Menstrual cycle prediction device, menstrual cycle prediction method, menstrual cycle prediction program, and menstrual cycle prediction system

Abstract

To propose a menstrual cycle prediction device, a menstrual cycle prediction method, a menstrual cycle prediction program and a menstrual cycle prediction system capable of correctly predicting a menstrual cycle with consideration of various factors that may occur in living.SOLUTION: According to this disclosure, a menstrual cycle prediction device includes an acquisition part for acquiring learning data obtained by combining a basal body temperature between menstrual cycles and event information of a date when the basal body temperature is measured, and a generation part for generating a prediction model for predicting a menstrual cycle on the basis of the learning data acquired by the acquisition part.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure relates to a menstrual cycle prediction device, a menstrual cycle prediction method, a menstrual cycle prediction program, and a menstrual cycle prediction system.

Background Art

[0002] By predicting the menstrual cycle, a woman can accurately estimate the ovulation day to increase the success rate of pregnancy and appropriately manage her monthly physical condition. For this reason, there is a high need to accurately predict the menstrual cycle. For example, techniques for predicting the menstrual cycle based on the periodicity of basal body temperature are known (Patent Documents 1 and 2 below). In addition, techniques for deriving the periodicity of health status based on data such as body temperature and blood pressure are also known (Patent Document 3 below).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Patent Document 3

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, it is difficult to accurately predict the periodicity of the menstrual cycle based only on data that is quantitatively measured such as body temperature. In social activities, people have diverse lifestyles, so events in life (events) affect the basal body temperature, and stress affects the menstrual cycle.

[0005] Therefore, the present disclosure proposes a menstrual cycle prediction device, a menstrual cycle prediction method, a menstrual cycle prediction program, and a menstrual cycle prediction system that can accurately predict the menstrual cycle in consideration of various factors that may occur in daily life.

Means for Solving the Problems

[0006] In order to solve the above problems, the menstrual cycle prediction device according to the present disclosure includes an acquisition unit that acquires learning data combining basal body temperature data during the menstrual cycle and event information on the day when the basal body temperature data is measured, and a generation unit that generates a prediction model for predicting the menstrual cycle based on the learning data acquired by the acquisition unit.

Brief Description of the Drawings

[0007]

Figure 1

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Mode for Carrying Out the Invention

[0008] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In the following embodiments, the same parts are denoted by the same reference numerals, and redundant descriptions are omitted.

[0009] (1. Embodiment) [1-1. An Example of the Menstrual Cycle Prediction Process According to the Embodiment] FIG. 1 is a diagram schematically showing the flow of the menstrual cycle prediction process according to the embodiment. The menstrual cycle prediction process according to the embodiment is executed by the menstrual cycle prediction system 1 shown in FIG. 1. The menstrual cycle prediction system 1 includes a prediction device 100 which is an example of the menstrual cycle prediction device according to the present disclosure, a user terminal 200, and a measuring instrument 300. Each device included in the menstrual cycle prediction system 1 can transmit and receive data to and from each other by wireless communication or the like.

[0010] The prediction device 100 is an information processing device that generates a prediction model for predicting the menstrual cycle by machine learning. For example, the prediction device 100 is a cloud server or the like.

[0011] The user terminal 200 is a terminal device used by the user 10 or the user 20. For example, the user terminal 200 is a smartphone, a tablet terminal, or the like.

[0012] Note that the user 10 is a general term for users who provide learning data used by the prediction device 100 when generating a prediction model. Also, the user 20 is a general term for users who transmit measurement data to the prediction device 100 and obtain prediction results regarding the physiological cycle based on the transmitted data. There are multiple users 10 and multiple users 20. Also, in the following description, when there is no need to distinguish between the user 10 and the user 20, they are simply referred to as users. Also, the user 10 and the user 20 may indicate the same user.

[0013] The measuring device 300 is a measuring instrument having a function of measuring the body temperature of a user. For example, the measuring device 300 is stored in clothing such as the user's underwear and measures the user's body temperature regularly (for example, at a fixed time every night).

[0014] Incidentally, the menstrual cycle has a periodicity in which it enters a high-temperature period under the influence of hormones secreted along with ovulation and then enters a low-temperature period after the start date of menstruation (period). Therefore, by statistically processing the basal body temperature data, it is possible to predict the pattern of the menstrual cycle to a certain extent. However, simply measuring the basal body temperature alone may make it difficult to accurately predict the menstrual cycle, such as when drinking alcohol or taking medications affects the basal body temperature, or when the presence or absence of stress delays the start date of menstruation itself.

[0015] Therefore, the physiological cycle prediction system 1 according to the embodiment predicts the physiological cycle in consideration of the influence of daily events such as drinking alcohol and taking medications (hereinafter collectively referred to as "events") by the processes described below. Thereby, the physiological cycle prediction system 1 can predict the physiological cycle more accurately. Hereinafter, the outline of the prediction process by the physiological cycle prediction system 1 will be described with reference to FIG. 1.

[0016] First, the process (learning phase) until the prediction device 100 generates a prediction model will be described. The measuring device 300 measures the basal body temperature data of the user 10 while the user 10 wears and goes to bed with the clothes or the like in which the measuring device 300 is housed (step S1). The measuring device 300 transmits the measured basal body temperature data to the user terminal 200 (step S2).

[0017] The user terminal 200 displays the basal body temperature data acquired from the measuring device 300 in a dedicated application or the like provided by the prediction device 100. Further, the user terminal 200 receives event information corresponding to the day on which the basal body temperature data is measured from the user 10 (step S3). Note that the day on which the basal body temperature data is measured is not necessarily the measurement date. For example, when the measuring device 300 acquires the basal body temperature after midnight, the user terminal 200 may receive the input of event information on the previous day (that is, event information assumed to have affected the measured basal body temperature).

[0018] The input of event information will be described with reference to FIGS. 2 to 4. FIG. 2 is a diagram (1) showing an example of a user interface according to the embodiment.

[0019] FIG. 2 shows an example of a screen display when an application for displaying basal body temperature data is activated on the user terminal 200. As shown in FIG. 2, the user terminal 200 displays the latest data of the measured basal body temperature data and a graph 251 showing the history of the basal body temperature data. The graph 251 includes a display 252 showing the event information recorded in the history. For example, on the date when the event information is input by the user 10, a display such as "〇" is shown in the corresponding item of the display 252.

[0020] The user 10 can start inputting event information corresponding to the measured basal body temperature data by selecting the button 253 included in the graph 251. When the button 253 is selected by the user 10, the user terminal 200 changes the display to the screen shown in FIG. 3.

[0021] Figure 3 is a diagram (2) showing an example of the user interface according to the embodiment. In Figure 3, an example is shown in which the user terminal 200 displays a screen for the user 10 to input event information.

[0022] The user 10 can check the measured basal body temperature data 261 and correct the basal body temperature on the screen if there is an error. Also, if there is a change in the registered weight, the user 10 can correct the weight data.

[0023] In addition, the user 10 can select an event corresponding to himself / herself from the event icons 262 and 263 displayed on the screen.

[0024] The event icon 262 lists event items related to the body of the user 10. For example, when the user 10 is in the menstrual period, by pressing the event icon corresponding to menstruation, information indicating that there is event information of "menstruation" can be input. Similarly, when the user 10 is in poor health, the user presses the event icon corresponding to "poor health". Similarly, when the user 10 has abnormal bleeding, sexual intercourse, or childbirth, the user presses the event icons corresponding to them.

[0025] In addition, the event icon 263 lists event items related to the actions of the user 10. For example, when the user 10 is sleep-deprived, by pressing the event icon corresponding to sleep deprivation, information indicating that there is event information of "sleep deprivation" can be input. As shown in Figure 3, when the user 10 selects the event icon 264 corresponding to "sleep deprivation", the event icon 264 changes to a different display mode (for example, a different color from other event icons). Also, when the user 10 drinks alcohol, the user presses the event icon corresponding to "drinking alcohol". Similarly, when the user 10 exercises or when the day is a holiday, the user presses the event icons corresponding to them.

[0026] When user 10 scrolls the screen down, user terminal 200 changes the screen display to FIG. 4. FIG. 4 is a diagram (3) showing an example of the user interface according to the embodiment.

[0027] Similar to FIG. 3, the screen display example of FIG. 4 includes event icons 271 and event icon 272. Further, the screen display example of FIG. 4 includes a memo input field 273.

[0028] Event icon 271 lists event items related to the hospital and medicine of user 10. For example, when user 10 visits the hospital, by pressing the event icon corresponding to the hospital visit, user 10 can input information indicating that there is event information of "hospital visit". Similarly, when user 10 takes medicine, user 10 presses the event icon corresponding to "taking medicine".

[0029] Event icon 272 lists event items related to the mood of user 10. For example, user 10 selects one of "bad", "normal", and "good" as the mood of the day according to the mood of the day (such as the presence or absence of stress). In this way, by user 10 himself / herself selecting the mood, user terminal 200 can obtain information such as the presence or absence of stress, which is difficult to measure as quantitative data, as event information.

[0030] In addition, user 10 may memo the events of the day in memo input field 273. In this case, user terminal 200 may analyze the text data input in memo input field 273 and automatically input event information based on the analyzed information. For example, user terminal 200 may use a text analysis model to determine whether the text is negative or positive, determine whether the memo content is negative or positive, and obtain the goodness or badness of the mood corresponding to the determination result as event information. Alternatively, user terminal 200 may perform morphological analysis on the text data, extract text data corresponding to drinking alcohol, taking medicine, etc., and obtain event information such as drinking alcohol and taking medicine based on such extracted data.

[0031] Returning to FIG. 1, the description will be continued. The user terminal 200 transmits, as learning data, data obtained by combining the event information input by the user 10 and the basal body temperature data to the prediction device 100 (step S4). In FIG. 1, only the user 10 is described for the sake of explanation, but in reality, the learning data is assumed to be periodically transmitted from a considerable number of users. Further, the user terminal 200 may transmit the learning data every time the event information is input, or may transmit a learning data set in which data for one menstrual cycle is accumulated with the physiological start date as a flag.

[0032] The prediction device 100 stores the learning data acquired from the user terminal 200 as measurement and event data 50. Thereafter, when the learning data is sufficiently accumulated, the prediction device 100 generates a prediction model for predicting the menstrual cycle (step S5). For example, when the basal body temperature data and the event information are input, the prediction device 100 generates a prediction model that outputs the physiological start date or ovulation date of the user 10 who transmitted the data. Details of the prediction model will be described later.

[0033] Next, the process (inference phase) in which the prediction device 100 predicts the menstrual cycle using the prediction model will be described. While the user 20 wears and goes to bed with the clothes or the like in which the measuring device 300 is housed, the measuring device 300 measures the basal body temperature data of the user 20 (step S11). The measuring device 300 transmits the measured basal body temperature data to the user terminal 200 (step S12).

[0034] The user terminal 200 displays the basal body temperature data acquired from the measuring device 300 on the application and accepts the input of event information from the user 20 (step S13). When the user terminal 200 accepts the input of the event information, it transmits the measurement data obtained by combining the basal body temperature data and the event information to the prediction device 100 (step S14). Note that when there is no input of event information from the user 20 for a predetermined time, the user terminal 200 may transmit the measurement data in which only the basal body temperature data not including the event information is recorded.

[0035] When the prediction device 100 acquires measurement data from the user terminal 200, it inputs the measurement data into the prediction model 60. Then, the prediction device 100 outputs, as a prediction result, the next physiological start date or ovulation date of the user 20 derived from the input measurement data (step S15).

[0036] The prediction device 100 transmits the output result to the user terminal 200 (step S16). At this time, the prediction device 100 may control the content displayed in the application of the user terminal 200. For example, the prediction device 100 controls to display a message such as "Your next physiological start date is 'XX month YY day'" in the application. Alternatively, the prediction device 100 controls to display a message such as "Your next ovulation date is 'XX month YY day'" in the application.

[0037] Note that the prediction device 100 may display other expressions, advice, etc. instead of directly displaying the physiological start date or ovulation date. In this case, the prediction device 100 may utilize various information regarding the menstrual cycle in order to generate the message or advice to be displayed. With reference to FIG. 5, various information regarding the menstrual cycle will be described. FIG. 5 is a diagram for explaining the menstrual cycle.

[0038] FIG. 5 shows a graph 280 indicating the body temperature change in the menstrual cycle. In the graph 280, the vertical axis represents the body temperature and the horizontal axis represents the number of days. The starting point of the horizontal axis of the graph 280 corresponds to the physiological start date. As shown in the graph 280, the menstrual cycle has a periodicity such that it becomes a low temperature period 282 near the start of menstruation (physiological start date), and becomes a high temperature period 281 with the ovulation date 283 as the boundary. Also, generally, the probability of pregnancy increases for several days after the ovulation date compared to other periods. Also, generally, many people experience physical discomfort such as mental instability immediately before the physiological start date. Also, when the high temperature period continues longer than normal and there is a deviation in the physiological cycle, the probability of pregnancy is high. Also, when the low temperature period continues longer than normal and there is a deviation in the physiological cycle, the probability of menstrual disorder is high.

[0039] The prediction device 100 transmits information regarding the predicted physiological start date, ovulation date, etc. to the user terminal 200 based on, for example, the information shown in FIG. 5 and known information that is generally observed. Further, the prediction device 100 may transmit information inferred from the predicted physiological start date, ovulation date, etc. to the user terminal 200 and display it on an application in the user terminal 200. That is, the prediction device 100 may display advice suggesting the possibility of pregnancy from the ovulation date prediction, advice suggesting the possibility of poor physical condition from the predicted physiological start date, etc. Specifically, the prediction device 100 displays advice such as "The success rate of pregnancy increases around XX month YY day" and "Be careful as the physical condition is likely to deteriorate around XX month YY day". Alternatively, the prediction device 100 may display advice suggesting the possibility of pregnancy or the presence of signs of menstrual irregularities, etc.

[0040] As described above with reference to FIGS. 1 to 5, the prediction device 100 according to the embodiment acquires learning data that combines basal body temperature data during the menstrual cycle and event information on the day when the basal body temperature data was measured. Then, the prediction device 100 generates a prediction model for predicting the physiological cycle based on the acquired learning data.

[0041] In this way, the prediction device 100 generates a prediction model based on learning data that includes not only quantitative information such as basal body temperature data but also event information assumed to affect the basal body temperature data. That is, the prediction device 100 performs machine learning taking into account the degree of influence based on the influencing factors that affect the basal body temperature data, the physiological start date, etc., and can generate a prediction model that can predict a more accurate physiological cycle compared to a prediction process using only the physiological cycle pattern.

[0042] [1-2. Configuration of the prediction device according to the embodiment] Next, the configuration of the prediction device 100 that executes the physiological cycle prediction process according to the embodiment will be described. FIG. 6 is a diagram showing a configuration example of the prediction device 100 according to the embodiment.

[0043] As shown in FIG. 6, the prediction device 100 includes a communication unit 110, a storage unit 120, and a control unit 130. Note that the prediction device 100 may also include an input unit (e.g., a keyboard, a mouse, etc.) that receives various operations from an administrator or the like who manages the prediction device 100, and a display unit (e.g., a liquid crystal display, etc.) for displaying various information.

[0044] The communication unit 110 is realized by, for example, a Network Interface Controller or the like. The communication unit 110 is connected to a network N (e.g., the Internet) by wire or wirelessly, and transmits and receives information to and from a user terminal 200 or the like via the network N. For example, the communication unit 110 may transmit and receive information using a communication standard or communication technology such as Wi-Fi (registered trademark), SIM (Subscriber Identity Module), LPWA (Low Power Wide Area).

[0045] The storage unit 120 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 120 includes a learning data storage unit 121, a model storage unit 122, and a measurement data storage unit 123.

[0046] The learning data storage unit 121 stores learning data used for learning a prediction model. FIG. 7 shows an example of information stored in the learning data storage unit 121. FIG. 7 is a diagram showing an example of the learning data storage unit 121 according to the embodiment. In the example shown in FIG. 7, the learning data storage unit 121 has items such as "dataset ID", "data ID", "date", "body temperature", and "event data". The "event data" has sub-items such as "body", "life", "physiology", "poor physical condition", "lack of sleep", and "drinking".

[0047] "Dataset ID" indicates the identification information that identifies the dataset when the data over one physiological cycle is grouped together. "Data ID" is the identification information that identifies each piece of data obtained by combining basal body temperature data and event information corresponding to the date on which the basal body temperature data was measured. "Date" indicates the date on which the basal body temperature was measured. "Body temperature" indicates the specific numerical value of the measured basal body temperature data.

[0048] "Event data" indicates the data obtained by digitizing each piece of event information acquired based on the input by user 10. For example, event data is represented by binary data of presence or absence. Each of "body", "life", "physiology", "poor physical condition", "lack of sleep", and "drinking" indicates information corresponding to each event icon shown in FIG. 3 and the like. For example, for the item selected by user 10 from the event icons, "1" is recorded as event data. Also, for the item not selected by user 10, "0" is recorded as event data.

[0049] The model storage unit 122 stores the prediction model generated by the prediction device 100. The prediction device 100 may update the prediction model stored in the model storage unit 122 at regular intervals or each time a predetermined amount of learning data is accumulated.

[0050] The measurement data storage unit 123 stores the measurement data used for the prediction process using the prediction model. FIG. 8 shows an example of the information stored in the measurement data storage unit 123. FIG. 8 is a diagram showing an example of the measurement data storage unit 123 according to the embodiment. In the example shown in FIG. 8, the measurement data storage unit 123 has items such as "user ID", "data ID", "date", "body temperature", and "event data". "Event data" has sub-items such as "body", "life", "physiology", "poor physical condition", "lack of sleep", and "drinking".

[0051] "User ID" indicates the identification information for identifying user 20. The items other than the user ID correspond to the same items shown in FIG. 7.

[0052] Returning to FIG. 6 and continuing the description, the control unit 130 is realized, for example, by a program stored inside the prediction device 100 being executed with a RAM (Random Access Memory) or the like as a work area by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), a GPU (Graphics Processing Unit), or the like. Further, the control unit 130 is a controller and is realized, for example, by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0053] As shown in FIG. 6, the control unit 130 includes an acquisition unit 131, a generation unit 132, a prediction unit 133, and a transmission unit 134.

[0054] The acquisition unit 131 acquires learning data that combines basal body temperature data during the menstrual cycle and event information on the day when the basal body temperature data was measured. For example, the acquisition unit 131 controls a program (app) installed in the user terminal 200 and acquires learning data from the user terminal 200 at the timing when the learning data is acquired by the user terminal 200, at a regular timing every day, or the like.

[0055] Note that the acquisition unit 131 may acquire, as learning data, not only basal body temperature data and the like, but also any body information that is assumed to affect the menstrual cycle, such as the age, height, and weight of each user. The acquisition unit 131 acquires this information, for example, by receiving an input from the user via user registration for an app or the like.

[0056] The acquisition unit 131 acquires, as event information, the daily behavior information (such as drinking alcohol and exercising) of the user 10 and information indicating whether the user 10 has visited a hospital or taken medicine.

[0057] In addition, the acquisition unit 131 can also acquire, as event information, a report of poor physical condition or a report regarding whether the user 10 is in a good or bad mood. Thereby, the acquisition unit 131 can acquire data regarding physical condition and mood that are difficult to quantify.

[0058] Furthermore, the acquisition unit 131 may acquire event information obtained by analyzing text data corresponding to the day on which the basal body temperature data was measured. For example, the acquisition unit 131 may acquire, as event information, the mood and physical condition of the user 10 estimated using a known text analysis model. Alternatively, the acquisition unit 131 may estimate, from the analysis of the meaning of words and context included in the text, that the user has consumed alcohol or visited a hospital, etc., and acquire the estimated information as event information.

[0059] Also, after the prediction model is generated, the acquisition unit 131 acquires daily measurement data from the user terminal 200. Specifically, the acquisition unit 131 acquires measurement data combining the basal body temperature data and the event information of the day on which the basal body temperature data was measured. For example, the acquisition unit 131 controls the program installed in the user terminal 200, and when a predetermined amount of measurement data has accumulated or when the start of menstruation is observed, etc., acquires a predetermined amount of measurement data sets (for example, measurement data for one menstrual cycle). Alternatively, the acquisition unit 131 may acquire measurement data from the user terminal 200 at the timing when the measurement data is acquired by the user terminal 200 or at a daily fixed timing, etc. The acquisition unit 131 identifies and stores the acquired data for each user 20.

[0060] The generation unit 132 generates a prediction model for predicting the menstrual cycle based on the learning data acquired by the acquisition unit 131. For example, the generation unit 132 performs predetermined machine learning on the learning data to generate a prediction model that outputs data regarding the menstrual cycle (menstruation start date, ovulation date, etc.) when new measurement data is input.

[0061] The generation unit 132 can generate a prediction model that takes into account the influence of the event information by using not only the basal body temperature data in the menstrual cycle but also the event information input together with each basal body temperature data as one of the elements. Note that the prediction model may be a model that predicts the low-temperature period schedule or the high-temperature period schedule, rather than predicting schedules such as the menstrual start date or ovulation date. Generally, since the high-temperature period schedule is considered to be relatively stable, the prediction model may be configured as a model that estimates the menstrual cycle of the prediction target by predicting the low-temperature period schedule.

[0062] The generation unit 132 may generate a prediction model using various machine learning methods. That is, the learning method by the generation unit 132 is not limited to any one method.

[0063] As an example, the generation unit 132 may use the number of days in the menstrual cycle of the user 10 as the correct data (objective variable), and the basal body temperature data and each event information as the influencing factors (explanatory variables) to perform learning by a regression analysis method. Thereby, the generation unit 132 can derive information such as which explanatory variables (event information) have an impact on the prediction of the number of days in the menstrual cycle of the user 10.

[0064] An example of model generation will be described below. Note that the learning method and model shown below are examples, and the generation unit 132 may generate any model using various known methods.

[0065] For example, the generation unit 132 uses the number of days in the menstrual cycle in the learning dataset as the objective variable in the regression analysis. Such a number of days in the menstrual cycle is calculated based on the menstrual start date input by the user 10. For example, when the user 10 selects "menstruation" after a series of days when "menstruation" has not been selected as the event information, the generation unit 132 determines that day as the menstrual start date. Also, the generation unit 132 uses the basal body temperature data and various event information included in the dataset as the explanatory variables in the multiple regression analysis.

[0066] For example, the generation unit 132 generates an expression showing the relationship between the number of physiological cycle days and each piece of event information. Further, the generation unit 132 calculates what weight each piece of event information has with respect to the event of the number of physiological cycle days of the user 10. Thereby, the generation unit 132 can obtain information such as how much each explanatory variable affects the event of the number of physiological cycle days of the user 10. For example, when generating a model related to the user 10, the generation unit 132 creates the following formula (1).

[0067] y (ユーザ10) = ω 1 ·x 1 + ω 2 ·x 2 + ω 3 ·x 3 ···+ ω N ·x N ···(1) (N is an arbitrary number)

[0068] In the above formula (1), "y (ユーザ10) " indicates the event of "the number of physiological cycle days of the user 10". Note that, in order to facilitate the calculation, the generation unit 132 may perform processing such as converting "the number of physiological cycle days of the user 10" into a value from "-1" to "1" through normalization or the like. For example, when the number of physiological cycle days is more than a specified numerical value (standard), the generation unit 132 may perform processing such as making "y (ユーザ10) " approach "1", and when the number of physiological cycle days is less than the specified numerical value, the generation unit 132 may perform processing such as making "y (ユーザ10) " approach "-1".

[0069] Also, in the above formula (1), "x" is an explanatory variable and corresponds to the event information obtained from the user terminal 200. Specifically, "x 1 " in the above formula (1) is assumed to be "poor physical condition" among the event information. Also, "x 2 " in the above formula (1) is assumed to correspond to "lack of sleep" among the event information. Also, "x 3Let "」" correspond to "drinking" among the event information. That is, the right side of the above formula (1) corresponds to the presence or absence of events in each learning data as shown in FIG. 7.

[0070] Also, in the above formula (1), "ω" is the coefficient of "x" and represents a predetermined weight value. Specifically, "ω 1 」 is the weight value of "x 1 」, and "ω 2 」 is the weight value of "x 2 」, and "ω 3 」 is the weight value of "x 3 」. Thus, the above formula (1) combines the explanatory variable "x" corresponding to the event information and the predetermined weight value "ω" to create a variable (for example, "ω 1 ·x 1」 ).

[0071] Then, the generation unit 132 generates an equation for each data set to be learned as in the above formula (1), and uses the generated equation as a sample for regression analysis. Then, the generation unit 132 derives a value corresponding to the predetermined weight value "ω" by performing arithmetic processing on the equation serving as a sample. Also, the generation unit 132 generates a sample equation such as the above formula (1) at any time. Then, the generation unit 132 determines a predetermined weight value "ω" that satisfies the above formula (1) regressively as the number of generated equations increases. In other words, the generation unit 132 determines the weight value "ω" that indicates the influence of a predetermined explanatory variable on the target variable "y".

[0072] If, for the event of the number of days of the user 10's menstrual cycle, the number of times of "drinking" contributes compared to other variables, it is estimated that the value of the weight value "ω 3 " corresponding to "drinking" will be calculated as a large positive value compared to other variables. Also, if the number of times of "lack of sleep" hardly contributes to the event of the number of days of the user 10's menstrual cycle, it is estimated that the value of the weight value "ω 2 " corresponding to "lack of sleep" will approach "0" as learning progresses.

[0073] In the above example, for simplicity of explanation, the formula (1) with only event information as the explanatory variable was shown. However, in reality, data based on basal body temperature data is included as an element. Also, in the above example, only three types of event information were shown as the explanatory variable. However, in reality, the formula (1) includes various explanatory variables corresponding to various event information acquired by the acquisition unit 131.

[0074] Also, in the above example, the correct answer data was set as the number of days of the menstrual cycle of user 10. However, the correct answer data is not limited to this, and the number of days of the low temperature period, the start date of menstruation, the ovulation date, etc. may be used as the correct answer data. Further, after the generation unit 132 performs learning based on the basal body temperature data and generates a prediction model for the menstrual cycle, the generation unit 132 may generate a prediction model by various learning methods, such as separately generating a model for predicting the influence that each event information gives to the numerical value predicted by the prediction model.

[0075] Also, the plurality of users 10 who provide the learning data do not always input all the event information that occurred to themselves without omission. For this reason, the generation unit 132 may generate a prediction model using semi-supervised learning instead of supervised learning, assuming a case where data with correct answers and data without correct answers are mixed.

[0076] The prediction unit 133 uses the prediction model generated by the generation unit 132 to predict the menstrual cycle of the user 20 corresponding to the measurement data from the measurement data obtained by combining the newly acquired basal body temperature data and event information.

[0077] Note that the prediction unit 133 may input data for one menstrual cycle as a data set into the prediction model for the measurement data and predict when the next start date of menstruation or ovulation date will be. Alternatively, each time the prediction unit 133 acquires daily measurement data from the user 20, it may sequentially input the data into the prediction model and update the prediction date based on the result output each time.

[0078] The transmission unit 134 transmits the result predicted by the prediction unit 133 to the user terminal 200. Further, the transmission unit 134 may transmit advice to the user based on the physiological cycle predicted by the prediction unit 133. For example, the transmission unit 134 transmits advice regarding the physical condition of the user 20 estimated from the physiological cycle and the possibility of pregnancy.

[0079] In this case, the transmission unit 134 may transmit different advice for each user 20. For example, the transmission unit 134 refers to the past history and determines that the user 20 has repeatedly shown that the event of "poor physical condition" occurred several days before the start of menstruation. In this case, the transmission unit 134 transmits advice to the user indicating the date several days before the predicted start date of menstruation and suggesting that there is a possibility of poor physical condition during that period. On the other hand, the transmission unit 134 may take the measure of not transmitting such advice to the user 20 who has no history of the event of "poor physical condition" occurring several days before the start of menstruation. Such processing for each user can be automated by machine learning the behavior history of each user, etc.

[0080] Next, the configuration of the user terminal 200 will be described. FIG. 9 is a diagram showing a configuration example of the user terminal 200. As shown in FIG. 9, the user terminal 200 includes a communication unit 210, a storage unit 220, and a control unit 230. Note that the user terminal 200 may include an input unit (for example, a touch panel, etc.) that receives various operations from the user and a display unit (for example, a liquid crystal display, etc.) that displays various information.

[0081] The communication unit 210 is realized by, for example, a network interface controller or the like. The communication unit 210 is connected to the network N by wire or wirelessly, and transmits and receives information to and from the prediction device 100, the measuring device 300, etc. via the network N.

[0082] The storage unit 220 is realized by, for example, a semiconductor memory element such as a RAM or a flash memory, or a storage device such as a hard disk or an optical disk. The measurement data storage unit 221 stores information combining the basal body temperature data measured by the measuring instrument 300 and the event information input from the user. The information stored in the measurement data storage unit 221 is transmitted to the prediction device 100 as learning data or measurement data. Note that the information stored in the measurement data storage unit 221 does not necessarily have to be held by the user terminal 200 itself, and may be held, for example, by a data server on the cloud or the like.

[0083] The control unit 230 is realized, for example, by a CPU, an MPU, a GPU, etc., when a program stored inside the user terminal 200 is executed using a RAM or the like as a work area. Further, the control unit 230 is a controller and is realized by, for example, an integrated circuit such as an ASIC or an FPGA. The control unit 230 includes an acquisition unit 231, a reception unit 232, and a transmission unit 233.

[0084] The acquisition unit 231 acquires the basal body temperature data measured by the measuring instrument 300 from the measuring instrument 300. Further, the acquisition unit 231 acquires the result predicted by the prediction device 100 and displays it within the application.

[0085] The reception unit 232 receives the input of event information from the user via the application. When the reception unit 232 receives the input of event information, it associates the basal body temperature data and the event information and stores them in the measurement data storage unit 221.

[0086] The transmission unit 233 transmits the data stored in the measurement data storage unit 221 to the prediction device 100 according to the request by the prediction device 100.

[0087] Next, the configuration of the measuring device 300 will be described. FIG. 10 is a diagram showing a configuration example of the measuring device 300. As shown in FIG. 10, the measuring device 300 includes a communication unit 310, a storage unit 320, a control unit 330, and a detection unit 340. Note that the measuring device 300 may have an input unit (such as a touch panel) that receives various operations from the user and a display unit (such as a liquid crystal display) that displays various information.

[0088] The communication unit 310 is realized by, for example, a network interface controller or the like. The communication unit 310 is connected to the network N by wire or wirelessly, and transmits and receives information to and from the prediction device 100, the user terminal 200, etc. via the network N.

[0089] The storage unit 320 is realized by, for example, a semiconductor memory element such as a RAM or a flash memory, or a storage device such as a hard disk or an optical disk. The measurement data storage unit 321 stores the basal body temperature data measured by the measuring device 300. The information stored in the measurement data storage unit 321 is transmitted to the user terminal 200 at a predetermined timing (such as at a fixed time every day). Note that the information stored in the measurement data storage unit 321 does not necessarily have to be held by the measuring device 300 itself, and may be held, for example, in a data server on the cloud.

[0090] The detection unit 340 is a sensor and detects various data. For example, the detection unit 340 is a temperature sensor that measures body temperature. Note that the detection unit 340 may detect not only the body temperature but also the external environmental temperature, humidity, etc.

[0091] The control unit 330 is realized, for example, when a program stored inside the measuring device 300 is executed with a RAM or the like as a work area by a CPU, an MPU, a GPU, etc. Also, the control unit 330 is a controller and is realized by, for example, an integrated circuit such as an ASIC or an FPGA. The control unit 330 includes a measurement unit 331 and a transmission / reception unit 332.

[0092] The measurement unit 331 measures the basal body temperature detected by the detection unit 340. The measurement unit 331 stores the measured data in the measurement data storage unit 321.

[0093] The transmission / reception unit 332 transmits the basal body temperature data measured by the measurement unit 331 to the user terminal 200. Further, the transmission / reception unit 332 may receive a transmission request or the like of measurement data issued from the user terminal 200. In this case, when the transmission / reception unit 332 receives the transmission request of the measurement data, it transmits the measurement data accumulated until then to the user terminal 200.

[0094] [1-3. Procedure of the menstrual cycle prediction process according to the embodiment] Next, with reference to FIGS. 11 and 12, the procedure of the process according to the embodiment will be described. First, with reference to FIG. 11, the procedure of the learning process according to the embodiment will be described. FIG. 11 is a flowchart showing the procedure of the learning process according to the embodiment.

[0095] As shown in FIG. 11, the prediction device 100 acquires learning data from the user terminal 200 (step S101). After that, the prediction device 100 determines whether or not a sufficient amount of learning data has been accumulated for learning (step S102). If the learning data has not been accumulated (step S102; No), the prediction device 100 repeats the process of acquiring the learning data.

[0096] On the other hand, when the learning data has been accumulated (step S102; Yes), the prediction device 100 generates a prediction model based on the learning data (step S103). Then, the prediction device 100 stores the generated prediction model in the storage unit 120 (step S104).

[0097] Subsequently, with reference to FIG. 12, the procedure of the prediction process according to the embodiment will be described. FIG. 12 is a flowchart showing the procedure of the prediction process according to the embodiment.

[0098] As shown in FIG. 12, the prediction device 100 determines whether it has received measurement data from the user (step S201). If it has not received the measurement data (step S201; No), the prediction device 100 waits until it receives the measurement data.

[0099] On the other hand, if it has received the measurement data (step S201; Yes), the prediction device 100 inputs the measurement data into the prediction model and executes prediction processing (step S202). Then, the prediction device 100 transmits the output result to the user (step S203).

[0100] (2. Modifications of the Embodiment) [2-1. Configuration of the Device] In the above embodiment, an example is shown in which the user terminal 200 according to the present disclosure acquires basal body temperature data measured by the measuring device 300. However, the user terminal 200 does not necessarily have to acquire basal body temperature data from the measuring device 300. For example, the user terminal 200 may handle basal body temperature data directly input by the user as measurement data or learning data.

[0101] Also, in the embodiment, a process is shown in which the user terminal 200 transmits learning data to the prediction device 100 and the prediction device 100 generates a prediction model. However, the user terminal 200 may generate a prediction model by itself based on the acquired learning data. That is, the user terminal 200 may have the function as the prediction device 100.

[0102] (3. Other Embodiments) The processing according to the above-described embodiment may be implemented in various different forms other than the above embodiment.

[0103] For example, among the various processes described in the above embodiments, all or part of the processes described as being automatically performed can also be manually performed, or all or part of the processes described as being manually performed can be automatically performed by a known method. In addition, regarding the processing procedures, specific names, and information including various data and parameters shown in the above documents and drawings, they can be arbitrarily changed unless otherwise specified. For example, the various information shown in each figure is not limited to the illustrated information.

[0104] Also, each component of each device shown in the drawings is a functional concept, and it is not necessarily physically configured as shown in the drawings. That is, the specific form of the distribution and integration of each device is not limited to that shown, and all or part of it can be functionally or physically distributed and integrated in any unit according to various loads and usage situations.

[0105] In addition, the above-described embodiments and modification examples can be appropriately combined within a range that does not conflict with the processing content.

[0106] Also, the effects described in this specification are merely examples and are not limited, and there may be other effects.

[0107] (4. Effects of the menstrual cycle prediction device and menstrual cycle prediction system according to the present disclosure) As described above, the menstrual cycle prediction device (prediction device 100 in the embodiment) according to the present disclosure includes an acquisition unit (acquisition unit 131 in the embodiment) and a generation unit (generation unit 132 in the embodiment). The acquisition unit acquires learning data that combines basal body temperature data during the menstrual cycle and event information on the day when the basal body temperature data was measured. The generation unit generates a prediction model for predicting the menstrual cycle based on the learning data acquired by the acquisition unit.

[0108] As described above, the menstrual cycle prediction device according to the present disclosure generates a prediction model based on learning data including not only quantitative information such as basal body temperature data but also event information assumed to affect the basal body temperature data. Thereby, the menstrual cycle prediction device can generate a prediction model that can predict a more accurate menstrual cycle as compared with a prediction process using only a menstrual cycle pattern.

[0109] Further, the menstrual cycle prediction device further includes a prediction unit (prediction unit 133 in the embodiment) and a transmission unit (transmission unit 134 in the embodiment). The prediction unit uses the prediction model generated by the generation unit to predict the menstrual cycle of the user corresponding to the measurement data from the measurement data combining newly acquired basal body temperature data and event information. The transmission unit transmits information regarding the menstrual cycle of the user predicted by the prediction unit to the user.

[0110] As described above, the menstrual cycle prediction device can provide the user with information regarding the accurately predicted menstrual cycle by performing a prediction process using a prediction model generated based on learning data including event information.

[0111] Further, the transmission unit transmits advice to the user based on the menstrual cycle predicted by the prediction unit.

[0112] As described above, the menstrual cycle prediction device can provide the user with more useful information by creating advice based on the accurately predicted menstrual cycle.

[0113] Further, the acquisition unit acquires, as event information, at least one piece of information on drinking, exercise, hospital visit, and medication.

[0114] As described above, the menstrual cycle prediction device acquires the daily behavior of the user assumed to affect the basal body temperature as event information. Thereby, the menstrual cycle prediction device can generate a prediction model taking into account the effects associated with these behaviors.

[0115] Further, the acquisition unit acquires, as event information, a report of poor physical condition or a report regarding whether one's mood is good or bad.

[0116] In this way, the physiological cycle prediction device acquires information that can be a stress factor, such as the user's poor physical condition and mood, which are difficult to measure quantitatively. Thereby, the physiological cycle prediction device can generate a prediction model that takes into account invisible influences such as stress.

[0117] Further, the acquisition unit acquires learning data obtained by combining basal body temperature data and event information obtained by analyzing text data corresponding to the day on which the basal body temperature data was measured.

[0118] In this way, the physiological cycle prediction device can generate a prediction model that can reflect stress, mood, etc. that the user himself / herself is not aware of as influencing factors in the prediction by estimating event information based on the user's memo or the like.

[0119] Further, the physiological cycle prediction system according to the present disclosure (physiological cycle prediction system 1 in the embodiment) includes a terminal device (user terminal 200 in the embodiment) and a physiological cycle prediction device. The terminal device includes a reception unit (reception unit 232 in the embodiment) and a transmission unit (transmission unit 233 in the embodiment). The reception unit receives an input of event information that occurred on the day corresponding to the measured basal body temperature data within a screen for displaying the measured basal body temperature data. The transmission unit transmits learning data obtained by combining the event information received by the reception unit and the basal body temperature data corresponding to the event information to the physiological cycle prediction device. The physiological cycle prediction device includes an acquisition unit and a generation unit. The acquisition unit acquires the learning data transmitted by the transmission unit. The generation unit generates a prediction model for predicting the physiological cycle based on the learning data acquired by the acquisition unit.

[0120] Thus, the menstrual cycle prediction system according to the present disclosure can obtain learning data through an excellent user interface, such as receiving input of event information from a user in a manner displayed together with basal body temperature data. Thereby, the menstrual cycle prediction system can obtain more learning data including event information input by the user, so that the accuracy of learning can be improved, and as a result, the prediction accuracy of the generated model can be enhanced.

[0121] (5. Hardware Configuration) Information devices such as the prediction device 100 and the user terminal 200 according to the above-described embodiments are realized by a computer 1000 having a configuration as shown in FIG. 13, for example. Hereinafter, the prediction device 100 according to the embodiment will be described as an example. FIG. 13 is a hardware configuration diagram showing an example of a computer 1000 that realizes the functions of the prediction device 100. The computer 1000 includes a CPU 1100, a RAM 1200, a ROM (Read Only Memory) 1300, an HDD (Hard Disk Drive) 1400, a communication interface 1500, and an input / output interface 1600. Each part of the computer 1000 is connected by a bus 1050.

[0122] The CPU 1100 operates based on a program stored in the ROM 1300 or the HDD 1400 and controls each part. For example, the CPU 1100 expands a program stored in the ROM 1300 or the HDD 1400 into the RAM 1200 and executes processing corresponding to various programs.

[0123] The ROM 1300 stores a boot program such as BIOS (Basic Input Output System) executed by the CPU 1100 when the computer 1000 is started up, and programs dependent on the hardware of the computer 1000.

[0124] The HDD 1400 is a computer-readable recording medium that non-temporarily records programs executed by the CPU 1100 and data used by such programs. Specifically, the HDD 1400 is a recording medium that records a program for executing the menstrual cycle prediction process according to the present disclosure, which is an example of the program data 1450.

[0125] The communication interface 1500 is an interface for the computer 1000 to connect to an external network 1550 (for example, the Internet). For example, the CPU 1100 receives data from other devices or transmits data generated by the CPU 1100 to other devices via the communication interface 1500.

[0126] The input / output interface 1600 is an interface for connecting the input / output device 1650 and the computer 1000. For example, the CPU 1100 receives data from input devices such as a keyboard and a mouse via the input / output interface 1600. Also, the CPU 1100 transmits data to output devices such as a display, a speaker, and a printer via the input / output interface 1600. Further, the input / output interface 1600 may function as a media interface for reading programs and the like recorded on a predetermined recording medium (media). The media is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or a PD (Phase change rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0127] For example, when the computer 1000 functions as the prediction device 100 according to the embodiment, the CPU 1100 of the computer 1000 realizes the functions of the control unit 130 and the like by executing the menstrual cycle prediction processing program loaded on the RAM 1200. Further, the HDD 1400 stores a program for executing the menstrual cycle prediction processing according to the present disclosure and data in the storage unit 120. Note that the CPU 1100 reads and executes the program data 1450 from the HDD 1400. As another example, these programs may be acquired from other devices via the external network 1550.

[0128] As described above, the embodiments of the present application have been described in detail with reference to the drawings. However, these are merely examples, and the present invention can be implemented in other forms with various modifications and improvements based on the knowledge of those skilled in the art, including the aspects described in the column of the disclosure of the invention.

Description of Reference Numerals

[0129] 1 Menstrual cycle prediction system 100 Prediction device 110 Communication unit 120 Storage unit 121 Learning data storage unit 122 Model storage unit 123 Measurement data storage unit 130 Control unit 131 Acquisition unit 132 Generation unit 133 Prediction unit 134 Transmission unit 200 User terminal 300 Measuring instrument

Claims

1. An acquisition unit that acquires learning data combining basal body temperature data during the menstrual cycle and event information on the day when the basal body temperature data was measured; A generation unit that generates a prediction model for predicting the menstrual cycle based on the learning data acquired by the acquisition unit; A menstrual cycle prediction device, characterized by comprising the above.

2. A prediction unit that predicts the menstrual cycle of a user corresponding to the measurement data from measurement data newly acquired by combining newly acquired basal body temperature data and event information, using the prediction model generated by the generation unit; A transmission unit that transmits information regarding the menstrual cycle of the user predicted by the prediction unit to the user; The menstrual cycle prediction device according to claim 1, further characterized by comprising the above.

3. The transmission unit: Transmits advice to the user based on the menstrual cycle predicted by the prediction unit. The menstrual cycle prediction device according to claim 2, characterized by the above.

4. The acquisition unit: Acquires at least one piece of information on drinking, exercise, hospital visit, and medication as the event information. The menstrual cycle prediction device according to any one of claims 1 to 3, characterized by the above.

5. The acquisition unit: Acquires a report of poor physical condition or a report regarding the user's mood as the event information. The menstrual cycle prediction device according to any one of claims 1 to 4, characterized by the above.

6. The acquisition unit: Acquires the learning data combining the basal body temperature data and the event information obtained by analyzing text data corresponding to the day when the basal body temperature data was measured. The menstrual cycle prediction device according to any one of claims 1 to 5, characterized by the above.

7. A computer: Acquires learning data combining basal body temperature data during the menstrual cycle and event information on the day when the basal body temperature data was measured; Generates a prediction model for predicting the menstrual cycle based on the acquired learning data. A menstrual cycle prediction method, characterized by including the above.

8. A computer is made to function as: An acquisition unit that acquires learning data combining basal body temperature data during the menstrual cycle and event information on the day when the basal body temperature data was measured; A generation unit that generates a prediction model for predicting the menstrual cycle based on the learning data acquired by the acquisition unit. A menstrual cycle prediction program, characterized by the above.

9. In a screen for displaying measured basal body temperature data, a reception unit that receives input of event information that occurred on the day corresponding to the basal body temperature data; A transmission unit that transmits learning data combining the event information received by the reception unit and the basal body temperature data corresponding to the event information to a menstrual cycle prediction device; A terminal device comprising: An acquisition unit that acquires the learning data transmitted by the transmission unit; A generation unit that generates a prediction model for predicting the menstrual cycle based on the learning data acquired by the acquisition unit; A menstrual cycle prediction device comprising: A menstrual cycle prediction system characterized by including:

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