Information processing device, ovulation date estimation method, and ovulation date estimation program

Abstract

This invention provides an information processing device that, based on the user's glucose level, suggests ways to improve the user's glucose level and thus improve their overall health. [Solution] The information processing device includes an acquisition unit that acquires information indicating the user's glucose value and information indicating the user's state when the glucose value is detected; a comment unit that presents comments to the user regarding the glucose value based on the glucose value acquired by the acquisition unit; and a suggestion unit that proposes improvements for the user in the user's state.

Description

Technical Field

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[0001] The present invention relates to an information processing apparatus, a proposed method, and an ovulation date estimation program for estimating a woman's ovulation date based on fluctuations in glucose levels.

Background Art

[0002] Conventionally, there is a method of specifying a woman's ovulation date by measuring her basal body temperature. Further, Patent Document 1 discloses a technique for predicting the ovulation date by estimating the concentrations of at least two types of biological substances from an imaging image obtained by imaging a region showing the reaction result of a specimen in an inspection instrument. The specimen is a body fluid such as blood, serum, urine, or saliva, and the biological substances are luteinizing hormone and estrogen.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] By the way, in some women, hormone secretion may be unstable, and the prediction according to Patent Document 1 may be incorrect.

[0005] Therefore, an object of the present invention is to provide an information processing apparatus, an ovulation date estimation method, and an ovulation date estimation program for estimating the ovulation date by a method different from that of Patent Document 1.

Means for Solving the Problems

[0006] An information processing apparatus according to an aspect of the present invention includes an acquisition unit that acquires fluctuation information of a woman's glucose level over a predetermined period, an estimation unit that estimates the woman's ovulation date based on the fluctuation information, and a provision unit that provides ovulation date information indicating the predicted ovulation date before the ovulation date estimated by the estimation unit.

[0007] Furthermore, in the above-mentioned information processing device, the estimation unit may estimate that the timing at which the glucose value gradually decreases and then gradually increases over a predetermined period is the ovulation day of the woman.

[0008] Furthermore, in the above-mentioned information processing device, the acquisition unit may sequentially acquire information on fluctuations in the woman's glucose level, and the estimation unit may detect that the glucose level fluctuation information acquired sequentially by the acquisition unit is gradually decreasing and estimate the next ovulation day.

[0009] Furthermore, in the above-mentioned information processing device, the estimation unit may input the fluctuation information acquired by the acquisition unit into a trained model that has been trained using the fluctuation information of glucose values ​​of multiple women as explanatory variables and the ovulation day in each fluctuation information as the objective variable, and estimate the ovulation day of the woman corresponding to the fluctuation information acquired by the acquisition unit.

[0010] Furthermore, in the above-mentioned information processing device, the estimation unit may use information on fluctuations in glucose levels measured at night during a predetermined period to estimate the ovulation day of the woman.

[0011] Furthermore, in the above-mentioned information processing device, the estimation unit may average the fluctuation information of glucose values ​​measured over a predetermined period on a daily basis, and estimate the ovulation day of the woman based on the averaged fluctuation information of glucose values.

[0012] Furthermore, in the above-mentioned information processing device, the predetermined period is at least 28 days.

[0013] Furthermore, in the above-mentioned information processing device, the acquisition unit may also acquire the woman's body temperature information, and the estimation unit may further use the woman's body temperature information to estimate the woman's ovulation day.

[0014] Furthermore, in one aspect of the present invention, the ovulation date estimation method includes an acquisition step in which a computer acquires information on fluctuations in a woman's glucose levels over a predetermined period of time; an estimation step in which the computer estimates the woman's ovulation date based on the fluctuation information; and an output step in which, before the ovulation date estimated in the estimation step, the computer outputs ovulation date information indicating the predicted ovulation date.

[0015] Furthermore, an ovulation date estimation program according to one aspect of the present invention provides a computer with an acquisition function to acquire information on fluctuations in a woman's glucose levels over a predetermined period, an estimation function to estimate the woman's ovulation date based on the fluctuation information, and an output function to output ovulation date information indicating the predicted ovulation date before the ovulation date estimated by the estimation function. [Effects of the Invention]

[0016] An information processing device according to one aspect of the present invention can estimate and provide a woman's ovulation date a few days in advance, based on fluctuations in her glucose levels. [Brief explanation of the drawing]

[0017] [Figure 1] This is a conceptual diagram illustrating the overview of the ovulation prediction system. [Figure 2] This is a block diagram showing an example of the configuration of an information processing device. [Figure 3] This diagram schematically illustrates the relationship between female hormones, ovulation, and glucose levels. [Figure 4] This diagram schematically illustrates glucose level fluctuations, basal body temperature, and the menstrual cycle over a predetermined period. [Figure 5] This flowchart shows an example of how an information processing device operates during learning. [Figure 6] This flowchart shows an example of how an information processing device operates during estimation. [Figure 7] This graph shows the first example of the fluctuation in glucose levels over one month for the first woman. [Figure 8] This graph shows a second example of the fluctuation in glucose levels over one month for the second woman. [Figure 9] It is a graph showing a third variation example of the glucose values of a third woman over one month. [Figure 10] It is a graph showing a fourth variation example of the glucose values of a fourth woman over one month. [Figure 11] It is a figure showing an example of a display screen for providing information.

Embodiment for Carrying Out the Invention

[0018] Hereinafter, the information processing apparatus 100 according to the present invention will be described in detail with reference to the drawings.

[0019] <Embodiment> <Configuration> FIG. 1 is a system diagram showing an operation example of an information processing apparatus according to the present invention.

[0020] As shown in FIG. 1, a woman 30 who wants to predict her ovulation day lives wearing a sensor 300 that collects information indicating her glucose values. The sensor 300 sequentially measures the glucose values (blood glucose values) of the woman 30 and transmits the information to the information processing terminal 200 of the woman 30 as appropriate. The sensor 300 is, for example, attached to a woman's arm or the like to continuously measure glucose values, and more precisely, measures interstitial fluid glucose values reflecting blood glucose levels. Therefore, in the present embodiment, the glucose value can also be replaced with the blood glucose value. The illustrated information processing terminal 200 shows a smartphone as an example, but this may be a wearable terminal or the like that can be realized in the form of a tablet terminal, a mobile phone, a PC (Personal Computer), a watch, glasses, or the like. The information processing terminal 200 transmits the glucose value information 21 transmitted from the sensor 300 to the information processing apparatus 100 via the network 400 as appropriate. The information processing apparatus 100 learns the glucose value information 21 of the woman 30, predicts the next ovulation day based on its variation, and transmits the ovulation day information 31 to the information processing terminal 200 of the woman 30. As a result, the woman 30 can recognize her ovulation day and utilize it, for example, for pregnancy activities. Details will be described below.

[0021] Figure 2 is a block diagram showing an example configuration of the information processing device 100.

[0022] As shown in Figure 2, the information processing device 100 comprises a communication unit 110, a control unit 130, and a storage unit 140. The information processing device 100 may also include an input unit 120 and an output unit 150. The information processing device 100 may be a computer system implemented by a server device or a PC.

[0023] The communication unit 110 is a communication interface that communicates with an external device via the network 400. The external device may, for example, be an information processing terminal 200 of a woman 30. The communication unit 110 receives glucose value information 21 of the woman 30 from the information processing terminal 200 and transmits it to the control unit 130. The communication unit 110 also transmits ovulation day information 31 transmitted from the control unit 130 to the information processing terminal 200. The glucose value information 21 is information that associates the date and time of measurement with information indicating the glucose value at that time. The glucose value information 21 may be transmitted from the information processing terminal 200 to the information processing device 100 each time a measurement is taken by the sensor 300, or measurements for a predetermined period (for example, one week, but not limited to this, it may be 3 days or 10 days) may be transmitted. The communication unit 110 may also receive ovulation day information input by the woman 30 from the information processing terminal 200 and transmit it to the control unit 130.

[0024] The input unit 120 is an input interface that receives input from the operator of the information processing device 100 and transmits it to the control unit 130. The input unit 120 may be implemented by, for example, a keyboard, mouse, microphone, etc., but is not limited to these.

[0025] The control unit 130 is a processor that controls each part of the information processing device 100. The control unit 130 realizes the functions that the information processing device 100 should perform by executing various programs stored in the memory unit 140. Specifically, it predicts the user's (female 30) next ovulation day based on the user's glucose value information 21 and transmits that information to the information processing terminal 200.

[0026] The control unit 130 may include an acquisition unit 131, an estimation unit 132, a provision unit 133, and a learning unit 134 in order to predict the ovulation day and transmit the information.

[0027] The acquisition unit 131 acquires glucose value information 21 transmitted from the information processing terminal 200 of the woman 30, which is received by the communication unit 110. The acquisition unit 131 stores the acquired glucose value information 21 in the storage unit 140, associating it with the user identification information of the woman 30.

[0028] The estimation unit 132 predicts the next ovulation day of the woman 30 based on the glucose value information 21 of the woman 30 stored in the memory unit 140. The estimation unit 132 uses the glucose value information 21 for a predetermined period and the ovulation day estimation program 141 stored in the memory unit 140 to predict the ovulation day of the woman 30.

[0029] The estimation unit 132 predicts the ovulation day of the woman 30 based on the trend of glucose value fluctuations indicated by glucose value information 21. The estimation unit 132 may predict the ovulation day based on the fluctuations in glucose value information during sleep, when fluctuations in glucose value due to food intake, etc., are less likely to occur. The estimation unit 132 may estimate that the ovulation day is a few days after the glucose value information 21 has gradually decreased over several days (for example, 4 days, but not limited to 4 days, an appropriate number of days may be determined from the average of sample data, etc.). Furthermore, if the ovulation day estimation program 141 estimates the ovulation day using a learning model that has learned the relationship between glucose value fluctuations and the ovulation day, the estimation unit 132 may input the glucose value information 21 of the woman 30 for a predetermined period into the learning model to predict the ovulation day. Furthermore, the estimation unit 132 may perform reinforcement learning on the learning model using glucose value information 21 of women 30 for a predetermined period, or perform fine tuning using glucose value information 21 of women 30 for a predetermined period, and then receive input of subsequent glucose value information of women 30 to predict the next ovulation day. In this embodiment, the estimation unit 132 predicts the ovulation day of women 30 using the learning model.

[0030] The estimation unit 132 predicts the ovulation day a few days before it occurs (specifically, it may be around 2 or 3 days before, but is not limited to this). The estimation unit 132 then transmits the estimated ovulation day to the provision unit 133.

[0031] The provisioning unit 133 provides the ovulation day information 31, which predicts the next ovulation day of the woman 30 as estimated by the estimation unit 132, to the woman 30's information processing terminal 200 via the communication unit 110.

[0032] When the ovulation estimation program 141 uses a learned model that has learned the relationship between glucose levels and ovulation to predict the ovulation day of woman 30, the learning unit 134 may retrain the learned model using the fluctuations in woman 30's glucose levels and the information on the ovulation day associated with those fluctuations. That is, the learning unit 134 may learn the fluctuations in the glucose levels of woman 30 (preferably fluctuations for at least one menstrual cycle, but fluctuations for a few days before ovulation, including the ovulation day) annotated with the ovulation day associated with those fluctuations as training data. The ovulation day in this case may be the date indicated by the ovulation day information transmitted from woman 30's information processing terminal 200. Alternatively, the learning unit 134 may learn the fluctuations in woman 30's glucose levels and the information on the ovulation day associated with those fluctuations as reference data (data for fine-tuning) during estimation. By learning the relationship between the fluctuations in woman 30's glucose levels and the ovulation day once, the estimation of woman 30's ovulation day in the next menstrual cycle can be made more accurate. Furthermore, if the ovulation day is to be estimated without performing learning, the learning unit 134 does not need to be provided.

[0033] The memory unit 140 is a storage medium that has the function of storing various programs and data required for the operation of the information processing device 100. The memory unit 140 may be implemented as an HDD (Hard Disk Drive), SSD (Solid State Drive), flash memory, etc., but is not limited to these. The memory unit 140 may also be implemented as cloud storage. The memory unit 140 may store an ovulation date estimation program 141 that predicts the ovulation date of woman 30 based on the glucose value information 21 of woman 30. The ovulation date estimation program 141 may predict the ovulation date based on whether or not the fluctuation of glucose value shows a predetermined pattern. The ovulation date estimation program 141 may also predict the ovulation date of woman 30 using a trained model that has learned the relationship between the fluctuation of glucose value information of many women and their ovulation dates. In other words, the trained model may be a model that has been trained on training data in which the fluctuation of a woman's glucose level information over a predetermined period (which may be about one month, but is not limited to one month) is used as the explanatory variable and the ovulation day is used as the dependent variable.

[0034] The output unit 150 is an interface that has the function of outputting specified information according to instructions from the control unit 130. The output unit 150 may be implemented by, for example, a monitor or a speaker, but is not limited to these. The output unit 150 may output information indicating the ovulation day, for example. In this embodiment, the information processing device 100 provides the ovulation day information 31 by communication, but this may also be provided by outputting from the output unit 150 and showing the contents to the woman 30.

[0035] The above describes the configuration of the information processing device 100.

[0036] The information processing terminal 200 is generally implemented using a smartphone or tablet device, and any computer system with similar functions to those information processing devices will suffice; therefore, a detailed explanation of its configuration will be omitted here. The information processing terminal 200 only needs to be a computer capable of communicating with the sensor 300 to receive glucose value information from the sensor 300, transmitting glucose value information 21 to the information processing device 100, and receiving and outputting ovulation day information 31 from the information processing device 100. The sensor 300 is attached to the arm of a woman 30 to collect bodily fluids such as blood, saliva, and sweat from the woman 30, and uses a detection element to acquire the glucose value in the collected bodily fluids and transmit it to the information processing terminal 200.

[0037] <Regarding the relationship between glucose levels and ovulation day> Figure 3 shows the relationship between glucose levels, ovulation day, and hormone secretion.

[0038] Figure 3 shows the relationship between fluctuations in glucose levels around ovulation and the secretion of various hormones related to ovulation, with the horizontal axis representing time, and illustrates an example of fluctuations during a menstrual cycle.

[0039] As shown in Figure 3, the hormones secreted by women differ before and after ovulation. The period before ovulation is called the follicular phase, and the period after ovulation is called the luteal phase. In women, a hormone called estradiol is gradually secreted leading up to ovulation, reaching its peak secretion a few days before ovulation. Estradiol is a steroid hormone with strong biological effects on the uterine lining and uterine muscle, and it functions to mature follicles, thicken the uterine lining, and support the implantation of a fertilized egg. Estradiol is a type of estrogen. In addition, luteinizing hormone is secreted in large quantities just before ovulation. Luteinizing hormone induces ovulation and, after ovulation, luteinizes the follicle and promotes the secretion of progesterone. It reaches its peak secretion before ovulation and continues to be secreted after ovulation. Progesterone is a hormone that thickens the uterine lining, creating an environment conducive to the implantation of a fertilized egg, and also regulates the menstrual cycle along with estradiol. Progesterone is secreted in gradually increasing amounts after ovulation, and its secretion decreases if fertilization does not occur.

[0040] In response, the inventors discovered that glucose levels fluctuate around the time of ovulation.

[0041] Specifically, as shown in Figure 3, the inventors discovered that glucose levels gradually decrease towards ovulation and gradually increase after ovulation.

[0042] Figure 4 shows the relationship between glucose level fluctuations, ovulation day, menstrual cycle, and basal body temperature changes. Figure 4 shows an example of how glucose level fluctuations and basal body temperature fluctuations change over the number of days in the menstrual cycle.

[0043] As shown in Figure 4, the menstrual cycle begins with the menstrual phase, progresses through the proliferative phase (follicular phase) to ovulation, then through the secretory phase (luteal phase), and finally transitions to the next menstrual phase. The menstrual cycle is said to be approximately 28 days long, with the first 5 days being the menstrual phase, the proliferative phase (follicular phase) from the 6th to around the 12th day, ovulation occurring on the 14th or 15th day, and the secretory phase (luteal phase) continuing until the next menstrual phase. Traditionally, ovulation was predicted based on fluctuations in basal body temperature.

[0044] Basal body temperature is the body temperature when the body is at rest, consuming only the energy necessary to maintain life. It is typically measured in the morning immediately after waking up while the body is still at rest.

[0045] During the menstrual cycle, as shown by the fluctuations in basal body temperature, when ovulation occurs, basal body temperature rises sharply, as indicated by arrow 41 in Figure 4. This is due to the action of progesterone, which is secreted after ovulation. Therefore, by measuring basal body temperature daily, it is possible to predict the approximate day of ovulation. However, actual ovulation occurs before this, which can be seen as late for women trying to conceive. This is because the success rate of conception is generally considered to be highest two days before ovulation.

[0046] On the other hand, the inventors discovered that, as shown in the glucose level fluctuations, glucose levels tend to decrease gradually as ovulation approaches. That is, as indicated by arrow 42 in the glucose level fluctuations in Figure 4, the amount of glucose secreted decreases as ovulation approaches. Since this downward trend begins to appear a few days before ovulation, the inventors discovered that if this downward trend in glucose levels or its signs can be detected, ovulation can be estimated earlier than with the basal body temperature method. The ovulation prediction method shown in this embodiment utilizes this finding.

[0047] <Operation> Figure 5 is a flowchart illustrating an example of the operation of the information processing device 100, specifically an example of its operation during learning.

[0048] As shown in Figure 5, the communication unit 110 sequentially receives glucose value information 21, which indicates the glucose value sensed by the sensor 300, from the woman's information processing terminal 200 via the network 400 (step S501). The communication unit 110 transmits the received glucose value information 21 to the control unit 130.

[0049] Furthermore, the communication unit 110 receives information indicating the date of ovulation from the woman's information processing terminal 200 (step S502). The communication unit 110 transmits the received date of ovulation to the control unit 130.

[0050] The learning unit 134 of the control unit 130 performs learning using information indicating the fluctuation of glucose values ​​over a predetermined period based on the transmitted glucose value information 21 as the explanatory variable, and the transmitted information indicating the ovulation day as the target variable (step S503). This learning may be additional learning or reinforcement learning of the learning model as the ovulation day estimation program 141, or a process of adding it as reference data.

[0051] This makes it easier to estimate the next ovulation day of woman 30 by receiving glucose value information for the next cycle, in other words, it improves the accuracy of the estimation.

[0052] Figure 6 is a flowchart illustrating an example of the operation of an information processing device, specifically an example of its operation during estimation.

[0053] As shown in Figure 6, the communication unit 110 of the information processing device 100 sequentially receives glucose value information 21 of the woman 30 from the information processing terminal 200. The communication unit 110 transmits the received glucose value information 21 to the control unit 130. The acquisition unit 131 of the control unit 130 acquires the glucose value information transmitted from the communication unit 110 (step S601). The acquisition unit 131 transmits the acquired glucose value information to the estimation unit 132.

[0054] The estimation unit 132 takes the transmitted glucose value information, combines it to form information showing the fluctuation of glucose values, inputs this into the trained model, and estimates the next ovulation day (step S602).

[0055] In this case, the estimation unit 132 may use only the glucose value information of the woman 30 at bedtime (nighttime) from the transmitted glucose value information to generate information indicating fluctuations in glucose value. For example, the estimation unit 132 may use only the glucose value information received between 0:00 and 6:00 to generate information indicating fluctuations in glucose value and estimate the ovulation day. The estimation unit 132 transmits the estimated next ovulation day information to the provision unit 133. In this case, if the ovulation day cannot be estimated from the transmitted glucose value information at this time (i.e., there are no signs, or even if there are signs, the possibility that it is the ovulation day is low), the ovulation day does not need to be estimated.

[0056] When the providing unit 133 (output unit 150) receives the ovulation date from the estimation unit 132, it transmits it via the communication unit 110 to the information processing terminal 200 that transmitted the glucose value information (step S604). When the information processing terminal 200 receives information indicating the ovulation date from the information processing device 100, it displays the information indicating the ovulation date. The woman 30 can see this and recognize her own ovulation date, which can be used, for example, for fertility planning.

[0057] The above explains the process for estimating the ovulation date.

[0058] <Specific examples of the correspondence between glucose level fluctuations and ovulation day> Figures 7 to 10 are graphs showing examples of glucose levels measured in four female subjects.

[0059] Figure 7 is a graph showing the fluctuations in glucose levels measured over one month for the first woman. Figure 8 is a graph showing the fluctuations in glucose levels measured over one month for the second woman. Figure 9 is a graph showing the fluctuations in glucose levels measured over one month for the third woman. Figure 10 is a graph showing the fluctuations in glucose levels measured over one month for the fourth woman.

[0060] In the graphs shown in Figures 7 to 10, the horizontal axis represents the day, and the vertical axis represents the amount of glucose secretion. Each plot of glucose value for each day represents the average amount of glucose secretion for each time period. The circular plots represent the average glucose value measured between 0:00 and 6:00 each day. The square plots represent the average glucose value measured between 6:00 and 24:00 each day. The triangular plots represent the average glucose value measured from 0:00 to 24:00, i.e., for the entire day.

[0061] Arrow 71 in the graph shown in Figure 7 indicates the ovulation day. That is, the first woman ovulated on August 20, 2024. From the fluctuations in the circular plots in Figure 7, it can be confirmed that the average glucose value gradually decreased from August 16 to August 20, as shown by arrow 72. The square and triangular plots include glucose value information from the first woman's daily life, that is, information that includes increases in glucose value due to eating and drinking, etc., and therefore it is not suitable for qualitatively observing fluctuations in glucose value.

[0062] Arrow 81 in the graph shown in Figure 8 indicates the ovulation day. That is, the second woman's ovulation day was August 21, 2024. From the fluctuations of the circular plots in Figure 8, as shown by arrow 82, it can be confirmed that the average glucose value gradually decreased overall from August 15 to August 22, although there were some ups and downs. The square and triangular plots include glucose value information from the second woman's daily life, that is, information that includes increases in glucose value due to eating and drinking, etc., and therefore it is not suitable for looking at qualitative fluctuations in glucose value.

[0063] Arrow 91 in the graph shown in Figure 9 indicates the ovulation day. That is, the third woman ovulated on August 23, 2024. From the fluctuations in the circular plots in Figure 9, as shown by arrow 92, it can be seen that the average glucose value gradually decreased from August 14 to August 23. A large increase can be seen on August 21, which is due to the woman having intercourse during the night. The square and triangular plots include glucose value information from when the third woman was going about her daily life, that is, information that includes increases in glucose value due to eating and drinking, etc., and therefore it is not suitable for seeing qualitative fluctuations in glucose value.

[0064] Arrow 101 in the graph shown in Figure 10 indicates the ovulation day. That is, for the fourth woman, ovulation occurred on August 13, 2024. From the fluctuations of the circled plots in Figure 10, it can be seen that the average glucose level gradually decreased from August 7 to August 13, as shown by arrow 102. This can be seen because the fourth woman has a more nocturnal lifestyle than the other women, and her glucose levels measured between midnight and 6 am are sometimes higher than those of the other averages. However, even for such a woman, a gradual decrease in glucose levels can be observed starting a few days before ovulation.

[0065] As these results show, glucose secretion gradually decreases in all women starting a few days before ovulation. Therefore, by sequentially acquiring glucose level information from women 30 and detecting a gradual decrease in the fluctuations of these glucose levels, it is possible to estimate that ovulation will occur a few days later.

[0066] <Output Example> Figure 11 shows an example of output from the information processing terminal 200. The output example shown in Figure 11 is merely an example, and output may be performed in other ways. Figure 11 shows an output example 1100 provided by the information processing device 100 and displayed by the information processing terminal 200, which includes at least information 1102 indicating the next ovulation day estimated by the information processing device 100. In addition, as shown in Figure 11, a graph 1101 showing the fluctuations in the glucose value of the woman 30, which is the basis for predicting the ovulation day, may also be output. Furthermore, if the woman 30 is measuring her basal body temperature, the fluctuations in her basal body temperature may also be output.

[0067] <Summary> The information processing device 100 according to this embodiment can estimate the ovulation day a few days before it occurs by observing the actual glucose level fluctuations of the woman 30. Therefore, it can be useful for the woman 30's efforts to conceive.

[0068] <Supplement> The information processing apparatus according to the above embodiment is not limited to the above embodiment, and each configuration may be realized by other methods. Various modifications will be described below.

[0069] (1) In the above embodiment, the information processing device 100 receives glucose value information 21 from the information processing terminal 200 of the woman 30, and transmits ovulation day information 31 predicting the next ovulation day of the woman 30 to the information processing terminal 200 of the woman 30 for display, thereby notifying the woman 30 of her predicted ovulation day. However, this is not limited to this. The prediction of the ovulation day shown in the above embodiment may also be realized by a native application that operates on the information processing terminal 200 alone. The native application may implement each function of the information processing device 100 through an application. Furthermore, when implemented by an application, the prediction of the ovulation day shown in the above embodiment may be realized through cooperation between the application operating on the information processing terminal 200 and the information processing device 100. In other words, the prediction of the ovulation day shown in the above embodiment may be realized by a browser-type, native application-type, or client-server-type application.

[0070] (2) In the above embodiment, the information processing device 100 learns the fluctuations in the glucose value of woman 30 before predicting the next ovulation day. However, the information processing device 100 may predict the next ovulation day without learning. That is, the process shown in Figure 5 of the above embodiment may be omitted. Since the learning model of the information processing device 100 is a model that has learned the relationship between the fluctuations in glucose value and ovulation days of many women, it is also possible to predict the ovulation day of woman 30 by providing glucose value information. However, learning the glucose value information of woman 30 in advance can improve the accuracy of the ovulation day prediction.

[0071] (3) In the above embodiment, the ovulation day is estimated using glucose value information obtained from the woman 30 between 0:00 and 6:00. However, it is preferable that this is glucose value information measured during the woman 30's sleep, and is not limited to information obtained between 0:00 and 6:00. Therefore, the information processing terminal 200 may receive input from the woman 30 regarding when she went to sleep and when she woke up, and transmit this information to the information processing device 100. The information processing device 100 may then estimate the ovulation day using only the glucose value information obtained between the transmitted bedtime and wake-up time for each day.

[0072] (4) In the above embodiment, the information processing device 100 estimates the ovulation day based on fluctuations in glucose values, but it may also use information other than glucose values ​​as the basis for the determination. For example, in addition to glucose values, the information processing device 100 may also use information on the basal body temperature of the woman 30, as in the conventional method, to estimate the ovulation day. In this case, the information processing device 100 may use a trained model that has learned the relationship between fluctuations in the basal body temperature of the woman 30 and the ovulation day to estimate the ovulation day. For example, when using basal body temperature, it may be used as information to reinforce the ovulation day predicted using glucose values. Reinforcement means, for example, if the estimation based on basal body temperature deviates from the estimation based on glucose values, the ovulation day estimated based on glucose values ​​may be corrected to the ovulation day estimated based on basal body temperature, or corrected towards the ovulation day estimated based on basal body temperature.

[0073] (5) In the above embodiment, the method for estimating and providing a woman's ovulation day based on the glucose value in the information processing device is to have the processor of the information processing device 100 execute an ovulation day estimation program, etc., to estimate and provide the day. However, this may be realized in the device by logic circuits (hardware) or dedicated circuits formed on an integrated circuit (IC (Integrated Circuit) chip, LSI (Large Scale Integration)), etc. Furthermore, these circuits may be realized by one or more integrated circuits, and the functions of the multiple functional units shown in the above embodiment may be realized by a single integrated circuit. Depending on the degree of integration, LSIs may also be called VLSI, super LSI, ultra LSI, etc.

[0074] The ovulation date estimation program described above may be recorded on a processor-readable recording medium, and the recording medium can be a "non-temporary tangible medium," such as tape, disk, card, semiconductor memory, or programmable logic circuit. Furthermore, the ovulation date estimation program may be supplied to the processor via any transmission medium capable of transmitting it (such as a communication network or broadcast wave). In other words, for example, the ovulation date estimation program may be downloaded and executed from a network using an information processing device such as a smartphone. The present invention can also be realized in the form of a data signal embedded in a carrier wave, where the ovulation date estimation program is embodied by electronic transmission.

[0075] The ovulation estimation program described above can be implemented using scripting languages ​​such as ActionScript and JavaScript®, or object-oriented programming languages ​​such as Objective-C, Java®, C++, Python®, and R.

[0076] (6) The various configurations shown in the above embodiments and supplements may be combined as appropriate. [Explanation of Symbols]

[0077] 100 Information Processing Devices 110 Communications Department 120 Input section 130 Control Unit 131 Acquisition Department 132 Estimation Department 133 Output section 134 Learning Department 140 Storage section 141 Ovulation Day Estimation Program 150 Output section 200 Information Processing Terminals 300 sensors

Claims

1. An acquisition unit that acquires information on the fluctuations in a woman's glucose levels over a predetermined period of time, An estimation unit that estimates the ovulation day of the woman based on the aforementioned fluctuation information, The system includes a providing unit that, before the ovulation date estimated by the estimation unit, provides the woman's information processing terminal with ovulation date information indicating the predicted ovulation date for use in the woman's fertility treatment, The estimation unit receives information from the woman indicating when she went to sleep and when she woke up, averages the glucose value fluctuation information for the time period from going to sleep to waking up during the predetermined period on a daily basis, and estimates the woman's ovulation day based on the averaged glucose value fluctuation information. Information processing device.

2. The estimation unit estimates that the timing at which the glucose level gradually decreases and then gradually increases during the predetermined period is the woman's ovulation day. The information processing apparatus according to feature 1.

3. The acquisition unit sequentially acquires information on the fluctuations in the woman's glucose level, The estimation unit detects that the glucose value fluctuation information acquired sequentially by the acquisition unit is gradually decreasing, and estimates the next ovulation day. The information processing apparatus according to feature 2.

4. The estimation unit uses the glucose value fluctuation information of multiple women as explanatory variables and the ovulation day for each fluctuation information as the target variable. The estimation unit then inputs the fluctuation information acquired by the acquisition unit into a trained model and estimates the ovulation day of the woman corresponding to the fluctuation information acquired by the acquisition unit. The information processing apparatus according to feature 1.

5. The aforementioned predetermined period is a period of at least 28 days. The information processing apparatus according to feature 1.

6. The acquisition unit also acquires the woman's body temperature information. The estimation unit further estimates the woman's ovulation day by also using the woman's body temperature information. The information processing apparatus according to feature 1.

7. Computers An acquisition step to obtain information on the fluctuations in a woman's glucose levels over a predetermined period, An estimation step to estimate the ovulation day of the woman based on the aforementioned fluctuation information, Before the ovulation day estimated in the estimation step, an output step is performed in which ovulation day information indicating the predicted ovulation day is output to the woman's information processing terminal for use in the woman's fertility treatment. The estimation step involves receiving information from the woman indicating when she went to sleep and when she woke up, averaging the glucose value fluctuations during the time between going to sleep and waking up within the predetermined period on a daily basis, and estimating the woman's ovulation day based on the averaged glucose value fluctuations. How to estimate ovulation day.

8. On the computer, A function to acquire information on the fluctuations in glucose levels of women over a predetermined period, Based on the aforementioned fluctuation information, an estimation function is provided to estimate the ovulation day of the woman, The estimation function provides an output function that outputs ovulation date information indicating the predicted ovulation date to the woman's information processing terminal for use in the woman's fertility treatment, before the ovulation date estimated by the estimation function. The estimation function receives input from the woman indicating when she goes to sleep and when she wakes up, averages the glucose value fluctuations during the time between going to sleep and waking up within the predetermined period on a daily basis, and estimates the woman's ovulation day based on the averaged glucose value fluctuations. Ovulation day estimation program.

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