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 .
Status of Claims
The following Office Action is responsive to the request for continued examination filed February 26, 2025.
Claims 2 and 12 have been previously canceled.
Claims 7 and 17 have been canceled.
Claims 1, 3, 11 and 13 have been amended.
Claims 1, 3-6, 8-11, 13-16, and 18-24 are currently pending and have been fully examined.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-6, 8-11, 13-16, and 18-24 are rejected under 35 U.S.C. 103 as being obvious over U.S. Publication No. US20210166137A1 to Neumann (“Neumann”) in view of WIPO Patent Publication No. WO2019166859A1 to Barreto Nogueria (“Barreto”) in further view of U.S. Publication No. US20170290516A1 to Nguyen et al. (“Nguyen”) in further view of U.S. Publication No. US20170363618A1 to Studer (“Studer”)
Regarding claim 1, Neumann discloses:
An apparatus for enhancing longevity, wherein the apparatus comprises: (0015: a system)
at least a processor; and (0015: processor) a memory communicatively connected to the at least a processor, (0015: data storage) the memory containing instructions configuring the at least a processor to: (0034: software can be used to implement the system using instructions stored on a machine-readable storage medium, see 0035)
receive a longevity measurement pertaining to a user; (0025: user-reported data)
identify one or more compositional longevity parameters as a function of the longevity measurement, (0025: determining an element of the user data, such as a longevity element, as a function of the user-reported data)
wherein identifying the one or more compositional longevity parameters comprises: assigning a first compositional longevity parameter a first weight as a function of a user input, wherein the user input comprises a center of interest of the user; and assigning a second compositional longevity parameter a second weight (par. [0019]: “Supervised machine-learning algorithms, as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function”; the relationships between the inputs and the outputs are the weights assigned to the variables because weights in a machine learning model are used to assign some relationship between the type of data being input into the system to an output of the system; see also par. [0021]; par. [0025], “One or more tables may include, without limitation, a heuristic table 220, which may include one or more inputs describing potential mathematical relationships between at least an element of user-reported data 108 and longevity element 112, compensatory supplement, and/or supplement plan, as described in further detail below.”; par. [0028], “As described in this disclosure, “an instruction set for a longevity element plan,” refers to a series of numerical values that describe at least a longevity element 112 amount, an associated dosage, dosage form, frequency of dosage, delivery information for supplement, and/or a quality grade of supplement, with the goal of weaning a user off of a supplement, or the like, to address a user-reported symptom.”; this shows that the correlations are made based on the goal of the “longevity element plan”, such as “the goal of weaning a user off of a supplement”.).
generate a symphonic longevity plan pertaining to the user as a function of the first weight of the first compositional longevity parameter and the second weight of the second compositional longevity parameter, (par. [0025], “One or more tables may include, without limitation, a personalized supplement table 204, which may correlate user-reported data 108 and/or combinations thereof to one or more measures of a longevity element 112, such as dose or frequency of a personalized supplement for responding to a symptom. One or more tables may include, without limitation, a compensatory supplement table 208, which may contain a plurality of entries associating at least a longevity element 112 data with a relationship to a second nutrient in a user. One or more tables may include, without limitation, an instruction set table 212 which may contain one or more inputs identifying one or more categories of data, for instance dose, frequency, and delivery mode for a particular longevity element 112 used to address a particular symptom in users. One or more tables may include, without limitation, a cohort category table 216 which may contain one or more inputs identifying one or more categories of data, for instance physiological data, symptoms, longevity elements 112, or the like, with regard to which users having matching or similar data may be expected to have similar longevity elements 112 and/or compensatory supplements as a result of user-reported 108 data. One or more tables may include, without limitation, a heuristic table 220, which may include one or more inputs describing potential mathematical relationships between at least an element of user-reported data 108 and longevity element 112, compensatory supplement, and/or supplement plan, as described in further detail below.”; 0029: the element is used to generate instructions for a longevity element plan) wherein generating the symphonic longevity plan further comprises:
training a machine-learning process using compositional longevity training data, (0029: the longevity element plan is generated using a machine learning model, and the machine learning model is trained with training data, including the longevity element) wherein training the machine-learning process further comprises:
generating a training data classifier (0020: a classifier) configured to classify training data to a compositional longevity parameter classification utilizing a classification algorithm, (0020: classification algorithms classifies training data and input data (see 0023) to generate the classifier, and the training data is input to “categories,” e.g. compositional longevity parameter classification) wherein the classification algorithm comprises a supervised machine-learning process (0020: classification algorithm uses supervised machine learning) that iteratively derives the training data classifier, (0016: steps can be performed iteratively) and wherein the compositional longevity training data contains a plurality of inputs (0029: training data includes data from a plurality of user-reported entries) containing compositional longevity parameters correlated to a plurality of outputs containing symphonic longevity plans; and (0029: training data correlates any input data to any output data, and the plan includes dosage information (para. 0028) to address patient symptoms, para 0025)
training the machine-learning process using the classified compositional longevity training data (0020: the classifier is used to train the machine-learning model)
generating the symphonic longevity plan pertaining to the user as a function of the trained machine-learning process; and (0029: the machine learning model is used to generate the longevity element plan)
update the symphonic longevity plan (0032: generating a new instruction set for the longevity element plan, previously construed as the symphonic longevity plan) after a time interval (0032: the new instruction set cancels and replaces the earlier longevity element, mean it comes after the earlier longevity set, e.g., after a time interval) based on a deficiency in the compositional longevity parameter, (0032: an unintended consequence (e.g., a deficiency) of using a longevity element, previously construed as the compositional longevity parameter) wherein the compositional longevity parameter comprises a life energy measurement (Neumann, 0028: a series of numerical values that describe the longevity element).
Neumann does not explicitly recite, but Barreto teaches wherein the compositional longevity parameter classification (Barreto, 0013: an age parameter, such as biological age) comprises data indicating an age of systems associated with the user’s longevity (Barreto, 0026: the biological age includes data associated with arterial blood pressure, which is part of the circulatory system).
Therefore, it would have been obvious to one having ordinary skill in the art to modify Neumann to include an age classification and a mental state parameter, as taught by Barreto, because Barreto teaches that using such data improves disease prediction with regard to rate of aging (Barreto, para. 0016).
Neumann does not explicitly recite, but Nguyen teaches wherein the symphonic longevity plan comprises a health age, the health age comprises an evaluation of one or more lifestyle choices of the user.
Nguyen et al. teaches collecting data on one or more lifestyle-related parameters which can include one or more of data on smoking habits and data on activity level. Lifestyle parameters are used along with various biomarkers and physiological markers to determine a physiological age (e.g., health age) for an individual. Lifestyle parameters may be used to determine the physiological age, examples of such lifestyle parameters including, for example, activity level (e.g., amount of exercise done per day), smoker status (e.g., whether or not a smoker, time since quitting, number of cigarettes or other units smoked per day, etc.). Age adjustment factors can be computed and stored for one or more life style parameters, such as smoker status and level of athletic activities (0007, 0026, 0032, 0052-0053).
Therefore, it would have been obvious to one having ordinary skill in the art to modify the combination to include a health age comprising an evaluation of one or more lifestyle choices of the user in the symphonic longevity plan because Nguyen teaches that biomarkers, physiological markers and lifestyle parameters can be used to increase the accuracy of the estimated physiological age (Nguyen, para. 0079) and can further provide personalized recommendations to alter the physiological age of the individual which is updated based on feedback from sensors or devices related to the estimated physiological age (Nguyen, para. 0080).
The combination does not explicitly recite, but Studer teaches wherein the longevity measurement comprises one or more longevity markers, and (Studer, 0115: a marker signature, which indicates cell aging) wherein the one or more longevity markers comprises telomerase length; (Studer, 0025: markers include telomere shortening)
Therefore, it would have been obvious to one having ordinary skill in the to modify the combination to include longevity markers comprising telomerase length, as taught by Studer, because Studer teaches that telomere length indicates cellular age (Studer, para. 0155), and it would have been obvious to include data that indicates cellular age when developing patient plans, such as the longevity element plan of Neumann.
Regarding claim 3, the combination discloses each of the limitations of claim 1 as discussed above. Neumann does not explicitly recite, but Barreto teaches wherein the compositional longevity parameter further comprises a health age (Barreto, 0013: an age parameter).
Therefore, it would have been obvious to one having ordinary skill in the to modify the combination to include an age parameter, as taught by Barreto, because Barreto teaches that using such data improves disease prediction. (Barreto, para. 0016).
Regarding claim 4, the combination discloses each of the limitations of claim 1 as discussed above. Neumann does not explicitly recite, but Barreto teaches wherein the compositional longevity parameter further comprises a longevity age (Barreto, 0013: a chronological age parameter.
Therefore, it would have been obvious to one having ordinary skill in the to modify the combination to include an age parameter, as taught by Barreto, because Barreto teaches that using such data improves disease prediction. (Barreto, para. 0016).
Regarding claim 5, the combination discloses each of the limitations of claim 1 as discussed above. Neumann does not explicitly recite, but Barreto teaches wherein the compositional longevity parameter further comprises a performance age. (Barreto, 0013: a biological age parameter).
Therefore, it would have been obvious to one having ordinary skill in the to modify the combination to include an age parameter, as taught by Barreto, because Barreto teaches that using such data improves disease prediction. (Barreto, para. 0016).
Regarding claim 6, the combination discloses each of the limitations of claim 1 as discussed above. Neumann does not explicitly recite, but Barreto teaches wherein the compositional longevity parameter further comprises an assigned weight. (Barreto, 0014: the biological age is a function of the chronological age and T ratio, e.g., the assigned weight)
Therefore, it would have been obvious to one having ordinary skill in the to modify the combination to include weighting, as taught by Barreto, because Barreto teaches that using such data improves disease prediction. (Barreto, para. 0016).
Regarding claim 7, the combination discloses each of the limitations of claim 6 as discussed above.
wherein generating the symphonic longevity plan further comprises: assigning a first compositional longevity parameter a first weight; (Neumann, 0020: a training algorithm adjusts the connections and weights between nodes in layers of the neural network)
assigning a second compositional longevity parameter a second weight; and (Neumann, 0021: a second data element belonging to a second category of data element)
generating the symphonic longevity plan as a function of the first weight and the second weight. (Neumann, Fig. 4: the longevity element plan is a function of the first and second elements, see para. 0033)
Regarding claim 8, Neumann discloses each of the limitations of claim 1 as discussed above, and further discloses:
wherein the symphonic longevity plan identifies a problematic area of the user. (Neumann, 0025: the elements, which the plans are based off, are used to address a particular symptom of the users)
Regarding claim 9, Neumann discloses each of the limitations of claim 1 as discussed above, and further discloses:
wherein the symphonic longevity plan identifies a set of actions designed to improve the compositional longevity parameter and correct the deficiency of the user. (Neumann, 0028: an instruction set for a longevity element plan refers a longevity element amount, such as dosage, delivery information, and/or a quality grade of a supplement)
Regarding claim 10, Neumann discloses each of the limitations of claim 1 as discussed above, and further discloses:
wherein identifying the compositional longevity parameter pertaining to the user further comprises: training an additional (0026: a second machine-learning process) machine-learning process using longevity training data, (0029: training a machine learning model with training data) wherein the longevity training data contains a plurality of inputs 0029: training data includes data from a plurality of user-reported entries) containing longevity measurements correlated to a plurality of outputs containing compositional longevity parameters; and (0029: training data correlates any input data as described in this disclosure to any output data, and the plan includes dosage information (para. 0028) to address patient symptoms, para 0025)
generating the compositional longevity parameter pertaining to the user as a function of the trained additional (0026: a second machine-learning process) machine-learning process. (0029: the machine learning model is used to generate the longevity element plan)
It would have been prima facie obvious to one of ordinary skill in the art to train an additional machine-learning process and to generate a parameter as a function of the additional machine-learning process from the teaching of Neumann. Neumann teaches a second machine-learning process (Neumann, 0026), so it would be obvious to use an additional machine-learning process given the finite number of possible ways to generate the above parameters. Indeed, a person of ordinary skill has good reason to pursue the known options within his or her technical grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
Regarding claim 11, Neumann discloses:
A method for enhancing longevity, wherein the method comprises: (Title: a method)
receiving, using a processor, (0015: processor is part of computing device 104, which includes user database 120, see Fig. 1) a longevity measurement pertaining to a user; (0025: user-reported data is included in database 120)
identifying, using the processor, a compositional longevity parameter as a function of the longevity measurement, (0025: determining an element of the user data, such as a longevity element, as a function of the user-reported data)
generating, using the processor, a symphonic longevity plan pertaining to the user as a function of the compositional longevity parameter, (0029: the element is used to generate instructions for a longevity element plan) wherein the generating the symphonic longevity plan further comprises:
training a machine-learning process using compositional longevity training data, (0029: the longevity element plan is generated using a machine learning model, and the machine learning model is trained with training data, including the longevity element) wherein training the machine-learning process further comprises:
generating a training data classifier (0020: a classifier) configured to classify training data to a compositional longevity parameter classification utilizing a classification algorithm, (0020: classification algorithms classifies training data and input data (see 0023) to generate the classifier, and the training data is input to “categories,” e.g. compositional longevity parameter classification) wherein the classification algorithm comprises a supervised machine-learning process (0020: classifier uses supervised machine learning) that iteratively derives the training data classifier, (0016: steps can be performed iteratively) and wherein the compositional longevity training data contains a plurality of inputs (0029: training data includes data from a plurality of user-reported entries) containing compositional longevity parameters correlated to a plurality of outputs containing symphonic longevity plans; and (0029: training data correlates any input data as described in this disclosure to any output data, and the plan includes dosage information (para. 0028) to address patient symptoms, para 0025)
training the machine-learning process using the classified compositional longevity training data (0020: the classifier is used to train the machine-learning model)
generating the symphonic longevity plan pertaining to the user as a function of the trained machine-learning process. (0029: the machine learning model is used to generate the longevity element plan)
updating the symphonic longevity plan (0032: generating a new instruction set for the longevity element plan, previously construed as the symphonic longevity plan) after a time interval (0032: the new instruction set cancels and replaces the earlier longevity element, mean it comes after the earlier longevity set, e.g., after a time interval) based on a deficiency in the compositional longevity parameter. (0032: an unintended consequence (e.g., a deficiency) of using a longevity element, previously construed as the compositional longevity parameter) wherein the compositional longevity parameter comprises a life energy measurement (Neumann, 0028: a series of numerical values that describe the longevity element).
Neumann does not explicitly recite, but Barreto teaches wherein the compositional longevity parameter classification (Barreto, 0013: an age classification, such as biological age) comprises data indicating an age of systems associated with the user’s longevity. (Barreto, 0026: the biological age includes data associated with arterial blood pressure, which is part of the circulatory system).
Barreto further teaches a parameter which includes a mental health status of the user regarding a relationship health. (Barreto, 0024: parameters including an altered state of consciousness, brain temperature, and brain oxygenation, and brain electric activity (0028), which relate to brain death (0042), e.g., the equivalent of death).
Therefore, it would have been obvious to one having ordinary skill in the art to modify Neumann to include an age classification and a mental state parameter, as taught by Barreto, because Barreto teaches that using such data improves disease prediction with regard to rate of aging. (Barreto, para. 0016).
Neumann does not explicitly recite, but Nguyen teaches wherein the symphonic longevity plan comprises a health age, the health age comprises an evaluation of one or more lifestyle choices of the user.
Nguyen et al. teaches collecting data on one or more lifestyle-related parameters which can include one or more of data on smoking habits and data on activity level. Lifestyle parameters are used along with various biomarkers and physiological markers to determine a physiological age (e.g., health age for an individual. Lifestyle parameters may be used to determine the physiological age, examples of such lifestyle parameters including, for example, activity level (e.g., amount of exercise done per day), smoker status (e.g., whether or not a smoker, time since quitting, number of cigarettes or other units smoked per day, etc.). Age adjustment factors can be computed and stored for one or more life style parameters, such as smoker status and level of athletic activities (0007, 0026, 0032, 0052-0053).
Therefore, it would have been obvious to one having ordinary skill in the art to modify the combination to include a health age comprising an evaluation of one or more lifestyle choices of the user in the symphonic longevity plan because Nguyen teaches that biomarkers, physiological markers and lifestyle parameters can be used to increase the accuracy of the estimated physiological age (Nguyen, para. 0079) and can further provide personalized recommendations to alter the physiological age of the individual which is updated based on feedback from sensors or devices related to the estimated physiological age (Nguyen, para. 0080).
The combination does not explicitly recite, but Studer teaches wherein the longevity measurement comprises one or more longevity markers, and (Studer, 0115: a marker signature, which indicates cell aging) wherein the one or more longevity markers comprises telomerase length; (Studer, 0025: markers include telomere shortening)
Therefore, it would have been obvious to one having ordinary skill in the to modify the combination to include longevity markers comprising telomerase length, as taught by Studer, because Studer teaches that telomere length indicates cellular age (Studer, para. 0155), and it would have been obvious to include data that indicates cellular age when developing patient plans, such as the longevity element plan of Neumann.
Regarding claim 13, the combination discloses each of the limitations of claim 11 as discussed above. Neumann does not explicitly recite, but Barreto teaches wherein the compositional longevity parameter further comprises the health age. (Barreto, 0013: an age parameter)
Therefore, it would have been obvious to one having ordinary skill in the to modify the combination to include an age parameter, as taught by Barreto, because Barreto teaches that using such data improves disease prediction. (Barreto, para. 0016).
Regarding claim 14, the combination discloses each of the limitations of claim 11 as discussed above. Neumann does not explicitly recite, but Barreto teaches wherein the compositional longevity parameter further comprises a longevity age. (Barreto, 0013: a chronological age parameter)
Therefore, it would have been obvious to one having ordinary skill in the to modify the combination to include an age parameter, as taught by Barreto, because Barreto teaches that using such data improves disease prediction. (Barreto, para. 0016).
Regarding claim 15, the combination discloses each of the limitations of claim 11 as discussed above. Neumann does not explicitly recite, but Barreto teaches wherein the compositional longevity parameter further comprises a performance age. (Barreto, 0013: a biological age parameter)
Therefore, it would have been obvious to one having ordinary skill in the to modify the combination to include an age parameter, as taught by Barreto, because Barreto teaches that using such data improves disease prediction. (Barreto, para. 0016).
Regarding claim 16, the combination discloses each of the limitations of claim 11 as discussed above. Neumann does not explicitly recite, but Barreto teaches wherein the compositional longevity parameter further comprises an assigned weight. (Barreto, 0014: the biological age is a function of the chronological age and T ratio, e.g., the assigned weight)
Therefore, it would have been obvious to one having ordinary skill in the to modify the combination to include an age parameter, as taught by Barreto, because Barreto teaches that using such data improves disease prediction. (Barreto, para. 0016).
Regarding claim 17, the combination discloses each of the limitations of claim 16 as discussed above.
wherein generating the symphonic longevity plan further comprises: assigning a first compositional longevity parameter a first weight; (Neumann, 0020: a training algorithm adjusts the connections and weights between nodes in layers of the neural network)
assigning a second compositional longevity parameter a second weight; and (Neumann, 0021: a second data element belonging to a second category of data element)
generating the symphonic longevity plan as a function of the first weight and the second weight. (Neumann, Fig. 4: the longevity element plan is a function of the first and second elements, see para. 0033)
Regarding claim 18, Neumann discloses each of the limitations of claim 11 as discussed above, and further discloses:
wherein the symphonic longevity plan identifies a problematic area of the user. (Neumann, 0025: the elements, which the plans are based off of, are used to address a particular symptom of users)
Regarding claim 19, Neumann discloses each of the limitations of claim 11 as discussed above, and further discloses:
wherein the symphonic longevity plan identifies a set of actions designed to improve the compositional longevity parameter and correct the deficiency of the user. (Neumann, 0028: an instruction set for a longevity element plan refers a longevity element amount, such as dosage, delivery information, and/or a quality grade of a supplement)
Regarding claim 20, Neumann discloses each of the limitations of claim 11 as discussed above, and further discloses:
wherein identifying the compositional longevity parameter pertaining to the user further comprises: training an additional (0026: a second machine-learning process) machine-learning process using longevity training data, (0029: training a machine learning model with training data) wherein the longevity training data contains a plurality of inputs 0029: training data includes data from a plurality of user-reported entries) containing longevity measurements correlated to a plurality of outputs containing compositional longevity parameters; and (0029: training data correlates any input data as described in this disclosure to any output data, and the plan includes dosage information (para. 0028) to address patient symptoms, para 0025)
generating the compositional longevity parameter pertaining to the user as a function of the trained additional (0026: a second machine-learning process) machine-learning process. (0029: the machine learning model is used to generate the longevity element plan)
It would have been prima facie obvious to one of ordinary skill in the art to train an additional machine-learning process and to generate a parameter as a function of the additional machine-learning process from the teaching of Neumann. Neumann teaches a second machine-learning process (Neumann, 0026), so it would be obvious to use an additional machine-learning process given the finite number of possible ways to generate the above parameters. Indeed, a person of ordinary skill has good reason to pursue the known options within his or her technical grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
Regarding claim 21, Neumann discloses each of the limitations of claim 1 as discussed above, and further discloses:
wherein the life energy measurement (Neumann, 0028: a series of numerical values that describe the longevity element)
Neumann does not explicitly recite, but Barreto teaches a measurement further includes a life energy comparison metric (Barreto, 0010: the variation, which is the ratio or gradient between the individual's parameter and the parameter available for a comparable population, see also 0034, 0039, 0043, etc).
Therefore, it would have been obvious to one having ordinary skill in the to modify the combination to include a comparison metric, as taught by Barreto, because Barreto teaches that using such data improves disease prediction with regard to rate of aging. (Barreto, para. 0016).
Regarding claim 22, Neumann discloses each of the limitations of claim 11 as discussed above, and further discloses:
wherein the life energy measurement (Neumann, 0028: a series of numerical values that describe the longevity element)
Neumann does not explicitly recite, but Barreto teaches a measurement further includes a life energy comparison metric (Barreto, 0010: the variation, which is the ratio or gradient between the individual's parameter and the parameter available for a comparable population, see also 0034, 0039, 0043, etc).
Therefore, it would have been obvious to one having ordinary skill in the to modify the combination to include a comparison metric, as taught by Barreto, because Barreto teaches that using such data improves disease prediction with regard to rate of aging. (Barreto, para. 0016).
Regarding claim 23, the combination of Neumann, Barreto, and Studer teaches all of the limitations of claim 1. Neumann further teaches
Determining a focus by comparing a plurality of compositional longevity parameters to a plurality of average compositional longevity parameters; and assigning the second weight as a function of the focus (Par. [0026], “As a non-limiting illustrative example, a longevity element 112 may be a personalized supplement does of a daily zinc supplement for addressing sickle cell disease, and a compensatory supplement 132 may be an amount of daily copper supplement to counteract or balance any leeching or displacing of copper due to acute, increased zinc intake. In further non-limiting illustrative examples, the correlation between increased zinc uptake and copper displacement may be the second element of data denoting a side effect and may be retrieved from a database to calculate a compensatory supplement 132. In non-limiting embodiments, a second machine-learning process 136 may input a longevity element 112 and a second element of data retrieved from a database to output an amount of a compatible compensatory supplement 132. In non-limiting illustrative examples, an amount of a compensatory supplement 132 may be no amount. In further non-limiting illustrative examples, an amount of a compensatory supplement 132 to address a deficiency may be within the daily recommended allowance of what a user already receives without supplementation.” This determines a focus of the treatment based on comparing a user’s zinc levels required to address sickle cell disease and the patient’s copper level to address the leakage of copper from the planned increase of zinc. Therefore, the correlation between the input data and the output data would be made in light of the need for increased zinc and the need to supplement any copper lost).
Regarding claim 24, the combination of Neumann, Barreto, and Studer teaches all of the limitations of claim 11. Neumann further teaches
Determining a focus by comparing a plurality of compositional longevity parameters to a plurality of average compositional longevity parameters; and assigning the second weight as a function of the focus (Par. [0026], “As a non-limiting illustrative example, a longevity element 112 may be a personalized supplement does of a daily zinc supplement for addressing sickle cell disease, and a compensatory supplement 132 may be an amount of daily copper supplement to counteract or balance any leeching or displacing of copper due to acute, increased zinc intake. In further non-limiting illustrative examples, the correlation between increased zinc uptake and copper displacement may be the second element of data denoting a side effect and may be retrieved from a database to calculate a compensatory supplement 132. In non-limiting embodiments, a second machine-learning process 136 may input a longevity element 112 and a second element of data retrieved from a database to output an amount of a compatible compensatory supplement 132. In non-limiting illustrative examples, an amount of a compensatory supplement 132 may be no amount. In further non-limiting illustrative examples, an amount of a compensatory supplement 132 to address a deficiency may be within the daily recommended allowance of what a user already receives without supplementation.” This determines a focus of the treatment based on comparing a user’s zinc levels required to address sickle cell disease and the patient’s copper level to address the leakage of copper from the planned increase of zinc. Therefore, the correlation between the input data and the output data would be made in light of the need for increased zinc and the need to supplement any copper lost).
Response to Applicant’s Arguments
Applicant’s arguments and amendments, filed on December 3, 2025, with respect to the 35 USC § 103 rejection have been considered but are not persuasive.
Applicant argues that the claims, as amended, are not taught by the art of record. Specifically, Applicant argues that Neumann, Barreto and Studer do not disclose “the symphonic longevity plan comprises a health age, the health age comprises an evaluation of one or more lifestyle choices of the user”. The rejection above, has been updated to specifically address the amendment made to claims 1 and 11, as presented on December 3, 2025. In particular, the Nguyen reference has been cited to teach physiological age (e.g., health age) and evaluating one or more lifestyle choices of an individual.
For the reasons set forth in the 35 USC § 103 rejection above, the references cited in the rejection render the claims obvious under 35 USC § 103. Applicant’s arguments and amendments are not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Summerell (US Patent 5,937,387)
Perls (US PGPub 20070118398)
Maus (US Patent 6,602,469)
Gerjets (US PGPub 20080195594)
Shiga (US PGPub 20110201902)
Gobeyn (US PGPub 20080294016)
Casruita et al. “Genetic, Social and Lifestyle Drivers of Healthy Aging and Longevity” (Sept 2022)
Govindaraju et al. “Genetics, lifestyle and longevity: Lessons from centenarians” (2015)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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