JP2021086556A - Information processing device, information processing method, and computer program - Google Patents

Abstract

To provide an information processing device, an information processing method, and a computer program which can improve the enterobacterial composition of each of subjects.SOLUTION: An information processing device 1 has: an acquisition unit 111 that acquires first information including enterobacterial information of a subject; a determination unit 112 that determines enterobacterial control information recommended to the subject on the basis of the first information, second information on enteric bacteria, and third information on enterobacterial nutrition, food components, or supplements, and an output unit 116 that outputs the determined control information.SELECTED DRAWING: Figure 2

Description

The present invention relates to an information processing device, an information processing method, and a computer program for improving the intestinal environment of a subject.

There are various types of bacteria in the intestine, and these are the health of the human body such as digestive organs, circulatory organs, immunity, and brain through the production of metabolites by each vital activity and the interaction with the host human cells. It is known to have a great effect on. Bacterial communities that live in the intestine are called the intestinal flora and are one of the main factors that make up the intestinal environment. By changing this, it is expected to improve the health condition and prevent diseases. , Various pharmaceuticals, food compositions and supplements have been developed.
Patent Document 1 discloses a food composition for improving the intestinal bacterial flora, which contains plant-derived glucoraphanin (GR) and / or sulforaphane (SFN) as an active ingredient.

JP-A-2017-178805

The optimal intestinal environment for maintaining each person's health depends on the acquired constitution, age, lifestyle, and preferences, in addition to the genetic information possessed by each person, so the optimal intestinal environment for each person is determined. It is difficult to do, and general intestinal environment improvement information is not always valid. The food composition of Patent Document 1 is not prepared for each consumer and does not improve the intestinal bacterial flora of each consumer.
The present invention has been made in view of such circumstances, and it is intended to provide an information processing device, an information processing method, and a computer program capable of improving the composition of bacteria in the intestine for each subject. The purpose.

The information processing apparatus according to one aspect of the present invention includes an acquisition unit that acquires first information including intestinal bacterial information of a subject, the first information, second information related to intestinal bacteria, and intestinal bacteria. Based on the third information related to the nutrition, food components, or supplements of the above, a specific part for specifying the adjustment information of the intestinal bacteria recommended for the subject and an output part for outputting the specified adjustment information. Be prepared.

The information processing method according to one aspect of the present invention acquires the first information including the intestinal bacterium information of the subject, the first information, the second information concerning the intestinal bacterium, and the nutrition of the intestinal bacterium. Based on the third information relating to the ingredients of the food or the supplement, the adjustment information of the intestinal bacteria recommended for the subject is specified, and the specified adjustment information is output.

The computer program according to one aspect of the present invention acquires the first information including the intestinal bacterium information of the subject, the first information, the second information concerning the intestinal bacterium, and the nutrition and food of the intestinal bacterium. Based on the component of the above or the third information relating to the supplement, the adjustment information of the intestinal bacteria recommended for the subject is specified, and the computer is made to execute the process of outputting the specified adjustment information.

According to the present invention, it is possible to improve the composition of the type, amount (ratio), combination, etc. of bacteria in the intestine for each subject.

It is a schematic diagram which shows an example of the structure of the information processing system which concerns on Embodiment 1. FIG. It is a block diagram which shows an example of the structure of an information processing apparatus. It is explanatory drawing which shows an example of the record layout of the target person information DB. It is explanatory drawing which shows an example of the record layout of the intestinal bacterial flora DB. It is explanatory drawing which shows an example of the record layout of the auxotrophy DB. It is explanatory drawing which shows an example of the record layout of the time nutrition information DB. It is explanatory drawing which shows an example of the record layout of the recipe DB. It is explanatory drawing which shows an example of the record layout of a menu DB. It is explanatory drawing which shows an example of the record layout of the processed food DB. It is explanatory drawing which shows an example of the record layout of a supplement DB. It is explanatory drawing which shows an example of the record layout of adjustment DB. It is explanatory drawing which shows the decomposition by the fungus of the component of vegetable A such as dietary fiber. It is a flowchart which shows the procedure of the specific processing of adjustment in adjustment DB by a control part. It is explanatory drawing which shows an example of the record layout of the recipe history DB. It is a block diagram which shows an example of the configuration of a terminal. It is a block diagram which shows an example of the configuration of a terminal. It is a block diagram which shows an example of the structure of a vegetable / supplement vending machine. It is a flowchart which shows the procedure of the specific processing of adjustment by a control part. It is a flowchart which shows the procedure of the process which identifies and displays a recipe, a menu, a processed food, and a supplement. It is explanatory drawing which shows an example of the display screen of a recipe. It is explanatory drawing which shows an example of the display screen of a menu. It is explanatory drawing which shows an example of the display screen of a processed food. It is explanatory drawing which shows an example of the display screen of a supplement. It is a flowchart which shows the processing procedure of the display of a vegetable / supplement. It is explanatory drawing which shows an example of the display screen of the vegetable / supplement vending machine. It is a block diagram which shows an example of the structure of the information processing apparatus which concerns on Embodiment 2. It is a schematic diagram which shows an example of a learning model. It is a flowchart which shows the procedure of the adjustment acquisition process by a control unit. It is explanatory drawing which shows an example of the record layout of the 1st DB. It is explanatory drawing which shows an example of the record layout of 2nd DB. It is a schematic diagram which shows an example of the 1st learning model and the 2nd learning model. It is a flowchart which shows the procedure of the adjustment acquisition process by a control unit. It is a block diagram which shows an example of the structure of the information processing apparatus which concerns on Embodiment 3. It is a schematic diagram which shows an example of a learning model. It is a flowchart explaining the generation procedure of a learning model. It is a flowchart which shows the processing of the subroutine of reward acquisition. It is a flowchart which shows the procedure of the specific processing of adjustment using a learning model.

(Outline of Embodiment)
The information processing apparatus according to the embodiment includes an acquisition unit that acquires first information including intestinal bacterial information of the subject, the first information, second information related to intestinal bacteria, and nutrition of intestinal bacteria. It includes a specific unit that specifies the adjustment information of the intestinal bacteria recommended for the subject based on the third information related to the food ingredient or the supplement, and an output unit that outputs the specified adjustment information.
According to the above configuration, adjustment information for adjusting the composition such as the type, amount (ratio), and combination of intestinal bacteria can be specified for each subject, and the intestinal environment of the subject is good. It will be improved.

Intestinal bacteria in the present specification include eukaryotes and archaea, which are not generally classified as bacteria, in addition to bacteria living in the intestine.

The first information preferably further includes at least one of a group consisting of biological information other than the intestinal bacterial information of the subject, living information of the subject, and preference information of the subject.
Examples of biological information include stool properties, genomic information, gut microbiota, antibody reactions, metabolites such as bile acids and cholesterol, metabolites such as organic acids, pH, and electrical conductivity. The above-mentioned biological information can be obtained, for example, by analyzing excrement such as feces and urine of the subject. Further, the biometric information can also be acquired by using, for example, a capsule endoscope or the like. The genomic information of the subject can be obtained not only from excrement such as feces and urine, but also by collecting and analyzing cells in the oral cavity. By grasping these biological information, it is possible to grasp the intestinal environment of the subject such as the type and amount of intestinal bacteria and the ratio of various intestinal bacteria.
The preference information includes the taste of meat / fish of the subject, the likes and dislikes of vegetables, and the like. By acquiring the preference information, it becomes possible to specify the adjustment information that is more easily accepted by the target person.
Life information includes gender, age, height, weight, normal fever, eating habits, drinking, smoking habits, blood analysis, blood pressure, medical history, medication history, supplement taking history, family structure, family history, favorite menu, seasoning. And so on.

The second information preferably includes information on the degree of contribution of the intestinal bacteria to human health, or information on the intestinal bacteria of a healthy person or a sick person. Examples of the information related to the degree of contribution to health include information on the classification and score of bacteria, which will be described later. Examples of the intestinal bacterial information of a healthy person or a sick person include information on the intestinal bacterial flora by age and gender. Gut microbiota information preferably includes information on both healthy and sick individuals.

The third information is a group consisting of nutritional requirement, saccharification, temporal nutritional information of intestinal bacteria, information on adjustment of composition of intestinal bacteria, nutritional component information of foods, and component information of supplements. It is preferable to include at least one.

What kind of nutritional component is required for the growth of bacteria is called auxotrophy. If you want to increase the amount of a bacterium, and the bacterium has a biotin requirement, the subject needs to eat a food high in biotin.
Which sugar is required for the growth of bacteria is called saccharification. For example, B. ruminantium can grow using mannitol as a carbon source, but mannitol is abundant in celery. If the subject has a large amount of B. ruminantium and the bowel upset is due to B. ruminantium, the subject should avoid celery.

The information related to the adjustment of the composition of intestinal bacteria is not particularly limited as long as it is information on improvement of the intestinal environment, but for example, academic paper information on ingestion of specific foods and supplements and increase / decrease of specific intestinal bacteria, Culture experiment data that a predetermined intestinal bacterium increases in a culture solution containing a specific intestinal bacterium or food, information on the relationship between intestinal pH and the proliferative power of the intestinal bacterium, body temperature and the proliferative power of the intestinal bacterium Related information can be mentioned. In addition, the intestinal environment is constructed by a symbiotic relationship between intestinal bacteria, and the amount of other intestinal bacteria in a symbiotic relationship can be indirectly adjusted by changing the amount of a certain intestinal bacterium. Therefore, the information related to the adjustment includes the above-mentioned information on the symbiotic relationship of the intestinal bacteria. The symbiotic relationship is, for example, a relationship in which a metabolite excreted by one intestinal bacterium serves as a nutrient source for another intestinal bacterium.
In addition, antibiotics are components that suppress the growth of intestinal bacteria and have a killing effect, and are known to upset the balance of the intestinal bacterial flora. Therefore, information on the growth suppression and killing effects of intestinal bacteria by antibiotics and change data on the intestinal bacterial flora disturbed by antibiotics before and after treatment are also important as information related to the above adjustment.

The adjustment information includes at least one of a group consisting of information on nutritional components to be ingested or avoided by the subject, information on foods to be ingested or avoided by the subject, and information on supplements to be ingested or avoided. Is preferable.
According to the above configuration, the type, amount (ratio), and combination of intestinal bacteria can be improved satisfactorily and efficiently.

Plant-derived foods are preferable as foods, and vegetables are more preferable among plant-derived foods.
Vegetables contain secondary metabolites such as dietary fiber, vitamins, sugars, minerals, alkaloids, terpenoids, and peptides, which are components that have the effect of growing or reducing certain bacteria.

In the above-mentioned information processing apparatus, based on the second information and the third information, a database for storing adjustment information is provided corresponding to the first information, and according to the first information acquired by the acquisition unit. Adjustment information may be specified from the database.
According to the above configuration, adjustment information can be easily and satisfactorily specified from the database.

The above-mentioned information processing apparatus includes a database that stores adjustment information in response to the first information based on the second information and the third information, and the specific unit uses the data of the database as teacher data. When the first information is input, the adjusted information may be specified by inputting the acquired first information of the target person into the learning model that outputs the adjustment information.
According to the above configuration, the adjustment information can be easily and satisfactorily specified by using the learning model.

In the above-mentioned information processing apparatus, the specific unit identifies the composition of the intestinal bacterium suitable for the subject based on the first information and the second information, and the identified composition of the intestinal bacterium and the identified composition of the intestinal bacterium. The adjustment information may be specified based on the third information.
According to the above configuration, after identifying the composition of intestinal bacteria suitable for the subject based on the information on the intestinal bacteria of the subject, it is possible to satisfactorily identify the food or supplement for obtaining the composition. ..

In the above-mentioned information processing apparatus, based on the second information, the first database that stores the composition of intestinal bacteria suitable for the subject corresponding to the first information, and the intestine based on the third information. A second database that stores adjustment information corresponding to the composition of bacteria is provided, and the specific unit uses the data of the first database as teacher data, and when the first information is input, the intestine suitable for the subject. The composition of the intestinal bacterium is specified by inputting the acquired first information of the subject into the first learning model that outputs the composition of the bacterium, and the data of the second database is used as the teacher data of the intestinal bacterium. When the configuration is input, the configuration of the specified intestinal bacterium may be input to the second learning model that outputs the adjustment information to specify the adjustment information.
According to the above configuration, the adjustment information can be easily and satisfactorily specified by using the learning model.

The information processing apparatus according to the embodiment is based on the acquisition unit that acquires the first information including the intestinal bacteria information of the subject, the first information, and the adjustment information of the intestinal bacteria recommended for the subject. A generation unit that generates a deep reinforcement learning model that expresses the evaluation of adjustment information using a function, and a specific unit that inputs the acquired first information of the target person into the deep reinforcement learning model and specifies adjustment information. It is provided with an output unit that outputs the specified adjustment information.
According to the above configuration, the adjustment information can be easily and satisfactorily specified and output by using the learning model.

The above-mentioned information processing device may include a setting unit for setting adjustment information based on the second information related to the intestinal bacteria and the third information related to the nutrition of the intestinal bacteria, food components, or supplements. Good.
According to the above configuration, the adjustment information can be set satisfactorily.

In the above-mentioned information processing apparatus, the generation unit determines the ratio of intestinal bacteria beneficial to the subject in the first information of the subject after the adjustment related to the specified adjustment information is the first information before ingestion. If it increases compared to, a positive reward is specified according to the ratio, and if the ratio decreases compared to the first information before ingestion, a negative reward is specified according to the ratio. A unit and an update unit that updates the function based on the specified reward may be provided.
According to the above configuration, the accuracy of evaluation of adjustment information is improved.

The information processing device described above may further include a cooking specific unit that specifies a cooking method for the food. When the adjustment information is the information of the food, the cooking specifying unit specifies the cooking method of the food. In addition, the output unit may output the specified cooking method.
According to the above composition, the subject can easily ingest the nutritional components necessary for obtaining a suitable composition of intestinal bacteria in the form of cooking.

The information processing device described above may further include a menu specifying unit for specifying a menu related to the food, and the output unit may output the specified menu.
When the adjustment information is the information of the food, the menu specifying unit specifies the menu related to the food, and the output unit outputs the specified menu.
According to the above configuration, the subject can easily ingest the nutritional components required to obtain a suitable intestinal bacterial composition.

The information processing device described above may further include a processed food specifying unit that specifies the processed food to be used together with the food, and the output unit may output the specified processed food.
The processed food identification unit specifies the processed food to be used together with the food when the adjustment information is the information of the food, and the output unit outputs the specified processed food.
According to the above composition, the subject can easily ingest the nutritional components necessary for obtaining a suitable composition of intestinal bacteria.

In the above-mentioned information processing device, the output unit may output the timing of ingesting the food or the supplement.
According to the above configuration, foods or supplements can be eaten at a time when nutritional components are easily absorbed, based on time nutrition information and the like.

The information processing method according to the embodiment acquires the first information including the intestinal bacterium information of the subject, the first information, the second information concerning the intestinal bacterium, the nutrition of the intestinal bacterium, and the components of the food. Or, based on the third information relating to the supplement, the adjustment information of the intestinal bacteria recommended for the subject is specified, and the specified adjustment information is output.
According to the above configuration, it is possible to obtain adjustment information for adjusting the type, amount (ratio), and combination of bacteria in the intestine for each subject, and the intestinal environment is satisfactorily improved.

The computer program according to the embodiment acquires the first information including the intestinal bacterium information of the subject, and the first information, the second information concerning the intestinal bacterium, the nutrition of the intestinal bacterium, and the ingredients of the food. Alternatively, the computer is made to execute a process of identifying the adjustment information of the intestinal bacteria recommended for the subject based on the third information related to the supplement and outputting the specified adjustment information.
According to the above configuration, it is possible to obtain adjustment information for adjusting the type, amount (ratio), and combination of intestinal bacteria for each subject, and the intestinal environment is satisfactorily improved.

Hereinafter, the present invention will be described in detail with reference to the drawings showing the embodiments thereof.
(Embodiment 1)
FIG. 1 is a schematic diagram showing an example of the configuration of the information processing system 10 according to the first embodiment. Hereinafter, vegetables will be mentioned as an example of food. In the information processing system 10, the information processing device 1, the terminal 2 of the user (target person), the terminal 3 of the analyst, and the vegetable / supplement vending machine 4 are connected via a network N such as the Internet. The vegetable / supplement vending machine 4 may internally manufacture and sell salads and the like. The vegetable / supplement vending machine 4 has a display panel 45 for displaying the proposed vegetables or supplements, and an outlet 47 for taking out the vegetables or supplements.

FIG. 2 is a block diagram showing an example of the configuration of the information processing device 1. The information processing device 1 includes a control unit 11, a main storage unit 12, a communication unit 13, and an auxiliary storage unit 14 that control the entire device. The information processing device 1 can be composed of one or a plurality of servers.
The control unit 11 can be composed of a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and the like. The control unit 11 may include a GPU (Graphics Processing Unit). Moreover, you may use a quantum computer.
The control unit 11 has a first acquisition unit 111, an adjustment identification unit 112, a cooking identification unit 113, a menu identification unit 114, a processed food identification unit 115, and an output unit 116 as functional units. Each functional unit of the control unit 11 is a program module, and functions as follows by executing an information processing program (hereinafter referred to as a program) 140.
The first acquisition unit 111 acquires the first information relating to the intestinal bacteria of the subject.
The adjustment specifying unit 112 reads out the adjustment DB 154, which will be described later, according to the first information, and specifies the adjustment.
The cooking specifying unit 113 specifies the cooking method based on the specified adjustment.
The menu specifying unit 114 identifies the menu based on the specified adjustment.
The processed food identification unit 115 specifies the processed food to be used in combination with the food based on the specified adjustment.
The output unit 116 outputs the specified adjustments, recipes, menus, processed foods, and supplements to the terminal 2 and the like by the communication unit 13.

The main storage unit 12 is a temporary storage area for SRAM (Static Random Access Memory), DRAM (Dynamic Random Access Memory), flash memory, etc., and temporarily stores data necessary for the control unit 11 to execute arithmetic processing. Remember.

The communication unit 13 has a function of communicating with the terminal 2, the terminal 3, and the vegetable / supplement vending machine 4 via the network N, and can transmit and receive necessary information. Specifically, the communication unit 13 receives the first information transmitted by the terminal 2 and / or the terminal 3. The communication unit 13 transmits the proposed adjustments, foods, supplements, menus, processed foods, and the like to the terminal 2 and / or the terminal 3.

The auxiliary storage unit 14 is a large-capacity memory, a hard disk, or the like, and includes a program required for the control unit 11 to execute processing, a program 140 for performing information processing described later, a target person information DB 141, a score DB 142, and a scored information DB 143. , Intestinal flora DB 144, nutritional requirement DB 145, glycation-utilizing DB 146, time nutrition information DB 147, adjustment information DB 148, nutritional component information DB 149, recipe DB 150, menu DB 151, processed food DB 152, supplement DB 153, adjustment DB 154 , And the recipe history DB 155 is stored. The control unit 11 acquires the first information of the target person from the terminal 2 and the terminal 3 and stores it in the target person information DB 141.

The control unit 11 has a score, scored information, second information on the intestinal flora, nutritional requirement, glycation, temporal nutrition information, information on adjustment of the composition of intestinal bacteria, nutritional component information, And the third information of the ingredient information of the supplement, the recipe, the menu, the processed food, and the information related to the supplement are acquired via the network N, and the score DB 142, the scored information DB 143, the intestinal flora DB 144, the nutritional requirement DB 145, and the sugar It is stored in assimilation DB 146, time nutrition information DB 147, adjustment information DB 148, nutritional component information DB 149, recipe DB 150, menu DB 151, processed food DB 152, and supplement DB 153. The data in each DB is updated at an appropriate timing.
The control unit 11 sets the adjustment corresponding to the above-mentioned first information based on the second information and the third information, and stores the adjustment in the adjustment DB 154. Details will be described later. The control unit 11 reads the adjustment DB 154 and has a No. 1 having the contents of the column items having a high match rate with the first information of the target person. Identify the adjustment of.

The program 140 stored in the auxiliary storage unit 14 may be provided by a recording medium 160 in which the program 140 is readablely recorded. The recording medium 160 is, for example, a portable memory such as a USB memory, an SD card, a micro SD card, or a compact flash (registered trademark). The program 140 recorded on the recording medium 160 is read from the recording medium 160 using a reading device (not shown) and installed in the auxiliary storage unit 14. Further, the program 140 stored in the auxiliary storage unit 14 may be provided by communication via the communication unit 13.

FIG. 3 is an explanatory diagram showing an example of the record layout of the target person information DB 141.
The target person information DB 141 is No. Column, healthy intestinal bacterial information (intestinal flora information) column, (current) intestinal bacterial information (intestinal bacterial flora information) column, biological information column, preference column, living information column, genetic information column , And the first information string of the geographical information string, the ideal gut microbiota information string, the adjusted information string, and the adjusted gut microbiota information (gut microbiota information) string are stored. No. The columns are No. 1 for identifying the target person. I remember. As biological information, stool properties, genome, gut microbiota, antibody reaction, metabolites such as bile acid and cholesterol 1, metabolites such as organic acid 2, pH, and electrical conductivity are stored. The preference column remembers the subject's preferences such as those who like greasy foods and who are not good at celery. Living information columns include gender, age, height, weight, normal fever column, eating habits column, drinking, smoking habits column, blood analysis, blood pressure column, medical history, medication history, supplement taking history column, family composition, family history column. I remember my favorite menu and seasoning line.

Since the identification of the ideal intestinal bacterial model (ideal intestinal bacterial flora information) for the subject depends on the subject's living information and biological information, it is necessary to identify it for each subject. Gut microbiota information when a person is healthier (eg, younger) can be an ideal gut microbiota model for the subject. Gut microbiota information can be obtained by extracting gut microbiota nucleic acids from stool and performing 16S rRNA sequence analysis. This makes it possible to obtain information on the type, amount and diversity of intestinal bacteria. The ideal gut microbiota information sequence is identified and stored as described below.
The current gut microbiota information sequence stores current gut microbiota information.
The adjustment information string stores the adjustment information specified by using the adjustment DB 154 as described later.
The adjusted gut microbiota information sequence is stored when the adjusted gut microbiota information is acquired.
The stool may be collected by itself at the time of defecation, or may be collected at the time of intestinal lavage at the time of colonoscopy, for example. Further, a sample may be collected at various places in the intestine such as the small intestine and the large intestine at the time of examination using a capsule endoscope.

As intestinal bacterium information, for example, by culturing intestinal bacteria collected from the feces of a subject in a laboratory, information on metabolites 2 such as organic acids can be obtained in addition to the type and amount of intestinal bacteria. be able to.
By culturing with the addition of specific intestinal bacteria and food, it is possible to acquire information on the intestinal bacteria and food to be given to the subject, and to acquire information on adjustment. When the information related to the adjustment is acquired, it is stored in the information DB 148 related to the adjustment.

In Table 1 below, it is stored in the stool property sequence of biological information, the genome information sequence, the gut microbiota metagenomic information sequence, the antibody reaction sequence, the metabolite 1 column, the metabolite 2 column, the pH column, and the electrical conductivity column. The method and purpose of measuring stool properties, genome, gut microbiota, antibody reaction, metabolite 1, metabolite 2, pH, and electrical conductivity are shown.

The subject's genomic information is obtained by, for example, acquiring the subject's cells from the oral cavity and performing genetic analysis. The subject's metagenomic information can be obtained by extracting the nucleic acid of Gut microbiota from stool and performing DNA sequence analysis.

The items in Table 1 are not only the first information, but also related to the effective factors described later, which are methods for adjusting the intestinal bacteria and the amount. For example, the pH can be adjusted to improve the intestinal flora.
Although it is described as intestinal bacteria in Table 1, the analysis target may be eukaryotes or archaea that are not classified as bacteria. Examples of eukaryotes present in the intestine include yeasts of the genus Candida.

Pre-test intake may also affect the analysis of the intestinal environment. For example, when ingesting natto containing natto bacteria classified into the genus Bacillus, the proportion of nucleic acids of the genus Bacillus in the collected sample increases, which reduces the accuracy of intestinal environment analysis. It is regarded as a problem.
As a method for improving the accuracy of intestinal flora analysis, it is conceivable to record meals before collecting excrement and analyze nucleic acids contained in ingestion to correct the results of intestinal flora analysis in feces. .. For example, in a hospital, it is conceivable to leave a part of the school lunch. Specifically, there is a method in which meals are mixed together with a mixer or the like, and then a part thereof is cultured and analyzed by the same method as the above-mentioned culture for intestinal bacterial flora analysis.
Further, the correction based on the analysis of the ingestion is not limited to the one based on the gut microbiota analysis, and can be carried out by other test items such as the antibody test listed in the measurement items.

The gender, age, height, weight, and normal heat column of the living information column memorize the subject's gender, age, height, weight, and normal fever. The eating habits column remembers the eating habits of the subject, such as skipping breakfast. The drinking and smoking habits column memorizes the subject's drinking, the presence or absence of smoking habits, and the degree (drinking frequency and amount of drinking). The blood analysis and blood pressure columns store the blood test results corresponding to the subject's liver function, renal function, uric acid, lipid, glucose metabolism, blood cells, infectious diseases, and blood pressure, as well as blood pressure. The medical history, medication history, and supplement taking history record the subject's medical history, medication history, and supplement taking history. The family structure and family history column memorize the family structure and family history of the subject. Favorite menus and seasoning columns remember the target person's favorite menus and seasonings. Favorite menus and seasonings are information necessary for making menu proposals that are easily accepted by users (target people). For example, it is possible to eliminate the mismatch between the user and the adjustment method, such as suggesting summer vegetable curry for people who are not good at spices and suggesting raw vegetable smoothies for people who dislike raw vegetables. The genetic information stores genetic information such as race, and the geographical information stores geographical information such as residential area.

Target person information DB No. The healthy intestinal bacterium information in row 1 includes 20% of intestinal bacterium a1 belonging to A bacterium, 30% of intestinal bacterium b2 belonging to B bacterium, and intestinal bacterium belonging to C bacterium in the scored information described later. It is remembered that it contains 40% of the bacterium c5, 10% of the intestinal bacterium d6 belonging to the D bacterium, and 0% of the intestinal bacterium e2 belonging to the E bacterium. Similarly, in the intestinal bacterium information, the ideal intestinal bacterium information, and the adjusted intestinal bacterium information, each intestinal bacterium and the ratio are memorized.

Table 2 below shows an example of a correspondence table between the intestinal bacteria (denoted as bacteria) stored in the score DB 142 and the score.

The classifications in Table 2 are, in the order of A, B, C, D, and E, which are preferable bacteria from the viewpoint of improving health.

Tables 3-1, 3-2, and 3-3 below show an example of scored information stored in the scored information DB 143. A list of bacteria corresponding to classifications A, B, C, D, and E in the correspondence table of Table 2 is shown.

Tables 3-1, 3-2, and 3-3 are examples, and the type and amount of intestinal bacteria necessary to improve the intestinal environment and the judgment of good or bad bacteria are not limited to this example. Absent. As illustrated in Tables 3-1 and 3-2, 3-3, the appropriate type and amount of intestinal bacteria are determined according to the subject's information.
For example, even if it is an intestinal bacterium of A, if the ratio exceeds 10%, it may be classified as an intestinal bacterium of C, and the score is based on the type and amount (ratio) of the intestinal bacterium. Is determined.

FIG. 4 is an explanatory diagram showing an example of the record layout of the intestinal bacterial flora DB 144.
The intestinal bacterial flora DB 144 is No. It remembers columns, age columns, and gut microbiota sequences. No. The columns are No. 1 for identifying the intestinal flora. I remember. The age column remembers the age. The gut microbiota sequence stores gut microbiota data of healthy and sick individuals by age and gender.

FIG. 5 is an explanatory diagram showing an example of the record layout of the auxotrophic DB 145.
The auxotrophic DB 145 is No. It remembers the columns, the gut microbiota sequence, and the nutritional component sequence. No. The columns are No. 1 for identifying each auxotrophy. I remember. The gut microbiota sequence remembers the gut microbiota name. The nutritional component sequence remembers the nutritional components required by the intestinal bacteria.

Table 4 shows an example of the saccharification property stored in the saccharification property DB146.
In Table 4, the items in the vertical direction are the types of intestinal bacteria (denoted as bacteria), and the items in the horizontal direction are the types of sugar. In Table 4, "+" means that bacteria of the same genus and the same species can generally assimilate sugar, "-" can generally assimilate sugar, and "W" can generally assimilate but grow slowly. "±" indicates that it cannot be assimilated or grows very slowly, and "V" indicates that neither can be said.

FIG. 6 is an explanatory diagram showing an example of the record layout of the time nutrition information DB 147.
The time nutrition information DB 147 is No. It remembers columns, nutritional components, foods, and time zones. No. The columns are No. 1 for identifying each nutritional information. I remember. The nutritional component sequence remembers the nutritional components. The food column remembers foods that are high in nutrients. The time zone column remembers the time zone when food should be ingested.

As described above, the information DB 148 relating to the adjustment contains information on academic papers on the intake of specific foods and supplements and the increase / decrease of specific intestinal bacteria, and predetermined intestinal bacteria in a culture solution containing specific intestinal bacteria and foods. Cultivation experiment data (research data) such as an increase in the number of gut microbiota, change data before and after treatment for the gut microbiota disturbed by antibiotics, symbiotic relationship between gut microbiota, etc. It stores related information, information on the relationship between body temperature and the proliferative capacity of intestinal bacteria, and information on the growth suppression and killing effects of intestinal bacteria by antibiotics. The balance of intestinal bacteria is maintained by the symbiotic relationship between intestinal bacteria. This symbiotic relationship is constructed, for example, by a mechanism in which metabolites excreted by one gut bacterium serve as a nutrient source for another gut bacterium. By changing the amount of one intestinal bacterium, the amount of another intestinal bacterium having a symbiotic relationship can be indirectly adjusted.

Table 5 below shows an example of the nutritional component information stored in the nutritional component information DB 149.

The nutritional component information DB149 contains, for example, a food column, an energy column, a protein column, a lipid column, a sodium column, a potassium column, a calcium column, a vitamin K column, a vitamin B1 column, a water-soluble dietary fiber column, an insoluble dietary fiber column, and a dietary fiber. Remembers the total quantity column. The food column stores food names such as brown rice, green peas, and carrots. The energy column stores the total energy of the food. The protein column, lipid column, sodium column, potassium column, calcium column, vitamin K column, vitamin B1 column, water-soluble dietary fiber column, insoluble dietary fiber column, and total dietary fiber amount column are the amount of protein and lipid contained in food. It stores the amount of sodium, potassium, calcium, vitamin K, vitamin B1, water-soluble dietary fiber, insoluble dietary fiber, and total dietary fiber.

Since the type of dietary fiber decomposition product (for example, oligosaccharide) produced in the intestinal environment differs depending on the type of intestinal bacterium, for example, when bacterium a decomposes carrot, what kind of (oligosaccharide) sugar is produced. It is necessary to obtain it by experiment. Specifically, the components of vegetables that have undergone acid treatment (alternative to gastric acid decomposition) following amylase treatment (alternative to saliva decomposition) are added to the medium and cultured for a certain period of time, and the difference between the medium components before and after culture is LC-MS /. Decomposes are identified by analysis by MS. This row of dietary fiber degradation products may be added to the nutritional information DB 149.

FIG. 7 is an explanatory diagram showing an example of the record layout of the recipe DB 150.
Recipe DB 150 is No. I remember the column, the dish name column, the source column, and the arrangement column. No. The columns are No. 1 for identifying each recipe. I remember. The dish name column remembers the dish name. The source column stores the URL of the recipe source, the book name, and the like. The arrangement column remembers the arrangement part of the recipe.

FIG. 8 is an explanatory diagram showing an example of the record layout of the menu DB 151.
Menu DB 151 is No. The column, the menu column, and the store name column are memorized. No. The columns are No. 1 for identifying each menu. I remember. The menu column remembers the name of the dish. The store name column stores the store name of the restaurant having the menu.

FIG. 9 is an explanatory diagram showing an example of the record layout of the processed food DB 152.
The processed food DB 152 is No. The column, the common name column, the manufacturer name column, the product name column, and the component column are stored. No. The columns are No. 1 for identifying each processed food. I remember. The common name sequence remembers the common names of processed foods. The manufacturer name column remembers the manufacturers and distributors of processed foods. The product name column remembers the product name of the processed food. The ingredient sequence stores the ingredients of the processed food.

FIG. 10 is an explanatory diagram showing an example of the record layout of the supplement DB 153.
Supplement DB153 is No. The column, the product name column, the manufacturer name column, the ingredient column, and the daily intake guideline amount column are memorized. No. The columns are No. 1 for identifying each supplement. I remember. The product name column stores the product name of the supplement. The manufacturer name column remembers the names of the manufacturers and distributors of the supplements. The ingredient sequence remembers the ingredient names contained in the supplement. The daily intake guideline stores the daily intake guideline of the supplement. The component is not particularly limited as long as it is a component contained in the supplement, but may be, for example, an intestinal bacterium, a nutritional component as described in [0066], or an intestinal bacterium as a nutritional component. It may be the required component.

FIG. 11 is an explanatory diagram showing an example of the record layout of the adjustment DB 154.
The adjustment DB 154 is No. The ideal intestine with a column, the first information column of the intestinal bacterial information string at the time of health, the intestinal bacterial information column, the biological information column, the preference column, the living information column, the genetic information column, and the geographical information column. It stores the bacterial information sequence and the adjustment sequence of the intestinal bacterial column, the nutritional component column, the food column, the supplement column, and other effective factor columns. In FIG. 11, the genetic information sequence and the geographical information sequence are omitted. When the adjustment DB 154 acquires the first information of the subject, in order to specify the adjustment, a large amount of the first information, the ideal gut microbiota information according to the first information, and the ideal gut microbiota information It stores adjustment information according to the above.

No. The columns are No. 1 for identifying adjustments. I remember. The healthy intestinal bacterium information string, the intestinal bacterium information string, the biological information column, the preference column, the living information column, the genetic information column, and the geographical information string are the healthy intestinal bacterium information of the subject information DB 141. It stores the same contents as the column, gut microbiota information string, biometric information column, preference column, living information column, genetic information column, and geographical information column. The ideal gut microbiota information sequence stores ideal gut microbiota information based on the first information and the second information. The gut microbiota sequence remembers the name of the gut microbiota that corresponds to the ideal gut microbiota information. The nutritional component sequence remembers the nutritional components for the increase of intestinal bacteria. The food column stores the foods containing a large amount of the nutritional components. Vegetables are preferable as food. On the other hand, if there are intestinal bacteria to be reduced, the food containing a large amount of nutritional components for increasing the intestinal bacteria is memorized as a food to be avoided. The supplement column remembers the name of the supplement containing a large amount of nutritional components for increasing the intestinal bacteria and the name of the supplement containing the intestinal bacteria to be increased. In addition, the food column and the supplement column represent, for example, the daily intake in addition to the names of foods and supplements to be ingested. Other effective factor columns store effective factors other than nutritional components among the effective factors described later.

Adjustment DB 154 No. It is memorized that the intestinal bacterial information at the time of health of 1 contains 15% of a1, 25% of b2, 50% of c5, 10% of d6, and 0% of e2. As intestinal bacterial information, it is remembered that a1 is contained in 10%, b2 is contained in 15%, c5 is contained in 55%, d6 is contained in 15%, and e2 is contained in 5%. Based on these gut microbiota information and the second information, the control unit 11 sets a1 as 20%, b2 as 30%, c5 as 40%, d6 as 10%, and e2 as ideal gut microbiota information. Remember to include 0%.
B. ruminantium, which belongs to B fungus, requires mannitol as a nutritional component. The required mannitol is obtained by eating 100 g of celery and 50 mg of supplement A.
Enterococcus.faecalis, which belongs to Bacteria C, requires biotin as a nutritional component. The required biotin is obtained by eating 50 g of beans.
Similarly, in order to obtain the ideal gut microbiota, identify the nutritional components according to the gut microbiota, identify the food or supplement based on the identified nutritional components, and if necessary, other effective factors. Identify.
The specific method will be described later.

FIG. 12 is an explanatory diagram showing decomposition of a component of vegetable A such as dietary fiber by an intestinal bacterium (denoted as a bacterium). Dietary fiber consists of parts a, b, c, and d. The fungus (1) decomposes dietary fiber into a and b and c and d. The fungus (2) decomposes dietary fiber into a, b, c and d.

Table 6 below shows the types, proliferative power, and effective factors of sugars that are preferentially assimilated by bacteria (1), bacteria (2), bacteria (3), and evil bacteria X.

In the case of Table 6, the decomposition product by the bacterium (1) promotes the growth of the evil bacterium X by ingesting the vegetable A. When the amount of the bacterium (2) is large, the bacterium (1) to (3) grows and the diversity (balance of the intestinal environment) is maintained.

The effective factor is the adjustment for improving the intestinal environment through the intake of foods and vegetables, and the adjustment of the assimilation and proliferative power of the intestinal bacteria.
Examples of effective factors include nutritional components of foodstuffs, intestinal pH, body temperature, other intestinal bacteria, antibiotics and the like.
Nutritional components of foodstuffs: The nutritional components of foodstuffs are nutritional components that have the effect of growing or reducing specific intestinal bacteria, and are secondary metabolites such as dietary fiber, vitamins, sugars, minerals, alkaloids, terpenoids, and peptides. Etc. are included. For example, intestinal bacteria that require specific vitamins for growth are known, and the intestinal bacterial flora can be adjusted by adjusting the vitamins ingested. Examples of secondary metabolites include capsaicin derived from chili pepper, which is known to have an antibacterial effect. This component makes it possible to regulate the intestinal flora. Secondary metabolites such as curcumin and quercetin, which have anti-inflammatory effects, are also useful in regulating the intestinal flora. When the intestine is inflamed, the mucosal barrier becomes thin and an unusual living environment for the intestinal bacteria is formed. Therefore, the growth environment of the intestinal bacteria can be changed by suppressing the inflammation.

Intestinal pH: The growth potential of microorganisms is greatly influenced by pH. Since the imbalance of gastric acid and bile acid secretion and the state of indigestion have a great influence on the growth of intestinal bacteria, adjusting the intestinal pH is also useful for adjusting the intestinal flora and improving the intestinal environment. is there.

Body temperature: Since the proliferative power of intestinal bacteria (and the activity of digestive organs) depends on temperature, maintaining proper body temperature is also useful for regulating the intestinal flora and improving the intestinal environment. In order to maintain proper body temperature, improvement of metabolism by improving eating habits and lifestyle, direct heating by bathing and eating, etc. can be mentioned. In addition, reducing subcutaneous fat in the abdomen by dieting also has a positive effect on the temperature control function.

Other gut bacteria: The balance of gut bacteria is maintained by the symbiotic relationship between gut bacteria. This symbiotic relationship is constructed, for example, by a mechanism in which metabolites excreted by one intestinal bacterium serve as a nutrient source for another microorganism. It is also possible to adjust the amount of other intestinal bacteria that have a symbiotic relationship by changing the amount of one intestinal bacterium. In this way, the intestinal flora can be adjusted by indirectly adjusting the amount of specific intestinal bacteria and the ratio of multiple intestinal bacteria while utilizing the symbiotic relationship between intestinal bacteria. , Can improve the intestinal environment.

Antibiotics: Antibiotics are components that have the effect of suppressing the growth of bacteria or killing them, and are widely used as agents against pathogens that have invaded the body, for example. However, it has been a problem that antibiotics also eliminate indigenous bacteria unrelated to the disease and upset the balance of the intestinal flora. Repellent of antibiotics and appropriate use according to the purpose are required for the optimization of the intestinal bacterial flora.

FIG. 13 is a flowchart showing a procedure for specifying adjustment in the adjustment DB 154 by the control unit 11.
The control unit 11 has the No. 1 of the adjustment DB 154. Based on the first information and the second information of the above, the composition of the ideal gut microbiota is identified and stored in the ideal gut microbiota information sequence (S1). The control unit 11 increases the ratio in the intestine based on the intestinal bacterial information when healthy or young, the first information including the current intestinal bacterial information, the score information, the scored information, the intestinal bacterial flora information, and the like. Specify the type of intestinal bacteria that you want to have or reduce, and specify the ratio of each intestinal bacterium.

The control unit 11 reads out the auxotrophic DB 145, the auxotrophic DB 146, and the information DB 148 related to the adjustment, and based on the identified composition of the gut microbiota, the auxotrophic or auxotrophic nature of the gut microbiota, etc. In consideration of the information related to the adjustment of the symbiotic relationship of the intestinal bacteria and the like, the above-mentioned nutritional components such as dietary fiber, vitamins, sugars, minerals, and secondary metabolites are identified and stored in the adjustment DB 154 (S2). ).

The control unit 11 reads out the information DB 148, the nutritional component information DB 149, and the supplement DB 153 related to the adjustment, identifies the food and the supplement based on the specified nutritional component, and stores them in the adjustment DB 154 (S3).
The control unit 11 reads out the time nutrition information DB 147 and the information DB 148 related to the adjustment, identifies an effective factor other than the nutritional component, stores it in the adjustment DB 154 (S4), and ends the process. Other effective factors include intestinal pH, body temperature, repellent or proper use of antibiotics, time of day when food should be ingested, and the like. Since the imbalance of gastric acid and bile acid secretion and the state of indigestion have a great influence on the growth of intestinal bacteria, the intestinal pH can be adjusted to improve the intestinal environment. Since body temperature affects the proliferative capacity of intestinal bacteria, it is possible to make adjustments to maintain body temperature appropriately. Antibiotics also eliminate disease-independent indigenous bacteria and upset the intestinal flora, so they can be adjusted for proper antibiotic use.

No. of adjustment DB 154 in FIG. Line 1 remembers the intake of 100 g of celery, 50 mg of supplement A, and 50 g of beans as an adjustment. No. Line 2 remembers taking supplement A 100 mg and beans 50 g as an adjustment because celery is not good as a taste. No. Line 3 avoids celery because it needs to reduce B. ruminantium.

FIG. 14 is an explanatory diagram showing an example of the record layout of the recipe history DB 155.
The recipe history DB 155 is No. It stores a row, an intestinal bacterium row, a nutritional component row, a food row, a supplement row, a recipe row, a menu row, and a processed food row. No. The columns are No. 1 for identifying the target person. I remember. The gut microbiota sequence remembers the names of gut microbiota that are increased or decreased for the subject. The nutritional component sequence stores the nutritional components for increasing the intestinal bacteria involved in the regulation. The food column and the supplement column store the food containing a large amount of nutritional components and the daily intake, the supplement name and the daily intake related to the adjustment. The supplement column remembers the supplements described below when they are added. The recipe column and menu column store recipes and menus using food. The processed food column stores processed foods such as dressings that are used in conjunction with foods. It should be noted that each line is not limited to storing all recipes, menus, and processed foods.

FIG. 15 is a block diagram showing an example of the configuration of the terminal 2. The terminal 2 includes a control unit 21, a main storage unit 22, a communication unit 23, an operation unit 24, a display panel 25, and an auxiliary storage unit 26 that control the entire terminal 2.

The terminal 2 can be composed of, for example, a smartphone, a desktop computer, a notebook personal computer, a tablet, or the like.
The control unit 21 can be composed of a CPU, a ROM, a RAM, and the like. The control unit 21 may be configured to include a GPU.

The main storage unit 22 is a temporary storage area for SRAM, DRAM, flash memory, etc., and temporarily stores data necessary for the control unit 21 to execute arithmetic processing.
The communication unit 23 has a function of communicating with the information processing device 1 via the network N, and can transmit and receive required information.

The operation unit 24 is composed of, for example, a hardware keyboard, a mouse, a touch panel, etc., and can operate icons and the like displayed on the display panel 25, input characters and the like, and the like.

The display panel 25 can be composed of a liquid crystal panel, an organic EL (Electro Luminescence) display panel, or the like. The control unit 21 controls to display the required information on the display panel 25.

The auxiliary storage unit 26 is a large-capacity memory or the like, and stores the program 261 required for the control unit 21 to execute the process. In addition, the auxiliary storage unit 26 has a first information acquired from the information processing device 1, a target person information DB 262 that stores adjustments, and a recipe history DB 263. A part of the first information may be acquired by input by the operation unit 24 of the target person.

FIG. 16 is a block diagram showing an example of the configuration of the terminal 3. The terminal 3 includes a control unit 31, a main storage unit 32, a communication unit 33, an operation unit 34, a display panel 35, and an auxiliary storage unit 36 that control the entire terminal 3. The configuration of the control unit 31, the main storage unit 32, the communication unit 33, the operation unit 34, and the display panel 35 is the same as the configuration of the control unit 21, the main storage unit 22, the communication unit 23, the operation unit 24, and the display panel 25. .. An analyzer 37 such as HPLC (High Performance Liquid Chromatography) is connected to the terminal 3.
The auxiliary storage unit 36 is a large-capacity memory, a hard disk, or the like, and stores a program 361 required for the control unit 31 to execute a process. Further, the auxiliary storage unit 36 stores the target person information acquired from the analyzer 37 in the target person information DB 362.

FIG. 17 is a block diagram showing an example of the configuration of the vegetable / supplement vending machine 4. The vegetable / supplement vending machine 4 includes a control unit 41, a main storage unit 42, a communication unit 43, an operation unit 44, a display panel 45, and an auxiliary storage unit 46 that control the entire vegetable / supplement vending machine 4. The configuration of the control unit 41, the main storage unit 42, the communication unit 43, the operation unit 44, and the display panel 45 is the same as the configuration of the control unit 21, the main storage unit 22, the communication unit 23, the operation unit 24, and the display panel 25. ..
The auxiliary storage unit 46 is a large-capacity memory, a hard disk, or the like, and stores a program 461 required for the control unit 41 to execute a process. Further, the auxiliary storage unit 46 stores the recipe history acquired from the information processing device 1 in the recipe history DB 462.

FIG. 18 is a flowchart showing a procedure for specifying adjustment processing by the control unit 11.
The control unit 21 transmits the first information to the information processing device 1 (S41).
The control unit 11 acquires the first information from the terminal 2 and the terminal 3 (not shown) (S11).
The control unit 11 reads the adjustment DB 154 and has a No. 1 having the contents of the column items having a high match rate with the first information of the target person. Is specified and stored in the target person information DB 141 (S12).
The control unit 11 transmits the adjustment to the terminal 2 (S13), and ends the process.
The control unit 21 of the terminal 2 receives the adjustment (S42) and stores it in the target person information DB 262.
The control unit 21 displays the adjustment on the display panel 25 and proposes it (S43), and ends the process.

The process of identifying and displaying recipes, menus, processed foods, and supplements based on the identified adjustments will be described below.
FIG. 19 is a flowchart showing a processing procedure for identifying and displaying (suggesting) menus, recipes, processed foods, and supplements.
The control unit 11 acquires the adjustment from the target person information DB 141 (S21). Adjustments shall include foods and / or supplements. The above-mentioned No. In the case of the subject of 1, the adjustment is "celery 100 g, supplement A 50 mg, beans 50 g".
The control unit 11 determines whether or not the adjustment includes food and specifies a cooking menu (S22). When the control unit 11 determines that the cooking menu is specified (S22: YES), the control unit 11 determines whether or not to specify the menu (S23).

When the control unit 11 determines that the menu is not specified (S23: NO), the control unit 11 determines whether or not to specify the recipe (S28).
When the control unit 11 determines that the menu is specified (S23: YES), the control unit 11 reads the menu DB 151 and selects the menu related to the adjusted food (S24).
The control unit 11 reads out the nutritional component information DB 149 and calculates the nutritional components of the ingredients in the menu (S25).
The control unit 11 determines whether or not the amount of the nutritional component is an amount capable of increasing the target intestinal bacteria (S26).
When the control unit 11 determines that the amount can increase the target intestinal bacteria (S26: YES), the process proceeds to S38. When the control unit 11 determines that the amount is not capable of increasing the target intestinal bacteria (S26: NO), it determines whether or not to reselect (S27). For example, it is determined whether or not the number of selections exceeds the threshold value, and if the number of selections is less than the threshold value (S27: YES), the process is returned to S24. At this time, select the one that was not selected last time. When the control unit 11 determines that the reselection is not performed (S27: NO), the process proceeds to S28.

When the control unit 11 determines that the recipe is specified (S28: YES), the control unit 11 reads out the "preference" and "favorite menu, seasoning" of the recipe DB 150 and the target person information DB 141, and selects a recipe including the adjusted food (S29). ).
The control unit 11 arranges the recipe based on the adjusted intestinal bacterial information, "preference", "favorite menu, seasoning", and the like (S30). If the arrangement is unnecessary, S30 can be skipped.
The control unit 11 reads out the nutritional component information DB 149 and calculates the nutritional component of each ingredient in the recipe (S31). Table 7 below shows an example of the calculation of nutritional components when the recipe is bean rice.

The control unit 11 determines whether or not the amount of the nutritional component is an amount capable of increasing the target intestinal bacteria of the subject (S32).
When the control unit 11 determines that the amount can increase the target intestinal bacteria (S32: YES), the process proceeds to S38. When the control unit 11 determines that the amount is not capable of increasing the target intestinal bacteria (S32: NO), it determines whether or not to reselect (S33). For example, it is determined whether or not the number of selections exceeds the threshold value, and if the number of times is less than the threshold value (S33: YES), the process is returned to S29. At this time, select the one that was not selected last time. When the control unit 11 determines that the reselection is not performed (S33: NO), the process proceeds to S34.

When the control unit 11 determines that the recipe is not specified (S28: NO), the control unit 11 reads the processed food DB and selects the processed food (S34).
The control unit 11 acquires the nutritional components of the processed food (S35).
The control unit 11 determines whether or not the target intestinal bacteria can be increased by the nutritional component (S36). Processed foods are added to the foods and supplements specified as adjustments, and can the ingredients of the processed foods increase the target gut bacteria without suppressing the increase of the target gut bacteria? When the control unit 11 determines whether or not the target intestinal bacteria can be increased (S36: YES), the process proceeds to S38. When the control unit 11 determines that the target intestinal bacteria cannot be increased (S36: NO), it determines whether or not to reselect (S37). For example, it is determined whether or not the number of selections exceeds the threshold value, and if the number of selections is less than the threshold value (S37: YES), the process is returned to S34. At this time, select the one that was not selected last time. When the control unit 11 determines that the reselection is not performed (S37: NO), the process proceeds to S38.

The control unit 11 determines whether or not it is necessary to add a supplement (S38). The control unit 11 determines, for example, whether supplements need to be added to supplement the deficient nutrients. When the control unit 11 determines that it is necessary to add the supplement (S38: YES), the control unit 11 reads the supplement DB 153, selects the supplement to be added (S39), and proceeds to the process to S50. When the control unit 11 determines that it is not necessary to add the supplement (S38: NO), the process proceeds to S50.

When the control unit 11 determines that the cooking menu is not specified (S22: NO), that is, when the adjustment does not include food, the control unit 11 specifies the supplement as the adjustment (S40).
The control unit 11 stores the menu, the recipe, or the processed food and the supplement to be added as needed in the recipe history DB 155, and the recipe history information of the menu, the recipe, or the processed food, and the supplement when added, and the adjustment. The supplement in the case of containing the supplement is transmitted to the terminal 2 (S50), and the process is terminated.

The control unit 21 of the terminal 2 receives the recipe history information and the supplement when the adjustment includes the supplement (S51), and stores the supplement in the recipe history DB 263.

The control unit 21 displays (suggests) on the display panel 25 (S52) a menu, a recipe, a processed food, or a supplement when the supplement is included in the preparation (S52).
The control unit 21 accepts whether or not the content proposed by the user is OK (S53). When the control unit 21 determines that it is not OK (S53: NO), it transmits an instruction to repeat the process of S22 to the information processing device 1. When the control unit 21 determines that the process is OK (S53: YES), the process ends.
The processing order of S23, S28, and S34 is not limited to the above case. Further, the request of the target person may be received to specify any of the menu, the recipe, and the processed food, or the request of the target person may be received and specified in order.
Then, the processes of S23, S28, and S34 may be performed in series to specify all of the menu, recipe, and processed food. Recipe history DB155 No. In the case of 1, the control unit 11 stores the recipe, the menu, the processed food, and the supplement to be added as needed, and transmits the recipe, the menu, the processed food, and the supplement to be added to the terminal 2. The control unit 21 may display all items or may display items specified in advance by the target person.

FIG. 20 is an explanatory diagram showing an example of a recipe display screen.
In FIG. 20, since biotin is required for the subject to grow the desired intestinal bacteria and the subject is a vegetarian, the control unit 11 identifies the beans as a prepared food containing a large amount of biotin, and the beans. As a recipe containing green peas, a case where bean rice containing green peas is specified is shown. The control unit 21 displays a picture of the bean rice, the ingredients, and the amount thereof on the display panel 25, and also displays a "how to make" button. When the target person taps the "How to make" button, "How to make" is displayed.

FIG. 21 is an explanatory diagram showing an example of a menu display screen.
In FIG. 21, since biotin is required for the subject to grow the desired intestinal bacteria and the subject is a vegetarian, the control unit 11 identifies the beans as a prepared food containing a large amount of biotin, and the beans. Indicates a case where a menu containing is specified. The control unit 21 displays the menu "chawanmushi A" of the "chawanmushi light" of the chawanmushi specialty store on the display panel 25, and displays the location, business hours, URL for reservation, telephone number, etc. of the "chawanmushi light". ..

FIG. 22 is an explanatory diagram showing an example of a display screen of processed food.
In FIG. 22, since biotin is required for the subject to grow the desired intestinal bacteria and the subject is a vegetarian, the control unit 11 identifies the beans as a prepared food containing a large amount of biotin, and the beans. The case where a dressing suitable for a salad containing the above is specified as a processed food is shown. The control unit 21 displays the “Morimori vegetable dressing” manufactured by ABC and the product information button on the display panel 25. When the target person taps the "product information" button, the details of the "product information" are displayed.

FIG. 23 is an explanatory diagram showing an example of a supplement display screen.
FIG. 23 shows a supplement for the subject to grow the desired gut microbiota. The control unit 21 displays the supplement "lactic acid bacterium R037" and the product information button on the display panel 25. When the target person taps the "product information" button, the details of the "product information" are displayed. This supplement can reduce the amount of triglycerides in the blood for subjects who prefer to eat greasy foods.

Hereinafter, a process of displaying the vegetables / supplements specified as adjustments on the vegetable / supplement vending machine 4 will be described.
FIG. 24 is a flowchart showing a processing procedure for displaying vegetables and supplements.
The control unit 11 acquires vegetables / supplements as adjustments from the recipe history DB 155 (S61).
The control unit 11 notifies the vegetable / supplement vending machine 4 of the vegetables / supplements (S62).
The control unit 41 acquires vegetable / supplement information from the information processing device 1 (S71) and stores it in the recipe history DB 155.
The control unit 41 receives the input using the operation unit 44 of the target person to obtain the ID No. of the target person. Is determined (S72). The control unit 41 has an ID No. When it is determined that the above is not acquired (S72: NO), this determination process is repeated.
The control unit 41 has an ID No. (S72: YES), vegetables and supplements are displayed on the display panel 45 (S73), and the process is terminated.

FIG. 25 is an explanatory diagram showing an example of the display screen 45 of the vegetable / supplement vending machine 4.
The case where carrot and lettuce and lactic acid bacterium R037 are specified as a prepared food and supplement in order to grow a desired intestinal bacterium for a subject is shown. The subject is ID No. By inputting, the control unit 21 has the ID No. The foods and supplements according to the above are displayed on the display panel 45. In FIG. 25, the ID No. Ingestion of carrots, lettuce and lactic acid bacterium R037 is recommended in 1 subject.

According to the present embodiment, it is possible to satisfactorily obtain adjustment information for adjusting the composition such as the type, amount (ratio), and combination of intestinal bacteria in the intestine for each subject. Then, by proposing a cooking method, a menu, processed foods to be added to foods, etc., the nutritional components necessary for obtaining the ideal intestinal flora can be easily ingested, and the intestinal environment is satisfactorily improved.

(Embodiment 2)
As shown in FIG. 26, in the information processing apparatus 1 according to the second embodiment, the control unit 11 has the second acquisition unit 117, the auxiliary storage unit 14 stores the learning model 156, and the recording medium 163. It has the same configuration as the information processing device 1 according to the first embodiment, except that the information processing program 162 is stored in the auxiliary storage unit 14.
The second acquisition unit 117 inputs the first information into the learning model 156 to acquire the adjustment.

FIG. 27 is a schematic diagram showing an example of the learning model 156.
The learning model 156 is a learning model that is expected to be used as a program module that is a part of artificial intelligence software, and a multi-layer neural network (deep learning) can be used. For example, a convolutional neural network: CNN) can be used, but a recurrent neural network (RNN) may also be used. Other machine learning such as decision trees, random forests, support vector machines, etc. may be used. The control unit 11 operates so as to perform an operation on the first information input to the input layer of the learning model 156 and output the adjustment and its probability value according to the command from the learning model 156. In FIG. 27, two intermediate layers are shown for convenience, but the number of layers of the intermediate layer is not limited to two and may be three or more. In the case of CNN, it includes a convolution layer and a pooling layer. The number of nodes (neurons) is also not limited to the case of FIG. 27.

One or more nodes (neurons) exist in the input layer, the output layer, and the intermediate layer, and the nodes of each layer are connected to the nodes existing in the preceding and following layers in one direction with a desired weight. A vector having the same number of components as the number of nodes in the input layer is given as input data (input data for learning and input data for adjustment identification) of the learning model 156. The input data includes, as the first information, intestinal bacterium information at the time of health, (current) intestinal bacterium information, biological information, preference, living information, genetic information, geographical information, and the like. The first information includes at least gut microbiota information. The output data includes adjustments.

When the data given to each node of the input layer is input to the first intermediate layer and given, the output of the intermediate layer is calculated using the weight and activation function, and the calculated value is transferred to the next intermediate layer. Given, the output of the output layer is transmitted to the subsequent layers (lower layers) one after another until the output of the output layer is obtained in the same manner. All the weights that connect the nodes are calculated by the learning algorithm.

The output layer of the learning model 156 generates adjustment information as output data. The number of nodes in the output layer corresponds to the number of adjustments. For example, if there are 100 types of adjustments consisting of at least one of foods, supplements, and other effective factors, the number of nodes in the output layer can be 100. The output layer outputs each adjustment and the probability value of each adjustment.

Although the learning model 156 has been described as being a CNN, the RNN can be used as described above. In RNN, the intermediate layer of the previous time is used for learning together with the input layer of the next time.

The control unit 11 has the No. 1 of the adjustment DB 154. Learning is performed using the teacher data in which the first information of the above and the adjustment information are associated with each other.

The control unit 11 inputs the first information, which is the teacher data, to the input layer, performs arithmetic processing in the intermediate layer, and acquires adjustments and probability values from the output layer.

The control unit 11 compares the specific result output from the output layer with the information labeled with respect to the first information in the teacher data, that is, the correct answer value, so that the output value from the output layer approaches the correct answer value. Optimize the parameters used for arithmetic processing in the intermediate layer. The parameters are, for example, the above-mentioned weight (coupling coefficient), coefficient of activation function, and the like. The method of optimizing the parameters is not particularly limited, but for example, the control unit 11 optimizes various parameters by using the backpropagation method.

The control unit 11 performs the above processing on the first information of each teacher data included in the adjustment DB 154, and generates the learning model 156. When the control unit 11 acquires the first information, the control unit 11 uses the generated learning model 156 and adjusts to specify the adjustment showing a high probability value based on the adjustment output by the learning model 156 and its probability value. Get as.
The output layer is
For example, 100 g of celery, 50 mg of supplement A, 50 g of beans ... 0.91
Supplement A 100mg, beans 50g ... 0.08
Output as ...

FIG. 28 is a flowchart showing a procedure of adjustment acquisition processing by the control unit 11.
The control unit 11 acquires the first information of the target person from the target person information DB 141 (S81).
The control unit 11 inputs the first information to the learning model 156 (S82).
The control unit 11 acquires the adjustment having the highest probability value among the adjustments output by the learning model 156 (S83), and ends the process.

The control unit 11 uses the learning model 156 to input the first information of the subject to identify the subject, and the specific result is based on the subject's intestinal bacterial information after the adjustment. The learning model 156 can be retrained so that the reliability of the model 156 is improved. For example, when the intestinal bacterial information of the subject is improved, the probability value of the adjustment is increased by inputting a large number of teacher data associated with the adjustment to the first information and re-learning. Can be done.

According to the present embodiment, the learning model 156 can be used to easily and satisfactorily identify the adjustment.

(Embodiment 3)
In the third embodiment, the adjustment DB 154 of the second embodiment becomes the ideal intestinal bacterium information based on the first DB that stores the ideal intestinal intestinal bacterium information corresponding to the first information and the third information. It has the same configuration as the information processing device 1 of the second embodiment except that the second DB correspondingly stores the adjustment information is provided and the learning model 156 includes the first learning model and the second learning model.

FIG. 29 is an explanatory diagram showing an example of the record layout of the first DB.
The first DB is No. The ideal intestine with a column, the first information column of the intestinal bacterial information string at the time of health, the intestinal bacterial information column, the biological information column, the preference column, the living information column, the genetic information column, and the geographical information column. It remembers the bacterial information sequence. In FIG. 29, the genetic information sequence and the geographical information sequence are omitted. No. The columns are No. 1 for identifying the ideal gut microbiota information for the first information. I remember. The healthy intestinal bacterium information string, the intestinal bacterium information string, the biological information column, the preference column, the living information column, the genetic information column, and the geographical information string are the healthy intestinal bacterium information of the subject information DB 141. It stores the same contents as the column, gut microbiota information string, biometric information column, preference column, living information column, genetic information column, and geographical information column. The ideal gut microbiota information sequence stores ideal gut microbiota information based on the first information and the second information.

FIG. 30 is an explanatory diagram showing an example of the record layout of the second DB.
The second DB is No. It stores columns, ideal gut microbiota information columns, and adjustment information columns for gut microbiota column, nutritional component column, food column, supplement column, and other effective factor columns. No. The columns are No. 1 for identifying adjustment information for ideal gut microbiota information. I remember. The ideal intestinal bacterium information sequence and the adjustment information sequence of the intestinal bacterium sequence, the nutritional component sequence, the food column, the supplement column, and other effective factor columns are the ideal intestinal bacterium information sequence in the adjustment DB 154 and the intestine. It stores the same contents as the adjustment information column of the endobacterial column, the nutritional component column, the food column, the supplement column, and other effective factor columns.

FIG. 31 is a schematic diagram showing an example of the first learning model and the second learning model.
The first learning model and the second learning model are learning models that are expected to be used as program modules that are a part of artificial intelligence software, and can use a multi-layer neural network, for example, CNN. However, RNN may be used, or other machine learning may be used.
The control unit 11 performs an operation on the first information input to the input layer of the first learning model according to a command from the first learning model, and outputs ideal intestinal bacterial information and its probability value. Operate. In FIG. 31, for convenience, two intermediate layers are shown, but the number of layers of the intermediate layer is not limited to two and may be three or more. In the case of CNN, it includes a convolution layer and a pooling layer. The number of nodes is also not limited to the case of FIG. 31.

The input layer, the output layer, and the intermediate layer have one or more nodes, and the nodes of each layer are connected to the nodes existing in the preceding and following layers in one direction with a desired weight. A vector having the same number of components as the number of nodes in the input layer is given as input data of the first learning model (input data for training and input data for specifying ideal intestinal bacterial information). The input data includes, as the first information, intestinal bacterium information at the time of health, intestinal bacterium information, biological information, preference, living information, genetic information, geographical information and the like.

The output layer of the first learning model generates ideal intestinal bacterial information as output data. The number of nodes in the output layer corresponds to the ideal number of gut microbiota information. The output layer outputs each ideal intestinal bacterium information and a probability value of each intestinal bacterium information.

The control unit 11 has each No. 1 of the first DB. Learning is performed using the teacher data in which the first information of the above and the ideal intestinal bacterial information are associated with each other.
The control unit 11 inputs the first information, which is the teacher data, to the input layer, and acquires the ideal gut microbiota information and the probability value from the output layer through the arithmetic processing in the intermediate layer.

The control unit 11 compares the specific result output from the output layer with the information labeled with respect to the first information in the teacher data, that is, the correct answer value, so that the output value from the output layer approaches the correct answer value. Optimize the parameters used for arithmetic processing in the intermediate layer. The method of optimizing the parameters is not particularly limited, but for example, the control unit 11 optimizes various parameters by using the backpropagation method.

The control unit 11 performs the above processing on the first information of each teacher data included in the first DB, and generates the first learning model. After the generation of the first learning model, when the control unit 11 acquires the first information, the control unit 11 uses the generated first learning model, inputs the first information to the first learning model, and outputs the ideal that the first learning model outputs. Based on the intestinal bacterium information and its probability value, the intestinal bacterium information showing a high probability value is specified as the ideal intestinal bacterium information.
The output layer is
For example, a1: 20%, b2: 30%, c5: 40%, d6: 10%, e2: 0% ... 0.92
a1: 15%, b2: 30%, c5: 45%, d6: 10%, e2: 0% ... 0.06
Output as ...

The control unit 11 operates so as to calculate the ideal intestinal bacterial information input to the input layer of the second learning model according to the command from the second learning model, and output the adjustment and its probability value. .. In FIG. 31, for convenience, two intermediate layers are shown, but the number of layers of the intermediate layer is not limited to two and may be three or more. In the case of CNN, it includes a convolution layer and a pooling layer. The number of nodes is also not limited to the case of FIG. 31.

The input data of the second learning model includes ideal intestinal bacterial information.
The output layer of the second training model generates adjustments as output data. The number of nodes in the output layer corresponds to the number of adjustments. The output layer outputs each adjustment and the probability value of each adjustment.

The control unit 11 has each No. 2 of the second DB. Learning is performed using the teacher data in which the ideal intestinal bacterial information and the adjustment are associated with each other.
The control unit 11 inputs ideal intestinal bacterial information, which is teacher data, to the input layer, performs arithmetic processing in the intermediate layer, and acquires adjustments and probability values from the output layer.

The control unit 11 compares the specific result output from the output layer with the information labeled for the ideal gut microbiota information in the teacher data, that is, the correct answer value, and the output value from the output layer approaches the correct answer value. As described above, the parameters used for the arithmetic processing in the intermediate layer are optimized. The method of optimizing the parameters is not particularly limited, but for example, the control unit 11 optimizes various parameters by using the backpropagation method.

The control unit 11 performs the above processing on the ideal intestinal bacterium information of each teacher data included in the second DB, and generates a second learning model. After the generation of the second learning model, when the ideal intestinal bacterium information is specified by the first learning model, the control unit 11 uses the second learning model and uses the second learning model as the specified ideal intestinal bacterium information. Is input, and the adjustment showing the high probability value is specified as the adjustment information to be specified based on the adjustment output by the second learning model and the probability value thereof.
The output layer is
For example, 100 g of celery, 50 mg of supplement A, 50 g of beans ... 0.91
Supplement A 100mg, beans 50g ... 0.08
Output as ...

FIG. 32 is a flowchart showing the procedure of the adjustment acquisition process by the control unit 11.
The control unit 11 acquires the first information of the target person from the target person information DB 141 (S91).
The control unit 11 inputs the first information to the first learning model (S92).
The control unit 11 acquires the information having the highest probability value among the ideal intestinal bacteria information output by the first learning model (S93).
The control unit 11 inputs the acquired ideal intestinal bacterial information into the second learning model (S94).
The control unit 11 acquires the adjustment having the highest probability value among the adjustments output by the second learning model (S95), and ends the process.

According to the present embodiment, the adjustment can be easily and satisfactorily specified by using the first learning model and the second learning model.

(Embodiment 4)
As shown in FIG. 33, in the information processing apparatus 1 according to the fourth embodiment, the control unit 11 has the adjustment setting unit 121 and the generation unit 118, and the auxiliary storage unit 14 stores the learning model 157. It has the same configuration as the information processing device 1 according to the second embodiment, except that the information processing program 164 is stored in the auxiliary storage unit 14 by the medium 165.
The adjustment setting unit 121 sets the adjustment information based on the second information and the third information.
The generation unit 118 generates a learning model (deep reinforcement learning model) 157 that expresses the evaluation of the adjustment information using a function based on the first information and the adjustment information of the intestinal bacteria recommended for the subject. .. When the ratio of intestinal bacteria beneficial to the subject in the first information of the subject after the adjustment related to the specified adjustment information is increased as compared with the first information before ingestion, the generation unit 118 is the ratio. If the positive reward is specified according to the above ratio and the ratio is reduced compared to the first information before ingestion, the reward specific unit 119 that specifies the negative reward according to the ratio and the specified reward are used. It includes an update unit 120 that updates the function.

FIG. 34 is a schematic diagram showing an example of the learning model 157.
The learning model 157 is a learning model that is expected to be used as a program module that is a part of artificial intelligence software, and is, for example, a learning model constructed by DQN (Deep Q Network). As the DQN, a multi-layer neural network is used in which the state s is input and the value of the action value function Q (s, a) corresponding to the selectable action (adjustment) a is output. Hereinafter, the value of the action value function Q (s, a) is also referred to as a Q value. The multi-layer neural network constituting the learning model 157 includes, for example, an input layer 157a, an intermediate layer 157b, and an output layer 157c.

The input layer 157a includes a plurality of nodes. The number of nodes of the input layer 157a is prepared as many as the number of states s to be input. In this embodiment, the state s is the first information. The input information is not limited to this case. The input information includes at least the subject's (current) gut microbiota information. When the input information is only intestinal bacteria information, for example, a1: 10%, b2: 15%, c5: 55%, d6: 15%, e2: 5%, etc. of the type and amount (ratio) of intestinal bacteria. Information.
When the input information includes intestinal pH information as biological information in addition to intestinal bacterial information, pH has a great influence on the growth of bacteria. Therefore, adjusting pH is cited as an effective factor according to the bacteria to be increased or decreased. , Can improve the intestinal environment. As the input information, for example, "pH 8" can be mentioned. Under alkaline pH 8, spoilage bacteria grow and the intestinal environment is poor. When, for example, "metagenome information" is included as input information, information related to the intestinal metabolic environment such as the amount of lipid-metabolizing enzyme can be obtained, the intestinal environment of the subject can be grasped well, and adjustments suitable for the subject can be made. Can be set.
When the input information includes the living information of the subject in addition to the intestinal bacterial information, adjustments suitable for the subject can be set based on the living information of the subject. For example, since body temperature affects the growth of bacteria (and the activity of digestive organs), it is possible to improve the intestinal environment by maintaining proper body temperature, such as direct heating by bathing or eating, based on the normal heat of the subject. As the input information, for example, "normal heat 36 ° C." can be mentioned. In addition, if there is a history of antibiotic use, antibiotics will eliminate indigenous bacteria unrelated to the disease and upset the balance of the intestinal flora, so adjust for proper use. As input information, for example, "history of antibiotic use" can be mentioned.

The intermediate layer 157b is composed of a plurality of layers. Each layer of the intermediate layer 157b includes a plurality of nodes. The nodes of each layer in the intermediate layer 157b are connected to the nodes existing in the preceding and following layers by a desired weight and bias in one direction. The weights and biases that characterize the connection state between nodes are updated by learning. The intermediate layer 157b calculates using each node and outputs the calculation result to the output layer 157c.

The output layer 157c includes a plurality of nodes. The number of nodes of the output layer 157c is prepared as many as the number of actions a to be distinguished. In this embodiment, the action a is an adjustment, specifically information including a food or supplement, or other effective factor.

Each node of the output layer 157c outputs a Q value for each action a. The Q value is an expected value of profits obtained in the future when the action a is taken in the state indicated by the state s, and is information on changes in intestinal bacteria. In the learning model 157, the Q value is increased and the intestinal environment is deteriorated in the adjustment so as to improve the intestinal environment of the subject, that is, to approach the ideal intestinal flora model of the subject. It is learned so that the Q value becomes low in the adjustment.
The control unit 11 operates so as to calculate the information input to the input layer of the learning model 157 and output the Q value according to the command from the learning model.

Hereinafter, a method of generating the learning model 157 will be described.
The control unit 11 generates the learning model 157 by updating the behavioral value function Q (s, a) based on the following equation and improving the parameters that characterize the learning model 157.
_{Q (s t, a t)} ← Q (s t, a t) + α (r t + 1 + γmax a 'Q (s t + 1, a') - Q (s t, a t)) ... formula (1 )

The action value function Q (s, a) is the expected value of the profit to be obtained in the future when the action a is taken in the state s. r is the reward. The subscript t of the state s, the action a, and the reward r is a number indicating one step in the thinking process that repeats in time series. The subscript t is also referred to as a trial number. If the state changes after the action is decided, the trial number is incremented. Reward r _{t + 1} in the formula (1) may act a _t in state s _t is selected, a reward condition is obtained when it becomes s _{t + 1.} α is the learning rate and γ is the discount rate. The action a'is an action that maximizes the action value function Q (s _{t + 1} , at _{+ 1 ) among the actions a t + 1 that} can be taken _{in the state st + 1. max a 'Q (s t +} 1, a') is a maximized action value function by the action a 'is selected.
In the learning model 157, for example, the amount of the bacterium (2) is large, and the Q (s, a) corresponding to the adjustment in which diversity is maintained is high.

As shown in FIG. 34, the behavioral value function Q provides a plurality of behavioral options for adjusting the type and amount of intestinal bacteria. Specifically, the type of food or supplement to be ingested and the amount of food or supplement are prepared. For example, in the case of Q (s, supplement A 100 mg, beans 50 g), it is proposed to take "supplement A 100 mg, beans 50 g" as an adjustment. In the case of Q (s, celery 100 g, supplement A 50 mg, beans 50 g), it is proposed to take "celery 100 g, supplement A 50 mg, beans 50 g" as an adjustment. In addition to foods and supplements, the other effective factors mentioned above may also be added to the behavioral options.

As an example of the behavioral value function Q (s, a), when you want to obtain the ideal intestinal bacterial flora in the subject's intestine based on the intestinal bacterial information of the subject's first information,
Q (s, supplement A 100 mg, beans 50 g): 7
Q (s, celery 100g, supplement A 50mg, beans 50g): 8
Q (s, supplement B 60 mg, lettuce 100 g, beans 50 g): 5
Q (s, do nothing): 0
Can be mentioned. Here, supplement A contains 50% of b2 bacteria, and supplement B contains 40% of b2 bacteria. The Q value corresponds to the change information of intestinal bacteria, and when the Q value is high, the ratio of b2 bacteria increases in the intestine of the subject. Therefore, it is possible to identify the adjustment of "celery 100 g, supplement A 50 mg, beans 50 g" having the highest Q value.
When the adjusted intestinal bacterium information is acquired as the first information of the subject and the ratio of b2 bacteria increases as compared with the first information, the score DB 142 and the scored information DB 143 are read out to specify the reward. Based on the specified reward, the parameter θ included in the definition information of the learning model 157 can be improved, and the accuracy of the Q value output by the learning model 157 can be improved.

An example of the behavioral value function Q (s, a) when the first information includes the intestinal pH information in addition to the intestinal bacterial information and the pH is 8.
Q (s, supplement A 100 mg, beans 50 g, yogurt A 150 g): 9
Q (s, celery 100g, supplement A 50mg, beans 50g, yogurt A 200g): 10
Q (s, supplement B 60 mg, lettuce 100 g, beans 50 g, yogurt A 150 g): 7
Q (s, do nothing): 0
Yogurt A contains a large amount of b2 bacteria and also contains a large amount of lactic acid bacteria that change the pH in the intestine to the acidic side.

An example of the behavioral value function Q (s, a) when the first information includes information on normal temperature in addition to information on intestinal bacteria and the normal temperature is 36 ° C. is shown.
Q (s, supplement A 100 mg, beans 50 g, soak in bathtub for 30 minutes): 8
Q (s, celery 100g, supplement A 50mg, beans 50g, celery warmed): 9
Q (s, supplement B 60 mg, lettuce 100 g, beans 50 g, lettuce warmed): 6
Q (s, do nothing): 0

An example of the behavioral value function Q (s, a) when the first information includes the information of "history of antibiotic use" in addition to the intestinal bacterial information is shown.
Q (s, supplement A 100 mg, beans 50 g, use of antibiotics once a day): 8
Q (s, celery 100g, supplement A 50mg, beans 50g, discontinue use of antibiotics): 10
Q (s, supplement B 60 mg, lettuce 100 g, beans 50 g, antibiotics once a day): 6
Q (s, do nothing): 0

An example of the behavioral value function Q (s, a) when the first information includes the intestinal bacterial metagenomic information in addition to the intestinal bacterial information and the amount of lipid-metabolizing enzyme is small is shown.
Q (s, supplement A 100 mg, beans 50 g, avoid greasy foods): 8
Q (s, celery 100g, supplement A 50mg, beans 50g, avoid greasy foods): 9
Q (s, supplement B 60 mg, lettuce 100 g, beans 50 g, avoid greasy foods): 5
Q (s, do nothing): 0

FIG. 35 is a flowchart illustrating the generation procedure of the learning model 157.
The control unit 11 initializes the definition information of the learning model 157 (S101). The definition information includes structural information of the learning model 157, various parameters such as weights and biases between nodes used in the learning model 157, and the like. Hereinafter, various parameters included in the definition information of the learning model 157 will be referred to as parameter θ for convenience.

The control unit 11 acquires the first information (S102).

The control unit 11 inputs the acquired first information to the input layer 157a of the learning model 157 and executes the calculation by the learning model 157 to acquire the Q value (S103). The information input to the input layer 157a is given to the intermediate layer 157b. In the intermediate layer 157b, each node performs an operation using weight multiplication, bias addition, and activation function on the input from the immediately preceding layer, and the operation result is output to the output layer 157c. The output layer 157c outputs a Q value through each node. The control unit 11 acquires the calculated Q value of each action (adjustment).

The control unit 11 selects the adjustment to be taken by the target person. The control unit 11 selects a food or supplement as an adjustment based on the Q value (S104). In the above case, Q (s, celery 100 g, supplement A 50 mg, beans 50 g) is the highest, so the adjustment of "celery 100 g, supplement A 50 mg, beans 50 g" is selected.

The control unit 11 acquires the intestinal bacterial information of the subject after ingesting the food or supplement (S105).

The control unit 11 acquires the reward (S106).
FIG. 36 is a flowchart showing the processing of the reward acquisition subroutine.
The control unit 11 reads out the score DB 142 and the scored information DB 143 (S111).
The control unit 11 acquires the adjusted intestinal bacterium change information based on the intestinal bacterium information acquired in S105 (S112).
The control unit 11 calculates the reward (S113) based on the change information of the intestinal bacteria, the score DB 142, and the scored information DB 143, and returns.
For example
a1 bacteria b2 bacteria c5 bacteria d6 bacteria e2 bacteria Previous intestinal bacteria information 10% 15% 55% 15% 5%
Adjusted intestinal bacterial information 15% 30% 45% 10% 0%
And. Here, the a1 bacterium, the b2 bacterium, the c5 bacterium, the d6 bacterium, and the e2 bacterium belong to the bacterium A, the bacterium B, the bacterium C, the bacterium D, and the bacterium E, respectively, of the scored information DB143. In the case of the above-mentioned example of adjustment based on intestinal bacterial information, the Q value based on the types and ratios of all bacteria in the intestine is "8". When the ratio of bacteria of E is large, the Q value becomes a negative value such as "-5".
The a1 bacterium is "Lactobacillus plantarum" in the scored information DB 143, and the increase amount is 5%, so the reward is "+2". Since the b2 bacterium is "B. ruminantium", the reward is "+1". In this case, the Q value is "11". If the a1 bacterium increases by 20%, it belongs to the bacterium C from the scored information DB 143, so the reward is "0". In the case of "Salmonella" belonging to the fungus of E, the reward will be "-2" just because it exists, regardless of the ratio. _{The reward is r t + 1} in equation (1).

The control unit 11 calculates the value of the objective function (S107). In order to properly update the behavioral value function Q (s, a), it is necessary to optimize the learning model 157 that outputs the behavioral value function Q (s, a). Formula _{(1) (r t + 1} + γmax a 'Q (s t + 1, a') - Q (s t, represented by a _t), the objective function to minimize TD (Temporal Difference) error , Improve the parameter θ that characterizes the learning model 157.
Specifically, the control unit 11 stores a large number of combinations of the input intestinal bacterial information and the Q value after the reward is given in the auxiliary storage unit 14. The control unit 11 trains the learning model 157 by the error backpropagation method or the like, using the combination of the accumulated intestinal bacterial information and the Q value after the reward is given as the teacher data. In addition, the control unit 11 uses the simulator stored in the auxiliary storage unit 14 to reward based on the input intestinal bacterial information and the intestinal bacterial information after taking the supplement or the like by the action proposed based on the Q value. May be sought. Further, the control unit 11 may select an arbitrary action with a predetermined probability by using the ε-greedy method. Further, in the present embodiment, an example of calculating the immediate reward based on the ingestion-post-ingestion intestinal bacterium information is shown, but the reward based on the ingestion-post-ingestion ingestion bacterium information may be given. Further, although the control unit 11 has input the current intestinal bacterium information, in addition to the information, the intestinal bacterium information for the past several days may be input for learning.

The control unit 11 determines whether or not the learning has been completed (S108). When the threshold value for determining whether or not the TD error is sufficiently small is determined in advance and the value of the objective function is equal to or less than the threshold value, the control unit 11 determines that learning has been completed.

When the control unit 11 determines that the learning has not been completed (S108: NO), the control unit 11 updates the action value function Q (s, a) (S109). For example, the control unit 11 specifies the changing direction of the parameter θ that reduces the objective function based on the partial differential according to the parameter θ of the TD error, and changes the parameter θ. The specification of the change direction of the parameter θ is not limited to the partial differential according to the parameter θ of the TD error, and various gradient descent methods can be used. By the above processing, the parameter θ can be changed so that the action value function Q (s, a) approaches the target. After updating the action value function Q (s, a), the control unit 11 returns the process to S103.

When the control unit 11 determines in S108 that the learning has been completed (S108: YES), the control unit 11 updates the definition information of the learning model 157 (S110) and ends the learning. The control unit 11 stores the θ obtained by learning in the auxiliary storage unit 14 as definition information of the learning model 157 to be referred to when evaluating the adjustment.

As described above, in the present embodiment, the process of selecting the action a that maximizes the value of the action value function (s, a) and the adjusted intestinal bacteria based on the selected action (adjustment) a. The learning model 157 can be generated by optimizing the action value function (s, a) while repeating the process of acquiring information. The learned learning model 157 may be delivered to terminals 2 and 3.

Next, a procedure for specifying adjustment using the learning model 157 will be described.
FIG. 37 is a flowchart showing a procedure for specifying adjustment processing using the learning model 157.
The control unit 11 acquires the first information and stores the first information in the target person information DB 141 (S121). The first information includes intestinal bacterial information. In addition, information on healthy intestinal bacteria, biological information other than intestinal bacteria, living information, and preferences may be acquired.

The control unit 11 inputs the acquired information to the input layer 157a of the learning model 157 to execute the calculation by the learning model 157 (S122). The first information input to the input layer 157a is given to the intermediate layer 157b. In the intermediate layer 157b, each node performs an operation using weight multiplication, bias addition, and activation function on the input from the immediately preceding layer, and the operation result is output to the output layer 157c. The output layer 157c outputs a Q value through each node.

The control unit 11 acquires the Q value obtained as the calculation result of the learning model 157 from the output layer 157c (S123).

The control unit 11 evaluates the adjustment (S124). The learning model 157 outputs a Q value for each of the adjustments a from each node included in the output layer 157c. The control unit 11 evaluates the adjustment by referring to the Q value output from each node of the output layer 157c. For example, the control unit 11 can evaluate the adjustment a corresponding to the gut microbiota information showing the highest value among the Q values output from each node of the output layer 157c as the most preferable adjustment a. The gut microbiota information showing the highest value approximates the gut microbiota model ideal for the subject. In the case of the above example, the evaluation of the adjustment of "celery 100 g, supplement A 50 mg, beans 50 g" is the highest.
The control unit 11 identifies the adjustment a evaluated as the most preferable as the adjustment and stores it in the target person information DB 141 (S125). Target person information DB 141 No. In the case of 1, "mannitol" is stored as a nutritional component, "celery 100 g" and "beans 50 g" as foods, and "supplement A 50 mg" as a supplement, corresponding to the bacteria a (B. ruminantium) to be increased. The control unit 11 transmits the adjustment a to the terminal 2 (S126), and ends the process.
The control unit 21 of the terminal 2 receives the adjustment a (S131) and stores it in the target person information DB 262.
The control unit 21 displays the adjustment a on the display panel 25 and makes a proposal (S132), and ends the process.

As described above, the control unit 11 can satisfactorily evaluate the adjustment according to the intestinal bacterial information of the subject by using the learning model 157.

According to the present embodiment, the first information relating to the intestinal bacteria of the subject is input to the learning model 157, the evaluation of the adjustment is obtained, the adjustment is specified, and the intestinal bacteria in the intestine of the subject is identified. The type and quantity can be improved satisfactorily.
Then, the information of the food or supplement according to the type and amount of the intestinal bacteria to be adjusted is output to the terminal of the subject, and the predetermined amount of the intestinal bacteria of the subject is satisfactorily adjusted by the predetermined ingredient or supplement of the food. It can be increased or decreased.
In this embodiment, the case where DQN is used as an example of deep reinforcement learning is shown, but the present invention is not limited to this. Other learning methods such as SARSA, Actor-Critic, Policy gradients, etc. may be used.

The embodiments disclosed this time should be considered as exemplary in all respects and not restrictive. The scope of the present invention is indicated by the scope of claims, not the above-mentioned meaning, and is intended to include all modifications within the meaning and scope equivalent to the scope of claims.

1 Information processing device 2, 3 Terminal 4 Vegetable / supplement vending machine 10 Information processing system 11, 21, 31, 41 Control unit 111 1st acquisition unit 112 Adjustment specific unit 113 Cooking specific unit 114 Menu specific unit 115 Processed food specific unit 116 Output unit 117 2nd acquisition unit 118 Generation unit 119 Reward identification unit 120 Update unit 121 Adjustment setting unit 12, 22, 32, 42 Main storage unit 13, 23, 33, 43 Communication unit 14, 26, 36, 46 Auxiliary storage unit 140 Information processing program 141 Target person information DB
154 Adjustment DB
155 Recipe history DB
156, 157 learning model

Claims (19)

The acquisition department that acquires the first information including the intestinal bacterial information of the subject,
Based on the first information, the second information on the intestinal bacteria, and the third information on the nutrition of the intestinal bacteria, food components, or supplements, the adjustment information on the intestinal bacteria recommended for the subject. With a specific part that identifies
An information processing device including an output unit that outputs the specified adjustment information. Based on the second information and the third information, a database storing adjustment information corresponding to the first information is provided.
The information processing apparatus according to claim 1, wherein adjustment information is specified from the database according to the first information acquired by the acquisition unit. Based on the second information and the third information, a database storing adjustment information corresponding to the first information is provided.
The specific unit uses the data of the database as teacher data, and when the first information is input, inputs the acquired first information of the target person into the learning model that outputs the adjustment information to specify the adjustment information. The information processing apparatus according to claim 1. The specific part is
Based on the first information and the second information, the composition of intestinal bacteria suitable for the subject was identified.
The information processing apparatus according to claim 1, wherein the adjustment information is specified based on the identified composition of the intestinal bacterium and the third information. Based on the second information, corresponding to the first information, the first database storing the composition of the intestinal bacteria suitable for the subject, and based on the third information, corresponding to the composition of the intestinal bacteria. Equipped with a second database that stores adjustment information
The specific part is
When the data of the first database is used as the teacher data and the first information is input, the acquired first information of the subject is used in the first learning model that outputs the composition of the intestinal bacteria suitable for the subject. Enter to identify the composition of gut microbiota
When the data of the second database is used as the teacher data and the composition of the intestinal bacterium is input, the composition of the specified intestinal bacterium is input to the second learning model that outputs the adjustment information to specify the adjustment information. , The information processing apparatus according to claim 4. The acquisition department that acquires the first information including the intestinal bacterial information of the subject,
A generator that generates a deep reinforcement learning model that expresses the evaluation of the adjustment information using a function based on the first information and the adjustment information of the intestinal bacteria recommended for the subject.
A specific unit that inputs the acquired first information of the target person into the deep reinforcement learning model and specifies adjustment information, and
An information processing device including an output unit that outputs the specified adjustment information. The information processing according to claim 6, further comprising a setting unit for setting adjustment information based on the second information relating to the intestinal bacteria and the third information relating to the nutrition of the intestinal bacteria, food components, or supplements. apparatus. The generator
When the ratio of intestinal bacteria beneficial to the subject in the first information of the subject after the adjustment related to the specified adjustment information is increased as compared with the first information before ingestion, it corresponds to the ratio. When the positive reward is specified and the ratio decreases compared to the first information before ingestion, the reward specific part that specifies the negative reward according to the ratio, and
The information processing apparatus according to claim 6 or 7, further comprising an update unit that updates the function based on the specified reward. The first information further includes at least one of a group consisting of biological information other than the intestinal bacterial information of the subject, living information of the subject, and preference information of the subject, according to claims 1 to 8. The information processing apparatus according to any one of the above items. The information processing device according to any one of claims 1 to 5, and 7, wherein the second information includes information on the degree of contribution to health or information on intestinal bacteria of a healthy person or a sick person. The third information is a group consisting of nutritional requirement, saccharification, temporal nutritional information of intestinal bacteria, information on adjustment of composition of intestinal bacteria, nutritional component information of foods, and component information of supplements. The information processing apparatus according to any one of claims 1 to 5, 7, and 10, which comprises at least one. The adjustment information includes at least one of a group consisting of information on nutritional components to be ingested or avoided by the subject, information on foods to be ingested or avoided by the subject, and information on supplements to be ingested or avoided. , The information processing apparatus according to any one of claims 1 to 11. The information processing device according to claim 12, wherein the food is a vegetable. The adjustment information is information on the food product, and is
It is equipped with a cooking specific part that specifies the cooking method of the food.
The information processing device according to claim 12 or 13, wherein the output unit outputs a specified cooking method. The adjustment information is information on the food product, and is
It is equipped with a menu specifying part that specifies the menu related to the food.
The information processing device according to any one of claims 12 to 14, wherein the output unit outputs the specified menu. The adjustment information is information on the food product, and is
It is equipped with a processed food identification unit that specifies processed foods to be used with the foods.
The information processing device according to any one of claims 12 to 15, wherein the output unit outputs the specified processed food. The information processing device according to any one of claims 12 to 16, wherein the output unit outputs the timing of ingesting the food or the supplement. Obtain the first information including the intestinal bacterial information of the subject,
Based on the first information, the second information on the intestinal bacteria, and the third information on the nutrition of the intestinal bacteria, food components, or supplements, the adjustment information on the intestinal bacteria recommended for the subject. Identify and
An information processing method that outputs the specified adjustment information. Obtain the first information including the intestinal bacterial information of the subject,
Based on the first information, the second information on the intestinal bacteria, and the third information on the nutrition of the intestinal bacteria, food components, or supplements, the adjustment information on the intestinal bacteria recommended for the subject. Identify and
A computer program that causes a computer to execute a process that outputs the specified adjustment information.

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