model, such that the predictive model generates a weighted sum of the polygenic score and the set of clinical parameters (see pg. 1411, sect. “Patient Subgrouping” and pg. 1413, sect. “PS and Predictive Model Development”: system employs the use of logistic regression modelling on baseline demographic and medication differences).
As per claim 16, the combination of KHERA, UDLER, MARIAM and BONDI teaches all of the limitations noted in the base claim(s) as outlined above, wherein MARIAM further teaches of the method of claim 11, wherein the set of clinical parameters includes at least a clinical parameter representing alcohol consumption by the patient (see pg. 1414, col. 3: alcohol intake was considered as a clinical parameter).
Conclusion
The applicant is strongly encouraged to contact the examiner if further clarifications are needed with respect to interpretation of currently presented claims and/or cited prior art.
A reference to specific paragraphs, columns, pages, or figures in a cited prior art reference is not limited to preferred embodiments or any specific examples. It is well settled that a prior art reference, in its entirety, must be considered for all that it expressly teaches and fairly suggests to one having ordinary skill in the art. Stated differently, a prior art disclosure reading on a limitation of Applicant's claim cannot be ignored on the ground that other embodiments disclosed were instead cited. Therefore, the Examiner's citation to a specific portion of a single prior art reference is not intended to exclusively dictate, but rather, to demonstrate an exemplary disclosure commensurate with the specific limitations being addressed. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006,1009, 158 USPQ 275, 277 (CCPA 1968)). In re: Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); In re Fritch, 972 F.2d 1260, 1264, 23 USPQ2d 1780, 1782 (Fed. Cir. 1992); Merck& Co. v. BiocraftLabs., Inc., 874 F.2d 804, 807, 10 USPQ2d 1843, 1846 (Fed. Cir. 1989); In re Fracalossi, 681 F.2d 792,794 n.1,215 USPQ 569, 570 n.1 (CCPA 1982); In re Lamberti, 545 F.2d 747, 750, 192 USPQ 278, 280 (CCPA 1976); In re Bozek, 416 F.2d 1385, 1390, 163 USPQ 545, 549 (CCPA 1969).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KELVIN BOOKER whose telephone number is (571)272-7827. The examiner can normally be reached on M-F 9am-5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached on (571) 272-4105. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/Kelvin Booker/
Examiner, Art Unit 2119
/MOHAMMAD ALI/ Supervisory Patent Examiner, Art Unit 2119
Prosecution Insights