Method and device for recommending dietary supplement and providing weight-loss exercise and meal schedules on basis of artificial intelligence model

Abstract

A method for recommending a dietary supplement and providing weight-loss exercise and meal schedules may comprise the steps of: acquiring customer data including body composition measurement data and physical constitution test data; acquiring service data including dietary supplement data and exercise and meal schedule data; inputting the customer data and the service data to a tagging artificial intelligence model so as to acquire a keyword for the customer data and a keyword for the service data, respectively, from the tagging artificial intelligence model; inputting the keyword of the customer data and the keyword of the service data to an association analysis artificial intelligence model so as to acquire the service data matching the customer data; and on the basis of the service data matching the customer data, recommending a dietary supplement and providing weight-loss exercise and meal schedules for each customer.

Description

Method and device for recommending diet supplements and providing diet exercise and meal schedules based on an artificial intelligence model The present invention relates to a method and device for providing a diet supplement recommendation and a diet exercise and meal schedule based on an artificial intelligence model using an electronic device. As interest in health has increased recently, interest in diet supplements has also increased. Diet supplements can be manufactured using health functional foods approved by the Ministry of Food and Drug Safety for their body fat reduction effects. These ingredients come in a variety of forms, each with its own unique mechanism of action and role. If you take various types of diet supplements at the same time to enjoy the different roles of each ingredient, side effects may occur if you are not aware of the interactions between the ingredients or precautions when taking them. Additionally, each person has a different constitution and health condition, and because people take diet supplements based on reviews from other customers, side effects such as allergic reactions may occur. The present invention provides a method and device for recommending a diet supplement product and providing a diet exercise and diet schedule based on an artificial intelligence (AI) model, which uses artificial intelligence (AI) technology to recommend a diet supplement suitable for a customer's constitution and health condition and to provide a sustainable diet exercise and diet schedule. A method for providing a diet supplement recommendation and a diet exercise and diet schedule based on an artificial intelligence model by an electronic device according to the present invention may include the steps of: obtaining customer data consisting of body composition measurement data and constitution examination data; obtaining service data consisting of diet supplement data and exercise and diet schedule data; inputting the customer data and the service data into a tagging artificial intelligence model that outputs keywords for the customer data and the service data when the customer data and the service data are input, thereby obtaining keywords for the customer data and keywords for the service data from the tagging artificial intelligence model, respectively; inputting keywords of the customer data and keywords of the service data into an association analysis artificial intelligence model that outputs service data matching the customer data through keyword association analysis when keywords of the customer data and keywords of the service data are input, thereby obtaining the service data matching the customer data; and recommending a diet supplement and providing a diet exercise and diet schedule for each customer based on the service data matched with the customer data. According to one embodiment of the present invention, in the step of acquiring the customer data, the body composition measurement data may include body weight, skeletal muscle mass, and body fat mass. According to one embodiment of the present invention, in the step of acquiring keywords for the customer data and keywords for the service data, the customer's body composition measurement data is classified into three types: type C in which body weight and body fat mass are greater than skeletal muscle mass, type I in which skeletal muscle mass, body weight, and body fat mass are similar, and type D in which skeletal muscle mass is greater than body weight and body fat mass, and keywords for the body composition measurement data can be acquired for each type. According to one embodiment of the present invention, in the step of acquiring the customer data, the physical examination data may include body nutrient concentration, health management reference factors, exercise ability, eating habits, and personal characteristics. An electronic device for recommending a diet supplement and providing a diet exercise and diet schedule based on an artificial intelligence model according to the present invention comprises a memory storing one or more instructions and at least one processor executing the one or more instructions, wherein the processor, by executing the one or more instructions, obtains customer data consisting of body composition measurement data and constitution examination data, obtains service data consisting of diet supplement data and exercise and diet schedule data, and inputs the customer data and the service data into a tagging artificial intelligence model that outputs keywords for the customer data and the service data when the customer data and the service data are input, thereby obtaining keywords for the customer data and keywords for the service data from the tagging artificial intelligence model, respectively, and inputs keywords of the customer data and keywords of the service data into an association analysis artificial intelligence model that outputs service data matching the customer data through keyword association analysis when keywords of the customer data and keywords of the service data are input, thereby obtaining the service data matching the customer data, and recommending a diet supplement and providing a diet exercise and diet schedule for each customer based on the service data matched with the customer data. According to one embodiment of the present invention, the body composition measurement data may include body weight, skeletal muscle mass, and body fat mass. According to one embodiment of the present invention, the processor executes the one or more instructions to classify the customer's body composition measurement data into three types: type C, in which body weight and body fat mass are greater than skeletal muscle mass; type I, in which skeletal muscle mass, body weight, and body fat mass are similar; and type D, in which skeletal muscle mass is greater than body weight and body fat mass, and obtain keywords for the body composition measurement data for each type. According to one embodiment of the present invention, the constitution test data may include body nutrient concentration, health management reference factors, exercise ability, eating habits, and personal characteristics. The present invention utilizes AI models to recommend diet supplements suited to each customer's constitution and health based on the aforementioned customer data, and to provide diet exercise and meal plans. This can help minimize side effects and increase the likelihood of successful dieting. FIG. 1 is a diagram illustrating a process in which an electronic device according to one embodiment of the present invention provides a diet supplement recommendation and a diet exercise and meal schedule based on an artificial intelligence model. FIG. 2 is a flowchart illustrating a method for recommending diet supplements and providing diet exercise and meal schedules based on an artificial intelligence model according to one embodiment of the present invention. FIG. 3 is a graph for explaining Type C among the types classified based on body composition measurement data according to one embodiment of the present invention. FIG. 4 is a graph for explaining Type I among the types classified based on body composition measurement data according to one embodiment of the present invention. FIG. 5 is a graph for explaining type D among types classified based on body composition measurement data according to one embodiment of the present invention. FIG. 6 is a block diagram illustrating a device for providing an artificial intelligence model-based diet supplement recommendation and diet exercise and meal schedule according to one embodiment of the present invention. Figure 7 is a block diagram of a server according to one embodiment of the present invention. The advantages and features of the present invention, and the methods for achieving them, will become clearer with reference to the embodiments described in detail below together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms. These embodiments are provided solely to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims. In connection with the description of the drawings, similar reference numerals may be used for similar components. In this document, the expressions "has," "may have," "includes," or "may include" indicate the presence of a feature (e.g., a number, function, operation, or component such as a part), but do not exclude the presence of additional features. In this document, the expressions "A or B," "at least one of A and / or B," or "one or more of A or / and B" can include all possible combinations of the listed items. For example, "A or B," "at least one of A and B," or "at least one of A or B" can all refer to (1) including at least one A, (3) including at least one B, or (3) including both at least one A and at least one B. The terms "first," "second," "first," or "second," as used herein, may describe various components, regardless of order and / or importance, and are only used to distinguish one component from another, without limiting the components. For example, a first user device and a second user device may represent different user devices, regardless of order or importance. For example, without departing from the scope of the rights set forth in this document, a first component may be referred to as a second component, and similarly, a second component may also be referred to as a first component. When it is said that a component (e.g., a first component) is "(operatively or communicatively) coupled with / to" or "connected to" another component (e.g., a second component), it should be understood that the component is directly coupled to the other component, or can be connected via another component (e.g., a third component). Conversely, when it is said that a component (e.g., a first component) is "directly coupled to" or "directly connected to" another component (e.g., a second component), it should be understood that no other component (e.g., a third component) exists between the first component and the other component. The expression "configured to" as used herein can be used interchangeably with, for example, "suitable for," "having the capacity to," "designed to," "adapted to," "made to," or "capable of." The term "configured to" does not necessarily mean something is "specifically designed to" in hardware terms. Instead, in some contexts, the expression "a device configured to" can mean that the device, together with other devices or components, is "capable of." For example, the phrase "a processor configured (or set) to perform A, B, and C" may mean a dedicated processor (e.g., an embedded processor) for performing those operations, or a general-purpose processor (e.g., a CPU or application processor) that can perform those operations by executing one or more software programs stored in a memory device. The terms used in this document are used only to describe specific embodiments and may not be intended to limit the scope of other embodiments. The singular expression may include the plural expression unless the context clearly indicates otherwise. Terms used herein, including technical or scientific terms, may have the same meaning as commonly understood by those of ordinary skill in the art described in this document. Terms defined in general dictionaries among the terms used in this document may be interpreted as having the same or similar meaning in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense unless explicitly defined in this document. In some cases, even if a term is defined in this document, it cannot be interpreted to exclude the embodiments of this document. The artificial intelligence-related functions according to the present disclosure are operated via a processor and memory. The processor may be comprised of one or more processors. In this case, one or more processors may be a general-purpose processor such as a CPU, an AP, a Digital Signal Processor (DSP), a graphics-only processor such as a GPU or a Vision Processing Unit (VPU), or an artificial intelligence-only processor such as an NPU. One or more processors control the processing of input data according to predefined operating rules or artificial intelligence models stored in memory. Alternatively, if one or more processors are artificial intelligence-only processors, the artificial intelligence-only processor may be designed with a hardware structure specialized for processing a specific artificial intelligence model. The predefined operation rules or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that the basic artificial intelligence model is trained using a learning algorithm using a plurality of learning data, thereby creating a predefined operation rules or artificial intelligence model set to perform a desired characteristic (or purpose). This learning may be performed on the device itself on which the artificial intelligence according to the present disclosure is performed, or may be performed through a separate server and / or system. Examples of the learning algorithm include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. An AI model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple nodes and weights, and performs neural network operations through operations between the computational results of the previous layer and the multiple weights. The multiple weights of the multiple neural network layers may be optimized based on the learning results of the AI ​​model. For example, the multiple weights may be updated during the learning process to reduce or minimize the loss or cost values ​​obtained from the AI ​​model. Furthermore, to minimize the loss or cost values, the multiple weights may be updated in a direction that minimizes the gradient associated with the loss or cost values. The artificial neural network may include a deep neural network (DNN), for example, a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), or deep Q-networks, but is not limited to the examples described above. The features of each of the various embodiments of the present invention are partially or wholly mutually

[0036] It is possible to combine or combine, and various technical connections and operations are possible as can be fully understood by those skilled in the art, and each embodiment may be implemented independently of each other or may be implemented together in a related relationship. Hereinafter, various embodiments of the present invention will be described in detail with reference to the attached drawings. FIG. 1 is a diagram illustrating a process in which an electronic device according to one embodiment of the present invention provides a diet supplement recommendation and a diet exercise and meal schedule based on an artificial intelligence model. Referring to FIG. 1, a method for providing a diet supplement recommendation and a diet exercise and meal schedule for each customer based on an artificial intelligence model can be performed by an electronic device (100). An electronic device (100) according to one embodiment may be implemented in various forms. For example, the electronic device (100) may include, but is not limited to, a mobile terminal, a smart phone, a laptop computer, a tablet PC, an e-book terminal, a digital broadcasting terminal, a PDA (Personal Digital Assistant), etc. According to one embodiment, the electronic device (100) may include a tagging artificial intelligence model (200) and an association analysis artificial intelligence model (210). Based on the tagging artificial intelligence model (200) and the association analysis artificial intelligence model (210), service data provided may be used to provide recommendations for diet supplements and diet exercise and meal schedules. Specifically, by using a tagging artificial intelligence model (200), customer data, diet supplement data, and service data can be input, and keywords for the customer data and service data can be output. By using a correlation analysis artificial intelligence model (210), keywords for customer data and keywords for service data can be input, and service data matching the customer data can be output through correlation analysis of the keywords, thereby recommending diet supplements for each customer and providing diet exercise and meal schedules based on the service data matching the customer data. According to one embodiment, the tagging artificial intelligence model (200) and the association analysis artificial intelligence model (210) used by the electronic device (100) are artificial neural network (ANN) models, which refer to computing systems inspired by biological neural networks. Examples of artificial neural network models include a deep neural network (DNN), a convolution neural network (CNN), a recurrent neural network (RNN), a restricted boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), and deep Q-networks. Hereinafter, for convenience, the tagging artificial intelligence model (200) and the association analysis artificial intelligence model (210) will be described as an example of a deep neural network (DNN) among artificial neural network models. An electronic device (100) according to one embodiment can obtain service data matching customer data using a tagging artificial intelligence model (200) and an association analysis artificial intelligence model (210), and can recommend diet supplements for each customer based on the service data and provide sustainable diet exercise and meal schedules. Specifically, the electronic device (100) can input customer data consisting of body composition measurement data and constitution examination data and service data consisting of diet supplement data and diet exercise and meal data into a tagging artificial intelligence model (200) to output keywords for the customer data and the service data. The electronic device (100) can input keywords of the customer data and keywords of the service data into a correlation analysis artificial intelligence model (210) to output the service data that matches the customer data through correlation analysis of the keywords. Furthermore, the electronic device (100) can recommend diet supplements for each customer and provide sustainable diet exercise and meal schedules based on the service data matched with the customer data. FIG. 2 is a flowchart for explaining a method for recommending a diet supplement and providing a diet exercise and meal schedule based on an artificial intelligence model according to an embodiment of the present invention, FIG. 3 is graphs for explaining type C among types classified based on body composition measurement data according to an embodiment of the present invention, FIG. 4 is graphs for explaining type I among types classified based on body composition measurement data according to an embodiment of the present invention, and FIG. 5 is graphs for explaining type D among types classified based on body composition measurement data according to an embodiment of the present invention. Referring to FIGS. 2 to 5, first, the electronic device (100) can obtain customer data. (S110) The above customer data may consist of body composition measurement data and physical examination data. The above customer data may further include information such as the customer's name, age, gender, and presence of underlying medical conditions. The above body composition measurement data may include body weight, skeletal muscle mass, and body fat mass. The body composition measurement data may be measured by a body composition measurement device. The above body composition measurement data can be obtained by a user's input to the electronic device (100) or a user's input to a server (not shown) connected to the electronic device (100). Alternatively, the body composition measurement data may be automatically input and acquired through a wireless or wired connection between the body composition measurement device and the electronic device (100) or the server. The above constitutional examination data may include body nutrient concentrations, health management reference factors, exercise ability, eating habits, and personal characteristics. The above constitution test data can be obtained by performing a constitution test using the customer's saliva. The above body nutrient concentrations may include concentrations of vitamin C, vitamin D, coenzyme Q10, magnesium, zinc, iron, potassium, arginine, omega-3 fatty acids, vitamin A, vitamin B6, vitamin E, vitamin K, vitamin B12, selenium, lutein & zeaxanthin, etc. The above health management reference factors may include susceptibility to degenerative arthritis inflammation, motion sickness, uric acid level, neutral fat concentration, body fat percentage, body mass index, blood sugar, bone mass, abdominal obesity (hip-to-waist ratio), weight loss effect of exercise, possibility of weight regain after weight loss (yo-yo possibility), systolic blood pressure, diastolic blood pressure, HDL cholesterol concentration, LDL cholesterol concentration, etc. The above exercise abilities include strength training suitability, aerobic exercise suitability, endurance exercise suitability, muscle development ability, short-distance sprinting ability, ankle injury risk, grip strength, and post-exercise recovery ability. The above eating habits may include appetite, satiety, sweet taste sensitivity, bitter taste sensitivity, salty taste sensitivity, etc. The above personal characteristics may include alcohol metabolism, alcohol dependence, nicotine metabolism, nicotine dependence, caffeine metabolism, caffeine dependence, insomnia, sleep habits / time, morning / evening type, etc. The above constitutional examination data can be obtained by a user's input to the electronic device (100) or a user's input to a server (not shown) connected to the electronic device (100). Next, the electronic device (100) can obtain service data. (S120) The above service data may include diet supplement data and diet exercise and meal plan data. The above diet supplement data may be data on a diet supplement product that helps reduce body fat. The diet supplement data may include information on the raw materials contained in the product, the product's effects, the product's ingredients, and precautions for the product, such as allergic reactions and side effects. The above raw materials include green tea extract, conjugated linoleic acid, garcinia cambogia extract, chitosan, chitooligosaccharide, green mate extract, green coffee bean extract, lemon balm extract, green apple extract, soybean germ extract, sesame leaf extract powder, aloe vera leaf, seaweed complex extract (xanthigen), ginkgo leaf extract, butter mixed powder, whey protein, and soy protein isolate. The above diet exercise data may include the type of exercise, duration, or frequency of exercise that is helpful for dieting, such as weight loss, skeletal muscle gain, or body fat reduction, depending on the customer's body composition or constitution. Examples of the above exercise types include, but are not limited to, walking, running, and squats. The above dietary data may include the type of diet supplement product that aids dieting based on the customer's body composition or constitution, as well as the method of taking the diet supplement product. The method of taking the diet supplement product may include the duration of taking the diet supplement product, the time of taking it, the type of food consumed with it, and the fasting period. The above service data can be obtained by a user's input to the electronic device (100) or a user's input to a server (not shown) connected to the electronic device (100). In contrast, the service data can be obtained through wired or wireless communication between the electronic device (100) or a server (not shown) connected to the electronic device (100) and a separate database. Next, the electronic device (100) inputs the customer data and the service data into a tagging artificial intelligence model (200) that outputs keywords for the customer data and the service data when the customer data and the service data are input, thereby obtaining keywords for the customer data and keywords for the service data from the tagging artificial intelligence model (200). (S130) Since the above customer data includes the body composition measurement data and the constitution examination data, the tagging artificial intelligence model (200) can set keywords for each of the body composition measurement data and the constitution examination data. As illustrated in FIGS. 3 to 5, the tagging artificial intelligence model (200) classifies the customer's body composition measurement data into three types: type C, in which body weight and body fat mass are higher than skeletal muscle mass; type I, in which skeletal muscle mass, body weight, and body fat mass are similar; and type D, in which skeletal muscle mass is higher than body weight and body fat mass, and can set keywords for each type. The above C type can be classified into a capital C type (see Figure 2(a)) in which body weight and body fat mass are above standard and skeletal muscle mass is standard, and a small C type (see Figure 2(b)) in which body weight and body fat mass are standard and skeletal muscle mass is below standard. The above type I can be classified into a lean type I (see Figure 3(a)) with body weight, skeletal muscle mass, and body fat mass below standard, a normal type I (see Figure 3(b)) with body weight, skeletal muscle mass, and body fat mass above standard, and an overweight type I (see Figure 3(c)) with body weight, skeletal muscle mass, and body fat mass above standard. The above D type can be classified into the normal D type (see Figure 4(a)) in which body weight and skeletal muscle mass are standard, and body fat mass is below the standard, and the hyperskeletal muscle D type (see Figure 4(b)) in which body weight is standard, skeletal muscle mass is above the standard, and body fat mass is below the standard. For example, the keywords of body composition measurement data set by the tagging artificial intelligence model (200) are as follows: The keywords in the capital letter C above may be weight loss, body fat reduction, and the goal may be achieving type I, and the keywords in the lowercase letter c above may be skeletal muscle increase, muscle gain, and the goal may be achieving type I, etc. Keywords for the above lean type I may include skeletal muscle gain, goal is to achieve type D, etc. Keywords for the above normal type I may include weight loss, body fat loss, skeletal muscle gain, goal is to achieve type D, etc. Keywords for the above overweight type I may include weight loss, body fat loss, goal is to achieve type D, etc. Additionally, the tagging artificial intelligence model (200) can set keywords based on the input customer's constitutional examination data. The keywords for the constitutional examination data can be set based on body nutrient concentrations, health management reference factors, exercise ability, eating habits, and individual characteristics. For example, the keywords of the physical examination data set by the tagging artificial intelligence model (200) are as follows: Keywords for the above body nutrient concentrations may include vitamin C concentration, vitamin D coenzyme Q10 concentration, magnesium concentration, zinc concentration, iron concentration, potassium concentration, arginine concentration, omega-3 fatty acid concentration, vitamin A concentration, vitamin B6 concentration, vitamin E concentration, vitamin K concentration, vitamin B12 concentration, selenium concentration, lutein & zeaxanthin concentration, etc. Keywords of the above health management reference elements may include degenerative arthritis inflammation susceptibility, motion sickness, uric acid level, neutral fat concentration, body fat percentage, body mass index, blood sugar, bone mass, abdominal obesity (hip-to-waist ratio), weight loss effect of exercise, possibility of weight regain after weight loss (yo-yo possibility), systolic blood pressure, diastolic blood pressure, HDL cholesterol concentration, LDL cholesterol concentration, etc. Keywords for the above exercise ability may include strength training suitability, aerobic exercise suitability, endurance exercise suitability, muscle development ability, short-distance sprinting ability, ankle injury risk, grip strength, and post-exercise recovery ability. Keywords for the above eating habits may include appetite, satiety, sweet taste sensitivity, bitter taste sensitivity, and salty taste sensitivity. Keywords for the above personal characteristics may include alcohol metabolism, alcohol dependence, nicotine metabolism, nicotine dependence, caffeine metabolism, caffeine dependence, insomnia, sleep habits / time, morning / evening type person, etc. In addition, since the service data includes the diet supplement data and the diet exercise and diet menu data, the tagging artificial intelligence model (200) can set keywords for each of the diet supplement data, the diet exercise data, and the diet menu data. Keywords for the above diet supplement data may be set for each of the raw materials contained in the diet supplement product, the ingredients of the product, the effects of the product, and the precautions of the product. For example, keywords for the above raw materials may be green tea extract, conjugated linoleic acid, garcinia cambogia extract, chitosan, chitooligosaccharide, green mate extract, green coffee bean extract, lemon balm extract, green apple extract, soybean germ extract, Glycyrrhiza uralensis leaf extract powder, aloe vera leaf, seaweed complex extract (xanthigen), ginkgo leaf extract, butter mixed powder, whey protein, soy protein isolate, etc. Keywords for the ingredients of the above product include theanine, selenium, niacin, zinc, pantothenic acid, protein, vitamin A, vitamin B1, vitamin B2, vitamin C, vitamin E, calcium, carbohydrates, and sugars. Keywords for the efficacy of the above product may include: body fat reduction, improved blood circulation, improved memory, stress relief care, immune function, goal is to achieve type D, energy production, weight loss, waist circumference reduction, BMI reduction, abdominal visceral fat reduction, goal is to achieve type I, skeletal muscle increase, muscle gain, etc. Keywords for precautions for the above product may include apple allergy, pregnant women, breastfeeding women, milk allergy, and nut allergy. The above diet exercise data, like the body composition measurement data, can have keywords set by type, such as capital letter C, small letter c, thin type I, average type I, and overweight type I. In this case, the keywords of the diet exercise data can be substantially the same as those of the body composition measurement data. In contrast, keywords for the above diet exercise data can be set for each type of exercise. For example, since the effects of each type of exercise are different, keywords for the diet exercise data can be set based on the effects. Since the above dietary data includes the type of the above dietary supplement product, the keywords of the above dietary data may be substantially the same as keywords for the ingredients of the product, keywords for the efficacy of the product, and keywords for precautions for the product among the keywords for the above dietary supplement data. Thereafter, the electronic device (100) inputs the keywords of the customer data and the keywords of the service data into an association analysis artificial intelligence model (210) that outputs service data matching the customer data through keyword association analysis when the keywords of the customer data and the keywords of the service data are input, thereby obtaining the service data matching the customer data from the association analysis artificial intelligence model (210). (S140) Specifically, the keywords of the body composition measurement data by type in the customer data and the keywords of the constitution examination data according to the concentration of nutrients in the body, health management reference elements, exercise ability, eating habits, and personal characteristics are analyzed in association with the keywords of the diet supplement data, thereby obtaining the diet supplement data that matches the body composition measurement data and the constitution examination data. For example, by analyzing the association of the keywords, the diet supplement data can be obtained by identifying keywords identical to the keywords of the body composition measurement data of a specific customer and the keywords of the constitutional examination data among the keywords of the diet supplement data. Likewise, the keywords for each type of the body composition measurement data in the customer data and the keywords for the body nutrient concentration, health management reference elements, exercise ability, eating habits, and personal characteristics of the constitution examination data are analyzed in association with the diet exercise data and the diet menu data to obtain the diet exercise and menu data that match the body composition measurement data and the constitution examination data. For example, by analyzing the association of the above keywords, the diet exercise and diet data can be used to identify keywords identical to the keywords of the body composition measurement data of a specific customer and the keywords of the constitution examination data among the keywords of the diet exercise and diet data, thereby obtaining the diet exercise data and the diet diet data having the above keywords. Thereafter, the electronic device (100) can recommend diet supplements and provide diet exercise and meal schedules for each customer based on the service data matched with the customer data. (S150) Specifically, if the customer data is matched with the service data, the diet supplements included in the matched service data can be identified. Therefore, diet supplement products that are suitable for the customer's body composition type and constitution, and free of allergic reactions or side effects, can be recommended. Additionally, if the customer data matches the service data, the diet exercise and meal plan for the matched service data can be confirmed. Therefore, an exercise schedule, such as the type and frequency of exercise that is suitable for the customer's body composition type and constitution and helpful for dieting, such as weight loss, skeletal muscle gain, and body fat reduction, can be provided. Furthermore, a diet meal plan, such as the intake period and intake time of the diet supplement product, the type of food consumed together, and the fasting period, can be provided, tailored to the customer's body composition type and constitution. According to the method of recommending diet supplements and providing diet exercise and meal plans based on the above AI model, the AI ​​models can be used to recommend diet supplements and provide diet exercise and meal plans tailored to each customer's constitution and health based on the customer data. Therefore, side effects of dieting can be minimized and the likelihood of successful dieting can be increased. FIG. 6 is a block diagram illustrating an artificial intelligence model-based clothing and product matching device according to one embodiment of the present invention. Referring to FIG. 6, the electronic device (100) may include a processor (110) and a memory (120). However, not all of the illustrated components are essential components. The electronic device (100) may be implemented with more components than the illustrated components, or may be implemented with fewer components. For example, the electronic device (100) may further include a user input unit (130), a communication unit (140), and a display (150). The processor (110) controls the overall operation of the electronic device (100) by executing one or more instructions in the memory (120). For example, the processor (110) can control the user input unit (130), the communication unit (140), the display (150), etc., by executing one or more instructions stored in the memory (120). In addition, the processor (110) can perform the operations and functions of the electronic device (100) described with respect to FIGS. 1 and 2 by executing one or more instructions stored in the memory (120). The processor (110) may be composed of one or more processors, and the one or more processors may be a general-purpose processor such as a CPU, an AP, a DSP (Digital Signal Processor), a graphics-only processor such as a GPU, a VPU (Vision Processing Unit), or an artificial intelligence (AI)-only processor such as an NPU. According to one embodiment, when the processor (110) is implemented with a plurality of processors or graphics-only processors or artificial intelligence-only processors such as an NPU, at least some of the plurality of processors or graphics-only processors or artificial intelligence-only processors such as an NPU may be mounted on the electronic device (100) and other electronic devices or servers connected to the electronic device (100). According to one embodiment, the processor (110) obtains customer data consisting of body composition measurement data and constitution examination data by executing one or more instructions, obtains service data consisting of diet supplement data and exercise and diet schedule data, inputs the customer data and the service data into a tagging artificial intelligence model that outputs keywords for the customer data and the service data when the customer data and the service data are input, thereby obtaining keywords for the customer data and keywords for the service data from the tagging artificial intelligence model, and inputs keywords of the customer data and keywords of the service data into an association analysis artificial intelligence model that outputs service data matching the customer data through keyword association analysis when keywords of the customer data and keywords of the service data are input, thereby obtaining the service data matching the customer data, and recommending diet supplements and providing diet exercise and diet schedules for each customer based on the service data matched with the customer data. Specific descriptions of the above customer data, the above service data, keywords for the customer data, and keywords for the service data are substantially the same as the descriptions of the customer data, service data, keywords for the customer data, and keywords for the service data with reference to FIGS. 1 to 5. In another embodiment, the body composition measurement data may include body weight, skeletal muscle mass, and body fat mass. According to another embodiment, the processor (110) executes one or more instructions to classify the customer's body composition measurement data into three types: type C, in which body weight and body fat mass are greater than skeletal muscle mass; type I, in which skeletal muscle mass, body weight, and body fat mass are similar; and type D, in which skeletal muscle mass is greater than body weight and body fat mass, and obtain keywords for the body composition measurement data for each type. In another embodiment, the constitutional examination data may include body nutrient concentrations, health management reference factors, exercise ability, eating habits, and personal characteristics. The memory (120) may include one or more instructions for controlling the operation of the electronic device (100). The memory (120) may include artificial intelligence models used by the electronic device (100), for example, a tagging artificial intelligence model (200) and an association analysis artificial intelligence model (210). According to one embodiment, the memory (120) may include, but is not limited to, at least one type of storage medium among, for example, a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), a RAM (Random Access Memory), a SRAM (Static Random Access Memory), a ROM (Read-Only Memory), an EEPROM (Electrically Erasable Programmable Read-Only Memory), a PROM (Programmable Read-Only Memory), a magnetic memory, a magnetic disk, and an optical disk. The user input unit (130) can receive user input for controlling the operation of the electronic device (100). For example, the user input unit (130) can include, but is not limited to, a key pad, a dome switch, a touch pad (contact electrostatic capacitance type, pressure resistive film type, infrared detection type, surface ultrasonic conduction type, integral tension measurement type, piezo effect type, etc.), a jog wheel, a jog switch, etc. The customer data and the service data can be entered through the user input unit (130). The communication unit (140) may include one or more communication modules for communication with a server (not shown), a body composition measurement device, etc. For example, the communication unit (140) may include at least one of a short-range communication unit or a mobile communication unit. Accordingly, the electronic device (100) may obtain the body composition measurement data through the communication unit (140). The short-range wireless communication unit may include, but is not limited to, a Bluetooth communication unit, a BLE (Bluetooth Low Energy) communication unit, a near field communication unit, a WLAN (Wi-Fi) communication unit, a Zigbee communication unit, an infrared (IrDA, infrared Data Association) communication unit, a WFD (Wi-Fi Direct) communication unit, an UWB (ultra wideband) communication unit, an Ant+ communication unit, etc. The mobile communication unit transmits and receives wireless signals with at least one of a base station, an external terminal, or a server on a mobile communication network. Here, the wireless signals may include various forms of data, such as voice call signals, video call signals, or text / multimedia message transmission and reception. The display (150) can display and output information processed in the electronic device (100). For example, the display (150) can display an interface for controlling the electronic device (100), an interface for indicating the status of the electronic device (100), etc. In addition, the display (150) can display the results of a customer-specific diet supplement recommendation performed in the electronic device (100) and a diet exercise and meal schedule. Figure 7 is a block diagram of a server according to one embodiment of the present invention. Referring to FIG. 7, according to one embodiment, a method of creating an application using a recommendation template provided based on an artificial intelligence model performed by an electronic device (100) can be performed in a server (300) that is connected to and capable of communication with the electronic device (100). The server (300) may include a communication interface (310), a database (320), and a processor (330). For example, the communication interface (310) of the server (300) according to the present disclosure may correspond to the communication unit (140) of the electronic device (100), the database (320) of the server (300) may correspond to the memory (110) of the electronic device (100), and the processor (330) of the server (300) may correspond to the processor (110) of the electronic device (100). In addition, the processor (330) of the server (300) may perform a method for recommending a diet supplement and providing a diet exercise and meal schedule based on the artificial intelligence model described with reference to FIGS. 1 to 5. The present invention utilizes AI models to recommend diet supplements tailored to each customer's constitution and health based on the aforementioned customer data, and to provide diet exercise and meal plans. Therefore, it not only minimizes side effects of dieting but also increases the likelihood of successful dieting. Although the present invention has been described above with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various modifications and changes may be made to the present invention without departing from the spirit and scope of the present invention as set forth in the claims below.

Claims

1. A method for an electronic device to provide diet supplement recommendations and diet exercise and meal schedules based on an artificial intelligence model, A step of acquiring customer data consisting of body composition measurement data and physical examination data; A step of acquiring service data consisting of diet supplement data and exercise and diet schedule data; A step of inputting the customer data and the service data into a tagging artificial intelligence model that outputs keywords for the customer data and the service data when the customer data and the service data are input, thereby obtaining keywords for the customer data and keywords for the service data from the tagging artificial intelligence model, respectively; A step of obtaining service data matching the customer data by inputting the keywords of the customer data and the keywords of the service data into an association analysis artificial intelligence model that outputs service data matching the customer data through keyword association analysis when keywords of the customer data and keywords of the service data are input; and A method for recommending diet supplements and providing diet exercise and diet schedules, characterized by including a step of recommending diet supplements and providing diet exercise and diet schedules for each customer based on the service data matched with the customer data.

2. In paragraph 1, In the step of acquiring the above customer data, A method for providing a diet supplement recommendation and a diet exercise and meal schedule, characterized in that the above body composition measurement data includes body weight, skeletal muscle mass, and body fat mass.

3. In paragraph 2, In the step of obtaining keywords for the customer data and keywords for the service data, respectively, A method for recommending a diet supplement and providing a diet exercise and meal plan, characterized in that the body composition measurement data of the customer is classified into three types: type C, in which body weight and body fat mass are greater than skeletal muscle mass; type I, in which skeletal muscle mass, body weight, and body fat mass are similar; and type D, in which skeletal muscle mass is greater than body weight and body fat mass, and keywords for the body composition measurement data are acquired for each type.

4. In paragraph 1, In the step of acquiring the above customer data, A method for providing a diet supplement recommendation and a diet exercise and meal schedule, characterized in that the above constitution test data includes body nutrient concentration, health management reference factors, exercise ability, eating habits, and personal characteristics.

5. In an electronic device for recommending diet supplements and providing diet exercise and meal schedules based on an artificial intelligence model, Memory that stores one or more instructions; and comprising at least one processor executing one or more of the above instructions; The processor executes one or more of the instructions, Obtain customer data consisting of body composition measurement data and physical examination data, Obtain service data consisting of diet supplement data and exercise and diet schedule data, When customer data and service data are input, keywords for the customer data and service data are input into a tagging artificial intelligence model that outputs keywords for the customer data and service data, thereby obtaining keywords for the customer data and keywords for the service data from the tagging artificial intelligence model, respectively. When keywords of customer data and keywords of service data are input, the service data matching the customer data is obtained by inputting the keywords of the customer data and keywords of the service data into an association analysis artificial intelligence model that outputs service data matching the customer data through keyword association analysis. An electronic device for recommending diet supplements and providing diet exercise and diet schedules, characterized in that it recommends diet supplements and provides diet exercise and diet schedules for each customer based on the service data matched with the customer data.

6. In paragraph 5, An electronic device providing a diet supplement recommendation and a diet exercise and meal schedule, characterized in that the above body composition measurement data includes body weight, skeletal muscle mass, and body fat mass.

7. In paragraph 6, The processor executes one or more of the instructions, An electronic device that provides a diet supplement recommendation and a diet exercise and meal schedule, characterized in that it classifies the customer's body composition measurement data into three types: type C, in which body weight and body fat mass are greater than skeletal muscle mass; type I, in which skeletal muscle mass, body weight, and body fat mass are similar; and type D, in which skeletal muscle mass is greater than body weight and body fat mass, and obtains keywords for the body composition measurement data by type.

8. In paragraph 5, An electronic device that provides a diet supplement recommendation and a diet exercise and meal schedule, characterized in that the above constitution test data includes body nutrient concentration, health management reference elements, exercise ability, eating habits, and personal characteristics.

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