Pet food recommendation device and pet food recommendation method, supplement recommendation device, supplement recommendation method, and intestinal age calculation formula determination method and intestinal age calculation method

Abstract

Provided is a pet food recommendation device and a pet food recommendation method that recommend food suitable for a pet. A pet food recommendation device is provided that includes a recommendation unit that recommends food suitable for a pet based on the results of an examination of pet feces and attribute information of the pet.

Description

Technical Field

[0001] This invention relates to a pet food recommendation device and method for recommending suitable pet food, a supplement recommendation device and method for recommending suitable human supplements, and a method for determining intestinal age calculation formula and method for calculating intestinal age. Background Technology

[0002] Currently, various pet foods have been proposed (e.g., Patent Document 1). However, it is not easy to produce pet food that is universally suitable for all pets. Similarly, various human supplements have been proposed, but it is not easy to produce supplements that are universally suitable for all people. Furthermore, Patent Document 2 discloses a technique for recommending supplements (functional materials) to individuals. However, because Patent Document 2 primarily uses user-oriented questionnaires, it is heavily influenced by user subjectivity and may not be able to recommend appropriate supplements. Existing technical documents Patent documents

[0003] Patent Document 1: Japanese Patent Application Publication No. 2017-2053 Patent Document 2: Japanese Patent No. 6245487 Summary of the Invention The problem that the invention aims to solve

[0004] The present invention aims to provide a pet food recommendation device and method for recommending suitable pet food, and a supplement recommendation device and method for recommending suitable human supplements. Furthermore, another objective of the present invention is to provide a method for estimating intestinal age based on bacterial distribution, and a method for determining the formula for intestinal age calculation. Technical solutions for solving the problem

[0005] According to one aspect of the present invention, a pet food recommendation device is provided, comprising a recommendation unit that recommends food suitable for the pet based on the results of pet feces examination and the pet's attribute information.

[0006] Alternatively, the examination results may include information about the pet's gut microbiota, and the recommendation unit may recommend suitable food for the pet based on the gut microbiota information and the pet's attribute information.

[0007] According to one aspect of the present invention, a pet food recommendation device is provided, comprising a recommendation unit that recommends suitable food for the pet based on the pet's attribute information and information on the food the pet has consumed.

[0008] Alternatively, the recommendation unit may infer the pet's gut microbiota information based on the pet's attribute information and the information on the food the pet has consumed, and recommend suitable food for the pet based on the gut microbiota information and the pet's attribute information.

[0009] Alternatively, the recommendation unit may classify the pet into one of a number of predefined groups based on the gut microbiota information, and pre-set recommended foods for each of the multiple groups.

[0010] Alternatively, the recommendation unit may classify the animals based on the information about the intestinal flora, using the diversity of intestinal bacteria, the abundance of lactic acid bacteria, the abundance of butyric acid-producing bacteria, and the FB ratio as criteria.

[0011] Alternatively, the benchmark may correspond to the attribute information of the pet.

[0012] Alternatively, the food may include basic food corresponding to the pet's attribute information and beneficial bacteria based on the information of the intestinal flora.

[0013] Alternatively, the beneficial bacteria may include bacteria that the pet lacks, based on information about the intestinal flora.

[0014] Alternatively, the recommendation unit may calculate the pet's intestinal age based on the information about the intestinal flora.

[0015] According to one aspect of the present invention, a pet food recommendation method is provided, comprising the step of recommending suitable food for the pet based on the examination results of the pet's feces and the pet's attribute information.

[0016] According to one aspect of the present invention, a pet food recommendation method is provided, comprising the step of recommending suitable food for the pet based on the pet's attribute information and information on the food the pet has consumed.

[0017] According to one aspect of the present invention, a supplement recommendation device is provided, comprising a recommendation unit that classifies a person into one of a predefined plurality of groups based on intestinal flora information obtained through examination of a person's feces, and recommends a suitable supplement for the person based on the classified group.

[0018] Alternatively, the information on the gut microbiota may include quantitative data on the types and quantities of bacteria contained in the human gut.

[0019] Alternatively, the recommendation unit may classify the gut microbiota based on whether the types of gut bacteria held by the person exceed a first benchmark value, whether lactic acid bacteria exceed a second benchmark value, whether butyrate-producing bacteria exceed a third benchmark value, and whether the FB ratio exceeds a fourth benchmark value.

[0020] Alternatively, the recommendation unit may also consider the person's attribute information to recommend suitable supplements for the person.

[0021] According to one aspect of the present invention, a method for recommending supplements is provided, the method comprising examining a person's feces to obtain information on the intestinal flora; classifying the person into one of a predefined plurality of groups based on the obtained information on the intestinal flora; and recommending a supplement suitable for the person based on the classified group.

[0022] According to one aspect of the present invention, a method for determining an intestinal age calculation formula is provided, comprising: acquiring microbial data in a healthy individual; selecting bacteria with a high correlation to true age based on the distribution of each bacteria; performing dimensionality compression on the distribution of the selected bacteria as an independent variable to extract principal components; creating a first formula for calculating intermediate intestinal age based on the principal components obtained by dimensionality compression and the distribution of the selected bacteria through regression analysis, i.e., creating a first formula including a constant for predicting true age based on the calculated intermediate intestinal age; and creating a second formula for predicting true age based on the intermediate intestinal age calculated by the created first formula through regression analysis, i.e., creating a second formula including a constant for reducing the error inherent in the prediction model based on the first and second formulas.

[0023] According to one aspect of the present invention, a method for calculating intestinal age is provided, which calculates intestinal age by applying the first formula and the second formula to the distribution of the selected bacteria. Invention Effects

[0024] Because it uses attribute information, it can recommend food suitable for the pet or person. Additionally, it can estimate intestinal age based on the distribution of bacteria. Attached Figure Description

[0025] Figure 1 This is a block diagram showing the general structure of the pet food recommendation system according to the first embodiment. Figure 2 This is an example of a web page used to input pet attribute information. Figure 3 This diagram illustrates the group classification based on recommendation unit 3. Figure 4A This is an example of a recommended screen for pets classified as Young-F. Figure 4B This is an example of a recommended screen for pets classified as Young-LC. Figure 4C This is an example of a recommended screen for pets classified as Young-N. Figure 4D This is an example of a recommended screen for pets classified as Young-C. Figure 4E This is an example of a recommended screen for pets classified as Young-N. Figure 5 This is a diagram of the food manufacturing process. Figure 6 This is a block diagram illustrating the general structure of the pet food recommendation system according to the second embodiment. Figure 7 This is a block diagram illustrating the general structure of the pet food recommendation system according to the third embodiment. Detailed Implementation

[0026] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. Furthermore, while dogs are exemplified as pets, the invention can also be applied to other pets such as cats and birds.

[0027] (First Implementation) The first implementation method is roughly as follows: examine pet feces and recommend pet food based on the results and the pet's attribute information.

[0028] Figure 1 This is a block diagram illustrating the general structure of the pet food recommendation system according to the first embodiment. The pet food recommendation system includes a pet information acquisition unit 1, a microbiome analysis unit 2, a recommendation unit 3, a recommendation result presentation unit 4, and a food order receiving unit 5. These can be comprised of a single device or distributed among multiple devices. As an example, the pet information acquisition unit 1, the recommendation result presentation unit 4, and the food order receiving unit 5 are implemented on a web server. The microbiome analysis unit 2 is located at a designated inspection agency. The recommendation unit 3 is located on the server of the system administrator (of course, this server can also be integrated with the aforementioned web server).

[0029] Pet information acquisition unit 1 acquires the pet's attribute information. More specifically, pet information acquisition unit 1 acquires the pet's attribute information manually entered by the owner from a designated platform on a website. Ideally, the pet's attribute information here includes breed, weight, age, sex, and living environment (indoor / outdoor), but it can be a part of these, or other information, as long as it is related to pet food, there are no particular restrictions.

[0030] As a specific example, the pet information acquisition unit 1 displays the following on the monitor of the owner's terminal device (not shown): Figure 2 The web page shown receives input from the owner regarding the pet's name, breed, and other attributes. When the "Apply" button is selected, the input attribute information is sent to recommendation unit 3. Additionally, a fecal examination kit is delivered to the owner.

[0031] return Figure 1 The microbial community analysis unit 2 performs a fecal examination on the pet. More specifically, the owner receives a fecal examination kit, collects a sample of the pet's feces, and returns it. Then, the microbial community analysis unit 2 examines and analyzes the feces to obtain information on the intestinal microbiota as the examination results. The intestinal microbiota refers to the ecology of bacteria growing in the pet's intestines. Moreover, the information on the intestinal microbiota may include, for example, the diversity of intestinal bacteria held by the pet, the number of lactic acid bacteria, the number of butyrate-producing bacteria (which may be both *Faecalibacterium* and *Clostridium*, or one of them), the ratio of Firmicutes to Bacteroidetes, and the FB ratio, which indicates the predisposition to obesity, but is not limited to these. For example, it may also include one or more of the following phyla: Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Fusobacterium, Clostridium, Lactobacillus, Bifidobacterium, Treponema, Broutella, SMB53, Fusobacterium, Prevotella, Ruminococcus, Turicibacter, Streptococcus, O2d06, Satetleella, Roselle, Dorea, and Eubacteria.

[0032] In this embodiment, since information on the intestinal flora, including objective and quantitative data such as the types and quantities of bacteria contained in the intestine, can be obtained from the results of fecal examination, an appropriate supplement can be provided.

[0033] Recommendation unit 3 recommends suitable pet food based on the pet's fecal examination results and pet attribute information. In this embodiment, the fecal examination results are set to include information on intestinal flora. The recommended food is not limited to one type; multiple foods with different flavors can also be recommended. Specific recommendation methods and recommended foods will be described later.

[0034] The recommendation result presentation unit 4 presents information about the recommended food to the pet owner. As a specific example, the recommendation result presentation unit 4 posts the recommended food information on a web page accessible to the pet owner, preferably also posting the pet's examination results. Additionally, the recommendation result presentation unit 4 can also send the recommended food information and a diagnostic report displaying the examination results to the pet owner.

[0035] The food ordering unit 5 accepts orders for recommended food presented to pet owners. For example, the food ordering unit 5 displays an icon on the web page listing the recommended food for purchasing it. Upon selection of this icon, the food ordering unit 5 performs the necessary checkout processing. The recommended food is then provided to the pet owner. The quantity may be set, for example, based on the pet's weight, to be consumed over a certain period (e.g., one month).

[0036] Next, the specific recommendation method performed by recommendation unit 3 is illustrated.

[0037] Recommendation unit 3 categorizes pets into one of several predefined groups based on gut microbiota information. Recommended food is then pre-set for each group. In other words, recommendation unit 3 categorizes pets into specific groups and recommends food specific to those groups.

[0038] Figure 3 This diagram illustrates the group classification performed in Recommendation Unit 3. Recommendation Unit 3 performs group classification based on the diversity of intestinal bacteria and based on lactic acid bacteria, butyrate-producing bacteria, and FB ratio.

[0039] First, Unit 3 categorizes gut bacteria diversity based on gut microbiota information into multiple groups (four examples are shown here). Generally, younger individuals tend to have more diverse gut bacteria; therefore, for convenience, the groups are named Young, Adult, Senior, and High Senior in order of diversity abundance. Gut bacterial diversity is assessed, for example, by whether the number of bacterial species in the gut exceeds a baseline value.

[0040] Next, Recommendation Unit 3 further categorizes the groups classified based on diversity into one of two groups according to the abundance of lactic acid bacteria. For example, classification can be based on whether the number of lactic acid bacteria exceeds a baseline value.

[0041] Then, Recommendation Unit 3 further categorizes the group with a higher proportion of lactic acid bacteria into one of two groups based on the FB ratio. For example, the FB ratio can be classified as higher than the baseline value. The group with the higher FB ratio is designated as type F. On the other hand, the group with a lower FB ratio is further categorized into two groups based on the quantity of butyrate-producing bacteria. For example, the quantity of butyrate-producing bacteria can be classified as higher than the baseline value. The group with more butyrate-producing bacteria is designated as type LC, and the group with fewer butyrate-producing bacteria is designated as type L.

[0042] Furthermore, Recommendation Unit 3 further categorizes the group with fewer lactic acid bacteria into one of two groups based on the abundance of butyrate-producing bacteria. For example, the categorization can be based on whether the number of butyrate-producing bacteria exceeds the baseline value. The group with more butyrate-producing bacteria is designated as type C, and the group with fewer butyrate-producing bacteria is designated as type N.

[0043] It can be said that type F is a group with more lactic acid bacteria but also higher FB levels, type LC is a group with more of both lactic acid bacteria and butyric acid-producing bacteria, type L is a group with more lactic acid bacteria but fewer butyric acid-producing bacteria, type C is a group with more butyric acid-producing bacteria but fewer lactic acid bacteria, and type N is a group with fewer of both lactic acid bacteria and butyric acid-producing bacteria.

[0044] Thus, based on the diversity of intestinal bacteria, the pet is classified into one of four groups, and based on the ratio of lactic acid bacteria, butyrate-producing bacteria, and FB, it is classified into one of five groups. This results in a total of twenty groups for the pet. For example, if the pet is classified as Young based on diversity and as F based on the ratio of lactic acid bacteria, butyrate-producing bacteria, and FB, then the pet is classified as Young-F.

[0045] Furthermore, it is preferable to set the baseline values ​​for each group classification to values ​​corresponding to the pet's attribute information such as breed, age, and weight. Additionally, the classification operation based on the abundance of lactic acid bacteria and butyric acid-producing bacteria includes a classification operation based on their presence or absence.

[0046] The recommended food can be a food containing a few percent of necessary beneficial bacteria mixed into a base food. The base food may also be unrelated to gut microbiota information, but preferably corresponds to the pet's attribute information; specifically, it can be a type suitable for the pet's actual age and a weight suitable for the pet's weight. On the other hand, the beneficial bacteria preferably correspond to groups classified according to gut microbiota information, including deficient bacteria and (supportive) substances that activate those bacteria.

[0047] Type F probiotics are preferred in terms of containing lactic acid bacteria, but they have a higher fiber content (FB) and can easily lead to obesity. Therefore, it is recommended to choose probiotic foods that contain soluble dietary fiber and oligosaccharides to enhance diversity, in addition to the basic food.

[0048] LC type is an ideal bacterial group with a high content of both lactic acid bacteria and butyric acid-producing bacteria. Therefore, probiotic foods are not necessary, and basic foods are recommended.

[0049] L-type bacteria are a group with a high proportion of lactic acid bacteria but a low proportion of butyric acid-producing bacteria. Therefore, it is recommended that foods containing butyric acid-producing bacteria and water-soluble dietary fiber that activates them, in addition to the basic food, be considered probiotic foods.

[0050] Type C bacteria are a group with a high proportion of butyric acid-producing bacteria but a low proportion of lactic acid bacteria. Therefore, it is recommended that foods containing lactic acid bacteria and oligosaccharides that activate them, in addition to the basic food, be considered as probiotic foods.

[0051] Type N bacteria are a group with relatively low levels of both lactic acid bacteria and butyric acid-producing bacteria. Therefore, it is recommended that foods containing lactic acid bacteria and butyric acid-producing bacteria, in addition to basic food products, be considered as probiotic foods.

[0052] In addition, for Senior and High Senior students, due to the low diversity of intestinal bacteria, probiotic foods containing water-soluble dietary fiber and oligosaccharides can be recommended not only for type F, but also for type LC, type L, type C and type N.

[0053] Furthermore, when comparing Young-F and Adult-F gut bacteria, it is generally recommended to use probiotic supplements containing soluble dietary fiber and oligosaccharides. However, because Adult-F gut bacteria lack diversity, probiotic supplements containing even more soluble dietary fiber and oligosaccharides are recommended. Thus, even within the same F type, the order of increasing soluble dietary fiber and oligosaccharides as probiotic supplements should be Young, Adult, Senior, and High Senior. This also applies to LC, L, C, and N types.

[0054] In addition, even for specific types, it is preferable to choose food that corresponds to the pet's attribute information. For example, Chihuahuas, as a breed of dog, require more calcium (because of their finer bones), oligosaccharides or dietary fiber (due to their delicate digestive system), and omega-3 fatty acids (to maintain joint health) compared to other breeds. Therefore, for Chihuahuas, such as those classified as Young-F, it is preferable to choose foods containing more calcium, oligosaccharides, dietary fiber, and omega-3 fatty acids compared to other breeds also classified as Young-F.

[0055] As another example, older dogs are prone to constipation due to decreased energy expenditure, insufficient exercise, and reduced intestinal activity. Therefore, for older dogs classified as Young-F (e.g., 8 years and older), compared to puppies (e.g., under 1 year old) or adult dogs (e.g., 1 to 8 years old) also classified as Young-F, it is preferable to recommend foods that contain more dietary fiber and are lower in calories.

[0056] In addition, as another example, indoor dogs are prone to obesity. Therefore, for indoor dogs, such as those classified as Young-F, in order to maintain muscle mass and increase basal metabolic rate, a diet with higher protein and lower calories is preferred compared to that of outdoor dogs also classified as Young-F.

[0057] Recommended pet food is determined as described above. Then, the recommendation result presentation unit 4 displays the following web page (hereinafter referred to as the recommendation screen).

[0058] Figures 4A-4EThese are diagrams showing an example of a recommendation screen for pets classified into Young-F type, Young-LC type, Young-N type, Young-C type and Young-N type, respectively. As shown in the figure, the recommendation screen includes descriptions of the classified types and their characteristics. Furthermore, specific food products are displayed. Furthermore, preferably, the recommendation screen numerically displays the diversity of intestinal bacteria, FB ratio, lactic acid bacteria, and butyric acid-producing bacteria used for group classification, and also displays simple comments and precautions.

[0059] In addition, the recommendation screen may also include the "intestinal age" calculated by the recommendation unit 3. Intestinal age is a parameter indicating the age of the intestinal flora, and is calculated based on information of the intestinal flora, attribute information, etc. Intestinal age can also be calculated according to the species and gender of the pet. A specific example of the intestinal age calculation method is described in the fourth embodiment.

[0060] Incidentally, as described above, the recommended food includes a base food and a beneficial bacteria food. With this configuration, the manufacturing process can be made efficient.

[0061] Figure 5 This is a flowchart of a food manufacturing process. The base food is manufactured by the following steps. First, a raw material preparation process such as pulverizing each material is performed (step S1a). Next, the raw materials are stirred (step S2a), and extrusion molding is performed with an extruder while heating (step S3a). Next, drying is performed at a high temperature (step S4a). Then, deformed particles and the like are removed (step S5a), and the base food is completed.

[0062] On the other hand, the beneficial bacteria food is manufactured by the following steps. First, a raw material preparation process such as burning chicken, drying and pulverizing, and mincing (original Japanese term: ミンチ加工) is performed (step S1b). Next, beneficial bacteria are added, the raw materials are stirred (step S2b), and extrusion molding is performed with an extruder without heating (step S3b). Next, drying is performed at a low temperature that does not kill the beneficial bacteria (step S4b). Then, deformed particles and the like are removed (step S5b), and the beneficial bacteria food is completed.

[0063] Then, required amounts of the base food and the beneficial bacteria food are weighed separately (steps S6a, S6b), bagged and labeled (step S7), and the food product is completed.

[0064] Thus, in the first embodiment, information about the intestinal flora (bacterial diversity, FB ratio, butyrate-producing bacteria, lactic acid bacteria, etc.) and pet attribute information (dog breed, weight, age, sex, living environment, etc.) can be considered to recommend suitable pet food. Specifically, in this embodiment, quantitative measured values ​​such as intestinal bacterial diversity, the quantity of each type of bacteria (lactobacilli, butyrate-producing bacteria, etc.), and FB ratio are obtained by examining feces. Therefore, pets can be objectively classified, and appropriate food can be recommended.

[0065] Furthermore, in this embodiment, both gut microbiota information and pet attributes are used, but it is also possible to use only gut microbiota information. Additionally, the recommended food based on gut microbiota information can be modified according to attributes.

[0066] (Second Implementation) The second embodiment omits the inspection of pet feces as in the first embodiment and instead uses information about the food the pet has ingested.

[0067] Figure 6 This is a block diagram illustrating the general structure of the pet food recommendation system according to the second embodiment. The following description focuses on the differences from the first embodiment.

[0068] In addition to the pet's attribute information, the pet information acquisition unit 1 in this pet food recommendation system also acquires information about the food the pet has consumed. As a specific example, a web page is displayed on the owner's terminal device for inputting the pet's (preferably recently) consumed food, and input from the owner is received.

[0069] Furthermore, the recommendation unit 3 recommends suitable food for the pet based on the pet's attribute information and information about the food the pet has consumed. Specifically, the recommendation unit 3 includes an intestinal flora information estimation unit 6. The intestinal flora information estimation unit 6 estimates the pet's intestinal flora information based on the pet's attribute information and information about the food the pet has consumed. As a specific example, the intestinal flora information estimation unit 6 maintains a database representing the relationship between the pet's attribute information, information about the food the pet has consumed, and the intestinal flora; by referring to this database, the intestinal flora information can be estimated.

[0070] In addition, it is common to the first embodiment except for the point that it uses inferred intestinal flora information instead of information based on flora analysis.

[0071] According to the second embodiment, since it is not necessary to examine pet feces, it is easier to recommend suitable food for pets.

[0072] (Third Implementation) The first and second embodiments described above recommend pet food. In contrast, the third embodiment described below recommends human food (mainly supplements).

[0073] Figure 7 This is a block diagram illustrating the general structure of the human supplement recommendation system according to the third embodiment. The human supplement recommendation system includes a human information acquisition unit 11, a microbiome analysis unit 12, a recommendation unit 13, a recommendation result presentation unit 14, and a supplement order receiving unit 15. These can be comprised of a single device or distributed among multiple devices. As an example, the human information acquisition unit 11, the recommendation result presentation unit 14, and the supplement order receiving unit 15 are implemented on a web server. The microbiome analysis unit 12 is located at a designated inspection agency. The recommendation unit 13 is located on the server of the system administrator (of course, this server can also be integrated with the aforementioned web server).

[0074] The human information acquisition unit 11 acquires the attribute information of the person. More specifically, the human information acquisition unit 11 acquires the attribute information of the person manually entered by the person being recommended (or a family member familiar with the person, etc.) from a designated platform on a website. Preferably, the attribute information of the person here includes weight, age, gender, and dietary and lifestyle information, but it can be a part of these, or it can be other information, as long as it is related to human supplements, there are no particular restrictions.

[0075] As a specific example, the human information acquisition unit 11 causes the display to show the same information as the human information acquisition unit 11. Figure 2 A similar webpage (where "dog breed" and "living environment" are not required). Then, it receives input of the attribute information of the person being tested. Then, when the "Apply" button is selected, the input attribute information is sent to recommendation unit 13. Additionally, a fecal examination kit is delivered to the person being tested.

[0076] The microbiota analysis unit 12 performs a stool examination on a person as the subject. More specifically, the person as the subject receives a stool examination kit, collects their own stool sample, and returns it. Furthermore, the microbiota analysis unit 12 examines and analyzes the stool sample to obtain information on the intestinal microbiota as the examination result. The intestinal microbiota refers to the ecology of bacteria growing in the human gut. Moreover, the information on the intestinal microbiota may include, for example, the diversity of intestinal bacteria present in humans, the number of lactic acid bacteria, the number of butyrate-producing bacteria (which may be both Clostridium and Clostridium, or one of them), the ratio of Firmicutes to Bacteroidetes, and the FB ratio, which indicates the susceptibility to obesity, but is not limited to these. It may also include one or more of the following phyla: Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Fusobacterium, Clostridium, Lactobacillus, Bifidobacterium, Treponema, Broutella, SMB53, Fusobacterium, Prevotella, Ruminococcus, Zurich bacillus, Streptococcus, O2d06, Sartella, Rosette, Durococcus, and Eubacteria.

[0077] In this embodiment, since information on the intestinal flora, including objective and quantitative data such as the types and numbers of bacteria contained in the intestine, can be obtained based on the results of stool examination, an appropriate supplement can be provided.

[0078] The recommendation unit 13 recommends suitable foods for a person based on the results of a fecal examination and the person's attribute information. In this embodiment, the fecal examination results are set to include information on intestinal flora. The recommended supplements are not limited to one type; multiple supplements with different flavors can also be recommended.

[0079] The recommendation result presentation unit 14 presents information about the recommended supplements to the recipient (or their family members, etc.). As a specific example, the recommendation result presentation unit 14 posts the recommended supplement information on a web page accessible to the recipient, preferably also posting the recipient's medical examination results. Alternatively, the recommendation result presentation unit 4 may also deliver the recommended supplement information and a diagnostic report showing the examination results to the recipient.

[0080] The food order-taking unit 15 accepts orders for recommended supplements. As a specific example, the supplement order-taking unit 15 displays an icon on the web page listing the recommended supplement for purchasing it. Upon selecting this icon, the supplement order-taking unit 15 performs the necessary checkout processing. The recommended supplement is then delivered to the individual.

[0081] Furthermore, the specific recommendation method performed by recommendation unit 13 can be the same as that described in the first embodiment. In this embodiment, quantitative measured values ​​such as the diversity of intestinal bacteria, the number of various bacteria such as lactic acid bacteria and butyric acid-producing bacteria, and the FB ratio can also be obtained by examining feces. Therefore, people can be objectively classified, and appropriate foods can be recommended.

[0082] Similarly to the second embodiment, the fecal examination can be omitted, and information on the food consumed by the person can be used instead. That is, the person information acquisition unit 11 acquires information on the food consumed by the person in addition to the person's attribute information. As a specific example, a web page for inputting the food consumed by the person (preferably recently) is displayed on the person's terminal device to receive the input.

[0083] Then, the recommendation unit 13 recommends suitable supplements for the person based on the person's attribute information and information about the food the person has consumed. Specifically, the recommendation unit 13 infers the person's gut microbiota information based on the person's attribute information and information about the food the person has consumed. As a specific example, the recommendation unit 13 maintains a database representing the relationship between the person's attribute information, information about the food the person has consumed, and gut microbiota, and can infer gut microbiota information by referring to this database.

[0084] In addition, it is common to the third embodiment described above, except for the point that uses inferred intestinal flora information instead of information based on flora analysis.

[0085] Thus, in the third embodiment, it is possible to recommend suitable food for a person by considering both gut microbiota information (bacterial diversity, FB ratio, butyrate-producing bacteria, lactic acid bacteria, etc.) and personal attribute information (weight, age, sex, diet, lifestyle, etc.). Furthermore, this embodiment uses both gut microbiota information and personal attributes, but it is also possible to use only gut microbiota information. Additionally, the food recommended based on gut microbiota information can be modified using attributes.

[0086] (Fourth Implementation) In the fourth embodiment described below, a specific example of the above-described method for calculating intestinal age will be explained. Intestinal age is an indicator that quantifies the health status of the gut using data on the distribution of intestinal bacteria, with chronological age as a baseline. Assuming that the distribution of intestinal bacteria has a certain tendency to correlate with age, and that in healthy individuals, intestinal age is consistent with chronological age, a predictive model for intestinal age will be explained.

[0087] In this prediction model, when the true age is set to y0, the intestinal age y can be calculated based on the following formula. y={(y1-y0)+a} / b+y0···(1) Here, y1=k1{f(a1,a2…an)}+k2{g(a1,a2···an)}···(2)

[0088] Furthermore, for convenience, the variable y1 will be referred to as "intermediate intestinal age". The independent variables a1~an, functions f and g, and constants k1, k2, a and b in equations (1) and (2) will be explained in turn.

[0089] The independent variables a1 to an are the distribution quantities and indicators of bacteria that are highly correlated with the true age and have a high probability of predicting the true age, and are selected through statistical methods. Specifically, they can be selected as follows.

[0090] First, based on past medical records, current medical records, medication status, and health status, certain baselines were established, and gut microbiota data were collected from healthy individuals who met these baselines. Then, the distribution of each bacterium (the proportion of each bacterium resolved by the sequencer relative to the total bacterial count, expressed as a continuous value between 0 and 1) was standardized using Logit transformation to create an analytical dataset. Finally, based on the contribution rates obtained through the bootstrap method, bacteria and indicators highly correlated with actual age were used in the gut age calculation model.

[0091] According to the inventor's analysis, the distribution of bacteria such as Bifidobacteria (k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Bifidobacteriales; f__Bifidobacteriaceae; g__Bifidobacterium), lactic acid bacteria (k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales; f__Lactobacillaceae; g__Lactobacillus), and butyric acid-producing bacteria (k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__Clostridium) can be used as independent variables.

[0092] Alternatively, other indicators different from bacteria (diversity indicators, or the distribution of multiple bacterial species contained in the feed) can be used as independent variables.

[0093] The independent variables a1 to an are determined as described above ("n" is the sum of the number of bacteria used and the number of other indicators). Alternatively, bacteria other than those listed, or other indicators, can be used as independent variables.

[0094] Secondly, principal component analysis was performed on the independent variables a1 to an to visualize the relationships between the independent variables and extract principal components based on dimensionality compression.

[0095] According to the inventor's analysis, the first and second principal components can be used based on their explanatory power. In equation (2) above, the function of the first principal component is f, and the function of the second principal component is g. Alternatively, the third and subsequent principal components can also be used.

[0096] After calculating the first principal component and the second principal component, the first principal component and the second principal component are adjusted based on the average distribution of each bacterium to determine the functions f and g in the above equation (2).

[0097] In addition, regression analysis was used to determine the constants k1 and k2 used to predict the true age y0 based on the intermediate intestinal age y1 in equation (2) above.

[0098] Furthermore, regression analysis is used to determine constants a and b for predicting the true age y0 based on the intermediate gut age y1. Constants a and b can also be described as constants used to adjust the error inherent in the prediction model to a smaller value.

[0099] Therefore, by determining the independent variables and constants used to calculate the intestinal age and applying the above equations (1) and (2) to the intestinal flora (a1~an), the intestinal age y can be calculated.

[0100] Furthermore, the calculation of intestinal age is primarily based on the assumption that it applies to pets, but it can also be applied to humans.

[0101] The purpose of describing the above embodiments is to enable those skilled in the art to implement the present invention. Of course, those skilled in the art can implement various modifications of the above embodiments, and the technical concept of the present invention can also be applied to other embodiments. Therefore, the present invention is not limited to the described embodiments, but should be considered within the maximum scope defined by the claims. Symbol Explanation

[0102] 1 Pet Information Acquisition Unit 2. Microbial community analysis unit 3 Recommended Units 4. Recommendation Results Presentation Unit 5 Food Order Receiving Unit 6. Intestinal flora information estimation unit 11-person information acquisition unit 12 Microbial Community Analysis Unit 13 Recommended Units 14. Recommendation Results Presentation Unit 15 Supplement Order Taking Units

Claims

1. A pet food recommendation device, comprising a recommendation unit that recommends suitable food for pets, wherein, The recommendation unit recommends suitable food for the pet based on the information of the intestinal flora obtained from the examination results of the pet's feces and the pet's attribute information. The information of the intestinal flora is related to the types and quantities of bacteria contained in the pet's intestines. The pet's attribute information includes the pet's species, weight, age, sex, and at least one of the following: Based on the information about the intestinal flora, the recommendation unit classifies the pet into one of a number of predefined groups, taking into account the diversity of the intestinal bacteria held by the pet and the abundance of lactic acid bacteria or butyric acid-producing bacteria. Recommended foods were pre-set for each of the multiple groups; The food contains: Beneficial bacteria containing bacteria that the pet lacks based on information about the intestinal flora, and Basic food items corresponding to the attribute information of the pet.

2. The pet food recommendation device according to claim 1, wherein, The recommendation unit classifies the animals based on the information about the intestinal flora, using the diversity of intestinal bacteria, the abundance of lactic acid bacteria, and the abundance of butyric acid-producing bacteria as criteria.

3. The pet food recommendation device according to claim 2, wherein, The benchmark corresponds to the attribute information of the pet.

4. The pet food recommendation device according to any one of claims 1 to 3, wherein, The food also contains substances that activate bacteria that the pet lacks.

5. A pet food recommendation method, comprising a recommendation step for recommending suitable pet food, wherein, In the recommendation step, suitable food for the pet is recommended based on the information of the intestinal flora obtained from the examination results of the pet's feces and the pet's attribute information. The information of the intestinal flora is related to the types and quantities of bacteria contained in the pet's intestines. The pet's attribute information includes the pet's species, weight, age, sex, and at least one of the following: In the recommended step, based on the information about the intestinal flora, the pet is classified into one of a number of predefined groups, according to the diversity of the intestinal bacteria held by the pet and the abundance of lactic acid bacteria or butyric acid-producing bacteria. Recommended foods were pre-set for each of the multiple groups; The food contains: Beneficial bacteria containing bacteria that the pet lacks based on information about the intestinal flora, and Basic food items corresponding to the attribute information of the pet.

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