Information processing device, information processing method, and program

Abstract

It is difficult to acquire a score indicating the physical state of a user. A score indicating the present or future physical state of each user can be acquired by means of an information processing device (200), the information processing device (200) being provided with: a biological information acquisition unit (143) that acquires measurement information relating to the biological state on the basis of measurement results of biological potentials of a user at two or more times; and a score acquisition unit (251) that acquires score information including scores indicating the present or future physical state of the user on the basis of the measurement information.

Description

Technical Field

[0001] The present invention relates to an information processing device, an information processing method, and a program capable of acquiring information related to a user's current or future physical condition. Background Art

[0002] In recent years, users have become more aware of their own health, and various devices or services have been provided to assist users in understanding their health status. For example, the following patent document 1 discloses a system configured to determine a health risk signal for a user based on a cumulative heart rate value determined based on biometric data obtained from the user and provide the signal to the user.

[0003] Patent Document 2 discloses a relaxation level determination device that can determine the relaxation level simply and accurately using a heartbeat (pulse) signal of a user, namely, a subject.

[0004] It is also known that various rhythms can affect a person's sleep-wake rhythm. For example, there is a rhythm called a circadian rhythm. In addition, regarding a person's sleep-wake rhythm, a so-called dual-process rhythm is widely known (for example, the following non-patent document 1, etc.).

[0005] In addition, as a machine learning method that can be used for future prediction of time series data, in addition to the conventionally known Transformer, a method called Informer as described in the following non-patent document 2 has been proposed.

[0006] Patent Literature Patent Document 1: Japanese Patent No. 6531161 Patent Document 2: Japanese Patent Application Laid-Open No. 9-70399

[0007] Non-patent literature Non-patent document 1: Daan S, Beersma DG, Borbely AA. Timing of human sleep: recovery process gated by a circadian pacemaker. Am J Physiol. 1984; 246(2Pt2): R161-83. Non-patent literature 2: Zhou, Haoyi, et al. "Informer: Beyond efficient transformer for long sequence time series forecasting." Proceedings of the AAAI Conference on Artificial Intelligence.Vol.35.No.12. Summary of the invention

[0008] If a user wants to maintain health, it is more effective for the user to easily understand or evaluate his or her health status. In order to enable the user to understand his or her health status, it is considered useful to use a score representing the user's physical condition. However, it is difficult to obtain such a score in the past.

[0009] An object of the present invention is to provide an information processing device, an information processing method, and a program that can obtain a score representing the current or future physical condition of each user.

[0010] The information processing device of the first invention is as follows, comprising: a bio-information acquisition unit, which acquires measurement information related to the bio-state based on the measurement results of the user's bio-potential at more than two moments; and a score acquisition unit, which acquires score information including a score representing the user's current or future physical state based on the measurement information.

[0011] With such a configuration, it is possible to obtain a score representing the current or future physical condition of each user.

[0012] Furthermore, the information processing device of the second invention is as follows: with respect to the first invention, the measurement information is time-series information.

[0013] With such a configuration, it is possible to obtain score information representing the physical condition more accurately.

[0014] Furthermore, the information processing device of the third invention is as follows: with respect to the first or second invention, the score information includes information indicating transition of the score.

[0015] With such a configuration, it is possible to obtain information indicating the transition of the score of each user.

[0016] Furthermore, the information processing device of the fourth invention is as follows, regarding any one of the first to third inventions, further comprising: an output information acquisition unit that acquires output information related to the score based on the score information; and an output unit that outputs the output information.

[0017] With such a configuration, it is possible to present the user with information corresponding to the user's current or future physical condition.

[0018] In addition, the information processing device of the fifth invention is as follows: with respect to the fourth invention, the output information acquisition unit acquires the maximum value of the score regarding the score information obtainable by the user, and acquires output information corresponding to the relationship between the maximum value and the score information already acquired by the score acquisition unit.

[0019] With such a configuration, it is possible to present to the user information corresponding to the relationship between the state in which the score is the maximum and the current or future physical state.

[0020] Furthermore, the information processing device of the sixth invention is as follows: with respect to the fourth invention, the measurement information is information obtained by measuring the bioelectric potential of the user by a measurement device having two or more electrodes in contact with the body surface of the user.

[0021] With such a configuration, it is possible to easily obtain the measurement result of the user's bioelectric potential.

[0022] Furthermore, the information processing device of the seventh invention is as follows, with respect to the sixth invention, wherein the output information acquisition unit acquires output information for outputting sound from the measuring device.

[0023] With such a configuration, the user can be informed of information on the score by outputting a sound from the measuring device.

[0024] In addition, the information processing device of the eighth invention is as follows: regarding any one of the fourth to seventh inventions, the output information acquisition unit acquires suggestions related to the user's eating behavior as output information, so as to be used to obtain a higher score in the future than the score of the scoring information already acquired by the scoring acquisition unit.

[0025] With such a configuration, the user can be informed of advice on eating behavior to achieve a better state in the future.

[0026] In addition, the information processing device of the ninth invention is as follows: regarding any one of the first to eighth inventions, the score acquisition unit acquires score information based on information obtained by measuring at least one of the user's blood pressure, pulse, blood oxygen concentration, body temperature, respiratory rate and acceleration.

[0027] With such a configuration, it is possible to obtain score information that reflects the user's physical condition with high accuracy.

[0028] Furthermore, the information processing device of the tenth invention is as follows, regarding any one of the first to ninth inventions, wherein the score acquisition unit acquires the score information based on dietary information related to the dietary behavior of the user.

[0029] With such a configuration, it is possible to obtain score information that reflects the user's physical condition with high accuracy.

[0030] In addition, the information processing device of the eleventh invention is as follows: with respect to any one of the first to tenth inventions, the score acquisition unit uses learning information constructed in a manner of taking input information related to measurement information as input and taking future measurement information as output, acquires future measurement information based on the input information, and acquires score information based on the measurement information already acquired.

[0031] With such a configuration, it is possible to obtain score information that reflects the user's physical condition with high accuracy.

[0032] In addition, the information processing device of the twelfth invention is as follows. With respect to any one of the first to tenth inventions, the score acquisition unit uses learning information constructed in a manner of taking input information related to measurement information as input and outputting information related to the score, acquires information related to the score based on the input information, and acquires the score information using the acquired information.

[0033] With such a configuration, it is possible to predict a score reflecting the user's future physical condition with high accuracy.

[0034] Furthermore, the information processing device of the thirteenth invention is as follows: regarding the twelfth invention, the score acquisition unit uses, as the learning information, learning information formed using input information including measurement information measured in the past for two or more users.

[0035] With such a configuration, even if there is little measurement information about the user himself, it is possible to obtain highly accurate score information.

[0036] In addition, the information processing device of the fourteenth invention is as follows, regarding the twelfth invention, further comprising a learning information acquisition unit that acquires learning information by re-learning using evaluation information corresponding to the score information acquired by the score acquisition unit and input information used in acquiring the score information.

[0037] With such a configuration, as the score information is continuously acquired, it is possible to acquire score information with higher accuracy.

[0038] According to the information processing device and the like according to the present invention, it is possible to obtain a score indicating the current or future physical condition of each user. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a diagram showing an overview of an information processing system according to Embodiment 1 of the present invention. Figure 2 is a block diagram of the information processing device. Figure 3 This is a block diagram of the terminal device and the measuring device. Figure 4 This is a diagram for explaining the structure of learning information used in the information processing device. Figure 5 This is a flowchart showing an example of the operation of the information processing device. Figure 6 This is a flowchart showing an example of prediction information acquisition processing performed by the information processing device. Figure 7 This is a diagram for explaining time series information in a specific example of this embodiment. Figure 8 This is a diagram showing an example of time series information before filtering in a specific example of the present embodiment. Fig. 9 This is a diagram showing an example of time series information after filtering in a specific example of this embodiment. Fig.10 This is a diagram showing an example of prediction information acquired in a specific example of this embodiment. Fig.11 This is a diagram showing an overview of an information processing system according to Embodiment 2 of the present invention. Fig.12 is a block diagram of the information processing device. Fig.13 This is a flowchart showing an example of the operation of the information processing device. Fig.14 This is a flowchart showing an example of the scoring information acquisition process of the information processing device. Fig.15 This is a flowchart showing an example of a process of obtaining a life score by the information processing device. Fig.16 FIG. 1 is a first diagram showing a specific example of screen transition of the terminal device. Fig.17 FIG. 2 is a specific example of screen transition of the terminal device. Fig.18 It is an external view of the computer system in the above-mentioned embodiment. Fig.19 is a block diagram of the computer system. Explanation of symbols 1, 2-information processing system; 100, 200-information processing device; 110-storage unit; 111-learning information storage unit; 115-user information storage unit; 120-receiving unit; 130-accepting unit; 140-processing unit; 143-biological information acquisition unit; 145-prediction information acquisition unit; 146-pre-processing unit; 147-future information output unit; 170-transmitting unit; 241-evaluation information acquisition unit; 242-life information acquisition unit Department; 249-learning information acquisition department; 251-score acquisition department; 257-output information acquisition department; 260-output department; 600-terminal device; 610-terminal storage department; 620-terminal receiving department; 630-terminal acceptance department; 640-terminal processing department; 660-terminal output department; 661-display department; 670-terminal sending department; 680-sensor department; 700-measuring device; 702-electrode department; 706-meter output department. DETAILED DESCRIPTION

[0040] Hereinafter, embodiments of the information processing device and the like will be described with reference to the drawings. In the embodiments, components denoted by the same reference numerals perform the same operations, and therefore duplicate descriptions may be omitted.

[0041] Furthermore, the terms used below are generally defined as follows. Furthermore, the semantics of these terms should not always be interpreted as the meanings shown here, but should be interpreted with reference to the description thereof when they are described separately below, for example.

[0042] An identifier for a certain item is a word or symbol that specifically represents the item. Although the identifier is, for example, an ID, its type is not limited as long as it is information that can identify the corresponding item. That is, the identifier can be the name of the item it represents, or it can be a combination of symbols in a specifically corresponding manner.

[0043] Acquisition may include acquisition of items input by a user or the like, and may also include acquisition of information stored in the own device or other devices (which may be information that has been stored in advance or information generated by information processing in the device). Acquisition of information stored in other devices may include acquisition of information stored in other devices via an API or the like, and may also include acquisition of the contents of documents (including the contents of web pages, etc.) provided by other devices.

[0044] In addition, the acquisition of information can also utilize the so-called machine learning method. Regarding the utilization of the machine learning method, for example, it can be done as follows. That is, a learning machine (learning information) is constructed using the machine learning method, which takes a specific type of input information as input and outputs the type of output information you want to obtain. For example, more than two groups of input information and output information are prepared in advance, and the two or more groups of information are provided to the modules of the learning machine for machine learning to constitute the learning machine, and the constructed learning machine is accumulated in the storage unit. In addition, the learning machine can also be referred to as a classifier. In addition, as a method of machine learning, there are, for example, deep learning, random forest, SVM, etc., and their types are not limited. In addition, in machine learning, for example, functions in various machine learning frameworks such as fastText, tinySVM, random forest, TensorFlow, and various existing program libraries can be used. Sometimes the acquisition of information using such a learning machine is referred to as acquisition based on machine learning.

[0045] In addition, it is not limited to obtaining a learning machine through machine learning. The learning machine can also be, for example, a table representing the correspondence between an input vector based on input information and output information. At this time, the output information corresponding to the feature vector based on the input information can be obtained from the table, or two or more input vectors in the table and parameters for weighting each input vector can be used to generate a vector that is similar to the feature vector based on the input information, and the final output information is obtained using the output information and parameters corresponding to each input vector used in the generation. Sometimes the information obtained using such a learning machine is referred to as the acquisition using the correspondence. In addition, the learning machine can also be, for example, a function representing the relationship between an input vector based on input information and information used to generate output information. At this time, for example, the information corresponding to the feature vector based on the input information can be obtained by a function, and the output information can be obtained using the obtained information. Sometimes the information obtained using such a learning machine is referred to as the acquisition using a function.

[0046] The output of information is a concept that includes display on a display, projection using a projector, typing using a printer, audio output, transmission to an external device, accumulation on a storage medium, transmission of processing results to other processing devices or other programs, etc. Specifically, it includes, for example, display of information on a web page, transmission as an e-mail, etc., or output of information for printing.

[0047] The acceptance of information is a concept that includes the acceptance of information input from input devices such as a keyboard, mouse, or touch screen, the reception of information sent from other devices via wired or wireless communication lines, or the acceptance of information read from storage media such as optical disks, magnetic disks, and semiconductor memories.

[0048] (Implementation Method 1)

[0049] In this embodiment, an information processing system is formed using an information processing device. The information processing device is configured to obtain prediction information related to the future bio-signal of the user from the measurement results of the bio-signal of the provider, i.e., the user, and output future information related to the future state of the user based on the prediction information. The information processing device can be called a prediction information output device, and the information processing system can be called a prediction information output system.

[0050] Preferably, the information processing device can also be constructed as follows. For example, the measurement result is time series information, and the prediction information can be obtained from the time series information after a specified pre-processing. For example, the pre-processing is a singular spectrum analysis process, and the processed time series information can also be constructed based on the results up to a predetermined number of times based on the singular spectrum analysis process. For example, the prediction information is obtained using a neural network model, and in particular, a Transformer model can also be used. Here, the decoder of the Transformer model can also be constructed so as not to perform autoregression.

[0051] Furthermore, as a specific example, the information processing device may be configured to use information indicating the transition of LF, HF, or LF / HF based on an electrocardiogram (ECG) as the time series information. In addition, for example, the information processing device may be configured to determine a time zone in which the user is likely to feel nervous or a time zone in which the user is likely to relax during a predetermined period from the prediction information and output the information.

[0052] An example of an information processing system using the information processing device configured as above will be described below.

[0053] Figure 1 This is a diagram showing an overview of an information processing system 1 according to Embodiment 1 of the present invention.

[0054] In this embodiment, the information processing system 1 includes an information processing device 100, a terminal device 600, and a measuring device 700. The information processing device 100 and the terminal device 600 can communicate with each other via a network such as a local area network or the Internet. In addition, the measuring device 700 and the terminal device 600 can communicate with each other. In addition, the configuration of the information processing system 1 is not limited to this. The number of each device included in the information processing system 1 is not limited, and the information processing system 1 may also include other devices.

[0055] The measuring device 700 is, for example, a so-called wearable device. The measuring device 700 has an electrode portion 702 that contacts the surface of the user's body. The measuring device 700 is configured to measure a bioelectric signal related to the user's body activity potential or resting potential, etc., by means of the electrode portion 702. In the present embodiment, the measuring device 700 is configured to measure a bioelectric signal of a time series such as a brain wave or a pulse.

[0056] Furthermore, the measuring device 700 may be configured to measure the temperature of the user's body, etc., through the electrode unit 702 or other parts not shown. In addition, the measuring device 700 may include a measuring unit for measuring the user's biological signal instead of the electrode unit 702 or in addition to the electrode unit 702. The measuring unit may be configured to detect an object other than bioelectricity. For example, the measuring unit may also receive light emitted or reflected from the user's body to obtain a time series of measurement results. That is, the measuring device 700 may also be configured to receive light emitted or reflected from the user's body, and based on the result, information related to the user's body may be measured. The measuring device 700 may include, for example: a storage unit (not shown) for storing measurement results or a control program of the measuring device 700, etc.; a processing unit (not shown) for performing various processing operations using the information stored in the storage unit; and a transceiver (not shown) for transmitting and receiving information, etc.

[0057] The measuring device 700 is, for example, an earphone of the earhook type, which is configured so that the user can always wear it. The measuring device 700 is configured so that when worn, the two electrode parts 702 contact the vicinity of the left and right mastoids of the user, thereby measuring the user's brain waves or pulse. Since the structure of such a measuring device 700 is already known, a detailed description of its structure is omitted. In addition, the measuring device 700 is not limited to the earphone type, but can also adopt various forms such as glasses type, watch type, ring type, necklace type, belt type, clothing type, etc. that the user can wear continuously. In addition, the measuring device 700 can also have a sound output unit (not shown) that outputs a sound that the user can hear through the vibration of the air or the object, or a display (not shown) that the user can visually recognize. The measuring device 700 can also be configured so that information can be output to the user through these sound output units or displays. The measuring device 700 can also be configured so that two or more physically separated devices can collaboratively measure more than one biological signal. The measuring device 700 may also include an electrode unit 702 that is used only in contact with the user's body surface during measurement. In addition, the measuring device 700 is not limited to such a wearable device, but may also be a device used by the user to measure a biosignal under specific conditions, such as a blood pressure meter, a thermometer, a weight scale, an electroencephalogram measuring device, or an electrocardiogram.

[0058] Furthermore, in this embodiment, the measurement results of the time series of biological signals or information based thereon are referred to as measurement information about the biological state. The measurement information may be, for example, raw data of the measurement results obtained by measuring the biological signals, or information obtained by changing or processing the raw data. It can also be said that the measurement information is time series information related to the biological state based on the measurement results of the biological signals of a user, that is, time series information. That is, the time series information is the measurement results of the time series of biological signals or the time series information obtained therefrom.

[0059] In the present embodiment, as the measurement result of the biological signal, for example, pulse or brain wave such as ECG (electrocardiogram) or PPG (Photoplethysmogram) can be cited, but the present invention is not limited to these.

[0060] In this embodiment, the measuring device 700 is connected to the terminal device 600 wirelessly or by wire, and is configured to be able to send and receive information with the terminal device 600. For example, the measuring device 700 is configured to be able to perform short-range wireless communication with the terminal device 600. The measuring device 700 is configured, for example, to be able to send the measurement result to the terminal device 600. The measurement result can be sent to the terminal device 600 as time series information accumulated in the measuring device 700, or can be sent to the terminal device 600 in sequence. The terminal device 600 is configured to perform various processes using the information received from the measuring device 700, or to be able to send the received information or information processed from the received information to the information processing device 100. In addition, the measuring device 700 itself can also communicate with the information processing device 100 or the terminal device 600 connected to the network via a network such as a local area network or the Internet. The measurement result can also be sent to the information processing device 100. The measuring device 700 may be configured to independently perform operations of measuring a biological signal or performing predetermined processing on a measurement result, or may be configured to perform these operations by cooperating with the terminal device 600 such as transmitting and receiving signals.

[0061] The user of the information processing system 1 can use the terminal device 600 and the measuring device 700 to use the information processing system 1. Figure 1 For example, although a portable information terminal device such as a so-called smart phone is shown as the terminal device 600, it is not limited to such a portable information terminal device as the terminal device 600. For example, a personal computer (PC) such as a notebook computer, a tablet-type information terminal device, or other devices may be used as the terminal device 600. In the following examples, although the case where a portable information terminal device such as a smart phone is used as the terminal device 600 is described, it is not limited to this.

[0062] Furthermore, the measuring device 700 may be built into the terminal device 600 .

[0063] Figure 2 is a block diagram of the information processing device 100 . Figure 3 This is a block diagram of the terminal device 600 and the measuring device 700.

[0064] like Figure 2 As shown, the information processing device 100 includes a depositing unit 110, a receiving unit 120, a receiving unit 130, a processing unit 140, and a sending unit 170. The information processing device 100 is, for example, a server device.

[0065] The storage unit 110 includes a learning information storage unit 111 and a user information storage unit 115 .

[0066] The learning information storage unit 111 stores previously acquired learning information. In the present embodiment, the learning information is generated by a so-called machine learning method. For example, although the learning information is generated by the processing unit 140 and stored in the learning information storage unit 111 as described later, this is not limited to this. That is, the learning information storage unit 111 may also store learning information generated in a device different from the information processing device 100. The details of the learning information will be described later.

[0067] In this embodiment, learning information is formed for each user who is the object of measurement of the bio-signal. Learning information corresponding to each user is generated and stored in the learning information storage unit 111. Each piece of learning information is stored in correspondence with a user identifier that identifies the user. Moreover, it is not limited to this, and learning information that can be used in common for more than two users can also be prepared. In addition, learning information can also be prepared for each group of users with common attributes. In this case, for example, learning information can be stored in correspondence with an identifier that identifies the group.

[0068] User information is stored in the user information storage unit 115. In the present embodiment, user information is information that associates an identifier for identifying a user who uses the information processing system 1, i.e., a user identifier, with information about the user. The user information may include various information. For example, it may include information sent from a terminal device 600 used by the user and information obtained from the user through the information processing device 100 as described later. For example, time series information or information input into the terminal device 600 by the user corresponds to information sent from the terminal device 600 used by the user. In addition, for example, information related to the user's sleep-wake rhythm or predicted information based thereon as described later corresponds to information obtained from the user through the information processing device 100. The user information storage unit 115 may also have stored therein user information sent from other external devices, etc.

[0069] In addition, the user information storage unit 115 stores the time series information transmitted from the terminal device 600 of each user. The time series information is, for example, the measurement result measured by the user through the measuring device 700, and is information stored in the terminal storage unit 610 of the terminal device 600. The process until the time series information is stored in the user information storage unit 115 is not limited to this. It is also possible to configure that the time series information prepared in advance is stored in the user information storage unit 115 and the time series information is used.

[0070] The receiving unit 120 receives information sent from other devices. The receiving unit 120 stores the received information in, for example, the storage unit 110. In this embodiment, the user uses, for example, the terminal device 600 to input information, etc., and sends it to the information processing device 100. The receiving unit 120 can store each sent information in the storage unit 110 in correspondence with the user identifier. In addition, in this embodiment, the receiving unit 120 receives time series information sent from each terminal device 600, and stores it in the storage unit 110 in correspondence with the user identifier. And, when the receiving unit 120 receives this information from the terminal device 600, the user identifier of the user involved in the transmission can be determined based on the sent information.

[0071] The receiving unit 130 receives information inputted using an input means (not shown) connected to the information processing device 100. The receiving unit 130 stores the received information in, for example, the storage unit 110. The input means may be any one of a numeric keypad, a keyboard, a mouse, or a menu screen. The receiving unit 130 may also receive information inputted by an input operation (for example, including information read by the device) using a reading device (for example, an encoder, etc.) connected to the information processing device 100.

[0072] Furthermore, the receiving unit 130 may be regarded as receiving the information received by the receiving unit 120 as information input to the information processing device 100. That is, the input of information to the information processing device 100 may be interpreted as the information being indirectly input to the information processing device 100 by the user via the terminal device 600 or the like, or as being directly input to the information processing device 100 by the user using input means. In addition, the user providing information to the information processing device 100 by executing a program that automatically generates information or providing various information to the program to make it function, etc., may also be regarded as the input of information to the information processing device 100.

[0073] The processing unit 140 includes a biological information acquisition unit 143, a prediction information acquisition unit 145, and a future information output unit 147. In this embodiment, the prediction information acquisition unit 145 includes a pre-processing unit 146. The processing unit 140 performs various processes. The various processes are, for example, processes performed by each unit of the processing unit 140 as follows.

[0074] The bio-information acquisition unit 143 acquires time series information for one user who is the prediction target. In this embodiment, the bio-information acquisition unit 143 acquires the time series information transmitted from the user's terminal device 600 and received by the receiving unit 120 from the user information storage unit 115. The bio-information acquisition unit 143 acquires the time series information of the user from the user information storage unit 115 based on the user identifier of the user who is the processing target.

[0075] The prediction information acquisition unit 145 uses the time series information of a user and the learning information corresponding to the user to acquire prediction information related to the future time series information of the user. The acquired prediction information is stored in, for example, the storage unit 110. The future is, for example, a period or time later than the end of the period of the acquired time series information for the user as the prediction target, and in particular, a period or time including a time later than the time when the prediction information is acquired.

[0076] The prediction information is obtained by a machine learning method. That is, in the present embodiment, the time series information (which can also be called the past time series information) obtained from the measurement results of the past biological signals is used to form the learning information by a machine learning method. The past time series information can be the information of the measurement results of the time series itself, or it can be the information obtained by using the measurement results of the time series. The prediction information is the information of the information continuous in the time series predicted from the past time series information. It can also be said that the prediction information is the prediction result of the time series information in a specified period later than the end of the period of the past time series information.

[0077] The learning information is information used in the construction of a learning model that is constructed in a manner that uses past time series information as input information and predictive information related to future time series information as output information. For example, the processing unit 140 can generate learning information as follows. That is, the processing unit 140 uses a machine learning method to construct learning information that uses past time series information as input and predictive information related to future time series information as output. For example, two or more groups of past time series information and output values ​​for learning are prepared in advance, and the two or more groups of information are provided to a module for constructing learning information for machine learning to construct learning information, and the constructed learning information is accumulated in the learning information storage unit 111. In addition, as a method of machine learning, a recurrent neural network (RNN) such as long short-term memory is used, but it is not limited to this. In machine learning, for example, functions in various machine learning frameworks such as TensorFlow and PyTorch and various existing program libraries can be used. For each user, a group of information for learning can be prepared in advance using the measured results. Such learning information can also be generated by a device other than the information processing device 100.

[0078] In this embodiment, the prediction information acquisition unit 145 is configured to acquire prediction information based on the time series information processed by the pre-processing unit 146. That is, the processed past time series information is used as input to generate the above-mentioned learning information. The processing by the pre-processing unit 146 is described below, for example, but is not limited thereto.

[0079] The pre-processing unit 146 performs a predetermined filtering process on the time series information (referred to as pre-processing information) acquired by the bio-information acquisition unit 143, and acquires the processed time series information. In other words, the pre-processing unit 146 performs dimensionality reduction (dimensional compression) on the pre-processing information. The pre-processing unit 146 is configured, for example, to perform a known singular spectrum analysis process as a predetermined filtering process. Thus, prediction information can be acquired using the processed time series information from which the fluctuation component or the trend component has been extracted. Such filtering process can be performed, for example, using a numerical calculation library such as NumPy.

[0080] The pre-processing unit 146 is configured to obtain the time series information after the processing by using the results up to the upper specified number of times in the singular spectrum analysis processing. The specified number of times is, for example, 1 or more and 10 or less, preferably 5 or more and 10 or less. In this embodiment, the pre-processing unit 146 is configured to obtain the time series information after the processing by using the results up to the upper 10 times in the singular spectrum analysis processing.

[0081] Furthermore, the method of dimensionality reduction performed by the pre-processing unit 146 is not limited thereto. For example, the pre-processing unit 146 may also be configured to perform dimensionality reduction of the pre-processed information by using an autoencoder of a convolutional neural network. Dimensionality reduction using an autoencoder may be configured by a known method. For example, the pre-processed information may be input into a probabilistic encoder to extract a vector representing a potential fluctuation component or a vector representing a potential trend component.

[0082] The future information output unit 147 outputs future information related to the future state of the user who is the prediction object based on the prediction information. The future information may be, for example, the prediction information itself or information obtained based on the prediction information. For example, the information obtained based on the prediction information may be information or a score that represents the situation or state of the user at a future moment or period in a prescribed category, but is not limited thereto. For example, it may be information related to the comparison result of comparing the benchmark value of each user obtained from the past measurement information with the prediction information, or it may be information related to the comparison result of comparing the prescribed benchmark value commonly used for multiple users with the prediction information.

[0083] In this embodiment, the future information output unit 147 may also be configured to obtain information related to a time zone in which a user is prone to feel nervous or a time zone in which a user is prone to relax during a specified period as future information based on the prediction information, and output the obtained future information. The specified period is, for example, the period of the prediction information, but is not limited to this. The future information output unit 147, for example, determines a time zone that satisfies a specified determination condition based on the prediction information, and obtains information related to a specific time zone as future information. As an example, it is assumed that the prediction information is related to time series information representing the activity of sympathetic nerves and the activity of parasympathetic nerves, respectively. As such prediction information, for example, information related to LF or HF related to heartbeat fluctuations or information related to brain wave activity can be cited, but is not limited to this. In such a case, for example, it is possible to set the state in which either the activity of the sympathetic nerves or the activity of the parasympathetic nerves is more dominant than the other as a specified determination condition. In addition, for example, it is possible to set the state in which either the activity of the sympathetic nerves or the activity of the parasympathetic nerves is more dominant than the other to a specified degree or more as a specified determination condition.

[0084] For example, the future information is output by sending information from the transmission unit 170 to the user's terminal device 600, but the present invention is not limited thereto. For example, the output of the future information may be delivery to the processing performed in the information processing device 100. In addition, for example, the prediction information may be output by displaying it on a display provided in the information processing device 100 in the form of text or images. In addition, the output of the future information may also mean accumulating the future information in the storage unit 110 for the purpose of performing these processes.

[0085] The transmission unit 170 transmits information to other devices constituting the information processing system 1 via the network. The transmission unit 170 transmits information to the terminal device 600, for example. In other words, the transmission unit 170 outputs information to the terminal device 600, for example.

[0086] Next, the configuration of the terminal device 600 will be described.

[0087] like Figure 3 As shown, the terminal device 600 includes a terminal deposit unit 610, a terminal receiving unit 620, a terminal accepting unit 630, a terminal processing unit 640, a terminal output unit 660, a terminal transmitting unit 670, and a sensor unit 680. The terminal output unit 660 includes a display unit 661.

[0088] Various information is stored in the terminal storage unit 610. The terminal storage unit 610 includes a measurement information storage unit 611. The measurement information storage unit 611 stores, for example, measurement information measured by the measurement device 700. In addition, the terminal storage unit 610 stores future information transmitted from the information processing device 100.

[0089] The terminal receiving unit 620 receives information transmitted from the information processing device 100 or the measuring device 700 via the network. The terminal receiving unit 620 accumulates the received information in the terminal storage unit 610, for example, and makes the terminal processing unit 640 and the like available.

[0090] The terminal accepting unit 630 accepts various input operations performed on the terminal device 600 by a user using the terminal device 600. For example, the operation is performed using an input device (not shown), but the present invention is not limited to this.

[0091] The terminal processing unit 640 performs various information processing operations using each unit of the terminal device 600. For example, the terminal output unit 660 outputs future information transmitted from the information processing device 100 to the user. Thus, the user can know the future information.

[0092] The terminal output unit 660 outputs information by displaying the information on, for example, a display device, namely, a display unit 661. The method of outputting information is not limited thereto, and the information may be output by outputting sound from a speaker or the like.

[0093] The terminal transmission unit 670 transmits the information acquired by the terminal processing unit 640 and the like, for example, via the network.

[0094] The sensor unit 680 is, for example, a microphone, an acceleration sensor, or an air pressure sensor, but is not limited thereto. The user's biosignal may be measured by the sensor unit 680. The sensor unit 680 may further include an electrode unit that can measure the user's biosignal.

[0095] Here, a method of machine learning that can be used in this embodiment is described.

[0096] Information that can constitute a neural network model having an encoder and decoder using a so-called Transformer is used as learning information. The neural network model called such a Transformer is already known as a model for predicting time series information, but a part of it can also be changed. It can also be said that the neural network model using the Transformer has an encoder-decoder structure with a self-attention mechanism (Self-Attention). It can also be said that the neural network model using the Transformer has an encoder and decoder structure using a multi-head attention mechanism. In the present embodiment, the learning information is a neural network model that uses the so-called Informer method in part as described in the above-mentioned non-patent document 2. It can also be said that the Informer is a neural network model derived from the Transformer. The decoder of the neural network model constituting the learning information is configured so as not to perform autoregression. That is, the learning information constitutes a neural network model that uses the so-called Informer method in part and has a decoder structure with a generative formula that is not accompanied by autoregression, and a model of the encoder and decoder using a conventional Transformer. The learning information can constitute a neural network model having an encoder and decoder that does not use the ProbSparse Self Attention and Encoder-distilling method in the so-called Informer method.

[0097] Figure 4 This is a diagram for explaining the structure of learning information used in the information processing device 100 .

[0098] The encoder-decoder structure for learning information is schematically shown in the figure. The generative decoder can be described as follows. That is, in a normal Transformer, the input data corresponding to the data column output by the decoder lacks the last one, is shifted backward as a whole, and only the first data is padded. In contrast, in the generative decoder, as shown in the figure, the part of the input data corresponding to the output data column in the inference unit is all padded, and the output data part is generated in one go during inference.

[0099] Since the generative decoder is configured to use such an Informer, it is possible to obtain prediction information more quickly.

[0100] Next, an example of the operation of the information processing apparatus 100 when the user uses the information processing system 1 according to the present embodiment will be described. In the present embodiment, the user can use the information processing system 1, for example, by connecting to the information processing apparatus 100 or receiving information sent from the information processing apparatus 100 through the terminal device 600 and at the same time operating a predetermined application program in the terminal device 600. And the predetermined application program can be, for example, a dedicated application program that operates using the information sent from the information processing apparatus 100, or a web browser that displays the web application program provided in the information processing apparatus 100 so that it can be used.

[0101] In the present embodiment, the information processing system 1 is typically used as follows. That is, the user measures his / her own biological signals while wearing the measurement device 700 for a predetermined period. The time series information obtained by the measurement is transferred to the terminal device 600. After that, the user sends the time series information stored in the terminal storage unit 610 from the terminal device 600 to the information processing apparatus 100. In this way, the information processing apparatus 100 obtains prediction information and outputs future information. The terminal device 600 receives the output future information and displays the information on the display device through the terminal output unit 660 according to the future information. Thus, the user can confirm his / her own future information or information based on this.

[0102] When such an information processing system 1 operates, the information processing apparatus 100 performs various operations as follows, for example. The processing unit 140 performs control operations and the like while using each part, thereby performing these operations.

[0103] Figure 5 It is a flowchart showing an example of the operation of the information processing apparatus 100.

[0104] (Step S11) The processing unit 140 determines whether information sent from the terminal device 600 or the like has been received through the receiving unit 120. When it is determined that the information has been received, the process advances to Step S12; otherwise, it advances to Step S13.

[0105] (Step S12) The processing unit 140 identifies the user based on the received information and accumulates the received information in the user information storage unit 115 in association with the user identifier. For example, if time-series information is sent from the terminal device 600 in association with the user identifier, the processing unit 140 accumulates the received time-series information in the user information storage unit 115 in association with that user identifier.

[0106] (Step S13) The processing unit 140 determines whether an opportunity related to processing such as obtaining prediction information or outputting future information has occurred. In other words, the processing unit 140 determines whether the conditions for starting processing such as obtaining prediction information or outputting future information are satisfied. When it is determined that an opportunity has occurred, the process advances to Step S14; otherwise, it returns to Step S11.

[0107] Here, for example, an instruction by the user through the terminal device 600 related to the execution of processing such as obtaining prediction information or outputting future information can be used as the above-mentioned opportunity. Additionally, for example, sending a specified information corresponding to such an instruction through the terminal device 600 can also be used as the above-mentioned opportunity. The opportunity is not limited to this. For example, reaching a specified time can be used as the above-mentioned opportunity, and in this case, future information can be output regularly. Additionally, for example, various conditions such as having received time-series information or other information again can be used as opportunities.

[0108] (Step S14) The processing unit 140 performs a prediction information acquisition process through the prediction information acquisition unit 145. The prediction information acquisition process will be described later. Through the prediction information acquisition process, the prediction information of the user is obtained and accumulated in the user information storage unit 115.

[0109] (Step S15) The processing unit 140 obtains future information based on the prediction information through the future information output unit 147.

[0110] (Step S16) The processing unit 140 outputs the future information through the future information output unit 147. In this embodiment, the processing unit 140 outputs the obtained future information to the target user. That is, the processing unit 140 outputs the future information to the terminal device 600 used by the target user through the sending unit 170. As a result, on the terminal device 600 that has received the future information, a display related to the future state of the user can be performed.

[0111] Figure 6This is a flowchart showing an example of the prediction information acquisition process of the information processing apparatus 100.

[0112] (Step S111) The prediction information acquisition unit 145 acquires time series information about the user from the user information storage unit 115 based on the user identifier of the target user.

[0113] (Step S112) The prediction information acquisition unit 145 performs filtering processing of the measurement information through the preprocessing unit 146.

[0114] (Step S113) The prediction information acquisition unit 145 acquires learning information corresponding to the user identifier of the target user from the learning information storage unit 111.

[0115] (Step S114) The prediction information acquisition unit 145 applies the time series information processed by the preprocessing unit 146 to the learning information, thereby obtaining prediction information. In addition, the obtained prediction information is accumulated in the storage unit 110 in association with the user identifier of the target user. Then, it returns to Figure 4 the processing of.

[0116] Next, a specific example of the acquisition of prediction information by the information processing apparatus 100 according to the present embodiment will be described.

[0117] Figure 7 This is a diagram for explaining the time series information in a specific example of the present embodiment.

[0118] In this specific example, the time series information is information obtained by taking, as the measurement result, information representing the ECG of a user. The time series information is information representing the changes in LF and HF related to the heartbeat variation of a user. The time series information can also be information related to the change in LF / HF (the ratio of LF to HF). As the prediction information, for example, it is possible to obtain the prediction of the change in LF or HF for a specified future period, or the prediction of the change in LF / HF.

[0119] Here, LF and HF are values obtained by integrating in their respective frequency bands (low frequency band, high frequency band) the energy spectra obtained by performing frequency analysis on the change in the heartbeat interval. LF can be used as an index representing the activity of the user's sympathetic nerve, and HF can be used as an index representing the activity of the user's parasympathetic nerve. That is, LF, HF, or LF / HF can be used as information related to the activity of the user's autonomic nerve.

[0120] As shown in the figure, when an electrocardiogram signal (S1) for a specified measurement period (e.g., 5 minutes) is obtained as a measurement result, the RRI (R - R interval: the interval between R waves) is acquired from it (S2). Based on the energy spectrum obtained by frequency - analyzing the trend of the RRI during the specified measurement period, LF and HF can be obtained (S3). By aggregating the LF and HF obtained for each specified measurement period, time - series information can be obtained.

[0121] And, for example, it is sufficient to obtain the time - series information from such an ECG in the terminal device 600, but it can also be obtained in the measurement device 700, or it can also be obtained in the information - processing device 100 that has obtained the measurement result.

[0122] Figure 8 This is a diagram showing an example of the time - series information before the filtering process in a specific example of the present embodiment.

[0123] In the figure, the time - series information showing the trend of the obtained LF and HF over time is represented. In this figure and the next figure, for example, the time - series information for approximately one week is shown. Regarding such time - series information, if it is subjected to a filtering process by the pre - processing unit 146, it becomes as follows.

[0124] Fig. 9 This is a diagram showing an example of the time - series information after the filtering process in a specific example of the present embodiment.

[0125] In the figure, as corresponding to the previous figure, the time - series information after the process of restoring the time - series information using the singular spectrum analysis up to the top 10 times is represented. Such processed time - series information is information that can well extract the main components of the time - series information and well represent the trend of the user's LF and HF. By using such processed time - series information, prediction information can be obtained with high precision.

[0126] Fig.10 This is a diagram showing an example of the prediction information obtained in a specific example of the present embodiment.

[0127] In the figure, the prediction information (Predicted HF) of HF for a future specified period (e.g., 24 hours) obtained from the time - series information (True HF) of HF in the past specified period (e.g., 24 hours) is represented. In the future specified time, the time - series information actually based on the subsequent measurement results is also represented. In this way, future time - series information, that is, prediction information, can be obtained from past time - series information. The prediction information becomes information that can predict the trend of the actual time - series information with high precision.

[0128] Moreover, in the prediction information thus obtained, in a specified period in the future, a time zone in which HF has only a specified degree of advantage over LF can also be output as future information indicating a time zone (a time zone in which it is difficult to relax) when the target user is likely to feel tense. In addition, in a specified period in the future, a time zone in which HF has only a specified degree of advantage over LF can also be output as future information indicating a time zone (a time zone in which it is difficult to feel tense) when the target user is likely to relax.

[0129] Moreover, as another specific example, instead of using the information constituting the above electrocardiogram as a measurement result, time series information obtained by using the pulse as a measurement result can also be used to obtain the prediction information of the user. For example, time series information indicating the change of LF or HF can be obtained from the change of PPI (pulse interval), and prediction information related to the change of LF or HF can be obtained in the same manner as above.

[0130] In addition, information indicating the PPG waveform of a user based on the pulse of the user can be obtained as time series information, and based on this time series information, the future change of the heart rate (HR) of the user can be obtained. For example, when obtaining the change of the heart rate, for example, data for learning including an ECG waveform paired with the PPG waveform is prepared, and information indicating the change of the heart rate obtained from the ECG waveform is used in a manner paired with the corresponding PPG waveform to constitute learning information for machine learning. By applying the time series information based on the measurement result to such learning information, the change of the heart rate can be obtained. As such learning information, information that can constitute a neural network model can be used, and the neural network model uses a structure that has been adopted in a Transformer in an encoder structure. Using the PPG waveform that can be obtained relatively easily, the prediction information related to the heart rate with high accuracy can be obtained in the same manner as when obtained from the ECG. In addition, the learning information can be configured in such a way that the instantaneous value of the heart rate can be obtained. In addition, the change of the value of an index other than the heart rate can be obtained to obtain prediction information. From such prediction information, for example, future information related to the activity of the peripheral nerves of the user can be output.

[0131] In addition, information indicating the brain wave can be obtained as time series information, and thus prediction information related to the future brain wave of the user can be obtained. The prediction information thus obtained can also be used to output future information related to the activity of the central nervous system of the user. For example, by appropriately predicting the degree of awakening of the brain during sleep or the degree of awakening of the brain during waking up, useful information for the user can be obtained.

[0132] As described above, for each user, the information processing apparatus 100 can obtain prediction information related to future time-series information from the time-series information regarding the biological state. Thus, information related to the future state of the user can be obtained. For example, the user can measure a biological signal by a simple method using a wearable device, i.e., the measurement apparatus 700, and thereby can provide the time-series information to the information processing apparatus 100. Thus, information related to the future state of the user can be easily obtained. In the present embodiment, learning information constituting a neural network model adopting a Transformer is used to obtain the prediction information. Thus, highly accurate prediction information can be easily obtained.

[0133] Moreover, although it is preferable that the above-described storage unit 110 and the terminal storage unit 610 are non-volatile storage media, they can also be implemented by volatile storage media. Information such as information that has been obtained in each device is stored therein, respectively, but the process of storing information and the like is not limited thereto. For example, information and the like can be stored by a storage medium, or information and the like transmitted through a communication line or the like can be stored, or information and the like input through an input device can be stored.

[0134] In addition, the above-described processing unit 140 or the terminal processing unit 640 can generally be implemented by an MPU, a memory, or the like. Generally, the processing sequence of the processing unit 140 or the terminal processing unit 640 is implemented by software, and this software is recorded in a storage medium such as a ROM. However, it can also be implemented by hardware (a dedicated circuit).

[0135] In addition, the input means that can be used to input information that can be accepted by the acceptance unit 130 or the terminal acceptance unit 630 can be any one of a numeric keypad, a keyboard, a mouse, a menu screen, or the like. The acceptance unit 130 or the terminal acceptance unit 630 can be implemented by a device driver of an input means such as a numeric keypad or a keyboard, or control software of a menu screen.

[0136] In addition, although the receiving unit 120 or the terminal receiving unit 620 is generally implemented by wireless or wired communication means, it can also be implemented by means of receiving a broadcast.

[0137] In addition, although the transmitting unit 170 or the terminal transmitting unit 670 is generally implemented by wireless or wired communication means, it can also be implemented by means of a broadcast.

[0138] Moreover, the information processing apparatus 100 can be constituted by one server, respectively, or can be constituted by a plurality of servers that cooperate with each other, or can be an electronic computer or the like built in other instruments. And the server can be a so-called cloud server, or an ASP server, etc., and needless to say, its type can be arbitrary.

[0139] Also, the processing in this embodiment can also be implemented by software. Moreover, the software can be distributed through software downloads or the like. Additionally, the software can be recorded on a storage medium such as an optical disc and transmitted. And the software that implements the information processing apparatus 100 in this embodiment is the following program. That is to say, this program is a program executed in the computer of the information processing apparatus 100, which causes the computer of the information processing apparatus 100 to function as a biological information acquisition unit, a prediction information acquisition unit, and a future information output unit. The biological information acquisition unit acquires time series information related to the biological state based on the measurement result of the biological signal of one user. The prediction information acquisition unit uses the learning information and time series information constituted by using the time series information obtained from the measurement results of past biological signals, and acquires prediction information related to the future time series information of one user by means of machine learning. The future information output unit outputs future information related to the future state of the user based on the prediction information.

[0140] (Embodiment 2)

[0141] In Embodiment 2, the information processing system 2 uses an information processing apparatus capable of acquiring scoring information including a score indicating the current or future physical state of each user. The scoring information is acquired using measurement information related to the biological state based on the measurement result of the user's bioelectric potential. In this embodiment, output information regarding the score is output based on the acquired scoring information. Thereby, the user can know their current or future physical state. Hereinafter, the information processing system 2 configured as such will be described.

[0142] And the score indicating the current or future physical state of the user is, for example, a value representing the degree of goodness of the user's physical state in the form of a score. For example, the score is a concept such that the higher the score, the better the user's physical state. It can also be a concept such that the lower the score, the better the physical state. Additionally, as the score, a value represented by a ranking can be used, or a sentence indicating the physical state can be used as the score. The degree of goodness of the physical state is, for example, the degree of tension. That is, the score can be used as a concept representing the user's tension level (the degree of feeling tense). And the physical state can be either the degree of wakefulness, the degree of fatigue, body temperature, etc., or a concept representing a combination of two or more of these elements.

[0143] Fig.11 It is a diagram showing an outline of the information processing system 2 according to Embodiment 2 of the present invention.

[0144] In this embodiment, the information processing system 2 includes an information processing device 200, the above-described terminal device 600, and the above-described measurement device 700. That is, the information processing system 2 is different from the information processing system 1 according to Embodiment 1 in that the information processing device 200 is provided instead of the above-described information processing device 100. The information processing device 200 and the terminal device 600 are communicable with each other via a network. Moreover, the configuration of the information processing system 2 is not limited to this. The number of each device included in the information processing system 2 is not limited, and the information processing system 2 may further include other devices.

[0145] Moreover, in Embodiment 2, the earphone, i.e., the measurement device 700, has a detector output unit 706. The detector output unit 706 is, for example, a speaker that is arranged near the user's ear in a state where the user is wearing the measurement device 700. The detector output unit 706 is, for example, a sound output unit that outputs sound audible to the user through air or the vibration of an object. The measurement device 700 is configured to be able to output sound from the detector output unit 706. By using the measurement device 700, the user can listen to the sound output from the measurement device 700. Moreover, the detector output unit 706 may have a display or the like that is visually recognizable by the user instead of or together with the speaker. At this time, the form of the display is not limited.

[0146] Even in Embodiment 2, the measurement information includes information obtained by measuring a value related to a human bioelectric potential. It can also be said that this information is information obtained by measuring a value related to a human bioelectric potential. Even in this embodiment, time series information, i.e., measurement information, is used. That is, the time series information described in the above Embodiment 1 may also be included in the measurement information of Embodiment 2. Moreover, the measurement information in Embodiment 2 is not limited to this. That is, the information processing device 200 is configured to use the measurement information related to the bioelectric potential at two or more times measured by the measurement device 700. In other words, the measurement information is not limited to information that is necessarily called time series. In this embodiment, information obtained by measuring a single user in time series is used as the measurement information.

[0147] The user of the information processing system 2 can use the information processing system 2 by using the terminal device 600 and the measurement device 700. Moreover, in the figure, although a portable information terminal device such as a so-called smartphone is shown as the terminal device 600, the configurations of the terminal device 600 or the measurement device 700 can be variously changed, which is the same as in Embodiment 1.

[0148] Fig.12 This is a block diagram of the information processing device 200.

[0149] The information processing device 200 is, for example, a server device. The information processing device 200 includes a storage unit 110, a receiving unit 120, an accepting unit 130, a processing unit 140, and a transmitting unit 170. Hereinafter, in the configuration of the information processing device 200, the differences from the configuration of the information processing device 100 according to the first embodiment will be mainly described.

[0150] In the second embodiment, in addition to the same learning information as in the first embodiment, learning information for score acquisition that has been previously obtained is also stored in the learning information storage unit 111. In this embodiment, the information to be input is input information including measurement information that has been measured for two or more users in the past, and the information to be output is information related to scores as described later, thereby creating learning information for score acquisition. A method called machine learning is used to generate the learning information. For example, although the learning information is generated by the learning information acquisition unit 249 and stored in the learning information storage unit 111 as described later, it is not limited thereto. That is, learning information generated in a device different from the information processing device 200 may also be stored in the learning information storage unit 111.

[0151] In addition, in the second embodiment, the user information stored in the user information storage unit 115 may include the user's living information. The living information is, for example, sent from the terminal device 600 used by the user, received by the receiving unit 120, and stored in the user information storage unit 115.

[0152] The living information is information related to the living conditions of a user. The living conditions may include various elements related to the user's actions, states, etc. In the following embodiments, the living information includes, for example, diet information or activity status information. That is, the living conditions include, for example, the user's diet status, the user's activity status, etc. However, it is not limited thereto, and the living information may include information related to other elements or only a part of these elements.

[0153] Dietary information is information related to a user's dietary status. As dietary information, for example, it can include information from various perspectives such as water, alcohol consumption, and the content of meals. Dietary information can also be conceptually divided into meal information related to meals or eating habits and beverage information related to the intake of beverages for representation. More specifically, for example, in this embodiment, although the dietary information includes at least one of information related to the amount of water consumed, information related to the presence or absence of alcohol consumption or the amount of alcohol consumed, information related to the presence or absence of meals or the content of meals, and information related to the presence or absence of a specific group of foods consumed or the amount of intake, it is not limited thereto. And a specific group of foods, for example, refers to a specific group of foods belonging to a group classified as meat, dairy products, vegetables, etc., but the classification perspective or granularity of such a "group" is not limited thereto. In addition, in either or both of the meal information and the beverage information, it can also include content related to so-called supplements (health foods). And although dietary information is information input by an evaluator such as a user to evaluate the dietary status, it is not limited thereto, but for example, it can also use information on the results measured or evaluated by an evaluation instrument, etc. For example, the dietary information can also include measurement values related to the time zone or chewing of the user's diet, which are obtained based on the measurement results of the electromyogram measured by the measurement device 700.

[0154] Activity status information is information related to the user's activity status. The activity status referred to herein means a status related to the user's state or lifestyle (referring to behaviors or actions repeatedly performed by the user in daily life) other than the diet status. That is, in addition to diet information, activity status information may include information related to lifestyle. In this embodiment, the activity status information is at least one of basic information, sleep information (such as sleep time or sleep quality) about sleep, and exercise information, but is not limited thereto. Basic information is information related to the user's manner or characteristics. For example, the basic information may include various information such as the user's height, weight, age, gender, body fat percentage, muscle mass, lifestyle (such as the presence or absence of smoking and drinking), etc. In addition, exercise information is information related to the user's exercise or state. For example, the exercise information may include information on the measurement results of pulse (pulse rate), respiratory rate, body temperature, blood pressure, and blood oxygen concentration. In addition, the exercise information may include information related to the acceleration measured for the user. For example, in addition to the values measured at the head, wrist, and other specified parts, the acceleration may also include the value measured in the state where the user holds the measurement instrument, etc. The information about acceleration may also be information regarded as the number of steps, calories consumed, exercise intensity, or exercise frequency, etc. And, for example, the activity status information may be information obtained through an activity meter (not shown) provided in a terminal device 600 or a measurement device 700 held or carried by the user, or may be information input after being evaluated by an evaluator such as the user. In addition, the activity status information may include the user's questionnaire answers or information generated based on the answers, as well as information related to the user's past history, etc., and may also include information obtained from an external database or the like that records user information.

[0155] For example, similar to Embodiment 1, the processing unit 140 includes a biological information acquisition unit 143, a prediction information acquisition unit 145, and a future information output unit 147. In this embodiment, the biological information acquisition unit 143 acquires past time series information based on actual measurement results as measurement information. In addition, the same processing as in Embodiment 1 is performed accordingly, whereby future time series information, that is, prediction information, is acquired and output by the future information output unit 147. That is, using learning information configured to take input information related to past time series information, that is, measurement information, as input and future measurement information as output, prediction information is acquired based on the input information. In Embodiment 2, the output of future information, for example, means delivering the prediction result, that is, future measurement information, to subsequent processing in the processing unit 140 or accumulating it in the storage unit 110.

[0156] In addition, the processing unit 140 includes an evaluation information acquisition unit 241, a lifestyle information acquisition unit 242, a learning information acquisition unit 249, a score acquisition unit 251, an output information acquisition unit 257, and an output unit 260.

[0157] The evaluation information acquisition unit 241 acquires evaluation information corresponding to the score information. The score information is the information acquired by the score acquisition unit 251 as described later. It can also be said that the evaluation information is, for example, information indicating the degree of correspondence between the score (which can be said to be the predicted score) based on the score information and the actual physical state of the user (hereinafter simply referred to as the physical state here). The evaluation information can be either information representing whether the score indicates the physical state in binary, or a value indicating the degree of correspondence between the score and the physical state. In addition, it can also be the score itself representing the physical state. The evaluation information acquisition unit 241 can acquire, as the evaluation information, information representing the result of the user's subjective judgment input by the user, or can also acquire the evaluation information based on the measurement information obtained by the measurement device 700, etc. For example, when the score information representing the score at a certain future time has been acquired, the evaluation information can also be acquired based on the comparison result between the score calculated based on the measurement information measured at that time and the acquired score after that. It can be said that such evaluation information is information related to the score reflecting the actual physical state.

[0158] The lifestyle information acquisition unit 242 acquires lifestyle information. In the present embodiment, the lifestyle information includes diet information and activity status information (i.e., exercise information or sleep information), but is not limited thereto. The lifestyle information acquisition unit 242 acquires, for example, the information stored in the user information storage unit 115. In addition to this, the lifestyle information acquisition unit 242 can also acquire the basic information of the user.

[0159] The learning information acquisition unit 249 generates learning information for score acquisition by using a machine learning method. For example, the machine learning method can be used as follows. That is, a learning machine that takes input information including at least measurement information as input and outputs information related to the score (output value) is constructed using a machine learning method. For example, two or more sets of input information and output values are prepared in advance, and the information of two or more such sets is provided to a module for constructing a learning machine for machine learning to construct a learning machine. That is, the learning information acquisition unit 249 generates learning information by using two or more pieces of teaching data including input information with at least measurement information and scores related to the user's physical state. Also, the learning machine can be referred to as a classifier. Also, as a machine learning method, for example, there are deep learning such as convolutional neural network (CNN), random forest, SVR, etc., and the type is not limited. In addition, functions in various machine learning frameworks such as fastText, tinySVM, random forest, TensorFlow, etc. and various existing libraries can be used in machine learning.

[0160] Also, the information related to the score can be, for example, the score itself, or information having a specified correspondence with the score (for example, information corresponding to a variable in a specified function or a score in a specified table, etc.). The information having a specified correspondence with the score can also be a value corresponding to the measurement information with the same elements (brain wave, electromyogram, electrocardiogram, etc.). The information related to the score can also be the following score information.

[0161] The learning information acquisition unit 249 accumulates the constructed learning machine as learning information in the learning information storage unit 111. Thus, when using the generated learning information, the processing unit 140 can obtain the learning information from the learning information storage unit 111.

[0162] Here, it is only necessary to prepare in advance a combination of input information and output values used in the generation of learning information for score acquisition. In this embodiment, for a user who needs to re-acquire score information, learning information constructed using input information including measurement information that has been measured in the past for two or more users is used. Thus, even for a user who has not obtained a score in the past, it is possible to obtain a score from the beginning of measurement. Also, for each user, a combination of input information and output values corresponding to the user can be prepared. In this case, the generated learning information can be accumulated in the learning information storage unit 111 corresponding to the user identifier of the user. Also, the learning information for score acquisition can be generated in a manner that can be commonly used for multiple users. In this case, for example, for multiple users who can be regarded as equivalent regarding a specified attribute, it can be generated in a commonly used manner. By centrally using teaching data related to multiple users respectively, such learning information can be constructed by various methods.

[0163] In addition, in Embodiment 2, for one user, the learning information acquisition unit 249 acquires evaluation information corresponding to the scoring information that has been acquired by the scoring acquisition unit 251, and at the same time acquires learning information for re-learning using the input information and evaluation information used in the acquisition of the scoring information. In other words, for each user, the learning information acquisition unit 249 uses a combination of input information including the measurement information newly obtained from the measurement device 700 and information related to the score reflecting the actual physical state corresponding thereto, and regenerates the learning information for scoring acquisition at a specified time. Further, such re-learning processing can also be performed for each group of two or more users. In addition, when using learning information common to all users, the re-learning processing can also be appropriately performed without distinguishing users.

[0164] Moreover, the learning information acquisition unit 249 can also be configured to acquire the learning information stored in a device other than the information processing device 200 from that device. For example, when the learning information stored in another device is always used, the information processing device 200 may not be provided with the learning information storage unit 111.

[0165] The scoring acquisition unit 251 acquires scoring information from the measurement information as follows, for example. The scoring information is information including a score indicating the current or future physical state of the user. As the measurement information, for example, the measurement information acquired by the biological information acquisition unit 143 and the future information output by the future information output unit 147, i.e., the measurement information, can be used. It can be said that the future information is information acquired using learning information configured to take the input information related to the past measurement information as input and output the future measurement information.

[0166] Moreover, although future measurement information, i.e., future information, can be acquired and generated in the same manner as in Embodiment 1 described above, such processing can also be interpreted as being executed by the scoring acquisition unit 251. That is, in Embodiment 2, it can also be interpreted that the scoring acquisition unit 251 performs the processing of the prediction information acquisition unit 145 and the future information output unit 147.

[0167] The scoring information is, for example, information that correlates information related to a future time with a score indicating the physical state at that time, or information indicating the change in the score. That is, the scoring information can also include information related to the score indicating the physical state at a specific one or more times, either currently or in the future. In addition, the scoring information can also include information explaining the change in the score indicating the physical state during a specified period, and the specified period may or may not include a future period. The scoring information can also be the score value itself.

[0168] Also, specifically, for example, when using measurement information that changes over time and represents HF or LF, it is possible to numerically determine the user's level of tension. That is, it is possible to numerically determine the score indicating a lower level of tension felt by the user or the score value indicating a higher level of tension felt by the user. And not limited to this, for example, when using electroencephalogram, which is measurement information, the level of the user's sleep-wakefulness (an example of the physical state) is numerically determined as a score, and thus can be determined. For example, when the user is awake and in a drowsy state, the score at this time is lower than when not in such a state. On the contrary, when the user can maintain a high level of concentration, the score is higher than when not in such a state. Additionally, for example, when obtaining information related to the heartbeat as measurement information, based on this, the physical strength or physical fatigue level (an example of the physical state) is numerically determined as a score, and thus can be determined. For example, when the heartbeat is high for the user's activity level, the score is lower than when not in such a state. In addition to this, according to measurement information such as electroencephalogram, pulse, and electromyogram, various states can be numerically determined as scores and determined accordingly.

[0169] In Embodiment 2, the score acquisition unit 251 acquires a measurement score based on measurement information and a life score based on life information, respectively. Then, score information representing the physical state is acquired based on the measurement score and the life score.

[0170] The score acquisition unit 251 acquires a measurement score based on input information that includes at least measurement information at multiple times. The score acquisition unit 251, for example, applies the input information to the learning information stored in the learning information storage unit 111 to acquire the measurement score. The measurement score is a value obtained based on the measurement information. In Embodiment 2, the score acquisition unit 251, for example, can also apply learning information configured to output future measurement information to input information including measurement information at multiple present or past times to acquire future measurement information. Then, the acquired measurement information can also be used to acquire a future measurement score. The score acquisition unit 251 accumulates the acquired measurement score corresponding to the user identifier in the user information storage unit 115.

[0171] Also, learning information does not necessarily have to be used in the acquisition of the measurement score or the score information. For example, the score acquisition unit 251 can also be configured to obtain the measurement information at a specified time by statistical methods such as linear regression based on the measurement information at two or more times, and then obtain the measurement score corresponding to this measurement information through a specified function or table, etc.

[0172] In addition, the score acquisition unit 251 may also compare the content of the measurement information (e.g., the trend of the biological signal during a specified period) with a specified reference, and calculate a measurement score using a specified calculation formula or the like based on the result. Additionally, multiple thresholds serving as references may be prepared, and specified points corresponding within the range of conditions that match the content of the measurement information may be reflected in the measurement score. Further, the points to be reflected may be determined in advance based on information mapped in a space composed of multiple view axes (e.g., an n-dimensional look-up table), and the determined points may be reflected in the measurement score.

[0173] In addition, the score acquisition unit 251 obtains a life score based on the life information acquired by the life information acquisition unit 242. The score acquisition unit 251 accumulates the acquired life score corresponding to the user identifier in the user information storage unit 115.

[0174] The life score is a value indicating the degree of goodness of the state of matters such as the user's living habits, state, or eating behavior, or a symbol indicating a grade, etc. The score acquisition unit 251 obtains a life score based on the life information, for example, according to whether the content of various life information satisfies specified conditions. For each element included in the life information, specified conditions are set for each view. For example, when the specified conditions are satisfied, the first specified points are reflected in the life score, and conversely, the second specified points are reflected in the life score, whereby the life score can be obtained. Also, for each element included in the life information, the score acquisition unit 251 may separately obtain scores and use the obtained scores to obtain a life score. For example, the sum value or average value of the scores, or a value derived through a specified function including variables regarding each score, may be obtained as the life score. Additionally, multiple thresholds serving as references may be prepared, and specified points corresponding within the range of conditions that match the content of the life information may be reflected in the life score. Further, the points to be reflected may be determined in advance based on information mapped in a space composed of multiple view axes (e.g., an n-dimensional look-up table), and the determined points may be reflected in the life score.

[0175] For example, in the case where the measured value of blood pressure is used as life information, when the measured value of blood pressure during the most recent specified period is greater than the specified value, the life score at this time is calculated to be lower than when it is not the case. Additionally, for example, when the calories ingested in the past few days are greater than the specified value as life information, the life score at this time is calculated to be lower than when it is not the case. Also, for example, when the calories consumed through exercise in the past few days are within the specified range as life information, the life score is calculated to be higher. And the calculation of the life score is not limited to this, but may be set according to the type of life information actually used, etc.

[0176] The score acquisition unit 251 acquires score information using the acquired measurement score and life score. For example, the score acquisition unit 251 uses, as the score indicating the physical state, a value obtained by a prescribed function including variables of each score, such as the sum value of each score or the average value of each score, and acquires this score information. The score acquisition unit 251 accumulates the acquired score information corresponding to the user identifier in the user information storage unit 115. The score acquisition unit 251 may also use learning information configured by a method using machine learning to acquire score information. As the learning information, for example, information configured to take the measurement score and the life score as inputs and the score indicating the physical state as an output can be used. Also, as needed, it can be used for acquiring score information after standardizing the measurement score or the life score or the like.

[0177] Also, in Embodiment 2, life information may not be used. The score acquisition unit 251 may also acquire score information using only measurement information. Additionally, the score acquisition unit 251 may be configured to acquire information related to the score using input information including measurement information and life information and learning information, and acquire score information based on this information.

[0178] For example, the input information may include, in addition to the measurement information, information obtained by measuring at least one of the user's blood pressure, pulse, blood oxygen concentration, body temperature, respiratory rate, and acceleration. In this case, the acquired score can represent the user's physical state with higher accuracy.

[0179] Also, for example, the input information may include, in addition to the measurement information, diet information related to the user's diet behavior. In this case, the acquired score can represent the user's physical state with higher accuracy.

[0180] The score acquisition unit 251 may also acquire a score based on the measurement score and life score acquired in the past for this user or the input information and life information at a past time that have been stored in the user information storage unit 115. For example, the score for this time may also be acquired by reflecting the points for this time in the score acquired in the previous time. Additionally, the input information and life information in a past prescribed period and the input information and life information for this time can be used, and the average value or the like of these can be used to acquire the score.

[0181] Moreover, factors such as conditions set for obtaining such scores, reference values for comparison, reference scores, points reflected in the scores, and methods of reflecting points in the scores (addition, subtraction, multiplication, etc.) can be set appropriately, or the learning information to be used can be selected. Such processing can be performed based on the user's life information, and more specifically, for example, it can be performed based on basic information, etc. That is, the score acquisition unit 251 can also acquire scores based on the user's basic information. Specifically, for example, the score acquisition unit 251 can also be configured to acquire scores by applying factors or learning information that vary depending on the user's gender or age.

[0182] The output information acquisition unit 257 acquires output information related to the score based on the score information. Output information about the score is, for example, the following information. In Embodiment 2, the output information is information used in the terminal device 600 to let the user know the score that has been obtained. For example, the output information can be information having characters or symbols indicating the score.

[0183] In addition, for example, the output information can also be information for outputting information such as the value of the score through an image or sound. For example, the output information acquisition unit 257 can also acquire output information for outputting sound from the measurement device 700. As such output information, for example, various information such as sound data or data for playing a sound source provided in the instrument in a prescribed form can be used. For example, by outputting information such as the value of the score from the measurement device 700 through sound, the user can easily know information related to their own physical state without using vision.

[0184] Here, the output information acquisition unit 257 can also acquire the maximum value of the score regarding the score information that the user can obtain, and acquire output information corresponding to the relationship between the maximum value and the score information already obtained by the score acquisition unit 251. For example, it can also be said that the maximum value is the score in the best physical state. For example, although the score determined based on the measurement information measured when the physical state is considered the best can be used as the maximum value, it is not limited thereto. A prescribed maximum value can be uniformly acquired for any user. For example, as a concept corresponding to the relationship between the maximum value and the score information, the output information acquisition unit 257 can also express the score indicating the current physical state as a percentage when the best physical state is regarded as 100%. In this way, as a value relative to the maximum value, the score can be expressed as a relativized value, so that the user can intuitively grasp the current physical state. And, for example, the maximum value can also be the score in the worst physical state.

[0185] In Embodiment 2, the output information acquisition unit 257 acquires advice related to the user's eating behavior so as to obtain a score higher than the score obtained by the score acquisition unit 251 in the future. The output information acquisition unit 257 acquires the obtained advice together with the information about the score as output information. By outputting such output information, the user can refer to the content of the advice and take actions in a better physical state in the future.

[0186] The output information acquisition unit 257 can acquire various forms of advice as follows, for example. For example, the scores at at least two moments on the time axis from the past to the future are compared to determine the type or change amount of the change in the physical state, which is the comparison result. After that, the advice corresponding to the determined type or change amount is extracted from a table prepared in advance and acquired as the advice to be included in the output information.

[0187] Such advice can also be appropriately prepared based on the type of measurement information or life information used for score output. For example, when the score represents the stress level, the following can be considered as advice. For example, advice related to activities that make it difficult for the user to feel stressed, such as "There is a possibility of easily feeling stressed in the future. Do some exercise" or "Tomorrow may be a day when it is easy to feel stressed. How about taking a longer break", can be acquired. In addition, for example, advice related to diet, such as "It seems that the stress level will increase in an hour. How about taking a break while drinking herbal tea", can be acquired. Also, advice that helps the user reconsider the action plan, such as "The score will reach the peak in two hours. Adjust the plan", can be acquired. In addition, information related to the expected score when a prescribed action is taken according to the content of the advice can be included in the advice. For example, advice such as "If you drink coffee now, it will help maintain concentration with an appropriate stress level for about two hours" can be acquired. And when the score represents the sleep-wake level, for example, advice that helps the user reconsider the action plan, such as "The score will reach the peak in two hours. Adjust the plan", can be acquired. For example, information related to the expected score when a prescribed action is taken according to the content of the advice, such as "If you drink 200 ml of green tea now, you can maintain a higher wake level for another two hours", can be included.

[0188] The output unit 260 outputs the output information acquired by the output information acquisition unit 257. In the present embodiment, although the output unit 260 outputs by, for example, sending the output information to the terminal device 600, it is not limited thereto. Output can also be performed by any one of display on a display or the like, printing on paper or the like, and sending information to other instruments. For example, based on the scoring information, output information based on the scoring can also be displayed on the display included in the information processing device 200 by text, image, or the like for output. Also, it can be considered that the output unit 260 includes or does not include output devices such as a display or a speaker. The output unit 260 can be implemented by the driving software of the output device or the driving software of the output device and the output device, etc.

[0189] Next, an example of the operation of the information processing device 200 when the user uses the information processing system 2 according to Embodiment 2 will be described. In the present embodiment, the user can use the information processing system 2, for example, by connecting to the information processing device 200 via the terminal device 600 or receiving information sent from the information processing device 200, and at the same time, causing a prescribed application program to operate in the terminal device 600. And the prescribed application program can be, for example, a dedicated application program that operates using the information sent from the information processing device 200, or a web browser or the like that displays the web application program provided in the information processing device 200 so that it can be used.

[0190] In the present embodiment, the information processing system 2 is typically used as follows. That is, the user measures his or her own biological signal while wearing the measurement device 700. The measurement information obtained by the measurement is transferred to the terminal device 600. After that, the user sends the measurement information stored in the terminal storage unit 610 from the terminal device 600 to the information processing device 200. In addition, the terminal storage unit 610 can also send the stored life information and the like from the terminal device 600 to the information processing device 200.

[0191] In this way, the information processing device 200 acquires the scoring information and outputs the output information. The terminal device 600 receives the output output information and displays the score on the display device through the terminal output unit 660. Thus, the user can confirm information related to his or her current or future physical state. Since the physical state is represented as a score, the user can easily confirm information related to his or her physical state. In addition, at this time, the advice to the user acquired by the output information acquisition unit 257 is also displayed, so that the user can use it as a reference to make himself or herself aware or take actions to be in a better physical state.

[0192] When such an information processing system 2 operates, the information processing device 200 performs various operations as follows. The processing unit 140 performs control operations and the like while using each part, thereby performing these operations.

[0193] Fig.13 It is a flowchart showing an example of the operation of the information processing device 200.

[0194] (Step S21) The processing unit 140 determines whether information sent from the terminal device 600 or the like has been received through the receiving unit 120. When it is determined that the information has been received, the process proceeds to step S22; otherwise, the process proceeds to step S23.

[0195] (Step S22) The processing unit 140 identifies the user based on the received information and accumulates the received information in the user information storage unit 115 in correspondence with the user identifier. For example, if life information or measurement information is sent from the terminal device 600 in correspondence with the user identifier, the processing unit 140 accumulates the received information in the user information storage unit 115 in correspondence with the user identifier.

[0196] Here, as in the first embodiment, the processing unit 140 outputs future information related to future measurement information based on the received measurement information. Thus, the measurement information including the information received from the terminal device 600 and the future information is accumulated in the user information storage unit 115. Also, instead of performing such processing, only the measurement information received from the terminal device 600 may be used in obtaining the scoring information.

[0197] Here, for example, when the evaluation information has been sent from the terminal device 600 corresponding to the case where the previous output information has been output, the learning information can also be relearned using the evaluation information. Thus, a higher-precision scoring can be obtained.

[0198] (Step S23) The processing unit 140 determines whether an opportunity related to obtaining the scoring indicating the physical state has occurred. In other words, the processing unit 140 determines whether the condition for starting to obtain the scoring is satisfied. When it is determined that the opportunity has occurred, the process proceeds to step S24; otherwise, the process returns to step S21.

[0199] And, for example, an instruction related to obtaining the scoring performed by the user through the terminal device 600 (transmission of specified information corresponding to the instruction) or the like can be used as the above-mentioned opportunity, but the opportunity is not limited thereto. For example, various conditions such as reaching a specified time or newly receiving measurement information can also be used as the opportunity.

[0200] (Step S24) The processing unit 140 performs a scoring information acquisition process through the scoring acquisition unit 251. The scoring information acquisition process will be described later. Scoring information is acquired through the scoring information acquisition process and accumulated in the user information storage unit 115.

[0201] (Step S25) The processing unit 140 acquires output information through the output information acquisition unit 257, using the acquired scoring information and the like.

[0202] (Step S26) The processing unit 140 outputs the acquired output information to the target user through the output unit 260. That is, the processing unit 140 outputs the output information to the terminal device 600 used by the user who is the calculation target of the scoring through the transmission unit 170. Thereby, in the terminal device 600 that has received the information, display related to the scoring can be performed.

[0203] Fig.14 It is a flowchart showing an example of the scoring information acquisition process of the information processing device 200.

[0204] (Step S211) The scoring acquisition unit 251 acquires measurement information, life information, etc. about the target user from the user information storage unit 115 according to the user identifier of the target user.

[0205] (Step S212) The scoring acquisition unit 251 uses the measurement information and learning information to perform a process of acquiring a measurement score.

[0206] (Step S213) The scoring acquisition unit 251 uses the life information to perform a process of acquiring a life score. The calculation of the life score will be described later.

[0207] (Step S214) If the measurement score and the life score are obtained, the scoring acquisition unit 251 acquires scoring information representing the physical state score. At this time, the scoring acquisition unit 251 calculates the score by adding, multiplying, etc. using the measurement score and the life score through a prescribed method as described above, for example.

[0208] (Step S215) The scoring acquisition unit 251 accumulates the acquired scoring information and the like corresponding to the user identifier of the target user in the user information storage unit 115. Then, it returns to the upper-level process.

[0209] Fig.15 It is a flowchart showing an example of the life score acquisition process of the information processing device 200.

[0210] Moreover, in this case, information related to each element that can be included in life information is not distinguished, and these are collectively referred to as life information for explanation. When information includes multiple elements as life information, it is only necessary to perform processing such as determining whether each element satisfies the following conditions. In the following description, although the first condition and the second condition related to life information are used, it is possible to perform only the determination of one condition, or the determination of three or more conditions.

[0211] (Step S251) The score acquisition unit 251 resets the life score to the initial value (for example, zero) respectively.

[0212] (Step S252) The score acquisition unit 251 determines whether the life information satisfies the specified first condition. When it is determined that the life information satisfies the first condition, the process advances to Step S253; otherwise, it advances to Step S254.

[0213] (Step S253) The score acquisition unit 251 adds 10 points to the life score. The process advances to Step S255.

[0214] (Step S254) The score acquisition unit 251 adds 5 points to the life score. The process advances to Step S255.

[0215] (Step S255) The score acquisition unit 251 determines whether the life information satisfies the specified second condition. When it is determined that the life information satisfies the second condition, the process advances to Step S256; otherwise, it advances to Step S257.

[0216] (Step S256) The score acquisition unit 251 adds 15 points to the life score.

[0217] (Step S257) The score acquisition unit 251 adds 5 points to the life score.

[0218] If Step S256 or Step S257 ends, the score acquisition unit 251 obtains the life score as the above result and returns to the upper-level process.

[0219] By operating the information processing device 200 as described above, score information about the user is obtained, and the output information is output.

[0220] Next, a specific example of obtaining the score information based on the information processing device 200 according to Embodiment 2 and outputting information to the user will be described.

[0221] A specific example of outputting information to a user is as follows. In the following specific example, it is assumed that the information processing system 2 provides a health support application for supporting a user to live a healthy life. The health support application accepts input operations related to the user's life information and provides information for maintaining health to the user. The user executes a prescribed application in the terminal device 600, and communication is performed between the terminal device 600 and the information processing device 200, thereby implementing the health support application. Under the control of the terminal processing unit 640, the following example of the screen of the health support application is displayed by the terminal output unit 660.

[0222] Fig.16 It is the first figure showing a specific example of the screen transition of the terminal device 600.

[0223] In the figure, a menu screen 901 of the health support application is displayed. The menu screen 901 includes, for example: an improvement information display section 911; a score information display section 912 for displaying information related to the score; a life information record button 913 for performing input or viewing of a resume related to life information, etc.; and a basic information button 914 for confirming or editing basic information, etc.

[0224] In the improvement information display section 911, information helpful for improving the user's physical condition is displayed, for example. When the improvement information display section 911 is operated, it transitions to an improvement information display screen (not shown). In the improvement information display screen, regardless of the user's state, preset information can also be displayed. In addition, information corresponding to the score information output in the past can also be displayed. For example, the content of the advice obtained previously can also be displayed, enabling the user to reconfirm the content of the advice.

[0225] The score information display section 912 is a button for instructing the information processing device 200 to execute the output of the score information. In this specific example, when the score information display section 912 is operated, the information processing device 200 obtains the score information as described above and sends the output information to the terminal device 600. If the terminal device 600 receives the output information, it transitions to the score display screen 904 (shown in the next figure).

[0226] The life information record button 913 is, for example, a button for inputting the time and content of eating and the time and content of exercise, etc. By operating the buttons corresponding to each element, the user can input and record information related to each element, or send it to the information processing device 200 and store it in the information processing device 200.

[0227] Fig.17 It is the second figure showing a specific example of the screen transition of the terminal device 600.

[0228] The figure shows a score display screen 904 of a health support application. The score display screen 904 includes, for example: a score display section 921 that indicates the overall score that has been obtained; a prediction display section 923 that indicates the predicted trend of the score in the future; and a recommendation display section 925 that displays recommendations. Through the score display screen 904, the user can confirm the score related to the physical condition.

[0229] In the score display section 921, for example, the overall score is displayed, which represents the current score and the predicted result of the score during a specified future period as a combined score. The overall score can be various values such as the average of each score or the score at a specified time. Through the overall score, the user can easily grasp the level of their own physical condition.

[0230] In addition, the prediction display section 923 displays the trend of the score in a specified future period. By confirming the prediction display section 923, the user can know the possibility of changes in their future physical condition.

[0231] In the recommendation display section 925, for example, the recommendations obtained by the output information acquisition section 257 are displayed. In the example shown in the figure, referring to the predicted result that the score will decrease around 14:00, a recommendation to drink a beverage that can be expected to reduce the stress level at the corresponding time is output. By displaying such recommendations, it is possible to provide the user with hints related to actions for improving the physical condition and effectively stimulate the user's motivation for improvement actions.

[0232] As described above, in the second embodiment, it is possible to obtain a score indicating the physical condition of the user. By presenting the score to the user, the user can effectively identify their own physical condition. Thus, it is possible to effectively make the user aware of maintaining a healthy and active state and improving their actions. By maintaining the user's good physical condition, it is possible to improve the user's productivity or make the society related to the user more active.

[0233] Moreover, the information processing device 200 can be composed of a single server, or can be composed of multiple servers that cooperate with each other, or can be a computer built into other instruments, etc. And the server can be a so-called cloud server or an ASP server, etc., and needless to say, its type can be arbitrary.

[0234] In addition, the processing in Embodiment 2 can also be implemented by software. Moreover, the software can be distributed through software downloads or the like. Additionally, the software can be recorded on a storage medium such as an optical disc and transmitted. The software that implements the information processing apparatus 200 in the present embodiment is the following program. That is to say, this program is a program executed in the computer of the information processing apparatus 200, and is used to cause the computer of the information processing apparatus 200, which can access the measurement information storage unit storing the measurement results of the biological potential of the user based on two or more times related to the biological state, to function as a biological information acquisition unit and a score acquisition unit. The biological information acquisition unit acquires the measurement information from the measurement information storage unit, and the score acquisition unit acquires score information including a score indicating the current or future physical state of the user based on the measurement information.

[0235] In addition, in Embodiment 2, the processing unit 140 may not include the prediction information acquisition unit 145 or the future information output unit 147 in these respective parts. In this case, it is sufficient to obtain a score using the measurement information based on the measurement results actually measured using the measurement device 700 or the like. In this case, it is possible to obtain score information including a score indicating the current physical state of the user, or to output output information based on the score information.

[0236] (Other)

[0237] Fig.18 is an external view of the computer system 800 in the above-described embodiment. Fig.19 is a block diagram of the computer system 800.

[0238] In these figures, a configuration example of a computer that executes the program described in this specification to implement the information processing apparatus and the like in the above-described embodiment is shown. The above-described embodiment can be implemented by computer hardware and a computer program executed thereon.

[0239] The computer system 800 includes a computer 801 having an optical disc drive, a keyboard 802, a mouse 803, and a monitor 804.

[0240] In addition to the optical disc drive (ODD) 8012, the computer 801 further includes: an MPU 8013; a bus 8014 connected to the optical disc drive 8012 and the like; a ROM 8015 for storing programs such as a startup program; a RAM 8016 connected to the MPU 8013 and used to temporarily store commands of application programs and provide a temporary storage space; and a hard disk drive (HDD) 8017 for storing application programs, system programs, and data. Here, although not shown, the computer 801 may further include a network card providing a connection to a LAN.

[0241] In the computer system 800, the program that executes the functions of the information processing apparatus and the like of the above-described embodiments can also be stored in the optical disc 8101 and further transferred to the hard disk 8017 by inserting it into the optical disc drive 8012. Alternatively, the program can also be sent to the computer 801 via a network (not shown) and stored in the hard disk 8017. The program is loaded into the RAM 8016 when it is executed. The program can also be directly loaded from the optical disc 8101 or the network.

[0242] The program does not necessarily include an operating system (OS) or third-party programs that cause the computer 801 to execute the functions of the information processing apparatus and the like of the above-described embodiments. As long as the program calls appropriate functions (modules) in a controlled form and only includes the command part that can obtain the desired result. It is well known how the computer system 800 works, and the detailed description is omitted.

[0243] Moreover, in the above program, in the transmission step of transmitting information or the reception step of receiving information, etc., there is no processing performed by hardware, such as the processing performed by a modem or an interface card in the transmission step (processing that can only be performed by hardware).

[0244] In addition, the computer that executes the above program can be either single or multiple. That is, either centralized processing can be performed, or distributed processing can also be performed.

[0245] In addition, in the above-described embodiments, two or more constituent elements existing in one device can physically be implemented by one medium.

[0246] In addition, in the above-described embodiments, each process (each function) can be implemented by a single device (system) through centralized processing, or can also be implemented by multiple devices through distributed processing (in this case, the entire system composed of multiple devices performing distributed processing can be regarded as one "device").

[0247] In addition, in the above-described embodiments, the exchange of information between constituent elements, for example, when the two constituent elements performing the exchange of this information are physically different, can be performed by the output of information of one constituent element and the reception of information of the other constituent element, or when the two constituent elements performing the exchange of this information are physically the same, it can also be performed by transferring from the processing phase corresponding to one constituent element to the processing phase corresponding to the other constituent element.

[0248] In addition, in the above-described embodiments, even if not explicitly described in the above explanation, information related to the processing performed by each component may be temporarily or permanently stored on a storage medium (not shown). Such information includes, for example, information received, obtained, selected, generated, transmitted, or received by each component, as well as thresholds, arithmetic expressions, addresses, and other information used by each component in the processing. In addition, each component or an unillustrated accumulation unit may accumulate information on the storage medium (not shown). In addition, each component or an unillustrated reading unit may read information from the storage medium (not shown).

[0249] In addition, in the above-described embodiments, when information used in each component, etc., such as thresholds, addresses, and various setting values used by each component in the processing, can be changed by the user, even if not explicitly described in the above explanation, the user may appropriately change this information or may not change it. When the user can change this information, the change can be achieved, for example, by an unillustrated reception unit that receives a change instruction from the user and an unillustrated change unit that changes the information according to the change instruction. The reception of the change instruction by the unillustrated reception unit may be, for example, reception from an input device, reception of information transmitted via a communication line, or reception of information read from a prescribed storage medium.

[0250] The present invention is not limited to the above embodiments and can be variously modified, and the modified content is also included in the scope of the present invention.

[0251] An embodiment can also be configured by appropriately combining the components of the above-described multiple embodiments or modified examples. For example, not limited to the configurations of the above embodiments themselves, with respect to the respective components of the above embodiments or modified examples, they can be appropriately replaced or combined with the components of other embodiments. In addition, some components or functions in the above embodiments or modified examples can be omitted. Industrial Applicability

[0252] As described above, the information processing apparatus according to the present invention has the effect of being able to obtain a score representing the current or future physical state of each user and is useful as an information processing apparatus or the like.

Claims

1. An information processing apparatus, characterized in that: it includes: a biological information acquisition unit that acquires measurement information related to a biological state, which is a measurement result of a user's bioelectric potential at two or more times; and a score acquisition unit that acquires score information including a score indicating the current or future physical state of the user based on the measurement information.

2. The information processing apparatus according to claim 1, characterized in that the measurement information is time-series information.

3. The information processing apparatus according to claim 1, characterized in that the score information includes information indicating the change of the score.

4. The information processing apparatus according to claim 1, characterized in that it further includes: an output information acquisition unit that acquires output information related to the score based on the score information; and an output unit that outputs the output information.

5. The information processing apparatus according to claim 4, characterized in that the output information acquisition unit acquires the maximum value of the score of the score information that can be obtained by the user, and acquires output information corresponding to the relationship between the maximum value and the score information already obtained by the score acquisition unit.

6. The information processing apparatus according to claim 4, characterized in that the measurement information is information obtained by measuring the user's bioelectric potential with a measurement device having two or more electrodes that contact the user's body surface.

7. The information processing apparatus according to claim 6, characterized in that the output information acquisition unit acquires output information for outputting sound from the measurement device.

8. The information processing apparatus according to claim 4, characterized in that the output information acquisition unit acquires, as the output information, advice related to the user's eating behavior for obtaining a higher score in the future compared to the score of the score information already obtained by the score acquisition unit.

9. The information processing apparatus according to claim 1, characterized in that the score acquisition unit acquires the score information based on information obtained by measuring at least one of the user's blood pressure, pulse, blood oxygen concentration, body temperature, respiratory rate, and acceleration.

10. The information processing apparatus according to claim 1, characterized in that the score acquisition unit acquires the score information based on diet information related to the user's eating behavior.

11. The information processing apparatus according to claim 1, characterized in that the score acquisition unit uses learning information configured to take input information related to the measurement information as input and output future measurement information, acquires future measurement information based on the input information, and acquires the score information based on the acquired measurement information.

12. The information processing apparatus according to claim 1, characterized in that the score acquisition unit uses learning information configured to take input information related to the measurement information as input and output information related to the score as output, acquires information related to the score based on the input information, and uses the acquired information to acquire the score information.

13. The information processing apparatus according to claim 12, characterized in that The scoring acquisition unit uses, as the learning information, learning information constituted by input information including measurement information that has been measured for two or more users in the past.

14. The information processing apparatus according to claim 12, wherein it further includes a learning information acquisition unit that performs relearning using evaluation information corresponding to the scoring information acquired by the scoring acquisition unit and input information used in the acquisition of the scoring information to acquire learning information.

15. An information processing method implemented using a measurement information storage unit that stores measurement information related to a biological state, the measurement information being based on measurement results of a biological potential of a user at two or more times, wherein it includes: a biological information acquisition step of acquiring the measurement information from the measurement information storage unit; and a scoring acquisition step of acquiring scoring information including a score indicating the current or future physical state of the user based on the measurement information.

16. A program, wherein it causes a computer that can access a measurement information storage unit storing measurement information related to a biological state, the measurement information being based on measurement results of a biological potential of a user at two or more times, to function as a biological information acquisition unit and a scoring acquisition unit, the biological information acquisition unit acquires the measurement information from the measurement information storage unit, and the scoring acquisition unit acquires scoring information including a score indicating the current or future physical state of the user based on the measurement information.

Images