DETAILED ACTION
Double Patenting
1. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees.
A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
2. Claims 1-15 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 of U.S. Patent No. 12193739, and claims 1-14 of U.S. Patent No. 12471770.
When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim.
The subject matter claimed in the instant application is fully disclosed in the claims 1-13 of U.S. Patent No. 12193739, and claims 1-14 of U.S. Patent No. 12471770, as follows:
Instant application:
1. A method for determining at least one recommendation for management of wellbeing of a first individual, comprising:
determining an indication of relative cardiovascular aging of the first individual, based at least in part on a predicted risk of cardiovascular disease (CVD) of the individual determined by a deep learning model based on one or more fundus images; and
determining the at least one recommendation for management of the individual's wellbeing based at least in part on the determined indication of relative cardiovascular aging of the first individual.
2. The method of claim 1, wherein determining an indication of relative cardiovascular aging of the first individual comprises: determining a degree of similarity between a predicted risk of cardiovascular disease (CVD) of the first individual and the risk of CVD for a set of individuals belonging to a chronological age bracket to which the first individual also belongs, wherein the predicted risk of CVD is determined by a deep learning model based on one or more fundus images; determining an average chronological age of individuals closest to the first individual in terms of predicted risk of CVD; determining a mean expected chronological age for a person with the predicted risk of CVD of the first individual; and determining the indication of relative cardiovascular aging of the first individual based on the determined degree of similarity, the determined average chronological age, and the determined mean expected chronological age.
3. The method of claim 1, comprising determining a difference between an actual chronological age of the individual and the indication of relative cardiovascular aging of the first individual determined for the individual.
4. The method of claim 3, comprising comparing the age difference of the first individual with the age differences of a set of individuals belonging to a chronological age group to which the first individual belongs.
5. The method of claim 4, comprising determining a relative position of the first individual within the set of individuals based on age difference.
6. The method of claim 5, wherein determination of the at least one recommendation for management of the first individual's wellbeing is based at least in part on the relative position of the first individual within the set of individuals based on age difference.
7. The method of claim 1, comprising determining the relative contribution of one or more risk contributing factors to the indication of relative cardiovascular aging.
8. The method of claim 7, wherein the risk contributing factors include two or more of: blood pressure, glycated haemoglobin A1c (HbA1c), total cholesterol, and glycaemic control.
9. The method of claim 7, wherein the relative contribution of the one of more of the risk contributing factors is used to determine the at least one recommendation for management of the first individual's wellbeing.
10. The method of claim 1, wherein determining the indication of relative cardiovascular aging of the first individual comprises: determining a degree of similarity between a predicted risk of cardiovascular disease (CVD) of the first individual and the risk of CVD for a set of individuals belonging to a chronological age bracket to which the first individual also belongs, wherein the predicted risk of CVD is determined by a deep learning model based on one or more fundus images; determining an average chronological age of individuals closest to the first individual in terms of predicted risk of CVD; determining a mean expected chronological age for a person with the predicted risk of CVD of the first individual; and determining the indication of relative cardiovascular aging of the first individual based on the determined degree of similarity, the determined average chronological age, and the determined mean expected chronological age.
11. The method of claim 10, wherein determining the indication of relative cardiovascular aging of the first individual ("Cardiac BioAge") comprises the calculation: Cardiac BioAgex = P(risk(x)|xEX) * age(X) + (1 - P(risk(x)|xEX )) * age(Yx~Y) where x refers to the first individual, and X is the set of individuals who are in the same age group as the first individual;where P(risk(x) |xE X )is the conditional probability that first individual x has CVD risk, risk(x), given that that the first individual belongs the set X of individuals who have similar age;where age(X) is the mean age for set X; andwhere age(Y~x~Y) is the mean age of patient points who are close to the first individual x.
12. The method of claim 11, wherein the set of individuals comprises individuals of the same gender as the first individual.
13. The method of claim 11, wherein implementation of age(Yx~Y) comprises the components similarity magnitude * cosine similarity age + (1 - similarity magnitude) * risk extrapolated age.
14. A system for determining at least one recommendation for management of wellbeing of a first individual, the system comprising one or more processors and one or more storage devices storing instructions that when executed by the one or more processors cause the one or more processors to perform operations comprising: determining an indication of relative cardiovascular aging of the first individual, based at least in part on a predicted risk of cardiovascular disease (CVD) of the individual determined by a deep learning model based on one or more fundus images; and determining the at least one recommendation for management of the individual's wellbeing based at least in part on the indication of relative cardiovascular aging of the first individual determined.
15. A computer program product for determining a recommendation for management of wellbeing of a first individual, the computer program product comprising a non-transitory computer- readable storage medium containing computer program code for: determining an indication of relative cardiovascular aging of the first individual, based at least in part on a predicted risk of cardiovascular disease (CVD) of the individual determined by a deep learning model based on one or more fundus images; and determining the at least one recommendation for management of the individual's wellbeing based at least in part on the indication of relative cardiovascular aging of the first individual determined.
Patent No. 12193739:
1. A method for determining at least one recommendation for management of wellbeing of a first individual, comprising:
determining an indication of relative cardiovascular aging of the first individual, wherein determining an indication of relative cardiovascular aging of the first individual comprises:
determining a degree of similarity between a predicted risk of cardiovascular disease (CVD) of the first individual and the risk of CVD for a set of individuals belonging to a chronological age bracket to which the first individual also belongs, wherein the predicted risk of CVD is determined by a deep learning model based on one or more fundus images;
determining an average chronological age of individuals closest to the first individual in terms of predicted risk of CVD;
determining a mean expected chronological age for a person with the predicted risk of CVD of the first individual; and
determining the indication of relative cardiovascular aging of the first individual based on the determined degree of similarity, the determined average chronological age, and the determined mean expected chronological age; and
determining the at least one recommendation for management of the individual's wellbeing based at least in part on the determined indication of relative cardiovascular aging of the first individual.
2. The method of claim 1, comprising determining an age difference between an actual chronological age of the first individual and the indication of relative cardiovascular aging determined for the first individual, wherein determining the at least one recommendation for management of the first individual's wellbeing is based at least in part on the determined age difference.
3. The method of claim 2, comprising comparing the age difference of the first individual with the age differences of a set of individuals belonging to a chronological age group to which the first individual belongs.
4. The method of claim 3, comprising determining a relative position of the first individual within the set of individuals based on age difference.
5. The method of claim 4, wherein determination of the at least one recommendation for management of the first individual's wellbeing is based at least in part on the relative position of the first individual within the set of individuals based on age difference.
6. The method of claim 1, comprising determining the relative contribution of one or more risk contributing factors to the indication of relative cardiovascular aging.
7. The method of claim 6, wherein the risk contributing factors include two or more of: blood pressure, glycated haemoglobin A1c (HbA1c), total cholesterol, and glycaemic control.
8. The method of claim 6, wherein the relative contribution of the one of more of the risk contributing factors is used to determine the at least one recommendation for management of the first individual's wellbeing.
9. The method of claim 1, wherein determining the indication of relative cardiovascular aging of the first individual comprises the calculation:
CardiacBioAge.sub.x=P(risk(x)|x∈X)*age(X)+(1−P(risk(x)|x∈X))*age(Y|x˜Y) where x refers to the first individual, and X is the set of individuals who are in the same age group as the first individual; where P(risk(x)|x∈X) is the conditional probability that first individual x has CVD risk, risk(x), given that that the first individual belongs the set X of individuals who have similar age; where age(X) is the mean age for set X; and where age(Y|x˜Y) is the mean age of patient points who are close to the first individual x.
10. The method of claim 9, wherein the set of individuals comprises individuals of the same gender as the first individual.
11. The method of claim 9, wherein implementation of age(Y|x˜Y) comprises the components similarity magnitude*cosine similarity age+(1−similarity magnitude)*risk extrapolated age.
12. A system for determining at least one recommendation for management of wellbeing of a first individual, the system comprising one or more processors and one or more storage devices storing instructions that when executed by the one or more processors cause the one or more processors to perform operations comprising: determining an indication of relative cardiovascular aging of the first individual, wherein determining an indication of relative cardiovascular aging of the first individual comprises: determining a degree of similarity between a predicted risk of cardiovascular disease (CVD) of the first individual and the risk of CVD for a set of individuals belonging to a chronological age bracket to which the first individual also belongs, wherein the predicted risk of CVD is determined by a deep learning model based on one or more fundus images; determining an average chronological age of individuals closest to the first individual in terms of predicted risk of CVD; determining a mean expected chronological age for a person with the predicted risk of CVD of the first individual; and determining the indication of relative cardiovascular aging of the first individual based on the determined degree of similarity, the determined average chronological age, and the determined mean expected chronological age; and determining the at least one recommendation for management of the first individual's wellbeing based at least in part on the determined indication of relative cardiovascular aging.
13. A computer program product for determining a recommendation for management of wellbeing of a first individual, the computer program product comprising a non-transitory computer-readable storage medium containing computer program code for: determining an indication of relative cardiovascular aging of the first individual, wherein determining an indication of relative cardiovascular aging of the first individual comprises: determining a degree of similarity between a predicted risk of cardiovascular disease (CVD) of the first individual and the risk of CVD for a set of individuals belonging to a chronological age bracket to which the first individual also belongs, wherein the predicted risk of CVD is determined by a deep learning model based on one or more fundus images; determining an average chronological age of individuals closest to the first individual in terms of predicted risk of CVD; determining a mean expected chronological age for a person with the predicted risk of CVD of the first individual; and determining the indication of relative cardiovascular aging of the first individual based on the determined degree of similarity, the determined average chronological age, and the determined mean expected chronological age; and determining the recommendation for management of the first individual's wellbeing based at least in part on the determined indication of relative cardiovascular aging.
Patent No. 12471770:
1. A method for determining at least one recommendation for management of wellbeing of a first individual, comprising:
determining an indication of relative cardiovascular aging of the first individual, based at least in part on a predicted risk of cardiovascular disease (CVD) of the individual determined by a deep learning model based on one or more fundus images;
determining a difference between an actual chronological age of the individual and the indication of relative cardiovascular aging of the first individual determined for the individual; and
determining the at least one recommendation for management of the individual's wellbeing based at least in part on the determined difference between the actual chronological age of the individual and the indication of relative cardiovascular aging of the first individual determined for the individual.
2. The method of claim 1, wherein determining an indication of relative cardiovascular aging of the first individual comprises: determining a degree of similarity between a predicted risk of cardiovascular disease (CVD) of the first individual and the risk of CVD for a set of individuals belonging to a chronological age bracket to which the first individual also belongs, wherein the predicted risk of CVD is determined by a deep learning model based on one or more fundus images; determining an average chronological age of individuals closest to the first individual in terms of predicted risk of CVD; determining a mean expected chronological age for a person with the predicted risk of CVD of the first individual; and determining the indication of relative cardiovascular aging of the first individual based on the determined degree of similarity, the determined average chronological age, and the determined mean expected chronological age.
3. The method of claim 1, comprising comparing the age difference of the first individual with the age differences of a set of individuals belonging to a chronological age group to which the first individual belongs.
4. The method of claim 3, comprising determining a relative position of the first individual within the set of individuals based on age difference.
5. The method of claim 4, wherein determination of the at least one recommendation for management of the first individual's wellbeing is based at least in part on the relative position of the first individual within the set of individuals based on age difference.
6. The method of claim 1, comprising determining the relative contribution of one or more risk contributing factors to the indication of relative cardiovascular aging.
7. The method of claim 6, wherein the risk contributing factors include two or more of: blood pressure, glycated haemoglobin A1c (HbA1c), total cholesterol, and glycaemic control.
8. The method of claim 6, wherein the relative contribution of the one of more of the risk contributing factors is used to determine the at least one recommendation for management of the first individual's wellbeing.
9. The method of claim 1, wherein determining the indication of relative cardiovascular aging of the first individual comprises: determining a degree of similarity between a predicted risk of cardiovascular disease (CVD) of the first individual and the risk of CVD for a set of individuals belonging to a chronological age bracket to which the first individual also belongs, wherein the predicted risk of CVD is determined by a deep learning model based on one or more fundus images; determining an average chronological age of individuals closest to the first individual in terms of predicted risk of CVD; determining a mean expected chronological age for a person with the predicted risk of CVD of the first individual; and determining the indication of relative cardiovascular aging of the first individual based on the determined degree of similarity, the determined average chronological age, and the determined mean expected chronological age.
10. The method of claim 1, wherein determining the indication of relative cardiovascular aging of the first individual comprises: determining a degree of similarity between a predicted risk of cardiovascular disease (CVD) of the first individual and the risk of CVD for a set of individuals belonging to a chronological age bracket to which the first individual also belongs, wherein the predicted risk of CVD is determined by a deep learning model based on one or more fundus images; determining an average chronological age of individuals closest to the first individual in terms of predicted risk of CVD; determining a mean expected chronological age for a person with the predicted risk of CVD of the first individual; and determining the indication of relative cardiovascular aging of the first individual based on the determined degree of similarity, the determined average chronological age, and the determined mean expected chronological age.
11. The method of claim 10, wherein determining the indication of relative cardiovascular aging of the first individual ("Cardiac BioAge") comprises the calculation: Cardiac BioAgex = P(risk(x)|xEX) * age(X) + (1 - P(risk(x)|xEX )) * age(Yx~Y) where x refers to the first individual, and X is the set of individuals who are in the same age group as the first individual;where P(risk(x) |xE X )is the conditional probability that first individual x has CVD risk, risk(x), given that that the first individual belongs the set X of individuals who have similar age;where age(X) is the mean age for set X; andwhere age(Y~x~Y) is the mean age of patient points who are close to the first individual x.
11. The method of claim 10, wherein the set of individuals comprises individuals of the same gender as the first individual.
12. The method of claim 10, wherein implementation of age (Y|x˜Y) comprises the components similarity magnitude * cosine similarity age+ (1-similarity magnitude) * risk extrapolated age.
13. A system for determining at least one recommendation for management of wellbeing of a first individual, the system comprising one or more processors and one or more storage devices storing instructions that when executed by the one or more processors cause the one or more processors to perform operations comprising: determining an indication of relative cardiovascular aging of the first individual, based at least in part on a predicted risk of cardiovascular disease (CVD) of the individual determined by a deep learning model based on one or more fundus images; determining a difference between an actual chronological age of the individual and the indication of relative cardiovascular aging of the first individual determined for the individual; and determining the at least one recommendation for management of the individual's wellbeing based at least in part on the determined difference between the actual chronological age of the individual and the indication of relative cardiovascular aging of the first individual determined for the individual.
14. A computer program product for determining a recommendation for management of wellbeing of a first individual, the computer program product comprising a non-transitory computer-readable storage medium containing computer program code for: determining an indication of relative cardiovascular aging of the first individual, based at least in part on a predicted risk of cardiovascular disease (CVD) of the individual determined by a deep learning model based on one or more fundus images; determining a difference between an actual chronological age of the individual and the indication of relative cardiovascular aging of the first individual determined for the individual; and determining the at least one recommendation for management of the individual's wellbeing based at least in part on the determined difference between the actual chronological age of the individual and the indication of relative cardiovascular aging of the first individual determined for the individual.
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-10 and 14-15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Vaghefi Rezaei et al. (US 20250308701 A1).
Considering claim 1, Vaghefi Rezaei teaches a method for determining at least one recommendation for management of wellbeing of a first individual, comprising:
determining an indication of relative cardiovascular aging of the first individual ([0090] model that represents changes in the retinal photograph is used to estimate a risk for the cardiovascular event within a time period (e.g. 5 to 10 years), [0091]), based at least in part on a predicted risk of cardiovascular disease (CVD) of the individual determined by a deep learning model based on one or more fundus images ([0090] identify the relative contribution of various components of the estimated cardiovascular risk, [0091] prediction of CVD risk, [0009] predicting a risk of cardiovascular disease (CVD) from one or more fundus images); and
determining the at least one recommendation for management of the individual's wellbeing based at least in part on the determined indication of relative cardiovascular aging of the first individual (Fig.4A-D, [0040] recommendation for management of an individual's condition based on the determined risk, ([0090] identify the relative contribution of various components of the estimated cardiovascular risk…estimate a risk for the cardiovascular event within a time period, [0109] Check your blood sugar and keep it under control as recommended by your doctor).
Considering claim 14, Vaghefi Rezaei teaches a system for determining at least one recommendation for management of wellbeing of a first individual, the system comprising one or more processors and one or more storage devices storing instructions that when executed by the one or more processors cause the one or more processors to perform operations (Fig.1, [0056]) comprising:
determining an indication of relative cardiovascular aging of the first individual ([0090] model that represents changes in the retinal photograph is used to estimate a risk for the cardiovascular event within a time period (e.g. 5 to 10 years), [0091]), based at least in part on a predicted risk of cardiovascular disease (CVD) of the individual determined by a deep learning model based on one or more fundus images ([0090] identify the relative contribution of various components of the estimated cardiovascular risk, [0091] prediction of CVD risk, [0009] predicting a risk of cardiovascular disease (CVD) from one or more fundus images); and
determining the at least one recommendation for management of the individual's wellbeing based at least in part on the determined indication of relative cardiovascular aging of the first individual (Fig.4A-D, [0040] recommendation for management of an individual's condition based on the determined risk, ([0090] identify the relative contribution of various components of the estimated cardiovascular risk…estimate a risk for the cardiovascular event within a time period, [0109] Check your blood sugar and keep it under control as recommended by your doctor).
Considering claim 15, Vaghefi Rezaei teaches a computer program product for determining a recommendation for management of wellbeing of a first individual, the computer program product [0012]) comprising a non-transitory computer- readable storage medium containing computer program code for:
determining an indication of relative cardiovascular aging of the first individual ([0090] model that represents changes in the retinal photograph is used to estimate a risk for the cardiovascular event within a time period (e.g. 5 to 10 years), [0091]), based at least in part on a predicted risk of cardiovascular disease (CVD) of the individual determined by a deep learning model based on one or more fundus images ([0090] identify the relative contribution of various components of the estimated cardiovascular risk, [0091] prediction of CVD risk, [0009] predicting a risk of cardiovascular disease (CVD) from one or more fundus images); and
determining the at least one recommendation for management of the individual's wellbeing based at least in part on the determined indication of relative cardiovascular aging of the first individual (Fig.4A-D, [0040] recommendation for management of an individual's condition based on the determined risk, ([0090] identify the relative contribution of various components of the estimated cardiovascular risk…estimate a risk for the cardiovascular event within a time period, [0109] Check your blood sugar and keep it under control as recommended by your doctor).
Considering claims 2, 10, Vaghefi Rezaei teaches wherein determining an indication of relative cardiovascular aging of the first individual comprises:
determining a degree of similarity between a predicted risk of cardiovascular disease (CVD) of the first individual and the risk of CVD for a set of individuals belonging to a chronological age bracket to which the first individual also belongs ([0040] identify, [0134] deep learning algorithms can use retinal images to predict modifiable CVD risk factors… cardiovascular event or chronological age), wherein the predicted risk of CVD is determined by a deep learning model based on one or more fundus images (Fig.2, [0005] artificial intelligence (AI) deep learning, [0134] deep learning algorithms can use retinal images to predict modifiable CVD risk factors, including diabetes, hypertension, and cholesterol and non-modifiable risk factors such as chronological age and gender); determining an average chronological age of individuals closest to the first individual in terms of predicted risk of CVD; determining a mean expected chronological age for a person with the predicted risk of CVD of the first individual (Fig.4A-D, [0005] studies have used the chronological age as the “label” for training, and the outcome of the model is called “retinal age”, [0116] an individual may be presented in comparison with values for people in a comparable cohort (e.g. of a similar age, gender, and ethnicity)… determining if their level of cardiovascular risk is “normal”); and determining the indication of relative cardiovascular aging of the first individual based on the determined degree of similarity, the determined average chronological age, and the determined mean expected chronological age ([0116] an individual may be presented in comparison with values for people in a comparable cohort (e.g. of a similar age, gender, and ethnicity)… determining if their level of cardiovascular risk is “normal”)[0112] cohort cardiovascular risk profile and its contributing factors are displayed. In this example, the population reporting interface 4050 includes a group average indicators 4052 in various categories of risk).
Considering claim 3, Vaghefi Rezaei teaches determining a difference between an actual chronological age of the individual and the indication of relative cardiovascular aging of the first individual determined for the individual ([0005], [0134]).
Considering claim 4, Vaghefi Rezaei teaches comparing the age difference of the first individual with the age differences of a set of individuals belonging to a chronological age group to which the first individual belongs ([0005], [0134]).
Considering claim 5, Vaghefi Rezaei teaches determining a relative position of the first individual within the set of individuals based on age difference ([0090], [0121] The “attribution score” for an input field, such as age, represents the amount of difference this particular field contributed to the value difference between the predicted CVD risk for this particular patient and that of the entire source dataset… 1. Age, 2. Gender, 3. BMI, 4. Smoking status (represented by model predicted smoking status), 5. Glycemic control (represented by model predicted “effect” of HbA1C) 6. Blood pressure (represented by model predicted “effect” of systolic and Diastolic blood pressure), and 7. Cholesterol/HDL (represented by model predicted “effect” of TCHDL ratio).
Considering claim 6, Vaghefi Rezaei teaches wherein determination of the at least one recommendation for management of the first individual's wellbeing is based at least in part on the relative position of the first individual within the set of individuals based on age difference ([0090], [0121] The “attribution score” for an input field, such as age, represents the amount of difference this particular field contributed to the value difference between the predicted CVD risk for this particular patient and that of the entire source dataset… 1. Age, 2. Gender, 3. BMI, 4. Smoking status (represented by model predicted smoking status), 5. Glycemic control (represented by model predicted “effect” of HbA1C) 6. Blood pressure (represented by model predicted “effect” of systolic and Diastolic blood pressure), and 7. Cholesterol/HDL (represented by model predicted “effect” of TCHDL ratio).
Considering claim 7, Vaghefi Rezaei teaches determining the relative contribution of one or more risk contributing factors to the indication of relative cardiovascular aging ([0090], [0121] The “attribution score” for an input field, such as age, represents the amount of difference this particular field contributed to the value difference between the predicted CVD risk for this particular patient and that of the entire source dataset… 1. Age).
Considering claim 8, Vaghefi Rezaei teaches wherein the risk contributing factors include two or more of: blood pressure, glycated haemoglobin A1c (HbA1c), total cholesterol, and glycaemic control ([0004]).
Considering claim 9, Vaghefi Rezaei teaches wherein the relative contribution of the one of more of the risk contributing factors is used to determine the at least one recommendation for management of the first individual's wellbeing ([0040] recommendation for management of an individual's condition based on the determined risk, ([0090] identify the relative contribution of various components of the estimated cardiovascular risk…estimate a risk for the cardiovascular event within a time period, [0109] Check your blood sugar and keep it under control as recommended by your doctor), [0116] an individual may be presented in comparison with values for people in a comparable cohort (e.g. of a similar age, gender, and ethnicity)… determining if their level of cardiovascular risk is “normal”))
Allowable Subject Matter
Claims 11-13 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KHAI MINH NGUYEN whose telephone number is (571)272-7923. The examiner can normally be reached on 6-3.
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/KHAI M NGUYEN/Primary Examiner, Art Unit 2641