KR20210019905A - Korean food health index deducting method and system - Google Patents

Abstract

In one aspect of the present invention, a method for deriving a K-index, which is an index of a Korean food diet, includes the steps of: providing preset questions related to the Korean food diet to a user; receiving the user′s response to the preset questions; and, as a step of deriving the K-index based on the user′s response, deriving the K-index from a total score given according to the user′s response to all predetermined questions in case a pre-allocated predetermined score is given when the user′s response satisfies a scoring criterion and the scoring criterion is set for each predetermined question. The preset questions may be grouped into at least one group based on at least one metabolic disease associated therewith.

Description

Korean food health index deducting method and system}

The present invention relates to a method and system for deriving a Korean food health index (hereinafter, K-index) indicating how much a user's eating habits conform to the traditional Korean diet.

The most important thing in human life is food, clothing, and shelter, of which food is particularly necessary, and dietary habits for proper intake of nutrients and control of calorie amount (Kcal) are the most basic important factors that protect the person's physical health.

In recent years, the dietary life of modern people has become abundant with economic growth, and foods that are satisfactory in terms of nutritional and taste can be easily obtained at any time. The average life expectancy of Koreans is also extending due to such improvements in diet and advances in medical technology. However, in recent years, due to the “westernization” of diet and the bias of nutritional balance, it has emerged as a major social problem due to the increase in lifestyle-related diseases such as cancer, circulatory system diseases, obesity, and allergic diseases from young to old.

The incidence of such lifestyle-related diseases is also epidemiologically pointed out that the close relationship with food or dietary habits is pointed out, and the need for health safety oriented food and food health maintenance disease prevention among the public is rapidly increasing.

However, the objective evaluation criteria and evaluation methods for the user's eating habits are in fact insufficient, and it is also difficult to recommend an improved diet tailored to users based on evaluation.

An object of the present invention is to propose a method of evaluating a user's diet more objectively and efficiently by deriving a K-index through questions specialized in Korean traditional diet.

In addition, it is an object of the present invention to propose a method for predicting/evaluating a user's disease risk by correlating a question for deriving the K-index with a specific disease to derive the K-index.

In addition, an object of the present invention is to recommend a customized improved diet based on the derived K-index.

The technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems that are not mentioned will be clearly understood by those of ordinary skill in the technical field to which the present invention belongs from the following description. I will be able to.

An aspect of the present invention provides a method for deriving a K-index, which is a Korean food diet index, comprising: providing a user with preset questions related to the Korean food diet; Receiving a response from the user to the preset questions; And deriving the K-index based on the user's response, wherein a score assignment criterion is set for each preset question, and if the user's response satisfies the score assignment criterion, a pre-allocated predetermined score is given. If yes, deriving a total score assigned according to the user's response to all preset questions as the K-index; Including, the preset questions may be grouped into at least one group based on at least one linked metabolic disease.

In addition, the preset questions may be defined in advance based on the number of times, the amount of intake, and/or the type of the food specialized for the Korean food diet.

In addition, the lower the score given according to the user's response for each group, the higher the probability that the user possesses or develops the metabolic disease associated with each group, and the user's response for each group Accordingly, the higher the assigned score, the lower the probability of developing metabolic diseases associated with each group may be evaluated by the user.

In addition, the method of deriving the K-index may include receiving information on metabolic diseases currently held by the user from the user; It may further include.

Also, the group associated with the received metabolic disease may be sub-grouped into a sub-group to which a subtraction weight is applied and a sub-group to which an addition weight is applied.

In addition, when information on metabolic diseases held by the user is input from the user, when assigning a score for a group associated with the metabolic disease of the user, the user's response to the subgroup applying the subtraction weight Accordingly, a score lower than the predetermined score may be applied, and a score higher than the predetermined score may be applied to the subgroup applying the added weight according to the user's response.

In addition, the higher the K-index, the closer the user's diet is to the Korean diet, and the lower the K-index, the farther the user's diet may be from the Korean diet.

In addition, recommending a customized diet to the user based on the K-index; It further includes, but the customized diet may be recommended in a direction in which the K-index value is high.

In addition, when the user has or is evaluated as having a high probability of developing a specific metabolic disease, the K-index derivation method includes information on the epigenetic diet material related to the specific metabolic disease into the epigenetic diet material database. Reading from; And recommending a customized diet including information on the epigenetic diet material to the user. It may further include.

In addition, the metabolic diseases may include obesity, hypertension and hyperlipidemia.

In addition, the preset questions may be defined in Table 1 of the present specification.

In addition, another aspect of the present invention is a K-index system for deriving a K-index, which is a Korean food diet index, comprising: an input/output unit for receiving an input from a user and outputting information; A database for storing data; A processor for controlling the input/output unit and the database; Including, wherein the processor provides preset questions related to the Korean food meal to the user through the input/output unit, receives the user's response to the preset questions through the input/output unit, and the user's response The K-index is derived based on, but a score assignment criterion is set for each preset question. If the user's response satisfies the score assignment criterion, a pre-allocated predetermined score is assigned, all preset questions The total score assigned according to the user's response to the K-index is derived as the K-index, and the preset questions may be grouped into at least one group based on at least one associated metabolic disease.

In addition, the preset questions may be defined in advance based on the number of times, the amount of intake, and/or the type of the food specialized for the Korean food diet.

In addition, the lower the score given according to the user's response for each group, the higher the probability that the user possesses or develops the metabolic disease associated with each group, and the user's response for each group Accordingly, the higher the assigned score, the lower the probability of developing metabolic diseases associated with each group may be evaluated by the user.

Also, the processor may receive information on metabolic diseases currently held by the user from the user through the input/output unit.

Also, the group associated with the received metabolic disease may be sub-grouped into a sub-group to which a subtraction weight is applied and a sub-group to which an addition weight is applied.

In addition, when information on metabolic diseases held by the user is input from the user, the processor, when assigning a score for a group associated with the metabolic disease of the user, the sub-group to which the subtraction weight is applied A score lower than the predetermined score may be applied according to the user's response, and a score higher than the predetermined score may be applied to the subgroup applying the added weight according to the user's response.

In addition, the higher the K-index, the closer the user's diet is to the Korean diet, and the lower the K-index, the farther the user's diet may be from the Korean diet.

Further, the processor may recommend a customized diet to the user based on the K-index through the input/output unit, but the customized diet may be recommended in a direction in which the K-index value is high.

In addition, when the user has or is evaluated as having a high probability of developing a specific metabolic disease, the processor reads epigenetic diet material information related to the specific metabolic disease from the database, and the epigenetic diet material A customized diet including information may be recommended to the user through the input/output unit.

In addition, the metabolic diseases may include obesity, hypertension and hyperlipidemia.

In addition, the preset questions may be defined in Table 1 of the present specification.

According to an exemplary embodiment of the present invention, since the user's diet is evaluated based on an objective K-index, the user's diet can be evaluated more objectively and efficiently, and comparison with others is possible.

In addition, according to an embodiment of the present invention, it is possible to predict in advance the risk of diseases or the like that may occur according to the user's diet, so that it is possible to recommend an efficient customized diet to prevent this.

The effects that can be obtained in the present invention are not limited to the above-mentioned effects, and other effects not mentioned will be clearly understood by those of ordinary skill in the art from the following description. .

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included as part of the detailed description to aid in understanding of the present invention, provide embodiments of the present invention, and together with the detailed description, the technical features of the present invention will be described.
1 is a table illustrating a question for deriving a K-index according to an embodiment of the present invention.
2 is a diagram showing an example of deriving K-index for a traditional diet developed by the Korea Food Research Institute.
3 is a diagram showing an example of deriving K-index for a control diet developed by the Korea Food Research Institute.
4 is a diagram showing a normal distribution diagram of K-index derived in FIGS. 2 and 3.
FIG. 5 is a graph showing changes in high-density lipoprotein cholesterol (HDL) levels according to a clinical diet for each K-index index according to an embodiment of the present invention.
6 is a diagram illustrating a method of deriving a K-index in connection with a disease according to an embodiment of the present invention, and predicting a user's onset disease through the K-index.
7 is a diagram illustrating an algorithm for recommending customized meals for each user based on a K-index according to an embodiment of the present invention.
8 is a diagram illustrating a K-index-based healthy diet recommendation service according to an embodiment of the present invention.
9 is a flowchart illustrating a method of deriving a K-index according to an embodiment of the present invention.
10 is a block diagram illustrating a configuration of a K-index derivation system according to an embodiment of the present invention.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. DETAILED DESCRIPTION OF THE INVENTION The detailed description to be disclosed hereinafter together with the accompanying drawings is intended to describe exemplary embodiments of the present invention, and is not intended to represent the only embodiments in which the present invention may be practiced. The following detailed description includes specific details to provide a thorough understanding of the present invention. However, one of ordinary skill in the art appreciates that the invention may be practiced without these specific details.

In some cases, in order to avoid obscuring the concept of the present invention, well-known structures and devices may be omitted, or may be shown in a block diagram form centering on core functions of each structure and device.

In the present specification, the K-index is a Korean food health index, and is defined as an index indicating how much a user's diet conforms to the traditional Korean diet. This K-index may be derived based on a user's response to a plurality of questions related to the Korean food diet. The reason for deriving this K-index in the present specification is that the Korean traditional diet has a very good effect on improving/alleviating metabolic diseases (eg, obesity, high blood pressure, hyperlipidemia, etc.), so whether the user's diet conforms to the traditional diet. It is to evaluate objectively through the K-index and to recommend a more traditional diet to users according to the evaluation results.

This K-index may be evaluated through a plurality of questions derived through various studies/surveys. In this specification, FIG. 1 is illustrated as an example of a plurality of questions, but the present specification is not limited thereto.

1 is a table illustrating a question for deriving a K-index according to an embodiment of the present invention.

Referring to FIG. 1, a total of 14 questions for deriving the K-index may be formed. The questions in this drawing correspond to questions for evaluating how well the current user's diet matches the Korean traditional diet (ie, K-index).

The questions in this example were selected to derive the K-index using the traditional/controlled clinical diet developed by the Korea Food Research Institute, and the national health nutrition information and national health examination information, and correspond to one embodiment and the other It goes without saying that questions can be used.

The K-index derivation method/system of this specification can present a plurality of questions to the user to confirm whether it conforms to the Korean traditional diet, and can derive the K-index by converting the user's response to the score. have.

For example, in the case of the embodiment of FIG. 1, the user can respond to a total of 14 questions presented by the K-index derivation method/system, and the K-index derivation method/system is for each question in FIG. K-index can be derived by scoring according to the defined evaluation unit and scoring criteria. Specifically, if the user answers “Yes” in question 1, 1 point, and when responding that he/she consumes more than one type of multigrain rice/brown rice in question 2, 1 point may be given. In this case, the K-index is' It can be derived as '2 points (=1 point + 1 point)'. A specific embodiment of a method/system for deriving a K-index based on the question of FIG. 1 will be described later in detail with reference to FIGS. 2 and 3.

The higher the K-index is, the closer the user's diet is to Korean/traditional food, so it can be classified as a healthy diet. Therefore, the K-index derivation method/system can recommend a user-customized diet based on the K-index derived as described above, which will be described later with reference to FIG. 8.

Hereinafter, for convenience of explanation, a method/system for deriving a K-index based on the 14 questions in FIG. 1 is described, but is not limited thereto, and the questions for deriving the K-index are It goes without saying that it can consist of a variety of questions to efficiently derive whether it matches.

2 to 4 are diagrams showing an example of deriving a K-index and a result of distribution of K-index by diet according to an embodiment of the present invention.

More specifically, FIG. 2 is an example of deriving K-index for a traditional diet developed by the Korea Food Research Institute, FIG. 3 is an example of deriving K-index for a control diet developed by the Korea Food Research Institute, and FIG. And the normal distribution map of K-index derived in 3, respectively. Questions 1 to 14 in FIGS. 2 and 3 correspond to the 14 questions in FIG. 1.

Referring to Figures 2 and 3, it can be seen that the K-index has different characteristics for each diet. More specifically, in the case of the traditional diet, the K-index has a value between 8 and 11, so the deviation is not severe and the average value (9=45/5) is high, whereas in the case of the control diet, the K-index is 2-7. It has a value between, so the deviation is severe and the average value (4.14=29/7) is low.

This difference is also confirmed by a normal distribution chart. Referring to FIG. 4, the K-index of the traditional diet is centered on '10', but has a high concentration distribution at the center, and the K-index of the control diet is '5'. It can be seen that it is centered, but has a low concentration in the center.

As described above, by deriving the K-index proposed in this specification for the traditional and control diet developed by the Korea Food Research Institute, it can be confirmed that the K-index is actually effective as an objective index indicating the degree of conformity with the Korean traditional diet.

Hereinafter, the results of clinical trials when a subject ingested such a clinical diet for each K-index index will be described later.

FIG. 5 is a graph showing changes in high-density lipoprotein cholesterol (HDL) levels according to a clinical diet for each K-index index according to an embodiment of the present invention. More specifically, the experiment of FIG. 5 includes clinical subjects having a high K-index value classified as a traditional diet (n=10) and clinical subjects having a low K-index value classified as a control diet (n=10). It is a graph showing the change in HDL levels for 2 weeks. Through this graph, it is possible to verify the validity of the classification of traditional/control meals according to the K-index and confirm the effect of reducing HDL levels according to consumption of traditional meals.

Referring to the graph of FIG. 5, the HDL of subjects who ingested the control diet (diet with a low K-index) did not change significantly after 2 weeks, but subjects who consumed the traditional diet (diet with a high K-index derived) It could be confirmed that their HDL decreased significantly after 2 weeks. In other words, it was confirmed that the K-index can be an objective indicator for classifying traditional meals, and that HDL levels decrease when eating a diet classified with a high K-index value. In addition, although not shown in this graph, it was confirmed that the insulin and blood sugar levels of the subjects who consumed the traditional diet also decreased.

6 is a diagram illustrating a method of deriving a K-index in connection with a disease according to an embodiment of the present invention, and predicting a user's onset disease through the K-index.

Referring to FIG. 6, first, as an embodiment, a specific question item may be associated with a specific disease. In other words, all of the question items may be grouped into at least one group according to diseases associated with each item.

For example, as shown in Figure 6,

-In the case of obesity, question items 1, 5, 10, 11, 13 and 14 in Fig. 1 (grouped into the first group),

-In the case of hypertension, question items 4, 5, and 10 of FIG. 1 (grouped into a second group),

-In the case of hyperlipidemia, question items 10, 11, and 14 of FIG. 1 can be linked to (grouped into a third group), respectively.

In this case, the lower the K-index score derived for each group's question, the higher the likelihood of having or developing a disease associated with the group can be predicted.

For example, if a user answers “no” to all six question items of the first group and '0' is derived as the K-index value for the first group, the user is currently obese. It can be predicted. In addition, it can be predicted that the user is not currently obese when K-index = 1 to 6 points are obtained for the six question items of the first group, but in the case of K-index = 1 to 2 points, the user is Although it is not currently obese, it can be predicted that there is a high possibility of becoming obese.

That is, the question items for deriving the K-index may be grouped into at least one group according to the disease linked by each item, and whether or not a disease corresponding to each group is possessed based on the K-index calculated for each group or The likelihood of an outbreak can be predicted.

And/or, as an independent/additional embodiment, the K-index may be derived by applying subtraction/addition weights to question items for deriving the K-index according to the user's current disease.

For example, as shown in Figure 6,

-In the case of obesity, subtraction weights may be applied to question items 1, 5, 10, 11, 13, and 14 of FIG. 1, and additive weights may be applied to question items 2, 4, 6, 9 and 12 of FIG. 1, respectively. ,

-In the case of hypertension, subtraction weights may be applied to question items 4, 5, and 10 of FIG. 1, and additive weights may be applied to question items 2, 6, and 12 of FIG. 1, respectively,

-In the case of hyperlipidemia, subtraction weights may be applied to question items 10, 11, and 14 of FIG. 1, and additive weights may be applied to question items 2, 6, and 12 of FIG. 1, respectively.

Accordingly, in the case of a user with the disease, even if all questions are answered in the same manner as a user without the disease, the K-index value may be derived differently (usually smaller).

For example, in the case of obese users, 0.8 points are added as the K-index value when'yes' to question 1 is answered, and even if'yes' to question 1 is the same for non-obese users, K-index Since 1 point is added as a value, a difference of 0.2 points occurs.

The question item to which the subtraction/addition weight is applied and/or the degree of the subtraction/addition weight may be updated to a different value by the administrator according to the correlation analysis between diet and disease and research results.

According to the present embodiment, since the user's disease is taken into account when the K-index is derived, it is possible to recommend not only the user's eating habits but also a diet (ie, a user/patient customized diet) for disease improvement.

To summarize the embodiment of FIG. 6, the question items for deriving the K-index may be grouped into at least one group according to the associated disease, and further grouped into subgroups of the subtraction/addition question items within each group. I can. The user's current diet (whether or not it is close to a traditional diet), disease, and possible disease can be predicted through the K-index obtained according to the user's response to the grouped question items, and a customized diet is recommended based on this. Can be.

7 is a diagram illustrating an algorithm for recommending customized meals for each user based on a K-index according to an embodiment of the present invention.

As described above in FIG. 6, the K-index derivation question items may be grouped into at least one group according to the associated disease, and subtraction/addition maximum values are applied for each question item within each group to derive the final K-index. Can be.

To this end, the K-index derivation method/system can receive information on the disease possessed by the user in advance, and recommend a user-customized diet by using the acquired disease information and the derived K-index. At this time, the recommended diet may be designed in a direction that can alleviate/improve a user's disease while increasing the K-index.

To this end, the K-index derivation method/system can use information on dietary material for epigenetic control by disease and information on food material for improved food by disease, and the information may be stored in advance in a database (Data Base; DB). . Here, the epigenetic control material refers to a material for treating metabolic diseases such as obesity, hypertension, and hyperlipidemia by being activated or inactivated as a specific gene is methylated when continuously ingested.

Examples of such epigenetic control materials include the following materials:

-Sesame seeds, pepper, spinach, Brazil nuts, sunflower seeds, liver, milk, egg yolk, soybeans, beets, sugar beets, red wine, broccoli, dietary fiber, garlic, fish, shellfish, leafy vegetables, whole grains, fermented beans, yeast

Therefore, the K-index derivation method/system design/recommends a diet using information on epigenetic control materials for each disease and/or information on improved food materials for each disease, improving the user's diet to the traditional style and at the same time improving the user's disease. Or it can improve / alleviate a disease that is likely to develop.

8 is a diagram illustrating a K-index-based healthy diet recommendation service according to an embodiment of the present invention.

Referring to FIG. 8, the K-index derivation method/system may receive basic information (especially possession disease information) and/or a response to a question from a user, and derive a K-index based on this. In addition, the K-index derivation method/system may recommend a user-customized diet based on user input information and/or the derived K-index.

Basically, the K-index derivation method/system can recommend a diet (that is, a traditional diet) in the direction of increasing the K-index. To this end, the K-index derivation method/system includes various information stored in the DB in advance (e.g., nutrients, diet pattern evaluation, traditional diet eating habits evaluation, epigenetic control diet material, diet information, etc.) and diet recommendations multidimensional. Analysis, diet/bio monitoring, and healthy diet recommendation algorithms can be used.

This K-index derivation method/system can be serviced to users in the form of web or application.

The user's dietary evaluation information and/or K-index-based recommended diet information obtained by the K-index derivation method/system may be shared with the Korean Food Research Institute, public health centers, related companies, and/or health examination/promotion centers.

9 is a flowchart illustrating a method of deriving a K-index according to an embodiment of the present invention. In relation to this flowchart, at least one step may be deleted or a new step may be added, and the description of the above-described embodiments may be applied in the same/similar manner.

First, the K-index derivation system may provide the user with preset questions related to the Korean food diet (S901). The preset questions are defined in advance based on the number of times, intake amounts, and/or types of foods that are specific to the Korean diet, and can be grouped into at least one group based on at least one metabolic disease linked to each question. have. As an example of such a preset question, there may be 14 items of FIG. 1, and as metabolic diseases, obesity, high blood pressure, and hyperlipidemia may exist.

Next, the K-index derivation system may receive a user's response to preset questions (S902).

Next, the K-index derivation system may derive the K-index based on the user's response (S903). More specifically, a score assignment criterion is set for each preset question, and if the user's response satisfies the score assignment criterion, a predetermined score assigned in advance may be assigned. In this case, the K-index derivation system may derive the total score assigned according to the user's response to all preset questions as K-index. A specific embodiment of deriving the K-index is as described above with reference to FIGS. 1 to 3.

The lower the score given according to the user's response for each group, the higher the probability that the user possesses or develops the metabolic disease associated with each group, and the score given according to the user's response for each group is evaluated. It may be evaluated that the higher the value is, the lower the probability of occurrence of the metabolic disease associated with each group is not possessed by the user.

Although not shown in this flowchart, the K-index derivation system may receive information on metabolic diseases currently held by the user from the user. In this case, the group associated with the received metabolic disease may be sub-grouped into a subgroup to which a subtraction weight is applied and a subgroup to which an addition weight is applied. When the user inputs information on metabolic diseases held by the user, the K-index derivation system applies subtraction weights to the user's response when assigning a score for the group associated with the user's metabolic disease. Accordingly, a score lower than the predetermined score may be applied, and a score higher than the predetermined score may be applied to a subgroup to which an additive weight is applied, according to a user's response. This embodiment is as described above with reference to FIGS. 6 and 7 above.

The higher the K-index, the closer the user's diet is to the Korean diet, and the lower the K-index, the farther the user's diet is from the Korean diet. Based on this, the K-index derivation system can recommend a customized diet to the user based on the final derived K-index. In this case, the K-index derivation system may recommend a customized diet to the user in a direction in which the K-index value is derived high.

If the user has or is evaluated as having a high probability of developing a specific metabolic disease, the K-index derivation system reads the information on the epigenetic diet material related to the specific metabolic disease from the epigenetic diet material database. It is possible to recommend a customized diet including material information to users. An example of information on an epigenetic dietary material is as described above with reference to FIGS. 6 and 7.

10 is a block diagram illustrating a configuration of a K-index derivation system according to an embodiment of the present invention.

In this specification, the K-index derivation system 1000 may correspond to a terminal, an electronic device, an application server, a web server, and the like according to an implementation method of the K-index derivation method. Hereinafter, for convenience of explanation, it is collectively referred to as the K-index derivation system 1000.

Referring to FIG. 10, the K-index derivation system 1000 is an input/output unit 1010, a memory unit/DB 1020, and a processor 1030, which are at least one means for performing the embodiments described herein. , And/or a communication unit (not shown).

The input/output unit 1010 is a component for receiving various types of input from a user and outputting certain information/data, and may be referred to as a user interface unit. The input/output unit 1010 may include at least one sensor for sensing a user's input, and may include a display for outputting data/information. For example, the input/output unit 1010 may correspond to a display capable of sensing a user's touch input.

The memory unit/DB 1020 may store various types of information/data. The memory unit/DB 1020 includes a flash memory, a hard disc, a memory card, a ROM (Read-Only Memory), a RAM (Random Access Memory), a memory card, and an EEPROM (Electrically Erasable Programmable Read-Only Memory), Programmable Read-Only Memory (PROM), magnetic memory, magnetic disk, and optical disk. In particular, in the present specification, the memory unit/DB 1020 may include the nutrient/diet pattern evaluation DB, Korean food eating habit evaluation DB, epigenetic diet material DB, and/or diet DB, etc. described above in FIG. 8.

The processor 1030 implements the functions, embodiments, and/or methods proposed herein. To this end, the processor 1030 may communicate with the input/output unit 1010, the memory unit/DB 1020, and/or the communication unit.

The communication unit (not shown) may include one or more modules that enable wired/wireless communication with a remote server. For example, the communication unit may include a broadcast reception module, a mobile communication module, a wireless Internet module, and a short-range communication module.

The embodiment according to the present invention may be implemented by various means, for example, hardware, firmware, software, or a combination thereof. In the case of implementation by hardware, an embodiment of the present invention is one or more ASICs (application specific integrated circuits), DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs ( field programmable gate arrays), processors, controllers, microcontrollers, microprocessors, etc.

In the case of implementation by firmware or software, an embodiment of the present invention may be implemented in the form of a module, procedure, or function that performs the functions or operations described above. The software code can be stored in a memory and driven by a processor. The memory may be located inside or outside the processor, and may exchange data with the processor through various known means.

It is obvious to those skilled in the art that the present invention can be embodied in other specific forms without departing from the essential features of the present invention. Therefore, the above detailed description should not be construed as restrictive in all respects, but should be considered as illustrative. The scope of the present invention should be determined by rational interpretation of the appended claims, and all changes within the equivalent scope of the present invention are included in the scope of the present invention.

1000: K-index derivation system
1010: input/output unit
1020: memory unit/DB
1030: processor

Claims (22)

In the method of deriving K-index, which is a Korean food diet index,
Providing a user with preset questions related to the Korean food diet;
Receiving a response from the user to the preset questions; And
Deriving the K-index based on the user's response,
A score assignment criterion is set for each preset question, and if the user's response satisfies the score assignment criterion, a pre-allocated predetermined score is given, the assigned according to the user's response to all preset questions. Deriving a total score as the K-index; Including,
The predetermined questions are grouped into at least one group based on at least one linked metabolic disease. The method of claim 1,
The predetermined questions are defined in advance based on the number of times, intake amount, and/or the type of the food specialized in the Korean food diet. The method of claim 2,
The lower the score assigned to each group according to the user's response, the higher the probability that the user possesses or develops the metabolic disease associated with each group, and is given according to the user's response for each group. A method of deriving K-index, the higher the score is, the less the user does not possess the metabolic disease associated with each group and the probability of developing it is also low. The method of claim 3,
Receiving information on metabolic diseases currently held by the user from the user; Further comprising, the K-index derivation method. The method of claim 4,
The group associated with the received metabolic disease is sub-grouped into a sub-group to which a subtraction weight is applied and a sub-group to which an addition weight is applied. The method of claim 5,
When the user inputs information on metabolic diseases possessed by the user,
When assigning a score to a group associated with the user's metabolic disease, a score lower than the predetermined score is applied to the sub-group to which the subtraction weight is applied, according to the user's response, and to the sub-group to which the added weight is applied, Applying a score higher than the predetermined score according to the user's response, K-index derivation method. The method of claim 6,
A method of deriving a K-index, wherein the higher the K-index, the closer the user's diet is to the Korean diet, and the lower the K-index, the farther the user's diet is from the Korean diet. The method of claim 7,
Recommending a customized diet to the user based on the K-index; Further include,
The customized diet is recommended in a direction in which the K-index value is high. The method of claim 8,
If the user has a specific metabolic disease or is evaluated as having a high probability of developing it,
Reading epigenetic diet material information related to the specific metabolic disease from an epigenetic diet material database; And
Recommending a customized diet including information on the epigenetic diet material to the user; Further comprising, the K-index derivation method. The method of claim 2,
The metabolic disease, including obesity, hypertension and hyperlipidemia, K-index derivation method. The method of claim 2,
The predetermined questions are defined in Table 1 below, K-index derivation method.
[Table 1]

In the K-index system that derives K-index, which is a Korean food diet index,
An input/output unit for receiving a user's input and outputting information;
A database for storing data;
A processor for controlling the input/output unit and the database; Including,
The processor,
Providing preset questions related to the Korean food diet to the user through the input/output unit,
Receiving the user's response to the preset questions through the input/output unit,
Derive the K-index based on the user's response,
A score assignment criterion is set for each preset question, and if the user's response satisfies the score assignment criterion, a pre-allocated predetermined score is given, the assigned according to the user's response to all preset questions. The total score is derived as the K-index,
The predetermined questions are grouped into at least one group based on at least one associated metabolic disease, K-index derivation system. The method of claim 12,
The preset questions are defined in advance based on the number of times, intake amount, and/or the type of the food specialized in the Korean food diet. The method of claim 13,
The lower the score assigned to each group according to the user's response, the higher the probability that the user possesses or develops the metabolic disease associated with each group, and is given according to the user's response for each group. The K-index derivation system is evaluated that the higher the score is, the less the user does not possess the metabolic disease associated with each group and the probability of developing it is also low. The method of claim 14,
The processor, through the input/output unit, receives information on metabolic diseases currently held by the user from the user through the input/output unit. The method of claim 15,
The group associated with the received metabolic disease is sub-grouped into a sub-group to which a subtraction weight is applied and a sub-group to which an addition weight is applied. The method of claim 16,
When receiving information about metabolic diseases held by the user from the user, the processor,
When assigning a score to a group associated with the user's metabolic disease, a score lower than the predetermined score is applied to the sub-group to which the subtraction weight is applied, according to the user's response, and to the sub-group to which the added weight is applied, A K-index derivation system for applying a score higher than the predetermined score according to the user's response. The method of claim 17,
The higher the K-index, the closer the user's diet is to the Korean diet, and the lower the K-index, the farther the user's diet is from the Korean diet. The method of claim 18,
The processor, based on the K-index, recommends a customized diet to the user through the input/output unit, and the customized diet is recommended in a direction in which the K-index value is high. The method of claim 19,
When the user has a specific metabolic disease or is evaluated as having a high probability of developing, the processor,
A K-index derivation system for reading epigenetic dietary material information related to the specific metabolic disease from the database, and recommending a customized diet including the epigenetic dietary material information to the user through the input/output unit. The method of claim 13,
The metabolic disease, including obesity, hypertension and hyperlipidemia, K-index derivation system. The method of claim 13,
The preset questions are defined in Table 1 below, K-index derivation system.
[Table 1]

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