Prediction device, learning device, prediction method, learning information production method, and program

Abstract

To present predicted values of inspection values regarding two or more target body weight values, which cannot be done by prior art.SOLUTION: Predicted values of inspection values regarding each of two or more target body weight values can be presented by a prediction device that comprises: a learning information storage unit storing learning information acquired using two or more teaching data associated with one measured human body weight value and one or more other kinds of inspection values; a target acquisition unit for acquiring two or more future target body weight values of one user; a prediction unit for acquiring, using learning information, the predicted values of the respective one or more future inspection values corresponding to the respective two or more target body weight values; and a prediction output unit for outputting the one or more predicted values.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present invention relates to a prediction device and the like that predicts one or more test values ​​corresponding to a weight value. [Background technology]

[0002] There has been an information processing device that can provide a subject with useful information related to health management. This information processing device includes a factor extraction unit that evaluates, for each subject, the correlation between each item of a lifestyle index related to physical activity or lifestyle habits and each item of a bioindicator related to the physiological state of the body, and extracts, as factors that may affect health, a set of a lifestyle index and a bioindicator for an item for which the evaluated correlation is higher than a predetermined correlation, and an input support unit that, when one of the acquired lifestyle index and the bioindicator is a life index or a bioindicator for an item related to the set extracted by the factor extraction unit, outputs an input guide for the other related to the set to which the one belongs, in accordance with a change from a past value of the other (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2016-189085 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the prior art, it was not possible to present predicted values ​​of test values ​​for a target body weight (hereinafter referred to as "target body weight" where appropriate). In particular, in the prior art, it was not possible to present predicted values ​​of test values ​​for each of two or more target body weights, so that the user could not grasp the step-by-step status of weight to improve to or approach the target test value, and thus was not sufficiently motivated to lose weight. Note that test values ​​include, for example, blood pressure, triglycerides, HDL, LDL, and blood glucose levels. [Means for solving the problem]

[0005] The prediction device of the first invention is a prediction device comprising: a learning information storage unit that stores learning information acquired using two or more teaching data in which a person's weight measurement value is associated with one or more other types of test values; a target acquisition unit that acquires two or more future target weight values ​​for a user; a prediction unit that acquires predicted values ​​for each type of one or more future test values ​​corresponding to each of the two or more target weight values ​​using the learning information; and a prediction output unit that outputs one or more predicted values.

[0006] This configuration makes it possible to present predicted test values ​​for two or more target weight values, thereby providing the subject with useful information regarding health management.

[0007] In addition, the prediction device of the second invention is a prediction device in which, compared to the first invention, the training data includes test values ​​of the same type at two or more different time periods and weight measurements at two or more time periods.

[0008] With this configuration, it is possible to present predicted values ​​of test values ​​for each of two or more target weight values ​​using information on the history of test values.

[0009] Furthermore, the prediction device of the third invention is a prediction device in which, compared to the first or second invention, the learning information is an arithmetic formula obtained by performing linear regression analysis on two or more teaching data.

[0010] With this configuration, predicted test values ​​for two or more target weight values ​​can be easily presented.

[0011] In addition, the prediction device of the fourth invention is a prediction device in which, compared to the third invention, the teacher data has two or more other types of test values, and the learning information is information obtained using two or more degenerate teacher data that are pairs of one or more test values ​​and weight measurements corresponding to each of the other one or more types selected based on the importance of each of the other two or more types of test values.

[0012] With this configuration, predicted test values ​​for two or more target weight values ​​can be presented simply and more accurately.

[0013] Furthermore, the prediction device of the fifth invention is a prediction device according to any one of the first to fourth inventions, in which the target acquisition unit acquires two or more target weight values ​​using the most recent weight measurement value of one user.

[0014] With this configuration, predicted test values ​​for two or more appropriate target weight values ​​can be presented.

[0015] Furthermore, a prediction device according to a sixth aspect of the present invention is a prediction device according to the fifth aspect of the present invention, wherein the target acquisition unit acquires a value obtained by subtracting two or more predetermined values ​​from the most recent weight measurement value of one user.

[0016] With this configuration, predicted test values ​​for two or more appropriate target weight values ​​can be presented.

[0017] Furthermore, the prediction device of the seventh invention is a prediction device in which, compared to any one of the first to sixth inventions, the learning information storage unit stores learning information corresponding to the type of each of the two or more predicted values ​​to be output, the prediction unit acquires learning information corresponding to each of the two or more types of predicted values ​​and uses the learning information to acquire predicted values ​​corresponding to each of the two or more target weight values, and the prediction output unit outputs the predicted value for each of the two or more types of predicted values ​​acquired by the prediction unit.

[0018] With this configuration, two or more types of highly accurate predicted values ​​can be presented by using learning information corresponding to the acquired type of predicted value.

[0019] Furthermore, the prediction device of the eighth invention is a prediction device in which, compared to any one of the first to seventh inventions, the teacher data corresponds to one or more attribute values ​​of a single person, the learning information storage unit stores learning information for each of two or more conditions using one or more attribute values, and the prediction unit obtains one or more predicted values ​​corresponding to each of two or more target weight values ​​using the learning information corresponding to the condition in which one or more attribute values ​​of a single user match.

[0020] With this configuration, highly accurate predicted values ​​can be presented by using learning information according to the attribute values ​​of the user.

[0021] In addition, the prediction device of the ninth invention is a prediction device that, compared to any one of the first to eighth inventions, further includes a component that creates output information that associates two or more target weight values ​​with one or more types of predicted values ​​acquired by the prediction unit, and the prediction output unit outputs the output information.

[0022] With this configuration, the relationship between each of two or more target weight values ​​and the predicted value can be presented in an easy-to-understand manner.

[0023] Furthermore, the prediction device of the tenth invention is a prediction device in which, compared to any one of the first to eighth inventions, it further comprises a reference value storage unit in which reference values ​​for one or more other types of test values ​​are stored, and further comprises a configuration unit that composes output information according to the comparison result between the predicted value acquired by the prediction unit and the reference value corresponding to the predicted value, and the prediction output unit outputs the output information.

[0024] With this configuration, it is possible to present the predicted value appropriately.

[0025] In addition, the learning device of the eleventh invention is a learning device that includes a teacher data storage unit that stores two or more teacher data that correspond to weight measurement values ​​obtained from a single person at the same time and one or more other types of test values, a learning information acquisition unit that acquires learning information using the two or more teacher data, and a learning information storage unit that accumulates the learning information.

[0026] With this configuration, it is possible to obtain information for obtaining predicted test values ​​for two or more target weight values. [Effects of the Invention]

[0027] The prediction device according to the present invention can present predicted test values ​​for two or more target body weight values. [Brief explanation of the drawings]

[0028] [Figure 1] Block diagram of learning device 1 according to embodiment 1 [Figure 2] A diagram illustrating an example of the acquisition process of the training data. [Figure 3] A flowchart illustrating an example of the operation of the learning device 1. [Figure 4] A flowchart illustrating an example of the explanatory variable selection process [Figure 5] Figure showing the teacher data management table [Figure 6] Block diagram of a prediction device 2 according to a second embodiment [Figure 7] A flowchart illustrating an example of the operation of the prediction device 2. [Figure 8] A flowchart illustrating an example of the target weight acquisition process. [Figure 9] Figure showing an example of the output [Figure 10] Block diagram of the prediction device 3 [Figure 11] Block diagram of the prediction system A [Figure 12] Overview of the computer system in the above embodiment [Figure 13] Block diagram of the computer system DETAILED DESCRIPTION OF THE INVENTION

[0029] Hereinafter, embodiments of a learning device, a prediction device, etc. 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, a learning device that acquires learning information using two or more pieces of teacher data will be described.

[0031] 1 is a block diagram of a learning device 1 according to this embodiment. The learning device 1 includes a storage unit 11, a learning information acquisition unit 12, and a learning information accumulation unit 13. The storage unit 11 includes a teacher data storage unit 111.

[0032] Various types of information are stored in the storage unit 11. The various types of information include, for example, teacher data (to be described later), a learning device (to be described later), a selection condition (to be described later), and an attribute value condition (to be described later).

[0033] The teacher data storage unit 111 stores two or more teacher data. The teacher data is associated with, for example, a user identifier and one or more attribute values ​​of a person. The user identifier is an attribute value of a person, such as an ID, name, email address, or telephone number. The person is the person from whom the test values ​​contained in the teacher data were obtained.

[0034] The attribute values ​​are attribute values ​​of the person from whom the test values ​​were obtained, such as gender, age, generation, occupation, and information about lifestyle habits (e.g., whether or not the person smokes, how much they smoke, whether or not they exercise, the amount of exercise they do in a given period, how much they exercise, whether or not they drink alcohol, and how much they drink).

[0035] The training data is information that associates a weight measurement value (hereinafter referred to as "weight value" or "weight") with one or more other types of laboratory values. The weight measurement value is usually a measured weight value, but it does not need to be accurate and may be a self-reported value by the user, although this is not preferred.

[0036] The training data is, for example, information having a weight measurement value and one or more other types of test values. The training data is, for example, link information that links a weight measurement value with one or more other types of test values. Note that the data structure, etc., is not important as long as the weight measurement value and one or more other types of test values ​​can be obtained using the training data. Furthermore, the one or more other types of test values ​​are, for example, values ​​resulting from a health checkup of a person.

[0037] The weight measurement value corresponding to one training data and one or more other types of test values ​​are information obtained from one person at the same time. "Same time" means a similar time period, for example, on the same day, within one week, within one month, etc., and the time period can vary.

[0038] The one or more other types of test values ​​are test values ​​other than body weight. The one or more other types of test values ​​are, for example, values ​​related to obesity, values ​​related to blood pressure, values ​​related to lipids, and values ​​related to blood glucose. Values ​​related to obesity are, for example, waist circumference and BMI. Values ​​related to blood pressure are, for example, systolic blood pressure and diastolic blood pressure. Values ​​related to lipids are, for example, triglycerides, HDL cholesterol, and LDL cholesterol. Values ​​related to blood glucose are, for example, fasting blood glucose level, HbA1C, and the presence or absence of urinary sugar. Note that test values ​​are usually any numerical value among multiple values, but may also be any binary information (for example, (-) or (+)).

[0039] The training data is a set of values ​​of one or more explanatory variables and values ​​of one or more objective variables.

[0040] For example, the value of one or more explanatory variables is weight, and the value of one or more response variables is a single laboratory value. An example of the single laboratory value is any one or more other types of laboratory values.

[0041] For example, the values ​​of one or more explanatory variables are one or more past weights and one or more past test values, and the value of one or more dependent variables is a test value. For example, the values ​​of one or more explanatory variables are (weight this year, weight last year, weight two years ago, weight three years ago, blood pressure last year, blood pressure two years ago, blood pressure three years ago), and the value of one or more dependent variables is (blood pressure this year). For example, the values ​​of one or more explanatory variables are (weight this year, weight last year, weight two years ago, weight three years ago, blood pressure this year, blood pressure last year, blood pressure two years ago, blood pressure three years ago), and the value of one or more dependent variables is (blood pressure this year).

[0042] For example, the values ​​of one or more explanatory variables may be a set of a person's most recent weight and two or more different past time points for that person, and the value of the dependent variable may be a set of one or more most recent test values. The set may include a weight at a time point and one or more other test values. The value of the dependent variable is preferably one other test value, but may also be two or more other test values.

[0043] The other types of test values ​​included in the training data may be only one type of test value (for example, blood pressure) or may be two or more types of test values ​​(for example, blood pressure and blood glucose level).

[0044] It is preferable that each value constituting the training data is associated with time information that specifies when it was acquired. The time information may be, for example, information indicating the date, time, month, year, or period of time elapsed since the present, but the level of detail and data structure are not important.

[0045] The learning information acquisition unit 12 acquires two or more pieces of teacher data from the teacher data storage unit 111 and acquires learning information using the two or more pieces of teacher data. The learning information is information for acquiring values ​​of one or more objective variables when values ​​of one or more explanatory variables are given. The learning information acquisition unit 12 may acquire learning information using two or more pieces of degenerate teacher data, which will be described later. Note that degenerate teacher data can also be considered teacher data.

[0046] When the training data for one person in the training data storage unit 111 is a set of laboratory values ​​including weights for α years (α is a natural number greater than or equal to 2), it is preferable that the learning information acquisition unit 12 acquires two or more sets of laboratory values ​​including weights for β years (β<α) from the training data for one or more people, staggering the years, acquires the two or more partial training data, and uses the two or more partial training data to acquire learning information. Note that the partial training data is also training data for acquiring learning information.

[0047] For example, if a set of test values ​​for one individual exists for six years (teaching data "A"), the learning information acquisition unit 12 shifts the base year and acquires three partial teaching data ("A1," "A2," and "A3") for four years from the set of test values ​​for that individual. As a result, if there are R records of teaching data for six years, for example, the learning information acquisition unit 12 acquires 3R pieces of partial teaching data (see FIG. 2). Then, the learning information acquisition unit 12 acquires learning information using the 3R pieces of partial teaching data.

[0048] For example, for each of two or more attribute value conditions, the learning information acquisition unit 12 acquires two or more pieces of training data that satisfy the attribute value condition from the training data storage unit 111, and acquires learning information using the two or more pieces of training data. Note that the attribute value conditions are conditions related to one or more attribute values. Examples of the attribute value conditions are "male and 60 years or older," "male and in his 40s," and "yes, smoking habit." The content of the attribute value conditions is not important. The attribute value conditions are stored in the storage unit 11.

[0049] The training data may contain two or more candidate explanatory variable values, and the learning information acquisition unit 12 may select one or more explanatory variables whose importance satisfies a selection condition from among the two or more candidate explanatory variables. Importance refers to the degree of influence on the target variable, and may also be referred to as influence or contribution. The selection condition refers to a condition for the explanatory variables selected to acquire the training data, and is a condition related to importance. Examples of the selection condition include "importance equal to or greater than a threshold," "importance greater than a threshold," and "importance within the top N (N is a natural number equal to or greater than 1)." The selection condition is stored in the storage unit 11. Training data in which training data contains two or more candidate explanatory variables whose importance does not satisfy the selection condition is called reduced training data. Reduced training data may also be called dimension-reduced training data.

[0050] The learning information acquisition unit 12, for example, calculates a correlation coefficient between the value of each objective variable contained in two or more teacher data and the value of a candidate explanatory variable, and acquires an importance that is the correlation coefficient, or an importance that increases as the value of the correlation coefficient increases.

[0051] The learning information acquiring unit 12 acquires a learning device by performing a learning process using a random forest algorithm on two or more pieces of training data, for example. Next, the learning information acquiring unit 12 acquires the importance of each explanatory variable using the learning device, for example.

[0052] The method by which the learning information acquiring unit 12 acquires the importance of each candidate for the explanatory variable is not important.

[0053] The learning information is, for example, an arithmetic expression, a learning device, and a correspondence table. Below, an example of processing by the learning information acquiring unit 12 for each of the three types of learning information will be described. (1) When the learning information is an arithmetic formula

[0054] The learning information acquisition unit 12 acquires two or more pieces of teacher data from the teacher data storage unit 111, and performs regression analysis (e.g., multiple regression analysis) on the two or more pieces of teacher data to acquire an arithmetic formula. The arithmetic formula is typically an equation that takes the values ​​of two or more explanatory variables as input and outputs the value of one objective variable. Note that regression analysis itself is a well-known technique, so a detailed description will be omitted.

[0055] An example of the arithmetic formula is "other types of test values ​​for latest weight = f(latest weight, last year's weight, . . ., weight N years ago, last year's test value, . . ., test value N years ago)" (for example, N is a natural number greater than or equal to 2). An example of the arithmetic formula is "other types of test values ​​for latest weight = f(latest weight, last year's weight, . . ., weight N years ago)" (for example, N is a natural number greater than or equal to 2). An example of the arithmetic formula is "other types of test values ​​for latest weight = f(latest weight)".

[0056] Note that the "latest weight" in the above arithmetic formula may be substituted with a future target weight value. The input parameters in the arithmetic formula may be only a future target weight value, a set of two or more time-series weight values, a future target weight value and the most recent test value, or a future target weight value and a set of two or more time-series test values. The test values ​​that are input parameters in the arithmetic formula may be the same type of test value as the output test value, or may be different types of test values.

[0057] The learning information acquiring unit 12 acquires, for example, two or more pieces of teacher data from the teacher data storage unit 111, performs linear regression analysis on the two or more pieces of teacher data, and acquires a linear arithmetic expression.

[0058] The learning information acquiring unit 12 acquires, for example, two or more pieces of teacher data from the teacher data storage unit 111, performs nonlinear regression analysis on the two or more pieces of teacher data, and acquires a nonlinear arithmetic expression.

[0059] When the training data has only one explanatory variable value, the learning information acquisition unit 12 may perform a simple regression analysis on two or more training data to acquire an arithmetic formula. In such a case, the arithmetic formula may be, for example, "other type of test value for latest weight = f (latest weight)". Note that the latest weight may be substituted with a target weight value.

[0060] It is preferable that the learning information acquiring unit 12 acquires an arithmetic formula using only the values ​​of one or more explanatory variables selected from two or more sets of teacher data using a selection condition and the value of the objective variable. In other words, it is preferable that the learning information acquiring unit 12 reduces the data contained in the teacher data (dimensionality compression), and then performs regression analysis on the dimensionally compressed two or more sets of teacher data (appropriately referred to as "compressed teacher data") to acquire an arithmetic formula. (2) When the learning information is a learning device

[0061] The learning information acquisition unit 12 acquires two or more pieces of teacher data from the teacher data storage unit 111, performs learning processing on the two or more pieces of teacher data using a machine learning algorithm, and acquires a learning device. The learning device may also be called a classifier, a model, or the like. The algorithm for the machine learning learning processing is not important. Examples of machine learning include deep learning, random forest, decision tree, SVR, and SVM. The algorithm for the machine learning prediction processing described below is also not important, as is the learning processing.

[0062] The learning information acquisition unit 12 provides the acquired two or more pieces of teacher data to a machine learning learning processing module and executes the module to acquire a learning device. The machine learning learning processing module is, for example, the TensorFlow library, fastText, tinySVM, or the random forest module of the R language. The machine learning prediction processing module, which will be described later, is also the same as the machine learning learning processing module.

[0063] It is preferable that the learning information acquiring unit 12 acquires an arithmetic formula using only the values ​​of one or more explanatory variables selected from two or more sets of teacher data using a selection condition and the value of the objective variable. In other words, it is preferable that the learning information acquiring unit 12 reduces the data contained in the teacher data (dimensionality compression), and then performs learning processing using a machine learning algorithm on the two or more compressed sets of teacher data to acquire a learner. (3) When the learning information is a correspondence table

[0064] The learning information acquisition unit 12 acquires two or more pieces of teacher data from the teacher data storage unit 111, and creates a correspondence table having correspondence information indicating the correspondence between the values ​​of one or more explanatory variables and the values ​​of one or more objective variables from each of the two or more pieces of teacher data. The correspondence information includes, for example, a vector having the values ​​of two or more explanatory variables as elements and the value of one objective variable. The correspondence information is, for example, link information that identifies the link between the vector having the values ​​of two or more explanatory variables as elements and the value of one objective variable.

[0065] It is preferable that the learning information acquisition unit 12 acquires compressed teacher data from each of two or more teacher data, and constructs a correspondence table from the two or more compressed teacher data having correspondence information indicating the correspondence between the values ​​of one or more explanatory variables and the values ​​of one or more objective variables.

[0066] The learning information accumulation unit 13 accumulates the learning information acquired by the learning information acquisition unit 12. When the learning information acquisition unit 12 acquires learning information for each of two or more attribute value conditions, the learning information accumulation unit 13 accumulates the learning information acquired by the learning information acquisition unit 12 in association with the attribute value conditions. When the learning information acquisition unit 12 acquires learning information for each of two or more objective variables, the learning information accumulation unit 13 accumulates the learning information acquired by the learning information acquisition unit 12 in association with the objective variables.

[0067] The learning information accumulation unit 13 accumulates the learning information in, for example, the storage unit 11. The learning information accumulation unit 13 accumulates the learning information in, for example, an external device (not shown).

[0068] The storage unit 11 and the teacher data storage unit 111 are preferably non-volatile recording media, but can also be realized as volatile recording media.

[0069] 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.

[0070] The learning information acquisition unit 12 and the learning information storage unit 13 can usually be realized by a processor, memory, etc. The processing procedures of the learning information acquisition unit 12, 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, for example, a CPU, MPU, GPU, etc., and its type does not matter.

[0071] Next, an example of the operation of the learning device 1 will be described with reference to the flowchart in Fig. 3. In this example of operation, learning information is configured for each objective variable.

[0072] (Step S301) The learning information acquiring unit 12 assigns 1 to a counter i.

[0073] (Step S302) The learning information acquisition unit 12 determines whether or not the i-th objective variable constituting the learning information exists. If the i-th objective variable exists, the process proceeds to step S303, and if the i-th objective variable does not exist, the process ends.

[0074] (Step S303) The learning information acquiring unit 12 selects an explanatory variable corresponding to the i-th objective variable. An example of such explanatory variable selection processing will be described with reference to the flowchart in Fig. 4. Note that the processing of step S303 does not necessarily have to be performed.

[0075] (Step S304) The learning information acquisition unit 12 acquires two or more pieces of teacher data from the teacher data storage unit 111. The learning information acquisition unit 12 acquires compressed teacher data having values ​​corresponding to one or more explanatory variables selected in step S303 and a value corresponding to the i-th objective variable from among the elements of each acquired teacher data. Note that if the processing of step S303 does not exist, in this step the learning information acquisition unit 12 simply acquires two or more pieces of teacher data from the teacher data storage unit 111.

[0076] (Step S305) The learning information acquisition unit 12 acquires learning information using the two or more compressed teacher data acquired in step S304. The learning information may be, for example, an arithmetic formula, a learning device, or a correspondence table. Details of how to acquire each of these have been described above, so a detailed explanation will be omitted here.

[0077] (Step S306) The learning information accumulation unit 13 accumulates the learning information acquired in step S305 in association with the identifier of the i-th objective variable.

[0078] (Step S307) The learning information acquiring unit 12 increments the counter i by 1. The process returns to step S302.

[0079] Next, an example of the explanatory variable selection process in step S303 will be described with reference to the flowchart in FIG.

[0080] (Step S401) The learning information acquisition unit 12 acquires the values ​​of the objective variables from each of two or more pieces of teacher data stored in the teacher data storage unit 111. The learning information acquisition unit 12 constructs a vector whose elements are the values ​​of the objective variables acquired from each of the two or more pieces of teacher data. The value of the objective variable is the value of the i-th objective variable in the flowchart of FIG. 3.

[0081] (Step S402) The learning information acquiring unit 12 assigns 1 to a counter j.

[0082] (Step S403) The learning information acquiring unit 12 determines whether or not a candidate for the j-th explanatory variable exists. If a candidate for the j-th explanatory variable exists, the process proceeds to step S404; if not, the process proceeds to step S408.

[0083] (Step S404) The learning information acquisition unit 12 acquires candidate values ​​for the j-th explanatory variable from each of the two or more teacher data stored in the teacher data storage unit 111. The learning information acquisition unit 12 constructs a vector whose elements are the candidate values ​​for the j-th explanatory variable acquired from each of the two or more teacher data.

[0084] (Step S405) The learning information acquiring unit 12 acquires the correlation coefficient between the vector constructed in step S401 and the vector constructed in step S404.

[0085] (Step S406) The learning information acquiring unit 12 temporarily stores the importance in a buffer (not shown) in association with the identifier of the j-th explanatory variable. The importance is a correlation coefficient or information based on the correlation coefficient.

[0086] (Step S407) The learning information acquiring unit 12 increments the counter j by 1. The process returns to step S403.

[0087] (Step S408) The learning information acquisition unit 12 determines whether the corresponding importance for each of the identifiers of two or more explanatory variables satisfies the selection condition. Then, it acquires the identifiers of one or more explanatory variables paired with the importance that satisfies the selection condition. It then returns to the upper-level processing. Note that the explanatory variables identified by the one or more identifiers are the selected explanatory variables.

[0088] A specific example of the operation of the learning device 1 in this embodiment will be described below.

[0089] Currently, the teacher data management table shown in Figure 5 is stored in the teacher data storage unit 111 of the learning device 1. The teacher data management table stores two or more pieces of teacher data. The teacher data management table manages two or more records each having a "user ID," "test subject," "medical examination results for each year from this year up to N years ago," and "attribute values." In this case, the "attribute values" include "gender," "age," and "smoking habits."

[0090] In such a situation, the learning device 1 operates as follows. That is, the learning device 1 acquires learning information using the teacher data in the teacher data management table. The learning information here is information for accepting future (here, next year) weight and outputting a predicted test value from one or more test values ​​other than weight (e.g., abdominal circumference, systolic blood pressure, diastolic blood pressure, triglycerides, HDL cholesterol, LDL cholesterol, fasting blood glucose, HbA1C, urinary sugar). Here, the predicted test value is a predicted value for next year.

[0091] Here, for example, a case where the objective variable is "systolic blood pressure" will be described. That is, a case where the learning device 1 acquires learning information for outputting next year's systolic blood pressure when next year's target weight is input will be described.

[0092] First, the learning information acquisition unit 12 of the learning device 1 acquires the importance of each weight from one year ago to N years ago, which are candidates for explanatory variables, and each test value from one year ago to N years ago, using the method described above. Note that each test value is a type of test value other than weight, and in this case, it is abdominal circumference, systolic blood pressure, diastolic blood pressure, triglycerides, HDL cholesterol, LDL cholesterol, fasting blood glucose, HbA1C, and urinary sugar.

[0093] Next, the learning information acquisition unit 12 acquires the selection condition (here, for example, "importance >= threshold") from the storage unit 11. Next, the learning information acquisition unit 12 selects the systolic blood pressure for the past three years and the body weight for the past three years. In other words, the compressed training data has a structure, for example, (weight this year, weight one year ago, weight two years ago, weight three years ago, systolic blood pressure this year, systolic blood pressure one year ago, systolic blood pressure two years ago, systolic blood pressure three years ago).

[0094] Next, the learning information acquisition unit 12 acquires compressed teacher data from each record in the teacher data management table of FIG. 5. Then, the learning information acquisition unit 12 acquires teacher data to be used for linear regression analysis from the compressed teacher data. That is, here, for example, the learning information acquisition unit 12 acquires (WE 11 ,HB 11 )(WE 12 ,HB 12 )(WE 13 HB 13 )(WE 14 ,HB 14 ) from the record of the user ID "U002". 21 ,HB 21 )(WE 22 ,HB 22 )(WE 23 HB 23 )(WE 24 ,HB 24 ) is acquired. Then, the learning information acquiring unit 12 acquires four compressed teacher data from each of all records in the teacher data management table.

[0095] Next, the learning information acquiring unit 12 performs linear regression analysis on all the acquired compressed training data, and acquires the arithmetic formula "systolic blood pressure = α1 × body weight + C1" (α1 and C1 are positive constants).

[0096] Next, the learning information accumulation unit 13 accumulates the arithmetic formula "systolic blood pressure=α×weight+C" in the storage unit 11 in association with the identifier "systolic blood pressure" of the type of test value.

[0097] Similarly, the learning information acquisition unit 12 of the learning device 1 acquires two or more compressed teacher data for each of one or more other types of test value, performs linear regression analysis on the two or more compressed teacher data, and calculates the test value by the arithmetic formula "test value = α n ×Weight+C n " (α n , C n is a positive constant).

[0098] Next, the learning information accumulation unit 13 accumulates the arithmetic formula "systolic blood pressure=α×body weight+C" in the storage unit 11 in association with each of the other types of identifiers.

[0099] The above processing has enabled acquisition of learning information for the learning device 1. Note that in the above example, the learning device 1 acquires learning information by regression analysis, but as mentioned above, it may acquire learning information that is an arithmetic formula using another algorithm, or it may acquire learning information that is a learner through machine learning learning processing, or it may acquire learning information that is a correspondence table.

[0100] When the learning device 1 performs machine learning learning processing, for example, the learning information acquisition unit 12 acquires (weight this year, weight one year ago, . . ., weight N years ago, test value one year ago, . . ., test value N years ago) from each training data as a set of explanatory variable values. The learning information acquisition unit 12 also acquires (test value this year) as the value of the objective variable. Then, a learning process is performed on two or more compressed training data consisting of a set of explanatory variable values ​​and the objective variable value, and a learning device is acquired. The test value may be various, such as systolic blood pressure or diastolic blood pressure. Furthermore, although "3" is preferable for N, other values ​​may be used.

[0101] The learning device 1 may also acquire learning information for each of one or more conditions of attribute values ​​of a user. Examples of attribute value conditions are "male and smoking habit 'yes'," "male and non-smoking habit 'no'," "female and smoking habit 'yes'," and "female and non-smoking habit 'no'." In this case, four pieces of learning information are acquired for each type of test value of the objective variable.

[0102] As described above, according to this embodiment, learning information for obtaining predicted test values ​​for two or more target body weight values ​​can be obtained.

[0103] The processing in this embodiment may be implemented by software. This software may be distributed by software download, etc. This software may also be recorded on a recording medium such as a CD-ROM and distributed. This also applies to other embodiments in this specification. The software that implements the learning device 1 in this embodiment is the following program. In other words, this program causes a computer that can access a training data storage unit that stores two or more pieces of training data, each of which corresponds a weight measurement value obtained for a single person at the same time to one or more other types of test values, to function as a training information acquisition unit that acquires training information using the two or more pieces of training data, and a training information storage unit that accumulates the training information.

[0104] (Embodiment 2) In this embodiment, a prediction device will be described that applies learning information acquired by the learning device 1 to a plurality of future target weight values ​​of a single user, and acquires and outputs test values ​​(predicted values) of one or more objective variables corresponding to each of the user's future target weight values.

[0105] In addition, in this embodiment, a prediction device will be described that automatically obtains two or more target weight values ​​using the latest weight measurement value of a user, and obtains and outputs a predicted value for each of the two or more target weight values.

[0106] In this embodiment, a prediction device will be described that uses learning information corresponding to each of two or more types of dependent variables to obtain and output two or more types of predicted values.

[0107] In addition, in this embodiment, a prediction device will be described that selects learning information that matches the attribute value of a user, and obtains and outputs a predicted value using the selected learning information.

[0108] In this embodiment, a prediction device that constructs and outputs a table that associates predicted values ​​of one or more objective variables with each target weight value will be described.

[0109] Furthermore, in this embodiment, a prediction device that outputs a result according to the result of comparing a predicted value for one or more dependent variables with a predetermined threshold will be described.

[0110] 6 is a block diagram of a prediction device 2 according to this embodiment. The prediction device 2 includes a storage unit 21, a processing unit 22, and an output unit 23. The storage unit 21 includes a learning information storage unit 211 and a reference value storage unit 212. The processing unit 22 includes a target acquisition unit 221, a prediction unit 222, and a configuration unit 223. The output unit 23 includes a prediction output unit 231.

[0111] Various types of information are stored in the storage unit 21. The various types of information include, for example, learning information and reference values, which will be described later.

[0112] The learning information storage unit 211 stores one or more pieces of learning information. The learning information may be associated with a type identifier that identifies the type of each of two or more predicted values ​​to be output. The learning information may also be associated with an attribute value condition that is a condition using one or more attribute values. The learning information is information acquired by the learning device 1, and may be, for example, an arithmetic formula, a learning unit, or a correspondence table.

[0113] The reference value storage unit 212 stores reference values ​​for one or more other types of test values. It is preferable that the reference values ​​correspond to type identifiers. The type identifiers are, for example, "systolic blood pressure," "diastolic blood pressure," "triglycerides," "HDL cholesterol," "LDL cholesterol," "fasting blood glucose level," "HbA1C," and "presence or absence of urinary sugar." The reference value is, for example, a threshold value between normal and abnormal values.

[0114] The processing unit 22 performs various types of processing. The various types of processing are, for example, processing performed by a target acquisition unit 221, a prediction unit 222, and a configuration unit 223.

[0115] The goal acquisition unit 221 acquires two or more future goal weight values ​​for one user. The goal acquisition unit 221 acquires, for example, two or more goal weight values ​​input by the user.

[0116] The target acquisition unit 221 acquires two or more target weight values, for example, by using the most recent weight measurement value of a single user. The most recent weight measurement value of a single user is stored, for example, in the storage unit 21. The most recent weight measurement value of a single user is, for example, information input by the user and acquired by the target acquisition unit 221.

[0117] For example, the target acquisition unit 221 acquires a value obtained by subtracting two or more predetermined values ​​from the most recent weight measurement value of one user. For example, the target acquisition unit 221 acquires the most recent weight measurement value (X) of one user and acquires target weight values ​​"X," "X-2 kg," "X-4 kg," "X-6 kg," "X-8 kg," and "X-10 kg." Note that it is preferable for the target acquisition unit 221 to acquire two or more target weight values ​​by continuously subtracting a certain amount (for example, 2 kg).

[0118] The target acquisition unit 221 preferably acquires a different number of target weight values ​​depending on, for example, a user's most recent weight measurement value (X). That is, the target acquisition unit 221 preferably acquires a larger number of target weight values ​​based on the difference between a user's most recent weight measurement value (X) and an ideal weight (Y). The ideal weight (Y) is pre-stored in, for example, the storage unit 21. In addition, in a situation where the storage unit 21 stores information indicating the correspondence between height and ideal weight (Y) (for example, "ideal weight (Y) = height (cm) - 110", a correspondence table between height ranges and ideal weight (Y)), the target acquisition unit 221 preferably acquires the ideal weight (Y) corresponding to the received user's height, calculates the difference from the received most recent weight measurement value (X), and acquires N (N is a natural number equal to or greater than 1) target weight values ​​between the most recent weight measurement value (X) and the ideal weight (Y).

[0119] The prediction unit 222 uses the learning information to obtain predicted values ​​of each type of one or more future test values ​​corresponding to each of the two or more target weight values ​​obtained by the target obtaining unit 221.

[0120] For example, the prediction unit 222 acquires learning information corresponding to each type of two or more predicted values ​​from the storage unit 21, and uses the learning information to acquire predicted values ​​corresponding to each of two or more target body weight values.

[0121] The prediction unit 222, for example, acquires learning information corresponding to attribute value conditions that are matched by one or more attribute values ​​of a user from the storage unit 21, and uses the learning information to acquire one or more predicted values ​​corresponding to two or more target weight values.

[0122] The processing of the prediction unit 222 according to the type of learning information will be described below. (1) When the learning information is an arithmetic formula

[0123] The prediction unit 222 substitutes one or more target weight values ​​acquired by the target acquisition unit 221 into an arithmetic expression, executes the arithmetic expression, and acquires a predicted value.

[0124] When obtaining multiple types of predicted values, the prediction unit 222 obtains an arithmetic formula corresponding to each type from the storage unit 21, and for each type, substitutes one or more target weight values ​​into the obtained arithmetic formula, executes the arithmetic formula, and obtains the predicted value.

[0125] In addition, when the arithmetic formula uses explanatory variables other than the target weight value as input parameters, the prediction unit 222 obtains the values ​​of the explanatory variables other than the target weight value, substitutes the values ​​of the explanatory variables and the target weight value into the arithmetic formula, executes the arithmetic formula, and obtains the predicted value.

[0126] Note that, among the set of values ​​of one or more explanatory variables, information excluding the target weight value is, for example, information stored in the storage unit 21 or information received from the user. The same applies hereinafter. (2) When the learning information is a learning module

[0127] The prediction unit 222 acquires a set of one or more explanatory variable values ​​including the target weight value for each of one or more target weight values ​​acquired by the target acquisition unit 221. Next, the prediction unit 222 applies the set of one or more explanatory variable values ​​to a learning device for each of one or more target weight values, performs machine learning prediction processing, and acquires a predicted value.

[0128] When obtaining multiple types of predicted values, the prediction unit 222 obtains a learning device corresponding to each type from the storage unit 21, and obtains, for each type, a set of one or more explanatory variable values ​​including one or more target body weight values. Next, the prediction unit 222 applies the set of one or more explanatory variable values ​​to the learning device for each of the multiple types and for one or more target body weight values, performs machine learning prediction processing, and obtains a predicted value. (3) When the learning information is a correspondence table

[0129] For example, the prediction unit 222 acquires a set of one or more explanatory variable values ​​including one or more target weight values ​​acquired by the target acquisition unit 221. Next, the prediction unit 222 searches the correspondence table for a vector that most closely resembles the set (vector) of values ​​of one or more explanatory variables for each of the one or more target weight values. Next, the prediction unit 222 acquires from the correspondence table a test value (predicted value) that pairs with the most closely resembled vector.

[0130] The prediction unit 222 acquires, for example, a set of values ​​of one or more explanatory variables including one or more target body weight values ​​acquired by the target acquisition unit 221. Next, for each of the one or more target body weight values, the prediction unit 222 searches a correspondence table for one or more vectors that match the set (vector) of values ​​of the one or more explanatory variables and an approximation condition (e.g., distance is within a threshold, distance is within the top N). Next, the prediction unit 222 acquires, from the correspondence table, test values ​​(predicted values) that pair with each of the one or more vectors. Next, the prediction unit 222 calculates a weighted average of the one or more test values ​​(predicted values) using the distance from the set (vector) of values ​​of the one or more explanatory variables to calculate a single predicted value. Note that the prediction unit 222 typically calculates a weighted average with a larger weight as the distance becomes smaller.

[0131] When obtaining multiple types of predicted values, the prediction unit 222 uses a correspondence table corresponding to each type to obtain a predicted value for each type and for one or more target weight values ​​through the above-described process.

[0132] The configuration unit 223 configures output information that associates two or more target weight values ​​with one or more types of predicted values ​​acquired by the prediction unit 222. The output information may be, for example, a table or a graph, and the format is not limited.

[0133] The configuration unit 223 configures output information according to the comparison result between the predicted value acquired by the prediction unit 222 and the reference value corresponding to the predicted value. Note that it is preferable that the configuration unit 223 configures output information in different output formats when the predicted value is favorable relative to the reference value (for example, when "predicted value<=reference value") and when the predicted value is not favorable relative to the reference value (for example, when "predicted value>reference value"). The different output formats mean, for example, that the display attribute values ​​of the predicted value, such as the font, font color, and background color of the predicted value, are different. The different output formats mean that the target weight value and predicted value are not included in the output information when the predicted value is not favorable relative to the reference value, and the target weight value and predicted value are included in the output information when the predicted value is favorable relative to the reference value.

[0134] The output unit 23 outputs various types of information. Examples of such information include predicted values, target weight values, and output information. Here, output is a concept that includes displaying on a display, projecting using a projector, printing using a printer, outputting sound, transmitting to an external device, storing in a recording medium, and transferring processing results to other processing devices or other programs. Note that transmission to an external device refers to transmission to a user's end-of-line device (not shown).

[0135] The prediction output unit 231 outputs one or more predicted values ​​acquired by the prediction unit 222. For example, the prediction output unit 231 outputs a predicted value for each type of each of the two or more predicted values ​​acquired by the prediction unit 222. For example, the prediction output unit 231 outputs output information configured by the configuration unit 223.

[0136] The storage unit 21, the learning information storage unit 211, and the reference value storage unit 212 are preferably non-volatile recording media, but may also be realized as volatile recording media.

[0137] There is no restriction on the process by which information is stored in the storage unit 21 etc. For example, information may be stored in the storage unit 21 etc. via a recording medium, information transmitted via a communication line etc. may be stored in the storage unit 21 etc., or information input via an input device may be stored in the storage unit 21 etc.

[0138] The processing unit 22, the target acquisition unit 221, the prediction unit 222, and the configuration unit 223 can usually be realized by a processor, a memory, etc. The processing procedures of the processing unit 22, 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, for example, a CPU, an MPU, a GPU, etc., and the type is not important.

[0139] The output unit 23 and the prediction output unit 231 may or may not include output devices such as a display, a speaker, etc. The output unit 23, etc. may be realized by driver software for an output device, or a combination of driver software for an output device and an output device, etc.

[0140] The output unit 23 and the prediction output unit 231 may be realized by wireless or wired communication means. In such a case, the prediction output unit 231 transmits information to a user's terminal device (not shown).

[0141] Next, an example of the operation of the prediction device 2 will be described with reference to the flowchart of FIG.

[0142] (Step S701) The goal obtaining unit 221 obtains the latest weight of one user.

[0143] (Step S702) The target weight obtaining unit 221 obtains one or more target weights using the latest weight of one user. An example of such target weight obtaining processing will be described with reference to the flowchart of FIG.

[0144] (Step S703) The prediction unit 222 determines whether or not to use the user's attribute value when acquiring a predicted value. If the attribute value is to be used, the process proceeds to step S704, and if the attribute value is not to be used, the process proceeds to step S705. Note that whether or not to use the attribute value may be determined in advance, or may be determined based on whether or not the user's attribute value has been accepted.

[0145] (Step S704) The prediction unit 222 acquires one or more attribute values ​​of the user.

[0146] (Step S705) The prediction unit 222 assigns 1 to the counter i.

[0147] (Step S706) Prediction unit 222 determines whether or not to acquire a predicted value of the i-th type of test value. If a predicted value of the i-th type of test value is to be acquired, the process proceeds to step S707; if not, the process proceeds to step S714. Note that, for example, the type for which a predicted value is to be acquired is determined in advance.

[0148] (Step S707) The prediction unit 222 acquires learning information corresponding to the i-th type from the learning information storage unit 211. Note that the prediction unit 222 may acquire learning information corresponding to the i-th type and one or more attribute values ​​of the user from the learning information storage unit 211.

[0149] (Step S708) The prediction unit 222 assigns 1 to the counter j.

[0150] (Step S709) The prediction unit 222 determines whether or not the jth target weight is included in the target weights acquired in step S702. If the jth target weight is included, the process proceeds to step S710, and if the jth target weight is not included, the process proceeds to step S713.

[0151] (Step S710) Prediction unit 222 acquires the jth target weight and acquires a predicted value corresponding to the i-th type and the jth target weight using the target weight and the learning information acquired in step S707. Note that when acquiring the predicted value, if the value of an explanatory variable other than the target weight is required, prediction unit 222 acquires the value of that explanatory variable and also uses that value to acquire the predicted value.

[0152] (Step S711) Prediction unit 222 stores the predicted value acquired in step S710 in a buffer (not shown) in association with the ith type and the jth target weight. Note that here, configuration unit 223 may acquire a reference value corresponding to the ith type from reference value storage unit 212, compare the reference value with the acquired predicted value, acquire the comparison result, and store it in association with the predicted value in a buffer (not shown).

[0153] (Step S712) The prediction unit 222 increments the counter j by 1. The process returns to step S709.

[0154] (Step S713) The prediction unit 222 increments the counter i by 1. The process returns to step S706.

[0155] (Step S714) The configuration unit 223 configures output information using the predicted value stored in a buffer (not shown). Note that the configuration unit 223 may also configure output information using the comparison result stored in a buffer (not shown).

[0156] (Step S715) The prediction output unit 231 outputs the output information configured in step S714, and the process ends.

[0157] Next, an example of the target weight acquisition process in step S702 will be described with reference to the flowchart in FIG.

[0158] (Step S801) The target acquisition unit 221 acquires the height of the user.

[0159] (Step S802) The target acquisition unit 221 acquires the ideal weight corresponding to the height acquired in step S801.

[0160] (Step S803) The target acquisition unit 221 determines whether or not "latest weight>optimal weight". If "latest weight>optimal weight", the process proceeds to step S804, and if not, the process proceeds to step S805.

[0161] (Step S804) The target acquisition unit 221 subtracts "x kilograms (kg)" from the latest weight (X) until it reaches the ideal weight (Y), and acquires n target weights (n is a natural number greater than or equal to 1). The target acquisition unit 221 acquires, for example, "X," "Xx kilograms," "X-2x kilograms," ... "X-nx kilograms," and "ideal weight (Y)" as the target weight.

[0162] (Step S805) The target acquisition unit 221 acquires, for example, an optimum weight (Y) or the like as the target weight. Note that the optimum weight (Y) or the like may be just the optimum weight (Y), or may be "X+x kilograms," "X+2x kilograms," or "optimum weight (Y)."

[0163] A specific example of the operation of the prediction device 2 in this embodiment will be described below.

[0164] Now, it is assumed that the results of a health checkup for 2019, including the latest weight of a user, "74.1", are stored in the storage unit 21 of the prediction device 2. It is also preferable that the results of health checkups for the user prior to 2018 are also stored in the storage unit 21.

[0165] It is also assumed that learning information for each type of test value (test item) is stored in the learning information storage unit 211. The test items here are "systolic blood pressure," "diastolic blood pressure," "triglycerides," "HDL cholesterol," "LDL cholesterol," "fasting blood glucose level," "HbA1C," and "presence or absence of urinary sugar."

[0166] Furthermore, it is assumed that the storage unit 21 stores reference values ​​for each test item. The reference values ​​here are of three types: "reference value," "health insurance guidance determination value," and "visit recommendation determination value." The "reference value" is a value within the normal range. The "health insurance guidance determination value" is a value within a range in which guidance is required. The "visit recommendation determination value" is a value within a range in which visiting a medical institution is recommended.

[0167] The target acquisition unit 221 of the prediction device 2 first acquires the latest weight of one user, "74.1." Next, the target acquisition unit 221 acquires the latest weight "74.1" and the weight loss values ​​for each 2 kg up to a 10 kg loss, "74.1," "72.1," "70.1," "68.1," "66.1," and "64.1."

[0168] Next, using the learning information for each type of test value (test item) acquired by the learning device 1, the target acquisition unit 221 applies the weight value to the learning information for each of the six weights acquired for each test item to acquire a predicted value.

[0169] Next, the configuration unit 223 configures output information that associates two or more target weight values ​​with one or more types of predicted values ​​acquired by the prediction unit 222. Furthermore, when configuring the output information, the configuration unit 223 acquires the results of comparing the predicted values ​​acquired by the prediction unit 222 with the reference values ​​corresponding to the predicted values ​​(here, the "health insurance guidance determination value" and the "visit recommendation determination value"). Then, the configuration unit 223 configures output information in a manner that makes the predicted values ​​within the range of the "health insurance guidance determination value" or the "visit recommendation determination value" and the measured values ​​for 2019 stand out compared to other values.

[0170] Next, the prediction output unit 231 outputs the output information constructed by the construction unit 223. An example of such output is shown in FIG.

[0171] By looking at the table in Figure 9, a user can be sure of their weight loss goal for the next year (here, 2020).

[0172] As described above, according to this embodiment, predicted test values ​​for one or more target body weight values ​​can be presented.

[0173] Furthermore, according to this embodiment, it is possible to present predicted values ​​of test values ​​for two or more target weight values ​​using information on the history of test values.

[0174] Furthermore, according to this embodiment, predicted test values ​​for two or more appropriate target weight values ​​can be presented.

[0175] Furthermore, according to this embodiment, by using learning information corresponding to the acquired type of predicted value, it is possible to present two or more types of highly accurate predicted values.

[0176] Furthermore, according to this embodiment, by using learning information according to the attribute values ​​of the user, it is possible to present a highly accurate predicted value.

[0177] Furthermore, according to this embodiment, the relationship between each of two or more target weight values ​​and the predicted value can be presented in an easy-to-understand manner.

[0178] Note that the prediction device 2 in this embodiment may have the functions of the learning device 1. A block diagram of the prediction device 3 in such a case is shown in FIG. 10. The prediction device 3 includes a storage unit 31, a processing unit 32, and an output unit 23. The storage unit 31 includes a teacher data storage unit 111, a learning information storage unit 211, and a reference value storage unit 212. The processing unit 32 includes a learning information acquisition unit 12, a learning information accumulation unit 13, a target acquisition unit 221, a prediction unit 222, and a configuration unit 223.

[0179] The prediction device 2 may also function as a server, communicate with one or more terminal devices 4, and transmit output information and the like to the terminal devices 4. A block diagram of a prediction system A having the prediction device 2 and one or more terminal devices 4 in such a case is shown in FIG.

[0180] The terminal device 4 includes a terminal storage unit 41 that can store information, a terminal reception unit 42 that receives instructions and information, a terminal processing unit 43 that performs various processes, a terminal transmission unit 44 that transmits instructions and information to the prediction device 2, a terminal reception unit 45 that receives information, and a terminal output unit 46 that outputs information. Note that the terminal device 4 can be realized by a known device, and therefore a detailed description thereof will be omitted.

[0181] Furthermore, for example, the terminal device 4 transmits an instruction to the prediction device 2. Then, the prediction device 2 receives the instruction and executes the above-described process in accordance with the instruction. Then, the output unit 23 of the prediction device 2 transmits the output information etc. acquired by the processing unit 22 to the terminal device 4. Next, the terminal device 4 receives and outputs the output information etc. An example of such output is shown in FIG. 9.

[0182] 11, the prediction device 2 is a server, such as a cloud server or an ASP server, but the type does not matter. Also, the terminal device 4 is a device used by a user, such as a personal computer, a tablet terminal, or a smartphone, but the type does not matter.

[0183] The processing in this embodiment may be implemented 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 other embodiments in this specification. The software implementing the prediction device 2 in this embodiment is the following program. That is, this program causes a computer that can access a learning information storage unit that stores learning information acquired using two or more pieces of training data in which a person's weight measurement value is associated with one or more other types of test values ​​to function as a target acquisition unit that acquires two or more future target weight values ​​for a user, a prediction unit that acquires, using the learning information, predicted values ​​for each of one or more types of future test values ​​corresponding to each of the two or more target weight values, and a prediction output unit that outputs the one or more predicted values.

[0184] 12 shows the appearance of a computer that executes the programs described herein to realize the prediction devices and the like of the various embodiments described above. The above-described embodiments can be realized by computer hardware and a computer program executed thereon. FIG. 12 is an overview diagram of this computer system 300, and FIG. 13 is a block diagram of the system 300.

[0185] In FIG. 12, a computer system 300 includes a computer 301 including a CD-ROM drive, a keyboard 302, a mouse 303, and a monitor 304.

[0186] 13, 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.

[0187] A program that causes computer system 300 to execute the functions of prediction device 2 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.

[0188] 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 prediction device 2 of the above-described embodiment. The program only needs to include instructions that call appropriate functions (modules) in a controlled manner to achieve the desired results. How the computer system 300 operates is well known, and a detailed description thereof will be omitted.

[0189] 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).

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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]

[0194] As described above, the prediction device 2 according to the present invention has the effect of being able to present predicted test values ​​for two or more target body weight values, and is useful as a device for supporting health promotion, etc. [Explanation of symbols]

[0195] 1 Learning device 2, 3 Prediction device 4 Terminal Devices 11, 21, 31 Storage area 12 Learning information acquisition unit 13 Learning Information Storage Unit 22, 32 Processing section 23 Output section 111 Teacher data storage unit 211 Learning information storage unit 212 Reference value storage section 221 Target Acquisition Department 222 Prediction Department 223 Components 231 Prediction Output Unit

Claims

1. a learning information storage unit that stores learning information acquired using two or more pieces of training data in which a weight measurement value of one person is associated with two or more other types of test values; a goal acquisition unit that acquires two or more future goal weight values ​​of one user; a prediction unit that obtains predicted values ​​of each type of one or more future test values ​​corresponding to each of the two or more target weight values ​​using the learning information; a prediction output unit that outputs the one or more predicted values, the importance of each of the other two or more types of test values ​​is stored in association with an identifier of the other two or more types of test values; The learning information is information obtained using two or more degenerate teacher data sets that are pairs of one or more test values ​​corresponding to one or more types whose importance levels for each of the other two or more test value types satisfy the selection conditions and the weight measurement values.

2. a learning information acquisition unit that acquires the importance of each type of the other two or more test values ​​and stores the importance in association with each type's identifier; The learning information acquisition unit Using the two or more teacher data, for each type of the other two or more test values, calculate a correlation coefficient between the weight measurement value contained in each of the two or more teacher data and the two or more other types of test values ​​contained in each of the two or more teacher data, and obtain a degree of importance based on the correlation coefficient; or The prediction device according to claim 1, wherein a learning process using a random forest algorithm is performed on the two or more pieces of training data to obtain a learning device, and the learning device is used to obtain the importance of each type of the other two or more test values.

3. 3. The prediction device according to claim 1, wherein the training data includes test values ​​of the same type at two or more different time periods and weight measurements at each of the two or more time periods.

4. The prediction device according to claim 1 , wherein the learning information is an arithmetic expression obtained by performing a linear regression analysis on two or more pieces of training data.

5. The target acquisition unit The prediction device according to claim 1 , wherein the two or more target weight values ​​are obtained using a most recent weight measurement value of the one user.

6. The target acquisition unit The prediction device according to claim 5 , wherein a value obtained by subtracting two or more predetermined values ​​from the most recent weight measurement value of the one user is obtained.

7. The teacher data is associated with one or more attribute values ​​of the person, the learning information storage unit stores learning information for each of two or more conditions using one or more attribute values; The prediction unit obtaining one or more predicted values ​​corresponding to the two or more target weight values ​​using learning information corresponding to a condition that one or more attribute values ​​of the one user match; The prediction device according to claim 1 , wherein the one or more attribute values ​​include information related to lifestyle habits.

8. The prediction device of claim 7, wherein the information regarding lifestyle habits is whether or not the person has a smoking habit, the level of smoking habit, whether or not the person has an exercise habit, the amount of exercise during a specified period, the level of exercise habit, whether or not the person has a drinking habit, or the level of drinking habit.

9. a configuration unit that configures output information that associates each of the two or more target weight values ​​with one or more types of predicted values ​​acquired by the prediction unit; The prediction output unit The prediction device according to claim 1 , wherein the prediction device outputs the output information.

10. further comprising a reference value storage unit in which reference values ​​of the one or more other types of test values ​​are stored; a configuration unit that configures output information according to a comparison result between the predicted value acquired by the prediction unit and a reference value corresponding to the predicted value; The prediction output unit The prediction device according to claim 1 , wherein the prediction device outputs the output information.

11. A prediction method for causing a computer to execute the process performed by the prediction device according to claim 1 .

12. Computer, A program for causing the prediction device according to any one of claims 1 to 10 to function.

Images