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 .
Acknowledgements
This communication is in response to Application No. 19/006,932 filed on 12/31/2024 .
Claims 1-68 are currently pending and have been examined.
Claims 1-68 have been rejected as follows.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/21/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 1 objected to because of the following informalities: “(a) a plurality of features derived from at least the respective set of biomarker data collected at the respective first time and the respective first set HSD collected at the respective first time, and” should be “(a) a plurality of features derived from at least the respective set of biomarker data collected at the respective first time and the respective first set of HSD collected at the respective first time, and”. Appropriate correction is required.
Claim 12 objected to because of the following informalities: “wherein the first set of HSD or the second set of HSD include at least one of” should be “wherein the first set of HSD or the second set of HSD includes at least one of”. Appropriate correction is required.
Claim 14 objected to because of the following informalities: “wherein the period of time less than 5 years.” should be “The method of claim 1, wherein the period of time is less than 5 years.”. Appropriate correction is required.
Claim 29 objected to because of the following informalities: “is estimated using at least one the” should be “is estimated using at least one of the”. Appropriate correction is required.
Claim 35 objected to because of the following informalities: “estimating a cardiovascular disease risk based the risk value that the diabetic human subject will experience progressive decline in kidney function over the period of time” should be “estimating a cardiovascular disease risk based on the risk value that the diabetic human subject will experience progressive decline in kidney function over the period of time”. Appropriate correction is required.
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
Claims 1-30, 31-53, 54-55, 56-68 are provisionally rejected under 35 U.S.C. 101 as claiming the same invention as that of claim 1-30, 32-54, 72-73, 91-102 of Application No.17797400 (reference application). This is a provisional statutory double patenting rejection since the claims directed to the same invention have not in fact been patented.
Claim Rejections - 35 USC § 112(b)
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.
Claim 31-36, 41-44, 46 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 pre-AIA the applicant regards as the invention.
Claim 31-36, 41-44 recites the limitation “risk value/score". No risk value/score was previously cited. There is insufficient antecedent basis for this limitation in the claim.
Claim 46 recites the limitation "train a machine learning model" in “train a machine learning model against, for each respective human training subject in the plurality of diabetic human training subjects (a) a plurality of features derived from at least the respective set of biomarker data collected at the respective first time and the respective first set HSD collected at the respective first time, and (b) a respective indication of whether the respective human training subject experienced progressive decline in kidney function, to produce the trained machine learning model.”. A trained model was previously recited in Claim 45, thus it is unclear if this is the same model or a different model.
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 1-68 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 45, 56 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a method, non-transitory processor-readable medium, and method for determining if a human subject experienced progressive decline in kidney function over time.
The limitations of receiving, for each respective human training subject in a plurality of diabetic human training subjects: a respective set of biomarker data from a biological sample collected from the respective human training subject at a respective first time; a respective first set of human subject data (HSD) collected from the respective human training subject at the respective first time; and a respective second set of HSD collected from the respective human training subject at a respective second time after the respective first time; determining, for each respective human training subject in the plurality of diabetic human training subjects, a respective indication of whether the respective human training subject experienced progressive decline in kidney function based on at least the respective second set of HSD; training […], for each respective human training subject in the plurality of diabetic human training subjects (a) a plurality of features derived from at least the respective set of biomarker data collected at the respective first time and the respective first set HSD collected at the respective first time, and (b) a respective indication of whether the respective human training subject experienced progressive decline in kidney function; receiving a set of biomarker data and a first set of HSD, for a diabetic human test subject not included in the plurality of diabetic human training subjects, collected at a first time for the diabetic human test subject; and executing, after the training, […] to generate an indication of whether the diabetic human test subject will experience progressive decline in kidney function over the period of time, based on the set of biomarker data and the first set of HSD for the diabetic human test subject, as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. That is, other than reciting a system implemented by (claim 45) a non-transitory processor-readable medium (computer), the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the non-transitory processor-readable medium, this claim encompasses a person looking at data and determining if the subject will experience progressive decline in kidney function over time in the manner described in the identified abstract idea, supra. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of (claim 45) A non-transitory processor-readable medium that implements the identified abstract idea. The non-transitory processor-readable medium is not described by the applicant and is recited at a high-level of generality (i.e., a generic computer performing a generic computer functions of computing, determining, and selecting) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim 45 recites the additional element of a first compute device and a second compute device remote from the first compute device that implements the identified abstract idea. The first and second compute device are not described by the applicant and is recited at a high-level of generality (i.e., a generic device performing a generic computer functions of computing, determining, and selecting) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim further recites the additional element of using the trained machine learning model to generate an indication. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a non-transitory processor-readable medium to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a first and second device to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the trained machine learning model to generate an indication was found to represent mere instructions to implement the abstract idea on a generic computer. This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Dependent Claims
Claims 2-44, 46-55, 57-68 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim 3, 4, 8, 12, 23, 24, 48, 49, 52, 64, 65 merely describes human subject data. Claim 5 merely describes a subset if subjects have chronic kidney disease. Claim 6, 7, 50, 51 merely describes biomarker data. Claims 9, 10, 53, 57, 58 merely describes the metabolic factor. Claim 11, 59 merely describes the health-related factor. Claims 13, 60 merely describes the demographic related factor. Claim 14, 61 merely describes the period of time. Claims 15, 66 merely describes the splitting of human subject data into a derivation data and validation data. Claim 16, 17, 67, 68 merely describes where the human subjects are from. Claim 18 merely describes derivation data are split into training and test data. Claim 28, 30, 54 merely describes what progressive decline in kidney function is based upon. Claim 29, 55 merely describes how eGFR is estimated. Claim 31, 32 merely describes the risk value. Claim 33 merely describes sending a signal having instructions based on risk score. Claim 35 merely describes estimating a risk of cardiovascular disease based on risk value human subject will experience progressive decline in kidney function. Claim 36 merely describes detecting risk value above a threshold and sending an alarm. Claim 37 merely describes classifying human subject. Claim 38, 41 merely describes administering a therapy to reduce risk value and assessing treatment effect. Claim 39 merely describes a determining a trend in risk value over time. Claim 42, 43 merely describes the therapy. Claim 44 merely describes administering a first therapy and a second therapy based on threshold.
Claim 2, 19-22, 25-27, 34, 40, 46, 47, 63 also includes the additional element of “a machine learning model” which is analyzed the same as in the independent claim and does not provide a practical application or significantly more for the same reasons. Claim 2, 47, 63 merely describes determining a relationship between features. Claim 19 merely describes executing the machine learning model based on the validation data. Claim 20 merely describes machine learning models. Claim 21 merely describes validating the model. Claim 22 merely describes before training the model the human subject data is classified. Claim 25 merely describes before training the model, mapping medication data to RxNorm codes and training based on the codes. Claim 26 merely describes before training the model, mapping lab values to LOINC codes and training based on the codes. Claim 27 merely describes predicting a composite kidney endpoint of progressive decline in kidney function. Claim 34 merely describes monitoring for improvement of level of risk or biomarker data or factor of human subject data. Claim 40 merely describes retraining the model. Claim 46 merely describes training a model. Claim 62 merely describes training a model and executing the model to generate an indication of whether the human subject will experience a progressive decline in kidney function.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-15, 18-66 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cha (US 20200005900)
CLAIM 1
Cha teaches A method, comprising: receiving, for each respective human training subject in a plurality of diabetic human training subjects: a respective set of biomarker data from a biological sample collected from the respective human training subject at a respective first time; (Cha para 2 teaches a method. Para 77, 228, 229 teaches patients with diabetes. Para 24 teach biomarkers assays. Para 85 teach biomarkers tumor necrosis factor receptor-1 (“TNFR1”), tumor necrosis factor receptor-2 (“TNFR2”), kidney injury molecule-1 (“KIM1”) and/or endostatin. See also para 227, 228, 231)
a respective first set of human subject data (HSD) collected from the respective human training subject at the respective first time; and (Cha para 39-45 teach patient data collected at a first time)
a respective second set of HSD collected from the respective human training subject at a respective second time after the respective first time; (Cha para 66-68 teaches collecting patient data over time including at least 45, 90, 180, 365 day periods. Para 68 explicitly teaches lab tests within periods of 90 days and 90-185 days as an example. )
determining, for each respective human training subject in the plurality of diabetic human training subjects, a respective indication of whether the respective human training subject experienced progressive decline in kidney function based on at least the respective second set of HSD; (Cha para 24 teaches predicting renal function decline using electronic patient data, such as longitudinal EHRs, biomarker assays and/or genomics data, derived from any number of data sources)
training a machine learning model against, for each respective human training subject in the plurality of diabetic human training subjects (Cha para 25 teaches employing of machine learning models. Para 61, 92-93, 95-98 teaches machine learning model training.)
(a) a plurality of features derived from at least the respective set of biomarker data collected at the respective first time and the respective first set HSD collected at the respective first time, and (Cha para 25 teaches using machine learning models to determine predictive features based on patient data. Para 24 teaches data patient data may include as longitudinal EHRs, biomarker assays and/or genomics data, derived from any number of data sources. Para 61 teaches machine learning model training. )
(b) a respective indication of whether the respective human training subject experienced progressive decline in kidney function; (Cha para 76 teaches info used for training includes patient info including warning signs of CKD including polyneuropathy, edema, fatigue and weakness. Para 83 teaches trend line of eGFR (relates to decline in kidney function))
receiving a set of biomarker data and a first set of HSD, for a diabetic human test subject not included in the plurality of diabetic human training subjects, collected at a first time for the diabetic human test subject; and (Cha para 95 teaches training the model to generalize or extrapolating well to unseen test data. Para 108 teaches once trained and validated the model can determine risk information for new patient records.)
executing, after the training, the machine learning model to generate an indication of whether the diabetic human test subject will experience progressive decline in kidney function over the period of time, based on the set of biomarker data and the first set of HSD for the diabetic human test subject. (Cha para 108 teaches once trained and validated the model can determine risk information for new patient records. Para 29 teaches risk information relate to renal function over time such as decline to a certain level or percentage decline)
CLAIM 2, 47, 63
Cha teaches wherein the machine learning model determines a relationship between (i) a plurality of features derived from at least the set of biomarker data and the first set of HSD and (ii) an indication of whether a diabetic human will experience progressive decline in kidney function over a period of time. (Cha para 24 teaches patient data including biomarker assay. Para 25 teaches using patient data in a machine learning model to determine likelihood of risk. Para 29 teaches risk relates to renal function such as an example of decline to a certain level or percentage decline over time. )
CLAIM 3, 48, 64
Cha teaches receiving, for each respective human training subject in the plurality of diabetic human training subjects, a respective third set of HSD collected from the respective human training subject at a third time before the first time. (Cha para 66-68 teaches collecting patient data over time including at least 45, 90, 180, 365 day periods. Para 68 explicitly teaches lab tests within periods of 90 days and 90-185 days as an example. )
CLAIM 4, 49, 65
Cha teaches receiving, for each respective human training subject in the plurality of diabetic human training subjects, a respective fourth set of HSD collected from the respective human training subject at a third time after the second time. (Cha para 66-68 teaches collecting patient data over time including at least 45, 90, 180, 365 day periods. Para 68 explicitly teaches lab tests within periods of 90 days and 90-185 days as an example. )
CLAIM 5
Cha teaches The method of claim 1, wherein a subset of the plurality of diabetic human subjects has chronic-kidney-disease (CKD). (Cha Para 62, 76 teaches selecting a group of patients who may have CKD. para 32-34 teaches predicting patients as CKD stage 3, 3a, 3b, 4.)
CLAIM 6, 50
Cha teaches The method of claim 1, wherein the biomarker data of the plurality of diabetic human subjects indicates a level of at least one of the following biomarkers: sTNFR-1, sTNFR- 2, KIM-1, and ratios to one another of any of the preceding. (Cha para 85 teaches biomarkers include TNFR-1, TNFR-2, KIM1. Para 86 teaches soluble forms of TNFR1 AND TNFR2. Para 56 teaches ratio of first variable to second variable in lab tests information)
CLAIM 7, 51
Cha teaches The method of claim 6, further comprising: detecting the biomarker data of the diabetic human subject in a biological sample of the diabetic human subject. (Cha para 42 teaches lab test information including samples blood, blood serum, blood plasma, urine, saliva, sweat, tears, cerebrospinal fluid, biopsy, ascites, milk, lymph, bronchial and other lavage samples, or tissue extract from a patient. Para 85 teaches biomarkers include TNFR-1, TNFR-2, KIM1.)
CLAIM 8, 52
Cha teaches The method of claim 1, further comprising: obtaining the first set of HSD or the second set of HSD of the diabetic human subject, the first set of HSD or the second set of HSD including a metabolic factor, a health-related factor, or a demographic-related factor. (Cha para 12 teach serum albumin, serum calcium (i.e., metabolic factor). Para 81 teaches further lab test information including serum creatinine, blood urea nitrogen (“BUN”), sodium, potassium, chloride, bicarbonate, calcium and urine microalbumin. Other features employed by the system may relate to lab tests associated with: components of complete blood count (“CBC”), liver function tests (“LFTs”), lipid profile, coagulation tests, calcium, magnesium, phosphorus, brain natriuretic peptide (“BNP”), and/or uric acid. Para 38 teaches patient information including identification information, demographics information, diagnoses and procedures information (“DP information”), lab tests information, medications information, genetics information, and/or various information relating patient signs, symptoms and behaviors. In certain embodiments, such patient information may additionally or alternatively comprise medical device information (e.g., waveform data, biometrics, etc.), financial information, insurance information, claims information, and/or various patient-generated data (e.g., automated call responses, health risk assessment responses, patient surveys, etc. )
CLAIM 9, 53, 57
Cha teaches The method of claim 8, wherein the metabolic factor includes at least one of a Serum Albumin level, a Serum Calcium level, […], or [..]. (Cha 12 teach serum albumin, serum calcium. Other claim limitations interpreted as optional due to claim language "at least one of … or …")
CLAIM 10, 58
Cha teaches The method of claim 8, wherein the metabolic factor includes at least one of a Hemoglobin-AlC (HbA1C) level, a Urine Albumin-Creatinine Ratio (UACR), […], a systolic blood pressure value, a Glomerular Filtration Rate, or a diastolic blood pressure value. (Cha para 83 teaches eGFR (estimated glomerular filtration rate). Para 84 teaches UACR, hba1c. TABLE-US-00010 TABLE 10 teaches blood pressure. Para 231 teaches systolic and diastolic blood pressure. Other claim limitations interpreted as optional due to claim language "at least one of … or …")
CLAIM 11, 59
Cha teaches The method of claim 8, wherein the health-related factor includes a […], a status of past smoking, or a status of current smoking. (Cha para 45 teaches smoking status. Other claim limitations interpreted as optional due to claim language "includes … or …")
CLAIM 12
Cha teaches The method of claim 8, wherein the first set of HSD or the second set of HSD include at least one of a Serum Calcium level, […], Hemoglobin-Al C (HbA1 C) level, a Urine Albumin-Creatinine Ratio (UACR), a systolic blood pressure value, or a Glomerular Filtration Rate. (Cha para 12 teach serum calcium. Cha para 83 teaches eGFR (estimated glomerular filtration rate). Para 84 teaches UACR, hba1c. TABLE-US-00010 TABLE 10 teaches blood pressure. Para 231 teaches systolic and diastolic blood pressure. Other claim limitations interpreted as optional due to claim language "at least one of … or …")
CLAIM 13, 60
Cha teaches wherein the demographic-related factor includes age, gender, ethnicity, income, education, or employment history. (Cha para 40 demographics information including age, gender, ethnicity, income, education, occupation )
CLAIM 14, 61
Cha teaches wherein the period of time less than 5 years. (Cha para 66-68 teaches collecting patient data over time including at least 45, 90, 180, 365 day periods. Para 68 explicitly teaches lab tests within periods of 90 days and 90-185 days as an example. Para 137 teaches 2 years, about 2.5 years, about 3 years, about 3.5 years, about 4 years, about 4.5 years or about 5 years )
CLAIM 15, 66
Cha teaches The method of claim 1, wherein the biomarker data of the plurality of diabetic human subjects and the first set of HSD of the plurality of diabetic human subjects are split into derivation data and validation data. (Cha para 99, 101, 103, 105 teaches cross-validation in training a model. Para 203 teaches split of data into training and validation data.)
CLAIM 18
Cha teaches The method of claim 15, wherein the derivation data are split into training data and test data. (Cha para 101 teaches cross-validation in training a model. Para 95 teaches test set and training set. Para 105 teaches a validation set. Para 203 teaches split of data into training and validation data. )
CLAIM 19
Cha teaches The method of claim 15, further comprising: executing, after the training, the machine learning model based on the validation data. (Para 108 teaches using the model after training and being validated. Para 99, 101, 103, 105, 107 teaches validating based on validation data.)
CLAIM 20
Cha teaches The method of claim 1, wherein the machine learning model includes a random forest model, deep learning model, a least absolute shrinkage and selection operator (LASSO) model, an eXtreme Gradient Boosting (XGBoost) model, or a support vector machine (SVM). ([0027] Machine learning algorithms employed by the embodiments disclosed herein may include, but are not limited to, random forest (“RF”), least absolute shrinkage and selection operator (“LASSO”) logistic regression, regularized logistic regression, XGBoost, decision tree learning, artificial neural networks (“ANN”), deep neural networks (“DNN”), support vector machines, rule-based machine learning, and/or others.)
CLAIM 21
Cha teaches The method of claim 1, further comprising: performing, during training the machine learning model, a multi-fold cross-validation. (Cha para 99, 101 teaches K-fold cross-validation )
CLAIM 22
Cha teaches The method of claim 1, further comprising: classifying, before training the machine learning model, the first set of HSD or the second set of HSD of the plurality of diabetic human subjects into a plurality of non- overlapping categories. (Cha para 54 teaches grouping information into meaningful categories. Para 60-63 teaches generating patient cohorts from the population during preprocessing. )
CLAIM 23
Cha teaches The method of claim 1, wherein the first set of HSD or the second set of HSD of the plurality of diabetic human samples include Related Health Problems (ICD) codes or Current Procedures Terminology (CPT) codes, each ICD code from the ICD codes or each CPT code from the CPT codes are associated with a Boolean variable and a timestamp. (Cha para 41 teaches information includes data/time information. Para 75 teaches ICD codes. Para 128 teaches CPT codes. )
CLAIM 24
Cha teaches The method of claim 1, wherein the first set of HSD or the second set of HSD of the plurality of diabetic human subjects include medication data and laboratory values for each diabetic human subject from the plurality of diabetic human subjects. (Cha para 60 teaches lab tests. Para 16 teaches medication data. )
CLAIM 25
Cha teaches The method of claim 24, further comprising: mapping, before training the machine learning model, the medication data to RxNorm codes, the training the machine learning model including training the machine learning model based on the RxNorm codes. (Cha para 55 teaches naming of each medication corresponding to NDC code selected from RxNORM database. Cha para 25 teaches using machine learning models to determine predictive features and evaluate such features to determine the likelihood that each patient will experience the outcome within one or more timeframes. Para 38 teaches patient information used may include medication information. Para 43 further teaches medication information. Para 92 teaches employing training data based on information and resulting features to train the model. )
CLAIM 26
The method of claim 24, further comprising: mapping, before training the machine learning model, the laboratory values to Logical Observation Identifiers Names and Codes (LOINC) code, the training the machine learning model including training the machine learning model based on the LOINC codes. (Cha para 54 teaches map individual lab test names and/or codes found in an input data record to a corresponding Logical Observation Identifiers Names and Codes (“LOINC”) code. Para 38 teaches patient information used may include lab information. Para 92 teaches employing training data based on information and resulting features to train the model.)
CLAIM 27
The method of claim 1, wherein the machine learning model is configured to predict a composite kidney endpoint of progressive decline in kidney function. (Cha para 104 teaches a composite score of likelihood of risk. Para 123 teaches composite outcomes that combine patient endpoints regarding renal function. )
CLAIM 28, 54
The method of claim 1, wherein progressive decline in kidney function is based upon estimated glomerular filtration rate (eGFR) changes over the period of time. (Cha para 12 teaches using estimated glomerular filtration rate (eGFR) as part of data used. Para 94 teaches observation window periods.)
CLAIM 29, 55
The method of claim 28, wherein the eGFR is estimated using at least one the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) creatinine equation, the Modification of Diet in Renal Disease (MDRD) Study equation, or […]. (Cha para 30 teaches using MDRF, CKD-EPI equations to estimate eGFR. Additional limitations interpreted as optional due to claim language "at least one of … or …")
CLAIM 30
The method of claim 28, wherein progressive decline in kidney function includes an eGFR decline of > 5 ml/min/1.73 m2/year or 40% sustained decline in eGFR or kidney failure (sustained eGFR <15 ml/min/1.73 m2/year. (Cha para 34 teaches renal failure characterized as an eGFR value of less than 15 ml/min/1.73m.sup.2. para 137 teaches determine a significant eGFR decline 230 when the patient's eGFR value decreases by at least a certain amount from a baseline value over a time period. For example, a significant eGFR decline may require a decline from baseline of at least about 30%, at least about 35%, at least about 40% or at least about 45% within the time period. As another example, such decline may be required to occur over a time period of about 2 years, about 2.5 years, about 3 years, about 3.5 years, about 4 years, about 4.5 years or about 5 years. In one particular embodiment, a significant eGFR decline may require a 40% decline in eGFR value from baseline within about 2 years to about 3 years. A 40% decline in eGFR (renal function) in 2-3 years is a broadly accepted surrogate end point for the development of kidney failure in clinical trials of kidney disease progression. )
CLAIM 31
The method of claim 1, wherein the risk value is a number in a range between 0 and 100. (Cha Fig. 6 element 625 shows a risk score between a range of 0 and 100)
CLAIM 32
The method of claim 1, wherein the risk value indicates a likelihood of progressive decline in kidney function. (Cha para 25 teaches using patient data in a machine learning model to determine likelihood of risk. Para 29 teaches risk relates to renal function such as an example of decline to a certain level or percentage decline over time.)
CLAIM 33
The method of claim 1, further comprising: sending, when the risk score is greater than a predetermined threshold, a signal having instruction for administering a therapy to the diabetic human subject. (Cha para 26 teaches when risk score is within a certain range or is greater than a certain threshold then sending alerts, notifications such as treatment recommendations. )
CLAIM 34
The method of claim 1, further comprising: detecting the risk value generated by the machine learning model is within a preset range; and (Cha para 26 teaches when risk score is within a certain range or is greater than a certain threshold)
monitoring, after receiving the biomarker data and the first set of HSD or the second set of HSD, the human subject for an improvement in a level of the risk score or at least one biomarker from the biomarker data or at least one factor of the first set of HSD or the second set of HSD of the diabetic human subject. (Cha para 24 teaches patient data including biomarker assay. Para 25 teaches using patient data in a machine learning model to determine likelihood of risk. Para 29 teaches risk relates to renal function such as an example of decline to a certain level or percentage decline over time. )
CLAIM 36
The method of claim 1, further comprising: detecting the risk value is above a preset threshold; and sending an alarm to a compute device associated with the diabetic human subject to visit a healthcare provider. (Cha para 26 teaches when risk score is within a certain range or is greater than a certain threshold then sending alerts, notifications such as treatment recommendations. Para 117 teaches recommendation including screening the patient for CKD, scheduling an appointment with the patient, referring the patient to a specialist (e.g., a nephrologist), initiating venous access or other interventions to the patient, providing clinical educational materials to the patient, providing a structured diet program to the patient, providing one or more medications to the patient, increasing the patient's medication adherence, ordering one or more lab tests for the patient, improving the patient's blood pressure management, transitioning the patient to planned dialysis enrollment, decreasing or managing the patient's risk of infection, and/or escalating care team assessment.)
CLAIM 37
The method of claim 1, further comprising: classifying the diabetic human subject as a low risk patient, an intermediate risk patient, or a high-risk patient. (Cha para 235 teaches categorizing patients into low, medium, high risk)
CLAIM 38
The method of claim 1, further comprising: administering a therapy to reduce the risk value that the diabetic human subject will experience progressive decline in kidney function over the period of time; and assessing a treatment effect of the therapy by calculating a trend of risk values generated over time. (Cha para 26 teaches when risk score is within a certain range or is greater than a certain threshold then sending alerts, notifications such as treatment recommendations. Para 117 teaches recommendation including screening the patient for CKD, scheduling an appointment with the patient, referring the patient to a specialist (e.g., a nephrologist), initiating venous access or other interventions to the patient, providing one or more medications to the patient, increasing the patient's medication adherence, ordering one or more lab tests for the patient, improving the patient's blood pressure management, transitioning the patient to planned dialysis enrollment, decreasing or managing the patient's risk of infection, and/or escalating care team assessment. Para 119 teach assessing risk and successive interventions based on risk score. Para 197 teaches trend over time. )
CLAIM 39
The method of claim 1, wherein the risk value is a first risk value and the period of time is a first period of time, the method further comprising: determining a second risk value over a second period of time, a difference between the first risk value at the first period of time and the second risk value at the second period of time being informative about a trend of risk of progressive decline in kidney function in the diabetic human subject. (Cha para 119 teach assessing risk and successive interventions based on risk score. Para 197 teaches trend over time. )
CLAIM 40
The method of claim 39, further comprising: retraining, after executing, the machine learning model based on at least one of the first risk value or the second risk value. (Cha para 108 teaches newly available information may be re-ingested, preprocessed then features calculated for the ML model to calculate revised risk scores based on the relative feature weights generated on the training data. In one embodiment, the ML model may re-calculate the individual patient risk scores at regular intervals as new patient records are made available (e.g., daily, weekly or monthly). )
CLAIM 41
The method of claim 1, wherein the risk value is a first risk value and the period of time is a first period of time, the method further comprising: administering a therapy on the diabetic human subject; and determining a second risk value over a second period of time, a difference between the first risk value at the first period of time and the second risk value at the second period of time being informative about the therapy administered on the diabetic human subject. (Cha para 119 teach assessing risk, intervening, assessing risk and intervening if necessary again. )
CLAIM 42
The method of claim 41, wherein the therapy includes at least one of a therapy based on SGLT2i, angiotensin converting enzyme (ACE) inhibitors, or angiotensin-receptor blockers (ARBs). (Cha para 16 teaches medications including angiotensin II receptor blockers (“ARBs”), angiotensin-converting enzyme (“ACE”) inhibitors, sodium-glucose Cotransporter-2 (SGLT2) inhibitors. Para 117 teaches treatments including providing one or more medications to a patient. )
CLAIM 43
The method of claim 41, wherein the therapy includes at least one of a change in lifestyle, a change in diet, or a change in exercise. (Cha para 118 teaches treatment including exercise and diet)
CLAIM 44
The method of claim 1, further comprising: administering a first therapy in response to the risk value that the diabetic human subject will experience progressive decline in kidney function being above a pre-set threshold, and administering a second therapy in response to the risk value that the diabetic human subject will experience progressive decline in kidney function being below the pre-set threshold. (Cha para 119 teach assessing risk is above a threshold and then intervening, assessing risk below a threshold and intervening again. )
CLAIM 45
A non-transitory processor-readable medium storing code representing instructions to be executed by a processor of a first compute device, the code comprising code to cause the processor to: (Cha para 181 -183 teaches system include non-volatile memories and the modules may include one or more sequences of instructions stored as software or firmware in association with the system memory which instructions or code may be stored for execution by the processor)
(a) receive, from a second compute device remote from the first compute device, a trained machine learning model; (Cha para 160 teaches server configured to receive/transmit information from/to various system components, with or without user interaction via a network 430. The server 420 may also be configured to store such information in one or more local or remote databases. See also 161-163. Fig. 4 shows image relating remote server to ML engine with paths between via a network. )
(b) receive biomarker data and a first set of HSD for a diabetic human subject, the biomarker data indicating a level of at least one of the following biomarkers: sTNFR-1, sTNFR-2, KIM-1, and ratios to one another of any of the preceding, and (Cha para 85 teaches biomarkers include TNFR-1, TNFR-2, KIM1. Para 86 teaches soluble forms of TNFR1 AND TNFR2. Para 56 teaches ratio of first variable to second variable in lab tests information being used.)
the first set of HSD including a metabolic factor, a health-related factor, or a demographic-related factor; (Cha para 12 teach serum albumin, serum calcium (i.e., metabolic factor). Para 81 teaches further lab test information including serum creatinine, blood urea nitrogen (“BUN”), sodium, potassium, chloride, bicarbonate, calcium and urine microalbumin. Other features employed by the system may relate to lab tests associated with: components of complete blood count (“CBC”), liver function tests (“LFTs”), lipid profile, coagulation tests, calcium, magnesium, phosphorus, brain natriuretic peptide (“BNP”), and/or uric acid. Para 38 teaches patient information including identification information, demographics information, diagnoses and procedures information (“DP information”), lab tests information, medications information, genetics information, and/or various information relating patient signs, symptoms and behaviors. In certain embodiments, such patient information may additionally or alternatively comprise medical device information (e.g., waveform data, biometrics, etc.), financial information, insurance information, claims information, and/or various patient-generated data (e.g., automated call responses, health risk assessment responses, patient surveys, etc. )
and(c) execute the trained machine learning model to generate an indication of whether the diabetic human subject will experience a progressive decline in kidney function over a period of time. (Cha para 108 teaches once trained and validated the model can determine risk information for new patient records. Para 29 teaches risk information relate to renal function over time such as decline to a certain level or percentage decline)
CLAIM 46
The non-transitory processor-readable medium of claim 45, wherein the second compute device is configured to: (Cha para 181 -183 teaches system include non-volatile memories and the modules may include one or more sequences of instructions stored as software or firmware in association with the system memory which instructions or code may be stored for execution by the processor. Cha para 160 teaches server configured to receive/transmit information from/to various system components, with or without user interaction via a network 430. The server 420 may also be configured to store such information in one or more local or remote databases. See also 161-163. Fig. 4 shows image relating remote server to ML engine with paths between via a network. )
receive, for each respective human training subject in a plurality of diabetic human training subjects: a respective set of biomarker data from a biological sample collected from the respective human training subject at a respective first time; (Cha para 42 teaches lab test information including samples blood, blood serum, blood plasma, urine, saliva, sweat, tears, cerebrospinal fluid, biopsy, ascites, milk, lymph, bronchial and other lavage samples, or tissue extract from a patient. Para 85 teaches biomarkers include TNFR-1, TNFR-2, KIM1.)
a respective first set of human subject data (HSD) collected from the respective human training subject at the respective first time; and (Cha para 66-68 teaches collecting patient data over time including at least 45, 90, 180, 365 day periods. Para 68 explicitly teaches lab tests within periods of 90 days and 90-185 days as an example. )
a respective second set of HSD collected from the respective human training subject at a respective second time after the respective first time; (Cha para 66-68 teaches collecting patient data over time including at least 45, 90, 180, 365 day periods. Para 68 explicitly teaches lab tests within periods of 90 days and 90-185 days as an example. )
determine, for each respective human training subject in the plurality of diabetic human training subjects, a respective indication of whether the respective human training subject experienced progressive decline in kidney function based on at least the respective second set of HSD; and (Cha para 24 teaches patient data including biomarker assay. Para 25 teaches using patient data in a machine learning model to determine likelihood of risk. Para 29 teaches risk relates to renal function such as an example of decline to a certain level or percentage decline over time. )
train a machine learning model against, for each respective human training subject in the plurality of diabetic human training subjects (Cha para 63 teaches training a model based on patient information to determine various risk information)
(a) a plurality of features derived from at least the respective set of biomarker data collected at the respective first time and the respective first set HSD collected at the respective first time, and (Cha para 24 teaches patient data including biomarker assay. Cha para 42 teaches lab test information including samples blood, blood serum, blood plasma, urine, saliva, sweat, tears, cerebrospinal fluid, biopsy, ascites, milk, lymph, bronchial and other lavage samples, or tissue extract from a patient. Para 85 teaches biomarkers include TNFR-1, TNFR-2, KIM1. Para 25 teaches using patient data in a machine learning model to determine likelihood of risk. Para 29 teaches risk relates to renal function such as an example of decline to a certain level or percentage decline over time. )
(b) a respective indication of whether the respective human training subject experienced progressive decline in kidney function, to produce the trained machine learning model. (Cha para 108 teaches once trained and validated the model can determine risk information for new patient records. Para 29 teaches risk information relate to renal function over time such as decline to a certain level or percentage decline)
CLAIM 56
A method, comprising: detecting biomarker data collected from a plurality of biological samples from a plurality of diabetic human subjects, each biomarker datum from the biomarker data indicating a level of at least one of the following biomarkers: sTNFR-1, sTNFR-2, KIM- 1, and ratios to one another of any of the preceding; (Cha para 85 teaches biomarkers include TNFR-1, TNFR-2, KIM1. Para 86 teaches soluble forms of TNFR1 AND TNFR2. Para 56 teaches ratio of first variable to second variable in lab tests information being used.)
obtaining a first set of human subject data (HSD) of the plurality of diabetic human subjects at a first time, (Cha para 66-68 teaches collecting patient data over time including at least 45, 90, 180, 365 day periods. Para 68 explicitly teaches lab tests within periods of 90 days and 90-185 days as an example. )
obtaining a second set of human subject data (HSD) of the plurality of diabetic human subjects at a second time, (Cha para 66-68 teaches collecting patient data over time including at least 45, 90, 180, 365 day periods. Para 68 explicitly teaches lab tests within periods of 90 days and 90-185 days as an example. )
the first set of HSD or the second set of HSD each including a metabolic factor, a health-related factor, or a demographic-related factor; and (Cha para 12 teach serum albumin, serum calcium (i.e., metabolic factor). Para 81 teaches further lab test information including serum creatinine, blood urea nitrogen (“BUN”), sodium, potassium, chloride, bicarbonate, calcium and urine microalbumin. Other features employed by the system may relate to lab tests associated with: components of complete blood count (“CBC”), liver function tests (“LFTs”), lipid profile, coagulation tests, calcium, magnesium, phosphorus, brain natriuretic peptide (“BNP”), and/or uric acid. Para 38 teaches patient information including identification information, demographics information, diagnoses and procedures information (“DP information”), lab tests information, medications information, genetics information, and/or various information relating patient signs, symptoms and behaviors. In certain embodiments, such patient information may additionally or alternatively comprise medical device information (e.g., waveform data, biometrics, etc.), financial information, insurance information, claims information, and/or various patient-generated data (e.g., automated call responses, health risk assessment responses, patient surveys, etc. )
determining, for each diabetic human subject in the plurality of diabetic human subjects, an indication of whether the diabetic human subject experienced progressive decline in kidney function based on at least the second set of HSD. (Cha para 24 teaches predicting renal function decline using electronic patient data, such as longitudinal EHRs, biomarker assays and/or genomics data, derived from any number of data sources)
CLAIM 62
The method of claim 56, further comprising: training a machine learning model against, for each diabetic human subject in the plurality of diabetic human subjects (Cha para 63 teaches training a model based on patient information to determine various risk information)
(a) a plurality of features derived from at least the set of biomarker data collected at the first time and the first set HSD collected at the first time, and (Cha para 12 teach serum albumin, serum calcium (i.e., metabolic factor). Cha para 85 teaches biomarkers include TNFR-1, TNFR-2, KIM1. Para 86 teaches soluble forms of TNFR1 AND TNFR2. Para 56 teaches ratio of first variable to second variable in lab tests information being used. Cha para 66-68 teaches collecting patient data over time including at least 45, 90, 180, 365 day periods. Para 68 explicitly teaches lab tests within periods of 90 days and 90-185 days as an example. )
(b) a indication of whether the diabetic human subject experienced progressive decline in kidney function; (Cha para 24 teaches predicting renal function decline using electronic patient data, such as longitudinal EHRs, biomarker assays and/or genomics data, derived from any number of data sources. Para 76 teaches info used for training includes patient info including warning signs of CKD including polyneuropathy, edema, fatigue and weakness. Para 83 teaches trend line of eGFR (relates to decline in kidney function))
receiving a set of biomarker data and a first set of HSD, for a human test subject not included in the plurality of diabetic human subjects, collected at a first time for the human test subject; and (Cha para 12 teach serum albumin, serum calcium (i.e., metabolic factor). Cha para 85 teaches biomarkers include TNFR-1, TNFR-2, KIM1. Para 86 teaches soluble forms of TNFR1 AND TNFR2. Para 56 teaches ratio of first variable to second variable in lab tests information being used. Cha para 66-68 teaches collecting patient data over time including at least 45, 90, 180, 365 day periods. Para 68 explicitly teaches lab tests within periods of 90 days and 90-185 days as an example. Cha para 95 teaches training the model to generalize or extrapolating well to unseen test data. Para 108 teaches once trained and validated the model can determine risk information for new patient records.)
executing, after the training, the machine learning model to generate an indication of whether the human test subject will experience progressive decline in kidney function over the period of time, based on the set of biomarker data and the first set of HSD for the human test subject. (Cha para 95 teaches training the model to generalize or extrapolating well to unseen test data. Para 108 teaches once trained and validated the model can determine risk information for new patient records. Cha para 12 teach serum albumin, serum calcium (i.e., metabolic factor). Cha para 85 teaches biomarkers include TNFR-1, TNFR-2, KIM1. Para 86 teaches soluble forms of TNFR1 AND TNFR2. Para 56 teaches ratio of first variable to second variable in lab tests information being used.)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 16-17, 67-68 are rejected under 35 U.S.C. 103 as being unpatentable over Cha (US 20200005900) in view of Wu (US 20100106524).
CLAIM 16, 67
Cha teaches wherein the plurality of diabetic human subjects […] (Cha para 38 teaches patient information)
Cha does not teach wherein the plurality of diabetic human subjects is from a first population from a first geographical location and a second population from a second geographical location.
Wu does teach wherein the plurality of diabetic human subjects is from a first population from a first geographical location and a second population from a second geographical location. (Wu para 10 teaches receiving patient information from patients in a plurality of locations. )
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the plurality of patients as taught by Cha with the patients being from a first and a second geographical location as taught by Wu. It would be beneficial because there is a need to assess risk for many patients in different remote locations as taught by Wu para 8.
CLAIM 17, 68
Chat teaches wherein the plurality of diabetic human subjects […] (Cha para 38 teaches patient information)
Cha does not teach wherein the plurality of diabetic human subjects is from a first population from a first healthcare setting and a second population from a second healthcare setting.
Wu does teach wherein the plurality of diabetic human subjects is from a first population from a first healthcare setting and a second population from a second healthcare setting. (Wu para 10 teaches receiving patient information from patients in a plurality of locations. )
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the plurality of patients as taught by Cha with the patients being from a first and a second healthcare setting as taught by Wu. It would be beneficial because there is a need to assess risk for many patients in different remote locations as taught by Wu para 8.
Claim 35 are rejected under 35 U.S.C. 103 as being unpatentable over Cha (US 20200005900) in view of Shlipak, Rapid Decline of Kidney Function Increases Cardiovascular Risk in the Elderly, 2009 December 20
CLAIM 35:
Cha teaches The method of claim 1, further comprising: […] the risk value that the diabetic human subject will experience progressive decline in kidney function over the period of time. (Cha para 24 teaches patient data including biomarker assay. Para 25 teaches using patient data in a machine learning model to determine likelihood of risk. Para 29 teaches risk relates to renal function such as an example of decline to a certain level or percentage decline over time. )
Cha does not teach The method of claim 1, further comprising: estimating a cardiovascular disease risk based the risk value that the diabetic human subject will experience progressive decline in kidney function over the period of time.
Shlipak does teach The method of claim 1, further comprising: estimating a cardiovascular disease risk based the risk value that the diabetic human subject will experience progressive decline in kidney function over the period of time. (Shlipak “Discussion” para 1 teaches findings suggest that both the current level of kidney function and the trajectory are important for assessing cardiovascular risk. )
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the estimating risk score as taught by Cha with the estimating risk of cardiovascular disease as taught by Shlipak because it would be beneficial to estimate cardiovascular disease risk because CKD is an important risk factor for cardiovascular disease as taught by Shlipak “Abstract” para 1.
Prior Art Made of Record and Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20180166174 Lewis
[0271] Elevated HbA1c is associated with increased morbidity and mortality even in patients not diagnosed with diabetes. Mean glycaemia and HbA1c show consistent and stronger associations with cardiovascular disease risk factors than fasting glucose or postprandial glucose levels or measures of glucose variability in patients with diabetes. [Borg, R., et al. “HbA1c and mean blood glucose show stronger associations with cardiovascular disease risk factors than do postprandial glycaemia or glucose variability in persons with diabetes: the A1C-Derived Average Glucose (ADAG) study.” Diabetologia 54.1 (2011): 69-72.] In a study of more than 8000 subjects over 7 years patients who progressed to chronic kidney disease had higher mean HbA1c (7.8±1.3% vs 7.4±1.2%, p<0.001) and SD (1.0±0.8% vs 0.8±0.6%, p<0.001) than nonprogressors. Similarly, patients who developed cardiovascular disease had higher mean HbA1c (7.7±1.3% vs 7.4±1.2%, p<0.001) and SD (1.4±1.1% vs 1.1±0.8%, p<0.001) than patients who did not develop cardiovascular disease.
US 20120065514 Naghavi
[Claim1]. A method for assessment of cardiovascular health, comprising: calculating a risk score based on risk factors, measuring an indicator of cardiovascular function, measuring an indicator of cardiovascular structure, and combining the risk score, indicator of cardiovascular function, and indicator of cardiovascular structure to provide a comprehensive assessment of cardiovascular health.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW KYLE TAPIA whose telephone number is (703)756-1662. The examiner can normally be reached 830 - 530.
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, Mamon Obeid can be reached at (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/A.K.T./Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687