System and method for generating a dynamic weighted combination

A system and method for generating a dynamic weighted combination includes a computing device configured to gain a nourishment metric, determine a nourishment vector as a function of the nourishment metric, generate a nourishment programs relating to a plurality of aliments as a function of the nourishment vector, determine a quantitative signature as a function of the nourishment programs, and generate a dynamic weighted combination as a function of the quantitative signature, wherein generating further comprises identifying, for each dynamic weighted combination, a degree of refinement according to the refinement criterion, comparing the degree of refinement for each dynamic weighted combination to the degree of refinement for at least one other dynamic weighted combination, and generate a dynamic weighted combination as a function of the comparison.

FIELD OF THE INVENTION

The present invention generally relates to the field of artificial intelligence. In particular, the present invention is directed to a system and method for generating a dynamic weighted combination.

BACKGROUND

Efficient calculations of nutritional programs are often prevented due to the lack of data being available. Furthermore, even when data is present that accounts for extraneous circumstances, improper algorithms are utilized. This is further complicated by a lack of uniformity of nutritional programs, which results in dissatisfaction of individuals.

SUMMARY OF THE DISCLOSURE

In an aspect a system for generating a dynamic weighted combination, the system comprising a computing device, the computing device configured to gain at least a nourishment metric, determine at least a nourishment vector as a function of the nourishment metric, generate a plurality of nourishment programs relating to a plurality of aliments as a function of the nourishment vector, determine a quantitative signature as a function of the plurality of nourishment programs, and generate a dynamic weighted combination of a plurality of dynamic weighted combinations as a function of the quantitative signature, wherein generating further comprises identifying, for each dynamic weighted combination of the plurality of dynamic weighted combinations, a degree of refinement according to the at least a refinement criterion, comparing the degree of refinement for each dynamic weighted combination of the plurality of dynamic weighted combinations to the degree of refinement for at least one other dynamic weighted combination of the plurality of dynamic weighted combinations, and generate a dynamic weighted combination as a function of the comparison.

In another aspect a method for generating a dynamic weighted combination, the method comprising gaining, by a computing device, at least a nourishment metric, determining, by the computing device, at least a nourishment vector as a function of the nourishment metric, generating, by the computing device, a plurality of nourishment programs relating to a plurality of aliments as a function of the nourishment vector, determining, by the computing device, a quantitative signature as a function of the plurality of nourishment programs, and generating, by the computing device, a dynamic weighted combination of a plurality of dynamic weighted combinations as a function of the quantitative signature, wherein generating further comprises identifying, for each dynamic weighted combination of the plurality of dynamic weighted combinations, a degree of refinement according to the at least a refinement criterion, comparing the degree of refinement for each dynamic weighted combination of the plurality of dynamic weighted combinations to the degree of refinement for at least one other dynamic weighted combination of the plurality of dynamic weighted combinations, and generate a dynamic weighted combination as a function of the comparison.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed to systems and methods for generating a dynamic weighted combination. In an embodiment, the disclosure may determine at least a nourishment vector as a function of a nourishment metric. Aspects of the present disclosure can be used to generate a plurality of nourishment programs relating to a plurality of aliments. Aspects of the present disclosure can also be used to determine a quantitative signature as a function of the plurality of nourishment programs. Aspects of the present disclosure allow for generating a dynamic weighted combination of a plurality of dynamic weighted combinations as a function of the quantitative signature. Exemplary embodiments illustrating aspects of the present disclosure are described below in the context of several specific examples.

Still referring toFIG.1, computing device104is configured to gain at least a nourishment metric108. As used in this disclosure a “nourishment metric” is an element of data relating to a user nutrient, wherein a user nutrient is comprised of one or more qualitative elements that may indicate the current health status of a user. For example, nourishment metric108may include, without limitation, a biological extraction. A “biological extraction” as used in this disclosure includes at least an element of user biological data. As used in this disclosure, “biological data” is data indicative of a person's biological state; biological state may be evaluated with regard to one or more measures of health of a person's body, one or more systems within a person's body such as a circulatory system, a digestive system, a nervous system, or the like, one or more organs within a person's body, and/or any other subdivision of a person's body useful for diagnostic or prognostic purposes. For instance, and without limitation, a particular set of biomarkers, test results, and/or biochemical information may be recognized in a given medical field as useful for identifying various disease conditions or prognoses within a relevant field. As a non-limiting example, and without limitation, biological data describing red blood cells, such as red blood cell count, hemoglobin levels, hematocrit, mean corpuscular volume, mean corpuscular hemoglobin, and/or mean corpuscular hemoglobin concentration may be recognized as useful for identifying various nourishments such as dehydration, nutrient deficiencies, anemia, and/or blood loss. Biological extraction data may alternatively or additionally include any data used as a biological extraction as described in U.S. Nonprovisional application Ser. No. 16/502,835, filed on Jul. 3, 2019, and entitled “METHODS AND SYSTEMS FOR ACHIEVING VIBRANT CONSTITUTION BASED ON USER INPUTS,” the entirety of which is incorporated herein by reference. Nourishment metric108may be gained as a function of one or more monitoring inputs, wherein a monitoring input is an input from an external source that relates to a user health status, as described below in reference toFIG.2.

Still referring toFIG.1, computing device104determines at least a nourishment vector112as a function of nourishment metric108. As used in this disclosure “nourishment vector” is a vector that relates to the user nutrients. As used in this disclosure “vector” as defined in this disclosure is a data structure that represents one or more quantitative values and/or measures user nutrients. A vector may be represented as an n-tuple of values, where n may be at least a value and/or two or more values, as described in further detail below; a vector may alternatively or additionally be represented as an element of a vector space, defined as a set of mathematical objects that can be added together under an operation of addition following properties of associativity, commutativity, existence of an identity element, and existence of an inverse element for each vector, and can be multiplied by scalar values under an operation of scalar multiplication compatible with field multiplication, and that has an identity element is distributive with respect to vector addition, and is distributive with respect to field addition. Each value of n-tuple of values may represent a measurement or other quantitative value associated with a given category of data, or attribute, examples of which are provided in further detail below; a vector may be represented, without limitation, in n-dimensional space using an axis per category of value represented in n-tuple of values, such that a vector has a geometric direction characterizing the relative quantities of attributes in the n-tuple as compared to each other. Two vectors may be considered equivalent where their directions, and/or the relative quantities of values within each vector as compared to each other, are the same; thus, as a non-limiting example, a vector represented as [5, 10, 15] may be treated as equivalent, for purposes of this disclosure, as a vector represented as [1, 2, 3]. Vectors may be more similar where their directions are more similar, and more different where their directions are more divergent; however, vector similarity may alternatively or additionally be determined using averages of similarities between like attributes, or any other measure of similarity suitable for any n-tuple of values, or aggregation of numerical similarity measures for the purposes of loss functions as described in further detail below. Any vectors as described herein may be scaled, such that each vector represents each attribute along an equivalent scale of values. Each vector may be “normalized,” or divided by a “length” attribute, such as a length attribute/as derived using a Pythagorean norm: l=√{square root over (Σi=0nαi2)}, where a is attribute number i of the vector. Scaling and/or normalization may function to make vector comparison independent of absolute quantities of attributes, while preserving any dependency on similarity of attributes. As a non-limiting example nourishment vector112may be 12 as a function of a biological extraction that identifies low iron levels in an individual. As a further non-limiting example nourishment vector112may be as a that identifies lethargy due to reduced sleep and decreased caffeine consumption.

Still referring toFIG.1, computing device104may determine nourishment vector112by gaining at least a nourishment element from at least a nourishment directory, wherein a nourishment directory is a database of nutrients as described below in detail, in reference toFIG.3. As used in this disclosure a “nourishment element” is a nutrient and/or nourishment category that relates to one or more nutrients. As used in this disclosure a “nutrient” is a substance and/or consumable that produces a source of energy to an organism such that the organism may survive, grow, and/or reproduce. As a non-limiting example a nourishment element may consist of macronutrients and/or micronutrients. As used in this disclosure “macronutrients” are a chemical class of compounds that individuals consume in large quantities to provide the individual with the bulk of energy. Macronutrients may include, without limitation carbohydrates, such as glucose, sucrose, ribose, amylose, amylopectin, maltose, galactose, fructose, lactose, and the like thereof. Macronutrients may include, without limitation proteins, such as standard amino acids, wherein standard amino acids include, but are not limited to, alanine, arginine, aspartic acid, asparagine, cysteine, glutamic acid, glutamine, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, valine, and the like thereof. Macronutrients may include without limitation fats, such as saturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids, essential fatty acids, and the like thereof. As used in this disclosure “micronutrients” are essential elements required by organisms in varying quantities to orchestrate a range of physiologic functions to maintain health. As a non-limiting example, micronutrients may consist of vitamins, wherein vitamins include vitamins A, B1, B2, B3, B5, B6, B7, B9, B12, C, D, E, K, and the like thereof. As a further non-limiting example, micronutrients may consist of minerals, such as potassium, chlorine, sodium, calcium, phosphorous, magnesium, iron, zinc, manganese, copper, iodine, chromium, molybdenum, selenium, cobalt, and the like thereof.

With continued reference toFIG.1, nourishment vector112may be determined as a function of the nourishment element and at least a vector-machine-learning model. As used in this disclosure a “vector machine-learning model” is a machine-learning model to produce a nourishment vector output given nourishment elements and nourishment metrics provided as inputs; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language. Vector machine-learning model may include one or more vector machine-learning processes such as supervised, unsupervised, or reinforcement machine-learning processes that apparatus104and/or a remote device may or may not use in the determination of nourishment vector112. As used in this disclosure “remote device” is an external device to computing device104. A vector machine-learning process may include, without limitation machine learning processes such as simple linear regression, multiple linear regression, polynomial regression, support vector regression, ridge regression, lasso regression, elasticnet regression, decision tree regression, random forest regression, logistic regression, logistic classification, K-nearest neighbors, support vector machines, kernel support vector machines, naïve bayes, decision tree classification, random forest classification, K-means clustering, hierarchical clustering, dimensionality reduction, principal component analysis, linear discriminant analysis, kernel principal component analysis, Q-learning, State Action Reward State Action (SARSA), Deep-Q network, Markov decision processes, Deep Deterministic Policy Gradient (DDPG), or the like thereof.

Still referring toFIG.1, the vector machine-learning process may be trained as a function of a vector training set. As used in this disclosure a “vector training set” is a training set that correlates at least nourishment element and nourishment metric to a measurable value. For example, and without limitation, nourishment element of a macronutrient consisting of carbohydrates and a nourishment metric of a questionnaire to a measurable value of 33. As a further non-limiting example, a vector training set may relate nourishment element of a micronutrient consisting of minerals and a nourishment metric of a biological extraction to a measurable value of 71. The vector training set may be received as a function of user-entered valuations of nourishment elements, nourishment metrics, and/or measurable values. The vector training set may be received by one or more past iterations of the previous nourishment vectors. The vector training set may be received by one or more remote devices that at least correlate a nourishment element and nourishment metric to a measurable value, wherein a remote device is an external device to computing device104.

Still referring toFIG.1, computing device104may receive the vector machine-learning model from the remote device that utilizes one or more vector machine learning processes, wherein a remote device is described above in detail. For example, and without limitation, a remote device may include a computing device, external device, processor, and the like thereof. The remote device may perform the vector machine-learning process using the vector training set to generate nourishment metric112and transmit the output to computing device104. The remote device may transmit a signal, bit, datum, or parameter to computing device104that at least relates to nourishment metric112. Additionally or alternatively, the remote device may provide an updated machine-learning model. For example, and without limitation, an updated machine-learning model may be comprised of a firmware update, a software update, a vector machine-learning process correction, and the like thereof. As a non-limiting example a software update may incorporate a new nourishment metric that relates to a modified nourishment vector. Additionally or alternatively, the updated machine learning model may be transmitted to the remote device, wherein the remote device may replace the vector machine-learning model with the updated machine-learning model and determine the nourishment vector as a function of the nourishment metric using the updated machine-learning model. The updated machine-learning model may be transmitted by the remote device and received by computing device104as a software update, firmware update, or corrected vector machine-learning model. For example, and without limitation a vector machine-learning model may utilize a neural net machine-learning process, wherein the updated machine-learning model may incorporate polynomial regression machine-learning process.

With continued reference toFIG.1, computing device104generates a nourishment program116of a plurality of nourishment programs as a function of nourishment vector112. As used in this disclosure a “nourishment program” is a program consisting of aliments that are to be consumed over a given time period, wherein a time period is a temporal measurement such as seconds, minutes, hours, days, weeks, months, years, and the like thereof. Nourishment program116recommends at least an aliment120aof a plurality of aliments120m. As used in this disclosure “aliment” is a substance to be consumed to at least sustain an individual. Aliment120a-mmay consist of a plant, animal, and/or fungi. As a non-limiting example nourishment program116may consist of recommending steak for 3 days. As a further non-limiting example a nourishment program116may recommend chicken for a first day, spaghetti for a second day, and mushrooms for a third day. Nourishment program116may include one or more diet programs such as paleo, keto, vegan, vegetarian, and the like thereof.

Still referring toFIG.1, computing device104may generate nourishment program116by determining at least a nutrient deficiency. As used in this disclosure a “nutrient deficiency” is a deficiency of a nutrient that exists in the user's body. As a non-limiting example, a nutritional deficiency may include vitamin K that is 1 ng/mL, wherein vitamin K should be 100 ng/mL in the user's body. As a further non-limiting example a nutritional deficiency may include glucose that is 20 ng/mL, wherein glucose should be 200 ng/mL in the user's body. Computing device may generate nourishment program116using the nutrient deficiency, nourishment vector112, and at least a nutrient machine-learning model. As used in this disclosure a “nutrient machine-learning model” is a machine-learning model to produce a nourishment program output given nutrient deficiencies and nourishment vectors provided as inputs; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language. Nutrient machine-learning model may include one or more nutrient machine-learning processes such as supervised, unsupervised, or reinforcement machine-learning processes that apparatus104and/or a remote device may or may not use in the determination of nourishment program116. A nutrient machine-learning process may include, without limitation machine learning processes such as simple linear regression, multiple linear regression, polynomial regression, support vector regression, ridge regression, lasso regression, elasticnet regression, decision tree regression, random forest regression, logistic regression, logistic classification, K-nearest neighbors, support vector machines, kernel support vector machines, naïve bayes, decision tree classification, random forest classification, K-means clustering, hierarchical clustering, dimensionality reduction, principal component analysis, linear discriminant analysis, kernel principal component analysis, Q-learning, State Action Reward State Action (SARSA), Deep-Q network, Markov decision processes, Deep Deterministic Policy Gradient (DDPG), or the like thereof.

Still referring toFIG.1, the nutrient machine-learning process may be trained as a function of a nutrient training set. As used in this disclosure a “nutrient training set” is a training set that correlates at least nutrient deficiency and a nourishment vector to an aliment. For example, and without limitation, a nutrient deficiency of a low vitamin D and a nourishment metric of 2 for vitamin D may relate to an aliment of milk and/or cheese. As a further non-limiting example, a nutrient training set may relate the nutrient deficiency of a decreased potassium and a nourishment metric of 7 for sodium to an aliment of a banana. The nutrient training set may be received as a function of user-entered valuations of nutrient deficiencies, nourishment vectors, and/or aliments. The nutrient training set may be received by one or more past iterations of the previous nourishment programs. The nutrient training set may be received by one or more remote devices that at least correlate a nutrient deficiency and a nourishment vector to an aliment, wherein a remote device is an external device to computing device104.

Still referring toFIG.1, computing device104may receive the nutrient machine-learning model from the remote device that utilizes one or more nutrient machine learning processes, wherein a remote device is described above in detail. For example, and without limitation, a remote device may include a computing device, external device, processor, and the like thereof. The remote device may perform the nutrient machine-learning process using the nutrient training set to generate nourishment program116and transmit the output to computing device104. The remote device may transmit a signal, bit, datum, or parameter to computing device104that at least relates to nourishment program116. Additionally or alternatively, the remote device may provide an updated machine-learning model. For example, and without limitation, an updated machine-learning model may be comprised of a firmware update, a software update, a nutrient machine-learning process correction, and the like thereof. As a non-limiting example a software update may incorporate a new nutritional deficiency that relates to a modified nourishment program. Additionally or alternatively, the updated machine learning model may be transmitted to the remote device, wherein the remote device may replace the nutrient machine-learning model with the updated machine-learning model and determine the nourishment program using the updated machine-learning model. The updated machine-learning model may be transmitted by the remote device and received by computing device104as a software update, firmware update, or corrected nutrient machine-learning model. For example, and without limitation a nutrient machine-learning model may utilize a Naïve Bayes machine-learning process, wherein the updated machine-learning model may incorporate Random Forest machine-learning process.

Still referring toFIG.1, computing device104is configured to determine a quantitative signature124as a function of nourishment program116of the plurality of nourishment programs. As used in this disclosure “quantitative signature” is a signature relating to an element of cost of nourishment program116. Quantitative signature124may include an element of cost relating to the price of aliment120a-m. As a non-limiting example the price of a pork chop may be $7.99. Quantitative signature124may include an element of cost relating to the time of the nourishment program. As a non-limiting example the time associated with the nourishment program may consist of 3 months of expense to the individual. Quantitative signature124may include an element of cost relating to the production of aliment120a-m. As a non-limiting example the cost relating to the energy expenses such as gas, water, electric, and the like thereof may relate to the cost to produce aliment120a-m. Quantitative signature124may include an element of cost relating to the travel expenses in procuring aliment120a-m. As a non-limiting example, travel to and from a grocery store may result in a cost of $5.32 in fuel and maintenance costs and/or $1.25 for public transportation. Quantitative signature124may include an element of cost relating to the costs related to the delivery of aliment120a-m. As a non-limiting example a delivery cost may be incurred as a fee for a courier or transportation service to deliver aliment120a-m. Quantitative signature124may be generated as a result of one or more aliments relating to one or more nourishment programs. As a non-limiting example quantitative signature124may relate to a nourishment program associated with a keto diet as well as a quantitative signature for a vegan diet.

Still referring toFIG.1, quantitative signature124may be determined by gaining at least a geolocation element. As used in this disclosure “geolocation element” is an identification of a real-world geographical location of a user. Geolocation element may be obtained from a radar source, remote device such as a mobile phone, and/or internet connected device location. Geolocation element may include a global positioning system (GPS) of a user. Geolocation element may include geographic coordinates that may specify the latitude and longitude of a particular location where a user is located. Geolocation element may include one or more cell-tower triangulations, wherein a cell-tower triangulation identifies at least an alpha, beta, and gamma sector. Each of the sectors identify one or more distances that an individual may be from the cell-tower. One or more cell-towers may be used in the determination of the geolocation element. For example, and without limitation, a first cell-tower may identify a mobile phone located in sector beta with a distance of 8.4 miles, wherein a second cell-tower may identify the same mobile phone in sector alpha at 23.8 miles. This may be used iteratively until the exact location of the mobile phone, and/or internet connected device may be identified. Geolocation element may include one or more received signal strength indicators (RSSI), wherein a RSSI is a measurement of the power present in a received radio signal. For example, and without limitation, RSSI may include an IEEE 802.11 wireless networking device, wherein the relative received signal strength in the wireless environment is received in arbitrary units, such that a geolocation element may be identified. Quantitative signature124may utilize an element of user geolocation to identify one or more prices, costs, and/or expenses associated with nourishment program116.

With continued reference toFIG.1, quantitative signature124may be determined by selecting a set of aliments as a function of nutritional program116. As used in this disclosure a “set of aliments” is a category of aliments that at least ameliorate a particular nutritional deficiency. As a non-limiting example a set of aliments associated with high protein may be categorized together. Computing device104may generate quantitative signature124for a first nourishment program as a function of the set of aliments and at least a temporal element. As used in this disclosure “a temporal element” is an element of data describing a specific time range, wherein a time range may consist of milliseconds, seconds, minutes, hours, days, weeks, months, years, and the like thereof. As a non-limiting example a particular set of aliments may result in a smaller temporal element in amending the nutritional deficiency of decreased phenylalanine in a user's body. As a further non-limiting example a category of aliments consisting of enhancing the amino acid phenylalanine in a suer's body may consist of salmon, steak, pork, chicken, and tofu, wherein each aliment may have a particular quantitative signature associated with the same function.

With continued reference toFIG.1, computing device104is configured to generate a dynamic weighted combination128of a plurality of dynamic weighted combinations as a function of quantitative signature124. As used in this disclosure “dynamic weighted combinations” are weighted and combined values associated with nourishment program116as a function of one or more quantitative signatures and at least a refinement criterion132, wherein each quantitative signature and refinement criterion has an established weighted value and the weighted value is combined with the value of the nourishment program. Dynamic weighted combination128may vary as a function of one or more quantitative signatures and at least a refinement criterion and/or dynamic weighted combination128may be associated with a specific nourishment program that is a function of one or more quantitative signatures and at least a refinement criterion. Furthermore, a dynamic weighted combination may re-define a magnitude of a weight and/or combination as a function of the refinement criterion As used in this disclosure “refinement criterion” are values and/or ranges of values associated with one or more attributes of a user. Refinement criterion132may include at least a nourishment qualifier relating to the user. As used in this disclosure “nourishment qualifier” is a nourishment program that a user wants, wishes, and/or desires to complete. Nourishment qualifier may consist of one or more diet programs and/or nutritional programs such as keto, paleo, vegan, vegetarian, pescatarian, and the like thereof. Refinement criterion132may include at least a pecuniary constraint relating to the user. As used in this disclosure “pecuniary constraint” is one or more currency ranges that a user wants, wishes, and/or desires to maintain. Pecuniary constraint may consist of a single aliment threshold, wherein a single aliment may not exceed a specified currency range. Pecuniary constraint may consist of a nutritional program threshold, wherein nutritional program116may not exceed a specified currency range. Pecuniary constraint may be selected, wherein a singular aliment cost may be associated with a cost of a single aliment at a single point in time. Pecuniary constraint may be generated as a function of. As a non-limiting example dynamic weighted combination128may display a range of quantitative signatures associated with a nutritional program, wherein one or more of the potential nourishment programs recommended may be eliminated as a function of refinement criterion132.

Still referring toFIG.1, computing device is configured to generate dynamic weighted combination by identifying a degree of refinement136for each dynamic weighted combinations of the plurality of dynamic weighted combinations according to the refinement criterion132. As used in this disclosure “degree of refinement” is a metric that identifies one or more degrees of freedom from nourishment program116. As a non-limiting example, degree of refinement136may include identifying one or more pecuniary constraints associated with the plurality of nourishment programs. As a further non-limiting example, degree of refinement136may identify one or more nourishment qualifiers associated with the plurality of nourishment programs. Computing device104may compare degree of refinement136for each dynamic weighted combination of the plurality of dynamic weighted combinations to the degree of refinement for at least one other dynamic weighted combination of the plurality of dynamic weighted combinations. Dynamic weighted combination may be generated as a function of the comparison.

Still referring toFIG.1, computing device104may generate the dynamic weighted combination by identifying at least a desired outcome. As used in this disclosure “desired outcome” is one or more achievements that a user may want, wish, and/or desire. As a non-limiting example a desired outcome may be related to losing a particular amount of weight. As a further non-limiting example a desired outcome may be related to increasing overall protein consumption. As a further non-limiting example a desired outcome may be related to a user wanting to lower LDL levels in a user's circulatory system. Computing device104may identify desired outcomes by receiving at least a user input from a graphical user interface. As used in this disclosure ‘user input” is information received by a user from a graphical user interface pertaining to a desired outcome. As used in this disclosure “graphical user interface” is a form or other graphical element having data entry fields, where a user may select one or more fields to enter one or more elements rating to the desired outcome. Graphical user interface may provide a drop-down menu and display one or more desired outcomes where a user may select one or more elements relating to the desired outcome. Graphical user interface may list one or more categories relating to the desired outcome, such as burn fat, gain muscle, increase health, lower blood pressure, and the like thereof. As a non-limiting example, user input may be received from one or more user devices, such as a smartphone, tablet, computer, and the like thereof. Computing device104may determine a program modifier as a function of the desired outcome.

Still referring toFIG.1, computing device104may determine a program modifier as a function of the desired outcome. As used in this disclosure a “program modifier” is a set of degrees of freedom that at least relates to a modification variable associated with a desired outcome, such that the degrees of freedom may modified and/or alter the dynamic weighted combination. Program As a non-limiting example a program modifier may include one or more degrees of freedom associated with a specific modification variable associated with the desired outcome that at least minimizes and/or maximizes the dynamic weighted combination. As a further non-limiting example the a program modifier associated with a variable that increases the weighted value of high protein aliments may be determined as a function of a desired outcome of reduced fat percentage.

Still referring toFIG.1, computing device104may modify the nourishment program as a function of program modifier and generate dynamic weighted combination128as a function of the modified nourishment program. Program modifier may modify dynamic weighted combination128by altering weighted variables associated with quantitative signatures124and refinement criterion132. For example, and without limitation, program modifier may identify one or more variables associated with reduced meat consumption as a function of a desired outcome, wherein the program modifier is incorporated into the refinement criteria such that an altered weighted dynamic combination is generated. Program modifier may modify one or more weighted values associated with the nourishment program. As a non-limiting example the program modifier may determine a user desired outcome of decreased aliment frequency, wherein the program modifier may increase the weighted value of a nourishment program that is associated with intermittent fasting.

Now referring toFIG.2, an exemplary embodiment of200of a monitoring input204according to an embodiment of the invention is illustrated. As used in this disclosure “monitoring input” is an input an external source that relates to a user health status. Monitoring input204may include data input via a survey208. As used in this disclosure “survey” is a written or verbal set of questions, wherein the process of collecting, aggregating, and analyzing the responses relates to nourishment metric108. As a non-limiting example survey208may include a verbal set of questions that relate to one or more nutritional deficiencies of a user. Monitoring input204may include an external source of a questionnaire212. As used in this disclosure “questionnaire” is a written set of questions of a plurality of written questions that may indicate one or more nourishment metrics associated with the user. For example, and without limitation, questionnaire212may include providing a user with a written form in which the user has to answer about nutritional deficiencies they may be experiencing.

Still referring toFIG.2, monitoring input204may include a signal generated by a wearable device216. As used in this disclosure “wearable device” is an electronic device that is worn on the person of a user, such as without limitation close to and/or on the surface of the skin, wherein the device can detect, analyze, and transmit nourishment metrics relating to the user. The monitoring device my consist of, without limitation, near-body electronics, on-body electronics, in-body electronics, electronic textiles, smart watches, smart glasses, smart clothing, fitness trackers, body sensors, wearable cameras, head-mounted displays, body worn cameras, Bluetooth headsets, wristbands, smart garments, chest straps, sports watches, fitness monitors, and the like thereof. The monitoring device may include directed light monitoring devices such as spectrophotometric device that at least identifies nourishment metrics such as body mass index, fat percentage, water percentage, bone mass percentage, muscle mass percentage, and the like thereof. The monitoring device may include, without limitation, earphones, earbuds, headsets, bras, suits, jackets, trousers, shirts, pants, socks, bracelets, necklaces, brooches, rings, jewelry, AR HMDs, VR HMDs, exoskeletons, location trackers, and gesture control wearables.

Still referring toFIG.2, monitoring input204may include an external source of a medical device220. As used in this disclosure “medical device” is a device operated by one or more informed advisors, wherein an informed advisor may include any medical professional who may assist and/or participate in the medical treatment of a user, that relates to one or more biological status's of the user. As a non-limiting example, an informed advisor may include a medical doctor, nurse, physician assistant, pharmacist, yoga instructor, nutritionist, spiritual healer, meditation teacher, fitness coach, health coach, life coach, and the like. As a further non-limiting example, a medical device of may include a/an stethoscope, ultrasound device, MRI device, PET scanner, CT scanner, X-ray device, electrocardiogram device, and the like thereof. Monitoring input204may include an external source of an assessment tracker224. As used in this disclosure “assessment tracker” is data-tracking tool that provides incisive data about one or more nourishment metrics relating to the user. As a non-limiting example assessment tracker224may include a program that monitors a user's nutrition over a period of time, such as seconds, minutes, hours, days, months, and/or years. As a further non-limiting example, assessment tracker224may include a worksheet that the user may record nourishment metrics to be entered into computing device104.

Now referring toFIG.3, an exemplary embodiment300of a nourishment directory304according to an embodiment of the invention is illustrated. Nourishment directory304may be implemented, without limitation, as a relational databank, a key-value retrieval databank such as a NOSQL databank, or any other format or structure for use as a databank that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. Nourishment directory304may alternatively or additionally be implemented using a distributed data storage protocol and/or data structure, such as a distributed hash table or the like. Nourishment directory304may include a plurality of data entries and/or records as described above. Data entries in a databank may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and/or in linked tables such as tables related by one or more indices in a relational database. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in a databank may store, retrieve, organize, and/or reflect data and/or records as used herein, as well as categories and/or populations of data consistently with this disclosure. Nourishment directory304may include a carbohydrate tableset308. Carbohydrate tableset308may relate to types of carbohydrates that at least provide necessary nourishment vectors. As a non-limiting example, carbohydrate tableset308may include monosaccharides, disaccharides, oligosaccharides, polysaccharides, and the like thereof. Nourishment directory304may include a fat tableset312. Fat tableset312may relate to esterified fatty acids that at least provide necessary nourishment vectors. Fat tableset may include, without limitation, triglycerides, monoglycerides, diglycerides, phospholipids, sterols, waxes, and free fatty acids. Fiber tableset316may relate to types of fiber that at least provide necessary nourishment vectors. As a non-limiting example, fiber tableset316may include soluble fiber, such as beta-glucans, raw guar gum, psyllium, inulin, and the like thereof as well as insoluble fiber, such as wheat bran, cellulose, lignin, and the like thereof. Nourishment directory304may include a mineral tableset320. Mineral tableset320may relate to types of minerals that at least provide necessary nourishment vectors. As a non-limiting example, mineral tableset320may include calcium, phosphorous, magnesium, sodium, potassium, chloride, sulfur, iron, manganese, copper, iodine, zing, cobalt, fluoride, selenium, and the like thereof. Nourishment directory304may include a protein tableset324. Protein tableset324may relate to types of proteins that at least provide necessary nourishment vectors. As a non-limiting example, protein tableset316may include amino acids combinations, wherein amino acids may include, without limitation, alanine, arginine, asparagine, aspartic acid, cysteine, glutamine, glutamic acid, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, valine, and the like thereof. Nourishment directory304may include a vitamin tableset328. Vitamin tableset328may relate to types of vitamins that at least provide necessary nourishment vectors. As a non-limiting example, vitamin tableset328may include vitamin A, vitamin B1, vitamin B2, vitamin B3, vitamin B5, vitamin B6, vitamin B7, vitamin B9, vitamin B12, vitamin C, vitamin D, vitamin E, vitamin K, and the like thereof.

Now referring toFIG.5, an exemplary embodiment of a method500for generating is illustrated. At step505, a computing device104gains a nourishment metric108. Computing device104includes any of the computing device104as described above in reference toFIG.104. Nourishment metric includes any of the nourishment metric108as described above, in reference toFIGS.1-4. For instance, and without limitation nourishment metric may include any qualitative information relating to a user's health status.

Still referring toFIG.5, at step510, computing device104determines at least a nourishment vector112as a function of nourishment metric108. Nourishment vector112includes any of the nourishment vector as described above, in reference toFIGS.1-4.

Still referring toFIG.5, at step515, computing device104generates a nourishment program116of a plurality of nourishment programs relating to an aliment124aof a plurality of aliments124a-mas a function of nourishment vector112. Nourishment program116includes any of the nourishment program116as described above, in reference toFIGS.1-4. Aliment124a-mincludes any of the aliment124a-mas described above in reference toFIGS.1-4.

Still referring toFIG.5, at step520, computing device104determines a quantitative signature124as a function of nourishment program116. Quantitative signature124includes any of the quantitative signature124as described above in reference toFIGS.1-4.

With continued reference toFIG.5, at step525, computing device104generates a dynamic weighted combination128of a plurality of dynamic weighted combinations as a function of quantitative signature124. Computing device104generates dynamic weighted combination128identifying a degree of refinement136according to a refinement criterion132. Degree of refinement136includes any of the degree of refinement as described above, in reference toFIGS.1-4. Refinement criterion132includes any of the refinement criterion132as described above, in reference toFIGS.1-4. Computing device104compares degree of refinement136for each dynamic weighted combination of the plurality of weighted combinations to the degree of refinement for at least one other dynamic combination of the plurality of dynamic weighted combinations and generates dynamic weighted combination128as a function of the comparison.

Computer system600may also include a storage device624. Examples of a storage device (e.g., storage device624) include, but are not limited to, a hard disk drive, a magnetic disk drive, an optical disc drive in combination with an optical medium, a solid-state memory device, and any combinations thereof. Storage device624may be connected to bus612by an appropriate interface (not shown). Example interfaces include, but are not limited to, SCSI, advanced technology attachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and any combinations thereof. In one example, storage device624(or one or more components thereof) may be removably interfaced with computer system600(e.g., via an external port connector (not shown)). Particularly, storage device624and an associated machine-readable medium628may provide nonvolatile and/or volatile storage of machine-readable instructions, data structures, program modules, and/or other data for computer system600. In one example, software620may reside, completely or partially, within machine-readable medium628. In another example, software620may reside, completely or partially, within processor604.

Computer system600may also include an input device632. In one example, a user of computer system600may enter commands and/or other information into computer system600via input device632. Examples of an input device632include, but are not limited to, an alpha-numeric input device (e.g., a keyboard), a pointing device, a joystick, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), a cursor control device (e.g., a mouse), a touchpad, an optical scanner, a video capture device (e.g., a still camera, a video camera), a touchscreen, and any combinations thereof. Input device632may be interfaced to bus612via any of a variety of interfaces (not shown) including, but not limited to, a serial interface, a parallel interface, a game port, a USB interface, a FIREWIRE interface, a direct interface to bus612, and any combinations thereof. Input device632may include a touch screen interface that may be a part of or separate from display636, discussed further below. Input device632may be utilized as a user selection device for selecting one or more graphical representations in a graphical interface as described above.

A user may also input commands and/or other information to computer system600via storage device624(e.g., a removable disk drive, a flash drive, etc.) and/or network interface device640. A network interface device, such as network interface device640, may be utilized for connecting computer system600to one or more of a variety of networks, such as network644, and one or more remote devices648connected thereto. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. A network, such as network644, may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software620, etc.) may be communicated to and/or from computer system600via network interface device640.

Computer system600may further include a video display adapter652for communicating a displayable image to a display device, such as display device636. Examples of a display device include, but are not limited to, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. Display adapter652and display device636may be utilized in combination with processor604to provide graphical representations of aspects of the present disclosure. In addition to a display device, computer system600may include one or more other peripheral output devices including, but not limited to, an audio speaker, a printer, and any combinations thereof. Such peripheral output devices may be connected to bus612via a peripheral interface656. Examples of a peripheral interface include, but are not limited to, a serial port, a USB connection, a FIREWIRE connection, a parallel connection, and any combinations thereof.