Information processing device, information processing method, and program

Abstract

To solve the problem that conventionally, when a measure for the result of inspection is to be proposed, the reason for the proposal could not have been presented.SOLUTION: The above problem can be solved by an information processing device 1 comprising: a user information reception unit 121 that receives user information including result information that specifies a user's inspection result; a measure determination unit 133 that acquires learning information based on two or more training data that are associated with a measure identifier for identifying a measure taken by the user, and that include first result information that specifies the inspection result before the measure is taken and second result information that specifies the inspection result of the result after the measure is taken, and acquires a measure identifier that corresponds to the result information that the user information possesses, using the learning information and the received user information; a reason information acquisition unit 134 that, using the received user information and one or more training data items that corresponds to the acquired measure identifier, acquires reason information regarding the reason why the measure is recommended; and an information output unit 141 that outputs the measure identifier and the reason information.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to an information processing device or the like that proposes measures in accordance with the results of a biological examination of a user. [Background technology]

[0002] In the prior art, there is a technique for outputting information indicating recommended health foods according to test results, information indicating recommended lifestyle habits according to test results, and the like (see Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-148507 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the prior art, when proposing measures to deal with the test results, it was not possible to present the basis for the proposal. [Means for solving the problem]

[0005] The information processing device of the first invention is an information processing device comprising: a user information receiving unit that receives user information having one or more user attribute values ​​including result information that identifies test results related to a user's biological activity; a learning information storage unit that stores learning information based on two or more teacher data items that are associated with a treatment identifier that identifies a treatment taken by the user, the learning information being based on two or more teacher data items having two or more user attribute values ​​including first result information that identifies the test results before the treatment was taken and second result information that identifies the test results as a result of the treatment being taken; a treatment decision unit that uses the learning information and the user information received by the user information receiving unit to obtain a treatment identifier that identifies a treatment corresponding to the result information contained in the user information; a basis information acquisition unit that uses the one or more teacher data items corresponding to the treatment identifier obtained by the treatment decision unit and the user information received by the user information receiving unit to obtain basis information regarding the basis for recommending a treatment identified by the treatment identifier obtained by the treatment decision unit; and an information output unit that outputs the treatment identifier obtained by the treatment decision unit and the basis information obtained by the basis information acquisition unit.

[0006] With this configuration, when proposing measures to deal with the test results, the basis for the proposal can be presented.

[0007] Furthermore, in the information processing device of the second invention, compared to the first invention, the basis information acquisition unit uses one or more teacher data corresponding to the action identifier acquired by the action decision unit and the user information accepted by the user information acceptance unit to acquire basis information including one or more types of information from among a basis level that specifies the degree of strength of the basis for recommending the action, and reason information that is information indicating the reason for recommending the action and includes effectiveness information regarding the effectiveness of taking the action or satisfaction information regarding the satisfaction level after taking the action.

[0008] With this configuration, when proposing measures to deal with the test results, it is possible to present appropriate grounds for the proposal.

[0009] Furthermore, the information processing device of the third invention is an information processing device in which, compared to the first or second invention, the basis information acquisition unit acquires basis information, which is information indicating that there is no basis, when the basis information cannot be acquired.

[0010] With this configuration, when a countermeasure for the test results is proposed, it is also possible to clearly indicate that the proposal is unfounded.

[0011] In addition, the information processing device of the fourth invention is an information processing device that further includes a reward information acquisition unit that acquires reward information, which is information that identifies a reward for encouraging a user to take action for any one of the first to third inventions and is information that corresponds to the basis information, and the information output unit also outputs the reward information acquired by the reward information acquisition unit.

[0012] With this configuration, when a measure to deal with the test results is proposed, it is possible to provide the user with an incentive to take that measure.

[0013] Furthermore, an information processing device of a fifth invention is the information processing device of the fourth invention, wherein the remuneration information acquisition unit acquires remuneration information that specifies a remuneration according to an evidence level included in the evidence information.

[0014] With this configuration, when a measure to deal with the test results is proposed, it is possible to provide the user with an incentive to take that measure.

[0015] In addition, the information processing device of the sixth invention is an information processing device that, compared to any one of the first to fifth inventions, further includes a classification unit that classifies two or more pieces of teacher data into two or more classes using effect information, which is information regarding the effect of the countermeasure, for each countermeasure identifier, and associates each of the two or more pieces of teacher data with a class identifier that identifies the class, and the countermeasure decision unit uses the user information accepted by the user information accepting unit and learning information based on the two or more pieces of teacher data to determine the class to which the user information belongs for each of the two or more countermeasure identifiers, and distinguishes between and acquires countermeasure identifiers corresponding to classes where there is a large difference between the first result information and the second result information and countermeasure identifiers corresponding to classes where there is a small difference.

[0016] This configuration allows for determining appropriate actions to be taken in response to the results of the test.

[0017] In addition, the information processing device of the seventh invention is an information processing device in which, compared to the sixth invention, the teacher data also includes the user's satisfaction as a result of taking the action, and the classification unit classifies the two or more teacher data into two or more classes using the effect information and satisfaction for each of the two or more teacher data for each action identifier, and associates each of the two or more teacher data with a class identifier that identifies the class.

[0018] This configuration allows for more appropriate responses to the test results to be determined.

[0019] Furthermore, the information processing device of the eighth invention is an information processing device as described in claim 6, in which, compared to the sixth invention, each of the two or more classes classified by the classification unit is associated with a basis level based on effect information for the teacher data corresponding to the class, the basis information acquisition unit acquires the basis level corresponding to the class, acquires effectiveness information using effect information which is information regarding the effect of the countermeasure and is information acquired using second result information contained in the teacher data corresponding to the class determined by the countermeasure decision unit, and acquires basis information which includes the basis level and reason information which is information including the effectiveness information.

[0020] With this configuration, when proposing measures to deal with the test results, it is possible to present appropriate grounds for the proposal.

[0021] Furthermore, the information processing device of the ninth invention is an information processing device in which, compared to the eighth invention, the teacher data also includes user satisfaction as a result of taking measures, the classification unit classifies two or more teacher data into two or more classes using effect information and satisfaction for each of the two or more teacher data for each measure identifier, and associates each of the two or more teacher data with a class identifier that identifies the class, and the basis information acquisition unit acquires satisfaction information using the satisfaction level of the teacher data corresponding to the class to which the user information belongs, and acquires basis information that includes reason information that includes the satisfaction information.

[0022] With this configuration, when proposing measures to deal with the test results, it is possible to present more appropriate grounds for the proposal.

[0023] Furthermore, the information processing device of the tenth invention is an information processing device in which, compared to any one of the first to ninth inventions, it further comprises a learning information acquisition unit that acquires learning information using two or more teacher data having one or more user attribute values ​​that satisfy a similarity condition with one or more user attribute values ​​included in the user information accepted by the user information accepting unit, and the learning information storage unit of the learning information storage unit is the learning information acquired by the learning information acquisition unit.

[0024] This configuration allows for more appropriate responses to the test results to be determined.

[0025] Furthermore, the information processing device of the eleventh invention is an information processing device in which, compared to any one of the first to tenth inventions, the response is a challenge to consume a product for a certain period of time or more in order to improve test results, and the training data includes the degree of effort in the challenge, which is response information to a questionnaire regarding the challenge.

[0026] With this configuration, when proposing measures to deal with the test results, the basis for the proposal can be presented. [Effects of the Invention]

[0027] According to the information processing device of the present invention, when proposing measures to deal with the results of an inspection, the basis for the proposal can be presented. [Brief explanation of the drawings]

[0028] [Figure 1] Conceptual diagram of information system A in embodiment 1 [Figure 2] Block diagram of Information System A [Figure 3] A flowchart illustrating an example of the operation of the information processing device 1. [Figure 4] 10 is a flowchart illustrating a first example of the learning information creation process. [Figure 5] Flowchart illustrating an example of classification processing [Figure 6] A flowchart illustrating an example of the learning process [Figure 7] 10 is a flowchart illustrating a second example of the learning information creation process. [Figure 8] 10 is a flowchart illustrating a third example of the learning information creation process. [Figure 9] 10 is a flowchart illustrating a fourth example of the learning information creation process. [Figure 10] 10 is a flowchart illustrating an example of the output information acquisition process. [Figure 11] A flowchart illustrating an example of the handling information acquisition process [Figure 12] A flowchart illustrating an example of the handling information acquisition process [Figure 13] A flowchart illustrating an example of the handling information acquisition process [Figure 14] A flowchart illustrating an example of the handling information acquisition process [Figure 15] A flowchart illustrating an example of the basis information processing [Figure 16] A flowchart illustrating an example of the remuneration information processing [Figure 17] Figure showing the teacher data management table [Figure 18] The table showing the basis level judgment [Figure 19] A diagram showing the concept of classification of the training data [Figure 20] Figure showing an example of the output [Figure 21] Overview of the computer system [Figure 22] Block diagram of the computer system DETAILED DESCRIPTION OF THE INVENTION

[0029] Hereinafter, embodiments of an information processing 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.

[0030] (Embodiment 1) In this embodiment, we will describe an information processing device that accepts a user's inspection results, uses learning information to obtain and output measures (e.g., products) based on the inspection results and evidence information regarding the basis for recommending the measures.

[0031] In this embodiment, an information processing device that acquires and outputs remuneration information using basis information will be described.

[0032] Furthermore, in this embodiment, we will describe an information processing device that, after a user has taken a measure, also uses questionnaire response information regarding the measure to acquire and output measures according to the test results and evidence information regarding the basis for recommending the measure.

[0033] In this embodiment, information X being associated with information Y means that information Y can be obtained from information X, or information X can be obtained from information Y, and the method of association is not important. Information X and information Y may be linked, may exist in the same buffer, information X may be included in information Y, or information Y may be included in information X, etc.

[0034] 1 is a conceptual diagram of an information system A according to this embodiment. The information system A includes an information processing device 1 and one or more terminal devices 2.

[0035] The information processing device 1 is a device that outputs countermeasures and evidence information according to the user's test results. The information processing device 1 is a so-called server, such as a cloud server or an ASP server, but the type is not important. The information processing device 1 may also be a standalone device.

[0036] The terminal device 2 is a device used by a user. The terminal device 2 may be, for example, a so-called personal computer, a multi-function mobile phone such as a smartphone, a mobile phone, or a tablet terminal, but the type does not matter. The user is a person who uses the information processing device 1 or an administrator of the information processing device 1.

[0037] The information processing device 1 and one or more terminal devices 2 can communicate with each other via a network such as the Internet or a LAN.

[0038] 2 is a block diagram of an information system A in this embodiment. The information processing device 1 includes a storage unit 11, a reception unit 12, a processing unit 13, and an output unit 14. The storage unit 11 includes a teacher data storage unit 111 and a learning information storage unit 112. The reception unit 12 includes a user information reception unit 121. The processing unit 13 includes a classification unit 131, a learning information acquisition unit 132, a response determination unit 133, a basis information acquisition unit 134, and a reward information acquisition unit 135. The output unit 14 includes an information output unit 141.

[0039] The terminal device 2 includes a terminal storage unit 21, a terminal reception unit 22, a terminal processing unit 23, a terminal transmission unit 24, a terminal reception unit 25, and a terminal output unit 26.

[0040] Various types of information are stored in the storage unit 11 constituting the information processing device 1. The various types of information include, for example, teacher data (to be described later), learning information (to be described later), questionnaire information (to be described later), response information (to be described later), and various conditions (to be described later). The various conditions include, for example, effect conditions (to be described later), satisfaction conditions (to be described later), acquisition conditions (to be described later), reward conditions (to be described later), and information associating class identifiers with evidence levels.

[0041] The teacher data storage unit 111 stores one or more teacher data. Teacher data is information that serves as the basis for learning information. Teacher data is associated with an action identifier that identifies an action taken by a user (which may also be called a "subject"). The teacher data has two or more user attribute values. The two or more user attribute values ​​include first result information and second result information. The two or more user attribute values ​​include, for example, the user's gender, the user's age, the user's height, the user's weight, effort information, lifestyle information, goals, goal achievement rate (which may also be called To-Be realization rate), and satisfaction level. The teacher data may be obtained directly from the user himself / herself, or indirectly from academic papers, academic journals, etc.

[0042] It is preferable that the training data include effect information. The effect information is information regarding the effect of the countermeasure. The effect information is usually information obtained using the second result information. The effect information is, for example, information regarding the difference between the first result information and the second result information. The effect information is, for example, information indicating the degree of improvement, which is the difference between the first result information and the second result information, or the ratio of the difference between the first result information and the second result information to the first result information. The effect information is, for example, information regarding the difference between the second result information and a goal. The effect information is, for example, information indicating the degree of improvement, which is the difference between the second result information and the goal, or the ratio of the difference between the second result information and the goal to the goal, or the degree of achievement of the goal. When the training data includes effect information, the training data does not need to include the first result information and the second result information. The effect information is information regarding the effect of the countermeasure taken by the user. The effect information is information regarding the difference between the first result information and the second result information, such as the number of decreases (increases) in blood pressure, the number of decreases (increases) in blood glucose levels, information indicating whether or not there is an effect, the rate of improvement in indoxyl sulfate measurement values, the number of increases in intestinal environment health, the rate of increase in intestinal environment health, the amount of weight loss (increase), the amount of improvement in body composition, and the amount of increase or decrease in height.

[0043] The action is information about the user's actions in response to the test results. The action is, for example, a product consumed by the user, a service provided to the user, an action taken by the user, or a challenge taken by the user. A challenge taken by the user is, for example, the user consuming a product for a predetermined period of time (e.g., eating a diet using low-salt product A for two weeks), the user receiving a service for a predetermined period of time, or the user taking a predetermined action for a predetermined period of time (e.g., walking for 30 minutes in one month, running for one hour every day for two weeks, quitting or limiting smoking or drinking for one month, etc.). The product, service, action, etc. consumed by the user is, for example, a product for improving the test results.

[0044] The first result information is information that identifies the test results before the countermeasure identified by the countermeasure identifier is taken. The second result information is information that identifies the test results after the countermeasure identified by the countermeasure identifier is taken. The first result information and the second result information are, for example, blood glucose level, blood pressure, indoxyl sulfate measurement value, intestinal health, weight, body composition, and height. The body composition is, for example, muscle mass (percentage), body fat mass (percentage), visceral fat mass (percentage), subcutaneous fat mass (percentage), bone density, BMI, body age, etc.

[0045] The test is a test on the user's living body. Here, the test is performed using, for example, a specimen. The specimen is a biological sample or an in vitro sample. Examples of biological samples include urine, feces, blood, oral cells, saliva, hair, body hair, sebum, nails, skin flakes, semen, tears, sweat, breast milk, nasal discharge, phlegm, dental tartar, and tongue coating. Examples of in vitro samples include photographic data of the subject, video data of the subject, audio data of the subject, and house dust from the subject's residence. The test may be, for example, a test using a test kit, but is not limited to this. For example, the test may be a test using physical examination equipment such as a weighing scale, a body composition scale, or a height measuring device. The test may be, for example, a subjective stress level check or discomfort check using a questionnaire, or a cognitive function test. Examples of such tests include the Mini-Mental State Examination (MMSE) test, which evaluates cognitive status (see URL: http: / / www.shizuokamind.org / wp-content / uploads / 2013 / 10 / MMSE.pdf), and the Kupperman Menopause Index (KKSI) test, which evaluates menopausal symptoms (see URL: https: / / ohana-clinic-kinoshitacho.com / wp-content / themes / ohana / download / kuppaman.pdf).

[0046] A test kit is, for example, an item used for testing using substances produced by a subject's body. Examples of substances produced by a body include urine, blood, feces, and other bodily fluids. A test kit is, for example, an item used for testing to obtain indoxyl sulfate measurement values. Examples of test kits include an equol test kit, which is a urine test that measures whether equol is produced from soy isoflavones; an intestinal environment test kit, which is a urine test that measures the health of the intestinal environment by the amount of putrefactive substances derived from intestinal bacteria; an oxidative stress test kit, which is a urine test that measures DNA (8-OHdG) damaged by reactive oxygen; a salt reduction test kit, which is a urine test that measures the amount of salt consumed per day; and a urine test kit that measures the presence or absence of antibodies to Helicobacter pylori, which increases the risk of stomach cancer.

[0047] The test kit may be, for example, information for testing using information about the subject (e.g., rights information). The information about the subject may be, for example, the subject's voice data or the subject's image data, and the image data may be a still image or a video. Information other than the item may include both image data and voice data. Information about the subject's health condition is obtained as a test result from the subject's voice data or image data. Such information about the health condition may be, for example, the degree of frailty or depression, but the content of the information is not important.

[0048] Effort information is information about the status of the user's efforts toward the challenge. Effort information is, for example, the degree of effort the user has made toward the challenge. Effort information is, for example, the completion rate and the number of days on which measures were taken. The completion rate is, for example, the percentage of days on which measures were taken during a predetermined period. Effort information may include not only information about direct efforts toward the challenge, but also information about accompanying efforts. Information about accompanying efforts is, for example, information indicating that, in response to efforts in the challenge program (for example, eating yogurt), the user has stopped snacking, information indicating that the user has increased sleep time, information specifying the sleep time, information indicating that the user has increased exercise time, information specifying the exercise time, information indicating that the user has stopped smoking, information specifying the number of cigarettes smoked, information indicating that the user has stopped drinking, information specifying the amount of alcohol consumed, etc.

[0049] Lifestyle information is information about the user's lifestyle, such as whether or not they smoke, whether or not they drink, the amount of smoking per day, the frequency of drinking, the amount of drinking, whether or not they exercise, and the amount of exercise for a given period (e.g., one day).

[0050] A goal is a user's goal, and is typically a goal related to test results. A goal is a goal with evidence. A goal can be said to represent an ideal state based on, for example, research papers or statistical results. A goal can be, for example, a target blood pressure value or a target weight value. Other target values ​​corresponding to the first result information and the second result information may also be set, such as target values ​​for blood glucose level, blood pressure, indoxyl sulfate measurement value, intestinal health, etc.

[0051] The To-Be realization rate is the degree to which the user has achieved their ideal state (To-Be) from their current state (As-Is), in other words, it is the percentage of achievement of the goal set by the individual user (for example, systolic blood pressure "120").

[0052] Satisfaction is the degree of satisfaction after a measure has been taken. Satisfaction is usually the degree of satisfaction with the measure taken. For example, satisfaction is a number between 1 and 5, or an evaluation value on one of three levels: "Satisfied," "Average," or "Not Satisfied."

[0053] The user attribute values ​​other than the first result information and the second result information included in the training data are, for example, response information corresponding to a questionnaire given to the user, such as the user's gender, age, height, weight, activity information, lifestyle information, goal, To-Be realization rate, and satisfaction level.

[0054] The learning information storage unit 112 stores learning information based on two or more pieces of teacher data. The learning information storage unit 112 is, for example, a learning device (described later), a correspondence table (described later), or a teacher data set. The process of creating the learning information in the learning information storage unit 112 may be performed by the learning information acquisition unit 132 (described later), or by an external learning device (not shown). Note that the teacher data set is a collection of two or more pieces of teacher data.

[0055] The learning device is data created by a machine learning learning process using, for example, two or more pieces of training data. Such learning process may be performed by the learning information acquisition unit 132, which will be described later, or by an external learning device (not shown). When the external learning device performs the learning process, the training data storage unit 111 is not required. The learning device may also be called a classifier, a predictor, a learning model, a model, etc.

[0056] The correspondence table has two or more pieces of correspondence information. The correspondence information is, for example, information indicating the correspondence between one or more user attribute values ​​and a corresponding identifier. The correspondence information is, for example, information indicating the correspondence between one or more user attribute values ​​and a class identifier. The class identifier will be described later.

[0057] The receiving unit 12 receives various types of information and instructions, such as user information, which will be described later.

[0058] Here, "reception" usually refers to the reception of information transmitted from the terminal device 2 via a wired or wireless communication line. However, the concept of "reception" may also include the reception of information input from an input device such as a keyboard, mouse, or touch panel, or the reception of information read from a recording medium such as an optical disk, magnetic disk, or semiconductor memory.

[0059] The user information receiving unit 121 receives user information of one user. The user information has one or more user attribute values. The user attribute values ​​include result information. The result information is information that identifies the results of tests related to one user's living body. The result information is, for example, blood glucose level, blood pressure, indoxyl sulfate measurement value, and the health of the intestinal environment. The user information receiving unit 121 typically receives user information in order to obtain a treatment identifier and evidence information. The result information contained in the user information received by the user information receiving unit 121 is information on the test results before treatment was taken.

[0060] The processing unit 13 performs various types of processing. The various types of processing are, for example, processing performed by a classification unit 131, a learning information acquisition unit 132, a handling determination unit 133, a basis information acquisition unit 134, and a reward information acquisition unit 135.

[0061] The processing unit 13 may configure the output information using the basis information acquired by the basis information acquisition unit 134. The processing unit 13 may configure the output information using the basis information acquired by the basis information acquisition unit 134 and the remuneration information acquired by the remuneration information acquisition unit 135. The output information is information to be output.

[0062] The classification unit 131 classifies the two or more pieces of teacher data in the teacher data storage unit 111 into two or more classes and associates each of the two or more pieces of teacher data with a class identifier. The class identifier is information that identifies a class. The class identifier is, for example, one of "Class 1," "Class 2," "Class 3," and "Class 4."

[0063] The classification unit 131 classifies two or more pieces of teacher data into two or more classes using the effect information for each treatment identifier, and associates each of the two or more pieces of teacher data with a class identifier.

[0064] The classification unit 131 acquires, for example, effect information relating to the difference between the first result information and the second result information held by each of the two or more teacher data for each action identifier. Next, the classification unit 131 classifies the effect information corresponding to each of the two or more teacher data into two or more classes, classifies the two or more teacher data into two or more classes according to the classification of the effect information, and associates each of the two or more teacher data with a class identifier. For example, the classification unit 131 classifies the two or more teacher data into three classes, such as class 1 for teacher data where "effect information<=threshold 1," class 2 for teacher data where threshold 1<effect information<=threshold 2," and class 3 for teacher data where threshold 2<effect information. The number of classes is not important.

[0065] For example, the classification unit 131 classifies two or more pieces of teacher data into two or more classes using the effectiveness information and satisfaction level contained in each of the two or more pieces of teacher data for each treatment identifier, and associates each of the two or more pieces of teacher data with a class identifier that identifies the class.

[0066] The classification unit 131 classifies two or more teacher data into four classes, for example, classifying teacher data where "effectiveness information<=threshold 1 and satisfaction level<=threshold a" as "Class 1," teacher data where "effectiveness information<=threshold 1 and threshold a<satisfaction level" as "Class 2," teacher data where "threshold 1<effectiveness information and satisfaction level<=threshold a" as "Class 3," and teacher data where "threshold 1<effectiveness information and threshold a<satisfaction level" as "Class 4." The number of classes is not important.

[0067] Furthermore, the classification unit 131 may classify each of two or more sets of teacher data into two or more classes using a known cluster analysis algorithm. The known cluster analysis algorithm may be a hierarchical method such as the k-means method, the shortest distance method (nearest neighbor method), the shortest distance method (nearest neighbor method), the center of gravity method, or the group average method, or a non-hierarchical method such as the k-means method or the hypervolume method. Note that classifying two or more sets of teacher data into two or more classes means associating each of the two or more sets of teacher data with a class identifier.

[0068] The learning information acquisition unit 132 acquires learning information using two or more pieces of teacher data from the teacher data storage unit 111. It is preferable that the learning information acquisition unit 132 acquires learning information using the results of classification performed by the classification unit 131. The learning information is information used to acquire a countermeasure identifier. The learning information may also be information used to acquire a class identifier of a class to which a user belongs before acquiring a countermeasure identifier. The learning information may also be, for example, a learning device or a correspondence table.

[0069] The learning information acquisition unit 132 preferably acquires learning information using two or more pieces of training data having one or more user attribute values ​​that satisfy a similarity condition with one or more user attribute values ​​included in the user information accepted by the user information acceptance unit 121. The similarity condition may be, for example, that the similarity is equal to or greater than a threshold. The similarity is the similarity between a vector whose elements are one or more user attribute values ​​included in the user information accepted by the user information acceptance unit 121 and a vector whose elements are one or more user attribute values ​​included in the training data. The algorithm for calculating the similarity between two vectors is a well-known technique, and therefore will not be described here.

[0070] An example of a specific algorithm for acquiring learning information will be described below. (1) Learning information for obtaining class identifiers (1-1) When the learning information is a learning device (1-1-1) When the learning device is a learning device for outputting one class identifier among candidate class identifiers (e.g., multi-value classification)

[0071] The learning information acquisition unit 132 acquires, for each response identifier, two or more pieces of teacher data held by the teacher data storage unit 111. Next, for each response identifier, the learning information acquisition unit 132 performs a machine learning learning process using one or more user attribute values ​​held by each of the two or more pieces of teacher data as explanatory variables and a class identifier corresponding to each of the two or more pieces of teacher data as a target variable, acquires a learner, associates it with the response identifier, and stores it in the learning information storage unit 112. Note that the class identifier of the teacher data is information acquired by the classification unit 131.

[0072] The algorithm for performing the machine learning learning process may be, but is not limited to, deep learning, decision tree, random forest, SVR, etc. The algorithm for performing the machine learning prediction process described below may also be, but is not limited to, deep learning, decision tree, random forest, SVR, etc. 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, may be used. The module may also be referred to as a program, software, function, method, etc.

[0073] When creating a learning device, the learning information acquiring unit 132 does not need to use all of the user attribute values ​​constituting the teacher data, and may use only some of the user attribute values. (1-1-2) When the learning device is a binary classification learning device

[0074] The learning information acquisition unit 132 acquires, for each response identifier, two or more pieces of teacher data stored in the teacher data storage unit 111. Next, for each response identifier and each class identifier, the learning information acquisition unit 132 performs a machine learning learning process using teacher data corresponding to a class identifier of interest as a positive example and teacher data not corresponding to the class identifier as a negative example, acquires a learner, and stores the learner in the learning information storage unit 112 in association with the response identifier and the class identifier.

[0075] The learning information acquisition unit 132 performs a machine learning learning process using one or more user attribute values ​​contained in each of the two or more teacher data as explanatory variables and a class identifier corresponding to each of the two or more teacher data as a target variable, and acquires a binary classification learning device. (1-2) When the learning information is a correspondence table (1-2-1) When one correspondence information corresponds to one training data

[0076] The learning information acquisition unit 132 acquires two or more pieces of teacher data held by the teacher data storage unit 111 for each response identifier. Next, the learning information acquisition unit 132 constructs a vector for each response identifier, with elements being one or more user attribute values ​​held by each of the two or more pieces of teacher data. Then, the learning information acquisition unit 132 constructs a correspondence table for each response identifier, with two or more pieces of correspondence information having the vector and a class identifier corresponding to the teacher data, and stores the correspondence table in the learning information storage unit 112 in association with the response identifier. (1-2-2) When one correspondence information corresponds to one class identifier

[0077] The learning information acquisition unit 132 acquires two or more pieces of teacher data held by the teacher data storage unit 111 for each response identifier. Next, the learning information acquisition unit 132 constructs a vector whose elements are one or more user attribute values ​​held by one or more pieces of teacher data for each response identifier and class identifier. Next, the learning information acquisition unit 132 acquires a representative vector representing one or more vectors for each response identifier and class identifier. Next, the learning information acquisition unit 132 constructs a correspondence table having two or more pieces of correspondence information each having a representative vector and a class identifier for each response identifier and class identifier, and stores the correspondence table in the learning information storage unit 112 in association with the response identifier. (2) Learning information for obtaining a response identifier

[0078] When the learning information is information for acquiring a response identifier, there is no need to classify the training data, and the classifying unit 131 is unnecessary. (2-1) When the learning information is a learning device (2-1-1) When using effect information

[0079] The learning information acquisition unit 132 acquires effect information corresponding to each of the two or more pieces of teacher data stored in the teacher data storage unit 111. Next, the learning information acquisition unit 132 acquires one or more pieces of teacher data whose effect information satisfies a predetermined effect condition. Next, the learning information acquisition unit 132 performs a machine learning learning process using one or more user identifiers stored in each of the acquired one or more pieces of teacher data as explanatory variables and a response identifier corresponding to each of the one or more pieces of teacher data as objective variables, acquires a learning device, and stores the learned device in the learning information storage unit 112.

[0080] The effect condition is a condition for determining that the effect information is the same as or greater than a predetermined effect. The effect condition is, for example, that the effect information is equal to or greater than a threshold value (e.g., "blood glucose level reduction of 10 or more," "systolic blood pressure reduction of 20 or more," "target achievement rate of 80% or more"), or greater than a threshold value (e.g., "blood glucose level reduction of more than 10," "systolic blood pressure reduction of more than 20," "target achievement rate of more than 70%), or a specific value (e.g., "effective," "great improvement"). (2-1-2) When using effect information and satisfaction

[0081] The learning information acquisition unit 132 acquires effect information for each piece of teacher data and the satisfaction level of each piece of teacher data from each of the two or more pieces of teacher data stored in the teacher data storage unit 111. Next, the learning information acquisition unit 132 acquires one or more pieces of teacher data whose effect information satisfies a predetermined effect condition and whose satisfaction level satisfies a predetermined satisfaction condition. Next, the learning information acquisition unit 132 performs a machine learning learning process using one or more user identifiers stored in each of the acquired one or more pieces of teacher data as explanatory variables and a response identifier corresponding to each of the one or more pieces of teacher data as objective variables, acquires a learning device, and stores the learned device in the learning information storage unit 112.

[0082] The effect information for the teacher data is effect information obtained from information contained in the teacher data or effect information contained in the teacher data. The satisfaction condition is a condition for determining whether the satisfaction level is high, for example, whether the satisfaction level is equal to or greater than a threshold value or is greater than the threshold value (satisfied). (2-2) When the learning information is a correspondence table (2-2-1) When using effect information (2-2-1-1) When one correspondence information corresponds to one training data

[0083] The learning information acquisition unit 132 acquires effect information corresponding to each of the two or more pieces of teacher data stored in the teacher data storage unit 111. Next, the learning information acquisition unit 132 acquires one or more pieces of teacher data whose effect information satisfies a predetermined effect condition. Next, the learning information acquisition unit 132 constructs a vector whose elements are one or more user identifiers stored in each of the acquired one or more pieces of teacher data. Then, the learning information acquisition unit 132 constructs a correspondence table having two or more pieces of correspondence information each having the vector and a corresponding action identifier corresponding to the teacher data, and stores the correspondence table in the learning information storage unit 112. (2-2-1-2) When one correspondence information corresponds to one handling identifier

[0084] The learning information acquisition unit 132 acquires effect information corresponding to each of the two or more pieces of teacher data stored in the teacher data storage unit 111. Next, the learning information acquisition unit 132 acquires one or more pieces of teacher data whose effect information satisfies a predetermined effect condition. Next, the learning information acquisition unit 132 constructs a vector whose elements are one or more user attribute values ​​stored in each of the one or more pieces of teacher data for each action identifier. Next, the learning information acquisition unit 132 acquires a representative vector representing one or more vectors for each action identifier. Next, the learning information acquisition unit 132 constructs a correspondence table having two or more pieces of correspondence information each having a representative vector and an action identifier, and stores the correspondence table in the learning information storage unit 112. (2-2-2) When using effect information and satisfaction level (2-2-2-1) When one correspondence information corresponds to one training data

[0085] The learning information acquisition unit 132 acquires effect information and satisfaction levels corresponding to each of the two or more teacher data stored in the teacher data storage unit 111. Next, the learning information acquisition unit 132 acquires one or more teacher data whose effect information satisfies predetermined effect conditions and whose satisfaction levels satisfy predetermined satisfaction conditions. Next, the learning information acquisition unit 132 constructs a vector whose elements are one or more user identifiers stored in each of the acquired one or more teacher data. Then, the learning information acquisition unit 132 constructs a correspondence table having two or more pieces of correspondence information each having the vector and a corresponding action identifier corresponding to the teacher data, and stores the correspondence table in the learning information storage unit 112. (2-2-2-2) When one correspondence information corresponds to one handling identifier

[0086] The learning information acquisition unit 132 acquires effect information and satisfaction levels corresponding to each of the two or more teacher data stored in the teacher data storage unit 111. Next, the learning information acquisition unit 132 acquires one or more teacher data whose effect information satisfies predetermined effect conditions and whose satisfaction levels satisfy predetermined satisfaction conditions. Next, the learning information acquisition unit 132 constructs a vector whose elements are one or more user attribute values ​​stored in each of the one or more teacher data for each action identifier. Next, the learning information acquisition unit 132 acquires a representative vector representing one or more vectors for each action identifier. Next, the learning information acquisition unit 132 constructs a correspondence table having two or more pieces of correspondence information each having a representative vector and an action identifier, and stores the correspondence table in the learning information storage unit 112.

[0087] The handling determination unit 133 acquires the learning information, and uses the learning information and the user information accepted by the user information accepting unit 121 to acquire a handling identifier that identifies one or more handlings according to the result information contained in the user information.

[0088] The response determining unit 133, for example, uses the user information and learning information received by the user information receiving unit 121 to determine the class to which the user information belongs for each of two or more response identifiers, and distinguishes between and acquires response identifiers corresponding to classes with large differences between the first result information and the second result information (classes with large response effects) and those with small differences (classes with small response effects). Distinguishing and acquiring the response identifiers means, for example, sorting the response identifiers in descending order of difference, and acquiring only the response identifiers corresponding to differences that satisfy the effect conditions.

[0089] The handling determining unit 133 acquires the handling identifier by, for example, machine learning prediction processing. The handling determining unit 133 acquires the handling identifier by, for example, using a correspondence table. An example of the processing by the handling determining unit 133 will be described below. When the training information is a trainer (1-1) When the learning device acquires a class identifier (1-1-1) When there is only one learning device

[0090] The response determining unit 133 acquires, for each response identifier, one or more attribute values ​​included in the user information accepted by the user information accepting unit 121. Next, the response determining unit 133 constructs a vector having the one or more attribute values ​​as elements. Furthermore, the response determining unit 133 acquires a learning device corresponding to the response identifier from the learning information storage unit 112. Next, the response determining unit 133 provides the vector and the learning device for each response identifier to a module that performs machine learning prediction processing, executes the module, and acquires a class identifier.

[0091] Next, the handling determining unit 133 associates the acquired class identifier with the handling identifier and stores them in a buffer (not shown). Note that the handling determining unit 133 may acquire one or more handling identifiers corresponding to class identifiers that satisfy an acquisition condition (for example, identifiers of classes with high effect and high satisfaction level), and store them in a buffer (not shown). The acquisition condition is a condition for acquiring a handling identifier. The acquisition condition is an effect condition based on effect information. The acquisition condition may be an effect condition and a satisfaction level condition. For example, the effect condition is that the effect information is equal to or greater than a threshold. The satisfaction level condition is a condition based on satisfaction level, for example, that the satisfaction level is equal to or greater than a threshold. For example, the acquisition condition is that the effect indicated by the effect information is the highest, and the satisfaction level is the highest. (1-1-2) When the learning device is a binary classification learning device with two or more values

[0092] The response determining unit 133 acquires, for each response identifier, one or more attribute values ​​included in the user information accepted by the user information accepting unit 121. Next, the response determining unit 133 constructs a vector whose elements are the one or more attribute values. The response determining unit 133 also acquires a learning device corresponding to the response identifier and each class identifier from the learning information storage unit 112. Next, the response determining unit 133 provides the constructed vector and the acquired learning device for each response identifier and each class identifier to a module that performs machine learning prediction processing, executes the module, and acquires a prediction result indicating whether or not the user belongs to the class identified by the class identifier. The prediction result may include a score.

[0093] Next, the handling determining unit 133 acquires one or more class identifiers corresponding to the prediction result that includes the information "belonging to a class."

[0094] Next, the handling determining unit 133 associates the acquired class identifier with each handling identifier and stores them in a buffer (not shown). Note that the handling determining unit 133 may acquire one or more handling identifiers corresponding to class identifiers that satisfy an acquisition condition and store them in a buffer (not shown). The acquisition condition is, for example, that the effect information corresponding to the class identifier is equal to or greater than a threshold value (has a great effect). (1-2) When the learning device acquires the action identifier (1-2-1) When there is only one learning device

[0095] The handling determining unit 133 acquires, for each handling identifier, one or more attribute values ​​included in the user information accepted by the user information accepting unit 121. Next, the handling determining unit 133 constructs a vector having the one or more attribute values ​​as elements. The handling determining unit 133 also acquires a learning device from the learning information storage unit 112. Next, the handling determining unit 133 provides the vector and the learning device to a module that performs machine learning prediction processing, executes the module, and acquires a handling identifier. (1-2-2) When the learning device is a binary classification learning device with two or more values

[0096] The response determining unit 133 acquires, for each response identifier, one or more attribute values ​​included in the user information accepted by the user information accepting unit 121. Next, the response determining unit 133 constructs a vector whose elements are the one or more attribute values. Furthermore, the response determining unit 133 acquires a learning device from the learning information storage unit 112 for each response identifier. Next, the response determining unit 133 provides, for each response identifier, the vector and the learning device to a module that performs machine learning prediction processing, executes the module, and acquires a prediction result including information indicating whether or not the response belongs to the response identified by the response identifier. Then, the response determining unit 133 acquires the response identifier corresponding to the prediction result including information indicating that the response "belongs to the response identified by the response identifier." (2) When the learning information is a correspondence table (2-1) When the correspondence table is a correspondence table for obtaining class identifiers

[0097] For each handling identifier, the handling determining unit 133 acquires one or more attribute values ​​included in the user information accepted by the user information accepting unit 121. Next, the handling determining unit 133 constructs a vector having the one or more attribute values ​​as elements. Next, the handling determining unit 133 determines correspondence information having a vector that is most similar to the vector, and acquires a class identifier included in the correspondence information.

[0098] Next, the handling determining unit 133 associates the acquired class identifier with the handling identifier and stores them in a buffer (not shown). Note that the handling determining unit 133 may acquire one or more handling identifiers corresponding to class identifiers that satisfy an acquisition condition and store them in a buffer (not shown). Note that the acquisition condition is, for example, that the effect information corresponding to the class identifier is equal to or greater than a threshold value (has a great effect). (2-2) When the correspondence table is a correspondence table for obtaining a response identifier

[0099] The handling determining unit 133 acquires, for each handling identifier, one or more attribute values ​​included in the user information accepted by the user information accepting unit 121. Next, the handling determining unit 133 constructs a vector having the one or more attribute values ​​as elements. Next, the handling determining unit 133 determines correspondence information having a vector that is most similar to the vector, and acquires the handling identifier included in the correspondence information.

[0100] The response determining unit 133 may obtain the response identifiers of all two or more candidate responses from the storage unit 11.

[0101] The basis information acquisition unit 134 acquires basis information for each of the one or more action identifiers acquired by the action determining unit 133, using one or more teacher data items corresponding to the action identifier and the user information accepted by the user information accepting unit 121. The basis information is information relating to the basis for recommending the action identified by the action identifier acquired by the action determining unit 133.

[0102] The basis information acquiring unit 134 acquires, for example, a basis level corresponding to the class identifier associated by the classification unit 131. The basis information acquiring unit 134 acquires effectiveness information using, for example, effect information for the teacher data corresponding to the class identifier associated by the classification unit 131, and acquires reason information that is information including the basis level and the effectiveness information. It is preferable that the basis information acquiring unit 134 acquires basis information that includes, for example, one or more types of information from the basis level and the reason information.

[0103] It is preferable that the basis information acquisition unit 134 acquires satisfaction level information using the satisfaction level contained in the teacher data corresponding to the class to which the user information belongs, and acquires basis information including reason information that includes the satisfaction level information.

[0104] The basis may be called evidence. The basis information may be called evidence information. The basis information includes, for example, one or more pieces of information from the basis level and reason information. The reason information includes one or more pieces of information from the effectiveness information and satisfaction level information.

[0105] The evidence level is information that specifies the strength of the evidence for recommending a measure. The evidence level is, for example, one of "1," "2," or "3," but may be any information that has an order such as "A," "B," or "C."

[0106] Reason information is information relating to the reason why a countermeasure is recommended. Effectiveness information is information relating to the effectiveness of a countermeasure when implemented. Effectiveness information is, for example, the average effectiveness when a countermeasure is implemented, the percentage of users whose effectiveness is above a threshold when a countermeasure is implemented (percentage of teacher data), and the number of users whose effectiveness is above a threshold when a countermeasure is implemented. Satisfaction information is information relating to satisfaction after a countermeasure is implemented. Satisfaction information is, for example, the average satisfaction after a countermeasure is implemented, the percentage of users (which could be teacher data) whose satisfaction is above a threshold when a countermeasure is implemented, and the number of users whose satisfaction is above a threshold when a countermeasure is implemented.

[0107] For example, for each of the one or more action identifiers acquired by the action determining unit 133, the basis information acquiring unit 134 acquires a basis level by using one or more teacher data corresponding to the action identifier and the user information accepted by the user information accepting unit 121. For example, the basis information acquiring unit 134 acquires a class identifier paired with one or more teacher data corresponding to the action identifier acquired by the action determining unit 133. Next, the basis information acquiring unit 134 acquires, for example, a basis level corresponding to the class identifier. In this case, it is assumed that the class identifier is associated with a basis level and stored in the storage unit 11.

[0108] The basis information acquiring unit 134 acquires effectiveness information for each action identifier, for example, using one or more teacher data corresponding to the action identifier acquired by the action determining unit 133. The basis information acquiring unit 134 acquires effect information corresponding to one or more teacher data corresponding to each action identifier acquired by the action determining unit 133. Next, the basis information acquiring unit 134 acquires, for example, for each action identifier, the number of effective people, which is the number of effect information that satisfies the effect condition among the acquired effect information. Next, the basis information acquiring unit 134 acquires effectiveness information, which is the ratio of effectiveness, for example, using the number of all teacher data corresponding to the action identifier and the number of effective people. Note that the effectiveness information may be the number of effective people, etc. Furthermore, the effect condition is information indicating that the effect information is highly effective, such as the effect information being equal to or greater than a threshold, the effect information being greater than a threshold, or the effect information being "effective."

[0109] The basis information acquiring unit 134 acquires satisfaction level information for each action identifier, for example, using one or more teacher data corresponding to the action identifier acquired by the action determining unit 133. The basis information acquiring unit 134 acquires, for example, the satisfaction level held by each of the one or more teacher data corresponding to each action identifier acquired by the action determining unit 133. Next, the basis information acquiring unit 134 acquires, for example, for each action identifier, the number of satisfied people, which is the number of satisfaction levels that meet the satisfaction condition, from the acquired satisfaction levels. Next, the basis information acquiring unit 134 acquires satisfaction level information, which is the satisfaction rate, using, for example, the number of all teacher data corresponding to the action identifier and the number of satisfied people. Note that the satisfaction level information may also be the number of satisfied people, etc.

[0110] For example, the basis information acquiring unit 134 acquires, for each action identifier, validity information and satisfaction level information by using one or more teacher data items corresponding to the action identifier acquired by the action determining unit 133. Then, it is preferable that the basis information acquiring unit 134 constructs reason information having, for example, the acquired validity information and the acquired satisfaction level information.

[0111] It is preferable that the basis information acquisition unit 134 acquires recommendation information for strongly recommending a response identifier that satisfies a goal achievement condition. The goal achievement condition is that the degree of goal achievement is equal to or greater than a threshold. The goal achievement rate is the degree of achievement of a goal, which is a user attribute value. The degree of goal achievement is, for example, a representative value (e.g., average value, median value) of one or more goal achievement rates, the number of users who have achieved the goal, or the percentage of users who have achieved the goal.

[0112] It is preferable that the basis information acquisition unit 134 acquires basis information indicating that there is no basis for a handling identifier for which the basis information could not be acquired.

[0113] The basis information acquiring unit 134 normally associates the acquired basis information with the corresponding action identifier and temporarily stores the information in a buffer (not shown).

[0114] The remuneration information acquisition unit 135 acquires remuneration information, which is information that specifies a remuneration for encouraging a user to take a measure and is information that corresponds to the basis information. The remuneration information is, for example, information that recommends the purchase of a product or service that corresponds to the measure. The remuneration information is, for example, information that indicates a discount rate for the product or service that corresponds to the measure.

[0115] The reward information acquisition unit 135 acquires reward information that specifies a reward according to the evidence level included in the evidence information. For example, the lower the evidence level included in the evidence information, the higher the reward information that is acquired by the reward information acquisition unit 135. In such a case, the reward indicated by the reward information is a reward for the user's efforts to adopt something with a low evidence level. Furthermore, for example, the higher the evidence level included in the evidence information, the higher the reward information that is acquired by the reward information acquisition unit 135. In such a case, the administrator of the information processing device 1 may want the user to take an effort with a high evidence level. The relationship between such evidence level and reward information may be set by the administrator of the information processing device 1.

[0116] The reward information acquisition unit 135 acquires, for example, reward information associated with the acquired evidence level from the storage unit 11.

[0117] The output unit 14 outputs various types of information, such as a response identifier, basis information, and remuneration information.

[0118] Here, output usually means transmission to terminal device 2, 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 handing over the processing results to other processing devices or other programs.

[0119] The information output unit 141 outputs the response identifier acquired by the response determining unit 133 and the basis information acquired by the basis information acquiring unit 134. It is preferable that the information output unit 141 also outputs the remuneration information acquired by the remuneration information acquiring unit 135.

[0120] Various types of information are stored in the terminal storage unit 21 included in the terminal device 2. The various types of information are, for example, a user identifier. The user identifier may be the ID of the terminal device 2 or the like.

[0121] The terminal reception unit 22 receives various information and instructions. The various information and instructions may be, for example, user information. The various information and instructions may be input using any means, such as a microphone, touch panel, keyboard, mouse, or menu screen.

[0122] The device processing unit 23 performs various types of processing. For example, the various types of processing are processing for creating a data structure for transmitting instructions and information received by the terminal receiving unit 22. Also, for example, the various types of processing are processing for creating a data structure for outputting information received by the terminal receiving unit 25.

[0123] The terminal transmitting unit 24 transmits various types of information and instructions to the information processing device 1. The various types of information and instructions include, for example, user information.

[0124] The terminal receiving unit 25 receives various types of information from the information processing device 1. The various types of information include, for example, a response identifier, basis information, and remuneration information.

[0125] The terminal output unit 26 outputs various types of information, such as a response identifier, basis information, and remuneration information.

[0126] The storage unit 11, the teacher data storage unit 111, the learning information storage unit 112, and the terminal storage unit 21 are preferably non-volatile recording media, but may also be realized as volatile recording media.

[0127] There is no restriction on the process by which information is stored in the storage unit 11 etc. For example, information may be stored in the storage unit 11 etc. via a recording medium, information transmitted via a communication line etc. may be stored in the storage unit 11 etc., or information input via an input device may be stored in the storage unit 11 etc.

[0128] The reception unit 12 and the user information reception unit 121 are preferably realized by wireless or wired communication means, but may also be realized by a means for receiving broadcasts, a device driver for an input means such as a touch panel or keyboard, or control software for a menu screen.

[0129] The processing unit 13, learning information acquisition unit 132, classification unit 131, action determination unit 133, basis information acquisition unit 134, reward information acquisition unit 135, and device processing unit 23 can usually be realized by a processor, memory, etc. The processing procedures of the processing unit 13, etc. are usually realized by software, and the software is recorded on a recording medium such as a ROM. However, they may also be realized by hardware (dedicated circuit). The processor may be a CPU, MPU, GPU, etc., and the type does not matter.

[0130] The output unit 14, the information output unit 141, and the terminal transmitting unit 24 are usually realized by wireless or wired communication means, but may also be realized by broadcasting means.

[0131] The terminal reception unit 22 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.

[0132] The terminal receiving unit 25 is usually realized by a wireless or wired communication means, but may also be realized by a means for receiving broadcasts.

[0133] The terminal output unit 26 may or may not include an output device such as a display, a speaker, etc. The terminal output unit 26 may be realized by driver software for an output device, or by a combination of driver software for an output device and the output device, etc.

[0134] Next, a description will be given of an example of the operation of the information system A. First, a description will be given of an example of the operation of the information processing device 1 with reference to the flowchart of FIG.

[0135] (Step S301) The reception unit 12 determines whether one or more pieces of teacher data have been received. If one or more pieces of teacher data have been received, the process proceeds to step S302. If no teacher data has been received, the process proceeds to step S303. Note that the reception here refers to, for example, reception from the terminal device 2.

[0136] (Step S302) The processing unit 13 accumulates the one or more pieces of teacher data accepted in step S301 in the teacher data storage unit 111. The process returns to step S301.

[0137] (Step S303) The processing unit 13 determines whether or not to create learning information. If it is determined that learning information should be created, the process proceeds to step S304; if it is determined that learning information should not be created, the process proceeds to step S305. The processing unit 13 determines to create learning information, for example, when the receiving unit 12 receives an instruction to create learning information. The processing unit 13 also determines to create learning information, for example, when a predetermined time has arrived. The processing unit 13 also determines to create learning information, for example, when the number of teacher data stored in the teacher data storage unit 111 is equal to or greater than a threshold. However, the conditions under which the processing unit 13 determines to create learning information are not important.

[0138] (Step S304) The learning information acquisition unit 132 performs a learning information creation process. Return to step S301. An example of the learning information creation process will be described with reference to the flowcharts of FIGS.

[0139] (Step S305) The user information accepting unit 121 determines whether or not the user information has been accepted. If the user information has been accepted, the process proceeds to step S306, and if the user information has not been accepted, the process returns to step S301. Note that the acceptance here refers to, for example, receipt from the terminal device 2.

[0140] (Step S306) The processing unit 13 performs an output information acquisition process. An example of the output information acquisition process will be described with reference to the flowchart of FIG.

[0141] (Step S307) The information output unit 141 outputs the output information acquired in step S306. The process returns to step S301. Note that the output here is, for example, transmission to the terminal device 2.

[0142] In the flowchart of FIG. 3, the process ends when the power is turned off or an interrupt occurs to end the process.

[0143] Next, a first example of the learning information creation process in step S304 will be described with reference to the flowchart in Fig. 4. The first example of the learning information creation process is an example of the case where a learning device for obtaining a class identifier is obtained.

[0144] (Step S401) The processing unit 13 assigns 1 to a counter i.

[0145] (Step S402) The processing unit 13 determines whether or not the i-th handling identifier exists. If the i-th handling identifier exists, the process proceeds to step S403, and if the i-th handling identifier does not exist, the process returns to the upper processing.

[0146] (Step S403) The classification unit 131 classifies two or more pieces of teacher data corresponding to the i-th response identifier. An example of such classification processing will be described with reference to the flowchart of FIG.

[0147] (Step S404) The learning information acquisition unit 132 performs a learning process using the result of the classification process in step S403, and acquires a learning device. An example of the learning process will be described with reference to the flowchart in FIG.

[0148] (Step S405) The learning information acquiring unit 132 stores the learning device acquired in step S404 in the learning information storage unit 112 in association with the i-th action identifier.

[0149] (Step S406) The processing unit 13 increments the counter i by 1. The process returns to step S402.

[0150] Next, an example of the classification process in step S403 will be described with reference to the flowchart in FIG.

[0151] (Step S501) The classification unit 131 acquires from the training data storage unit 111 all training data corresponding to the treatment identifier of interest (the i-th treatment identifier in S402).

[0152] (Step S502) The classification unit 131 assigns 1 to a counter i.

[0153] (Step S503) The classification unit 131 determines whether or not the i-th teacher data exists among the teacher data acquired in step S501. If the i-th teacher data exists, the process proceeds to step S504, and if the i-th teacher data does not exist, the process returns to the upper level process.

[0154] (Step S504) The classification unit 131 acquires effect information for the i-th teacher data. For example, the classification unit 131 acquires first result information and second result information contained in the i-th teacher data, and acquires effect information related to the difference between the first result information and the second result information. For example, the classification unit 131 acquires the effect information contained in the i-th teacher data.

[0155] (Step S505) The classification unit 131 acquires the satisfaction level of the i-th teacher data.

[0156] (Step S506) The classification unit 131 uses the effect information acquired in step S504 and the satisfaction level acquired in step S505 to acquire a class identifier corresponding to the effect information and the satisfaction level.

[0157] (Step S507) The classification unit 131 associates the i-th teacher data with the class identifier acquired in step S506.

[0158] (Step S508) The classification unit 131 increments the counter i by 1. The process returns to step S503.

[0159] 5, the classification unit 131 determines the class of the training data using the effect information and the satisfaction level. However, the classification unit 131 may determine the class of the training data using either the effect information or the satisfaction level.

[0160] In the flowchart of FIG. 5, the classification unit 131 may classify two or more sets of teacher data using the known cluster analysis algorithm described above.

[0161] Next, an example of the learning process in step S404 will be described with reference to the flowchart in FIG.

[0162] (Step S601) The learning information acquiring unit 132 assigns 1 to a counter i.

[0163] (Step S602) The learning information acquisition unit 132 determines whether or not there is a class identifier for the i-th class classified by the classification unit 131 in step S403. If there is the i-th class identifier, the process proceeds to step S603; if there is no i-th class identifier, the process returns to the upper level process.

[0164] (Step S603) The learning information acquiring unit 132 acquires one or more positive examples. Note that a positive example is training data associated with the i-th class identifier.

[0165] (Step S604) The learning information acquisition unit 132 acquires one or more negative examples. Note that a negative example is training data that is not associated with the i-th class identifier. Training data that is not associated with the i-th class identifier is usually training data that is associated with a class identifier other than the i-th class identifier.

[0166] (Step S605) The learning information acquisition unit 132 performs machine learning learning processing using one or more positive examples acquired in step S603 and one or more negative examples acquired in step S604 to acquire a learning device. Note that this learning device is a learning device for determining whether or not a data item belongs to the i-th class, and is a learning device for performing binary classification.

[0167] (Step S606) The learning information acquiring unit 132 stores the learning device acquired in step S605 in the learning information storage unit 112 in association with the action identifier of interest and the i-th class identifier.

[0168] (Step S607) The learning information acquiring unit 132 increments the counter i by 1. The process returns to step S602.

[0169] 6, the learning information acquisition unit 132 may use two or more pieces of teacher data corresponding to the treatment identifier of interest, perform machine learning learning processing using one or more user attribute values ​​of each piece of teacher data as explanatory variables, and use the class identifier as a target variable, acquire a learning device, associate it with the treatment identifier of interest, and store the acquired learning device in the learning information storage unit 112. In this case, one treatment identifier corresponds to one learning device. This learning device is a learning device for predicting one of the class identifiers.

[0170] Next, a second example of the learning information creation process in step S304 will be described with reference to the flowchart in FIG. 7. The second example of the learning information creation process is an example of acquiring a learning device for acquiring an action identifier. The learning device acquired in the second example is a learning device for determining whether or not an action identified by the corresponding action identifier should be taken, and is acquired for each action. Such a learning device is a learning device that performs binary classification.

[0171] (Step S701) The learning information acquiring unit 132 assigns 1 to a counter i.

[0172] (Step S702) The learning information acquisition unit 132 determines whether or not the i-th handling identifier exists. If the i-th handling identifier exists, the process proceeds to step S703, and if not, the process returns to the upper level process.

[0173] (Step S703) The learning information acquiring unit 132 acquires, from the training data storage unit 111, two or more pieces of training data corresponding to the i-th action identifier.

[0174] (Step S704) The learning information acquiring unit 132 acquires one or more positive examples from the two or more training data acquired in step S703. A positive example is training data that meets the positive example condition.

[0175] The positive example condition is a condition for determining that an action should be taken. The positive example condition may be one or two of the conditions of satisfying an effect condition and satisfying a satisfaction level condition. The positive example condition is, for example, a condition based on one or more types of information of effect information and satisfaction level. The positive example condition is, for example, "effect information is equal to or greater than a threshold value," "satisfaction level is equal to or greater than a threshold value," or "effect information is equal to or greater than a threshold value and satisfaction level is equal to or greater than a threshold value."

[0176] (Step S705) The learning information acquiring unit 132 acquires one or more negative examples from the two or more pieces of training data acquired in step S703. A negative example is training data that does not meet the positive example conditions.

[0177] (Step S706) The learning information acquisition unit 132 performs machine learning learning processing using one or more positive examples acquired in step S704 and one or more negative examples acquired in step S705, and acquires a learning device.

[0178] (Step S707) The learning information acquiring unit 132 stores the learning device acquired in step S706 in the learning information storage unit 112 in association with the action identifier of interest.

[0179] (Step S708) The learning information acquiring unit 132 increments the counter i by 1. The process returns to step S702.

[0180] Next, a third example of the learning information creation process in step S304 will be described with reference to the flowchart in Fig. 8. The third example of the learning information creation process is an example of acquiring a single learning device for acquiring a response identifier. Note that this learning device is typically a learning device for performing multi-value classification.

[0181] (Step S801) The learning information acquiring unit 132 acquires one or more pieces of training data that meet the positive example condition from the training data storage unit 111.

[0182] (Step S802) The learning information acquisition unit 132 provides one or more pieces of teacher data acquired in step S801 to a machine learning learning module to acquire a learning device.

[0183] (Step S803) The learning information acquiring unit 132 accumulates the learning device acquired in step S802 in the learning information storage unit 112. The process returns to the upper level process.

[0184] Next, a fourth example of the learning information creation process in step S304 will be described using the flowchart in Fig. 9. In the fourth example of the learning information creation process, a correspondence table for each response identifier is created. In the flowchart in Fig. 9, explanations of steps that are the same as those in the flowchart in Fig. 4 will be omitted.

[0185] (Step S901) The learning information acquiring unit 132 assigns 1 to a counter j.

[0186] (Step S902) The learning information acquisition unit 132 determines whether or not the jth class identifier corresponding to the ith handling identifier exists. If the jth class identifier exists, the process proceeds to step S903; if not, the process proceeds to step S907.

[0187] (Step S903) The learning information acquiring unit 132 acquires one or more pieces of teacher data corresponding to the i-th action identifier and the j-th class identifier.

[0188] (Step S904) The learning information acquiring unit 132 acquires a representative vector of vectors formed from one or more pieces of teacher data.

[0189] (Step S905) The learning information acquiring unit 132 temporarily stores the representative vector acquired in step S904 in association with the j-th class identifier.

[0190] (Step S906) The power learning information acquiring unit 132 increments the counter j by 1. The process returns to step S902.

[0191] (Step S907) The learning information acquisition unit 132 constructs a correspondence table having two or more pieces of correspondence information each having the representative vector and class identifier acquired in step S905, and stores the correspondence table in the learning information storage unit 112 in association with the i-th response identifier.

[0192] (Step S908) The learning information acquiring unit 132 increments the counter i by 1. The process returns to step S402.

[0193] Next, an example of the output information acquisition process in step S306 will be described with reference to the flowchart in FIG.

[0194] (Step S1001) The handling determining unit 133 acquires handling information. An example of the handling information acquisition process will be described with reference to the flowcharts in FIGS.

[0195] (Step S1002) The basis information acquisition unit 134 acquires the basis information. An example of the basis information processing will be described with reference to the flowchart in FIG.

[0196] (Step S1003) The remuneration information acquisition unit 135 acquires remuneration information. An example of remuneration information processing will be described with reference to the flowchart in FIG.

[0197] (Step S1004) The processing unit 13 composes output information using the handling information, the basis information, and the remuneration information, and returns to the upper processing.

[0198] Next, an example of the action information acquisition process in step S1001 will be described with reference to the flowchart in Fig. 11. Note that in the flowchart in Fig. 11, a learning device is used for each action and each class.

[0199] (Step S1101) The handling determining unit 133 acquires one or more user attribute values ​​included in the received user information.

[0200] (Step S1102) The handling determining unit 133 assigns 1 to a counter i.

[0201] (Step S1103) The handling determining unit 133 determines whether or not the i-th handling identifier exists. If the i-th handling identifier exists, the process proceeds to step S1104, and if not, the process returns to the upper level processing.

[0202] (Step S1104) The handling determining unit 133 assigns 1 to a counter j.

[0203] (Step S1105) The handling determining unit 133 determines whether or not the j-th class identifier exists. If the j-th class identifier exists, the process proceeds to step S1106, and if not, the process proceeds to step S1111.

[0204] (Step S1106) The action determining unit 133 acquires, from the learning information storage unit 112, a learning device corresponding to the i-th action identifier and the j-th class identifier.

[0205] (Step S1107) The handling determining unit 133 performs machine learning prediction processing using the learning device acquired in step S1106 and one or more user attribute values ​​acquired in step S1101, and acquires a prediction result. The prediction result includes information on whether or not the user belongs to the class identified by the j-th class identifier. The prediction result preferably includes a score.

[0206] (Step S1108) If the prediction result obtained in step S1107 is that the object “belongs to the class identified by the jth class identifier,” the handling determination unit 133 proceeds to step S1109; if the prediction result is that the object “does not belong to the class identified by the jth class identifier,” the handling determination unit 133 proceeds to step S1110.

[0207] (Step S1109) The handling determining unit 133 temporarily stores the jth class identifier in a buffer (not shown) in association with the i-th handling identifier.

[0208] (Step S1110) The handling determining unit 133 increments the counter j by 1. The process returns to step S1105.

[0209] (Step S1111) The handling determining unit 133 increments the counter i by 1. The process returns to step S1103.

[0210] 11, a class identifier is determined for each handling identifier. Then, the handling determining unit 133 may store in the storage unit 11 only the handling identifier corresponding to the determined class identifier only if the determined class identifier satisfies the acquisition condition.

[0211] Next, an example of the countermeasure information acquisition process in step S1001 will be described with reference to the flowchart in Fig. 12. In the flowchart in Fig. 12, explanations of the same steps as in the flowchart in Fig. 11 will be omitted. Note that in the flowchart in Fig. 12, a learner for each countermeasure is used as a learner for determining whether or not to take a countermeasure.

[0212] (Step S1201) The handling determining unit 133 acquires, from the learning information storage unit 112, a learning device corresponding to the i-th handling identifier.

[0213] (Step S1202) The action determining unit 133 performs machine learning prediction processing using the learning device acquired in step S1201 and one or more user attribute values ​​acquired in step S1101, and acquires a prediction result. The prediction result includes information on whether or not taking the action identified by the j-th action identifier is effective (whether or not it satisfies the requirement). It is preferable that the prediction result includes a score.

[0214] (Step S1203) If the prediction result in step S1202 includes "satisfied" (for example, "1"), the handling determining unit 133 proceeds to step S1204, and if it includes "not satisfied" (for example, "0"), the handling determining unit 133 proceeds to step S1207. Note that the prediction result being "satisfied" indicates that the handling identified by the handling identifier should be carried out.

[0215] (Step S1204) The handling determining unit 133 acquires the score of the prediction process in step S1202.

[0216] (Step S1205) The handling determining unit 133 determines whether the score acquired in step S1204 satisfies a condition. If the condition is satisfied, the process proceeds to step S1206, and if the condition is not satisfied, the process proceeds to step S1207. The condition is that the score is high, for example, "the score is equal to or greater than a threshold" or "the score is larger than a threshold."

[0217] (Step S1206) The handling determining unit 133 stores the j-th handling identifier in the storage unit 11. It is preferable that the handling determining unit 133 also stores the score acquired in step S1204.

[0218] (Step S1207) The handling determining unit 133 increments the counter i by 1. The process returns to step S1105.

[0219] Next, an example of the handling information acquisition process in step S1001 will be described with reference to the flowchart in Fig. 13. In the flowchart in Fig. 13, explanations of the same steps as in the flowchart in Fig. 11 will be omitted. Note that the flowchart in Fig. 13 uses a learning device that outputs a handling identifier.

[0220] (Step S1301 ) The handling determining unit 133 acquires a learning device from the learning information storage unit 112 .

[0221] (Step S1302) The handling determining unit 133 performs machine learning prediction processing using the learning device acquired in step S1301 and one or more user attribute values ​​acquired in step S1101, and acquires a prediction result. The prediction result includes a handling identifier. It is preferable that the prediction result includes a score.

[0222] (Step S1303) The handling determining unit 133 stores the handling identifier acquired in step S1302. The process returns to the upper level process. Note that it is preferable that the handling determining unit 133 also stores a score here.

[0223] Next, an example of the handling information acquisition process in step S1001 will be described with reference to the flowchart in Fig. 14. In the flowchart in Fig. 14, explanations of the same steps as in the flowchart in Fig. 11 will be omitted. Note that the flowchart in Fig. 14 uses a correspondence table for acquiring class identifiers.

[0224] (Step S1401) The handling determining unit 133 constructs a vector whose elements are one or more of the user attribute values ​​acquired in step S1101. Next, the handling determining unit 133 determines correspondence information having a vector that is most similar to the vector from the correspondence table in the learning information storage unit 112, which is paired with the i-th handling identifier.

[0225] (Step S1402) The handling determining unit 133 acquires the class identifier included in the correspondence information determined in step S1401.

[0226] (Step S1403) The handling determining unit 133 determines whether the class identifier acquired in step S1402 satisfies the condition. If the condition is satisfied, the process proceeds to step S1404, and if the condition is not satisfied, the process proceeds to step S1405. The condition here is that the class identifier is any one of one or more predetermined class identifiers (for example, the identifier of a class that satisfies the effect condition).

[0227] (Step S1404) The handling determining unit 133 stores the class identifier acquired in step S1402 in pairs with the i-th handling identifier.

[0228] (Step S1405) The handling determining unit 133 increments the counter i by 1. The process returns to step S1103.

[0229] 14, a correspondence table for acquiring a response identifier may be used. In such a case, in step S1402, the response determining unit 133 acquires and stores the response identifier included in the response information determined in step S1401. In such a case, steps S1403 and S1404 are unnecessary.

[0230] Next, an example of the basis information processing in step S1002 will be described with reference to the flowchart in FIG.

[0231] (Step S1501) The basis information acquiring unit 134 assigns 1 to a counter i.

[0232] (Step S1502) The basis information acquisition unit 134 determines whether the i-th action identifier exists among the action identifiers acquired in step S1002. If the i-th action identifier exists, the process proceeds to step S1503; if not, the process returns to the upper level process.

[0233] (Step S1503) The basis information acquisition unit 134 acquires raw information. The raw information is information used when acquiring the basis level. The raw information is, for example, a class identifier corresponding to the action identifier and a score corresponding to the action identifier.

[0234] (Step S1504) The basis information acquiring unit 134 acquires the basis level corresponding to the raw information acquired in step S1503.

[0235] The basis information acquiring unit 134 acquires, for example, the basis level corresponding to the class identifier acquired in step S1503. In this case, the storage unit 11 stores the basis level paired with each of two or more class identifiers.

[0236] Furthermore, the evidence information acquiring unit 134 acquires, for example, an evidence level corresponding to the score acquired in step S1503. In this case, evidence levels paired with two or more conditions of the score are stored in the storage unit 11. The conditions of the score are usually information indicating the range of the score. Note that the score is a score acquired in the machine learning prediction process.

[0237] (Step S1505) The basis information acquiring unit 134 acquires one or more pieces of teacher data that are the basis for acquiring the i-th action identifier.

[0238] (Step S1506) The basis information acquisition unit 134 acquires effect information corresponding to each of the teacher data, using the one or more pieces of teacher data acquired in step S1505. Next, the basis information acquisition unit 134 acquires effectiveness information using the one or more pieces of effect information, associates it with the i-th action identifier, and accumulates it in a buffer (not shown).

[0239] (Step S1507) The basis information acquiring unit 134 acquires the degree of satisfaction from each of the one or more pieces of teacher data acquired in step S1505.

[0240] (Step S1508) The basis information acquiring unit 134 acquires satisfaction level information using one or more satisfaction levels acquired in step S1507, associates it with the i-th action identifier, and stores it in a buffer (not shown).

[0241] (Step S1509) The basis information obtaining unit 134 increments the counter i by 1. The process returns to step S1502.

[0242] In the flowchart of FIG. 15, the evidence level, effect information, and satisfaction level information are acquired, but only one or two types of information may be acquired from among these pieces of information.

[0243] Next, an example of the remuneration information processing in step S1003 will be described with reference to the flowchart of FIG.

[0244] (Step S1601) The remuneration information acquisition unit 135 assigns 1 to a counter i.

[0245] (Step S1602) The remuneration information acquisition unit 135 determines whether or not the i-th handling identifier exists. If the i-th handling identifier exists, the process proceeds to step S1603, and if not, the process returns to the upper processing.

[0246] (Step S1603) The remuneration information acquisition unit 135 determines whether the i-th action identifier matches the remuneration condition. If the remuneration condition is met, the process proceeds to step S1604, and if the remuneration condition is not met, the process proceeds to step S1606.

[0247] The remuneration information acquisition unit 135 determines whether the i-th action identifier meets the remuneration conditions, for example, using one or more of the i-th action identifier, the evidence level corresponding to the i-th action identifier, the effect information, and the satisfaction level information.

[0248] The reward condition is a condition for acquiring the reward information. Examples of the reward condition include "reward information associated with the i-th action identifier is stored in the storage unit 11," "the evidence level associated with the i-th action identifier is 'information indicating no evidence,'" "the evidence level associated with the i-th action identifier is equal to or less than a threshold," "the effect indicated by the effect information associated with the i-th action identifier is low enough to satisfy the condition," and "the degree of satisfaction indicated by the satisfaction level information associated with the i-th action identifier is low enough to satisfy the condition."

[0249] (Step S1604) The reward information acquisition unit 135 acquires reward information from the storage unit 11. For example, the reward information acquisition unit 135 acquires reward information associated with the i-th action identifier from the storage unit 11. For example, the reward information acquisition unit 135 acquires reward information associated with a reward condition from the storage unit 11.

[0250] (Step S1605) The reward information acquisition unit 135 temporarily stores the reward information acquired in step S1604 in a buffer (not shown) as reward information to be output in association with the i-th action identifier.

[0251] (Step S1606) The remuneration information acquisition unit 135 increments the counter i by 1. The process returns to step S1602.

[0252] Next, an example of the operation of the terminal device 2 will be described. The terminal reception unit 22 of the terminal device 2 receives user information. Next, the device processing unit 23 uses the user information to compose user information to be transmitted. The terminal transmission unit 24 transmits the user information to the information processing device 1. Next, in response to the transmission of the user information, the terminal reception unit 25 receives output information from the information processing device 1. Next, the device processing unit 23 uses the output information to compose information to be output. The terminal output unit 26 outputs the composed information. The output information includes, for example, a response identifier, basis information, and reward information.

[0253] Furthermore, the terminal reception unit 22 of the terminal device 2 may receive teacher data. In this case, the device processing unit 23 uses the teacher data to generate teacher data to be transmitted. Next, The terminal transmitting unit 24 transmits the teacher data to the information processing device 1.

[0254] A specific example of the operation of the information system A in this embodiment will be described below.

[0255] Assume that the teacher data storage unit 111 of the information processing device 1 stores a teacher data management table containing two or more sets of teacher data transmitted from the terminal device 2. The teacher data is information about a user who has taken some kind of action and includes first result information and second result information. This teacher data management table is shown in FIG. 17. The teacher data management table manages two or more records, each of which includes an "ID," "action identifier," "first result information," "second result information," "gender," "age," "height," "weight," and "answer information." The "answer information" is a user's response to a questionnaire and includes "effort information," "lifestyle information," "goal," and "satisfaction level." The "first result information" is the test result before the action identified by the action identifier was taken. The "second result information" is the test result after the action identified by the action identifier was taken. Here, the test result is the systolic blood pressure. The "goal" is the systolic blood pressure the user aims for. The "satisfaction level" is the user's degree of satisfaction with the action identified by the action identifier.

[0256] It is also assumed that the storage unit 11 of the information processing device 1 stores action identifiers "product A (reduced-salt soy sauce)," "product B (reduced-salt miso)," and "product C (yogurt)." Here, the action corresponding to "product A" is a program (program A) for using product A continuously for one month, the action corresponding to "product B" is a program (program B) for using product B continuously for one month, and the action corresponding to "product C" is a program (program C) for using product C continuously for two months. A program refers to the continuous consumption of a product, service, etc., and is also referred to as a challenge program.

[0257] Furthermore, the storage unit 11 stores a basis level determination table having a basis level of "3" associated with the class identifier "Class A," a basis level of "2" associated with the class identifiers "Class B" and "Class D," a basis level of "1" associated with the class identifier "Class C," and a basis level of "0" associated with the class identifier "None."

[0258] The evidence level determination table may be a table for determining the evidence level using one or more pieces of information selected from the validity information and the satisfaction level information, as shown in FIG. 18. Such an evidence level determination table includes an "evidence level" and a "determination condition." The "determination condition" is a condition for determining the evidence level. That is, the evidence information acquisition unit 134 may acquire the evidence level using one or more pieces of information selected from the validity information and the satisfaction level information. The determination condition of "evidence level = 1 to 3" is applied, for example, when the number of pieces of teacher data is equal to or greater than a threshold value (e.g., 100 pieces). In addition, in the "evidence level = 1 to 3" in FIG. 18, the order of application is "evidence level = 3," "evidence level = 2," and "evidence level = 1."

[0259] It is also assumed that the storage unit 11 stores a goal achievement condition of "goal achievement rate >= 80%." The basis information acquisition unit 134 then acquires recommendation information to strongly recommend an action identifier that satisfies the goal achievement condition. Here, the recommendation information is assumed to be "necessary" (e.g., "1"). Note that "necessary" means that it is necessary to take the action identified by the action identifier in order to achieve the goal. It is also assumed that the basis information acquisition unit 134 acquires recommendation information "not required" (e.g., "0") for an action identifier that does not satisfy the goal achievement condition.

[0260] Also, it is assumed that the reward condition "evidence level = 0" and the reward information corresponding to the reward condition "30% OFF" are stored in the storage unit 11. In other words, it is assumed that the reward information of 30% OFF (information indicating that the product can be purchased at a 30% discount) is presented for the product corresponding to "evidence level = 0".

[0261] In this situation, the classification unit 131 of the information processing device 1 acquires the first result information, second result information, goal, and satisfaction level of each training data in FIG. 17 for each action identifier. Next, the classification unit 131 acquires effect information using the first result information, second result information, and goal for each action identifier and each training data. Here, the effect information is the goal achievement rate. The goal achievement rate is calculated, for example, by "(first result information - second result information) / (first result information - goal) * 100 (%)".

[0262] Next, the classification unit 131 classifies the teacher data for each action identifier using the effect information and the satisfaction level. Here, the classification unit 131 classifies the teacher data for each action identifier, for example, as follows: the class identifier of teacher data whose effect information is 60% or more and whose satisfaction level is "4 or 5" is "Class A," the class identifier of teacher data whose effect information is less than 60% and whose satisfaction level is "4 or 5" is "Class B," the class identifier of teacher data whose effect information is less than 60% and whose satisfaction level is "1, 2, or 3" is "Class C," and the class identifier of teacher data whose effect information is 60% or more and whose satisfaction level is "1, 2, or 3" is "Class D." FIG. 19 illustrates the concept of such teacher data classification.

[0263] Next, the learning information acquisition unit 132 acquires, for each treatment identifier, the "first result information," "gender," "age," "height," "weight," "lifestyle information," and "goal" for each teacher data from the teacher data management table in Figure 17, and constructs a vector with the "first result information," "gender," "age," "height," "weight," "lifestyle information," and "goal" as explanatory variables and the class identifier as the objective variable.

[0264] Next, the learning information acquisition unit 132 performs machine learning learning processing using two or more vectors for each treatment identifier, constructs a learning device for each treatment identifier, and stores the learning device for each treatment identifier in association with the treatment identifier in the learning information storage unit 112. Note that this learning device is a learning device for predicting a class identifier using vectors having "first result information," "gender," "age," "height," "weight," "lifestyle habit information," and "goal."

[0265] In the above situation, it is assumed that user A inputs user information having "<First result information> 183 <Gender> Male <Age> 68 <Height> 175 <Weight> 88 <Lifestyle information> Smoking <Goal> 140" into terminal device 2. Next, terminal device 2 accepts the user information and transmits it to the information processing device 1.

[0266] Next, the user information receiving unit 121 of the information processing device 1 receives the user information from the terminal device 2 of the user A.

[0267] Next, the processing unit 13 performs the output information acquisition process as follows. That is, first, the action determining unit 133 determines a class identifier for each of the action identifiers “Product A (reduced-salt soy sauce),” “Product B (reduced-salt miso),” and “Product C (yogurt).” That is, first, the action determining unit 133 acquires a learning device paired with the action identifier “Product A (reduced-salt soy sauce)” from the learning information storage unit 112. Next, the action determining unit 133 provides the user attribute values ​​“<First result information> 183 <Gender> Male <Age> 68 <Height> 175 <Weight> 88 <Lifestyle information> Smoking <Goal> 140” contained in the user information and the acquired learning device to a prediction module that performs machine learning prediction processing, executes the prediction module, and acquires a class identifier “Class A.” Similarly, the action determining unit 133 acquires a learning device paired with the action identifier “Product B (reduced-salt miso)” from the learning information storage unit 112. Next, the handling determining unit 133 provides two or more user attribute values ​​included in the user information and the acquired learning device to a prediction module that performs machine learning prediction processing, executes the prediction module, and acquires a class identifier "Class D." It is also assumed that the handling determining unit 133 was unable to acquire a learning device paired with the handling identifier "Product C (yogurt)" from the learning information storage unit 112.

[0268] Next, the basis information acquisition unit 134 acquires from the storage unit 11 a basis level of "3" that is paired with the class identifier "Class A" that corresponds to the action identifier "Product A (reduced-salt soy sauce)". The basis information acquisition unit 134 also acquires from the storage unit 11 a basis level of "2" that is paired with the class identifier "Class D" that corresponds to the action identifier "Product B (reduced-salt miso)". Furthermore, the basis information acquisition unit 134 acquires from the storage unit 11 a basis level of "0" that is paired with the action identifier "Product C (yogurt)" for which a class could not be determined.

[0269] Next, for each action identifier for which a class identifier has been acquired, the basis information acquisition unit 134 acquires the action identifier and one or more teacher data corresponding to the class identifier. Then, the basis information acquisition unit 134 acquires effect information for each teacher data using the acquired one or more teacher data. Next, the basis information acquisition unit 134 acquires "effectiveness information" which is the average of the effect information for each teacher data. It is assumed that the basis information acquisition unit 134 has acquired effect information of "80%" from one or more teacher data corresponding to the action identifier "Product A (reduced-salt soy sauce)" and the class identifier "Class A." Furthermore, the basis information acquisition unit 134 acquires the satisfaction level of each of the acquired one or more teacher data. Next, it is assumed that the basis information acquisition unit 134 has acquired satisfaction information of "60%", which is the percentage of teacher data in the same class that have a satisfaction level of "4" or "5".

[0270] Similarly, the basis information acquisition unit 134 acquires one or more training data corresponding to the class identifier "Class D" corresponding to the treatment identifier "Product B (reduced-salt miso)", and uses the training data to acquire effectiveness information "80%" and satisfaction information "30%".

[0271] The basis information acquisition unit 134 also acquires the goal achievement condition "goal achievement rate >= 80%" from the storage unit 11. Next, for each action identifier, the basis information acquisition unit 134 acquires the action identifier "product A (reduced-salt soy sauce)" for which the average of the goal achievement rates of one or more pieces of teacher data for the class identifier corresponding to the action identifier satisfies the goal achievement condition. Next, the basis information acquisition unit 134 associates the action identifier "product A (reduced-salt soy sauce)" with the action identifier "product A (reduced-salt soy sauce)" and acquires the recommendation information "necessary." Furthermore, the basis information acquisition unit 134 associates the action identifiers "product B (reduced-salt miso)" and "product C (yogurt)" that do not satisfy the goal achievement condition and acquires the recommendation information "not required."

[0272] Furthermore, the basis information acquiring unit 134 was unable to acquire the effectiveness information and satisfaction level information corresponding to the treatment identifier "product C (yogurt)" because the basis level was "0."

[0273] Next, the reward information acquisition unit 135 refers to the reward condition "evidence level=0" in the storage unit 11, and acquires reward information of 30% off for the treatment identifier "product C (yogurt)" corresponding to "evidence level=0".

[0274] Next, the processing unit 13 composes output information using the acquired handling information, basis information, recommendation information, and reward information. Next, the information output unit 141 transmits the acquired output information to the terminal device 2.

[0275] Next, the terminal device 2 receives and outputs the output information. An example of such output is shown in FIG. 20. In FIG. 20, 2001 is a basis level, which is referred to here as an "evidence level." 2002 is basis information having effectiveness information and satisfaction information, and is labeled "reason." 2003 is recommendation information, and is labeled "for achieving ToBe." 2004 is reward information, and is labeled "incentive."

[0276] As described above, according to this embodiment, it is possible to determine an appropriate response to the test results.

[0277] Furthermore, according to this embodiment, when a measure to be taken in response to the test results is proposed, the basis for the proposal can be presented.

[0278] Furthermore, according to this embodiment, when a countermeasure for the test results is proposed, it is also possible to clearly indicate that the proposal has no basis.

[0279] Furthermore, according to this embodiment, when a measure to deal with the test results is proposed, an incentive can be given to the user to take that measure.

[0280] 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 information processing device in this embodiment is the following program. In other words, this program causes a computer to function as: a user information receiving unit that receives user information having one or more user attribute values ​​including result information that identifies test results related to a user's biological activity; a learning information storage unit that stores learning information based on two or more pieces of teacher data that are associated with a treatment identifier that identifies a treatment taken by the user, the learning information being based on two or more pieces of teacher data having two or more user attribute values ​​including first result information that identifies the test results before the treatment was taken and second result information that identifies the test results as a result of the treatment being taken; a treatment decision unit that uses the learning information and the user information received by the user information receiving unit to obtain a treatment identifier that identifies a treatment that corresponds to the result information held by the user information; a basis information acquisition unit that uses one or more pieces of teacher data corresponding to the treatment identifier acquired by the treatment decision unit and the user information received by the user information receiving unit to obtain basis information regarding the basis for recommending the treatment identified by the treatment identifier acquired by the treatment decision unit; and an information output unit that outputs the treatment identifier acquired by the treatment decision unit and the basis information acquired by the basis information acquisition unit.

[0281] 21 shows the appearance of a computer that executes the programs described herein to realize the information processing device 1 and the like according to the various embodiments described above. The above-described embodiments can be realized by computer hardware and a computer program executed thereon. FIG. 21 is an overview of this computer system 300, and FIG. 22 is a block diagram of the system 300.

[0282] In FIG. 21, a computer system 300 includes a computer 301 including a CD-ROM drive, a keyboard 302, a mouse 303, and a monitor 304.

[0283] 22, 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.

[0284] A program that causes computer system 300 to execute the functions of information processing device 1 and the like of the above-described embodiment may be stored on CD-ROM 3101, inserted into CD-ROM drive 3012, and then transferred to hard disk 3017. Alternatively, the program may be transmitted to computer 301 via a network (not shown) and stored on hard disk 3017. The program is loaded into RAM 3016 when executed. The program may also be loaded directly from CD-ROM 3101 or the network.

[0285] 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 information processing device 1 of the above-described embodiment. The program only needs to include instructions that call appropriate functions (modules) in a controlled manner and achieve desired results. How the computer system 300 operates is well known, and a detailed description thereof will be omitted.

[0286] 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 transmission step (processing that can only be performed by hardware).

[0287] The computer that executes the program may be a single computer or a plurality of computers, that is, it may perform centralized processing or distributed processing.

[0288] 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.

[0289] Furthermore, 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.

[0290] 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]

[0291] As described above, the information processing device of the present invention has the effect of being able to present the basis for a suggestion when proposing a measure in response to test results, and is useful as a server, etc. that outputs a measure identifier that identifies the recommended measure and basis information. [Explanation of symbols]

[0292] 1. Information processing equipment 3 Terminal Devices 11 Storage area 12 Reception 13 Processing section 14 Output section 21 Terminal storage section 22 Terminal Reception 23 Terminal processing section 24 Terminal transmitter 25 Terminal receiving section 26 Terminal Output Unit 111 Teacher data storage unit 112 Learning information storage unit 121 User Information Reception Department 131 Classification Department 132 Learning Information Acquisition Unit 133 Response Decision-Making Department 134 Information Acquisition Department 134 Basis Information Acquisition Department 135 Compensation Information Acquisition Department 141 Information output unit

Claims

1. a user information receiving unit that receives user information having one or more user attribute values ​​including result information that identifies a test result related to a biological body of a single user; a countermeasure determination unit that acquires learning information from a learning information storage unit that stores learning information based on two or more pieces of teacher data having two or more user attribute values ​​including two or more user attribute values ​​that are associated with a countermeasure identifier that identifies a countermeasure taken by a user, the learning information being based on two or more pieces of teacher data including two or more user attribute values ​​that include first result information that identifies a test result before the countermeasure was taken and second result information that identifies a test result after the countermeasure was taken, and acquires a countermeasure identifier that identifies a countermeasure corresponding to the result information held by the user information, using the learning information and the user information accepted by the user information accepting unit; a basis information acquiring unit that acquires basis information regarding a basis for recommending the measure identified by the measure identifier acquired by the measure determining unit, using one or more pieces of teacher data corresponding to the measure identifier acquired by the measure determining unit and the user information accepted by the user information accepting unit; an information output unit that outputs the handling identifier acquired by the handling determining unit and the basis information acquired by the basis information acquisition unit.

2. Each of the two or more teacher data corresponds to a class, The basis information acquisition unit The information processing apparatus according to claim 1 , wherein the basis information is acquired using one or more pieces of teacher data corresponding to a class to which the user information belongs.

3. The one or more user attribute values ​​have one or more pieces of information of a goal or a goal achievement rate, The basis information acquisition unit determining a countermeasure identifier that satisfies the goal achievement condition stored in the storage unit, and obtaining recommendation information for strongly recommending the countermeasure identifier; The information output unit is 3. The information processing apparatus according to claim 1, further comprising: an output section for outputting the recommended information.

4. The basis information acquisition unit 4. The information processing device according to claim 1, further comprising: a processing unit that uses one or more teacher data corresponding to the action identifier acquired by the action decision unit and the user information accepted by the user information accepting unit to acquire basis information including one or more types of information selected from a basis level that identifies a degree of strength of the basis for recommending the action, and reason information that indicates a reason for recommending the action and includes effectiveness information regarding the effectiveness of the action when the action is taken or satisfaction information regarding the satisfaction level after the action is taken.

5. The basis information acquisition unit, obtaining the evidence level using the efficacy information or the satisfaction information; The information output unit is The information processing apparatus according to claim 1 , further comprising: an information processing device configured to output the basis information including the basis level.

6. The basis information acquisition unit The information processing apparatus according to claim 1 , further comprising: when the basis information cannot be acquired, acquiring basis information indicating that there is no basis.

7. A reward information acquisition unit that acquires reward information, which is information specifying a reward for encouraging a user to take the measure and corresponds to the basis information, The information output unit is The information processing device according to claim 1 , further comprising: a remuneration information acquisition unit that outputs the remuneration information acquired by the remuneration information acquisition unit.

8. The remuneration information acquisition unit, The information processing apparatus according to claim 7 , further comprising: acquiring remuneration information that specifies a remuneration according to an evidence level included in the evidence information.

9. A classification unit is further provided for classifying the two or more teacher data into two or more classes using effect information, which is information regarding the effect of the countermeasure and is obtained using the second result information possessed by each of the two or more teacher data for each countermeasure identifier, and associating each of the two or more teacher data with a class identifier that identifies the class; The handling determining unit, 8. The information processing device according to claim 1, wherein the user information received by the user information receiving unit and the learning information based on the two or more teacher data are used to determine a class to which the user information belongs for each of two or more action identifiers, and the action identifier corresponding to a class in which the difference between the first result information and the second result information is large and the action identifier corresponding to a class in which the difference is small are distinguished and obtained.

10. The teacher data also includes a degree of user satisfaction as a result of taking the action, The classification unit includes: The information processing device of claim 9, further comprising: a classifier for classifying the two or more teacher data into two or more classes using the effectiveness information and the satisfaction level for each of the two or more teacher data for each treatment identifier; and a class identifier for identifying the class for each of the two or more teacher data.

11. Each of the two or more classes classified by the classification unit is associated with a grounds level based on effect information for the teacher data corresponding to the class, The basis information acquisition unit 10. The information processing device of claim 9, further comprising: an information processing unit that obtains a basis level corresponding to the class; obtains the effectiveness information using effect information, which is information regarding the effectiveness of the countermeasure, and obtains basis information including the basis level and reason information, which is information including the effectiveness information, and obtains the basis information using the basis level and reason information, which is information including the effectiveness information.

12. The teacher data also includes a degree of user satisfaction as a result of taking the action, The classification unit includes: classifying the two or more teacher data into two or more classes using the effect information and the satisfaction level for each of the two or more teacher data for each of the two or more treatment identifiers, and associating each of the two or more teacher data with a class identifier that identifies the class; The basis information acquisition unit The information processing apparatus according to claim 11 , further comprising: acquiring the satisfaction level information using a satisfaction level included in teacher data corresponding to the class to which the user information belongs; and acquiring grounds information including reason information for including the satisfaction level information.

13. The method further includes a learning information acquisition unit that acquires learning information using two or more pieces of teacher data having one or more user attribute values ​​that satisfy a similarity condition with the one or more user attribute values ​​included in the user information accepted by the user information acceptance unit, The information processing device according to claim 1 , wherein the learning information of the learning information storage unit is learning information acquired by the learning information acquisition unit.

14. The information processing device of claim 1 , wherein the response is a challenge to consume a product for a certain period of time or longer in order to improve test results, and the teacher data includes a degree of commitment to the challenge, which is response information to a questionnaire regarding the challenge.

15. An information processing method comprising all steps performed by an information processing device described in any one of claims 1 to 14.

16. A computer comprising: A program for causing an information processing device to function as the information processing device according to any one of claims 1 to 14.

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