Information processing equipment, systems, and terminal devices

Abstract

To improve the quality of life for the target individuals. [Solution] The information processing device 20 includes a control unit that acquires time-series data including data showing changes in the heart rate of the subject U1, and uses the time-series data to perform a prediction process to predict autonomic nervous system symptoms associated with autonomic nervous system dysfunction.

Description

Technical Field

[0001] The present disclosure relates to an information processing apparatus, a system, and a terminal device.

Background Art

[0002] Patent Document 1 discloses an autonomic nerve regulator and a food composition for autonomic nerve regulation, which have an effect of regulating autonomic nerve function, and a method for regulating autonomic nerve.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The autonomic nerves are deeply involved in the automatic life support of the human body. The autonomic nerves include the sympathetic nerves that predominantly act during body activities and the parasympathetic nerves that predominantly act during body rest. The sympathetic nerves and the parasympathetic nerves have opposite effects on each other. Depending on the balance between the sympathetic nerves and the parasympathetic nerves, various symptoms occur in the human body. In the technology of Patent Document 1, these symptoms are dealt with after they occur, and it is not intended to predict changes in the autonomic nerves of the subject in advance.

[0005] Autonomic nervous system symptoms resulting from such imbalances in the autonomic nervous system are a cause for concern as they may affect the quality of life of the individuals concerned. If treatment is only provided after autonomic nervous system symptoms appear, it can cause inconvenience in daily life. For example, if abdominal pain suddenly occurs while out, it may be difficult to deal with on the spot, or plans may have to be changed, thus lowering the individual's quality of life. Therefore, it is necessary to accurately predict these autonomic nervous system symptoms and inform the individuals of the results. For example, if abdominal pain is known in advance, measures such as delaying the time of departure can be taken. As a result, the individual's quality of life can be improved.

[0006] In light of these circumstances, the purpose of this disclosure is to improve the quality of life of the subjects. [Means for solving the problem]

[0007] (1) An information processing apparatus according to one embodiment of the present disclosure is The system includes a control unit that acquires time-series data including data showing changes in the subject's heart rate, and uses the time-series data to perform predictive processing to predict autonomic nervous system symptoms associated with autonomic nervous system dysfunction.

[0008] (2) An information processing apparatus according to one embodiment of the present disclosure is the information processing apparatus described in (1), The control unit uses the time-series data to estimate changes in the sympathetic and parasympathetic nervous systems in the subject, and predicts the autonomic nervous system symptoms based on the estimated results.

[0009] (3) An information processing apparatus according to one embodiment of the present disclosure is the information processing apparatus described in (1) or (2), wherein the autonomic nervous system symptoms are abdominal pain, constipation, diarrhea, or irritable bowel syndrome.

[0010] (4) An information processing apparatus according to one embodiment of the present disclosure is an information processing apparatus according to any one of (1) to (3), The control unit acquires data as biometric data, which includes at least one of the following: data relating to the subject's stress, data relating to the subject's exercise level, data relating to the subject's body water balance, data relating to the subject's stomach or intestinal sounds, and data relating to the amount of contents in the subject's stomach or intestines. The control unit then uses the acquired biometric data to perform the prediction process.

[0011] (5) An information processing apparatus according to one embodiment of the present disclosure is an information processing apparatus according to any one of (1) to (4), The control unit acquires data as factor data, which includes at least one of the subject's dietary data, data on the subject's attributes, and data on the subject's water intake, and further uses the acquired factor data to perform the prediction process.

[0012] (6) An information processing device according to one embodiment of the present disclosure is the information processing device described in (1) or (2), wherein the autonomic nervous system symptom is a change in appetite.

[0013] (7) An information processing apparatus according to one embodiment of the present disclosure is the information processing apparatus described in (6), The control unit acquires data relating to the subject's exercise level or data relating to the subject's sleep as biometric data, and further uses the acquired biometric data to perform the prediction process.

[0014] (8) An information processing apparatus according to one embodiment of the present disclosure is an information processing apparatus described in (6) or (7), The control unit acquires data on the subject's diet as factor data, and further uses the acquired factor data to perform the prediction process.

[0015] (9) An information processing device according to one embodiment of the present disclosure is the information processing device described in (1) or (2), wherein the autonomic nervous system symptom is drowsiness.

[0016] (10) An information processing apparatus according to one embodiment of the present disclosure is the information processing apparatus described in (9), The control unit acquires data as biometric data, which includes at least one of the subject's sleep data, the subject's exercise level data, the subject's blood glucose level data, and the subject's stress data, and further uses the acquired biometric data to perform the prediction process.

[0017] (11) An information processing apparatus according to one embodiment of the present disclosure is an information processing apparatus described in (9) or (10), The control unit acquires data on the subject's diet as factor data, and further uses the acquired factor data to perform the prediction process.

[0018] (12) An information processing device according to one embodiment of the present disclosure is the information processing device described in (1) or (2), wherein the autonomic nervous system symptom is coldness.

[0019] (13) An information processing apparatus according to one embodiment of the present disclosure is the information processing apparatus described in (12), The control unit acquires data as biometric data, which includes at least one of the subject's exercise level, data on the subject's sleep, data on the subject's muscle mass, data on the subject's metabolism, and data on the subject's body water content, and further uses the acquired biometric data to perform the prediction process.

[0020] (14) An information processing apparatus according to one embodiment of the present disclosure is an information processing apparatus described in (12) or (13), The control unit acquires data including at least one of the subject's attributes and weather information as factor data, and further uses the acquired factor data to perform the prediction process.

[0021] (15) An information processing device according to one embodiment of the present disclosure is the information processing device described in (1) or (2), wherein the autonomic nervous system symptom is muscle tension.

[0022] (16) The information processing apparatus according to an embodiment of the present disclosure is the information processing apparatus described in (15), and the control unit acquires, as biological data, data including at least one of data related to the momentum of the subject, data related to the sleep of the subject, and data related to the muscle mass of the subject, and further uses the acquired biological data to execute the prediction process.

[0023] (17) The information processing apparatus according to an embodiment of the present disclosure is the information processing apparatus described in (15) or (16), and the control unit acquires, as factor data, data including at least one of data related to the attributes of the subject and data related to weather information, and further uses the acquired factor data to execute the prediction process.

[0024] (18) The information processing apparatus according to an embodiment of the present disclosure is the information processing apparatus described in (1) or (2), and the autonomic nerve symptom is numbness or sweating.

[0025] (19) The information processing apparatus according to an embodiment of the present disclosure is the information processing apparatus described in (18), and the control unit acquires, as biological data, data including at least one of information related to the momentum of the subject, data related to the sleep of the subject, data related to the stress of the subject, and data related to the muscle mass of the subject, and further uses the acquired biological data to execute the prediction process.

[0026] (20) The information processing apparatus according to an embodiment of the present disclosure is the information processing apparatus described in (18) or (19), and the control unit acquires, as factor data, data including at least one of data related to the attributes of the subject and data related to weather information, and further uses the acquired data to execute the prediction process.

[0027] (21) The information processing apparatus according to an embodiment of the present disclosure is the information processing apparatus described in (l) or (2), and the autonomic nerve symptom is nausea.

[0028] (22) An information processing apparatus according to one embodiment of the present disclosure is the information processing apparatus described in (21), The control unit acquires data on the subject's stress as biometric data, and further uses the acquired biometric data to perform the prediction process.

[0029] (23) An information processing apparatus according to one embodiment of the present disclosure is an information processing apparatus described in (21) or (22), The control unit acquires data as factor data, which includes at least one of the subject's dietary data and the subject's attributes, and further uses the acquired factor data to perform the prediction process.

[0030] (24) An information processing device according to one embodiment of the present disclosure is the information processing device described in (1) or (2), wherein the autonomic nervous system symptom is arrhythmia.

[0031] (25) An information processing apparatus according to one embodiment of the present disclosure is the information processing apparatus described in (24), The control unit acquires data on the subject's stress as biometric data, and further uses the acquired biometric data to perform the prediction process.

[0032] (26) An information processing apparatus according to one embodiment of the present disclosure is an information processing apparatus described in (25) or (26), The control unit acquires data relating to the attributes of the subject as factor data, and further uses the acquired factor data to perform the prediction process.

[0033] (27) An information processing device according to one embodiment of the present disclosure is the information processing device described in (1) or (2), wherein the autonomic nervous system symptom is hypertension.

[0034] (28) An information processing apparatus according to one embodiment of the present disclosure is the information processing apparatus described in (27), The control unit acquires data as biometric data, which includes at least one of the subject's exercise level, the subject's stress level, and the subject's sleep level, and further uses the acquired biometric data to perform the prediction process.

[0035] (29) An information processing apparatus according to one embodiment of the present disclosure is an information processing apparatus described in (27) or (28), The control unit acquires data as factor data, which includes at least one of the following: data relating to the subject's attributes, data relating to the subject's smoking habits, and data relating to the subject's drinking habits, and further uses the acquired factor data to perform the prediction process.

[0036] (30) An information processing device according to one embodiment of the present disclosure is the information processing device described in (1) or (2), wherein the autonomic nervous system symptom is anxiety.

[0037] (31) An information processing apparatus according to one embodiment of the present disclosure is the information processing apparatus described in (30), The control unit acquires data as biometric data, which includes at least one of the subject's exercise level, the subject's stress level, the subject's sleep level, and the subject's respiration level, and further uses the acquired biometric data to perform the prediction process.

[0038] (32) An information processing apparatus according to one embodiment of the present disclosure is an information processing apparatus according to (30) or (31), The control unit acquires data relating to the attributes of the subject as factor data, and further uses the acquired factor data to perform the prediction process.

[0039] (33) An information processing apparatus according to one embodiment of the present disclosure is the information processing apparatus described in (1) or (2), wherein the autonomic nervous system symptoms are asthma symptoms.

[0040] (34) An information processing apparatus according to one embodiment of the present disclosure is the information processing apparatus described in (33), The control unit acquires data as biometric data, which includes at least one of the subject's stress level, the subject's exercise level, and the subject's respiration level, and further uses the acquired biometric data to perform the prediction process.

[0041] (35) An information processing apparatus according to one embodiment of the present disclosure is an information processing apparatus described in (33) or (34), The control unit acquires data as factor data, which includes data relating to the subject's attributes, data relating to the subject's smoking habits, data relating to the subject's allergies, and data relating to weather information, and further uses the acquired factor data to perform the prediction process.

[0042] (36) An information processing device according to one embodiment of the present disclosure is the information processing device described in (1) or (2), wherein the autonomic nervous system symptom is tinnitus.

[0043] (37) An information processing apparatus according to one embodiment of the present disclosure is the information processing apparatus described in (36), The control unit acquires data including data on the subject's stress or data on the subject's sleep as biometric data, and further uses the acquired data to perform the prediction process.

[0044] (38) An information processing apparatus according to one embodiment of the present disclosure is an information processing apparatus described in (36) or (37), The control unit acquires data as factor data, which includes at least one of the subject's subjective symptoms of tinnitus, data on the earphones used by the subject, and data on the external environmental sounds of the location where the subject is, and further uses the acquired factor data to perform the prediction process.

[0045] (39) An information processing apparatus according to one embodiment of the present disclosure is an information processing apparatus according to any one of (1) to (38), The control unit acquires historical information indicating records of autonomic nervous system symptoms previously entered by the subject, and uses the acquired historical information and the time-series data to perform the prediction process.

[0046] (40) An information processing apparatus according to one embodiment of the present disclosure is an information processing apparatus according to any one of (1) to (39), The control unit displays the results of the prediction process on the subject's terminal device, alongside the changes in the sympathetic and parasympathetic nervous systems.

[0047] (41) An information processing apparatus according to one embodiment of the present disclosure is an information processing apparatus according to any one of (1) to (40), The control unit acquires diagnostic information indicating the type of autonomic nervous system symptoms of the subject diagnosed based on the results of the prediction process, and controls the unit to notify the subject of the diagnostic result indicated by the acquired diagnostic information, along with the results of the prediction process.

[0048] (42) An information processing apparatus according to one embodiment of the present disclosure is an information processing apparatus according to any one of (1) to (41), The control unit acquires countermeasure information indicating a preventive method or countermeasure determined based on the results of the prediction process, and performs control to notify the target person of the preventive method or countermeasure indicated in the acquired countermeasure information, along with the results of the prediction process.

[0049] (43) An information processing apparatus according to one embodiment of the present disclosure is an information processing apparatus according to any one of (1) to (42), The control unit performs control to notify a third party other than the target person of the results of the prediction process based on a request from the target person.

[0050] (44) An information processing apparatus according to one embodiment of the present disclosure is an information processing apparatus according to any one of (1) to (43), The control unit causes the target person's terminal device to display a selection screen for selecting the content to be notified to the third party.

[0051] (45) An information processing apparatus according to one embodiment of the present disclosure is an information processing apparatus according to any one of (1) to (44), The control unit causes the target person's terminal device to display a selection screen allowing the target person to select their preferred way of interacting with the third party.

[0052] (46) A system according to one embodiment of the present disclosure includes an information processing device described in any of (1) to (45), A terminal device that acquires data showing the results of the prediction process performed by the information processing device, and displays the results of the prediction process to the subject, alongside the changes in the sympathetic and parasympathetic nervous systems. It is equipped with.

[0053] (47) A terminal device according to one embodiment of the present disclosure is a terminal device equipped with the functions of the information processing device described in any of (1) to (45). [Effects of the Invention]

[0054] According to this disclosure, it is possible to improve the quality of life of the subjects. [Brief explanation of the drawing]

[0055] [Figure 1] This figure shows the configuration of a system according to one embodiment of the present disclosure. [Figure 2] This is a block diagram showing the configuration of an information processing device according to one embodiment of the present disclosure. [Figure 3] This is a block diagram showing the configuration of a terminal device according to one embodiment of the present disclosure. [Figure 4] This is a flowchart showing the operation of an information processing device according to one embodiment of this disclosure. [Figure 5] This figure shows an example of a screen displayed on a subject's terminal device according to one embodiment of this disclosure. [Figure 6] This figure shows an example of a screen displayed on a subject's terminal device according to one embodiment of this disclosure. [Figure 7] This figure shows an example of a screen displayed on a subject's terminal device according to one embodiment of this disclosure. [Figure 8] This figure shows an example of a screen displayed on a subject's terminal device according to one embodiment of this disclosure. [Figure 9] This figure shows an example of a screen displayed on a subject's terminal device according to one embodiment of this disclosure. [Figure 10] This figure shows an example of a screen displayed on a subject's terminal device according to one embodiment of this disclosure. [Figure 11] This figure shows an example of a screen displayed on a third-party terminal device according to one embodiment of this disclosure. [Modes for carrying out the invention]

[0056] Hereinafter, one embodiment of this disclosure will be described with reference to the figures.

[0057] In each figure, identical or corresponding parts are denoted by the same reference numerals. In the description of each embodiment, the description of identical or corresponding parts will be omitted or simplified as appropriate.

[0058] Referring to Figure 1, the configuration of the system 10 according to this embodiment will be described.

[0059] System 10 comprises at least one information processing device 20 and at least one terminal device 30. Note that there may be multiple information processing devices 20 and terminal devices 30.

[0060] The information processing device 20 can communicate with the terminal device 30 via the network 40.

[0061] Network 40 includes the Internet, at least one WAN, at least one MAN, or any combination thereof. "WAN" is an abbreviation for wide area network. "MAN" is an abbreviation for metropolitan area network. Network 40 may also include at least one wireless network, at least one optical network, or any combination thereof. Wireless networks are, for example, ad hoc networks, cellular networks, wireless LANs, satellite communication networks, or terrestrial microwave networks. "LAN" is an abbreviation for local area network.

[0062] The terminal device 30 is held by the subject U1. The terminal device 30 is, for example, a mobile device such as a mobile phone, smartphone, tablet, or wearable device (e.g., smartwatch), or a PC. "PC" is an abbreviation for personal computer.

[0063] The information processing device 20 is installed in a facility such as a data center. The information processing device 20 is, for example, a server belonging to a cloud computing system or other computing system.

[0064] The information processing device 20 may take any form as long as it has the function of performing the prediction processing described later. For example, in Figure 1, the information processing device 20 is connected to the terminal device 30 by a network 40, but the functions of the information processing device 20 may also be installed in the terminal device 30.

[0065] The outline of this embodiment will be described with reference to Figure 1.

[0066] The autonomic nervous system is the nervous system that regulates specific processes in the body, such as blood pressure and respiratory rate. The autonomic nervous system functions automatically (autonomously) without requiring conscious effort. The autonomic nervous system consists of the "sympathetic nervous system," which acts as an accelerator and is mainly active when we are active, and the "parasympathetic nervous system," which acts as a brake and is mainly active when we are resting or relaxing. The sympathetic nervous system works to tense the body, while the parasympathetic nervous system works to relax the body. Basically, all organs are controlled by the sympathetic and parasympathetic nervous systems, and the body is regulated by the alternating work of the two nerves. In other words, the sympathetic and parasympathetic nervous systems have opposite functions, but they balance each other and govern bodily functions. In humans, the sympathetic nervous system is dominant during the day, for example, when we are working, exercising, or engaging in other activities. On the other hand, the parasympathetic nervous system is dominant at night, allowing us to fall asleep more easily. In other words, if the sympathetic nervous system is more active during the day when you are working, and the parasympathetic nervous system is more active at night when you are resting, then that person's autonomic nervous system is in balance. Therefore, it is important to maintain the balance of the autonomic nervous system for good health. However, the autonomic nervous system is easily affected by mental stress, overwork, lifestyle rhythms, and diseases, making it difficult to maintain balance. Major triggers for imbalance in the autonomic nervous system include mental stress such as interpersonal relationships and work pressure, as well as physical stress such as light, sound, and temperature. When the balance between the sympathetic and parasympathetic nervous systems is disrupted, various symptoms occur in the body. When the function of the autonomic nervous system is disrupted, various physical and mental ailments may appear, such as abdominal pain, constipation, diarrhea, fatigue, difficulty sleeping, lack of motivation, and headaches. For example, if the sympathetic nervous system remains in a state of tension during the time when the parasympathetic nervous system should be dominant and you should fall asleep at night, you will not be able to fall asleep properly, leading to the ailment of "not being able to sleep well." When stressed, the balance of the autonomic nervous system is disrupted, and even during work when the sympathetic nervous system is dominant, bowel movements may become excessive or spasmodic, which can lead to irritable bowel syndrome (IBS) accompanied by a series of symptoms such as abdominal pain, constipation, and diarrhea.When the sympathetic nervous system is dominant, intestinal peristalsis slows down, while when the parasympathetic nervous system is dominant, peristalsis becomes more active, leading to increased appetite. When the sympathetic nervous system is dominant, the brain becomes more active, but when the parasympathetic nervous system is dominant, the brain relaxes, causing drowsiness. Furthermore, when the sympathetic nervous system is dominant, blood vessels constrict, reducing blood flow to the extremities and causing coldness. Other examples of symptoms associated with autonomic nervous system imbalances include stiff shoulders, numbness, sweating, nausea, high blood pressure, anxiety, asthma, and tinnitus.

[0067] Thus, the balance of the autonomic nervous system greatly influences the autonomic nervous system symptoms that occur in the subjects. However, the way in which physical and mental discomfort manifests varies greatly from person to person, with symptoms, their severity, and duration differing from individual to individual. Furthermore, even in the same person, the type, severity, and timing of the discomfort may change depending on attributes such as age, gender, and body type. In addition, since the balance of the autonomic nervous system is related to various factors such as stress, lack of sleep, and poor physical condition, it has been difficult to accurately predict the autonomic nervous system symptoms that will occur in the subjects.

[0068] In the system 10 shown in Figure 1, the information processing device 20 according to this embodiment acquires time-series data D1 including data showing changes in the heart rate of the subject U1, and uses the time-series data D1 to perform prediction processing to predict autonomic nervous system symptoms associated with autonomic nervous system dysfunction.

[0069] According to this embodiment, the technology for predicting autonomic nervous system symptoms in subjects can be improved. Therefore, the quality of life of the subjects can be improved.

[0070] Referring to Figure 2, the configuration of the information processing device 20 according to this embodiment will be described.

[0071] The information processing device 20 comprises a control unit 21, a storage unit 22, and a communication unit 23.

[0072] The control unit 21 includes at least one processor, at least one programmable circuit, at least one dedicated circuit, or any combination thereof. The processor is a general-purpose processor such as a CPU or GPU, or a dedicated processor specialized for a specific process. "CPU" is an abbreviation for central processing unit. "GPU" is an abbreviation for graphics processing unit. The programmable circuit is, for example, an FPGA. "FPGA" is an abbreviation for field-programmable gate array. The dedicated circuit is, for example, an ASIC. "ASIC" is an abbreviation for application specific integrated circuit. The control unit 21 controls each part of the information processing device 20 and executes processes related to the operation of the information processing device 20.

[0073] The storage unit 22 includes at least one semiconductor memory, at least one magnetic memory, at least one optical memory, or any combination of at least two of these. The semiconductor memory is, for example, RAM or ROM. "RAM" is an abbreviation for random access memory. "ROM" is an abbreviation for read-only memory. The RAM is, for example, SRAM or DRAM. "SRAM" is an abbreviation for static random access memory. "DRAM" is an abbreviation for dynamic random access memory. The ROM is, for example, EEPROM. "EEPROM" is an abbreviation for electrically erasable programmable read-only memory. The storage unit 22 functions, for example, as a main memory, auxiliary memory, or cache memory. The storage unit 22 stores data used for the operation of the information processing device 20 and data obtained by the operation of the information processing device 20. The data obtained by the operation of the information processing device 20 may include data received from the terminal device 30. In this embodiment, time-series data D1 is stored in the storage unit 22. Time-series data D1 includes data showing changes in the heart rate of subject U1. Time-series data D1 is acquired, for example, via a device such as a wearable device. The memory unit 22 may also store biometric data D2. Biometric data D2 is data that can be obtained from the body of subject U1, other than time-series data D1. Biometric data D2 is acquired, for example, via a device such as a wearable device. In this embodiment, biometric data D2 includes at least one of the following: data on subject U1's stress, data on subject U1's exercise level, data on subject U1's internal water balance, data on subject U1's stomach or intestinal sounds, and data on the amount of contents of subject U1's stomach or intestines. The memory unit 22 may also store factor data D3. Factor data D3 is data other than time-series data D1 and biometric data D2, and is data showing factors that are thought to influence the onset of autonomic nervous system symptoms.Factor data D3 can also be described as data that cannot be obtained from the subject U1's body, or data that, even if obtained from the subject U1's body, cannot be obtained with sufficient accuracy to be used for predicting autonomic nervous system symptoms. In this embodiment, factor data D3 includes at least one of the subject U1's dietary data, the subject U1's attributes, and the subject U1's water intake data. Factor data D3 may also represent results calculated based on time-series data D1 and / or biometric data D2. Furthermore, the storage unit 22 may store history information D4 showing records of autonomic nervous system symptoms previously entered by the subject U1, and diagnostic information D5 showing the type of autonomic nervous system symptoms of the subject U1. Time-series data D1, biometric data D2, factor data D3, history information D4, and diagnostic information D5 will be described later.

[0074] The communication unit 23 includes at least one communication interface, which is, for example, a LAN interface. The communication unit 23 receives data used in the operation of the information processing device 20 and transmits data obtained by the operation of the information processing device 20. In this embodiment, the communication unit 23 communicates with the terminal device 30.

[0075] The functions of the information processing device 20 are realized by executing the information processing program according to this embodiment on the processor acting as the control unit 21. In other words, the functions of the information processing device 20 are realized by software. The information processing program causes the computer to perform the operations of the information processing device 20, thereby causing the computer to function as the information processing device 20. That is, the computer functions as the information processing device 20 by performing the operations of the information processing device 20 according to the information processing program.

[0076] Information processing programs can be stored on non-temporary computer-readable media. Examples of non-temporary computer-readable media include flash memory, magnetic recording devices, optical discs, magneto-optical recording media, or ROM. Program distribution is carried out, for example, by selling, transferring, or lending portable media such as SD cards, DVDs, or CD-ROMs containing the programs. "SD" is an abbreviation for Secure Digital. "DVD" is an abbreviation for digital versatile disc. "CD-ROM" is an abbreviation for compact disc read only memory. Information processing programs may also be distributed by storing them in server storage and transferring them from the server to other computers. Information processing programs may also be provided as program products.

[0077] A computer, for example, stores an information processing program stored on a portable medium or transferred from a server in its main memory. Then, the computer reads the information processing program stored in the main memory with its processor and executes the processing according to the read information processing program. The computer may also directly read the information processing program from the portable medium and execute the processing according to the information processing program. The computer may also execute the processing according to the received information processing program sequentially each time an information processing program is transferred to the computer from a server. Processing may also be performed by a so-called ASP-type service, which does not transfer information processing programs from the server to the computer, but realizes its function only through execution instructions and result acquisition. "ASP" is an abbreviation for application service provider. Information processing programs include information used for processing by an electronic computer that is equivalent to a program. For example, data that is not a direct instruction to the computer but has the nature of defining the computer's processing falls under "equivalent to a program".

[0078] Some or all of the functions of the information processing device 20 may be implemented by a programmable circuit or a dedicated circuit as a control unit 21. In other words, some or all of the functions of the information processing device 20 may be implemented by hardware.

[0079] Referring to Figure 3, the configuration of the terminal device 30 according to this embodiment will be described.

[0080] The terminal device 30 comprises a control unit 31, a storage unit 32, a communication unit 33, an input unit 34, and an output unit 35.

[0081] The control unit 31 includes at least one processor, at least one programmable circuit, at least one dedicated circuit, or any combination thereof. The processor is a general-purpose processor such as a CPU or GPU, or a dedicated processor specialized for a specific process. The programmable circuit is, for example, an FPGA. The dedicated circuit is, for example, an ASIC. The control unit 31 controls each part of the terminal device 30 and executes processes related to the operation of the terminal device 30.

[0082] The storage unit 32 includes at least one semiconductor memory, at least one magnetic memory, at least one optical memory, or any combination thereof. The semiconductor memory is, for example, RAM or ROM. The RAM is, for example, SRAM or DRAM. The ROM is, for example, EEPROM. The storage unit 32 functions, for example, as a main memory, auxiliary memory, or cache memory. The storage unit 32 stores data used for the operation of the terminal device 30 and data obtained by the operation of the terminal device 30. The data obtained by the operation of the terminal device 30 may include data received from the information processing device 20. In this embodiment, the storage unit 32 may store time-series data D1, biological data D2, factor data D3, history information D4, and diagnostic information D5. Furthermore, the storage unit 32 may store, for example, the results of prediction processing performed by the information processing device 20, which are transmitted from the information processing device 20.

[0083] The communication unit 33 includes at least one communication interface, which is, for example, a LAN interface. The communication unit 33 receives data used for the operation of the terminal device 30 and transmits data obtained by the operation of the terminal device 30. In this embodiment, the communication unit 33 communicates with the information processing device 20.

[0084] The input unit 34 includes at least one input interface. The input interface may be, for example, a physical key, a capacitive key, a pointing device, a touchscreen integrated with a display, or a voice sensor. The input unit 34 accepts operations to input data used for the operation of the terminal device 30. Instead of being provided in the terminal device 30, the input unit 34 may be connected to the terminal device 30 as an external input device. Any connection method can be used, for example, USB, HDMI®, or Bluetooth®. "USB" is an abbreviation for Universal Serial Bus. "HDMI®" is an abbreviation for High-Definition Multimedia Interface. In this embodiment, the input unit 34 is a touchscreen.

[0085] The output unit 35 includes at least one output interface. The output interface is, for example, a display or a speaker. The display is, for example, an LCD or an organic EL display. "LCD" is an abbreviation for liquid crystal display. "EL" is an abbreviation for electroluminescence. The output unit 35 outputs data obtained by the operation of the terminal device 30. The output unit 35 may also output data transmitted from the information processing device 20. Instead of being provided in the terminal device 30, the output unit 35 may be connected to the terminal device 30 as an external output device. Any connection method can be used, for example, USB, HDMI®, or Bluetooth®. In this embodiment, the output unit 35 is a display.

[0086] The functions of the terminal device 30 are realized by executing the application program according to this embodiment on the processor acting as the control unit 31. In other words, the functions of the terminal device 30 are realized by software. The application program causes the computer to perform the operations of the terminal device 30, thereby causing the computer to function as the terminal device 30. That is, the computer functions as the terminal device 30 by performing the operations of the terminal device 30 according to the application program.

[0087] Application programs can be stored on non-temporary computer-readable media. Examples of non-temporary computer-readable media include flash memory, magnetic recording devices, optical discs, magneto-optical recording media, or ROM. Program distribution can be carried out, for example, by selling, transferring, or leasing portable media such as SD cards, DVDs, or CD-ROMs containing the programs. Application programs may also be distributed by storing them in server storage and transferring them from the server to other computers. Furthermore, application programs may be provided as program products.

[0088] A computer, for example, stores an application program stored on a portable medium or transferred from a server in its main memory. The computer then reads the application program stored in the main memory using its processor and executes the processing according to the read application program. Alternatively, the computer may directly read the application program from the portable medium and execute the processing according to the application program. The computer may also execute the processing according to the received application program sequentially each time an application program is transferred from a server to the computer. Furthermore, processing may be performed using a so-called ASP-type service, which does not transfer application programs from the server to the computer, but instead implements functionality only through execution instructions and result retrieval. An application program includes information used for processing by an electronic computer that is equivalent to a program.

[0089] Some or all of the functions of the terminal device 30 may be implemented by a programmable circuit or a dedicated circuit as the control unit 31. In other words, some or all of the functions of the terminal device 30 may be implemented by hardware.

[0090] Referring to Figure 4, the operation of the information processing device 20 according to this embodiment will be described. This operation corresponds to the information processing method according to this embodiment. That is, the information processing method according to this embodiment includes steps S1 to S4 shown in Figure 4. Hereinafter, each step in the flowchart will be identified by S and a number.

[0091] Changes in the autonomic nervous system can be estimated based on time-series data D1 of subject U1. One method for estimating changes in the autonomic nervous system in subject U1 is to do so based on changes in subject U1's heart rate. Information that enables the estimation of changes in the autonomic nervous system in subject U1 may include, for example, information obtained by analyzing pulse rate, heart rate variability, pulse wave, blood flow, blood pressure, blood glucose, sweat, body temperature, activity level, or skin electrical activity (hereinafter referred to as "detection information"). Changes in the subject's heart rate and this detection information can be detected, for example, by a wearable device worn by subject U1.

[0092] Furthermore, the method for estimating changes in the sympathetic and parasympathetic nervous systems in subject U1 is not limited to a method based on changes in subject U1's heart rate, but any other analysis method can be used. For example, the control unit 21 may acquire data as time-series data D1, such as the subject U1's exhaled gas (specifically, changes in oxygen consumption and carbon dioxide emission), respiratory waveform and ventilation (specifically, changes in trunk circumference and respiratory temperature), or skin electrical activity (specifically, sweat gland activity), and predict changes in the autonomic nervous system based on this data.

[0093] The following describes, as an example, the method described above, in which changes in the autonomic nervous system are estimated based on heart rate, and autonomic nervous system symptoms such as "abdominal pain," "constipation," "diarrhea," or "irritable bowel syndrome" are predicted to occur in subject U1. "Irritable bowel syndrome" is a general term for a series of symptoms such as "abdominal pain," "constipation," and "diarrhea." "Examples" are provided not to limit this disclosure, but to aid in understanding this embodiment.

[0094] In S1, the control unit 21 of the information processing device 20 acquires time-series data D1. The time-series data D1 includes data showing changes in the heart rate of the subject U1. The time-series data D1 may be acquired by any procedure, but for example, it may be acquired by the following procedure: A wearable device worn by the subject U1 monitors the subject U1's heart rate and detects the subject U1's heart rate interval. The control unit 21 communicates with the wearable device via the communication unit 23, receives data showing the heart rate interval transmitted from the wearable device, and acquires this data as time-series data D1. Alternatively, the wearable device may detect the pulse wave of the subject U1, and the control unit 21 may calculate the heart rate interval based on the detected pulse wave, thereby acquiring data showing the calculated heart rate interval as time-series data D1. Note that the time-series data D1 is not limited to data showing changes in the subject U1's heart rate, but may include any data, such as the detection information described above, as long as it can predict changes in the autonomic nervous system in the subject U1. For example, the control unit 21 may acquire data as time-series data D1 that shows the subject U1's exhaled gas (specifically, changes in oxygen consumption and carbon dioxide output), respiratory waveform and ventilation (specifically, changes in trunk circumference and respiratory temperature), or skin electrical activity (specifically, sweat gland activity).

[0095] In S2, the control unit 21 of the information processing device 20 estimates the changes in the sympathetic and parasympathetic nervous systems of subject U1 using the time-series data D1 acquired in S1. If the time-series data D1 includes data showing changes in the heart rate of subject U1, the changes in the sympathetic and parasympathetic nervous systems can be estimated, for example, by the following procedure.

[0096] A specific procedure for estimating autonomic nervous system balance from heart rate variability involves extracting high-frequency (HF) and low-frequency (LF) components from time-series heart rate variability data and comparing their magnitudes. "HF" is an abbreviation for "High Frequency," and is a fluctuating wave whose signal source is respiration with a period of approximately 3 to 4 seconds. "LF" is an abbreviation for "Low Frequency," and is a fluctuating wave whose signal source is blood pressure changes with a period of approximately 10 seconds, known as a Mayer wave. To measure the magnitudes of the HF and LF components, the HF component is calculated as the sum of power spectral components from 0.15 Hz to 0.40 Hz, and the LF component is calculated as the sum of power spectral components from 0.05 Hz to 0.15 Hz. When the parasympathetic nervous system is dominant (relaxed), the HF component increases. Conversely, when the sympathetic nervous system is dominant (stressed), the HF component decreases. When the parasympathetic nervous system is dominant, the HF component increases; therefore, the value of the HF component (in ms²) can be used as an indicator of parasympathetic nervous system activity. Conversely, when the sympathetic nervous system is dominant, the HF component decreases relatively. Conversely, when the parasympathetic nervous system is dominant, the HF component increases relatively. Therefore, a low LF / HF value indicates parasympathetic nervous system dominance. Conversely, a high LF / HF value indicates sympathetic nervous system dominance. For this reason, LF / HF can be used as an indicator of sympathetic nervous system activity. In addition, HF / (LF+HF) is an indicator that shows the proportion of parasympathetic nervous system activity within the total activity of autonomic nervous system function.

[0097] Furthermore, photoplethysmography (PHYW) allows for the continuous measurement of surrogate indicators of sympathetic and parasympathetic nervous system activity.

[0098] Furthermore, the method for estimating changes in the sympathetic and parasympathetic nervous systems in subject U1 is not limited to the method described above, and any other analysis method can be used. For example, the control unit 21 may acquire data as time-series data D1, such as the subject U1's exhaled gas (specifically, changes in oxygen consumption and carbon dioxide output), respiratory waveform and ventilation (specifically, changes in trunk circumference and respiratory temperature), or skin electrical activity (specifically, sweat gland activity), and predict autonomic nervous system fluctuations based on this data.

[0099] In S3, the control unit 21 of the information processing device 20 performs predictive processing to predict autonomic nervous system symptoms based on the estimation results of the changes in the autonomic nervous system in S2. Specifically, the control unit 21 predicts autonomic nervous system symptoms such as "abdominal pain," "constipation," "diarrhea," or "irritable bowel syndrome" based on whether the sympathetic or parasympathetic nervous system is excessively dominant. When the sympathetic nervous system is excessively dominant, intestinal peristalsis stagnates, and water is absorbed while the stool remains in the intestines, resulting in "constipation." On the other hand, when the parasympathetic nervous system is excessively dominant, intestinal peristalsis becomes active, and stool is expelled before sufficient water can be absorbed, resulting in "diarrhea." When the balance of the autonomic nervous system is disrupted, intestinal peristalsis does not function properly, and abdominal pain and constipation or diarrhea chronically recur, even without any digestive disease. This condition is called "irritable bowel syndrome." Therefore, the control unit 21 can predict autonomic nervous system symptoms such as "abdominal pain," "constipation," "diarrhea," or "irritable bowel syndrome" based on whether the sympathetic or parasympathetic nervous system is excessively dominant. As an example, let's explain the case where "abdominal pain" is predicted as an "autonomic nervous system symptom."

[0100] The control unit 21 of the information processing device 20 refers to the estimated results of changes in the sympathetic and parasympathetic nervous systems in S2 and determines whether the sympathetic or parasympathetic nervous system is excessively dominant. Specifically, the control unit 21 makes estimations based on LF / HF, which is an activity index of the sympathetic or parasympathetic nervous system. It is known that LF / HF is less than 2.0 in a very resting state, 2 to 3 in normal resting conditions, and 4.0 or higher when parasympathetic activity is suppressed or sympathetic activity is excited, and the reference range is 0.8 to 2.0. Therefore, the control unit 21 determines whether LF / HF is within the reference range. Here, suppose the value of LF / HF is 0.5. The control unit 21 determines that the parasympathetic nervous system is excessively dominant because the value of LF / HF is below the lower limit of the reference range.

[0101] As mentioned above, excessive dominance of the parasympathetic nervous system is expected to stimulate intestinal peristalsis and lead to diarrhea. In irritable bowel syndrome, abdominal pain generally accompanies diarrhea. Therefore, if the control unit 21 of the information processing device 20 determines that the parasympathetic nervous system is excessively dominant, it predicts "abdominal pain" as an autonomic nervous system symptom in subject U1.

[0102] As described above, the control unit 21 of the information processing device 20 acquires time-series data D1, which includes data showing changes in the heart rate of the subject U1, and uses the acquired time-series data D1 to perform prediction processing to predict autonomic nervous system symptoms associated with autonomic nervous system dysfunction. Specifically, the control unit 21 uses the time-series data D1 to estimate changes in the sympathetic and parasympathetic nervous systems in the subject U1. Based on the estimated results, the control unit 21 predicts autonomic nervous system symptoms such as abdominal pain, constipation, diarrhea, or irritable bowel syndrome.

[0103] According to this embodiment, the technology for predicting autonomic nervous system symptoms in subject U1 can be improved. Therefore, the quality of life of the subject can be improved.

[0104] As a modification of this embodiment, the control unit 21 of the information processing device 20 may further acquire biological data D2 in addition to the time-series data D1 acquired in S1. The biological data D2 includes, for example, at least one of the following: data relating to the stress of the subject U1, data relating to the amount of exercise of the subject U1, data relating to the body water balance of the subject U1, data relating to the sounds of the stomach or intestines of the subject U1, and data relating to the amount of contents of the stomach or intestines of the subject U1. The control unit 21 may further use the acquired biological data D2 to perform the prediction processing in S3.

[0105] Biometric data D2 may be acquired by any procedure, but for example, it can be acquired by the following procedure. A wearable device worn by subject U1 detects subject U1's sweat, body temperature, activity level, heart rate, pulse, heart rate variability, pulse wave, blood flow, blood pressure, blood glucose, or skin electrical activity (hereinafter referred to as "detection information") as detection information. The control unit 21 of the information processing device 20 communicates with the wearable device via the communication unit 23 and receives the detection information transmitted from the wearable device. The control unit 21 analyzes the received detection information and acquires the resulting data as biometric data D2. Alternatively, biometric data D2 may be stored in advance in the storage unit 22 of the information processing device 20. The control unit 21 of the information processing device 20 may acquire biometric data D2 by reading it from the storage unit 22.

[0106] In S3, the control unit 21 of the information processing device 20 performs prediction processing based on the acquired biometric data D2. Prediction processing using time-series data D1 and biometric data D2 may be performed in any procedure, but for example, it may be performed in the following procedure. As an example, suppose that biometric data D2 contains data on the body water balance of subject U1, and in S3, the control unit 21 of the information processing device 20 determines that the parasympathetic nervous system is excessively dominant and predicts "abdominal pain" as an autonomic nervous system symptom for subject U1. In this case, the control unit 21 refers to the biometric data D2 and predicts "diarrhea" or "constipation" based on the body water balance of subject U1. This is because the consistency of stool changes depending on the amount of water in the body. For example, if biometric data D2 indicates that subject U1's body water balance is disrupted and there is excess water, the control unit 21 predicts "diarrhea". On the other hand, if biometric data D2 indicates that subject U1 is dehydrated, the control unit 21 predicts "constipation". More specifically, the memory unit 22 stores a threshold for the body's water balance, and the control unit 21 determines whether there is excess or deficiency of water by comparing the acquired biological data D2 with this threshold.

[0107] As another example, prediction processing using time-series data D1 and biometric data D2 may be performed in the following procedure. Suppose that biometric data D2 contains data on the stomach or intestinal sounds of subject U1, and in S3, the control unit 21 of the information processing device 20 determines that the parasympathetic nervous system is excessively dominant and predicts "abdominal pain" as an autonomic nervous system symptom for subject U1. In this case, the control unit 21 refers to biometric data D2 and predicts "diarrhea" or "constipation" based on the stomach or intestinal sounds of subject U1. This is because the stomach or intestinal sounds change depending on whether there is diarrhea or constipation. That is, in the case of diarrhea, intestinal peristalsis is increased, so the frequency and / or intensity of intestinal peristalsis sounds tend to increase (there tends to be more sound because the contents in the intestines move rapidly). In the case of constipation, intestinal peristalsis is decreased, so the frequency and / or intensity of intestinal peristalsis sounds tend to decrease (there tends to be less sound because the contents remain in the intestines for a long time). For example, if the number of bowel sounds per minute calculated for subject U1 based on biometric data D2 is 35 or more, the control unit 21 predicts "diarrhea." Alternatively, if the number of bowel sounds per minute calculated based on biometric data D2 is 0, it predicts "constipation." More specifically, a threshold for bowel sounds is stored in the memory unit 22, and the control unit 21 counts the bowel sounds by comparing the acquired biometric data D2 with this threshold.

[0108] Autonomic nervous system activity maintains homeostasis through the interaction of three systems: circulation, respiration, and thermoregulation. Therefore, when estimating autonomic nervous system symptoms, it is desirable to use multiple indicators rather than a single indicator. According to this modification, the control unit 21 of the information processing device 20 acquires biological data D2 and further uses the acquired biological data D2 to perform prediction processing. In other words, the control unit 21 can perform prediction processing of autonomic nervous system symptoms based not only on time-series data D1 but also on biological data D2. Consequently, autonomic nervous system symptoms can be predicted with greater accuracy. As a result, the quality of life of the subject can be improved.

[0109] As another modification of this embodiment, the control unit 21 of the information processing device 20 may further acquire factor data D3 in addition to the time-series data D1 acquired in S1. Factor data D3 is data other than the time-series data D1 and the biological data D2, and is data indicating factors that are thought to influence the onset of autonomic nervous system symptoms. Factor data D3 includes, for example, at least one of data relating to the subject U1's diet, data relating to the subject U1's attributes, and data relating to the subject U1's water intake. The control unit 21 may further use the acquired factor data D3 to perform the prediction process in S3. That is, the control unit 21 can perform the prediction process for autonomic nervous system symptoms based not only on the time-series data D1 but also on the factor data D3. Therefore, autonomic nervous system symptoms can be predicted with greater accuracy. Furthermore, factor data D3 may be acquired in place of or together with the biological data D2. The control unit 21 may use the time-series data D1, the biological data D2, and the factor data D3 to perform the prediction process in S3. This will further improve the accuracy of predictive processing. As a result, it will be possible to further improve the quality of life for the target individuals.

[0110] Factor data D3 can be acquired by any procedure, but for example, it can be acquired by the following procedure. Subject U1 records details such as the amount and time of meals eaten, attributes such as gender, and fluid intake status via terminal device 30. The control unit 21 of the information processing device 20 communicates with the terminal device 30 via the communication unit 23, receives the data registered by subject U1, and acquires the received data as factor data D3. Alternatively, factor data D3 may be stored in advance in the storage unit 22 of the information processing device 20. The control unit 21 of the information processing device 20 may acquire factor data D3 by reading it from the storage unit 22.

[0111] In S3, the control unit 21 of the information processing device 20 performs prediction processing based on the acquired factor data D3. Prediction processing using time series data D1 and factor data D3 can be performed in any procedure, but for example, it can be performed in the following procedure. As an example, suppose that data on the meals of subject U1 is acquired as factor data D3. The data on subject U1's meals includes the amount and time of subject U1's meals. Also, suppose that in S3, the control unit 21 of the information processing device 20 determines that the parasympathetic nervous system is excessively dominant and predicts "abdominal pain" as an autonomic nervous system symptom for subject U1. In this case, the control unit 21 refers to the factor data D3 and predicts "diarrhea" or "constipation" based on the amount and time of subject U1's meals indicated in the factor data D3. Since peristalsis is induced when a predetermined amount of food reaches the stomach and / or intestines of subject U1, the control unit 21 predicts the timing T1 when a predetermined amount of food will reach the stomach and / or intestines of subject U1, based on the amount and timing of subject U1's meals as shown in factor data D3. If the predicted timing T1 coincides with a period of excessive parasympathetic dominance, the control unit 21 predicts "diarrhea." On the other hand, if the predicted timing T does not coincide with a period of excessive parasympathetic dominance, the control unit 21 predicts "constipation."

[0112] According to this modified version, the control unit 21 of the information processing device 20 acquires factor data D3 and further uses the acquired factor data D3 to perform prediction processing. That is, the control unit 21 can perform prediction processing of autonomic nervous system symptoms not only based on time series data D1 but also on factor data D3. Therefore, autonomic nervous system symptoms can be predicted with greater accuracy. In this modified version, if biological data D2 is also used along with factor data D3 in addition to time series data D1 for prediction processing, the accuracy of the prediction can be further improved, and as a result, the quality of life of the subject can be improved.

[0113] Furthermore, the biometric data D2 and factor data D3 are not limited to the data described above, and may include additional data considered to be correlated with the type of autonomic nervous system symptom to be predicted. Examples of the types of autonomic nervous system symptoms to be predicted include "changes in appetite," "drowsiness," "muscle tension," "numbness or sweating," "nausea," "arrhythmia," "high blood pressure," "anxiety," "asthma symptoms," and "tinnitus."

[0114] For example, "changes in appetite" can be predicted as an autonomic nervous system symptom. Appetite decreases when the sympathetic nervous system is dominant because peristalsis slows down and gastrointestinal function is suppressed, while appetite increases when the parasympathetic nervous system is dominant because peristalsis becomes more active, dopamine is secreted, and appetite increases. When predicting "changes in appetite" as an autonomic nervous system symptom, the control unit 21 of the information processing device 20 acquires data on the subject U1's exercise level or data on the subject U1's sleep level as biological data D2. The control unit 21 also acquires data on the subject U1's diet as factor data D3. The reason for acquiring data on the subject U1's exercise level as biological data D2 is that when the amount of exercise is high, ghrelin, an appetite-stimulating hormone, decreases and peptide YY, an appetite-suppressing hormone, increases, thus suppressing appetite. The reason for acquiring data on the subject U1's sleep level as biological data D2 is that when the amount of sleep is short, ghrelin increases and the hormone leptin decreases, thus increasing appetite. Furthermore, data on the meals of subject U1 is obtained as factor data D3 because the degree of hunger changes depending on the time elapsed since eating.

[0115] For example, "sleepiness" can be predicted as an autonomic nervous system symptom. When the sympathetic nervous system is dominant, the brain becomes active, causing drowsiness. Therefore, if the balance of the autonomic nervous system is disrupted and the sympathetic nervous system becomes dominant at night, the brain becomes excited, making it difficult to fall asleep. When the parasympathetic nervous system is dominant, the brain relaxes, causing drowsiness. When predicting "sleepiness" as an autonomic nervous system symptom, the control unit 21 of the information processing device 20 acquires data as biological data D2, which includes at least one of the following: data on subject U1's sleep, data on subject U1's exercise level, data on subject U1's blood glucose level, and data on subject U1's stress. The control unit 21 also acquires data on subject U1's diet as factor data D3. Data on subject U1's sleep is acquired as biological data D2 because "sleepiness" is more likely to occur if the sleep duration or quality of sleep the previous day was short. Data on exercise level is acquired as biological data D2 because moderate exercise affects nighttime sleep. The reason for acquiring blood glucose data as biometric data D2 is to detect the occurrence of postprandial blood glucose spikes that cause "drowsiness." The reason for acquiring stress data as biometric data D2 is that when a person experiences stress, the hormone corticotropin, which has a sleep-suppressing effect, increases, resulting in a decrease in the quality of nighttime sleep and making daytime drowsiness more likely.

[0116] For example, "coldness" can be predicted as an autonomic nervous system symptom. When the sympathetic nervous system is dominant, blood vessels constrict, preventing blood from reaching the extremities, resulting in "coldness." When the sympathetic nervous system is dominant, the heart rate increases, blood vessels constrict, and the extremities become cold. When the parasympathetic nervous system is dominant, blood vessels dilate, and heat is released, which can lead to excessive heat being released from the body and a decrease in internal body temperature. As a result, the hands and feet may be warm, but the inside of the body and / or internal organs may feel "cold." Some people also experience flushing due to dilated blood vessels in the face, etc. When predicting "coldness" as an autonomic nervous system symptom, the control unit 21 of the information processing device 20 acquires data as biological data D2 that includes at least one of the following: information on the subject U1's exercise level, data on the subject U1's sleep, data on the subject U1's muscle mass, data on the subject U1's metabolism, and data on the subject U1's body water content. Furthermore, the control unit 21 acquires data as factor data D3, which includes at least one of the attributes of the subject U1 and data related to weather information. This is because moderate exercise regulates the autonomic nervous system, while lack of sleep disrupts it and worsens sleep quality. Also, muscles circulate blood, but poor metabolism reduces heat production, and poor water metabolism (high body water content) causes heat to be lost.

[0117] For example, "muscle tension" can be predicted as an autonomic nervous system symptom. "Muscle tension" is, for example, "shoulder stiffness." When the sympathetic nervous system is dominant, blood vessels constrict and blood flow worsens, causing muscle tension and shoulder stiffness. When the parasympathetic nervous system is dominant, blood vessels dilate and blood circulation improves, reducing muscle tension. When predicting "muscle tension" as an autonomic nervous system symptom, the control unit 21 of the information processing device 20 acquires data as biological data D2, which includes at least one of the data relating to the subject U1's exercise level, the subject U1's sleep level, and the subject U1's muscle mass. The control unit 21 also acquires at least one of the data relating to the subject U1's attributes and weather information as factor data D3. This is because aerobic exercise improves blood circulation, but excessive sleep or insufficient physical activity can worsen blood flow. Another reason why women often experience stiff shoulders is that prolonged sitting at a desk can impair blood flow, leading to shoulder stiffness. Moving the muscles through exercise can improve blood circulation and alleviate shoulder stiffness.

[0118] For example, "numbness or sweating" can be predicted as an autonomic nervous system symptom. When the sympathetic nervous system is dominant, blood vessels constrict, preventing blood from reaching the extremities, causing numbness. Also, when the sympathetic nervous system is dominant, blood vessels constrict, blood circulation worsens, causing heat to build up and sweating to occur. When predicting "numbness or sweating" as an autonomic nervous system symptom, the control unit 21 of the information processing device 20 acquires data as biological data D2, which includes at least one of the following: information on the subject U1's exercise level, data on the subject U1's sleep, data on the subject U1's muscle mass, and data on the subject U1's stress. The control unit 21 also acquires data as factor data D3, which includes at least one of the subject's attributes and data on weather information. The reason for acquiring data on the subject U1's exercise level and sleep as biological data D2 is that aerobic exercise improves blood circulation, but excessive sleep or insufficient physical activity can worsen blood flow. The reason for acquiring muscle mass data as biodata D2 is that muscles circulate blood. The reason for acquiring stress data as biodata D2 is that when stressed, the sweat glands become more active, causing sweating, and poor blood flow can cause numbness.

[0119] For example, nausea can be predicted as an autonomic nervous system symptom. When the sympathetic nervous system is dominant, intestinal movement slows down and gastric juice secretion increases, leading to nausea. On the other hand, nausea can also be felt when the parasympathetic nervous system is dominant because gastrointestinal movement becomes more vigorous. When predicting nausea as an autonomic nervous system symptom, the control unit 21 of the information processing device 20 acquires data on the subject U1's stress as biological data D2. The control unit 21 also acquires data as factor data D3, which includes at least one of the subject U1's diet data and data on the subject U1's attributes. This is because peristalsis is induced when food reaches the gastrointestinal tract. It is also because factors such as gender, age, and past pregnancy experience are thought to have an influence. Furthermore, if subject U1 is pregnant, generally the parasympathetic nervous system is dominant in the early stages of pregnancy, and the sympathetic nervous system is dominant in the later stages. However, the more severe the morning sickness symptoms, the longer the parasympathetic nervous system remains dominant, making it difficult to eat properly and further worsening the symptoms.

[0120] For example, arrhythmia can be predicted as an autonomic nervous system symptom. Changes in the autonomic nervous system affect heart rate; when the sympathetic nervous system is dominant, the heart rate increases, electrical excitation becomes more likely, and arrhythmia (tachyarrhythmia, a fast pulse) is more likely to occur. Conversely, when the parasympathetic nervous system is dominant, bradyarrhythmia, a slow pulse, is more likely to occur. When predicting arrhythmia as an autonomic nervous system symptom, the control unit 21 of the information processing device 20 acquires data on the subject U1's stress as biological data D2. The control unit 21 also acquires data on the subject U1's attributes as factor data D3. This is because arrhythmia can also be induced by stress and other risk factors.

[0121] For example, "hypertension" can be predicted as an autonomic nervous system symptom. When the sympathetic nervous system is dominant, the person is in a state of tension, causing vasoconstriction and an increase in blood pressure. When predicting "hypertension" as an autonomic nervous system symptom, the control unit 21 of the information processing device 20 acquires data as biological data D2, which includes at least one of the following: data on the exercise level of subject U1, data on the sleep level of subject U1, and data on the stress level of subject U1. The control unit 21 also acquires data as factor data D3, which includes at least one of the following: data on the attributes of subject U1, data on the smoking habits of subject U1, and data on the drinking habits of subject U1. The control unit 21 acquires stress-related data as biological data D2 because "hypertension" is induced in stressful situations. More specifically, when a person experiences stress, large amounts of adrenaline and noradrenaline are secreted. Adrenaline increases heart rate, while noradrenaline constricts blood vessels. Because blood volume increases and blood vessels constrict, blood pressure rises. For example, the control unit 21 predicts "hypertension" using the resting stress value of the subject U1 stored in the memory unit 22. The control unit 21 determines "hypertension" if the current stress value acquired as biological data D2 exceeds the resting stress value by a predetermined percentage. The control unit 21 also acquires other data because these affect the likelihood of developing hypertension. For example, hypertension is more likely to occur if the amount of exercise is low (specifically, moderate exercise (approximately 40-60% of maximum oxygen uptake) for less than 150 minutes per week), sleep duration is short (less than 7 hours per day), weight is increasing (BMI of 25 or higher), smoking habits exist, or salt intake is high (8g or more per day). For example, if data other than stress-related data meets predetermined conditions, the prediction accuracy can be improved by making it easier to determine hypertension using stress-related data (reducing the "predetermined percentage" mentioned above).

[0122] For example, "anxiety" can be predicted as an autonomic nervous system symptom. When stress is felt, the sympathetic nervous system becomes dominant, and if the sympathetic nervous system becomes excessive, it causes anxiety. When predicting "anxiety" as an autonomic nervous system symptom, the control unit 21 of the information processing device 20 acquires data as biological data D2, which includes at least one of the following: data on the subject U1's exercise level, data on the subject U1's stress level, data on the subject U1's sleep level, and data on the subject U1's respiration level. The control unit 21 also acquires data on the subject's attributes as factor data D3. The reason for acquiring data on the subject U1's exercise level as biological data D2 is that if the subject has a moderate exercise habit (a habit of doing moderate exercise for about 30 minutes about 3 times a week), they are less likely to feel anxious (if the amount of exercise is extremely low, they are more likely to feel anxious). The reason for acquiring data on sleep as biological data D2 is that if the sleep duration is short (for example, less than 7 hours of sleep per day) or if sleep is frequently interrupted, the subject is more likely to feel anxious. The reason for acquiring respiratory data as biometric data D2 is that shallow and rapid breathing (for example, more than 20 breaths per minute) can increase the likelihood of feeling anxious.

[0123] For example, "asthma symptoms" can be predicted as an autonomic nervous system symptom. When predicting "asthma symptoms" as an autonomic nervous system symptom, the control unit 21 of the information processing device 20 acquires data as biological data D2, which includes at least one of the following: data on the subject U1's stress, data on the subject U1's exercise level, and data on the subject U1's respiration. The control unit 21 also acquires data as factor data D3, which includes data on the subject's attributes, and at least one of the following: data on the subject U1's smoking habits, data on the subject U1's allergies, and data on weather information. This is because stress can trigger asthma, and there is also exercise-induced asthma. Normal respiration can be confirmed based on respiratory rate or oxygen concentration. Furthermore, factors that cause asthma, such as pollen, yellow dust, and weather, are also useful in predicting asthma.

[0124] For example, tinnitus can be predicted as an autonomic nervous system symptom. When the body is under stress, the sympathetic nervous system becomes dominant, and blood vessels constrict. It is said that tinnitus is more likely to occur when blood circulation around the ears is poor. When predicting tinnitus as an autonomic nervous system symptom, the control unit 21 of the information processing device 20 acquires data including data on the subject U1's stress or data on the subject U1's sleep as biological data D2. This is because tinnitus may occur when stressed or when sleep-deprived. The control unit 21 also acquires data including at least one of the following as factor data D3: data on the subject U1's subjective symptoms of tinnitus, data on the earphones used by the subject U1, and data on the external environmental sounds of the location where the subject U1 is. This is because tinnitus is triggered by stress, and trends can be analyzed by recording symptoms. Also, since loud noises can cause tinnitus, it is necessary to consider the volume of the earphones and the volume of the external environment.

[0125] In yet another modification of this embodiment, the control unit 21 of the information processing device 20 may acquire history information D4 showing records of autonomic nervous system symptoms previously entered by the subject U1. The control unit 21 may then perform prediction processing using the acquired history information D4 and time-series data D1. The history information D4 can be acquired by any procedure. In this embodiment, the history information D4 is stored in the storage unit 22 of the information processing device 20. The control unit 21 of the information processing device 20 acquires the history information D4 by reading it from the storage unit 22. The reason for acquiring the history information D4 of the subject U1 is as follows: Changes in physical condition that occur in response to autonomic nervous system disturbances vary from person to person, and there are individual differences in which of the various changes in physical condition occur, as well as the timing and degree of such changes in physical condition.

[0126] The control unit 21 of the information processing device 20 associates the acquired history information D4 with the time-series data D1 acquired in S1. For example, based on the time-series data D1 and the history information D4, the control unit 21 associates the time-series data D1 and the history information D4 by linking the autonomic nervous system symptoms shown in the acquired history information D4 as labels to the changes in the sympathetic and parasympathetic nervous systems estimated using the time-series data D1. As an example, if subject U1 records "abdominal pain" during a period in which the parasympathetic nervous system is excessively dominant as shown in the time-series data D1, the control unit 21 uses the time-series data D1 to determine the period P1 in which the parasympathetic nervous system is excessively dominant, and based on the history information D4, determines that "abdominal pain" is the autonomic nervous system symptom entered by subject U1 during that period P1. Then, by linking the changes in the autonomic nervous system during period P1 (excessive dominance of the parasympathetic nervous system) and the determined autonomic nervous system symptom, "abdominal pain," as labels, the time-series data D1 and the historical information D4 are associated. As another example, if "constipation" is recorded by subject U1 during a period in which the sympathetic nervous system is excessively dominant, as shown in the time-series data D1, the control unit 21 uses the time-series data D1 to determine the period P2 in which the sympathetic nervous system is excessively dominant, and based on the historical information D4, determines that "constipation" is the autonomic nervous system symptom entered by subject U1 during that period P2. Then, by linking the changes in the autonomic nervous system during period P2 (excessive dominance of the sympathetic nervous system) and the determined autonomic nervous system symptom, "constipation," as labels, the time-series data D1 and the historical information D4 are associated. By associating time-series data D1 with historical information D4 in this way, the control unit 21 can predict "abdominal pain" as an autonomic nervous system symptom if it estimates that the parasympathetic nervous system is excessive based on the time-series data D1. Furthermore, the control unit 21 can predict "constipation" as an autonomic nervous system symptom if it estimates that the sympathetic nervous system is excessive based on the time-series data D1. Machine learning models can be used for these predictions, and the machine learning model can be trained using the historical information D4 as training data to improve prediction accuracy.

[0127] In this modified example, the autonomic nervous system symptoms recorded in the history information D4 may include the type of symptoms recorded by multiple subjects U1, the time of onset of the symptoms, and the intensity and / or duration of the symptoms. The control unit 21 of the information processing device 20 may then associate the time-series data D1 with the history information D4 by linking the changes in the sympathetic and parasympathetic nervous systems estimated using the time-series data D1 with the intensity and / or duration of the symptoms that occurred as indicated in the history information D4, using these as labels.

[0128] As described above, the control unit 21 of the information processing device 20 acquires history information D4 and performs prediction processing using the time-series data D1 acquired in S1 and the acquired history information D4. According to this modified example, the control unit 21 can perform prediction processing of autonomic nervous system symptoms not only based on the time-series data D1 but also on the history information D4. The control unit 21 can perform prediction processing of autonomic nervous system symptoms not only based on the time-series data D1 and history information D4 but also based on biological data D2 and / or factor data D3. Therefore, autonomic nervous system symptoms can be predicted with greater accuracy, and the quality of life of the subject U1 can be improved.

[0129] In this embodiment, the control unit 21 of the information processing device 20 further performs control to notify the target person U1 of the result of the prediction processing. Specifically, the control unit 21 further performs the processing shown in S4 of Figure 4.

[0130] In S4, the control unit 21 of the information processing device 20 controls the notification of the prediction processing results to the subject U1. Specifically, the control unit 21 transmits the prediction data d1, as a result of the prediction processing in S3, to the subject U1's terminal device 30 via the communication unit 23. The control unit 31 of the terminal device 30 receives the prediction data d1 transmitted from the information processing device 20 via the communication unit 33 and displays it on the display, which acts as the output unit 35. As a result, the autonomic nervous system symptoms indicated by the prediction data d1 are displayed on the display, which acts as the output unit 35. Specifically, the control unit 21 displays on the display, which acts as the output unit 35, the type of autonomic nervous system symptoms predicted to occur in the subject U1, the time of occurrence, the intensity, and / or duration of the autonomic nervous system symptoms indicated by the prediction data d1.

[0131] Figures 5 to 9 show examples of screens where the predicted data d1, as a result of the prediction processing in S3, is displayed on the output unit 35 of the terminal device 30 of the subject U1, as determined by the control unit 21 of the information processing device 20.

[0132] In this embodiment, the control unit 21 of the information processing device 20 displays the results of the prediction process in S3 alongside the time-series changes in the autonomic nervous system on the terminal device 30 of the subject U1. Figure 5 is an example in which the autonomic nervous system symptoms of subject U1 predicted as a result of the prediction process in S3 are displayed alongside the time-series changes in hormone values ​​represented by the sympathetic nervous system, which are estimated based on the time-series data D1.

[0133] As shown in Figure 5, the display, which serves as the output unit 35 of the terminal device 30, shows a screen titled "Your Symptoms and Autonomic Nervous System Activity." The displays "9:41" and "November 29, 2023" indicate the date and time when subject U1 is viewing the screen. In the graph on the screen in Figure 5, the horizontal axis represents the passage of time, and the vertical axis represents the fluctuations of subject U1's autonomic nervous system. On the graph, the predicted value of subject U1's parasympathetic nervous system is shown as a solid line, and the predicted value of the sympathetic nervous system is shown as a dotted line. On the graph, the upper limit of the normal range for the autonomic nervous system is shown as a dotted line, and areas exceeding the normal range are shaded. In Figure 5, the solid line showing the predicted value of the parasympathetic nervous system exceeds the normal range, indicating that the parasympathetic nervous system is excessively dominant. In Figure 5, subject U1's symptoms, such as changes in physical condition over time, are represented on the graph by facial icons alongside the changes in the autonomic nervous system. The face icons, from left to right, represent changes in physical condition: "abdominal pain," "abdominal pain," "headache," "sweating," and "good." When subject U1 taps the time slot "11:00," a speech bubble appears at the bottom of the screen. The display "November 30, 2023, 11:00" indicates that autonomic nervous system symptoms expected to occur at 11:00 on November 30, 2023, the following day, are displayed. The expected autonomic nervous system symptoms are shown as "symptom predictions," with "abdominal pain" and "headache" displayed. After the expected time of occurrence has passed, subject U1 can record whether the symptoms actually occurred by pressing or tapping the "symptoms present" or "symptoms absent" buttons to the right of "abdominal pain" and "headache." This "symptom prediction" display may be made to remain visible for a certain period even after the expected time of occurrence for each symptom has passed, allowing subject U1 to record whether the symptoms actually occurred even after the expected time of occurrence has passed. Additionally, an article describing countermeasures for the symptoms may be displayed under the heading "Autonomic Nervous System Balance," such as "Parasympathetic Nervous System Overactivity: The parasympathetic nervous system is working excessively, disrupting the balance of the autonomic nervous system. Please calm down and rest." Furthermore, although not shown in the example screen in Figure 5, if the predicted data d1 includes, for example, the severity and probability of occurrence of the symptoms, these may also be displayed.Subject U1 can scroll horizontally through the displayed graph to see whether the predicted symptoms actually occurred, and to view details of the autonomic nervous system balance results at the time the symptoms occurred. Furthermore, as described later, a recording icon may be displayed to allow Subject U1 to record autonomic nervous system symptoms, etc.

[0134] In this manner, the control unit 21 of the information processing device 20 displays the results of the prediction process on the terminal device 30 of the subject U1, alongside the changes in the sympathetic and parasympathetic nervous systems. According to this embodiment, the results of the prediction process in S3 are notified to the subject U1 along with the estimated changes in the autonomic nervous system. Specifically, the types of autonomic nervous system symptoms that are predicted to occur in the subject U1 are displayed on the display, which is the output unit 35 of the terminal device 30, as changes in physical condition indicated by the prediction data d1. The display may also show the time of occurrence, intensity, probability of occurrence, and / or duration of the symptom. By displaying the changes in the autonomic nervous system over time and the autonomic nervous system symptoms side by side in this way, the subject U1 can see the balance of the autonomic nervous system in correspondence with the symptom record, and can grasp the correlation between the state of the autonomic nervous system and their own symptoms. Once the correlation between the balance of the autonomic nervous system and the autonomic nervous system symptoms is understood, the subject U1 can gain a sense of relief, thinking, "So those symptoms were because the balance of my autonomic nervous system was disrupted." Furthermore, if subject U1, for example, avoids going out during the times when autonomic nervous system symptoms are expected to occur, they can more easily avoid situations where abdominal pain suddenly occurs while out or at work, thereby suppressing a decline in subject U1's quality of life.

[0135] As a modification of this embodiment, the control unit 21 of the information processing device 20 may acquire countermeasure information D6 indicating a preventive method or countermeasure determined based on the result of the prediction processing in S3, and perform control to notify the subject U1 of the preventive method or countermeasure indicated in the acquired countermeasure information D6 together with the result of the prediction processing. The countermeasure information D6 may be acquired by any procedure. For example, the storage unit 22 of the information processing device 20 stores a database that stores changes in physical condition in association with preventive methods or countermeasures. The control unit 21 may determine a preventive method or countermeasure associated with the changes in physical condition determined as a result of the prediction processing in S3 by referring to the database. The control unit 21 may then acquire the determined result as countermeasure information D6. In this embodiment, determining a preventive method or countermeasure includes diagnosing the type of symptoms of the subject U1 by continuing the prediction processing in S3 for a certain period of time, and determining a preventive method or countermeasure that matches the diagnosed type. For example, as a preventative or treatment method for the autonomic nervous system symptom "diarrhea," one could associate it with suppressing intestinal peristalsis by "avoiding irritants," "eating easily digestible foods," "taking anti-diarrheal medication," and "consuming dietary fiber and probiotics." For the autonomic nervous system symptom "drowsiness," one could associate it with "caffeine intake" and "getting sunlight during the day." As a preventative or treatment method for the autonomic nervous system symptom "abdominal pain," one could associate it with "delaying going out," "having brunch," and "taking medication."

[0136] Figure 6 shows an example of the screen displayed on the output unit 35 of the terminal device 30 when the predicted change in the physical condition of subject U1 as a result of the prediction processing in S3 is "abdominal pain," and the preventive or coping methods indicated in the countermeasure information D6 for this change in physical condition are "delay the time of going out" and "have a brunch."

[0137] As shown in Figure 6, the display, which serves as the output unit 35 of the terminal device 30, shows a screen titled "Predicted onset time of abdominal pain." The display "11:15~12:08" represents the predicted duration of the symptoms. The symptom duration can be the same as the recorded "abdominal pain" in the autonomic nervous system symptom record shown in the history information D4. Subject U1 can request that the results of this prediction process be sent to a third party by pressing or tapping the "Send SOS" button displayed at the top of the screen. The "Send SOS" button will be described later. In addition, "What you can do now" is displayed, along with the countermeasures "Delay your departure time" and "Have brunch." For the countermeasure "Delay your departure time," the message "Let's go out after 13:00 when the symptoms are likely to subside" is displayed, along with an illustration. For the countermeasure "Have brunch," the message "The symptoms are likely to worsen around lunchtime. Have an early lunch with breakfast" is displayed, along with specific advice for subject U1. Furthermore, the "hourly changes" and "7-day changes" of subject U1's symptoms are displayed using facial icons. In the "hourly changes," the facial icons indicate changes in physical condition: "currently" is "irritable," "10:00" is "good," and "11:00" and "12:00" are "stomach ache." In the "7-day changes," the facial icons show changes in physical condition during the "morning," "noon," "evening," and "night" for "today" and "Wednesday, December 6th," respectively. "Wednesday, December 6th" is either 7 days after or 7 days before "today." In "today," the changes in physical condition are "good" in the "morning," "stomach ache" at "noon," and "good" in the "evening" and "night." On the other hand, in "Wednesday, December 6th," the changes in physical condition are "good" in the "morning" and "noon," but the occurrence of "stomach ache" is indicated in the "evening" and "night." By highlighting the symptoms "irritability" and "stomach ache" with a border, we can draw attention to the target individual U1.

[0138] As described above, in this modified version, the control unit 21 of the information processing device 20 acquires countermeasure information D6 indicating a preventive or countermeasure method determined based on the results of the prediction process. The control unit 21 controls the system to notify the subject U1 of the preventive or countermeasure method indicated in the acquired countermeasure information D6, along with the results of the prediction process. By displaying the predicted symptoms and the preventive or countermeasure methods for those symptoms, the subject U1 can more easily take measures to alleviate their symptoms. In addition, by displaying the duration of the symptoms, the subject U1 can see how long it will take for the symptoms to subside, thus improving their suffering. As a result, the quality of life of the subject U1 is improved.

[0139] As another modification of this embodiment, the control unit 21 of the information processing device 20 may perform the prediction processing in S3 on a daily basis and notify the target person U1 of the results of the prediction processing in S3 on a daily basis.

[0140] Figure 7 shows an example of displaying the results of the prediction process in S3 for each date, June 5, 2024 and June 3, 2024.

[0141] In the example shown in Figure 7, it can be seen that at 5 PM on Wednesday, June 5, 2024, sympathetic nervous system activity is predicted to be high. It can also be seen that the weather at the same time is "sunny," stress levels are "low," and body temperature is 36.7°C. Furthermore, it can be seen that subject U1's sleep duration is 8.2 hours, and the average number of steps per hour is 18. The face icon indicates that subject U1 is "unwell," and the suggested treatment is taking "over-the-counter medication (XXX tablets)." Similarly, it can be seen that at 5 PM on Monday, June 3, 2024, the balance of the autonomic nervous system is predicted to be normal. It can also be seen that the weather at the same time is "cloudy," stress levels are "moderate," and body temperature is 36.3°C. Furthermore, it can be seen that subject U1's sleep duration is 6.2 hours, and the average number of steps per hour is 500. The face icon indicates that subject U1 is "slightly unwell," and suggests "exercise" and "stretching" as possible solutions. Furthermore, as will be discussed later, a recording icon may be displayed to allow subject U1 to record autonomic nervous system symptoms, etc.

[0142] Alternatively, the results of S3's prediction process can be displayed on a calendar to notify subject U1 of the results on a daily basis, showing past symptoms and autonomic nervous system balance. Specifically, symptom icons may be displayed on the days in the calendar when autonomic nervous system symptoms occurred. The calendar may also be color-coded based on the autonomic nervous system balance. For example, days with poor autonomic nervous system balance could be displayed in red, slightly poor days in yellow, and days with no problems in blue. As a result, the autonomic nervous system balance can be visualized. If subject U1 is female, the menstrual period may also be displayed on the calendar. Recording icons, as described later, may also be displayed on the calendar.

[0143] As another modification of this embodiment, the control unit 21 of the information processing device 20 may notify the subject U1 of the result of the prediction process in S3 at a predetermined timing T2. The timing T2 can be arbitrarily set, for example, 1 hour, 30 minutes, or 10 minutes before the onset of autonomic nervous system symptoms. The timing T2 may be set for each type of autonomic nervous system symptom. If the control unit 21 of the information processing device 20 predicts that autonomic nervous system symptoms will occur as a result of the prediction process in S3, it may display a message M1 as a notification to the subject U1 to warn them at timing T2. For example, if the prediction process in S3 predicts the onset of an autonomic nervous system symptom called "abdominal pain," the control unit 21 generates a message M1 that reads, "Abdominal pain will occur in Y minutes! Take your medicine." Alternatively, if the control unit 21 determines in S2 that the autonomic nervous system is out of balance as a result of estimating changes in the sympathetic and parasympathetic nervous systems of subject U1, the control unit 21 may generate a message M1 that reads, "Your autonomic nervous system is out of balance. You may feel anxious or irritable. Calm down and take a deep breath." The control unit 21 transmits the generated message M1 to the subject U1's terminal device 30. The terminal device 30 receives the message M1 transmitted from the information processing device 20 and displays it on the display, which acts as the output unit 25. The message M1 can be displayed as a banner, for example, as shown in Figure 8. A banner is a function that displays an object at the top of the screen. In the example in Figure 8, the text "Your autonomic nervous system is out of balance. You may feel anxious or irritable. Calm down and take a deep breath." is displayed as a banner message M1.

[0144] In this way, by notifying subject U1 in advance of autonomic nervous system imbalances and / or expected autonomic nervous system symptoms, subject U1 can prepare beforehand. If the autonomic nervous system symptoms are mental symptoms such as "depression," subject U1 can be reassured knowing that their depression is simply due to an autonomic nervous system imbalance. Therefore, subject U1's quality of life improves.

[0145] As another modification of this embodiment, the control unit 21 of the information processing device 20 may acquire diagnostic information D5 indicating the type of autonomic nervous system symptoms of the subject U1 diagnosed based on the results of the prediction process, and perform control to notify the subject U1 of the diagnostic result indicated by the acquired diagnostic information D5 along with the results of the prediction process. The diagnostic information D5 can be acquired by any procedure, but for example, it can be acquired by the following procedure.

[0146] Generally, autonomic nervous system disorders can be broadly divided into four types: essential autonomic nervous system disorders, neurotic autonomic nervous system disorders, psychosomatic autonomic nervous system disorders, and depressive autonomic nervous system disorders. Essential autonomic nervous system disorders are caused by an inherent imbalance in the autonomic nervous system. Neurotic autonomic nervous system disorders are caused by psychological factors that disrupt the balance of the autonomic nervous system. Psychosomatic autonomic nervous system disorders are caused by everyday stress, such as work and interpersonal relationships. It is said that as the symptoms of psychosomatic autonomic nervous system disorders progress, they can transition to depressive autonomic nervous system disorders. Therefore, it is conceivable to determine the type of autonomic nervous system disorder in subject U1 by pre-defining the autonomic nervous system symptoms that are likely to appear for each type, and having the control unit 21 determine which type the tendency of autonomic nervous system symptoms shown as a result of the prediction processing of subject U1 corresponds to. Specifically, the control unit 21 diagnoses the type of autonomic nervous system disorder of subject U1 by continuing the prediction processing in S4 for a certain period of time. Alternatively, as described below, subject U1 may record their symptoms etc. via the terminal device 30, and the control unit 21 of the information processing device 20 may determine the type of autonomic nervous system disorder of subject U1 based on the recording of symptoms etc. entered by subject U1. Alternatively, the control unit 21 may store diagnostic information D5 about subject U1 in advance in the storage unit 22, and the control unit 21 acquires information indicating the result of determining the type of autonomic nervous system disorder of subject U1 as diagnostic information D5. Alternatively, the control unit 21 may acquire diagnostic information D5 by reading the diagnostic information D5 from the storage unit 22.

[0147] Figure 9 shows an example of an input screen for recording symptoms displayed on the terminal device 30. Subject U1 can record their symptoms, etc., through this input screen. The input screen in Figure 9 displays "Physical Condition," "Autonomic Nervous System Symptoms," "Coping Strategies," and "Events" as items to be recorded. The face icons displayed for the "Physical Condition" item indicate, from left to right, that the physical condition is "Very Good," "Good," "Normal," "Poor," and "Very Poor." The four input icons displayed for the "Autonomic Nervous System Symptoms" item indicate, from left to right, that the symptoms are "Sleepiness," "Dizziness," "Abdominal Pain," and "Tinnitus." The three icons displayed for the "Coping Strategies" item indicate, from left to right, "Medication," "Exercise," and "Sleep." The two icons displayed for the "Events" item indicate, from left to right, "Hospital Visit" and "Travel."

[0148] Note that the display in Figure 9 is illustrative, and many more types of input icons may be displayed. For example, subject U1 may display input icons for other items by scrolling up and down on each input screen. User U may also display further input icons for an item by scrolling left and right on the area where an input icon for that item is displayed. In addition to the above input icons, the input screen may also display a record icon or a "Record" button. A memo field may also be provided so that other information necessary for symptom prediction can be entered as text.

[0149] Subject U1 can input their autonomic nervous system symptoms, as well as their physical condition, actions taken, and events related to those symptoms, by selecting a desired icon from among several input icons displayed on the input screen. For example, in the example shown in Figure 9, Subject U1 can input their "autonomic nervous system symptoms" by selecting one of the four input icons labeled "autonomic nervous system symptoms" for each item. Similarly, Subject U1 can record their "physical condition," "actions taken," and "events" when the autonomic nervous system symptoms occurred.

[0150] The control unit 21 of the information processing device 20 determines the type of autonomic nervous system dysfunction suffered by subject U1 based on the content of each item recorded by subject U1. The control unit 21 acquires information indicating the determination result as diagnostic information D5. The control unit 21 of the information processing device 20 notifies subject U1 of the diagnostic result indicated in the acquired diagnostic information D5, along with the result of the prediction process.

[0151] As described above, the control unit 21 of the information processing device 20 acquires countermeasure information D6 indicating a preventive or countermeasure method determined based on the results of the prediction process, and controls the subject U1 to notify it of the preventive or countermeasure method indicated in the acquired countermeasure information D6 along with the results of the prediction process. By displaying the predicted symptoms and the preventive or countermeasure methods for those symptoms, the subject U1 can more easily take measures to alleviate their symptoms. Similarly, by displaying the type of autonomic nervous system dysfunction, the subject U1 can also more easily take measures to alleviate their symptoms. Therefore, the technology for predicting changes in the physical and mental state of the subject U1 is improved.

[0152] In this embodiment, the control unit 21 of the information processing device 20 may perform control to notify a third party other than the subject U1 of the results of the prediction processing based on a request from the subject U1. The third party can be arbitrarily selected by the subject U1, but for example, it could be X's friends and family. The subject U1's terminal device 30 communicates with the third party's terminal device 30' via the network 40. When the terminal device 30 receives an instruction from the subject U1 to send the results of the prediction processing as a request, it sends the results of the prediction processing to the terminal device 30'.

[0153] Referring to Figures 5, 10, and 11, the procedure for notifying a third party other than the subject U1 of the prediction process will be explained. In Figure 5, when the subject U1 touches the "Send SOS" button displayed at the top of the screen, a screen like the one shown in Figure 10 will appear, which will serve as the screen for sending the prediction process results to a third party other than the subject U1.

[0154] Figure 10 shows an example screen for sending the results of the prediction process from "X" (as subject U1) to "Y" (as a third party). In Figure 10, "X" (as subject U1) has selected "Y" as the third party to whom the results will be sent. "X" (as subject U1) can select who they want to share the prediction process results with by clicking the "plus (+)" button to the right of "Y's" icon. "December 6, 2023, 9:22 AM" is the time subject U1 is viewing the screen. "X" can select the content they want to share with "Y". The content that "X" can share with "Y" can be selected from, for example, "Recent symptom onset time", "Recent symptom description", and "Today's forecast". In the example in Figure 10, subject U1 selects the content they want to share with "Y" by checking the checkboxes for "Next symptom onset time", "Specific symptoms that will occur next", and "Today's symptom forecast" in the "Choose what you want to tell about your symptoms" section. Furthermore, "X," as the subject U1, can also choose "I want to be left alone," "I want things to be as usual," or "I want to be treated kindly" as the "desire for Y to treat X." In the example in Figure 10, "X," as the subject U1, has chosen "I want things to be as usual" as the "desire for Y to treat X." Subject U1 can also enter a free-form message in the "Write a message" field at the top of the screen. For example, "X," as the subject U1, might write the message to the third party "Y" saying, "Sorry!!! My stomach ache is getting worse, so I'd like to skip lunch today." When subject U1 "X" presses the "Send" button at the bottom of the screen, the result of the prediction process is sent to the third party "Y."

[0155] Figure 11 shows an example of a screen displayed on the output unit 35' of terminal device 30' of "Y," a third party who received the result of the prediction processing sent from "X," the subject U1. In the example screen shown in Figure 11, "A message has arrived from X" is displayed below the icon representing the third party "Y." The message entered by X for Y is displayed as "Sorry!!! My stomach ache is getting worse, so I'll have to skip lunch today." "December 6, 2023, 9:22 AM" is the date and time the prediction processing result was received. In addition, in the "About X's Symptoms" section, the time of onset of subject U1's symptoms is displayed as "X's next symptoms will appear between 11:15 AM and 12:08 PM," the type of symptom predicted to occur is "stomach ache," and the change in physical condition over time, indicated by facial expressions, is "normal" at 8:00 AM, the present, and 10:00 AM, and "stomach ache" at 11:00 AM and 12:00 PM. From these displays, "Y" can understand that "X" is predicted to develop a stomach ache around lunchtime. Therefore, since the reason why "X" suddenly changed plans can be inferred, "Y" can reduce their distrust of "X". Also, since "preferred interaction" is displayed as "as usual", "Y" will have a guideline for how to interact with "X", making it easier to interact with "X" appropriately. As a result, the relationship between "X" and "Y" will be maintained in a good state. "Y" can also select the reaction button at the bottom of the screen and send their reaction to "X".

[0156] As described above, the information processing device 20 controls the notification of the prediction processing results to a third party other than the target person U1, based on a request from the target person U1. Specifically, the control unit 21 of the information processing device 20 controls the display of a selection screen on the target person U1's terminal device 30 for the target person U1 to select the content to be notified to the third party. Furthermore, the control unit 21 controls the display of a selection screen on the target person U1's terminal device 30 for the target person U1 to select the desired content regarding how the third party should interact with the target person U1.

[0157] According to this embodiment, subject U1 can more easily share their symptoms with a third party. For example, subject U1 may be able to alleviate their suffering by sharing their distressing symptoms with a third party, such as a friend or family member. Therefore, according to this embodiment, subject U1's quality of life is further improved.

[0158] In this embodiment, the information processing device 20 and the terminal device 30 communicate via the network 40 to perform a process that displays the results of the prediction process on the terminal device 30. However, some or all of the functions of the information processing device 20 may be installed on the terminal device 30. Furthermore, the process shown in Figure 4 may be performed solely by the terminal device 30, which is equipped with the information processing program according to this disclosure as an application program, without going through the information processing device 20. In addition, in this embodiment, as an example, the case of estimating changes in the autonomic nervous system of subject U1 and predicting autonomic nervous system symptoms such as abdominal pain, constipation, diarrhea, or irritable bowel syndrome that may occur in subject U1 as a result of the estimated changes in the autonomic nervous system was described. However, the prediction process according to this disclosure is not limited to this, and any change in physical condition that may occur in subject U1 as a result of changes in the autonomic nervous system may be predicted.

[0159] This disclosure is not limited to the embodiments described above. For example, multiple blocks described in the block diagram may be combined, or a single block may be divided. Instead of executing multiple steps described in the flowchart in chronological order as described, they may be executed in parallel or in a different order, depending on the processing capacity of the device performing each step, or as necessary. Other modifications are possible without departing from the spirit of this disclosure. [Explanation of Symbols]

[0160] 10 Systems 20 Information Processing Devices 21 Control Unit 22 Memory section 23 Communications Department 30,30' Terminal device 31 Control Unit 32 Storage section 33 Communications Department 34 Input section 35,35' Output section 40 Networks U1 Target

Claims

1. An information processing device comprising a control unit that acquires time-series data including data showing changes in the heart rate of a subject, and uses the time-series data to perform a predictive process for predicting autonomic nervous system symptoms associated with autonomic nervous system dysfunction.

2. The information processing apparatus according to claim 1, wherein the control unit estimates changes in the sympathetic and parasympathetic nerves in the subject using the time-series data, and predicts the autonomic nervous system symptoms based on the estimated results.

3. The information processing apparatus according to claim 2, wherein the autonomic nervous system symptoms are abdominal pain, constipation, diarrhea, or irritable bowel syndrome.

4. The control unit acquires data as biological data that includes at least one of the following: data relating to the subject's stress, data relating to the subject's exercise level, data relating to the subject's internal water balance, data relating to the subject's stomach or intestinal sounds, and data relating to the amount of contents of the subject's stomach or intestines, and further uses the acquired biological data to perform the prediction process, as described in claim 1.

5. The information processing apparatus according to claim 1, wherein the control unit acquires data including at least one of the subject's dietary data, the subject's attributes, and the subject's water intake as factor data, and further uses the acquired factor data to perform the prediction process.

6. The information processing apparatus according to claim 2, wherein the control unit acquires historical information indicating a record of autonomic nervous system symptoms previously entered by the subject, and uses the acquired historical information and the time-series data to perform the prediction process.

7. The information processing apparatus according to claim 2 or claim 6, wherein the control unit displays the results of the prediction process on the subject's terminal device in conjunction with the changes in the sympathetic and parasympathetic nervous systems.

8. The information processing apparatus according to claim 1, wherein the control unit acquires diagnostic information indicating the type of autonomic nervous system symptoms of the subject diagnosed based on the results of the prediction process, and controls the system to notify the subject of the diagnostic results indicated by the acquired diagnostic information together with the results of the prediction process.

9. The information processing apparatus according to claim 1, wherein the control unit acquires countermeasure information indicating a preventive method or countermeasure determined based on the result of the prediction process, and controls the target person to notify them of the preventive method or countermeasure indicated in the acquired countermeasure information together with the result of the prediction process.

10. The information processing apparatus according to claim 1, wherein the control unit performs control to notify a third party other than the subject of the result of the prediction process based on a request from the subject.

11. The information processing apparatus according to claim 10, wherein the control unit controls the display of a selection screen on the target person's terminal device for selecting content to be notified to the third party.

12. The information processing apparatus according to claim 10 or 11, wherein the control unit controls the display of a selection screen on the target person's terminal device for the third party to select their preferred way of interacting with the target person.

13. The information processing apparatus according to claim 2, A terminal device that acquires data showing the results of the prediction process performed by the information processing device, and displays the results of the prediction process to the subject, alongside the changes in the sympathetic and parasympathetic nervous systems. A system equipped with these features.

14. A terminal device equipped with the functions of the information processing device described in claim 1.

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