An instruction-dependent nutrient calculation and storage platform

Abstract

A method and system for monitoring nutrient levels and recommending dietary intake via a customizable, instruction-driven platform is presented. A method is provided that includes generating, by a computing device having one or more processors and via an API, an app that assesses nutrient needs and / or nutrient levels (e.g., collagen levels). The app can prompt for input of user attributes to assess the nutrient needs or nutrient levels. The computing device can receive the user attributes from a user device associated with the user. The computing device can store a user profile associated with the user based on the user device and the user attributes. An assessment of nutrient needs and / or nutrient levels can be generated for the user. Dietary intake recommendations can be provided to the user via the app.

Description

[Technical Field]

[0001]

[0001] Certain aspects of the present disclosure generally relate to a nutrient calculator platform that provides estimated serving sizes and customized factual information about nutritional needs, and further provides a method for saving the results for future use. Inputs for the nutrient calculator platform include, but are not limited to, an indication that collagen is being consumed, as well as collected information about the gender, weight, and age of the signed-up user. [Background technology]

[0002]

[0002] A large portion of the general public needs certain types of nutrients and dietary supplements to live a healthy lifestyle, achieve desired cosmetic improvements, or supplement their food and beverage choices in a diet-friendly manner. While consumers may desire specific physiological, medical, and / or cosmetic improvements for themselves or strive to be generally healthy, they often lack information regarding nutrient intake deficiencies. Furthermore, consumers may not be aware of the types of nutrients and dietary supplements available and how to supplement them in their daily lives. For example, consumers may lack knowledge about the possible uses and applications (e.g., recipes) of dietary supplements.

[0003]

[0003] Various embodiments of the present disclosure address one or more of the shortcomings presented above. Summary of the Invention

[0004] The present disclosure presents a novel and innovative method and system for monitoring nutrient levels and recommending dietary intakes via a customizable, instruction-driven platform. In one embodiment, a method is provided that includes a computing device (e.g., an application server) that generates (e.g., via an application programming interface) and an application for assessing nutrient levels (e.g., collagen levels) and / or nutritional needs (e.g., recommended collagen intake). The nutrient levels and / or nutritional needs may be user-specific (e.g., the degree may depend on the user's physiology). Furthermore, nutritional needs (e.g., the degree of deficiency or overconsumption) may be calculated based on the nutrient levels. The application, which may be accessible to the user on the user device, may prompt the user for input of user attributes to assess the nutrient levels. The computing device may receive one or more user attributes from a user device associated with the user to assess the user's nutrient levels. The computing device may store a user profile associated with the user based on the user device and the one or more user attributes. Furthermore, the computing device may generate an assessment of the user's nutrient levels and may generate a dietary intake recommendation for the user (e.g., based on the user profile). For example, the recommendations may include products whose contents contribute to the recommended dietary intake or recipes for preparing preparations that include the recommended dietary intake. In some aspects, the computing device may also display, via the application, predicted nutrient levels based on the recommended dietary intake.

[0005] In some aspects, the computing device can send a message to the user device via an application requesting that the user input physiological data (e.g., heart rate, blood pressure, etc.). The user device can provide the physiological data (e.g., via a wearable and / or accessory device communicatively associated with the user device), which can be received by the computing device (e.g., as one or more user attributes). Additionally or alternatively, the computing device can send a message to the user device via an application requesting the user input health goals (e.g., desired weight, desired body mass index, desired body fat percentage, etc.). The user can input their health goals via the application, and the user's health goals can be received by the computing device. The computing device can calculate dietary intake recommendations for the user based on a comparison of the physiological data to the health goals.

[0006] The computing device can determine a set of user-specific products for a user (e.g., based on one or more user attributes of the user). The generated recommendations for dietary intake can include one or more user-specific products (e.g., a subset) from the set of user-specific products. In some aspects, the user can be prompted (e.g., via a message received via an application on the user device) to input one or more filters for the user-specific products, including, but not limited to, activity level, food sensitivities, dietary preferences, or co-morbidities. The computing device can filter the user-specific products from the set of user-specific products based on the one or more filters entered by the user.

[0007] In some embodiments, a computing device can receive an indication of a user's intent from a user device. As used herein, user's intent may refer to a user's intent to use the systems and methods discussed herein to either treat a health condition (e.g., manage obesity, gain weight from an underweight state, lower blood sugar or cholesterol, overcome a nutritional deficiency, etc.) or maintain a healthy state. Applications, also referred to as customizable instruction-driven platforms, can be customized based on the indicated user's intent.

[0008] In some embodiments, the customizable instruction-dependent platform can invoke and / or recognize a user, e.g., based on a user profile and / or stored attributes, and can appropriately restore a specific configuration of the application. For example, the computing device can receive one or more user attributes from a second user device associated with the user to assess the user's nutrient levels. The computing device can recognize the user's user profile (e.g., by receiving the one or more user attributes). Thus, the computing device can appropriately configure the application based on the user (e.g., by customizing the application based on any indicated user intent).

[0009] In another embodiment, a system for monitoring nutrient levels and / or nutritional needs and recommending dietary intake via a customizable, instruction-dependent platform is disclosed. The system may include one or more processors and a memory. The memory stores instructions that, when executed by the one or more processors, cause the system to perform one or more methods described herein. In another embodiment, a non-transitory computer-readable medium for use on a computer system is disclosed, the medium including computer-executable programming instructions for monitoring nutrient levels and / or nutritional needs and recommending dietary intake via a customizable, instruction-dependent platform. The instructions may include one or more steps, methods, or processes described herein.

[0010]

[0010] The features and advantages described herein are not all-inclusive, and many additional features and advantages will become apparent to those skilled in the art upon consideration of the drawings and description, in particular. Furthermore, it should be noted that the language used herein has been chosen solely for ease of reading and instruction, and is not intended to limit the scope of the inventive subject matter. [Brief explanation of the drawings]

[0011]

[0011] [Figure 1] 1 illustrates a system for monitoring nutrient levels and recommending dietary intake via a customizable, instruction-driven platform, according to one embodiment of the present disclosure. [Figure 2] 1 illustrates an exemplary user profile database according to an exemplary embodiment of the present disclosure. [Figure 3] 1 illustrates an exemplary dietary supplement recommendation engine, according to an exemplary embodiment of the present disclosure. [Figure 4] 1 shows a flow diagram of an exemplary method for monitoring nutrient levels and recommending dietary intake via a customizable, instruction-driven platform, according to an exemplary embodiment of the present disclosure. [Figure 5]1A-1C illustrate screenshots of an exemplary user interface of a customizable, instruction-driven platform for monitoring nutrient levels and recommending dietary intake, according to one embodiment of the present disclosure. [Figure 6] 1A-1C illustrate screenshots of an exemplary user interface of a customizable, instruction-driven platform for monitoring nutrient levels and recommending dietary intake, according to one embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0012]

[0016] To live a healthy life, to achieve desired cosmetic improvements, or to supplement food and beverage choices in a diet-friendly way, a large proportion of the general public needs certain types of nutrients and dietary supplements. However, individuals do not always know the nutritional deficiencies they may have. For example, collagen is a protein that occurs naturally throughout the human body and is responsible for supporting healthy hair, skin, nails, bones, and joints. However, around the age of 25, the human body's natural collagen production may generally begin to decline. Despite recognizing the need for nutrients such as collagen, individuals may not know the types of nutrients and dietary supplements available and how to supplement them in their daily lives (e.g., through recipes and other applications).

[0013]

[0017] The present disclosure generally relates to a customizable, instruction-dependent platform for monitoring nutrient levels and recommending dietary intake. Furthermore, the present disclosure provides methods for monitoring and calculating various nutritional needs (e.g., recommended daily individual-specific collagen requirements). Nutritional needs may depend on, among other factors, a user's specific nutrient levels, a proposed user instruction for treatment or prevention, the proposed user's gender, weight, and age, and the proposed user's physical and / or behavioral information. In addition to generating recommendations for dietary intake (e.g., collagen dosage) based on such inputs, the platform can also present factual information regarding the proposed user's nutritional needs given the gender, weight, and / or age, physical and / or behavioral data. Furthermore, the platform can recommend various uses of nutritional and / or dietary supplements (e.g., recipes, products, etc.) to meet nutritional needs. In some aspects, the platform can utilize wearable biosensors to accurately and closely monitor a user's health status and adjust nutritional and / or dietary recommendations.

[0014]

[0018] 1 illustrates a system 100 according to one embodiment of the present disclosure. The system 100 includes a user device 102 associated with a user and an analysis server 120 for performing one or more steps or methods described herein. The user device 102 may be communicatively coupled to one or more biosensor and / or wearable devices 119. Additionally or alternatively, the biosensor and / or wearable 199 may be part of a stand-alone computing device (e.g., in a hospital and / or medical facility). Each of these devices and other external devices (not shown) may be able to communicate with each other via a communications network 150, which may be any wired or wireless network for disseminating information. Examples of wireless networks may include Wi-Fi, a Global System for Mobile Communications (GSM) network, a General Packet Radio Service (GPRS) network, an Enhanced Data GSM Environment (EDGE) network, an 802.5 communications network, a Code Division Multiple Access (CDMA) network, a Bluetooth network, or a Long Term Evolution (LTE) network, an LTE Advanced (LTE-A) network, or a Fifth Generation (5G) network. Furthermore, each device may include a respective network interface (e.g., network interfaces 118, 136, 118A, and 146) to facilitate communication over communications network 150. For example, each network interface may include a wired interface (e.g., an electrical interface, an RF interface (via coaxial cable), an optical interface (via fiber), a wireless interface, a modem, etc.). Furthermore, each of these devices may include one or more respective processors (e.g., processors 104 and 122) and memory (e.g., memories 110 and 128). A processor may comprise any one or more types of digital circuitry configured to perform operations on a data stream, including the functions described in this disclosure.The memory may include any type of long-term, short-term, volatile, non-volatile, or other memory and should not be limited to any particular type or number of memories or the type of medium the memory is stored on. The memory may store instructions that, when executed by a processor, cause the respective device to perform one or more of the methods discussed herein.

[0015]

[0019] The user device 102 may be implemented as a computing device, such as a computer, smartphone, tablet, smartwatch, or other wearable, that enables an associated user to communicate with the analysis server 120 through the use of applications 114. The user device 102 may further include a user interface (UI) 112, which may include a touch-sensitive display, a touchscreen, a keypad with a display device, or a combination thereof, and may function in conjunction with the display 106. The UI 112 may enable a first user to view audio, visual, and / or textual information presented by the analysis server 120 via the applications 114, access and use one or more applications 114, and enter input signals, for example, by touching and moving icons on the display 106. The display 106 may comprise any medium that outputs visual information (e.g., images, video, etc.). The applications 114 may include any program or software for performing the methods described herein. For example, the applications 114 may include applications hosted by the analysis server 120 for monitoring nutrient levels and / or nutritional needs and recommending dietary intake. A user may have a user profile 116 associated with the application, which may include multiple user attributes that may be utilized by the analytics server 120. The user attributes may include biographical details about the user (e.g., user identity, gender, weight, height, etc.), information about the first user's health (e.g., activity level, co-morbidities, health goals, etc.), and dietary requirements and preferences. In some embodiments, the user attributes may further include physiological data of the first user. The physiological data may be obtained via one or more biosensors and / or a wearable device (e.g., "biosensor / wearable" 119) that includes one or more biosensors. In some aspects, the biosensor / wearable 119 may be part of the user device 102 or may be communicatively linked to the user device 102.The biosensors / wearables may include, but are not limited to, glucose monitors, blood pressure monitors, heart rate measuring devices, and / or devices that measure activity levels (e.g., FITBIT®). The applications 14 may further include one or more social media applications hosted by a social media server 140, which may allow the first user to network with one or more followers.

[0016]

[0020] The analytics server 120 may include local or remote computing systems for requesting and receiving information received from user devices 102; processing information associated with users or followers; training machine learning models to learn from various user-specific data regarding nutrient levels and user attributes and predict nutrient level, nutritional needs, and dietary intake recommendations; generating recommended product lists and product usage; and transmitting recommendations.

[0017]

[0021] The one or more processors 122 of the analytics server 120 may include an image processor 124 and a natural language processor 126. The image processor 124 can digitally process image data generated by the user device 102 to avoid noise and other artifacts and prepare such image data for text and physical object recognition. The natural language processor 126 may be used to recognize text and determine meaning from text captured from the image data. The text may include user attributes and recognizable product identifiers (e.g., product names, company names, etc.). The image processor and / or natural language processor can rely on stored machine learning models from the machine learning module 124 to recognize information about various user attributes from the image and text data (e.g., age and weight information from natural language text, calories consumed per day from food images, health conditions from natural language text describing health conditions, etc.). In one aspect, the machine learning module 124 can perform supervised learning using reference image data so that relevant user attributes can be accurately identified. Additionally, the machine learning training module 124 may also be used to train a model to calculate and / or predict nutrient levels based on reference training data. In this example, the reference training data may include feature vectors, each based on a set of user attributes for a plurality of individual users and including corresponding values. The feature vectors may be associated with known nutrient levels for those plurality of individual users and may be trained as supervised machine learning models.

[0018]

[0022] The memory 128 of the analysis server 120 may further include a user database 130, a product database 132, and an application program interface 134. The user database 130 may store respective user profiles associated with users of the systems and methods presented herein (e.g., including users associated with the user devices 102). FIG. 2 illustrates an exemplary embodiment of the user database 130 in more detail and is further described below. The product database 132 may store information about multiple recognizable products. The recognizable products may provide nutritional or dietary supplements and may be the subject of various multimedia content. The product database 132 may list nutritional information for each product, how to use the product in recipes, and special instructions and warnings regarding their use. The products may include food and beverage products associated with nutritional and dietary supplements. Exemplary products may include collagen-infused edible products, such as collagen peptide powders, collagen-based additives for beverages, and collagen-infused water. The memory may also include an application program interface (API) 134 that hosts, manages, or facilitates one or more applications (e.g., application 114) within the user device 102. For example, the API 134 may manage an application that enables monitoring of nutrient levels and dietary intake recommendations. The diet recommendation engine 124 may include one or more programs, applications, or implementations that utilize the user database 130 and the product database 132 to generate appropriate recommendations and discounts associated with a set of one or more products for a given user (e.g., user).

[0019]

[0023] The analytic server 120 may further include an update interface 138. The update interface 138 may include a database management program or application for managing one or more databases (e.g., user database 130, product database 132, etc.) via create, read, update, or delete (CRUD) functions. In some embodiments, the update interface 138 may allow an external device (e.g., user device 102) to update one or more databases, such as when a new user wants to register with an application for monitoring nutrient levels and recommending dietary intake.

[0020]

[0024] 2 illustrates an exemplary user profile database 200 according to an exemplary embodiment of the present disclosure. As previously described, the user profile database 200 may be a component of the analysis server 120. The user profile database 200 enables the analysis server 120 to personalize diet and nutritional recommendations to users for various products and to assist users in achieving their health goals, among other functions. The user database 200 may include multiple user profiles 200A-200C corresponding to multiple users of the application.

[0021]

[0025] For example, user profile 200A, which may represent multiple user profiles, may include multiple user attributes 122. For example, user attributes 122 may include information regarding one or more of age 204, gender 206, weight 208, height 210, activity level 212, food sensitivities 220, diet preferences 222, co-morbidities 220, physiological data 212 (e.g., blood pressure 214, blood glucose levels 216, etc.), and user health goals 225. Some examples of food sensitivities 212 include lactose, egg, nut, shellfish, soy, fish, and gluten sensitivity. Some non-limiting examples of dietary preferences 214 include vegetarian, vegan, Mediterranean, kosher, halal, paleo, low-carb, and low-fat diets. Other non-limiting examples of physiological data 212 may include oxygen levels, heart rate, body temperature, body fat percentage, etc. Some non-limiting examples of comorbidities 220 include diabetes, obesity, high blood pressure, high cholesterol, celiac disease, and heartburn. In some aspects, the user may be prompted to provide physiological data via the application 114, for example through the wearable biosensor device 119. In some aspects, the user may be prompted to input the user's health goals, for example via a message on the application 114, for example via the user interface 112. Some non-limiting examples of the user's health goals may include a target weight, target physiological data (e.g., a target blood pressure, a target glucose level, a target body fat percentage, etc.).

[0022]

[0026] The user profile 200A may further include a stored user ID 230 associated with the user, and an authentication key 232. The user ID 230 may be used to identify the user, the user's user device 102 (or follower devices 102A-102C), or a social media profile associated with the user. For example, to onboard a new user to an application for monitoring nutrient levels and recommending dietary intake, the authentication key may include a public key and / or a private key generated by the authentication module 139 to verify the user.

[0023]

[0027] Additionally, user profile 200A may track and record a user's purchase history 240, for example, as it relates to products from product database 132. In some aspects, the purchase history may be able to determine various user attributes for the user, calculate nutrient levels based on consumption of purchased products, or generate a user-specific list of products based on purchasing behavior.

[0024]

[0028] The user database 200 may also include a user profile directory 244 that maps the various user profiles 200A-200C within the user database 200, and a link engine 246 that links the various data structures within the user database 200 (e.g., the user profiles 200A-200C) to data structures in other databases (e.g., the product database 132). Additionally, a query optimizer 248 may enable external components and devices to more efficiently and accurately retrieve information from the user database 200.

[0025]

[0029] 3 illustrates an exemplary dietary supplement recommendation engine (“diet recommendation engine”) 300, according to an exemplary embodiment of the present disclosure. As previously described, diet recommendation engine 300 may include one or more programs, applications, or implementations within analytics server 120 that utilize user attributes from user database 130 and product information from product database 132 to generate appropriate recommendations and discounts associated with a set of one or more products for a given user.

[0026]

[0030] In an exemplary embodiment, the diet recommendation engine 300 may include a user health plan unit 302, a dietary restriction filter unit 308, an optimization unit 318, and a product recommendation unit 326. The user health plan module 302 may include instructions for calculating a health plan for a user based on user health goals 304 retrieved from the user's user profile (e.g., from the user profile database 200) and the user's current health state 306. The user's current health state 306 may include a data structure that stores various user attributes indicative of the user's current health. For example, the user's current health state 306 may include a body mass index (BMI) calculated for a given user based on the given user's height 210, weight 208, gender 206, and age 204. The user's health goal may be a desired BMI that the user wishes to achieve. The user's health plan module 302 calculates the weight difference that may be required to achieve it. Additionally, based on the indicated activity level 218, the usage health plan module 302 may calculate dietary requirements for a given user, for example, to achieve the user health goals 304 indicated by the user.

[0027]

[0031] The dietary filter restrictions unit 144, which may limit the results provided by the product recommendation unit 326, may include filters for one or more of food sensitivities 310, diet preferences 312, co-morbidities 316, and physiological restrictions 316. The optimization unit 318 may include optimization rules based on one or more of calorie intake 320, specific nutrients 322, and / or collagen levels 324.

[0028]

[0032] The diet recommendation engine 300 may further include a product recommendation unit 326 that may store programs and instructions for generating a list (e.g., product offerings 328) of products relevant to the user (e.g., "user-specific products") based on the provided user attributes, the ratings provided by the user health plan module 302, the dietary filter restrictions 308, and the optimization unit 318. The product offerings 326 may include user-specific products including food and beverage products associated with specific nutrients (e.g., protein (e.g., collagen), vitamins, minerals, fat, water, etc.). The product offerings may be identifiable from the product database 132. The product recommendation unit 326 may further generate a list of recommended recipes 330 based on the product offerings. Furthermore, the product recommendation unit 326 may generate discount codes 332 that may enable the user to purchase and / or obtain products from the product offerings 328 at a discounted price or for free.

[0029]

[0033] 4 shows a flow diagram of an example method 400 for monitoring nutrient levels and recommending dietary intakes, according to an example embodiment of the present disclosure. Method 400 may be executed by one or more processors of analysis server 120. Furthermore, while method 400 may relate to the ability of a user of application 114 to enable analysis server 120 to monitor the user's nutrient levels, calculate nutrient needs, and recommend dietary intakes, method 400 may also be implemented by other users (e.g., in parallel) via their respective user devices.

[0030]

[0034] Referring to method 400, step 402 may include the analysis server generating an application for assessing nutrient levels (e.g., as in step 402). For example, the analysis server 120 may generate the application 114, which may be hosted by the API 134, and the application may be accessible to users via their respective user devices. The application may be web browser-enabled and / or a mobile application accessible on a mobile platform.

[0031]

[0035] Through the interactive tools presented herein, the application may present visual and / or textual information inviting the user to calculate their nutrient levels, encouraging the user to learn more about nutrients, and recommending dietary intake solutions. Exemplary screenshots of the application's user interface are shown in Figures 5 and 6.

[0032]

[0036] The application may also prompt the user to enter user attributes to assess nutrient levels, as shown in step 404. For example, a dashboard on the application may ask a series of questions for the user to answer in order to calculate the level of a particular nutrient (e.g., in collagen).

[0033]

[0037] In step 406, the analysis server 120 can receive responses to the user's attributes from the user device. For example, a user accessing a web-enabled version of the application can decide to find out what their collagen levels are and enter a response to the question into the application accordingly. The entered response can be processed and recognized by the analysis server 120 as a user attribute.

[0034]

[0038] As previously mentioned, user attributes may include biographical details about the user (e.g., user identification, gender, weight, height, etc.), information about the first user's health (e.g., activity level, comorbidities, health goals, etc.), dietary needs and preferences, and physiological data. In some aspects, prompting the user to input user attributes may include prompting the user to connect to a biosensor and / or wearable device (e.g., biosensor / wearable 119) to input physiological data. Additionally or alternatively, input of user attributes may be via image, audio, and / or video upload. For example, a patient data sheet may be scanned and uploaded to application 114, and the natural language processor 126 can parse and recognize user attributes related to comorbidities, underlying health conditions, and biographical details.

[0035]

[0039] In step 408, the analytic server 120 can determine whether to recognize a user profile based on, for example, the entered user attributes or the user device interacting with the application when the user attributes are entered. For example, the analytic server can track user devices that interact with the application (e.g., by storing a device identifier) ​​and recognize the same device when it returns to the application. Similarly, or alternatively, when a user enters a response for a user attribute, a threshold number of responses that are the same as another user's response can cause the analytic server 120 to identify the current user and the other user as the same user.

[0036]

[0040] If the analytic server 120 cannot recognize the user, the analytic server 120 can create and store a new user profile based on the input responses for user attributes. In some aspects, the analytic server 120 can prompt the user to input an intent to use the application ("user intent"), for example, via a message sent to the user device 102 via the application 114. Thus, the user can input an indication of the user intent via the application. The user intent may, for example, indicate an intent to treat a health condition. Additionally or alternatively, the user intent may indicate an intent to prevent a health condition. Additionally or alternatively, the user intent may indicate an intent to maintain a health condition.

[0037]

[0041] In response to the expressed intent, the analytic server 120 may customize the application accordingly, at step 412. For example, as discussed further herein, the analytic server 120 may provide tailored dietary intake recommendations, news articles, articles, promotions, and / or marketing materials based on the user's intent.

[0038]

[0042] If, at step 408, the analytic server 120 recognizes a user profile based on the input responses about the user attributes, then the analytic server 120 may restore a previously customized application based on the user profile at step 414. For example, a user associated with the user profile may have previously accessed the application and indicated a particular user intent, which resulted in the analytic server 120 customizing the application for the user.

[0039]

[0043] In step 416, the analytic server 120 can calculate an assessment of the user's nutrient levels. The analytic server can convert the user's responses for various user attributes into values. The values ​​of the set of user attributes can be compared to existing models to calculate or extrapolate the user's nutrient levels. In some embodiments, the values ​​of the set of user attributes can be used to create a feature vector. For a training dataset with known nutrient levels, a machine learning model trained using the same or similar set of user attributes can be identified. The feature vector can be input into the identified machine learning model to calculate an assessment of the user's nutrient levels. In some embodiments, the analytic server 120 can calculate how much a nutrient the user needs (e.g., how much collagen the user should consume per day) based on the calculated nutrient levels. In some embodiments, the assessment can include an estimate of one or more of the user's approximate amount or percentage of a nutrient and the approximate amount or percentage by which the nutrient is above or below the user's recommended amount.

[0040]

[0044] At step 418, the analysis server 120 may generate dietary intake recommendations for the user. The dietary intake recommendations may include amounts of nutrients that the user may need on a regular basis (e.g., daily, weekly, monthly, etc.) to achieve recommended levels of nutrients and / or to meet health goals that the user may have selected and entered via the application. Additionally, the dietary intake recommendations may include recommendations for one or more user-specific products (e.g., foods and / or beverages) known to have nutrients that will help the user reach the recommended nutrient levels.

[0041]

[0045] In some embodiments, the analysis server 120 can further assist the first user in achieving their health goals. For example, the user can input and / or upload various user attributes to the application 114 via the UI 112 (e.g., at step 406). The analysis server 120 can use these user attributes to develop a plan for the user to achieve their desired health goals and provide user-specific products that help the user meet their desired health goals. For example, the analysis server 120 can determine a set of user-specific products based on the user attributes. As discussed above in connection with FIG. 3 , the user-specific products may be based on ratings of products that can help the user meet their desired health goals, filtered (e.g., based on dietary restrictions), and optimized accordingly.

[0042]

[0046] In some aspects, user attributes, including real-time physiological data (e.g., heart rate, blood pressure, glucose levels, etc.), enable more accurate monitoring of nutritional needs and more effective product delivery to the user. For example, the analysis server can prompt the user to input physiological data by sending a message via the application 114 on the mobile device 102 requesting the user to input physiological data. For example, a user wishing to improve athletic performance over time may be prompted to provide their heart rate. The user can provide such physiological input using a biosensor or wearable associated with or communicatively linked to the user device 102. The analysis server 120 can then receive the physiological data from the mobile device.

[0043]

[0047] A user's health goals are yet another user attribute that can be used to determine a user-specific set of products for a first user. In one aspect, the analysis server 120 can send a message via the application 114 on the user device 102 requesting the user to input their health goals. The analysis server 120 can then receive the health goals indicated by the user. Based on the health goals, the analysis server can determine a user-specific set of products. In a further aspect, the user's indicated health goals can be compared with the user's current physiological data to determine a first user-specific set of products.

[0044]

[0048] 5 and 6 show screenshots of an exemplary user interface of a customizable, instruction-dependent platform for monitoring nutrient levels and recommending dietary intake, according to one embodiment of the present disclosure. The exemplary screenshots may be based on a graphical user interface provided by an application 114 and may be viewable by a user on a user device 102. The customizable, instruction-dependent platform may be an application 114 that may be accessible to various users (e.g., via their respective user devices) and managed by the analytics server 120 via an API 134.

[0045]

[0049] 5, an exemplary user interface may prompt the user to determine if they have a particular nutrient need (e.g., "How much collagen should I take daily?" 502). To invite or encourage the user to participate in assessing their nutritional levels, the exemplary user interface may also provide news alerts, stories, articles, and / or other multimedia content informing the user of the benefits of particular nutrients (e.g., "Latest Vital News: Read More" 504). The exemplary user interface may also provide functionality (e.g., data fields, tabbed options) for the user to select responses for various user attributes (e.g., "Why are you taking collagen?" 506, "I am female" 508, weight 508, and age 512). Some user attributes, such as "Why are you taking collagen?", may be used by the analytics server 120 to determine the user's intent and customize the application's user interface accordingly. The user may then select calculate 514 to calculate nutrient levels. In some aspects, a more accurate or comprehensive assessment of nutrient levels and / or dietary intake recommendations may be available to the user through an advanced version 516 of the application, which may prompt for responses for significantly more user attributes.

[0046]

[0050] FIG. 6 shows another screenshot of an exemplary user interface of a customizable, instruction-driven platform for monitoring nutrient levels and recommending dietary intake, according to one embodiment of the present disclosure. Specifically, FIG. 6 shows the exemplary user interface after a user selects to have the application calculate their nutrient levels (e.g., by clicking calculate 514 in FIG. 5). As shown in FIG. 6, the user may be directed to a dashboard 602 that rewards users who wish to commit to a healthier future with the application for monitoring nutrient levels and recommending dietary intake. For example, dashboard 502 shows the user's recommended daily collagen intake as "25g of collagen per day." Furthermore, the application provides a dietary intake recommendation to add collagen to the user's diet for at least 2-4 weeks for best results. As options for meeting this dietary intake recommendation, the user may be able to access or purchase an inventory of collagen products 608; browse collagen-based recipes (e.g., collagen recipes 610); and / or learn more about collagen generally (e.g., via collagen FAQs 612).

[0047]

[0051] All of the disclosed methods and procedures described in this disclosure can be implemented using one or more computer programs or components. These components may be provided as a series of computer instructions on any conventional computer-readable or machine-readable medium, including volatile and non-volatile memory such as RAM, ROM, flash memory, magnetic or optical disks, optical memory, or other storage media. The instructions may be provided as software or firmware, or may also be implemented in whole or in part in hardware components such as ASICs, FPGAs, DSPs, or any other similar devices. The instructions may be configured to be executed by one or more processors, which, when executing the series of computer instructions, perform or facilitate the performance of all or a portion of the disclosed methods and procedures.

[0048]

[0052] It should be understood that various changes and modifications to the examples described herein will be apparent to those skilled in the art. Such changes and modifications can be made without departing from the spirit and scope of the present subject matter and without diminishing its intended advantages. It is therefore intended that such changes and modifications be covered by the appended claims.

Claims

1. A method for monitoring collagen levels and recommending dietary intake, comprising: generating, by a computing device having one or more processors and via an application programming interface, an application for assessing a user's specific collagen level, the application prompting for input of user attributes for assessing the user's specific collagen level; receiving, by the computing device and from a user device associated with the user, one or more user attributes of the user to assess the user-specific collagen level of the user, the user device accessing the generated application; storing a user profile associated with the user based on a device identifier of the user device and the one or more user attributes; generating, by the computing device, the user-specific collagen level assessment for the user based on the user profile; generating, by the computing device, a dietary intake recommendation for the user based on the generated assessment of the user-specific collagen level; Including, receiving the one or more user attributes, sending a message to the user device via the application requesting the user to input physiological data; receiving the physiological data from the user device; Including, The method comprises: sending a message to the user device via the application requesting the user to input a health goal; receiving the health goal from the user device; calculating the dietary intake recommendation for the user based on a comparison of the physiological data and the health goals; The method further comprises:

2. The method comprises: determining a user-specific set of products based on the one or more user attributes; Further comprising: generating the dietary intake recommendation includes one or more user-specific products from the set of user-specific products; The method of claim 1.

3. sending a message to the user device via the application requesting the user to input one or more of activity level, food sensitivities, dietary preferences, and co-morbidities; filtering user-specific products from the set of user-specific products based on the one or more of the activity level, the food sensitivities, the dietary preferences, and the co-morbidities; The method of claim 2 further comprising:

4. receiving an indication of a user's intent from the user device via the application, the indication including one of a first intent to treat a health condition and a second intent to prevent the health condition; customizing, by the computing device, the application based on the indication of the user's intent; The method of claim 1 further comprising:

5. A method for monitoring collagen levels and recommending dietary intake, comprising: generating, by a computing device having one or more processors and via an application programming interface, an application for assessing a user's specific collagen level, the application prompting for input of user attributes for assessing the user's specific collagen level; receiving, by the computing device and from a user device associated with the user, one or more user attributes of the user to assess the user-specific collagen level of the user, the user device accessing the generated application; storing a user profile associated with the user based on a device identifier of the user device and the one or more user attributes; generating, by the computing device, the user-specific collagen level assessment for the user based on the user profile; generating, by the computing device, a dietary intake recommendation for the user based on the generated assessment of the user-specific collagen level; receiving an indication of a user's intent from the user device via the application, the indication including one of a first intent to treat a health condition and a second intent to prevent the health condition; customizing, by the computing device, the application based on the indication of the user's intent; A method comprising:

6. receiving, by the computing device and from a second user device associated with the user, the one or more user attributes of the user to assess the user's specific collagen level; recognizing, by the computing device and based on the one or more user attributes, the user profile of the user; The method of claim 1 or 5, further comprising:

7. 1. A system for monitoring nutritional needs and recommending dietary intakes, comprising: one or more processors; a memory for storing instructions that, when executed by the processor, cause the system to: generating, via an application programming interface, an application for assessing the nutritional needs, the application prompting for input of user attributes for assessing the nutritional needs; receiving one or more user attributes of the user from a user device associated with the user to assess the nutritional needs of the user; storing a user profile associated with the user based on a device identifier of the user device and the one or more user attributes; generating an assessment of the nutritional needs of the user based on the user profile; generating dietary intake recommendations for the user based on the user profile; When the instructions are executed, the system: sending a message to the user device via the application requesting the user to input physiological data; receiving the physiological data from the user device; receiving the one or more user attributes by When the instructions are executed, the system further sending a message to the user device via the application requesting the user to input a health goal; receiving the health goal from the user device; calculating the dietary intake recommendation for the user based on a comparison of the physiological data and the health goals; system.

8. When the instructions are executed, the system: determining a user-specific set of products based on the one or more user attributes; generating the dietary intake recommendation includes one or more user-specific products from the set of user-specific products. The system of claim 7.

9. When the instructions are executed, the system further sending a message via the application to the user device requesting the user to input one or more of an activity level, food sensitivities, dietary preferences, and co-morbidities; filtering user-specific products from the set of user-specific products based on the one or more of the activity level, the food sensitivities, the dietary preferences, and the co-morbidities; The system of claim 8.

10. When the instructions are executed, the system further receiving, via the application from the user device, an indication of a user's intent, the indication including one of a first intent to treat a health condition and a second intent to prevent the health condition; customizing the application based on the indication of the user's intent; The system of claim 7.

11. A system for monitoring nutritional needs and recommending dietary intake, comprising: one or more processors; a memory for storing instructions that, when executed by the processor, cause the system to: generating, via an application programming interface, an application for assessing the nutritional needs, the application prompting for input of user attributes for assessing the nutritional needs; receiving one or more user attributes of the user from a user device associated with the user to assess the nutritional needs of the user; storing a user profile associated with the user based on a device identifier of the user device and the one or more user attributes; generating an assessment of the nutritional needs of the user based on the user profile; generating dietary intake recommendations for the user based on the user profile; When the instructions are executed, the system further receiving, via the application from the user device, an indication of a user's intent, the indication including one of a first intent to treat a health condition and a second intent to prevent the health condition; customizing the application based on the indication of the user's intent; system.

12. When the instructions are executed, the system further receiving, by a computing device and from a second user device associated with the user, the one or more user attributes of the user to assess the nutritional needs of the user; recognizing, by the computing device and based on the one or more user attributes, the user profile of the user; 12. The system of claim 7 or 11.

13. A non-transitory computer-readable medium for use on a computer system containing computer-executable programming instructions for monitoring nutrient levels and recommending dietary intakes, the instructions comprising: generating, by a computing device having one or more processors and via an application programming interface, an application for assessing nutrient levels, the application prompting for input of user attributes to assess the nutrient levels; receiving, by the computing device and from a user device associated with the user, one or more user attributes of the user for assessing the nutrient level of the user; storing a user profile associated with the user based on a device identifier of the user device and the one or more user attributes; determining, by the computing device, an assessment of the nutrient level of the user based on the user profile; generating, by the computing device, a dietary intake recommendation for the user based on the determined assessment of the nutrient levels of the user; displaying predicted nutrient levels for the user via the application and based on the dietary intake recommendations; Including, said receiving one or more first user attributes; sending a message via the application to the user device requesting a first user to input physiological data; receiving the physiological data from the user device; Including, The instruction: sending a message to the user device via the application requesting the first user to input a health goal; receiving the health goal from the user device; calculating the dietary intake recommendation for the user based on a comparison of the physiological data and the health goal; Further comprising: Non-transitory computer-readable medium.

14. The instruction: determining a user-specific set of products based on the one or more user attributes; Further comprising: generating the dietary intake recommendation includes one or more user-specific products from the set of user-specific products.

14. The non-transitory computer-readable medium of claim 13.

15. The instruction: sending a message to the user device via the application requesting the user to input one or more of an activity level, food sensitivities, dietary preferences, and co-morbidities; filtering user-specific products from the set of user-specific products based on the one or more of the activity level, the food sensitivities, the dietary preferences, and the co-morbidities; 15. The non-transitory computer-readable medium of claim 14, further comprising:

16. The instruction: receiving an indication of a user's intent from the user device via the application, the indication including one of a first intent to treat a health condition and a second intent to prevent the health condition; customizing, by the computing device, the application based on the indication of the user's intent; 16. The non-transitory computer-readable medium of claim 15, further comprising:

17. A non-transitory computer-readable medium for use on a computer system containing computer-executable programming instructions for monitoring nutrient levels and recommending dietary intakes, the instructions comprising: generating, by a computing device having one or more processors and via an application programming interface, an application for assessing nutrient levels, the application prompting for input of user attributes to assess the nutrient levels; receiving, by the computing device and from a user device associated with the user, one or more user attributes of the user for assessing the nutrient level of the user; storing a user profile associated with the user based on a device identifier of the user device and the one or more user attributes; determining, by the computing device, an assessment of the nutrient level of the user based on the user profile; generating, by the computing device, a dietary intake recommendation for the user based on the determined assessment of the nutrient levels of the user; displaying predicted nutrient levels for the user via the application and based on the dietary intake recommendations; Including, The instruction: receiving an indication of a user's intent from the user device via the application, the indication including one of a first intent to treat a health condition and a second intent to prevent the health condition; customizing, by the computing device, the application based on the indication of the user's intent; Further comprising: Non-transitory computer-readable medium.

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