Information processing device, method, program, and system

Abstract

To provide technology with which it is possible to propose advice that is suitable for various PMS symptoms.SOLUTION: A program according to one embodiment of the present disclosure causes a computer to function as: means for acquiring first information based on a user's active input; means for acquiring second information based on sensing on a physical quantity that changes with at least one of the physiological phenomenon and the motion of the user's body; means for estimating the user's premenstrual syndrome (PMS) on the basis of the first and second information; and means for determining the advice presented to the user, on the basis of the estimation result of the user's PMS symptom.SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] The present disclosure relates to an information processing device, a method, a program, and a system. [Background technology]

[0002] Before menstruation, women may experience a condition called premenstrual syndrome (PMS).

[0003] Patent Document 1 proposes a lighting control system that aims to alleviate the symptoms of PMS depending on the severity of PMS. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2017-51375 A Summary of the Invention [Problem to be solved by the invention]

[0005] The lighting control system of Patent Document 1 controls the brightness, color temperature, and exposure time of lighting according to the severity of the user's PMS. However, various symptoms are known as PMS, and while controlling the brightness, color temperature, and exposure time of lighting may be effective in alleviating certain symptoms, it may not be effective in alleviating other symptoms.

[0006] An object of the present disclosure is to provide technology that can suggest appropriate advice for various PMS symptoms. [Means for solving the problem]

[0007] A program of one embodiment of the present disclosure causes a computer to function as a means for acquiring first information based on active input from a user, a means for acquiring second information based on sensing of a physical quantity that changes due to at least one of a physiological phenomenon or movement of the user's body, a means for estimating the user's PMS (Premenstrual Syndrome) symptoms based on the first information and the second information, and a means for determining advice to be presented to the user based on the estimation result of the user's PMS symptoms. [Brief description of the drawings]

[0008] [Figure 1] 1 is a block diagram showing a configuration of an information processing system according to an embodiment of the present invention; [Diagram 2] 1 is a block diagram showing a configuration of a wearable device according to an embodiment of the present invention. [Diagram 3] FIG. 2 is a block diagram showing a configuration of a client device according to the present embodiment. [Figure 4] FIG. 2 is a block diagram showing a configuration of a server according to the present embodiment. [Diagram 5] FIG. 1 is an explanatory diagram of one aspect of the present embodiment. [Figure 6] FIG. 4 is a diagram showing the data structure of a symptom / advice database according to the present embodiment. [Figure 7] FIG. 13 is a diagram illustrating an example of a hierarchical structure of symptom information. [Figure 8] FIG. 4 is a diagram showing a data structure of an advice history database according to the present embodiment. [Figure 9] 4 is a flowchart of information processing according to the present embodiment. [Figure 10] 4 is a flowchart of a first example of a PMS symptom estimation process in the information processing of this embodiment. [Figure 11] 11A to 11C are diagrams illustrating examples of screens displayed in the information processing of the present embodiment. [Figure 12] 11A to 11C are diagrams illustrating examples of screens displayed in the information processing of the present embodiment. [Figure 13] 11A to 11C are diagrams illustrating examples of screens displayed in the information processing of the present embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. In the drawings for explaining the embodiment, the same components are generally designated by the same reference numerals, and the repeated description will be omitted.

[0010] (1) Information Processing System Configuration The configuration of the information processing system will be described below. Fig. 1 is a block diagram showing the configuration of the information processing system according to the present embodiment.

[0011] As shown in FIG. 1, the information processing system 1 includes a wearable device 10, a client device 30, and a server 50. The wearable device 10 and the client device 30 are connected via a wireless link such as Bluetooth (registered trademark) or a communication cable. The client device 30 and the server 50 are connected via a network (eg, the Internet or an intranet) NW.

[0012] The wearable device 10 is an example of an information processing device that is configured to be wearable on the body of a user. The wearable device 10 is, for example, a smart watch, a smart ring, or smart glasses.

[0013] The client device 30 is an example of an information processing device that transmits a request to the server 50. The client device 30 is, for example, a smartphone, a tablet terminal, or a personal computer.

[0014] The server 50 is an example of an information processing device that provides the client device 30 with a response in response to a request transmitted from the client device 30. The server 50 is, for example, a server computer.

[0015] (1-1) Client device configuration The configuration of the client device will now be described with reference to Fig. 2, which is a block diagram showing the configuration of the wearable device according to this embodiment.

[0016] 2, the wearable device 10 includes a storage device 11, a processor 12, an input / output interface 13, and a communication interface 14. The wearable device 10 is connected to a display 21 and a sensor 22.

[0017] The storage device 11 is configured to store programs and data. The storage device 11 is, for example, a combination of a Read Only Memory (ROM), a Random Access Memory (RAM), and a storage (for example, a flash memory or a hard disk).

[0018] The programs include, for example, the following programs: ·OS (Operating System) programs · Programs for applications that perform information processing (e.g., health care applications)

[0019] The data includes, for example, the following data: Databases referenced in information processing Data obtained by performing information processing (i.e., the results of performing information processing)

[0020] The processor 12 is a computer that realizes the functions of the wearable device 10 by running a program stored in the storage device 11. The processor 12 is, for example, at least one of the following: ·CPU(Central Processing Unit) ·GPU(Graphic Processing Unit) ·ASIC(Application Specific Integrated Circuit) ·FPGA(Field Programmable Array)

[0021] The input / output interface 13 is configured to obtain information (e.g., a user's instructions or sensing results) from an input device connected to the wearable device 10, and to output information (e.g., an image) to an output device connected to the wearable device 10.

[0022] The input device is, for example, a sensor 22, a keyboard, a pointing device, a touch panel, a physical button, or a combination thereof. The output device is, for example, a display 21, a speaker, or a combination thereof.

[0023] The communication interface 14 is configured to control communication between the wearable device 10 and an external device (eg, a client device 30).

[0024] The display 21 is configured to display an image (a still image or a moving image). The display 21 is, for example, a liquid crystal display or an organic EL display.

[0025] The sensor 22 measures (senses) a physical quantity that changes due to at least one of a physiological phenomenon or a movement of the user's body. The sensor 22 may include at least one of the following, for example. Heart rate sensor Oxygen saturation sensor Electrodermal Activity (EDA) sensor Temperature Sensor Acceleration sensor Gyro sensor GPS (Global Positioning System) module ·Altimeter Ambient light sensor The sensing cycle of the sensor 22 may be determined for each item. For example, the sensor 22 may perform sensing every minute, every five minutes, or every day. The sensor 22 may also perform sensing only while the user is in a specific state (e.g., during sleep, during physical activity, etc.).

[0026] (1-2) Client device configuration The configuration of the client device will now be described with reference to Fig. 3, which is a block diagram showing the configuration of the client device of this embodiment.

[0027] 3, the client device 30 includes a storage device 31, a processor 32, an input / output interface 33, and a communication interface 34. The client device 30 is connected to a display 41.

[0028] The storage device 31 is configured to store programs and data, and is, for example, a combination of a ROM, a RAM, and a storage (for example, a flash memory or a hard disk).

[0029] The programs include, for example, the following programs: -OS programs Programs for applications that process information (e.g. web browsers or health care applications)

[0030] The data includes, for example, the following data: Databases referenced in information processing Results of information processing

[0031] The processor 32 is a computer that realizes the functions of the client device 30 by running a program stored in the storage device 31. The processor 32 is, for example, at least one of the following: ·CPU GPU ·ASIC FPGA

[0032] The input / output interface 33 is configured to obtain information (e.g., a user's instruction or a sensing result) from an input device connected to the client device 30, and to output information (e.g., an image) to an output device connected to the client device 30.

[0033] The input device is, for example, a keyboard, a pointing device, a touch panel, a sensor, a physical button, or a combination thereof. The output device is, for example, a display 41, a speaker, or a combination thereof.

[0034] The communication interface 34 is configured to control communication between the client device 30 and an external device (eg, the wearable device 10, or the server 50).

[0035] The display 41 is configured to display an image (a still image or a moving image). The display 41 is, for example, a liquid crystal display or an organic EL display.

[0036] (1-3) Server configuration The configuration of the server will now be described with reference to Fig. 4, which is a block diagram showing the configuration of the server according to this embodiment.

[0037] As shown in FIG. 4, the server 50 includes a storage device 51, a processor 52, an input / output interface 53, and a communication interface .

[0038] The storage device 51 is configured to store programs and data, and is, for example, a combination of a ROM, a RAM, and a storage (for example, a flash memory or a hard disk).

[0039] The programs include, for example, the following programs: -OS programs Application programs that perform information processing

[0040] The data includes, for example, the following data: Databases referenced in information processing Results of information processing

[0041] The processor 52 is a computer that realizes the functions of the server 50 by starting a program stored in the storage device 51. The processor 52 is, for example, at least one of the following: ·CPU GPU ·ASIC FPGA

[0042] The input / output interface 53 is configured to obtain information (eg, a user's instruction) from an input device connected to the server 50, and to output information (eg, an image) to an output device connected to the server 50.

[0043] The input device is, for example, a keyboard, a pointing device, a touch panel, or a combination thereof. The output device is, for example, a display.

[0044] The communication interface 54 is configured to control communication between the server 50 and an external device (eg, the wearable device 10, or the client device 30).

[0045] (2) One aspect of the embodiment An embodiment of the present invention will now be described with reference to Fig. 5, which is an explanatory diagram of an embodiment of the present invention.

[0046] As shown in FIG. 5, the wearable device 10 uses the sensor 22 to sense a physical quantity that changes due to at least one of a physiological phenomenon or a movement of the body of the user US1.

[0047] For example, when the client device 30 determines that the current date and time is within the user's PMS period, the client device 30 acquires the sensing result from the wearable device 10. Here, the PMS period refers to a period during the luteal phase when PMS symptoms may occur. In addition, the client device 30 acquires active input of the user US1 as necessary, which can be used as information for determining PMS symptoms. The client device 30 transmits user information based on the sensing result (or user information based on the sensing result and the active input of the user US1) to the server 50.

[0048] The server 50 estimates the PMS symptoms of the user US1 based on the user information from the client device 30. In the process of estimating the PMS symptoms, the server 50 may make an inquiry to the user US1 via the client device 30 and obtain a response (which is an example of active input from the user US1) to estimate the PMS symptoms with high accuracy.

[0049] The server 50 determines advice to be presented to the user US1 based on the estimation result of the PMS symptoms of the user US1. Then, the server 50 presents the advice to the user US1 via the client device 30.

[0050] In this way, according to the information processing system 1 of this embodiment, various pieces of advice can be provided depending on the type or severity of the PMS symptoms of the user US1, and the behavior of the user US1 can be changed to alleviate the symptoms.

[0051] (3) Database The database of this embodiment will be described.

[0052] (3-1) Symptom / Advice Database The symptom / advice database of this embodiment will be described below. Fig. 6 is a diagram showing the data structure of the symptom / advice database of this embodiment. Fig. 7 is a diagram showing an example of the hierarchical structure of symptom information.

[0053] The symptom / advice database may be stored in the storage device 51. The symptom / advice database stores symptom / advice information. The symptom / advice information is information relating to PMS symptoms and advice for the PMS symptoms.

[0054] 6, the symptom / advice database includes an "ID" field, a "symptom" field, a "recommended action" field, and a "recommended product" field. Each field is associated with the others.

[0055] The "ID" field stores an advice ID. The advice ID is information that identifies a combination of a PMS symptom and advice for that PMS symptom.

[0056] The "symptoms" field stores symptom information. The symptom information is information about PMS symptoms. The symptom information may include information about problems caused by PMS symptoms. PMS symptoms may be defined hierarchically, for example, as shown in FIG. 7. Specifically, the major category of "PMS symptoms" may be subdivided into the medium categories of "mental symptoms," "physical symptoms," and "difficulties in daily life."

[0057] The "mental symptoms" medium category can be further divided into the following subcategories, for example: "Becoming aggressive" (e.g. becoming angry, irritable, or lashing out at others) "Becoming negative" (e.g. becoming tearful or suddenly sad) "Feeling down" (e.g. feeling depressed, hopeless, or negative about oneself) "Increased anxiety" (e.g. feeling anxious, emotionally unstable, or lacking in confidence) "Feeling confused" (e.g. being unable to make decisions or feeling out of control) "I have trouble concentrating" "Apathy" (e.g., loss of interest (in work, school, hobbies, etc.)) "I want to be alone"

[0058] The medium category of "physical symptoms" can be further divided into the following subcategories, for example: "Increased appetite" (e.g. craving certain foods (e.g. sweets)) "I'm thirsty" "Breast pain and swelling" "I get acne" "Increased vaginal discharge" "I get constipated" "Weight gain" "I have stiff shoulders" "My stomach hurts" "My stomach feels bloated" "My head hurts" "My lower back hurts" "Feeling sleepy" (e.g. sleeping more than usual) ·"insomnia" "My body is swelling" "Fatigue" (e.g. feeling tired easily or losing energy)

[0059] The medium category of "impediments to daily life" can be further divided into the following subcategories, for example: "My work efficiency has been affected." "Housework has become difficult" "My relationships with people at work have been affected" (e.g., I avoid social interactions, I get angry at people, or I start fighting) "My relationship with my family has been affected" "My relationships with friends and acquaintances have been affected."

[0060] The symptom information may be defined as information on the presence or absence or severity of a symptom for one of the major, middle, or minor categories, or a combination of such information. For example, symptom information representing a symptom falling under both "mental symptoms" and "physical symptoms," symptom information representing a symptom falling under at least one of "insomnia" or "irritability," etc. may be defined.

[0061] In the example of FIG. 6, the "Symptoms" field includes a "General" subfield, a "Mental" subfield, a "Physical" subfield, and a "Details" subfield, however, such a structure is merely exemplary.

[0062] The "General" subfield stores general information. The general information is information about the presence or absence of the major category "PMS symptoms." If any of the symptoms in the medium or minor categories are occurring, the general information has a value ("YES") indicating that "PMS symptoms" are occurring.

[0063] The "mental" subfield stores mental information. The mental information is information about the presence or absence of the medium category "mental symptoms." If the "mental symptoms" or any symptom of its subcategory occurs, the mental information has a value ("YES") indicating that the "mental symptoms" occur.

[0064] The "Physical" subfield stores physical information. The physical information is information about the presence or absence of the medium category "physical symptoms." If a "physical symptom" or any symptom of its subcategory occurs, the physical information has a value ("YES") indicating that a "physical symptom" is occurring.

[0065] The "details" subfield stores detailed information. The detailed information supplements the details of the symptom information. The detailed information indicates, for example, which symptoms of the medium or small classification are emphasized (e.g., which medium or small classification symptoms of the general PMS symptoms are matched to, or which small classification symptoms of the medium classification are matched to), or a symptom alleviation policy (e.g., whether a long-term effect is expected or a short-term effect is expected). The detailed information may be represented, for example, by a vector consisting of weights assigned to symptoms of each classification.

[0066] The "recommended action" field stores recommended action information. The recommended action information is information about an action (an example of "advice") recommended to the user for alleviating a symptom identified by the corresponding symptom information. The actions recommended to the user are, for example, taking a specific nutrient, herb, or probiotic (e.g., lactic acid bacteria such as Lactobacillus gasseri (an example of lactic acid bacteria belonging to Lactobacillus)), reducing or stopping the intake of a specific luxury item, exercising (e.g., aerobic exercise), having breakfast, getting enough sleep, warming the body, or visiting a medical institution. The recommended action information may include information on the amount of the recommended action (e.g., the amount of intake, the amount of implementation, or the implementation time). Note that the symptom information and the recommended action information do not necessarily have a 1:1 relationship. A single symptom may be associated with multiple different recommended actions. Also, a single recommended action may be associated with multiple different symptoms.

[0067] The lactic acid bacteria may include cells of lactic acid bacteria belonging to Lactobacillus, treated cells, or extracts thereof. Examples of lactic acid bacteria belonging to the Lactobacillus genus include Lactobacillus gasseri, Lactobacillus acidophilus, Lactobacillus amylovorus, Lactobacillus curvatas, Lactobacillus paragasseri, Lactobacillus plantarum, Lactobacillus brevis, Lactobacillus casei, Lactobacillus paracasei, Lactobacillus rhamnosus, and Lactobacillus helveticus. helveticus, Lactobacillus fermentum, Lactobacillus brevis, Lactobacillus salivarius, Lactobacillus reuteri, Lactobacillus johnsonii, Lactobacillus jensenii, Lactobacillus crispatus, Lactobacillus delbrueckii, Lactobacillus zeae, Lactobacillus gallinarum, Lactobacillus kefiranofaciens kefiranofaciens, Lactobacillus sakei, Lactobacillus pentosuspentosus, Lactobacillus fructivorans, Lactobacillus kunkeei, Lactobacillus mucosae, Lactobacillus oris, etc.

[0068] The "recommended product" field stores recommended product information. The recommended product information is information about a product (an example of "advice") recommended to the user for alleviating the symptoms identified by the corresponding symptom information. The recommended product may include, for example, at least one of the following: Beverages, foods, or supplements that contain functional ingredients recommended for intake based on the corresponding recommended behavior information (especially those that can be efficiently taken) Beverages (e.g., low-alcohol or non-alcoholic beverages), foods (e.g., low-sugar or high-protein foods), or supplements that serve as substitutes for recreational products that are recommended to be reduced or eliminated by the corresponding recommended behavioral information Beverages (e.g. hot beverages to warm the body or beverages suitable for replacing fluids lost during exercise), foods or supplements (e.g. protein or sleep-enhancing supplements or probiotics) that support the implementation of the recommended behavior by the corresponding recommended behavior information Note that the symptom information and the recommended product information do not necessarily have a one-to-one relationship. A single symptom may be associated with multiple different recommended products, or there may be no associated recommended product. Also, the same recommended product may be associated with multiple different symptoms.

[0069] Here, functional ingredients are a general term for nutrients and functional ingredients with health functions contained in functional foods (e.g., foods for specified health uses, foods with nutrient functions, or foods with functional claims). Nutrients contained in foods with nutrient functions include vitamins, minerals, etc. Ingredients contained in foods with functional claims include lactic acid bacteria (e.g., Lactobacillus gasseri), Bacillus subtilis, yeast, processed yeast products, and ingredients and peptides obtained from these bacteria.

[0070] The symptom / advice database may also store information not shown in FIG. 6 (e.g., information regarding recommended foods (general foods, not specific products) and their intake amounts, or recommended types of exercise and the duration of the exercise).

[0071] (3-2) Advice History Database The advice history database of this embodiment will now be described with reference to Fig. 8, which is a diagram showing the data structure of the advice history database of this embodiment.

[0072] The advice history database may be stored in the storage device 51. The advice history database stores advice history information. The advice history information is information regarding advice presented to a user and the user's situation after the advice was presented (for example, the situation regarding the implementation of the advice, the situation regarding improvement of PMS symptoms, or a combination thereof).

[0073] 8, the advice history database is constructed for each user. However, the advice history database may be constructed in common for a plurality of users, and may store information (e.g., a user ID) capable of identifying a user corresponding to each record.

[0074] 8, the advice history database includes an "advice date and time" field, a "symptom" field, an "advice ID" field, a "confirmation date and time" field, a "practice status" field, and an "improvement status" field. Each field is associated with each other.

[0075] The "advice date and time" field stores advice date and time information. The advice date and time information is information relating to the date and time when the target advice (hereinafter referred to as "target advice") was presented to the user.

[0076] The "symptoms" field stores symptom information. The symptom information is information about the user's PMS symptoms that are estimated as a prerequisite for presenting the target advice.

[0077] The "advice ID" field stores an advice ID. The advice ID is information that can identify the contents of the target advice. The advice ID can correspond to an advice ID in the symptom / advice database (FIG. 6).

[0078] The "Confirmation Date and Time" field stores confirmation date and time information. The confirmation date and time information is information regarding the date and time when the user's situation was confirmed after the target advice was presented. The user's situation may be confirmed, for example, by presenting a questionnaire to the user and receiving responses to the questionnaire. Alternatively, the user's situation may be confirmed by estimation based on various sensing results or user information acquired through collaboration with other applications or SaaS.

[0079] The "practice status" field stores practice status information. The practice status information is information regarding whether or not, or to what extent, the user has practiced the advice after the advice has been presented. The degree of practice may include, for example, the frequency of practice (e.g., daily, once a week, etc.), the amount of practice (e.g., the intake of a particular product or nutrient, etc.), or a combination thereof.

[0080] The "improvement status" field stores improvement status information. The improvement status information is information regarding the presence or absence, or the degree of improvement, of the user's PMS symptoms after the presentation of the target advice. The degree of improvement may be expressed, for example, by a Likert scale or by a score. Furthermore, the improvement status information may be confirmed for multiple types of PMS symptoms, or may be confirmed for only one type of PMS symptom (e.g., "general").

[0081] (4) Information processing The information processing of this embodiment will be described. Fig. 9 is a flowchart of the information processing of this embodiment. Fig. 10 is a flowchart of a first example of a PMS symptom estimation process in the information processing of this embodiment. Fig. 11 is a diagram showing an example of a screen displayed in the information processing of this embodiment. Fig. 12 is a diagram showing an example of a screen displayed in the information processing of this embodiment. Fig. 13 is a diagram showing an example of a screen displayed in the information processing of this embodiment.

[0082] The information processing in FIG. 9 starts when a predetermined start condition is satisfied. A first example of the start condition is that the client device 30 accepts a user instruction to start information processing from the user. A second example of the start condition is that the current date and time is within the user's PMS period. In addition, a start condition may also be imposed that a predetermined period (e.g., one day, several days, or one month) has passed since the previous information processing was performed.

[0083] When the process of FIG. 9 starts, the client device 30 executes acquisition of user information (S130). Specifically, the client device 30 may read out previously acquired user information from the storage device 31, or may receive the user information in this step. The client device 30 may obtain the user information by receiving a user instruction via an input device, or by receiving information from an external device (e.g., the wearable device 10 or the server 50). The client device 30 may obtain the user information by linking with another application or SaaS (Software as a Service). Alternatively, the client device 30 may generate the user information based on at least one of the information read out from the storage device 31 or the information received in this step.

[0084] The user information includes the following information: - Information based on active input from the user (hereinafter referred to as "first information") Information based on sensing of a physical quantity that changes due to at least one of a physiological phenomenon or movement of the user's body (hereinafter referred to as "second information")

[0085] The first information may include information regarding at least one of the following: Menstrual cycle (e.g. a log of your period dates, or your usual menstrual cycle or duration) Basic attributes (e.g., age (which may include date of birth), height, weight, body fat percentage) Lifestyle habits (e.g., intake log of fluids, food, etc.) Symptoms (which may include, for example, mental symptoms, physical symptoms, or impairments in daily life)

[0086] The symptom-related information may be a response to an inquiry from a UI (User Interface) presented by the client device 30 (for example, a selection from one of the presented symptom candidates, or free description (which may include speech)). Alternatively, the symptom-related information may be a spontaneous report by the user of the client device 30. With regard to physical symptoms, the user may make the above response or report by selecting the part of the body in which he or she feels unwell from the body picture presented by the client device 30.

[0087] The first information may be generated by detecting an operation on a UI presented by the client device 30. Alternatively, the first information may be generated based on a voice signal corresponding to a user's speech, or a voice recognition result (text) thereof, or a recognition result of a gesture performed by the user.

[0088] The second information may include information regarding at least one of the following: User activity information User biometric information User's external environment information - User's stress information (stress information can be influenced by a combination of activity information, biological information, and external environmental information)

[0089] The user activity information may include at least one of the following: Physical activity information Sleep Information

[0090] The information regarding physical activity may include information regarding at least one of the following: Physical activity (e.g. number of steps, distance traveled, or number of swimming strokes, etc.) Exercise category (may include the relevant date and time for each exercise category) Calories burned (may include calories burned per exercise category or per heart rate zone, as described below) -Time for each activity intensity (e.g. light, moderate, rest, vigorous) Biological information during physical activity (specific examples of biological information are described below)

[0091] The information relating to sleep may include information relating to at least one of the following: Bedtime (may include bedtime date and time) Wake up (may include wake up date and time) Falling asleep (may include the date and time of falling asleep) Awakening (may include awakening date and time) -Time and / or frequency of sleep Time and / or frequency of waking Time when you couldn't fall asleep · Number of times per sleep stage (e.g. REM, deep or light sleep, etc.) Time per sleep stage (which may include statistics (e.g., averages) over a number of days (e.g., 30 days)) - The sleep stages identified during your sleep, as well as the start time and duration of each sleep stage Time in bed (time from going to bed to waking up) -Sleep efficiency (the percentage of time spent asleep compared to time in bed) Time from awakening (i.e. final awakening) to getting up Biological information during sleep (specific examples of biological information will be described later) Movement during sleep (e.g. acceleration, angular velocity, or analysis of these) Sleep results (e.g. sleep quality score, recovery score, sleep duration score, sleep pattern score)

[0092] The sleep quality score depends on the time spent in deep sleep and REM sleep, and the longer the time spent in these, the higher the score. The recovery score indicates how relaxed the user was while sleeping, and the lower the heart rate and body movement during sleep, and the shorter the time spent in light sleep, the higher the score. The sleep duration score is higher, for example, the longer the hours slept over the past week. The sleep pattern score is an overall indication of the quality of the user's sleep pattern, and the higher the sleep quality score, recovery score, and sleep duration score, the higher the score.

[0093] The user's biometric information may include information regarding at least one of the following: Heart rate (e.g., time spent in a certain heart rate zone, changes in the interval between each heartbeat while sleeping (heart rate variability), resting heart rate (which may include statistics (e.g., mean and standard deviation)), or beats per minute) Oxygen saturation (e.g., may include estimated oxygen variation during sleep, average, lower limit, or upper limit) Maximum oxygen intake Electrodermal activity Body temperature (which may include, for example, average skin temperature while sleeping) Respiration rate (which may include, for example, statistics (e.g., mean, standard deviation) or SNR (Signal Noise Ratio) of respiration rate by sleep stage (especially during deep sleep stages) or upon awakening)

[0094] Heart rate variability during sleep is indicative of the user's autonomic nervous activity. Heart rate variability during sleep can include at least one of the following: RMSSD during sleep -HF component of heart rate variability during sleep -LF component of heart rate variability during sleep Fluctuations in heart rate during sleep (e.g. histogram) Randomness of heartbeats during sleep (e.g., entropy) Heart rate coverage during sleep (e.g., the number of data points in an interval multiplied by the average beat duration of the interval divided by the interval length (e.g., 300 seconds)) - LF / HF ratio of heart rate during sleep (an index of balance between sympathetic and parasympathetic nervous activity) - HF / (LF+HF) of heart rate during sleep (an index showing the proportion of parasympathetic nervous activity)

[0095] RMSSD is the square root of the average of the squares of the differences between consecutive adjacent RR intervals. A larger RMSSD during sleep suggests that the user's parasympathetic nervous activity is dominant. HF is the high frequency component of heart rate variability (HRV). HF during sleep reflects the user's parasympathetic nervous activity. LF is the low frequency component of HRV. LF during sleep reflects the user's sympathetic nervous activity. Heart rate fluctuation during sleep represents the amplitude of heart rate variability, as described below.

[0096] In humans, parasympathetic nervous activity decreases during the late luteal phase when PMS symptoms occur. Therefore, PMS symptoms can be estimated by comparing parasympathetic nervous activity at rest with parasympathetic nervous activity during the late luteal phase. It is also known that autonomic nervous system disorders are related to PMS symptoms. Furthermore, people who have PMS symptoms or PMDD (Premenstrual Dysphoric Disorder) symptoms have smaller heart rate fluctuations than people who do not have the symptoms, so PMS symptoms can also be estimated from heart rate fluctuations.

[0097] Here, the heart rate fluctuation refers to the degree of dispersion of the heart rate interval (e.g., the RR interval). As an example, the heart rate fluctuation is the variance, standard deviation, or range of the heart rate. In a state without stress (in other words, a state in which parasympathetic activity is normal), the heart rate fluctuation is greater than in a state with stress (in other words, a state in which parasympathetic activity is reduced). For example, suppose a histogram of heart rate intervals is created. In a state without stress, a histogram with low peaks and a wide width is obtained. Conversely, in a state with stress, a histogram with high peaks and a narrow width is obtained.

[0098] Resting heart rate changes with age and the menstrual cycle. Therefore, menstruation and PMS periods can be estimated based on resting heart rate. Specifically, menstruation and PMS periods can be estimated based on the average heart rate during the follicular phase (about two weeks from the start of menstruation to the day of ovulation) and the luteal phase (about two weeks after ovulation until the start of menstruation).

[0099] The user's external environment information may include, for example, at least one of the following: ·Altitude ·temperature ·Humidity Ambient light (including total exposure time and total light exposure per unit time)

[0100] The user's stress information may include, for example, at least one of the following: Responsiveness score Sleep pattern score Activity balance score Overall stress score

[0101] The reactivity score represents the degree to which the user's body exhibited a fight-or-flight response. The reactivity score can be estimated, for example, based on information about the user's heart rate and information about electrodermal activity. Specifically, in a stressful situation, the heart rate increases and the sweat level changes due to autonomic nerve activity, so the user's stress can be evaluated, for example, based on at least one of the following: Heart rate variability during deep sleep Increase in resting heart rate (compared to the previous day) Sleeping heart rate (HR) exceeding resting heart rate (RHR) (e.g., for one night) Electrodermal activity based on EDA scans (e.g., for a day)

[0102] The sleep pattern score is as described above. Poor quality sleep (i.e. short or shallow sleep) negatively impacts your ability to cope with stress, and stress reduces the quality of your sleep.

[0103] The activity balance score quantitatively represents the balance of the user's physical activity. Too little physical activity can lead to an increase in stress hormones, while too much physical activity can lead to fatigue and physical stress. If the user's physical activity indicators (e.g., at least one of the distance traveled, acceleration, number of steps, or time spent moving in a way that increases the heart rate) are within the appropriate range, the activity balance score is determined to be high. On the other hand, if the user's physical activity indicators are outside the appropriate range, the activity balance score is determined to be low.

[0104] A total stress score may be calculated based on at least one of the reactivity score, the sleep pattern score, or the activity balance score.

[0105] As another example of stress assessment, the client device 30 may assess stress based on the user's facial expression or voice characteristics. A trained model may be used for such stress assessment. The trained model may be constructed to return output data representing the user's mental state (e.g., happy, depressed, calm, etc.) in response to input data based on, for example, an image or voice of the user.

[0106] The second information may be based on sensing results performed by the sensor 22 of the wearable device 10, or may be based on sensing results performed by a sensor built into the client device 30 or a sensor external to the client device 30.

[0107] After step S130, the client device 30 executes an analysis request (S131). Specifically, the client device 30 requests the server 50 to analyze the user's PMS symptoms. The client device 30 generates an analysis request based on the user information acquired in step S130, and transmits the analysis request to the server 50. The analysis request may include, for example, the user information itself, or information that can identify the user information (for example, an index, or information indicating a resource in which the user information is stored (for example, a Universal Resource Locator (URL))).

[0108] After step S131, the server 50 executes estimation of PMS symptoms (S150). Specifically, the server 50 receives the analysis request transmitted in step S131. The server 50 acquires user information based on the information included in the analysis request. The server 50 estimates the user's PMS symptoms based on the user information. As an example, the server 50 estimates the type of the user's PMS symptoms. Furthermore, the server 50 may estimate the severity of the user's PMS symptoms for the estimated type based on the user information. The server 50 may evaluate at least one of the user's physical activity, sleep, or stress based on the second information, and estimate the user's PMS symptoms based on the result of the evaluation.

[0109] Here, the server 50 may obtain user information (particularly the second information) for the user during the PMS period and the period other than the PMS period, and estimate the PMS symptoms based on both pieces of user information. For example, the server 50 can determine whether the user's sleep disorder is chronic or specific to the PMS period by comparing sleep during the PMS period with sleep during the period other than the PMS period. Similarly, the server 50 can determine whether the user's stress is chronic or specific to the PMS period by comparing stress during the PMS period with stress during the period other than the PMS period.

[0110] As a first example of estimating PMS symptoms (S150), the server 50 identifies at least one candidate for the user's PMS symptoms (e.g., a candidate for type or a candidate for severity) based on user information (particularly the second information). Then, the server 50 presents the identified candidates to the user via the client device 30. The server 50 receives first information corresponding to an answer to the validity of the presented candidates from the client device 30, and estimates the type of the user's PMS symptoms according to the first information. That is, in this example, the server 50 estimates the type of the user's PMS symptoms in stages.

[0111] As shown in FIG. 11, the server 50 executes estimation of candidates for PMS symptoms (S1501). Specifically, the server 50 estimates at least one candidate for PMS symptoms based on at least the second information. The server 50 may evaluate at least one of the user's physical activity, sleep, or stress based on the second information, and estimate the candidate based on the result of the evaluation. The server 50 transmits information capable of identifying the estimated candidate to the client device 30.

[0112] The server 50 can estimate multiple candidates, not just one candidate. For example, the server 50 may quantitatively evaluate the validity of the multiple candidates as a PMS symptom of the user, and estimate a predetermined number of candidates in descending order of evaluation value, or estimate candidates with evaluation values ​​equal to or greater than a threshold value. When the number of estimated candidates exceeds an upper limit, random sampling may be performed on some or all of the estimated candidates.

[0113] As a first example of estimating candidates for PMS symptoms (S1501), the server 50 estimates candidates for PMS symptoms of the user based on information about the user's sleep. If the user is able to sleep long and of good quality, especially during the luteal phase, it can be estimated that the user is not experiencing sleep disorders among the PMS symptoms. On the other hand, if the user is not able to sleep long and of good quality, especially during the luteal phase, it can be estimated that the user is experiencing sleep disorders, and as a result, it can be estimated that the user is experiencing severe symptoms such as fatigue or daytime sleepiness.

[0114] The server 50 may evaluate the length and quality of the user's sleep based on information on at least one of the user's heart rate, heart rate variability, oxygen saturation, and sleep behavior, and identify candidates for the user's PMS symptoms based on the evaluation results. The server 50 may evaluate, for example, at least one of the following indicators: Total sleep time Number of deep sleeps Time or percentage of deep sleep Bedtime (what time did you go to bed)

[0115] As a second example of estimating candidates for PMS symptoms (S1501), the server 50 identifies candidates for the user's PMS symptoms based on the user's stress information. It is known that there is a correlation between stress and the severity of PMS symptoms. In other words, it can be estimated that the user is experiencing PMS symptoms or that the user's PMS symptoms are severe under stressful circumstances.

[0116] After step S1501, the client device 30 executes the presentation of candidates (S1311). Specifically, the client device 30 displays information on the candidates estimated in step S1501 on the display 41. As an example, the client device 30 displays on the display 41 the screen of FIG.

[0117] The screen in Fig. 11 is an example of a screen for requesting an answer to the validity of a candidate. The screen in Fig. 11 includes objects J40 and J41.

[0118] Object J40 displays the question for which the user is requested to answer. In the example of Figure 11, an answer is requested as to the validity of the symptom candidates.

[0119] The object J41 receives an answer instruction from the user. The user selects the object J41 that corresponds to the content of the answer that he or she intends to give.

[0120] After step S1311, the client device 30 receives a response (S1312). Specifically, the client device 30 acquires, in response to a user instruction, information indicating which of the candidates presented in step S1311 was selected or which was not selected, as an answer (corresponding to first information). As an example, the client device 30 acquires, in response to a selection of any of the objects J41 on the screen of FIG. 11, information indicating the selected object J41 or the non-selected object J41 as an answer. The client device 30 transmits the accepted answer to the server 50.

[0121] After step S1312, the server 50 executes identification of PMS symptoms (S1502). Specifically, the server 50 receives the answer transmitted by the client device 30 in step S1312. Based on the received answer, the server 50 identifies any of the candidates estimated in step S1501 as the PMS symptom of the user.

[0122] If the user's answer indicates that none of the candidates are valid, the server 50 may re-execute the estimation of candidates for PMS symptoms (S1501). In this case, the server 50 excludes the candidates determined by the user to be invalid and estimates new candidates.

[0123] As a second example of estimating PMS symptoms (S150), the server 50 estimates the user's PMS symptoms (e.g., type, severity, or a combination thereof) based on the user information (i.e., the first information and the second information). That is, in this example, the server 50 estimates the user's PMS symptoms directly (i.e., without requesting an answer to the validity of candidates for the user's PMS symptoms).

[0124] After step S150, the server 50 executes advice determination (S151). Specifically, the server 50 determines advice to be presented to the user based on the results of the PMS symptoms estimated in step S150 (that is, the estimation results).

[0125] As an example, the server 50 refers to the symptom / advice database (FIG. 6) to identify a record having symptom information matching the estimation result. The server 50 determines advice based on at least one of recommended action information and recommended product information included in the identified record.

[0126] Note that the server 50 may identify multiple records, not limited to one record. When identifying multiple records, the server 50 may identify all records having symptom information whose suitability for the estimation result exceeds a threshold, or may identify a predetermined number of records in descending order of suitability. When identifying multiple records, the server 50 may narrow down the records to be referenced for determining advice (hereinafter, referred to as "reference records").

[0127] The records may be narrowed down by random sampling, or by filtering based on user information (which may include the user's advice history information). Information on the user's practice status or changes in condition after practicing advice previously presented to the user is useful for narrowing down the records. As a first example, a record corresponding to advice previously presented to the user that the user continues to practice but has not shown any improvement in condition may be excluded from the reference records since it may not suit the user's constitution. As a second example, advice previously presented to the user that the user did not practice may be difficult for the user to practice, so it may be set to be less likely to be selected as a reference record (for example, by decreasing the probability of being selected in random selection). As a third example, advice previously presented to the user that the user practiced and showed an improvement in condition may be set to be more likely to be selected as a reference record (for example, by increasing the probability of being selected in random selection) since it may suit the user's constitution.

[0128] The server 50 may estimate the user's habits based on the user information and narrow down the records based on the estimation result. The server 50 estimates whether the user has an aerobic exercise habit based on information on at least one of the user's steps over a period of more than one day or the user's heart rate (particularly, the time in a heart rate zone with a high heart rate). It is believed that PMS symptoms can be alleviated in the long term by making an aerobic exercise habit. The server 50 may exclude records corresponding to advice recommending aerobic exercise for a user who is estimated to have an aerobic exercise habit from records to be referenced for deciding advice, or may set the records to be less likely to be selected as records to be referenced for deciding advice.

[0129] After step S151, the server 50 executes an analysis response (S152). Specifically, the server 50 generates an analysis response including information capable of identifying the advice determined in step S151. The server 50 transmits the analysis response to the client device 30.

[0130] After step S152, the client device 30 executes the presentation of advice (S132). Specifically, the client device 30 receives the analysis response transmitted by the server 50 in step S152. The client device 30 presents advice to the user based on the received analysis response. As an example, the client device 30 displays the advice on the display 41.

[0131] The client device 30 may display the screen of Fig. 12 on the display 41. The screen of Fig. 12 is an example of a screen showing advice regarding a recommended action. The screen of Fig. 12 includes objects J42, J43, J44a, and J44b.

[0132] Object J42 displays a summary of recommended actions for the user. Object J43 displays details of the recommended action to the user (eg, the relationship of the action to PMS symptoms).

[0133] The object J43a accepts a user instruction to present information on foods recommended to the user. When the object J43a is selected, the client device 30 may display on the display 41 a screen (not shown) showing foods recommended to the user.

[0134] The object J43b accepts a user instruction to present information on products recommended to the user. When the object J43b is selected, the client device 30 may display on the display 41 a screen (e.g., FIG. 13) showing products recommended to the user.

[0135] The screen of Figure 13 includes objects J45, J46, J46a, J46b, J46c, J46d, J46e, J46f, and J46g.

[0136] Object J45 displays an overview of products recommended to the user. Object J46 displays individual information about products recommended to the user.

[0137] The object J46a displays the name of the product (hereinafter referred to as the "target product") that corresponds to the object J46. Object J46b displays an image of the target product.

[0138] The object J46c accepts a user instruction to present a detailed description of the target product. When the object J46c is selected, the client device 30 may expand and display the detailed description of the target product, or may display a screen (not shown) showing the detailed description on the display 41. Alternatively, an object displaying the detailed description of the target product may be placed instead of the object J46c.

[0139] The object J46d accepts a user instruction to purchase the target product. When the object J46d is selected, the client device 30 may access an EC site where the target product can be purchased, add the target product to a cart, or make a purchase payment. Alternatively, when the object J46d is selected, the client device 30 may guide a store where the target product can be purchased.

[0140] The object J46e accepts a user instruction to start a subscription purchase of the target product. When the object J46e is selected, the client device 30 may access a web page for applying for the subscription purchase of the target product, or may display on the display 41 a screen for making a payment for the subscription purchase of the target product.

[0141] The object J46f accepts a user instruction to obtain a coupon that can be used to purchase a target product. The coupon may be set to be usable at a brick-and-mortar store or an EC site. When the object J46f is selected, the client device 30 may access a web page for obtaining the coupon, or may display a coupon screen on the display 41.

[0142] The object J46g accepts a user instruction to view reviews of the target product. When the object J46g is selected, the client device 30 may access a web page for viewing the reviews, or may display the review information on the display 41.

[0143] (5) Summary As described above, the server 50 of the present embodiment estimates the PMS symptoms of the user based on the first information and the second information described above, and determines advice to be presented to the user based on the estimation result. This makes it possible to provide various advice according to the type or severity of the PMS symptoms of the user US1, and to change the behavior of the user US1 to alleviate the symptoms.

[0144] The server 50 may estimate at least one candidate PMS symptom based on at least the second information, and identify the user's PMS symptoms according to the first information corresponding to the user's response to the validity of the candidate. This reduces the burden on the user in answering questions, while improving the accuracy of estimating PMS symptoms and providing appropriate advice.

[0145] The server 50 may determine advice to be presented to the user based on the first information on the user's practice status or change in condition after practicing advice previously presented to the user. This makes it possible to suppress re-presentation of advice that the user does not like (that is, advice that has not been put into practice) or advice that may not have contributed to improving the user's PMS symptoms, or to proactively present advice that has not been presented to the user.

[0146] The advice may include information about functional ingredients, which may encourage the user to ingest functional ingredients that may contribute to improving PMS symptoms. The information about functional ingredients may also include information about Lactobacillus gasseri, which has been confirmed to be effective in alleviating certain types of PMS symptoms, such as depressed mood, anxiety, lethargy, fatigue, and drowsiness.

[0147] The second information may include at least one of the user's biological information and the user's activity information, whereby PMS symptoms can be estimated based on characteristics of the biological information or characteristics of the activity information.

[0148] The second information may include information regarding at least one of the user's heart rate, oxygen saturation, or the user's movements during sleep, which allows for an estimation of PMS symptoms in terms of sleep length or quality.

[0149] The second information may include information about the user's heart rate while sleeping, which allows for estimation of PMS symptoms from the perspective of autonomic nerve activity.

[0150] The second information may include information on at least one of the user's autonomic nerve activity, physical activity, or sleep, which allows for estimation of PMS symptoms in terms of stress.

[0151] The second information may include at least one of the user's biological information or activity information during a PMS period and at least one of the user's biological information or activity information during a period other than a PMS period. In this way, when anomalous biological information or activity information is observed, it is possible to determine whether the event is chronic or specific to a PMS period, thereby improving the accuracy of estimating PMS symptoms.

[0152] The server 50 may evaluate at least one of the user's physical activity, sleep, or stress based on the second information, and estimate the user's PMS symptoms based on the result of the evaluation. This makes it possible to estimate the PMS symptoms from the perspective of at least one of the physical activity, sleep, or stress.

[0153] The server 50 may determine advice to be presented to the user based on second information on at least one of the user's heart rate or the amount of physical activity. This makes it possible to avoid presenting advice to encourage a behavior that has already been practiced (particularly, has become a habit), such as advice to encourage a user who is presumed to have an exercise habit, and instead present advice that is considered to be beneficial to the user. Note that information on behavior that the user has already practiced may be managed as the advice history information described above, or may be collected separately by inquiring of the user, or may be acquired through cooperation with other applications (e.g., a diet app or a running app) or SaaS.

[0154] (7) Other modifications The storage device 31 may be connected to the client device 30 via a network NW. The display 41 may be integrated with the client device 30. The storage device 51 may be connected to the server 50 via the network NW.

[0155] The wearable device 10 or the client device 30 may notify the user of the onset of menstruation or PMS. This allows the user to be aware of the onset of menstruation or PMS and prepare for it. The estimation of the menstruation or PMS may be performed by any of the wearable device 10, the client device 30, or the server 50. As described above, the menstruation and PMS periods may be estimated based on the average resting heart rate during the follicular phase (8-10 days after menstruation) and the luteal phase (3-10 days before menstruation). This allows, for example, the timing of estimating PMS symptoms to be optimized and the calculation load related to the estimation to be reduced. Alternatively, the above notification may be performed appropriately. The menstruation period may be estimated based on, for example, information related to the user's menstrual cycle, the user's biological information (for example, body temperature), or a combination thereof.

[0156] In the above description, an example in which each step in the information processing is executed in a specific order has been described, but the execution order of each step is not limited to the described example as long as there is no dependency. In addition, each step of the above information processing may be executed by any of the wearable device 10, the client device 30, and the server 50. For example, a process described as being executed by any device may be executed by another device, or a process described as being executed by the interaction of multiple devices may be executed by a single device. Specifically, a part or all of the process described as being executed by the server 50 may be executed by the client device 30. A part or all of the process described as being executed by the client device 30 and the server 50 may be executed by the wearable device 10. The client device 30 may acquire user information from a built-in or external sensor instead of acquiring user information from the wearable device 10.

[0157] In the above description, an example has been shown in which the advice determination (S151) and the analysis response (S152) are performed immediately after the PMS symptom estimation (S150). However, before the advice determination (S151), a message for confirming whether or not advice on symptom improvement is required may be presented (for example, displayed on the display 21 or the display 41) together with the PMS symptom estimation result. As an example, a message such as "Are you experiencing such symptoms? Do you need advice on symptom improvement?" may be presented. The user corrects the PMS symptom estimation result or declines the presentation of advice via the wearable device 10 or the client device 30 as necessary. When the user corrects the PMS symptom estimation result, the server 50 may re-perform the PMS symptom estimation (S150) taking into account the user's response. When the user declines the presentation of advice, the server 50 omits the advice determination (S151) and the analysis response (S152). When a user requests the presentation of advice without correcting the estimated result of PMS symptoms, the server 50 executes the advice determination (S151) and the analysis response (S152). This makes it possible to prevent the presentation of advice corresponding to an estimated result that does not match the user's recognition. In addition, it is possible to present the estimated result of PMS symptoms without providing advice to a user who is interested in his / her own PMS symptoms but does not need advice.

[0158] Although the embodiment of the present invention has been described in detail above, the scope of the present invention is not limited to the above embodiment. Furthermore, the above embodiment can be improved or modified in various ways without departing from the spirit of the present invention. Furthermore, the above embodiment and the modified examples can be combined. [Explanation of symbols]

[0159] 1: Information processing system 10: Wearable devices 11:Storage device 12: Processor 13: Input / Output Interface 14: Communication interface 21: Display 22: Sensor 30: Client device 31:Storage device 32 : Processor 33: Input / Output Interface 34: Communication interface 41: Display 50: Server 51 :Storage device 52: Processor 53: Input / Output Interface 54: Communication interface

Claims

1. Computer, means for acquiring first information based on an active input from a user; a means for acquiring second information based on sensing a physical quantity that changes due to at least one of a physiological phenomenon or a movement of the user's body; means for estimating a change in physical condition of the user associated with a menstrual cycle based on the first information and the second information; a means for determining advice to be presented to the user based on the estimation result of the change in physical condition accompanying the menstrual cycle of the user; A program that functions as a

2. The means for estimating changes in physical condition associated with the user's menstrual cycle estimates the user's PMS (Premenstrual Syndrome) symptoms based on the first information and the second information, The means for determining advice to be presented to the user determines advice to be presented to the user based on the estimation result of the PMS symptoms of the user. The program according to claim 1.

3. The PMS symptom estimation means estimates at least one candidate PMS symptom based on at least the second information, and identifies the PMS symptom of the user in accordance with the first information corresponding to the user's response to the validity of the candidate PMS symptom. The program according to claim 2.

4. the means for determining advice determines advice to be presented to the user based further on the first information regarding the user's practice status or change in condition after practice of advice previously presented to the user; The program according to claim 2.

5. The advice includes information about functional ingredients. The program according to claim 2.

6. The information about the functional material includes information about lactic acid bacteria belonging to Lactobacillus. The program according to claim 5.

7. The second information includes at least one of biometric information of the user or activity information of the user. The program according to claim 2.

8. The second information includes information regarding at least one of the user's heart rate or oxygen saturation during sleep, or the user's movements during sleep. The program according to claim 7.

9. The second information includes information about the user's heart rate during sleep. The program according to claim 7.

10. The second information includes information regarding at least one of the user's autonomic nervous activity, physical activity, or sleep. The program according to claim 7.

11. The second information includes at least one of biological information or activity information of the user during a PMS period and at least one of biological information or activity information of the user during a period other than a PMS period. The program according to claim 7.

12. The means for estimating PMS symptoms evaluates at least one of the user's physical activity, sleep, or stress based on the second information, and estimates the user's PMS symptoms based on a result of the evaluation. The program according to claim 7.

13. the means for determining advice determines advice to be presented to the user further based on the second information relating to at least one of a heart rate or a physical activity amount of the user. The program according to claim 2.

14. and causing the computer to further function as a means for estimating at least one of the user's menstrual or PMS period based on the second information relating to the user's resting heart rate during each of the follicular and luteal phases. The program according to claim 2.

15. The computer is further configured to function as a means for notifying the user of the estimated onset of at least one of the user's menstrual period or PMS period. The program according to claim 14.

16. means for acquiring first information based on an active input from a user; means for acquiring second information based on sensing of a physical quantity that changes due to at least one of a physiological phenomenon or a movement of the user's body; means for estimating a change in physical condition of the user associated with a menstrual cycle based on the first information and the second information; a means for determining advice to be presented to the user based on the estimation result of the change in physical condition associated with the menstrual cycle of the user; An information processing device comprising:

17. The computer acquiring first information based on an active input from a user; acquiring second information based on sensing a physical quantity that changes due to at least one of a physiological phenomenon or a movement of the user's body; estimating a change in physical condition associated with the menstrual cycle of the user based on the first information and the second information; determining advice to be presented to the user based on the estimation result of the change in physical condition accompanying the menstrual cycle of the user; How to do it.

18. A system comprising a first computer and a second computer, The first computer means for acquiring first information based on an active input from a user; means for acquiring second information based on sensing of a physical quantity that changes due to at least one of a physiological phenomenon or a movement of the user's body; means for estimating a change in physical condition of the user associated with the menstrual cycle based on the first information and the second information; a means for determining advice to be presented to the user based on the estimation result of the change in physical condition accompanying the menstrual cycle of the user; Equipped with the second computer comprises means for presenting the advice; system.

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