Water intake estimation device, water intake estimation method, and program

Abstract

To provide a water intake amount estimation device that acquires a water intake amount on the basis of a response to a questionnaire including questions other than a question that asks an amount of water taken directly, a water intake amount estimation method, and a program.SOLUTION: In a water intake amount estimation system including: a learning unit; a water intake amount estimation device; and one or more user terminals, a water intake amount estimation device 2 includes: an original information storage 212 that stores original information as an original for estimating a water intake amount from response information to each of two or more questions including questions other than those that ask for an amount of water directly taken; a response reception unit 221 that receives user response information having response information to each of the two or more questions; an intake amount acquisition unit 232 that acquires intake amount information for determining the water intake amount by using two or more pieces of response information included in the user response information received by the response reception unit 221 and the original information stored in the original information storage 212; and an intake amount output unit 242 that outputs a piece of intake amount information.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to a water intake estimation device and the like that obtains water intake using responses to a questionnaire. [Background technology]

[0002] Conventionally, there has been a health advice device that uses answers to a questionnaire to provide health advice. This device is a health advice device that displays advice for improving lifestyle habits, and includes a lifestyle analysis questionnaire input means for inputting answers to a predetermined set of questions, a means for calculating a predicted increase or decrease in health risk using the answers input by the lifestyle analysis questionnaire input means, an influencing answer extraction means for selecting answers that have increased the health risk from the input answers when an increase in health risk is predicted, and an influencing lifestyle item display means for displaying a question corresponding to the selected answer or advice associated with the question (see Patent Document 1).

[0003] In addition, in the past, the physical condition of subjects was assessed using vital data such as water intake, body temperature, and blood pressure. There is a monitoring device that monitors a target person by making a judgment (see Patent Document 2). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2014-135027 A [Patent Document 2] JP 2020-052808 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, in the past, it was not possible to obtain the amount of fluid intake based on responses to a questionnaire that includes questions other than those directly asking about the amount of fluid intake. [Means for solving the problem]

[0006] The water intake estimation device of the first invention is a water intake estimation device comprising: a raw information storage unit that stores raw information that is the basis for estimating water intake from answer information to two or more questions including questions other than questions that directly ask about the amount of water ingested; an answer receiving unit that receives user answer information having answer information to the two or more questions; an intake acquisition unit that acquires intake information that specifies the water intake using the two or more answer information contained in the user answer information received by the answer receiving unit and the raw information stored in the raw information storage unit; and an intake output unit that outputs the intake information.

[0007] With this configuration, the amount of fluid intake can be obtained based on answers to a questionnaire that includes questions other than those directly asking about the amount of fluid intake.

[0008] In addition, compared to the first invention, the fluid intake estimation device of the second invention is a fluid intake estimation device having two or more questions from the following: beverage-derived questions, which are questions regarding beverage addictiveness; alcohol-derived questions, which are questions regarding alcohol addictiveness; diet-derived questions, which are questions regarding dietary addictiveness; and all-derived questions, which are questions regarding all origins.

[0009] With this configuration, an appropriate amount of fluid intake can be obtained based on responses to an appropriate questionnaire that includes questions other than those directly asking about the amount of fluid intake.

[0010] The water intake estimation device of the third invention is different from the water intake estimation device of the first or second invention in that it further comprises a questionnaire storage unit in which two or more pieces of questionnaire information each having question information for two or more questions are stored in correspondence with conditions related to a user attribute value, a questionnaire acquisition unit that acquires from the questionnaire storage unit questionnaire information corresponding to the conditions corresponding to the user attribute value of the user, and a questionnaire output unit that transmits the questionnaire information acquired by the questionnaire acquisition unit to a user terminal, wherein the raw information storage unit stores two or more pieces of raw information corresponding to the conditions corresponding to the user attribute value, the reception unit accepts user response information corresponding to the user attribute value, and the intake acquisition unit acquires intake information using the raw information corresponding to the user attribute value corresponding to the user response information and the two or more pieces of answer information contained in the user response information.

[0011] With this configuration, an appropriate amount of fluid intake can be obtained based on answers to a questionnaire that varies depending on the user's attribute values.

[0012] The water intake estimation device of the fourth invention is a water intake estimation device in which, compared to any one of the first to third inventions, the two or more questions have two or more types of question sets consisting of one or more questions, the raw information storage unit stores two or more pieces of raw information corresponding to the types of questions, the intake acquisition unit acquires intake information for each type using answer information corresponding to each question of each of the two or more types and the raw information corresponding to each type, and acquires intake information using the two or more intake information.

[0013] With this configuration, an appropriate amount of fluid intake can be obtained based on answers to a questionnaire that includes questions other than those directly asking about the amount of fluid intake.

[0014] The fluid intake estimation device of the fifth invention is a fluid intake estimation device according to any one of the first to fourth inventions, in which the raw information is a learning device constructed by performing a learning process using a machine learning algorithm using two or more teacher data having water intake information and answer information to two or more questions or answer-related information obtained from the answer information, and the intake acquisition unit performs a prediction process using a machine learning algorithm using two or more answer information or answer-related information obtained from each of the two or more answer information contained in the user answer information accepted by the answer accepting unit, and the learning unit stored in the raw information storage unit, to acquire the intake information.

[0015] With this configuration, an appropriate amount of fluid intake can be estimated using a machine learning algorithm.

[0016] Furthermore, a fluid intake estimation device according to a sixth aspect of the present invention is a fluid intake estimation device according to the fifth aspect of the present invention, in which the machine learning algorithm is a random forest.

[0017] With this configuration, the appropriate amount of fluid intake can be estimated using a random forest algorithm. Effect of the Invention

[0018] The fluid intake estimation device according to the present invention can obtain the fluid intake amount based on the answers to the questionnaire that includes questions other than those directly asking about the amount of fluid intake. [Brief description of the drawings]

[0019] [Figure 1] Concept of water intake estimation system A in embodiment 1 [Diagram 2] Block diagram of the water intake estimation system A [Diagram 3] Block diagram of the water intake estimation device 2 [Figure 4] A flowchart illustrating an example of the operation of the learning device 1. [Diagram 5] A flowchart illustrating an example of the operation of the water intake estimation device 2. [Figure 6] A flowchart for explaining an example of the operation of the user terminal 3. [Figure 7] A diagram showing an example of the questionnaire information [Figure 8] A diagram showing an example of the questionnaire information [Figure 9] FIG. 13 shows the user response information management table. [Figure 10] The figure shows the actual measurement value management table. [Figure 11] A diagram showing the identity information management table [Figure 12] A diagram showing an example of the output [Figure 13] A diagram showing an example of the output [Figure 14] Overview of the computer system [Figure 15] Block diagram of the computer system DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] Hereinafter, embodiments of a water intake estimation device and the like will be described with reference to the drawings. Note that components with the same reference numerals in the embodiments perform similar operations, and therefore repeated description may be omitted.

[0021] (Embodiment 1) In this embodiment, a water intake estimation system is described that includes a water intake estimation device that acquires and outputs a water intake amount using answer information to two or more questions included in a questionnaire that includes questions other than the question directly asking about the amount of water ingested, and raw information for obtaining the water intake amount using the answer information. Note that the questionnaire has two or more questions from, for example, beverage origin, alcohol origin, food origin, and all origin. In addition, each of the two or more questionnaires is associated with, for example, a user attribute value condition, and a different questionnaire is sent to a user terminal depending on the user attribute value.

[0022] In this embodiment, a water intake estimation system will be described which has two or more types of questions and includes a water intake estimation device which acquires the water intake amount for each type of question using raw information.

[0023] In addition, in this embodiment, a water intake estimation system including a water intake estimation device that acquires a water intake amount using a machine learning algorithm will be described. Note that the machine learning algorithm is, for example, a random forest.

[0024] 1 is a conceptual diagram of a fluid intake estimation system A according to the present embodiment. The fluid intake estimation system A comprises a learning device 1, a fluid intake estimation device 2, and one or more user terminals 3.

[0025] The learning device 1 is a device that acquires raw information (described later) using the actual measured value of the water intake and answer information to two or more questions included in a questionnaire. The learning device 1 may be, for example, a so-called personal computer, tablet terminal, smartphone, server, or the like, and the type does not matter. The server may be, for example, a cloud server or an ASP server, and the type does not matter. The learning device 1 may be a standalone device or a server that receives instructions from a user terminal 3 and operates accordingly.

[0026] The water intake estimation device 2 is, for example, a so-called server. The server may be, for example, a cloud server or an ASP server, and the type of server does not matter. However, the water intake estimation device 2 may also be a standalone device such as a so-called personal computer, a tablet terminal, or a smartphone.

[0027] The user terminal 3 may be a personal computer, a tablet terminal, a smartphone, or the like, and the type of the terminal is not important.

[0028] The learning device 1, the water intake estimation device 2, and the user terminal 3 may be capable of communicating with each other via the Internet, a LAN, or the like.

[0029] Fig. 2 is a block diagram of a water intake estimation system A according to this embodiment. Fig. 3 is a block diagram of a water intake estimation device 2.

[0030] The learning device 1 includes a learning storage unit 11, a learning reception unit 12, a learning processing unit 13, and a learning output unit 14. The learning storage unit 11 includes a response storage unit 111 and an actual measurement value storage unit 112.

[0031] The water intake estimation device 2 comprises a storage unit 21, a reception unit 22, a processing unit 23, and an output unit 24. The storage unit 21 comprises a questionnaire storage unit 211 and a raw information storage unit 212. The reception unit 22 comprises a response reception unit 221. The processing unit 23 comprises a questionnaire acquisition unit 231 and an intake amount acquisition unit 232. The output unit 24 comprises a questionnaire output unit 241 and an intake amount output unit 242.

[0032] The user terminal 3 includes a terminal storage unit 31, a terminal reception unit 32, a terminal processing unit 33, a terminal transmission unit , a terminal reception unit 35, and a terminal output unit .

[0033] Various types of information are stored in the learning storage unit 11 constituting the learning device 1. The various types of information are, for example, answer information, actual measurement values, and identification information for each of two or more pieces of original information.

[0034] The identification information is information used when acquiring two or more pieces of original information. The identification information is information that specifies the conditions of the answer information used when acquiring the original information. The identification information is information that corresponds to the acquired original information. The identification information is, for example, information that specifies the conditions related to the user attribute value. The identification information is, for example, information that identifies the type of question set. The identification information includes, for example, information that specifies the conditions related to the user attribute value and information that identifies the type of question set. The identification information is, for example, "male, female" which is a condition related to the user attribute value, and "beverage-derived, alcohol-derived, food-derived, all-derived" which is information that identifies the type of question set. The identification information is, for example, (gender = male & type of question set = beverage origin) (gender = male & type of question set = alcohol origin) (gender = male & type of question set = food origin) (gender = male & type of question set = all origins) (gender = female & type of question set = beverage origin) (gender = female & type of question set = alcohol origin) (gender = female & type of question set = food origin) (gender = female & type of question set = all origins).

[0035] The fluid intake from beverages is the amount of fluid taken in from beverages. The fluid intake from alcoholic beverages is the amount of fluid taken in from alcoholic beverages. The fluid intake from food is the amount of fluid taken in from food. The total fluid intake is the amount of fluid taken in from beverages, alcoholic beverages, and food.

[0036] The answer storage unit 111 stores two or more pieces of answer information. The answer information is information that identifies an answer to a question. The answer storage unit 111 stores answer information associated with an identifier, for example. The answer information is information that identifies the user's answer to each question in the questionnaire. The user is the respondent. The answer information is usually associated with a question identifier of each question in the questionnaire. The question identifier is information that identifies a question, for example, a question number or a question ID. The identifier is a user identifier, or a day identifier, or a user identifier and a day identifier. The user identifier is information that identifies a user, for example, an ID, an email address, a telephone number, or an ID of a terminal used by the user (for example, an IP address, a MAC address, a terminal identifier, etc.).

[0037] The answer information corresponds to, for example, the identification information. That is, the answer information may correspond to a user attribute value. The user attribute value is an attribute value of the user, such as gender, age, or occupation. The answer information may also correspond to a type identifier that identifies the type of question. The type is, for example, origin. The type may be, for example, beverage origin, alcohol origin, food origin, or all origin.

[0038] Two or more actual measured values ​​are stored in the actual measured value storage unit 112. The actual measured values ​​are usually associated with answer information. The actual measured values ​​are usually associated with a set of answer information for each question in the questionnaire. The actual measured values ​​are associated with, for example, an identifier.

[0039] The actual measurement value is, for example, the water intake of a user in one day. The actual measurement value is, for example, the water intake of a single user in one day, and is associated with a user identifier. The actual measurement value is, for example, the water intake of a single user in one day, and is associated with a user identifier and a day identifier.

[0040] The actual measurement value is associated with, for example, a type identifier. It is preferable that the actual measurement value storage unit 112 stores the actual measurement value for each origin. When the actual measurement value is the user's daily water intake, the origin is, for example, two or more of beverage origin, alcohol origin, food origin, and all origin.

[0041] The method of acquiring the actual measurement value does not matter. The actual measurement value is, for example, the daily water intake acquired from the user's answers to a multiple-choice questionnaire, which is a set of questions that asks the user to select the type and amount of food and drink ingested in a multiple-choice manner. The actual measurement value is, for example, the daily water intake acquired from the user's answers to a questionnaire based on a descriptive food record method. However, the actual measurement value may be a value input manually. In other words, it is preferable that the actual measurement value is an accurate water intake value, but there may be some error. The actual measurement value may be a value that is considered to be generally accurate.

[0042] The multiple-choice questionnaire above is a set of questions to obtain actual fluid intake, and asks participants to select the types and amounts of food and drink they ingested per day.

[0043] The written food record method involves giving subjects a record sheet to record what they have eaten and drunk over a certain period of time. The greatest advantage of the food record method is that it provides information about what they actually ate. Using the answers based on the food record method, we can obtain the actual amount of water intake of the user per day.

[0044] The learning reception unit 12 receives various instructions, information, and the like. The various instructions, information, and the like are, for example, learning instructions. A learning instruction is an instruction to obtain original information. The input means for the various instructions, information, and the like may be anything, such as a touch panel, a keyboard, a mouse, or a menu screen. The learning reception unit 12 may also receive various instructions, information, and the like from the user terminal 3.

[0045] The raw information is information that is the basis for estimating the amount of water intake from the answer information to each of the two or more questions. The two or more questions include questions other than those that directly ask about the amount of water intake. The amount of water intake here is the amount of water intake for a specified period. The specified period is preferably one day, but may be one week, half a day, etc.

[0046] The raw information is, for example, a learning device, which will be described later. The raw information is, for example, a correspondence table, which will be described later. The raw information is, for example, an arithmetic expression, which will be described later. The raw information is acquired by the learning processing unit 13, which will be described later.

[0047] The learning processing unit 13 acquires raw information. The process of the learning processing unit 13 is, for example, any one of the following (1) to (3). It is preferable that the learning processing unit 13 acquires a learning device by performing the following process (1). (1) When the source data is a learning device

[0048] The learning processing unit 13 acquires two or more sets from the learning storage unit 11. A set is a collection of an achievement value and two or more pieces of answer information. The achievement value in the set corresponds to the collection of two or more pieces of answer information in the set. The achievement value and the collection of answer information in the set correspond to the same identifier, for example.

[0049] The learning processing unit 13 performs learning processing by a machine learning algorithm using the two or more acquired pairs, and acquires a learning device. More specifically, the learning processing unit 13, for example, provides the two or more acquired pairs to a module that performs machine learning learning processing, executes the module, and acquires a learning device. Note that the learning device may be called a classifier or a model. In addition, this learning device is provided to a module that performs machine learning prediction processing together with a set of two or more pieces of answer information, and is a learning device for acquiring the amount of water intake.

[0050] The learning module of the machine learning may be a random forest, a decision tree, deep learning, SVR, etc., and the algorithm is not limited. For the machine learning, various machine learning functions such as the TensorFlow library, fastText, tinySVM, and the random forest module of the R language, as well as various existing libraries, can be used. (2) If the original information is a correspondence table

[0051] The learning processing unit 13 acquires two or more sets of answer information from the answer storage unit 111. In addition, the learning processing unit 13 acquires, from the actual measurement value storage unit 112, actual measurement values ​​associated with the sets of answer information.

[0052] For example, for each of two or more sets of answer information, the learning processing unit 13 constructs a vector having each answer information of each set as an element. Furthermore, the learning processing unit 13 acquires, for each set, correspondence information that associates the vector with an actual measurement value corresponding to the set of answer information that is the basis of the vector. Then, the learning processing unit 13 constructs a correspondence table having two or more pieces of correspondence information. Note that the correspondence information may be a set of answer information and an actual measurement value, or may be link information that associates a set of answer information with an actual measurement value, and may be any information that allows a set of answer information to be associated with an actual measurement value. (3) When the source data is an arithmetic expression

[0053] The learning processing unit 13 acquires two or more pairs from the learning storage unit 11. Then, the learning processing unit 13 acquires an arithmetic expression that outputs an actual measurement value of each of the two or more pairs, for example, using each answer information of a set of answer information owned by each of the two or more pairs as a parameter. The learning processing unit 13 acquires the arithmetic expression, for example, by multiple regression analysis. Note that the arithmetic expression is not limited to a linear expression, and may be an expression of quadratic or higher order. Furthermore, the learning processing unit 13 may reduce the number of dimensions of a vector having each answer information as an element by, for example, principal component analysis, and then acquire the arithmetic expression using the vector.

[0054] The learning output unit 14 outputs information. For example, the learning output unit 14 outputs raw information acquired by the learning processing unit 13. It is preferable that the learning output unit 14 stores the raw information in the raw information storage unit 212 of the water intake estimation device 2.

[0055] Here, output usually means storage on a recording medium, but it may also be a concept that includes display on a display, projection using a projector, printing on a printer, sound output, transmission to an external device, and handing over the processing results to other processing devices or other programs, etc.

[0056] Various types of information are stored in the storage unit 21 constituting the water intake estimation device 2. The various types of information are, for example, questionnaire information (described later), raw information, intake amount information (described later), and one or more pieces of user information.

[0057] The user information includes a user identifier and one or more user attribute values. The user attribute value is, for example, gender, but may also include various attribute values ​​such as age, type of work (e.g., manual labor, white collar, clerical work, construction industry, etc.), height, weight, etc.

[0058] The questionnaire storage unit 211 stores one or more pieces of questionnaire information. It is preferable that the questionnaire storage unit 211 stores two or more pieces of questionnaire information in association with conditions related to user attribute values. The questionnaire information has question information for two or more questions. The questionnaire information may exist for each type identifier that identifies the type of question.

[0059] The two or more questions in the questionnaire information include one or more questions other than a question directly asking about the amount of water ingested. The condition related to the user attribute value may be a condition using one user attribute value (e.g., "male", "age 20 or younger"), or a condition using two or more user attribute values ​​(e.g., "male & 50 or older", "male & manual labor", etc.).

[0060] One or more pieces of raw information are stored in the raw information storage unit 212. The raw information storage unit 212 may store two or more pieces of raw information corresponding to a condition corresponding to a user attribute value.

[0061] Two or more pieces of raw information corresponding to the types of questions may be stored in the raw information storage unit 212. In other words, the raw information may be associated with a type identifier.

[0062] The original information is information that serves as a basis for estimating the amount of fluid intake from answer information to two or more questions, including questions other than those directly asking about the amount of fluid intake. The original information is, for example, a learning device. The learning device is information acquired by performing a learning process using a machine learning algorithm using two or more teacher data sets that have answer information to two or more questions or answer-related information obtained from the answer information, and fluid intake information. The answer-related information (for example, "1", "2") is information that corresponds to answer information (for example, "Yes", "No") and may be called answer information. The answer information is information input by the user, and the answer-related information is information that has a one-to-one relationship with the answer information.

[0063] The reception unit 22 receives various instructions, information, and the like. The reception unit 22 receives, for example, a questionnaire output instruction. This is an instruction to output questionnaire information. It is preferable that the questionnaire output instruction includes a user identifier or one or more user attribute values. The reception unit 22 receives, for example, one or more pieces of user answer information.

[0064] Note that, in this case, acceptance typically refers to the reception of information transmitted via a wired or wireless communication line, but it may also be a concept that includes the acceptance of information input from an input device such as a keyboard, mouse, or touch panel, or the acceptance of information read from a recording medium such as an optical disk, magnetic disk, or semiconductor memory.

[0065] The answer receiving unit 221 receives user answer information having answer information for each of two or more questions. The user answer information has two or more pieces of answer information. Such answer information is associated with a question identifier. The user answer information may have two or more pieces of answer information and a user identifier. The user answer information may have two or more pieces of answer information and one or two or more user attribute values.

[0066] The processing unit 23 performs various processes. For example, the processes performed by the questionnaire acquisition unit 231 and the intake amount acquisition unit 232 are included in the processing unit 23.

[0067] The questionnaire acquisition unit 231 acquires questionnaire information from the questionnaire storage unit 211. For example, the questionnaire acquisition unit 231 acquires questionnaire information corresponding to a condition matching one or more user attribute values ​​that are paired with a user identifier included in the accepted questionnaire output instruction from the questionnaire storage unit 211. For example, the questionnaire acquisition unit 231 acquires questionnaire information corresponding to a condition matching one or more user attribute values ​​included in the accepted questionnaire output instruction from the questionnaire storage unit 211.

[0068] The intake amount obtaining section 232 obtains intake amount information that specifies the amount of water intake, using two or more pieces of answer information contained in the user answer information received by the answer receiving section 221 and the raw information stored in the raw information storage section 212.

[0069] The intake amount obtaining section 232 obtains the intake amount information, for example, by using raw information corresponding to the user attribute value corresponding to the user answer information received by the answer receiving section 221 and two or more pieces of answer information contained in the user answer information.

[0070] The intake amount acquiring unit 232 acquires intake amount information for each type, for example, using answer information corresponding to each question of two or more types, which is answer information included in the user answer information accepted by the answer accepting unit 221, and raw information corresponding to each type, and acquires intake amount information using two or more pieces of intake amount information. Note that for each type, this refers to each type identifier. The intake amount acquiring unit 232 acquires intake amount information for each of, for example, all origins, beverage origins, alcohol origins, and food origins, using answer information corresponding to questions corresponding to each origin and raw information corresponding to each origin.

[0071] The intake amount acquiring section 232 acquires the intake amount information, for example, by any one of the following methods (1) to (3). When the source information is a learner

[0072] The intake amount obtaining section 232 performs prediction processing and obtains intake amount information using a machine learning algorithm, using two or more pieces of answer information contained in the user answer information received by the answer receiving section 221 or answer-related information obtained from each of the two or more pieces of answer information, and a learning device stored in the raw information storage section 212. Note that the answer-related information is information obtained from the answer information input by the user, and may be regarded as the same as the answer information.

[0073] In addition, like the learning module, the machine learning prediction module may be a random forest, a decision tree, deep learning, SVR, or the like, and the algorithm is not important. Furthermore, for machine learning, various machine learning functions such as the TensorFlow library, fastText, tinySVM, and the random forest module of the R language, as well as various existing libraries, can be used.

[0074] More specifically, the intake amount obtaining section 232 obtains two or more pieces of answer information contained in the user answer information received by the answer receiving section 221, or answer-related information obtained from each of the two or more pieces of answer information.

[0075] Next, the intake amount acquiring unit 232 acquires a learning device from the raw information storage unit 212. For example, the intake amount acquiring unit 232 acquires a learning device corresponding to a user attribute value corresponding to user response information from the raw information storage unit 212. The intake amount acquiring unit 232 also acquires, for example, a learning device corresponding to each of two or more types for each type from the raw information storage unit 212. For example, the intake amount acquiring unit 232 is a learning device corresponding to a user attribute value corresponding to user response information, and acquires, for each type, a learning device corresponding to each of two or more types from the raw information storage unit 212.

[0076] Next, the intake amount acquiring unit 232 provides two or more pieces of answer information or two or more pieces of answer-related information and the learning device to a module that performs machine learning prediction processing, executes the module, and acquires the intake amount information. The intake amount acquiring unit 232 acquires the intake amount information, for example, by type. (2) If the original information is a correspondence table (2-1) Using a vector that is most similar to the vector obtained from the user response information

[0077] The intake amount obtaining section 232 constructs a vector having as its elements each piece of information contained in two or more pieces of answer information contained in the user answer information accepted by the answer accepting section 221 or each piece of answer-related information obtained from each of the two or more pieces of answer information.

[0078] Next, the intake amount obtaining section 232 searches the correspondence table for a vector that is most similar to the vector, and obtains the actual measurement value paired with the most similar vector as intake amount information.

[0079] The intake amount acquiring unit 232 acquires the intake amount information by the above-mentioned process, for example, using a correspondence table corresponding to user attribute values. The intake amount acquiring unit 232 acquires the intake amount information by the above-mentioned process, for each type, for example, using a correspondence table corresponding to each of two or more types. The intake amount acquiring unit 232 acquires the intake amount information by the above-mentioned process, for each type, for example, using a correspondence table corresponding to user attribute values ​​and corresponding to each of two or more types. (2-2) When using two or more vectors that meet the conditions

[0080] The intake amount obtaining section 232 constructs a vector having as its elements each piece of information contained in two or more pieces of answer information contained in the user answer information accepted by the answer accepting section 221 or each piece of answer-related information obtained from each of the two or more pieces of answer information.

[0081] Next, the intake amount acquiring section 232 determines two or more vectors that are included in each correspondence information in the correspondence table and that satisfy the approximation condition for the vector constructed by the intake amount acquiring section 232. Next, the intake amount acquiring section 232 acquires actual measurement values ​​that are paired with each of the two or more vectors.

[0082] Next, the intake amount acquiring section 232 acquires intake amount information using the two or more acquired actual measured values. For example, the intake amount acquiring section 232 acquires a representative value (for example, an average value, a median value) of the two or more acquired actual measured values ​​as the intake amount information. For example, the intake amount acquiring section 232 calculates a weighted average of the two or more acquired actual measured values ​​using the actual measured values ​​and the degree of approximation, and acquires the value as the intake amount information. For example, the approximation condition is that the distance between the two vectors is equal to or less than a threshold value or is less than a threshold value.

[0083] The intake amount acquiring unit 232 acquires the intake amount information by the above-mentioned process, for example, using a correspondence table corresponding to user attribute values. The intake amount acquiring unit 232 acquires the intake amount information by the above-mentioned process, for each type, for example, using a correspondence table corresponding to each of two or more types. The intake amount acquiring unit 232 acquires the intake amount information by the above-mentioned process, for each type, for example, using a correspondence table corresponding to user attribute values ​​and corresponding to each of two or more types. (3) When the source data is an arithmetic expression

[0084] The intake amount obtaining section 232 constructs a vector having as its elements each piece of information contained in two or more pieces of answer information contained in the user answer information accepted by the answer accepting section 221 or each piece of answer-related information obtained from each of the two or more pieces of answer information.

[0085] The intake amount acquiring section 232 acquires the arithmetic expression, which is raw information, from the raw information storage section 212. The intake amount acquiring section 232 provides the constructed vector to the arithmetic expression, executes the arithmetic expression, and acquires intake amount information.

[0086] The output unit 24 outputs various types of information, such as questionnaire information and intake amount information.

[0087] Here, output typically means transmission to a user terminal 3, but it may also be a concept that includes display on a display, projection using a projector, printing on a printer, sound output, storage on a recording medium, and delivery of processing results to other processing devices or other programs.

[0088] The questionnaire output unit 241 transmits the questionnaire information acquired by the questionnaire acquisition unit 231 to the user terminal 3 .

[0089] The intake output section 242 outputs the intake information acquired by the intake acquisition section 232.

[0090] Here, output typically means transmission to a user terminal 3, but it may also be a concept that includes display on a display, projection using a projector, printing on a printer, sound output, storage on a recording medium, and delivery of processing results to other processing devices or other programs.

[0091] Various types of information are stored in the terminal storage unit 31 constituting the user terminal 3. The various types of information are, for example, a user identifier and one or more user attribute values.

[0092] The terminal reception unit 32 receives various instructions and information. The various instructions and information are, for example, answer information, a questionnaire output instruction, and a transmission instruction. The transmission instruction is an instruction to transmit intake amount information. The transmission instruction includes user answer information. The transmission instruction includes, for example, a user identifier. The transmission instruction includes, for example, one or more user attribute values.

[0093] The means for inputting various instructions and information may be any means, such as a touch panel, a keyboard, a mouse, or a menu screen.

[0094] The device processing unit 33 performs various types of processing. For example, the various types of processing are to convert the instructions, information, etc. accepted by the device accepting unit 32 into a data structure for instructions, information, etc. to be transmitted. For example, the various types of processing are to convert the information received by the device receiving unit 35 into a data structure for output.

[0095] The terminal transmission unit 34 transmits various instructions, information, etc. to the water intake estimation device 2. The terminal transmission unit 34 transmits instructions, information, etc. configured by the device processing unit 33 to the water intake estimation device 2.

[0096] The terminal receiving unit 35 receives various types of information etc. The terminal receiving unit 35 receives, for example, questionnaire information or intake information from the water intake estimation device 2.

[0097] The terminal output unit 36 ​​outputs various types of information etc. For example, the terminal output unit 36 ​​outputs questionnaire information or intake amount information.

[0098] The learning storage unit 11, the answer storage unit 111, the actual value storage unit 112, the storage unit 21, the questionnaire storage unit 211, the raw information storage unit 212, and the terminal storage unit 31 are preferably non-volatile recording media, but can also be realized using volatile recording media.

[0099] There is no restriction on the process by which information is stored in the learning storage unit 11, etc. For example, information may be stored in the learning storage unit 11, etc. via a recording medium, information transmitted via a communication line, etc. may be stored in the learning storage unit 11, etc., or information inputted via an input device may be stored in the learning storage unit 11, etc.

[0100] The study reception unit 12 and the terminal reception unit 32 may be realized by a device driver for an input means such as a touch panel or a keyboard, control software for a menu screen, etc. The study reception unit 12 may be realized by a wireless or wired communication means.

[0101] The learning processing unit 13, the processing unit 23, the questionnaire acquisition unit 231, the intake acquisition unit 232, and the device processing unit 33 can usually be realized by a processor, a memory, etc. The processing procedure of the processing unit 23, etc. is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, they may also be realized by hardware (dedicated circuit). The processor may be, for example, a CPU, MPU, GPU, etc., and the type is not important.

[0102] The learning output unit 14 and the terminal output unit 36 ​​may or may not include an output device such as a display or a speaker. The learning output unit 14 and the like may be realized by driver software for an output device, or driver software for an output device and an output device, etc. The learning output unit 14 may be realized by wireless or wired communication means.

[0103] The reception unit 22, the response reception unit 221, and the terminal reception unit 35 are usually realized by wireless or wired communication means, but may be realized by means for receiving broadcast.

[0104] The output section 24, the questionnaire output section 241, the intake output section 242, and the terminal transmission section 34 are usually realized by wireless or wired communication means, but may also be realized by broadcasting means.

[0105] The terminal reception unit 32 can be realized by a device driver for an input means such as a touch panel or a keyboard, or control software for a menu screen.

[0106] The terminal output unit 36 ​​may or may not include an output device such as a display, a speaker, etc. The terminal output unit 36 ​​may be realized by driver software for an output device, or a combination of driver software for an output device and an output device, etc.

[0107] Next, a description will be given of an example of the operation of the water intake estimation system A. First, a description will be given of an example of the operation of the learning device 1 with reference to the flowchart of FIG.

[0108] (Step S401) The learning processing unit 13 assigns 1 to a counter i.

[0109] (Step S402) The learning processing unit 13 judges whether or not to acquire the i-th element information. If the i-th element information is to be acquired, the process proceeds to step S403, and if the i-th element information is not to be acquired, the process ends.

[0110] The learning processing unit 13 determines whether to acquire the i-th original information depending on whether the i-th identification information exists in the learning storage unit 11. In addition, when the identification information in the learning storage unit 11 is, for example, (gender=male & type of question set=drink origin) (gender=male & type of question set=alcohol origin) (gender=male & type of question set=food origin) (gender=male & type of question set=all origin) (gender=female & type of question set=drink origin) (gender=female & type of question set=alcohol origin) (gender=female & type of question set=food origin) (gender=female & type of question set=all origin), the counter i can be 1 to 8.

[0111] (Step S403) The learning processing unit 13 assigns 1 to a counter j.

[0112] (Step S404) The learning processing unit 13 judges whether or not the j-th set of answer information exists. If the j-th set of answer information exists, the process proceeds to step S405. If the j-th set of answer information does not exist, the process proceeds to step S410.

[0113] The j-th set of answer information is associated with an identifier, for example. The identifier is a user identifier, a day identifier, or a combination of a user identifier and a day identifier.

[0114] (Step S405) The learning processing unit 13 acquires the j-th set of answer information. Note that the acquired answer information may not be the entire j-th set of answer information. In other words, when a question corresponds to any of two or more types, the learning processing unit 13 acquires only the answer information corresponding to the question of the type corresponding to the i-th original information.

[0115] (Step S406) The learning processing unit 13 configures a vector using the set of answer information acquired in step S405.

[0116] (Step S407) The learning processing unit 13 acquires from the actual measurement value storage unit 112 the actual measurement values ​​that are paired with the set of answer information acquired in step S405.

[0117] (Step S408) The learning processing unit 13 constructs a record having the vector acquired in step S406 and the actual measurement value acquired in step S407, and temporarily stores the record in a buffer (not shown).

[0118] (Step S409) The learning processing unit 13 increments the counter j by 1. The process returns to step S404.

[0119] (Step S410) The learning processing unit 13 acquires two or more records from a buffer (not shown) and acquires raw information using the two or more records. The learning processing unit 13 acquires a learning device by, for example, machine learning prediction processing.

[0120] (Step S411) The learning processing unit 13 accumulates the raw information acquired in step S410. When acquiring two or more pieces of raw information, the learning processing unit 13 accumulates the raw information in association with the identification information. The raw information may be accumulated in, for example, the learning storage unit 11, but this does not matter.

[0121] (Step S412) The learning processing unit 13 increments the counter i by 1. The process returns to step S402.

[0122] Next, an example of the operation of the water intake estimation device 2 will be described with reference to the flowchart of FIG.

[0123] (Step S501) Reception unit 22 determines whether or not a questionnaire output instruction has been received. If a questionnaire output instruction has been received, the process proceeds to step S502, and if a questionnaire output instruction has not been received, the process proceeds to step S504. Here, reception unit 22 determines whether or not a questionnaire output instruction has been received from user terminal 3.

[0124] (Step S502) The questionnaire acquisition unit 231 acquires questionnaire information corresponding to the questionnaire output instruction received in step S501 from the questionnaire storage unit 211. The questionnaire acquisition unit 231 acquires, for example, one or more user attribute values ​​corresponding to the user identifier included in the questionnaire output instruction from the storage unit 21, and acquires questionnaire information corresponding to the one or more user attribute values ​​from the questionnaire storage unit 211.

[0125] (Step S503) The questionnaire output unit 241 outputs the questionnaire information acquired in step S502. Return to step S501. Note that the questionnaire output unit 241 transmits the questionnaire information to the user terminal 3, for example.

[0126] (Step S504) The answer receiving unit 221 judges whether or not the user answer information has been received. If the user answer information has been received, the process proceeds to step S505, and if the user answer information has not been received, the process returns to step S501. Here, the receiving unit 22 judges, for example, whether or not the user answer information has been received from the user terminal 3.

[0127] (Step S505) The intake amount obtaining section 232 assigns 1 to the counter i.

[0128] (Step S506) The intake amount obtaining section 232 judges whether or not the i-th question type exists. If the i-th question type exists, the process proceeds to step S507, and if the i-th question type exists, the process proceeds to step S511.

[0129] (Step S507) The intake amount obtaining section 232 obtains two or more pieces of answer information corresponding to the type of the i-th question from the user answer information. The intake amount obtaining section 232 uses the obtained two or more pieces of answer information to form a vector.

[0130] (Step S508) The intake amount obtaining section 232 obtains raw information corresponding to the i-th question type from the raw information storage section 212.

[0131] (Step S509) The intake amount acquisition unit 232 acquires the water intake amount using the vector constructed in step S507 and the raw information acquired in step S508. The intake amount acquisition unit 232 acquires the water intake amount by performing a machine learning prediction process using, for example, the vector constructed in step S507 and the raw information acquired in step S508.

[0132] (Step S510) The intake amount obtaining section 232 increments the counter i by 1. The process returns to step S506.

[0133] (Step S511) The intake amount obtaining section 232 uses one or more water intake amounts obtained in step S509 to configure intake amount information to be output.

[0134] (Step S512) The intake amount output section 242 outputs the intake amount information acquired in step S511.

[0135] Here, the intake amount output section 242 transmits, for example, the intake amount information acquired in step S511 to the user terminal 3.

[0136] In the flowchart of FIG. 5, the process ends when the power is turned off or an interrupt occurs to end the process.

[0137] Next, an example of the operation of the user terminal 3 will be described with reference to the flowchart of FIG.

[0138] (Step S601) The terminal reception unit 32 determines whether or not a questionnaire output instruction has been received. If the questionnaire output instruction has been received, the process proceeds to step S602, and if the questionnaire output instruction has not been received, the process returns to step S601.

[0139] (Step S602) The device processing unit 33 composes a questionnaire output instruction to be output. The terminal transmitting unit 34 transmits the composed questionnaire output instruction to the water intake estimation device 2.

[0140] The device processing unit 33, for example, reads out the user identifier from the terminal storage unit 31 and generates a questionnaire output instruction having the user identifier.

[0141] (Step S603) The terminal receiving unit 35 judges whether or not questionnaire information has been received. If questionnaire information has been received, the process proceeds to step S604, and if not, the process returns to step S603.

[0142] (Step S604) The terminal processing unit 33 composes questionnaire information to be output from the questionnaire information received in step S603. The terminal output unit 36 ​​outputs the questionnaire information.

[0143] (Step S605) The terminal reception unit 32 judges whether or not answer information has been received from the user. If answer information has been received, the process proceeds to step S606, and if answer information has not been received, the process proceeds to step S607.

[0144] (Step S606) The terminal processing unit 33 temporarily stores the answer information received in step S605 in a buffer (not shown) in association with the question identifier. Then, the process returns to step S605.

[0145] (Step S607) The terminal reception unit 32 judges whether or not a transmission instruction has been received from the user. If a transmission instruction has been received, the process proceeds to step S608, and if a transmission instruction has not been received, the process returns to step S605.

[0146] (Step S608) The device processing unit 33 reads two or more pieces of answer information from the buffer and configures the user answer information. Note that the user answer information includes, for example, a user identifier.

[0147] (Step S609) The terminal transmitting unit 34 transmits the user's answer information constructed in step S608 to the water intake estimation device 2.

[0148] (Step S610) The terminal receiving unit 35 determines whether or not intake information has been received from the water intake estimation device 2. If intake information has been received, the process proceeds to step S611, and if intake information has not been received, the process returns to step S610.

[0149] (Step S611) The device processing unit 33 uses the intake amount information received in step S610 to compose intake amount information to be output. The terminal output unit 36 ​​outputs the intake amount information. Return to step S601.

[0150] In the flowchart of FIG. 6, the process ends when the power is turned off or an interrupt occurs to end the process.

[0151] The specific operation of the water intake estimation system A in this embodiment will be described below. A conceptual diagram of the water intake estimation system A is shown in FIG.

[0152] Assume now that the learning storage unit 11 of the learning device 1 stores two or more pieces of questionnaire information including the questionnaire information shown in Fig. 7 and Fig. 8. The questionnaire information in Fig. 7 corresponds to information "male" that specifies a condition related to a user attribute value. The questionnaire information in Fig. 8 corresponds to information "female" that specifies a condition related to a user attribute value. Also, it is assumed that each question in the questionnaire information corresponds to a type identifier. Here, it is assumed that the type identifier is one of "beverage-derived," "alcohol-derived," "food-derived," and "all-derived."

[0153] In this situation, assume that many users input answer information to the questionnaire information, and as a result, a large number of sets of answer information are stored in the learning storage unit 11 in association with user identifiers. An example of such a large number of sets of answer information is the user answer information management table shown in FIG. 9. The user answer information management table is a table having "user identifier", "user attribute value", "question identifier", and "type identifier". In this example, the "question identifier" ranges from "1" to "40". Also, there are 10 questions for each type identifier. Also, the first record in FIG. 9 indicates that the user with user identifier "U001" is "male" and has input answer information "3"..."5", "3"..."5", "1"..."4", "3"..."3".

[0154] Also, it is assumed that the actual measured values ​​of many users are associated with user identifiers and stored in the learning storage unit 11. Note that the actual measured values ​​here are daily water intake amounts.

[0155] As described above, the learning processing unit 13 may obtain the actual measurement value for each type identifier from, for example, the user's response to a multiple-choice questionnaire. The learning processing unit 13 may also obtain the actual measurement value for each type identifier from the user's response to a questionnaire based on a descriptive food record method. Furthermore, the learning processing unit 13 may obtain the actual measurement value input by the user and associated with the user identifier. In other words, the method of obtaining the actual measurement management table is not important.

[0156] As a result of the above process, it is assumed that the actual measurement value management table shown in FIG. 10 is stored in the actual measurement value storage unit 112. The actual measurement value management table is a table showing the correspondence between the user identifier, the type identifier, and the amount of water intake. The amount of water intake is, for example, 001 ,a 002 ,···,b 001 ,···,c 001 , ,d 001 ,d 002 ,..., and such values ​​are, for example, numerical values ​​in units of milliliters.

[0157] The learning processing unit 13 of the learning device 1 then performs machine learning learning processing for each user identifier (male, female) and each type identifier (beverage-derived, alcohol-derived, food-derived, all-derived) using the user response information management table shown in Fig. 9 and the actual measured values ​​in the actual measured value management table shown in Fig. 10, by the processing etc. described with reference to Fig. 5, to obtain a learning device. That is, the learning processing unit 13 performs machine learning learning processing for each user identifier and each type identifier, to obtain eight learning devices. The learning processing unit 13 then stores each of the eight learning devices in the raw information storage unit 212 of the water intake estimation device 2 in association with identification information having the user identifier and the type identifier.

[0158] Through the above processing, the raw information management table shown in Figure 11 is stored in the raw information storage unit 212 of the water intake estimation device 2. The raw information management table has eight records, each having an "ID", "user identifier", "type identifier", and "raw information". Here, "ID" is information that identifies the record. "Raw information" is an identifier (e.g., file name) of a file of a machine learning (e.g., random forest) learning device. Note that the actual file is assumed to exist in the raw information storage unit 212. Furthermore, the "user identifier" and "type identifier" may be collectively referred to as identification information.

[0159] It is also assumed that questionnaire storage unit 211 stores two or more pieces of questionnaire information including the questionnaire information in FIG.

[0160] Furthermore, it is assumed that the storage unit 21 stores a user information management table (not shown) for managing two or more pieces of user information each having a user identifier and one or more user attribute values ​​(including gender).

[0161] In this situation, it is assumed that the user inputs an instruction to output a questionnaire to the user terminal 3. Then, by the process described in Figure 6, the user terminal 3 accepts the instruction to output a questionnaire and transmits the instruction to output a questionnaire including the user identifier to the water intake estimation device 2.

[0162] Next, the reception unit 22 of the water intake estimation device 2 receives the questionnaire output instruction. The questionnaire acquisition unit 231 then acquires a user attribute value (here, "male") corresponding to the user identifier included in the questionnaire output instruction from the user information management table in the storage unit 21, and acquires questionnaire information corresponding to the user attribute value from the questionnaire storage unit 211. Next, the questionnaire output unit 241 transmits the questionnaire information to the user terminal 3.

[0163] Next, the user terminal 3 receives and outputs the questionnaire information. An example of such output is shown in FIG.

[0164] 12, the user inputs answer information and selects the “Send” button 1201. The user terminal 3 then transmits user answer information including a user identifier and two or more pieces of answer information to the water intake estimation device 2.

[0165] Next, the answer receiving section 221 of the water intake estimation device 2 receives the user answer information from the user terminal 3.

[0166] Next, the intake amount acquiring unit 232 acquires a learning device paired with the user attribute value "male" for each type identifier from FIG. 11. Then, the intake amount acquiring unit 232 acquires two or more pieces of answer information corresponding to each type identifier from the user answer information for each type identifier, and configures a vector using the two or more pieces of answer information. Next, the intake amount acquiring unit 232 provides the vector and the learning device corresponding to the type identifier for each type identifier to a machine learning prediction module, executes the prediction module, and acquires intake amount information for each type identifier. Such intake amount information is intake amount information derived from beverages, alcoholic beverages, meals, and all. Next, the intake amount acquiring unit 232 adds up the four pieces of intake amount information and acquires intake amount information "1215 milliliters" indicating the user's daily water intake.

[0167] Next, the output unit 24 transmits the intake amount information to the user terminal 3. The output unit 24 may transmit information containing the intake amount information to the user terminal 3. Such information is, for example, a character string, and is a character string in which the acquired intake amount information is substituted for the variable <intake amount information> of a template (here, "Your average daily water intake is <intake amount information>").

[0168] Next, the user terminal 3 receives and outputs the intake amount information. An example of such output is shown in FIG.

[0169] As described above, according to this embodiment, the amount of fluid intake can be estimated based on the answers to the questionnaire that includes questions other than those directly asking about the amount of fluid intake.

[0170] Furthermore, in this embodiment, an appropriate amount of fluid intake can be estimated based on answers to a questionnaire that varies depending on the user's attribute values.

[0171] In addition, in this embodiment, it is possible to estimate an appropriate water intake amount for each origin.

[0172] Furthermore, in this embodiment, an appropriate amount of fluid intake can be estimated using a machine learning algorithm such as random forest.

[0173] The processing in this embodiment may be realized by software. This software may be distributed by software download or the like. This software may also be recorded on a recording medium such as a CD-ROM and distributed. This also applies to the other embodiments in this specification. The software that realizes the water intake estimation device 2 in this embodiment is a program such as the following. That is, this program: The program causes a computer that can access a raw information storage unit in which raw information that is the basis for estimating fluid intake from answer information to two or more questions, including questions other than those that directly ask about the amount of fluid intake, to function as an answer receiving unit that receives user answer information having answer information to the two or more questions, an intake amount acquisition unit that acquires intake information that specifies the amount of fluid intake using two or more answer pieces of answer information contained in the user answer information accepted by the answer receiving unit and the raw information stored in the raw information storage unit, and an intake amount output unit that outputs the intake amount information.

[0174] Fig. 15 shows the appearance of a computer that executes the programs described in this specification to realize the learning device 1 and water intake estimation device 2 of the various embodiments described above. The above-mentioned embodiments can be realized by computer hardware and a computer program executed thereon. Fig. 15 is an overview of this computer system 300, and Fig. 16 is a block diagram of the system 300.

[0175] In FIG. 15, a computer system 300 includes a computer 301 including a CD-ROM drive, a keyboard 302, a mouse 303, and a monitor 304.

[0176] 16, computer 301 includes, in addition to CD-ROM drive 3012, MPU 3013, bus 3014 connected to CD-ROM drive 3012 etc., ROM 3015 for storing programs such as a boot-up program, RAM 3016 connected to MPU 3013 for temporarily storing instructions of application programs and providing temporary storage space, and hard disk 3017 for storing application programs, system programs, and data. Although not shown here, computer 301 may further include a network card for providing connection to a LAN.

[0177] A program for causing the computer system 300 to execute the functions of the water intake estimation device 2 of the above-mentioned embodiment, etc., may be stored on a CD-ROM 3101, inserted into the CD-ROM drive 3012, and then transferred to the hard disk 3017. Alternatively, the program may be transmitted to the computer 301 via a network (not shown) and stored on the hard disk 3017. The program is loaded into the RAM 3016 when executed. The program may also be loaded directly from the CD-ROM 3101 or the network.

[0178] The program does not necessarily include an operating system (OS) or a third party program that causes the computer 301 to execute the functions of the water intake estimation device 2 of the above-mentioned embodiment. The program only needs to include an instruction portion that calls appropriate functions (modules) in a controlled manner to obtain a desired result. How the computer system 300 operates is well known, and a detailed description will be omitted.

[0179] In addition, in the above program, the steps of transmitting information and receiving information do not include processing performed by hardware, such as processing performed by a modem or interface card in the transmitting step (processing that is performed only by hardware).

[0180] Furthermore, the computer that executes the above program may be a single computer or multiple computers. That is, centralized processing or distributed processing may be performed. That is, the water intake estimation device 2, etc. may be a stand-alone device or may be composed of two or more devices.

[0181] Furthermore, in each of the above embodiments, it goes without saying that two or more communication means present in one device may be physically realized by one medium.

[0182] In each of the above embodiments, each process may be realized by centralized processing in a single device, or may be realized by distributed processing in a plurality of devices.

[0183] The present invention is not limited to the above-described embodiment, and various modifications are possible, and it goes without saying that these modifications are also included within the scope of the present invention. [Industrial Applicability]

[0184] As described above, the fluid intake estimation device of the present invention has the effect of being able to obtain fluid intake based on answers to questionnaires that include questions other than those directly asking about the amount of fluid ingested, and is useful as a server or the like that estimates fluid intake. [Explanation of symbols]

[0185] 1 Learning device 2. Water intake estimation device 3. User terminal 11 Learning Storage Unit 12. Learning Reception 13 Learning processing unit 14 Learning output section 21 Storage area 22 Reception 23 Processing section 24 Output section 31 Terminal storage section 32 Terminal Reception 33 Terminal Processing Section 34 Terminal transmitter 35 Terminal receiving section 36 Terminal Output Section 111 Answer Storage Unit 112 Actual measurement value storage section 211 Questionnaire Storage Unit 212 Original Information Storage Unit 221 Answer Reception Department 231 Questionnaire acquisition department 232 Intake acquisition unit 241 Questionnaire Output Unit 242 Intake output section

Claims

1. A questionnaire storage unit in which two or more pieces of questionnaire information each having question information for two or more questions are stored in association with conditions related to user attribute values; a raw information storage unit that stores two or more pieces of raw information corresponding to conditions corresponding to user attribute values, the raw information being two or more pieces of raw information that are used to estimate fluid intake from answer information to each of the above questions, including two or more types of questions among beverage-related questions, which are questions about beverage habituation, alcohol-related questions, which are questions about alcohol habituation, diet-related questions, which are questions about dietary habituation, and all-origin questions, which are questions about all origins; a questionnaire acquisition unit that acquires questionnaire information corresponding to a condition corresponding to a user attribute value of a user from the questionnaire storage unit; a questionnaire output unit that transmits the questionnaire information acquired by the questionnaire acquisition unit to a user terminal of the user; an answer receiving unit that receives user answer information corresponding to a user attribute value, the user answer information being a user answer to the questionnaire information transmitted by the questionnaire output unit; an intake acquisition unit that acquires intake information for identifying a water intake amount by using two or more pieces of answer information included in the user answer information accepted by the answer acceptance unit and raw information corresponding to the user attribute value corresponding to the user answer information; and an intake output unit that outputs the intake information, The raw information is A learning device constructed by performing a learning process using a machine learning algorithm using two or more pieces of teacher data having answer information to two or more questions and water intake information; or a correspondence table having two or more pieces of correspondence information that associate a vector having answer information for each of two or more questions as elements with an actual measured value that is a daily water intake; or an arithmetic formula that outputs an actual measurement value using answer information for each of two or more questions as a parameter, The intake amount acquisition unit is performing a prediction process using a machine learning algorithm by using two or more pieces of answer information included in the user answer information accepted by the answer accepting unit and the learning device, thereby obtaining the intake amount information; Alternatively, a vector is constructed having two or more pieces of answer information contained in the user answer information accepted by the answer accepting unit as elements, and after constructing the vector, a vector that is most similar to the vector is searched for in the correspondence table, and an actual measurement value paired with the most similar vector is obtained as the intake amount information, or two or more vectors that satisfy an approximation condition for the vector are determined, an actual measurement value paired with each of the two or more vectors is obtained, and a representative value of the two or more actual measurement values ​​is obtained as the intake amount information. Alternatively, a water intake estimation device that constructs a vector having two or more pieces of answer information contained in the user answer information accepted by the answer accepting unit as elements, applies the vector to the arithmetic formula, executes the arithmetic formula, and obtains intake information.

2. An original information storage unit that stores original information that is the basis for estimating fluid intake from answer information to two or more types of questions among beverage-derived questions that are questions about beverage addictiveness, alcohol-derived questions that are questions about alcohol addictiveness, dietary-derived questions that are questions about dietary addictiveness, and all-derived questions that are questions about all origins; an answer receiving unit that receives user answer information having answer information to each of two or more questions including the two or more types of questions; an intake acquisition unit that acquires intake information for identifying a water intake amount by using two or more pieces of answer information included in the user answer information accepted by the answer acceptance unit and the raw information; and an intake output unit that outputs the intake information, The raw information is a learning device constructed by performing a learning process using a machine learning algorithm using two or more pieces of teacher data having answer information to each of the two or more questions and water intake amount information, The intake amount acquisition unit is The water intake estimation device performs a prediction process using a machine learning algorithm using two or more pieces of answer information contained in the user answer information accepted by the answer accepting unit and the learning device, and obtains the intake information.

3. The water intake estimation device of claim 1, wherein the two or more questions include four or more types of questions, including beverage-related questions regarding beverage habituation, alcohol-related questions regarding alcoholic beverage habituation, diet-related questions regarding dietary habituation, and all-related questions regarding all origins.

4. A fluid intake estimation device as described in claim 2, wherein the two or more questions include four or more types of questions, including beverage-related questions that are questions regarding beverage addictiveness, alcohol-related questions that are questions regarding alcohol addictiveness, diet-related questions that are questions regarding dietary addictiveness, and all-origin questions that are questions regarding all origins.

5. The two or more questions include two or more types of question sets each including one or more questions, The raw information storage unit includes: Two or more pieces of source information corresponding to the type of question are stored, The intake amount acquisition unit is 5. The water intake estimation device according to claim 1, wherein intake information is obtained for each of the two or more types of questions using answer information corresponding to each of the two or more types and raw information corresponding to each of the types, and the intake information is obtained using the two or more pieces of intake information.

6. The water intake estimation device according to claim 1 or 2, wherein the machine learning algorithm is a random forest.

7. A method for estimating fluid intake, comprising: a questionnaire storage unit in which two or more pieces of questionnaire information each having question information for two or more questions is stored in correspondence with conditions related to user attribute values; a raw information storage unit in which two or more pieces of raw information corresponding to conditions corresponding to user attribute values ​​are stored, the raw information being two or more pieces of raw information that are the basis for estimating fluid intake from answer information to two or more questions including two or more types of questions selected from beverage-derived questions which are questions about beverage addictiveness, alcohol-derived questions which are questions about alcohol addictiveness, dietary-derived questions which are questions about dietary addictiveness, and all-origin questions which are questions about all origins; a questionnaire acquisition unit, a questionnaire output unit, a response acceptance unit, an intake acquisition unit, and an intake output unit, a questionnaire acquisition step in which the questionnaire acquisition unit acquires questionnaire information corresponding to a condition corresponding to a user attribute value of a user from the questionnaire storage unit; a questionnaire output step in which the questionnaire output unit transmits the questionnaire information acquired by the questionnaire acquisition unit to a user terminal; an answer receiving step in which the answer receiving unit receives user answer information corresponding to a user attribute value, the user answer information being user answer information to the questionnaire information transmitted by the questionnaire output unit; an intake acquisition step in which the intake acquisition unit acquires intake information for identifying a water intake amount, using two or more pieces of answer information included in the user answer information accepted in the answer acceptance step and raw information corresponding to a user attribute value corresponding to the user answer information; The intake output unit includes an intake output step of outputting the intake information, The raw information is A learning device constructed by performing a learning process using a machine learning algorithm using two or more pieces of teacher data having answer information to two or more questions and water intake information; or a correspondence table having two or more pieces of correspondence information that associate a vector having elements of answer information to each of two or more questions with an actual measured value that is a daily water intake; or an arithmetic formula that outputs an actual measurement value using answer information for each of two or more questions as a parameter, In the intake acquisition step, performing a prediction process using a machine learning algorithm by using two or more pieces of answer information included in the user answer information accepted by the answer accepting unit and the learning device, thereby obtaining the intake amount information; or forming a vector having two or more pieces of answer information included in the user answer information received by the answer receiving unit as elements; After constructing the vector, a vector that is most similar to the vector is searched for in the correspondence table, and an actual measurement value paired with the most similar vector is obtained as the intake information, or two or more vectors that satisfy an approximation condition for the vector are determined, an actual measurement value paired with each of the two or more vectors is obtained, and a representative value of the two or more actual measurement values ​​is obtained as the intake information. Alternatively, a method for estimating water intake includes constructing a vector having two or more pieces of answer information contained in the user answer information accepted by the answer accepting unit as elements, applying the vector to the arithmetic formula, executing the arithmetic formula, and obtaining intake information.

8. A method for estimating fluid intake from answer information to two or more types of questions among beverage-derived questions that are questions about beverage addictiveness, alcohol-derived questions that are questions about alcohol addictiveness, dietary-derived questions that are questions about dietary addictiveness, and all-origin questions that are questions about all origins, the method being implemented by an original information storage unit that stores original information that is the basis for estimating fluid intake from answer information to each of the following two or more types of questions: beverage-derived questions that are questions about beverage addictiveness, alcohol-derived questions that are questions about alcohol addictiveness, dietary-derived questions that are questions about dietary addictiveness, and all-origin questions that are questions about all origins; an answer receiving unit; an intake amount acquisition unit; and an intake amount output unit, an answer receiving step in which the answer receiving unit receives user answer information having answer information to each of two or more questions including the two or more types of questions; an intake acquisition step in which the intake acquisition unit acquires intake information for identifying a water intake amount by using two or more pieces of answer information included in the user answer information accepted by the answer acceptance unit and the raw information; The intake output unit includes an intake output step of outputting the intake information, The raw information is a learning device constructed by performing a learning process using a machine learning algorithm using two or more pieces of teacher data having answer information to each of the two or more questions and water intake amount information, In the intake acquisition step, The water intake estimation device performs a prediction process using a machine learning algorithm using two or more pieces of answer information contained in the user answer information accepted by the answer accepting unit and the learning device, and obtains the intake information.

9. A computer having access to a questionnaire storage section in which two or more pieces of questionnaire information each having question information for two or more questions is stored in correspondence with conditions related to user attribute values, and a raw information storage section in which two or more pieces of raw information corresponding to conditions corresponding to user attribute values ​​are stored, the raw information storage section storing two or more pieces of raw information that are the basis for estimating fluid intake from answer information to two or more questions including two or more types of questions selected from beverage-derived questions which are questions about beverage addictiveness, alcohol-derived questions which are questions about alcohol addictiveness, dietary-derived questions which are questions about dietary addictiveness, and all-origin questions which are questions about all origins, a questionnaire acquisition unit that acquires questionnaire information corresponding to a condition corresponding to a user attribute value of a user from the questionnaire storage unit; a questionnaire output unit that transmits the questionnaire information acquired by the questionnaire acquisition unit to a user terminal of the user; an answer receiving unit that receives user answer information corresponding to a user attribute value, the user answer information being a user answer to the questionnaire information transmitted by the questionnaire output unit; an intake acquisition unit that acquires intake information for identifying a water intake amount by using two or more pieces of answer information included in the user answer information accepted by the answer acceptance unit and raw information corresponding to the user attribute value corresponding to the user answer information; A program for causing an intake output unit to function as an intake output unit that outputs the intake information, The raw information is A learning device constructed by performing a learning process using a machine learning algorithm using two or more pieces of teacher data having answer information to two or more questions and water intake information; or a correspondence table having two or more pieces of correspondence information that associate a vector having answer information for each of two or more questions as elements with an actual measured value that is a daily water intake; or an arithmetic formula that outputs an actual measurement value using answer information for each of two or more questions as a parameter, The intake amount acquisition unit is performing a prediction process using a machine learning algorithm by using two or more pieces of answer information included in the user answer information accepted by the answer accepting unit and the learning device, thereby obtaining the intake amount information; or forming a vector having two or more pieces of answer information included in the user answer information received by the answer receiving unit as elements; After constructing the vector, a vector that is most similar to the vector is searched for in the correspondence table, and an actual measurement value paired with the most similar vector is obtained as the intake information, or two or more vectors that satisfy an approximation condition for the vector are determined, an actual measurement value paired with each of the two or more vectors is obtained, and a representative value of the two or more actual measurement values ​​is obtained as the intake information. Or a program for causing the computer to function as follows: constructing a vector having two or more pieces of answer information contained in the user answer information accepted by the answer accepting unit as elements, applying the vector to the arithmetic formula, executing the arithmetic formula, and obtaining intake amount information.

10. A computer that can access an original information storage unit that stores original information that is the basis for estimating fluid intake from answer information to two or more types of questions from among beverage-derived questions that are questions about beverage addictiveness, alcohol-derived questions that are questions about alcohol addictiveness, dietary-derived questions that are questions about dietary addictiveness, and all-derived questions that are questions about all origins, an answer receiving unit that receives user answer information having answer information to each of two or more questions including the two or more types of questions; an intake acquisition unit that acquires intake information for identifying a water intake amount by using two or more pieces of answer information included in the user answer information accepted by the answer acceptance unit and the raw information; A program for causing an intake output unit to function as an intake output unit that outputs the intake information, The raw information is a learning device constructed by performing a learning process using a machine learning algorithm using two or more pieces of teacher data having answer information to each of the two or more questions and water intake amount information, The intake amount acquisition unit is A program for causing the computer to function as performing prediction processing using a machine learning algorithm using two or more pieces of answer information contained in the user answer information accepted by the answer accepting unit and the learning device, and obtaining the intake amount information.

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