Information processing system, information processing program, and information processing method

Abstract

The present invention identifies, at an early stage, a risk posed to a newborn or infant child, or recommendation information regarding said risk. This information processing system acquires input information related to the mother of a newborn or infant child, and estimates bacterial flora information related to the intestinal bacterial flora of the child on the basis of the acquired input information. The information processing system identifies, on the basis of the estimation result, risk information indicating a risk related to at least one among health, growth, and development of the child, and outputs the risk information. The information processing system identifies, on the basis of the estimation result, recommendation information regarding at least one of the diet of the mother and childcare for the child, and outputs the recommendation information.

Description

Information Processing System, Information Processing Program, and Information Processing Method

[0001] The present invention relates to an information processing system, an information processing program, and an information processing method that output risk information regarding a child and / or recommendation information for a mother by estimating the gut microbiota of the child.

[0002] Conventionally, there is a technique for determining whether a subject has autism spectrum disorder and / or epilepsy based on information on the gut microbiota collected from the subject (see, for example, Patent Document 1).

[0003] Japanese Patent No. 7548648

[0004] Conventionally, since obtaining information on the gut microbiota requires a period for examining the gut microbiota, it has been difficult to early estimate risks related to growth, development, and development of newborns and infants based on the information on the gut microbiota.

[0005] Therefore, an object of the present invention is to provide an information processing system, an information processing program, and an information processing method capable of early identifying risks that occur in a child or recommendation information for the risks.

[0006] In order to solve the above problems, the present invention adopts the following first to tenth configurations.

[0007] (First Configuration) The information processing system according to the first configuration includes an acquisition unit that acquires input information regarding the mother of a child who is a newborn or an infant, an estimation unit that estimates microbiota information regarding the gut microbiota of the child based on the acquired input information, a specification unit that specifies risk information indicating at least one of the health, growth, and development of the child based on the estimation result by the estimation unit, and an output unit that outputs the risk information.

[0008] According to the first configuration, risk information regarding a child can be identified early.

[0009] (Second Configuration) The information processing system according to the second configuration comprises: an acquisition means for acquiring input information about the mother of a newborn or infant child; an estimation means for inferring bacterial flora information about the child's intestinal flora based on the acquired input information; an identification means for identifying recommended information about at least one of the mother's diet and childcare for the child based on the estimation results from the estimation means; and an output means for outputting the recommended information.

[0010] According to the second configuration, recommendations regarding risks to children can be identified early.

[0011] (Third configuration) In the first or second configuration, the acquisition means may be able to acquire prenatal information before the birth of the child as input information. The estimation means may be able to estimate the bacterial flora information before the birth of the child.

[0012] According to the third configuration, the above-mentioned risk information or recommendation information can be identified even before the child is born.

[0013] (Fourth configuration) In the first or second configuration, the acquisition means may be capable of acquiring prenatal information before the birth of the child and postnatal information after the birth of the child as input information. The estimation means may be capable of estimating the bacterial flora information before the birth of the child and after the birth of the child.

[0014] According to the fourth configuration, the above risk information or recommendation information can be identified whether the mother is before or after giving birth.

[0015] (Fifth configuration) In the fourth configuration, the input information may include at least one of the following: information about food to be given to the child and information about the type of birth. The acquisition means may acquire at least one of the following as prenatal information: information about the planned food to be given to the child and information about the planned type of birth; and as postnatal information, it may acquire at least one of the following: information about the history of giving the food to the child and information about the type of birth that was performed.

[0016] According to the fifth configuration, even if the input information includes at least one of the following: information about the food given to the child and information about the birthing method, the bacterial flora information can be estimated using the same method before and after birth.

[0017] (Sixth configuration) In any of the first to fifth configurations, the input information may include at least one of the following: information that affects the mother's breast milk; information about food or medicine the mother consumes; information about the mother's birthing method; information about the mother's living environment; and information about food given to the child.

[0018] (Seventh configuration) In the sixth configuration, the input information may include information relating to the mother, specifically at least one of the following: her gut microbiota, her living environment, her diet, her use of antibiotics, the manner of birth of the child, and the circumstances under which she provides food to the child.

[0019] (Eighth configuration) In the first configuration, the identification means may identify recommended information relating to at least one of the mother's diet and childcare based on the prediction results by the prediction means. The output means may output the recommended information.

[0020] According to the eighth configuration, risk information and recommendation information can be presented to the user.

[0021] (Composition 9) In Composition 2 or 8, the recommendation information may include information on foods that mothers or children should consume, and childcare advice relating to the child's physical growth, immune system development, allergy prevention, intellectual development, bowel movements, and / or sleep habit formation.

[0022] (Tenth configuration) In any of the first to ninth configurations, the input information may include first information, which includes at least one of information relating to the mother's dietary intake and information relating to the type of the mother's breast milk, and second information of a different type from the first information. The estimation means may estimate breast milk component information relating to the components of the mother's breast milk based on the first information, and estimate the bacterial flora information based on the estimated breast milk component information and the second information.

[0023] According to the tenth configuration, bacterial flora information can be inferred based on breast milk component information obtained through estimation.

[0024] Another example of the present invention is that each of the means in the first to ten configurations described above may be an information processing program for causing a computer to function, or an information processing method executed in the information processing system in the first to ten configurations described above.

[0025] According to the above-described information processing system, information processing program, and information processing method, risk information and / or recommendation information concerning children can be identified at an early stage.

[0026] A block diagram showing an example of the configuration of an information processing system. A diagram showing an example of the input / output relationship between input information and sub-microbiota information. A diagram showing another example of the input / output relationship between input information and sub-microbiota information. A diagram showing an example of input information before and after the birth of a child. A diagram showing an example of sub-microbiota information, which is the prediction result by the prediction unit. A diagram showing an example of the correspondence between sub-microbiota information, risk information and recommendation information. A flowchart showing an example of processing performed on the server. A diagram showing an example of an image displayed on a user terminal.

[0027] The following describes an example of an information processing system according to the present invention with reference to Figures 1 to 8. In this embodiment, the information processing system presents risk information regarding the child and recommended information to the mother, with the mother of a newborn or infant as the user. As will be described in detail later, in this embodiment, the information system infers information about the child's gut microbiota (hereinafter referred to as "child microbiota information" to distinguish it from the information about the mother's gut microbiota, which will be described later) based on the input information, and identifies the risk information and recommended information to be presented based on the inference results.

[0028] [1. Configuration of the Information Processing System] Figure 1 is a block diagram showing an example of the configuration of an information processing system. In this embodiment, the information processing system comprises a server 1 and a user terminal 2. The server 1 and the user terminal 2 can communicate with each other. The server 1 and the user terminal 2 communicate via a network such as the Internet and / or a mobile communication network.

[0029] Server 1 is composed of, for example, one or more general-purpose computers, and its hardware configuration includes at least a processor such as a CPU and a memory device. The memory device stores at least a program for operating Server 1. The term "server" above refers to a single information processing device (i.e., a server device), and also refers to the entire group of server devices (i.e., a server system) if the functions of that server are realized by multiple information processing devices.

[0030] User terminal 2 is an example of an information processing device used by the user, such as a smartphone, mobile phone, tablet, personal computer, or wearable device. In this embodiment, the user inputs information to user terminal 2 and confirms the risk information and recommendation information output by user terminal 2.

[0031] As shown in Figure 1, the server 1 comprises an acquisition unit 11, an estimation unit 12, a risk information identification unit 13, a recommendation information identification unit 14, and an output unit 15. In this embodiment, each of these units 11 to 15 is realized through the cooperation of the server 1's hardware (e.g., processor, storage device, input device, display device, and communication device, etc.) and a program stored in the server 1.

[0032] The acquisition unit 11 acquires input information entered by the user, the mother. As will be described in detail later, the input information is information about the mother. In this embodiment, the user terminal 2 receives input information from the user and sends the entered input information to the server 1. The acquisition unit 11 acquires the input information sent from the user terminal 2 to the server 1. The acquired input information is passed to the inference unit 12.

[0033] The estimation unit 12 estimates sub-microbiota information regarding the child's intestinal microbiota based on the input information. The sub-microbiota information can be any information indicating the state of the child's intestinal microbiota. As will be described in detail later, in this embodiment, the estimation unit 12 calculates information on microbiota types, which classifies the state of the intestinal microbiota into several types, as sub-microbiota information (Figure 5). The sub-microbiota information, which is the estimation result from the estimation unit 12, is passed to the risk information identification unit 13 and the output unit 15.

[0034] The risk information identification unit 13 identifies risk information based on the child microbiota information. The risk information indicates a risk related to at least one of the following: the child's health, growth, and development. As will be described in detail later, in this embodiment, the risk information indicates a risk corresponding to the microbiota type information indicated by the child microbiota information. The identified risk information is passed to the recommendation information identification unit 14 and the output unit 15.

[0035] The recommendation information identification unit 14 identifies recommendation information based on the risk information. As will be described in detail later, the recommendation information is information relating to at least one of the following: the mother's diet and the mother's childcare. The recommendation information may, for example, recommend ways to reduce the risks indicated by the risk information, or ways to respond to those risks. The identified recommendation information is passed to the output unit 15.

[0036] The output unit 15 outputs information on the microbiome, risk information, and recommendation information. In this embodiment, the output unit 15 transmits this information from the server 1 to the user terminal 2. The user terminal 2 displays this received information. In this way, the microbiome information, risk information, and recommendation information are presented to the user.

[0037] [2. Prediction of Sub-microbiota Information] Figure 2 is a diagram showing an example of the input-output relationship between input information and sub-microbiota information. In this embodiment, the prediction unit 12 takes the various information shown in Figure 2 as input information and predicts sub-microbiota information based on the input information. Specifically, in this embodiment, the input information includes diet information, breast milk type information, birthing method information, antibiotic information, formula milk usage information, and age information.

[0038] The dietary information, for example, shows the mother's diet during a specified period (for example, the most recent week). For instance, the user may input information about the menus of meals during the specified period, and this input information may be used as the dietary information. Alternatively, the dietary information may show the nutrients the mother consumed during the specified period. Since the mother's diet affects the composition of her breast milk, and the composition of breast milk affects the child's gut microbiota, the dietary information can be used to infer information about the child's gut microbiota.

[0039] Breast milk type information indicates the type of breast milk based on its composition. Here, breast milk can be classified into two types: Type 1, which is from a secretory individual (mother), and Type 2, which is from a non-secretory individual. It is known that there are differences in the composition of breast milk (e.g., breast milk oligosaccharides) between these two types. Therefore, breast milk type information can be used to infer information about the progenitor microbiome. The type of breast milk can be determined by examining the breast milk or saliva. For example, a mother (user) may obtain her own breast milk type information beforehand through testing and input this information as input data.

[0040] Delivery method information refers to information indicating the method of delivery when a mother gives birth to her child. In this embodiment, delivery method information indicates whether the delivery was vaginal or cesarean section. It is known that differences in delivery methods affect the neonatal gut microbiota, and therefore, delivery method information can be used to infer information about the neonatal gut microbiota.

[0041] Antibiotic information refers to information about the mother's use of antibiotics. For example, antibiotic information shows the history of antibiotic use during a specified period. This specified period is, for example, the pregnancy period if the mother is pre-birth, or the period from pregnancy to the present if the mother is postpartum. It is known that the mother's use of antibiotics affects the child's gut microbiota. Therefore, antibiotic information can be used to infer information about the child's gut microbiota.

[0042] The formula milk usage information indicates the status of formula milk administration to a child. For example, the formula milk usage information may indicate the frequency of formula milk administration, the ratio of breast milk to formula milk, or the amount of formula milk administered within a certain period. Furthermore, since the foods given to a child affect their gut microbiota, information on the foods given to a child can be used to infer information about the child's gut microbiota. Therefore, in other embodiments, information indicating the administration status of foods other than formula milk may be used as input information.

[0043] Age information indicates the mother's age. Since maternal age is associated with health risks to children (e.g., risk of developmental disorders), it is thought that this information can be used to predict health risks to children based on their microbiome.

[0044] As described above, the "input information regarding the mother" means information regarding the nature and / or state of the mother and information regarding the actions the mother takes towards the child (e.g., formula milk usage information). Further, the "input information regarding the mother" means information including information that affects the mother's breast milk, such as the above-described dietary information, breast milk type information, and antibiotic information. Further, the "input information regarding the mother" means information including information regarding the food or medicine the mother ingests, such as the above-described dietary information and antibiotic information.

[0045] Note that the various types of information shown in FIG. 2 are examples of input information. In other embodiments, some or all of the various types of information shown in FIG. 2 may not be included in the input information, or other information different from the various types of information shown in FIG. 2 may be included in the input information.

[0046] For example, the input information may include maternal microbiota information, which is information regarding the mother's gut microbiota. It is known that the mother's microbiota information affects the child's gut microbiota, and it is considered that the maternal microbiota information can be used to estimate the child's microbiota information. Note that the maternal microbiota information does not have to be in the same format as the child's microbiota information and may be information in any format. Also, the maternal microbiota information may be, for example, information obtained by having the mother input information regarding the diet content and estimating based on that information, or information obtained by prior examination.

[0047] Also, for example, the input information may include living environment information, which is information regarding the mother's living environment. The living environment information is, for example, information indicating the composition of the family living with the mother and / or the child, information indicating the presence or absence of pets, etc., information from which the hygiene status can be estimated. It is also known that these types of information affect the child's gut microbiota, and it is considered that they can be used to estimate the child's microbiota information.

[0048] Note that it is considered that the mode of delivery has a great influence on the child's microbiota information, and it is also easy for the user to input. Therefore, the input information may at least include mode of delivery information. This can improve the estimation accuracy of the child's microbiota information without significantly increasing the input burden on the user.

[0049] As described above, in this embodiment, child microbiota information can be inferred based on input information about the mother, and risk information and recommendation information can be identified. In contrast, methods that identify risk information and recommendation information based on test results by examining the child's gut microbiota require time for testing, making it difficult to immediately provide risk information and recommendation information, and it is also difficult to provide it in the early stages after birth. Furthermore, it is impossible to provide this information before birth using the above method. In contrast, in this embodiment, child microbiota information is inferred using input information about the mother without using test results about the child, so the user can easily receive risk information and recommendation information even before birth or in the early stages after birth.

[0050] Furthermore, in this embodiment, the input information does not include information indicating the results of tests performed on the mother or child at the time of input (for example, the results of a child's stool test or the results of a mother's breast milk test). In this embodiment, since the child microbiome information is inferred without using such information, risk information and recommendation information can be presented early from the time of input without requiring time for testing. Regarding the above-mentioned maternal microbiome information, it can be obtained without performing tests by having the user input information about their diet. Also, breast milk type information can be obtained by performing tests in advance. Therefore, even if the input information includes maternal microbiome information or breast milk type information, the user can easily input the information without requiring time when entering the information.

[0051] Furthermore, the information processing system may require the user to input their identification information when inputting information, and may store the input information in association with the user's identification information. In this case, if a user who has previously entered information is entering it again, the information processing system may reduce the effort required for re-entry by using the previous input as the current input as needed. For example, the breast milk type information and the birthing method information after childbirth mentioned above are types of information whose content does not change depending on when the information is entered. For such types of information, if the information processing system has stored the previously entered information for the user, it may use the stored information as the current input information, allowing the user to omit input for the current entry.

[0052] In other embodiments, the estimation unit 12 may be configured to output sub-microbiota information even if some of the input information is not provided. For example, information that can be obtained through prior testing, such as breast milk type information and mother microbiota information, may be treated as optional input items when the user inputs the information. This makes it easier for the user to input the information, thereby improving the convenience of the information processing system.

[0053] The specific method for inferring sub-microbiota information based on input information is arbitrary. For example, in this embodiment, the inference unit 12 infers sub-microbiota information using a trained model generated by machine learning. The trained model is generated using training data that associates input information obtained from a questionnaire given to the mother who is the subject with sub-microbiota information obtained from an examination of the mother's child. The trained model is pre-stored in a storage device provided by the server 1. When a trained model is used, the system may perform an examination (for example, an examination of the child's gut microbiota) on the mother and / or child using this information processing system, and use the examination results as training data to perform additional training on the trained model.

[0054] In the estimation of sub-microbiome information, the information entered by the user does not need to be directly input into the trained model; information obtained based on the entered information may be input into the trained model. Figure 3 shows another example of the input / output relationship between input information and sub-microbiome information. As shown in Figure 3, the estimation unit 12 may estimate breast milk component information based on diet information and breast milk type information among the input information. In the example shown in Figure 3, the estimation unit 12 estimates sub-microbiome information based on the estimated breast milk component information and other input information other than diet information and breast milk type information. In the estimation unit 12, a trained model for estimating breast milk component information and a trained model for estimating sub-microbiome information may be prepared separately.

[0055] In this embodiment, the information processing system can infer information about the child's microbiome based on input information entered after the mother gives birth, and can also infer information about the child's microbiome based on input information entered before the child gives birth. Figure 4 shows an example of input information before and after childbirth. Of the input information shown in Figure 2, information other than birthing method information and formula milk usage information is the type of information that can be entered according to the mother's situation and condition at the time of input, regardless of whether it is before or after childbirth (this includes information whose content does not change depending on the time of input, such as breast milk type information). In contrast, with regard to birthing method information among the input information, during the pre-birth period, information about the planned birthing method is entered as birthing method information, and during the post-birth period, information about the birthing method that was performed is entered as birthing method information (see Figure 4). Furthermore, with regard to formula milk usage information among the input information, during the pre-birth period, information about the plan to give formula milk to the child is entered as formula milk usage information, and during the post-birth period, information about the history of giving formula milk to the child is entered as birthing method information (see Figure 4). For example, the former information may indicate the planned frequency of using formula milk, while the latter information may indicate the actual frequency of using formula milk. Furthermore, if information regarding foods to be given to the child is used as input information, information regarding the planned frequency of giving such foods may be entered as input information, and during the postnatal period, information regarding the history of giving such foods to the child may also be entered as input information.

[0056] As described above, in this embodiment, pre-natal information (before childbirth) and post-natal information (after childbirth) can be obtained as input information, and the microbiome information is estimated based on at least one of the pre-natal or post-natal information. This makes it possible to present risk information and recommendation information based on the estimation results, whether before or after childbirth, thereby improving the convenience of users using the information processing system. As mentioned above, if a user who has previously entered input information enters it again, the information processing system may use the previous input content as new input content as necessary. Therefore, after childbirth, the microbiome information may be estimated based on both the newly entered post-natal information and the pre-natal information used as the current input content.

[0057] Furthermore, in this embodiment, with respect to information that is undetermined before birth (e.g., birthing method information and formula milk usage information), pre-birth information uses information indicating the planned content, and post-birth information uses information indicating the content that was actually performed. This allows for the estimation of the sub-microbiome information to be performed before birth, even when such types of information are included in the input information. In addition, in this embodiment, estimation can be performed without changing the type of input information before and after birth, so the information processing system can estimate the sub-microbiome information using the same method (e.g., using the same trained model) before and after birth.

[0058] In this embodiment, information on the type of delivery, which is considered to have a significant impact on the child's gut microbiota, is entered by the user as planned information, enabling predictions based on delivery type information even before birth. This improves the accuracy of predictions made before birth.

[0059] In other embodiments, the information processing system may, before birth, infer information about the child microbiota based on input information that does not include information of a type that is undetermined before birth, and after birth, infer information about the child microbiota based on input information that includes such information.

[0060] Figure 5 shows an example of sub-microbiota information, which is the result of estimation by the estimation unit 12. As shown in Figure 5, in this embodiment, the intestinal microbiota is classified into several types (four in this case), and sub-microbiota information is calculated that shows the probability of the child's intestinal microbiota belonging to each type. The sub-microbiota information exemplified in Figure 5 shows that the probability of type A is 70%, the probability of type B is 5%, the probability of type C is 20%, and the probability of type D is 5%. The type with the highest probability value in the estimated sub-microbiota information can also be said to be the type to which the estimated intestinal microbiota belongs. In this embodiment, type A is a type of microbiota centered on bacterium A (i.e., bacterium A is abundant), type B is a type of microbiota centered on bacterium B, type C is a type of microbiota centered on bacterium C, and type D is a type of microbiota where major bacteria are present in a balanced manner. As described above, in this embodiment, the sub-microbiota information is information that indicates the type of microbiota, so the sub-microbiota information can be presented in a format that is easy for the user to understand. In other embodiments, information indicating only the type with the highest probability value may be calculated as sub-bacterial information showing the type of bacterial flora. The sub-bacterial information may be in any format that shows the intestinal flora of a child. For example, in other embodiments, the sub-bacterial information may be information showing the relative abundance of each major type of bacteria.

[0061] [3. Identification of Risk Information and Recommendation Information] Figure 6 shows an example of the correspondence between sub-microbiota information and risk information and recommendation information. In this embodiment, the information processing system stores correspondence information that associates the type of microbiota with risk information and recommendation information, as illustrated in Figure 6. The correspondence information defines the relationship between the type of microbiota and the risk (or the degree of the risk) that may occur when the state of the intestinal microbiota indicated by the sub-microbiota information corresponds to that type. Such correspondence relationships are set in advance based on conventional research results showing the relationship between the state of the intestinal microbiota and the risks that may occur in that state, as well as experimental data related to that relationship.

[0062] In this embodiment, the risk information includes, as an example of risks related to a child's health, growth, and development, risks related to a child's physical growth, immune system development, sleep habit formation, allergy onset, and bowel movements (see Figure 6). Risks related to sleep habit formation, allergy onset, and bowel movements are examples of risks related to a child's health. Risks related to physical growth are examples of risks related to a child's growth. Risks related to immune system development are examples of risks related to a child's development. Since a child's sleep habits are formed by brain development, it can also be said that risks related to sleep habit formation are examples of risks related to a child's development. In other embodiments, some or all of the information on the various risks shown in Figure 6 may not be included in the risk information, or other information different from the various information shown in Figure 6 may be included in the risk information. For example, research results showing a correlation between a child's intellectual development and the gut microbiota are known. Therefore, in other embodiments, the risk information may include information indicating risks related to a child's intellectual development.

[0063] In this embodiment, the risk information indicates the degree of each of the above-mentioned risks. In this embodiment, the risk information is expressed in three levels. In the example shown in Figure 6, "++" indicates almost no risk, "+" indicates low risk, and "-" indicates high risk. The risk information may also be expressed numerically, or it may simply indicate the presence or absence of risk.

[0064] The risk information identification unit 13 identifies the risk information that corresponds to the type with the highest probability value among the multiple types included in the correspondence information, from among the multiple risk information in the correspondence information. For example, if the child microbiota information is as illustrated in Figure 5, the risk information corresponding to type A, which has the highest probability value among the multiple risk information in the correspondence information, is identified. In the example shown in Figure 6, for type A, where bacteria A is abundant, risk information indicating a high risk regarding immune system development and sleep habit formation is identified. For type B, where bacteria B is abundant, risk information indicating a high risk regarding physical growth and bowel movements is identified. For type C, where bacteria C is abundant, risk information indicating a high risk regarding physical growth and sleep habit formation is identified. For type D, where major bacteria are present in a balanced manner, risk information indicating a low risk regarding the physical growth of children, immune system development, sleep habit formation, allergy onset, and bowel movements is identified.

[0065] Furthermore, as shown in Figure 6, the correspondence information defines the relationship between risk information and the recommended information presented to the user when that risk information is presented. This correspondence is created in advance based on conventionally known research results, etc., regarding methods to reduce the risks indicated by the risk information, or the actions to be taken when that risk occurs.

[0066] In this embodiment, the recommended information includes information recommending foods that mothers or children should consume, and information providing childcare advice. The former, for example, is information recommending foods that improve the state of the gut microbiota, which is a risk factor indicated by the risk information. The latter, for example, is information recommending methods to mitigate the risks indicated by the risk information, or ways to deal with those risks.

[0067] For example, in the example shown in Figure 6, if the inferred sub-microbiota information indicates that type A is the most likely, the identified risk information indicates a high risk regarding immune system development and sleep habit formation. In this case, to reduce these risks, the recommended information identified would be, for example, information recommending probiotic materials to increase bacteria B and C in the gut microbiota, and / or information recommending dietary improvements to increase bacteria B and C in the gut microbiota. Note that the food or dietary recommendations may be recommendations for foods or diets consumed by the child, or, since breast milk, which affects the child's gut microbiota, is influenced by the mother's diet, it may also be recommendations for foods or diets consumed by the mother. In the above case, the recommended information identified would be, for example, information on childcare advice to promote immune system development and sleep habit formation.

[0068] If the inferred sub-microbiota information indicates that type B is the most likely, the identified risk information indicates a high risk regarding physical growth and bowel movements. In this case, the identified recommendation information may include, for example, information recommending probiotic materials to increase bacteria A and C in the gut microbiota, and / or information recommending dietary changes to increase bacteria A and C in the gut microbiota, in order to reduce these risks. In addition, in the above case, the identified recommendation information may include, for example, information on childcare advice to promote physical growth and bowel movements.

[0069] If the inferred sub-microbiota information indicates that type C is the most likely, the identified risk information indicates a high risk regarding physical growth and sleep habit formation. In this case, the identified recommendation information would be, for example, information recommending probiotic materials to increase bacteria A and B in the gut microbiota, and / or information recommending dietary improvements to increase bacteria A and B in the gut microbiota, in order to reduce these risks. In addition, in the above case, the identified recommendation information would be, for example, information regarding childcare advice to promote physical growth and sleep habit formation.

[0070] If the inferred sub-microbiota information indicates that type D is the most likely, the identified risk information will indicate a low risk for each of the risks shown in Figure 6. In this case, the recommended information will include, for example, advice on maintaining a balanced gut microbiota and a balanced selection of advice on each risk. Alternatively, in the above case, recommended information corresponding to the second most likely type in the inferred sub-microbiota information may be identified. For example, if the second most likely type is type A, the same recommended information as when the inferred sub-microbiota information indicates that type A is the most likely may be identified.

[0071] As described above, the recommendations may include information on foods that mothers or children should consume, and at least one of the following: childcare advice regarding improving immunity, preventing allergies, and / or establishing sleep habits. In other embodiments, some or all of this information may not be included in the recommendations, or other information different from this information may be included. For example, if the risk information includes information indicating risks related to physical growth or bowel movements, the recommendations may include information on foods to improve physical growth or bowel movements, or information on childcare advice to improve physical growth or bowel movements. Also, for example, if the risk information includes information indicating risks related to a child's intellectual development as described above, the recommendations may include information indicating childcare advice regarding intellectual development (e.g., advice recommending foods to improve the state of the gut microbiota that contributes to risks related to intellectual development).

[0072] In this embodiment, risk information and recommendation information are identified using the correspondence information described above. However, in other embodiments, the method for identifying risk information or recommendation information based on the microbiome information is arbitrary. For example, in other embodiments, identification may be performed using a trained model that takes the microbiome information as input and outputs risk information and / or recommendation information. In other embodiments, risk information or recommendation information may be identified using the breast milk component information described above as input in addition to the microbiome information.

[0073] [4. Processing Flow in the Information Processing System] Next, the details of the processing flow in the information processing system will be explained. Figure 7 is a flowchart of an example of processing performed on server 1. In this embodiment, the processor of server 1 executes a series of processes shown in Figure 7 by executing a program stored in the memory device. For example, when server 1 receives an instruction from user terminal 2 to generate microbiome information, risk information, and recommendation information from input information, it starts the processing shown in the flowchart of Figure 7.

[0074] The processor of server 1 executes the processing of each step shown in Figure 7 using a storage device such as memory. That is, the processor stores the information (in other words, data) obtained by each processing step in memory, and when it is necessary to use that information in subsequent processing steps, it reads the information from memory and uses it.

[0075] First, in step S1, the processor of server 1 outputs question information indicating questions for the user to input the above-mentioned input information. These questions may include, for example, "Please enter the contents of your meals for the past week" to prompt the user to input meal information, or "Will your delivery method (or planned delivery method if not yet delivered) be a cesarean section?" to prompt the user to input delivery method information. In this embodiment, the processor transmits the question information to user terminal 2 using the communication device of server 1.

[0076] User terminal 2 receives the question information and displays the question on its display device. User terminal 2 then accepts input of the answer to the displayed question. The user inputs the answer to the displayed question into user terminal 2. User terminal 2 transmits the input information to server 1.

[0077] In step S2, the processor of server 1 acquires input information transmitted from user terminal 2. Specifically, the processor acquires input information received by the communication device of server 1 and stores it in a storage device such as memory.

[0078] The process in steps S1 and S2 in which the server 1 acquires the input information entered by the user terminal 2 may be carried out in any way. For example, an application for inputting input information (and outputting risk information and recommendation information) may be installed on the user terminal 2. In this case, the question information sent in step S1 may be displayed on the application, and in step S2, the input information may be entered on the application. Alternatively, for example, the server 1 may provide a web page for inputting input information (and outputting risk information and recommendation information) in step S1. In this case, in step S2, the input information may be entered on the web page displayed on the user terminal 2.

[0079] In step S3, the processor of server 1 infers information about the child bacterial community based on the input information obtained in step S2. For example, the processor obtains the output information about the child bacterial community by inputting the input information into a trained model stored in memory.

[0080] In step S4, the processor of server 1 identifies risk information based on the microbiome information estimated in step S3. For example, the processor refers to the correspondence information stored in the memory device and identifies the risk information that corresponds to the estimated microbiome information in the correspondence information.

[0081] In step S5, the processor of server 1 identifies recommendation information based on the risk information identified in step S4. For example, the processor refers to the correspondence information stored in the memory device and identifies the recommendation information that is associated with the identified risk information in the correspondence information.

[0082] In step S6, the processor of server 1 outputs the microbiome information, risk information, and recommendation information obtained in steps S3 to S5. For example, the processor transmits this information to user terminal 2 using the communication device of server 1. User terminal 2 receives this information and displays it on its display device. This presents the information to the user.

[0083] Figure 8 shows an example of an image displayed on the user terminal 2. As shown in Figure 8, in this embodiment, the user terminal 2 displays sub-microbiota information 21, risk information 22, and recommendation information 23. This allows the mother to learn about the microbiota type of her child's gut microbiota, the risks associated with that type, and recommendations for those risks. In other embodiments, it is not necessary for all of this information 21-23 to be presented to the user; one or two of these pieces of information may be presented to the user. Furthermore, as shown in Figure 8, only the type with the highest probability may be displayed as sub-microbiota information, or the probability for each type may be displayed.

[0084] [5. Modifications] In the above embodiment, the case in which the information processing system includes multiple devices (specifically, a server 1 and a user terminal 2) was described as an example, but in other embodiments, the information processing system may consist of a single information processing device. Specifically, the information processing device may include the above-described parts 11 to 15 and present the user with sub-bacterial flora information, risk information, and recommendation information based on input information entered by the user. Furthermore, when the information processing system includes a server 1 and a user terminal 2, some of the above-described parts 11 to 15 may be provided by the user terminal 2. For example, the user terminal 2 may include a risk information identification unit 13, a recommendation information identification unit 14, and an output unit 15. In this case, the user terminal 2 may receive sub-bacterial flora information from the server 1, identify risk information and recommendation information based on the received sub-bacterial flora information, and display the identified sub-bacterial flora information, risk information, and recommendation information.

[0085] In the above embodiment, the explanation was given assuming that the user inputting the information is the child's mother, but the user inputting the information is not limited to the mother; it can be any person. For example, if this information processing system is installed in a hospital, the input may be performed by medical staff at that hospital.

[0086] The information processing system may output only either risk information or recommendation information based on the input information. Alternatively, the information processing system may output estimated information about the microbiome based on the input information, without specifying risk information or recommendation information.

[0087] In other embodiments, the information processing system may not have to include some of the configurations in the above embodiments, nor may it perform some of the processes executed in the above embodiments. For example, in order to obtain some specific results in the above embodiments, the information processing system may have to include the configurations for obtaining those results and perform the processes for obtaining those results, but it may not have to include other configurations or perform other processes.

[0088] The above embodiment can be used, for example, as an information processing system or information processing method that presents risk information and / or recommendation information to a user, with the aim of identifying risks to children or providing recommended information regarding such risks at an early stage.

[0089] 1 Server 2 User terminal 11 Acquisition unit 12 Estimation unit 13 Risk information identification unit 14 Recommendation information identification unit 15 Output unit

Claims

1. An information processing system comprising: an acquisition means for acquiring input information about the mother of a newborn or infant child; an estimation means for inferring bacterial flora information about the child's intestinal flora based on the acquired input information; an identification means for identifying risk information indicating a risk to at least one of the child's health, growth, and development based on the estimation results by the estimation means; and an output means for outputting the risk information.

2. An information processing system comprising: an acquisition means for acquiring input information about the mother of a newborn or infant child; an estimation means for inferring bacterial flora information about the child's gut microbiota based on the acquired input information; an identification means for identifying recommended information about at least one of the mother's diet and childcare for the child based on the estimation results from the estimation means; and an output means for outputting the recommended information.

3. The information processing system according to claim 1 or 2, wherein the acquisition means is capable of acquiring prenatal information before the birth of a child as input information, and the estimation means is capable of estimating the bacterial flora information before the birth of the child.

4. The information processing system according to claim 1 or 2, wherein the acquisition means is capable of acquiring prenatal information before the birth of a child and postnatal information after the birth of a child as input information, and the estimation means is capable of estimating the bacterial flora information before the birth of the child and estimating the bacterial flora information after the birth of the child.

5. The information processing system according to claim 4, wherein the input information includes at least one of information relating to food to be given to the child and information relating to the type of birth, the acquisition means acquires at least one of information relating to the planned food to be given to the child and information relating to the planned type of birth as pre-birth information, and acquires at least one of information relating to the history of giving the food to the child and information relating to the type of birth that was performed as post-birth information.

6. The information processing system according to claim 1 or 2, wherein the input information includes information relating to at least one of the following: information relating to the mother's breast milk, information relating to food or medicine consumed by the mother, information relating to the mother's birthing method, information relating to the mother's living environment, and information relating to food given to the child.

7. The information processing system according to claim 6, wherein the input information includes information relating to the mother, at least one of the following: her gut microbiota, her living environment, her diet, her use of antibiotics, the manner of birth of the child, and the circumstances under which she provides food to the child.

8. The information processing system according to claim 1, wherein the identification means identifies recommended information relating to at least one of the mother's diet and childcare based on the prediction results of the prediction means, and the output means outputs the recommended information.

9. The information processing system according to claim 2 or 8, wherein the recommended information includes information on foods that the mother or child should consume, and childcare advice relating to the child's physical growth, immune system enhancement, allergy prevention, intellectual development, bowel movements, and / or sleep habit formation.

10. The information processing system according to claim 1 or 2, wherein the input information includes first information which includes at least one of information relating to the mother's dietary intake and information relating to the type of the mother's breast milk, and second information which is of a different type from the first information, and the estimation means estimates breast milk component information relating to the components of the mother's breast milk based on the first information, and estimates the microbiome information based on the estimated breast milk component information and the second information.

11. An information processing program that causes the computer of an information processing device to function as an acquisition means for acquiring input information about the mother of a newborn or infant child; an estimation means for inferring bacterial flora information about the intestinal flora of the child based on the acquired input information; an identification means for identifying risk information indicating a risk to at least one of the health, growth, and development of the child based on the estimation results by the estimation means; and an output means for outputting the risk information.

12. An information processing program that causes a computer of an information processing device to function as: an acquisition means for acquiring input information about the mother of a newborn or infant child; an estimation means for inferring bacterial flora information about the child's intestinal flora based on the acquired input information; an identification means for identifying recommended information about at least one of the mother's diet and childcare for the child based on the estimation results by the estimation means; and an output means for outputting the recommended information.

13. An information processing method comprising: an acquisition step of acquiring input information about the mother of a newborn or infant child; an estimation step of inferring bacterial flora information about the child's intestinal flora based on the acquired input information; an identification step of identifying risk information indicating a risk to at least one of the child's health, growth, and development based on the estimation results from the estimation step; and an output step of outputting the risk information.

14. An information processing method comprising: an acquisition step of acquiring input information about the mother of a newborn or infant child; an estimation step of inferring bacterial flora information about the child's gut microbiota based on the acquired input information; an identification step of identifying recommended information about at least one of the mother's diet and childcare for the child based on the estimation results from the estimation step; and an output step of outputting the recommended information.

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