Support device, support method, and program

Abstract

To support concentration more sufficiently than before.SOLUTION: The assistance device includes a degree-of-concentration acquirer that acquires a time-series degree of concentration of a measurement subject, a tendency estimator that estimates, for each tendency type, a concentration tendency that is a tendency regarding concentration by using the time-series degree of concentration acquired by the degree-of-concentration acquirer, and an assistance information outputter that outputs assistance information regarding concentration on the basis of an estimation result by the tendency estimator.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The disclosed technology relates to an assistive technology that provides a user with assistance in terms of concentration. [Background technology]

[0002] There is a conventional technique for providing support regarding concentration to a user who is a support target. Patent Document 1 discloses a support device that calculates a concentration level of a subject, determines the current concentration state using the concentration level, and presents advice according to the current concentration state. Specifically, the support device of Patent Document 1 determines that the current concentration state is declining when the concentration level falls below a threshold, and provides advice such as switching to another subject (see paragraphs

[0027] ,

[0048] , etc.). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2023 / 032335 (WO2023 / 032335) (Panasonic IP Management) Summary of the Invention [Problem to be solved by the invention]

[0004] However, the support device of Patent Document 1 has a problem in that the support regarding concentration is likely to be insufficient because the concentration state used to generate the advice to be presented is only the current concentration state.

[0005] The present disclosure is intended to solve the above-mentioned problems and aims to provide more comprehensive support for concentration than ever before. [Means for solving the problem]

[0006] The support device of the present disclosure includes: a concentration level acquisition unit that acquires the concentration level of the person to be measured in time series; a tendency estimation unit that estimates a concentration tendency, which is a tendency related to concentration, for each tendency type using the time-series concentration degree acquired by the concentration degree acquisition unit; a support information output unit that outputs support information related to concentration based on the estimation result by the tendency estimation unit; An assistive device comprising: [Effects of the Invention]

[0007] According to the present disclosure, it is possible to provide more comprehensive support for concentration than ever before. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram illustrating an example of a configuration of a support device 100 according to a first embodiment of the present disclosure. [Figure 2] FIG. 2 is a flowchart showing an example of processing performed by the assistance device 100 according to the first embodiment of the present disclosure. [Figure 3] FIG. 3 is a diagram illustrating an example of the configuration of a support system 1 (1A) including a support device 100 (100A) according to the first embodiment of the present disclosure. [Figure 4] FIG. 4 is a diagram illustrating an example of a concentration tendency and an example of a tendency type of the concentration tendency according to the first embodiment of the present disclosure. [Figure 5] FIG. 5 is a flowchart showing an example of a data acquisition process in the assistance device 100 (100A) shown in FIG. [Figure 6] FIG. 6 is a diagram illustrating an example of the configuration of a support system 1 (1B) including a support device 100 (100B) according to the second embodiment of the present disclosure. [Figure 7] FIG. 7 is a flowchart showing an example of processing by the support device 100 (100B) according to the second embodiment of the present disclosure. [Figure 8] FIG. 8 is a flowchart showing an example of a data acquisition process in the assistance device 100 (100B) according to the second embodiment of the present disclosure. [Figure 9]FIG. 9 is a flowchart showing an example of a part of the trend estimation process of the support device 100 (100B) according to the second embodiment of the present disclosure. [Figure 10] FIG. 10 is a flowchart showing an example of a support information output process in the support device 100 (100B) according to the second embodiment of the present disclosure. [Figure 11] FIG. 11 is a diagram illustrating an example of the configuration of a support system 1 (1C) including a support device 100 (100C) according to the third embodiment of the present disclosure. [Figure 12] FIG. 12 is a flowchart showing an example of a part of the data acquisition process of the support device 100 (100C) according to the third embodiment of the present disclosure. [Figure 13] FIG. 13 is a flowchart showing an example of a part of the trend estimation process of the support device 100 (100C) according to the third embodiment of the present disclosure. [Figure 14] FIG. 14 is a flowchart showing an example of a support information output process of the support device 100 (100C) according to the third embodiment of the present disclosure. [Figure 15] FIG. 15 is a diagram illustrating an example of a concentration tendency and an example of a tendency type of the concentration tendency according to the third embodiment of the present disclosure. [Figure 16] FIG. 16 is a diagram illustrating an example of the configuration of a support system 1 (1D) including a support device 100 (100D) according to the fourth embodiment of the present disclosure. [Figure 17] FIG. 17 is a flowchart showing an example of a part of the data acquisition process of the support device 100 (100D) according to the fourth embodiment of the present disclosure. [Figure 18] FIG. 18 is a flowchart showing an example of a part of the trend estimation process of the support device 100 (100D) according to the fourth embodiment of the present disclosure. [Figure 19] FIG. 19 is a flowchart showing an example of a support information output process in the support device 100 (100D) according to the fourth embodiment of the present disclosure. [Figure 20]FIG. 20A is a first example of an image showing support information using concentration trends taking into account the implementation details, and FIG. 20B is a second example of an image showing support information using concentration trends taking into account the implementation details. [Figure 21] FIG. 21 is a diagram illustrating an example of the configuration of a support system 1 (1E) including a support device 100 (100E) according to the fifth embodiment of the present disclosure. [Figure 22] FIG. 22 is a flowchart showing an example of a part of the data acquisition process of the support device 100 (100E) according to the fifth embodiment of the present disclosure. [Figure 23] FIG. 23 is a flowchart showing an example of a part of the trend estimation process of the support device 100 (100E) according to the fifth embodiment of the present disclosure. [Figure 24] FIG. 24 is a flowchart showing an example of a part of the support information output process of the support device 100 (100E) according to the fifth embodiment of the present disclosure. [Figure 25] FIG. 25 is a flowchart showing an example of a support information output process in the support device 100 (100E) according to the fifth embodiment of the present disclosure. [Figure 26] FIG. 26 is a diagram illustrating an example of support information output by the support device 100 (100E) according to the fifth embodiment of the present disclosure. [Figure 27] FIG. 27 is a diagram illustrating an example of the configuration of a support system 1 (1F) including a support device 100 (100F) according to the sixth embodiment of the present disclosure. [Figure 28] FIG. 28 is a flowchart showing an example of a part of the data acquisition process of the support device 100 (100F) according to the sixth embodiment of the present disclosure. [Figure 29] FIG. 29 is a flowchart showing an example of a part of the trend estimation process of the support device 100 (100F) according to the sixth embodiment of the present disclosure. [Figure 30] FIG. 30 is a flowchart showing an example of a support information output process in the support device 100 (100F) according to the sixth embodiment of the present disclosure. [Figure 31]FIG. 31 is a diagram illustrating an example of support information output by the support device 100 (100F) according to the sixth embodiment of the present disclosure. [Figure 32] FIG. 32 is a diagram illustrating an example of the configuration of a support system 1 (1G) including a support device 100 (100G) according to the seventh embodiment of the present disclosure. [Figure 33] FIG. 33 is a flowchart showing an example of a part of the data acquisition process of the support device 100 (100G) according to the seventh embodiment of the present disclosure. [Figure 34] FIG. 34 is a flowchart showing an example of a part of the trend estimation process of the support device 100 (100G) according to the seventh embodiment of the present disclosure. [Figure 35] FIG. 35 is a flowchart showing an example of a support information output process in the support device 100 (100G) according to the seventh embodiment of the present disclosure. [Figure 36] FIG. 36 is a diagram illustrating an example of a fatigue tendency and an example of a tendency type of the fatigue tendency used in the assistance device 100 (100G) according to the seventh embodiment of the present disclosure. [Figure 37] FIG. 37 is a diagram for explaining an example of concentration tendency taking into account fatigue tendency in the assistance device 100 (100G) according to the seventh embodiment of the present disclosure. [Figure 38] FIG. 38 is a diagram illustrating an example of the configuration of a support system 1 (1H) including a support device 100 (100H) according to the eighth embodiment of the present disclosure. [Figure 39] FIG. 39 is a flowchart showing an example of a part of the data acquisition process of the support device 100 (100H) according to the eighth embodiment of the present disclosure. [Figure 40] FIG. 40 is a flowchart showing an example of a part of the trend estimation process of the support device 100 (100H) according to the eighth embodiment of the present disclosure. [Figure 41] FIG. 41 is a flowchart showing an example of a support information output process in the support device 100 (100H) according to the eighth embodiment of the present disclosure. [Figure 42]FIG. 42 is a diagram illustrating an example of the configuration of a support system 1 (1J) including a support device 100 (100J) according to the ninth embodiment of the present disclosure. [Figure 43] FIG. 43 is a flowchart showing an example of a part of the data acquisition process of the support device 100 (100J) according to the ninth embodiment of the present disclosure. [Figure 44] FIG. 44 is a flowchart showing an example of a part of the trend estimation process of the support device 100 (100J) according to the ninth embodiment of the present disclosure. [Figure 45] FIG. 45 is a flowchart showing an example of a support information output process in the support device 100 (100J) according to the ninth embodiment of the present disclosure. [Figure 46] FIG. 46 is a diagram illustrating a first example of a hardware configuration for realizing the functions according to the configuration of the present disclosure. [Figure 47] FIG. 47 is a diagram illustrating a second example of a hardware configuration for realizing the functions according to the configuration of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0009] In order to explain the present disclosure in more detail, embodiments of the present disclosure will be described below with reference to the accompanying drawings.

[0010] Embodiment 1 In the first embodiment, a configuration example of a basic form for realizing the assistance technology of the present disclosure will be described.

[0011] An example of the configuration of a support device according to a first embodiment of the present disclosure will be described. FIG. 1 is a diagram illustrating an example of a configuration of a support device 100 according to a first embodiment of the present disclosure. The assistance device 100 provides assistance to the user regarding concentration. The user is, for example, an operator who operates the assistance device 100. The support device 100 estimates a concentration tendency, which is a tendency of concentration, using the concentration level based on the biological information of the measurement subject, and outputs support information using the estimated concentration tendency. The subject to be measured may be the operator or a person other than the operator. For example, the operator may be a parent and the subject to be measured may be a child. The method of obtaining the concentration level from biometric information can be a known technique, for example, the method described below, but is not limited to the method described in this specification. The concentration trend is a trend expressed in the concentration level over time, and includes, for example, the high and low states of the concentration level over time, the duration of the high and low states of the concentration level, the cumulative amount of the concentration level over the duration of the high and low states of the concentration level, the high and low states of the concentration level by season, the high and low states of the concentration level by month, the high and low states of the concentration level by day of the week, or the high and low states of the concentration level by time period within a day. The concentration trend may include trends other than those exemplified above, as long as it is a trend expressed in the concentration level over time. The assistance device 100 is realized, for example, in the form of a mobile terminal or smartphone that can be carried by the user. The assistance device 100 is also realized, for example, in the form of a terminal placed on a table. The assistance device 100 is also realized, for example, in the form of a server (server device or cloud server) that generates assistance information by communicating with a smartphone or the like using an application on the user's smartphone or the like and outputs the generated assistance information to the smartphone or the like. The assistance device 100 may also be realized in a form other than the above-mentioned examples. The support device 100 shown in FIG. 1 includes a data acquisition unit 110, a tendency estimation unit 130, and a support information output unit 150.

[0012] The data acquisition unit 110 acquires data used for processing in the assistance device 100. 1 acquires a time-series concentration degree. Specifically, the data acquisition unit 110 includes a concentration degree acquisition unit 111. The concentration level acquisition unit 111 acquires the concentration level of the person to be measured in chronological order.

[0013] The trend estimation unit 130 estimates a concentration trend for each trend type using the time-series concentration degree. Specifically, the trend estimation unit 130 estimates a concentration trend, which is a trend related to concentration, for each trend type using the time-series concentration degree acquired by the concentration degree acquisition unit 111. The types of concentration trends included in the estimation results by the trend estimation unit 130 include one or more of the following: high and low levels of concentration over time, duration of high and low levels of concentration, cumulative amount of concentration for each duration of high and low levels of concentration, high and low levels of concentration by season, high and low levels of concentration by month, high and low levels of concentration by day of the week, or high and low levels by time period in a day.

[0014] The support information output unit 150 outputs support information related to concentration based on the estimation result by the tendency estimation unit 130. The support information includes one or more of the following information: the level of concentration over time, the duration of the level of concentration, the cumulative amount of concentration over the duration of the level of concentration, the level of concentration by season, the level of concentration by month, the level of concentration by day of the week, or the level of concentration by time period in a day.

[0015] In addition to the above components, the support device 100 also includes a control unit (not shown), a storage unit (not shown), and a communication unit (not shown). A control unit (not shown) controls the entire support device 100 and each of its components. The control unit (not shown) starts up the support device 100 in accordance with, for example, an external command. The control unit (not shown) also controls the state of the support device 100 (operating state = start-up, shutdown, sleep, etc.). A storage unit (not shown) stores each piece of data used in the support device 100. The storage unit (not shown) stores, for example, output (output data) from each component in the support device 100, and outputs data requested for sending or acquisition by each component to the component that responds to the request. A communication unit (not shown) communicates with external devices. For example, communication is performed between the support device 100 (100A) and peripheral devices (e.g., an information source device and an output destination device). For example, when the support device 100 is not connected to the information source device and the output destination device via a wired connection, the communication unit (not shown) has a function of communicating between the support device 100 and the information source device and the output destination device. Furthermore, when a terminal device and a server (server device, cloud server) having the functions of the support device 100 cooperate to realize the support technology of the present disclosure, the communication unit (not shown) has a function of communicating between the terminal device and the server device. The control unit (not shown), the storage unit (not shown), and the communication unit (not shown) are the same in the embodiments described below.

[0016] Next, a processing example of the support device according to the first embodiment of the present disclosure will be described. FIG. 2 is a flowchart showing an example of processing performed by the assistance device 100 according to the first embodiment of the present disclosure. The process shown in FIG. 2 is a support method performed by the support device 100. For example, when the assistance device 100 shown in FIG. 1 is activated by receiving an operation from a user, or when a processing start condition is met, such as when a time-series concentration level is received, the assistance device 100 starts the processing shown in FIG. 2 ("Start").

[0017] The support device 100 then executes a data acquisition process (step ST100). In the data acquisition process, the concentration level acquisition unit 111 in the data acquisition unit 110 of the support device 100 acquires the concentration level of the subject in chronological order from, for example, an external device. The concentration level acquisition unit 111 outputs the acquired concentration level to the tendency estimation unit 130.

[0018] The support device 100 then executes a trend estimation process (step ST200). In the trend estimation process, upon receiving the concentration level, the trend estimation unit 130 of the support device 100 estimates a concentration trend, which is a trend related to concentration, for each trend type using the time-series concentration level acquired by the concentration level acquisition unit 111 in the data acquisition unit 110. The trend estimation unit 130 estimates the concentration trend for each trend type, which includes one or more of the following: high and low states of the concentration level in the time series, the duration of the high and low states of the concentration level, the high and low states of the concentration level by season, the high and low states of the concentration level by month, the high and low states of the concentration level by day of the week, the high and low states by time zone in a day, or the cumulative amount of the concentration level over the duration of the high and low states of the concentration level. The trend estimation unit 130 outputs the concentration trend, which is the estimation result.

[0019] The support device 100 then executes support information output processing (step ST300). In the support information output processing, the support information output unit 150 of the support device 100 outputs support information related to concentration based on the estimation result by the tendency estimation unit 130. The support information output unit 150 generates and outputs support information using the tendency of concentration.

[0020] The assistance device 100 then ends the process and waits ("End").

[0021] Next, a configuration example of a system including the support device according to the first embodiment of the present disclosure will be described. FIG. 3 is a diagram illustrating an example of the configuration of a support system 1 (1A) including a support device 100 (100A) according to the first embodiment of the present disclosure. In this specification, different symbols are used for each of the following embodiments, but components with similar symbols, such as support device 100 and support device 100 (100A), can be configured by combining some or all of them, or by omitting some of them. The support system 1 (1A) is a system including a support device 100 (100A). The support system 1 (1A) shown in FIG. 3 includes a support device 100 (100A), an information source device 600 (600A), and an output destination device 700.

[0022] The information source device 600 (600A) is a device or a group of devices that are a source of information used in the processing of the support device 100 (100A). The information source device 600 includes, for example, a biometric device that outputs biometric information. The information source device 600 may be configured integrally with the support device 100 (100A).

[0023] The output destination device 700 receives support information output by the support device 100 (100A). The output destination device 700 is, for example, an output device such as a display device or a sound output device. The output destination device 700 may be configured integrally with the support device 100 (100A).

[0024] The support device 100 (100A) has the same functions as the already explained support device 100. The support device 100 (100A) further has a function of calculating a concentration level from biological information. The support device 100 (100A) shown in FIG. 3 includes a data acquisition unit 110 (110A), a tendency estimation unit 130 (130A), and a support information output unit 150 (150A).

[0025] The data acquiring unit 110 (110A) has the same functions as the already described data acquiring unit 110. The data acquiring unit 110 (110A) further acquires biometric information, and derives and acquires the concentration level using the acquired biometric information. The data acquiring section 110 (110A) shown in FIG. 3 includes a concentration level acquiring section 111 (111A) and a biological information acquiring section 112.

[0026] The biometric information acquiring unit 112 acquires biometric information of the measurement subject. The biometric information acquiring unit 112 acquires known biometric information necessary for the concentration level acquiring unit 111 (111A) to derive the concentration level. The biometric information is, for example, pre-conversion biometric information as a measurement result such as pulse, pre-conversion biometric information obtained by analyzing an image, or the concentration level itself. A detailed example of the biometric information acquisition unit 112 will be described. The biometric information acquiring unit 112 detects and collects biometric information of the subject at a predetermined cycle. The biometric information acquiring unit 112 acquires one or more types of biometric information. When there are multiple subjects, the biometric information acquiring unit 112 acquires the biometric information of each of the multiple subjects present in the room. The biometric information acquiring unit 112 stores the detected values ​​of the acquired biometric information of the subject in a biometric information storage unit (not shown) as pre-conversion biometric information values. When the biometric information of multiple subjects is acquired, the biometric information acquiring unit 112 stores the pre-conversion biometric information values ​​for each subject in a biometric information storage unit (not shown). Here, when the biological information acquired by the biological information acquisition unit 112 is an actual measurement value, it is a value before being converted into biological information to be acquired, such as a sleepiness level, a fatigue level, etc. For this reason, here, the value of the biological information detected by the biological information acquisition unit 112 is referred to as a pre-conversion biological information value. Furthermore, the values ​​of the biometric information such as drowsiness level and fatigue level into which the pre-conversion biometric information values ​​are converted are converted under predetermined conditions. Therefore, the values ​​of the biometric information into which the pre-conversion biometric information values ​​are converted are called converted biometric information values. Furthermore, the biometric information acquiring unit 112 may reflect the tendency of the biometric information specific to the subject to be measured in the pre-conversion biometric information value, and may set the value of the biometric information adapted to the tendency of the biometric information specific to the subject to be measured as the biometric information value. Therefore, here, the value of the biometric information adapted to the tendency of the biometric information specific to the subject to be measured, and reflecting the tendency of the biometric information specific to the subject to be measured, is referred to as the adapted biometric information value. Furthermore, the biological information acquiring unit 112 may convert the pre-conversion biological information value based on predetermined upper and lower limit values, instead of the value of the biological information adapted to the tendency of the biological information unique to the subject by reflecting the tendency of the biological information unique to the subject in the pre-conversion biological information value. Therefore, here, the value of the biological information obtained by converting the pre-conversion biological information value based on predetermined upper and lower limit values, instead of the value of the biological information adapted to the tendency of the biological information unique to the subject by reflecting the tendency of the biological information unique to the subject in the pre-conversion biological information value, is referred to as the default converted biological information value. Examples of the biological information of the subject to be measured acquired by the biological information acquiring unit 112 include stress, concentration level, sleepiness level, fatigue level, and emotions. The biological information of the subject to be measured acquired by the biological information acquiring unit 112 is not limited to one type. The biological information acquiring unit 112 can simultaneously acquire multiple types of biological information for one subject to be measured. The pre-conversion biometric information value is biometric information of the subject acquired by the biometric information acquiring unit 112, and is an actual measurement value acquired by the biometric information acquiring unit 112. The pre-conversion biometric information value measured by the biometric information acquiring unit 112 is sent to a biometric information storage unit (not shown) in association with date and time information, and is stored in the biometric information storage unit (not shown). The date and time information is information on the date and time when the pre-conversion biometric information value of the subject was measured by the biometric information acquiring unit 112. The bioinformation acquiring unit 112 collects the pre-conversion bioinformation values ​​of the subject at a predetermined cycle, so that changes in the physical condition of the subject can be recorded and can be used as information for detecting an abnormal condition of the subject. Note that the bioinformation acquiring unit 112 may acquire and collect the pre-conversion bioinformation values ​​of the subject at a timing designated by the operator of the support device 100 or the subject, or at a predetermined timing. The biometric information acquiring unit 112 acquires actual measurement values ​​(pre-conversion biometric information values) obtained by, for example, a Doppler sensor (information source device 600) and converts the pre-conversion biometric information values ​​to acquire biometric information. The biometric information acquiring unit 112 may also be configured to include a sensor (information source device 600). Note that the type of sensor used by the biometric information acquiring unit 112 to acquire actual measurement values ​​is not critical as long as it is a sensor that can appropriately acquire desired biometric information. Furthermore, when acquiring multiple types of biometric information, the biometric information acquiring unit 112 can use a sensor suitable for acquiring biometric information for each type of biometric information. For example, pulse data of the subject can be acquired using a Doppler sensor. The biological information acquiring unit 112 can acquire biological information of the subject, i.e., pre-conversion biological information values ​​of the subject, based on the pulse data of the subject acquired by the Doppler sensor. Considering stress level as an example, the biological information acquiring unit 112 can estimate the stress level, which is the balance of the subject's autonomic nerves, from fluctuations in the subject's pulse. The biological information acquiring unit 112 can acquire the pre-conversion biological information value of the subject's stress level using the formula stress level=LF / HF. In this case, the biological information acquiring unit 112 extracts a high frequency (HF) fluctuation component corresponding to respiratory fluctuation and a low frequency (LF) component corresponding to Mayer wave, which is blood pressure fluctuation, from the time series data of pulse fluctuation, and compares the magnitudes of both.The biological information acquiring unit 112 can then acquire the value of LF / HF as a stress index, which is the activity level of the sympathetic nerves. Furthermore, the biological information acquiring unit 112 can use sensors such as a frequency modulated continuous wave (FMCW) sensor and an acoustic wave sensor in addition to the Doppler sensor.

[0027] The concentration level acquiring unit 111 (111A) acquires the concentration level of the measurement subject in chronological order from the biological information acquiring unit 112.

[0028] The trend estimation unit 130 (130A), like the trend estimation unit 130 already described, estimates a concentration trend, which is a trend related to concentration, for each trend type using the time-series concentration degree acquired by the concentration degree acquisition unit 111. FIG. 4 is a diagram illustrating an example of a concentration tendency and an example of a tendency type of the concentration tendency according to the first embodiment of the present disclosure. FIG. 4 shows the concentration of measurement subjects over time on a single day, such as October 8, 2026 (Thursday), as an example of the concentration of measurement subjects over time. In Figure 4, the types of concentration trends represented by the concentration level can be seen, for example, as high or low levels of concentration (concentration trend (1)), duration of high or low levels of concentration (trend (2)), high or low levels of concentration by season or month (trend (3)), high or low levels of concentration by day of the week (concentration trend (4)), high or low levels by time period in a day (trend (5)), or the cumulative amount of concentration over the duration of high or low levels of concentration (trend (6)). The support information output unit 150 (150A) outputs support information related to concentration based on the estimation result by the tendency estimation unit 130, similar to the support information output unit 150 already described.

[0029] Next, among the processes in the support device 100 (100A), those that differ from those in the support device 100 will be described. FIG. 5 is a flowchart showing an example of a data acquisition process in the assistance device 100 (100A) shown in FIG. For example, the data acquisition unit 110A in the support device 100A shown in FIG. 3 starts the process shown in FIG. 5 when it is activated by receiving an operation from a user or when it meets a processing start condition such as receiving biometric information. ("Start")

[0030] The data acquiring unit 110A then executes a biometric information acquiring process (step ST1100). In the biometric information acquiring process, the biometric information acquiring unit 112 of the data acquiring unit 110A acquires biometric information from the information source device 600A. The biometric information acquiring unit 112 outputs the acquired biometric information to the concentration level acquiring unit 111.

[0031] The data acquiring section 110A then executes a concentration level acquiring process (step ST1200). In the concentration level acquiring process, the concentration level acquiring section 111 of the data acquiring section 110A acquires biometric information output from the biometric information acquiring section 112. If the biometric information is information before being converted into a concentration level, the concentration level acquiring section 111 calculates the concentration level using the biometric information.

[0032] After outputting the calculated concentration level to the tendency estimation unit 130, the data acquisition unit 110A ends the process shown in FIG. 5 and goes into standby mode ("end").

[0033] This embodiment shows a configuration including the following configuration. [1] a concentration level acquisition unit that acquires the concentration level of the person to be measured in time series; a tendency estimation unit that estimates a concentration tendency, which is a tendency related to concentration, for each tendency type using the time-series concentration degree acquired by the concentration degree acquisition unit; a support information output unit that outputs support information related to concentration based on the estimation result by the tendency estimation unit; An assistive device comprising: As a result, the present disclosure has the effect of providing an assistance device that enables more comprehensive assistance with concentration than ever before.

[0034] This embodiment shows a configuration including the following configuration.

[14] A support method using a support device, a concentration level acquisition unit of the support device acquires the concentration level of the measurement subject in chronological order; a tendency estimation unit of the assistance device estimating a concentration tendency, which is a tendency related to concentration, for each tendency type using the time-series concentration degree acquired by the concentration degree acquisition unit; an assistance information output unit of the assistance device outputs assistance information related to concentration based on the estimation result by the tendency estimation unit; A support method characterized by: As a result, the present disclosure has the effect of providing a support method that enables more comprehensive support for concentration than ever before.

[0035] This embodiment shows a configuration including the following configuration.

[15] Computer, a concentration level acquisition unit that acquires the concentration level of the person to be measured in time series; a tendency estimation unit that estimates a concentration tendency, which is a tendency related to concentration, for each tendency type using the time-series concentration degree acquired by the concentration degree acquisition unit; a support information output unit that outputs support information related to concentration based on the estimation result by the tendency estimation unit; an assist device comprising: A program characterized by operating as As a result, the present disclosure has the effect of being able to provide a program that enables more comprehensive support for concentration than ever before.

[0036] This embodiment further shows an example of an embodiment including the following configuration. [2] The support information is The information includes one or more of the following: a time series of high and low levels of concentration; a duration of high and low levels of concentration; an accumulated amount of concentration for each duration of high and low levels of concentration; a season-by-season level of concentration; a month-by-month level of concentration; a day-by-day level of concentration; or a time period-by-day level of concentration. 2. The support device according to claim 1. As a result, the present disclosure has the effect of providing an assistance device that enables more comprehensive support for concentration than the configurations such as [1] above. Furthermore, the present disclosure achieves the same effects as those described above by applying the above configuration to a system including the assistance device, the assistance method, or the program.

[0037] Embodiment 2 In the above-described first embodiment, a basic configuration example has been described in which support information regarding concentration is output based on the concentration tendency expressed in the time-series concentration degree. In the second embodiment, a configuration example will be described in which support information regarding concentration is output based on the concentration tendency for each individual and the concentration tendency after the current time. In embodiment 2, among the components of embodiment 2, those components that are similar to the components of embodiment 1 already described will be given the same names and similar symbols, and duplicate explanations will be omitted as appropriate.

[0038] An example of the configuration of a support system including a support device according to a second embodiment of the present disclosure will be described. FIG. 6 is a diagram illustrating an example of the configuration of a support system 1 (1B) including a support device 100 (100B) according to the second embodiment of the present disclosure. The support system 1 (1B) shown in Fig. 6 includes a support device 100 (100B), an information source device 600 (600B), and an output destination device 700. The output destination device 700 has the same configuration as the output destination device 700 already described.

[0039] The information source device 600 (600B) may be configured to further include, in addition to the information source device 600 already described, a personal authentication device that outputs object identification information for identifying the person to be measured. In this case, the information source device 600 (600B) performs personal authentication by known information analysis processing using, for example, a captured image and / or a signal from another sensor, and outputs object identification information for identifying the person to be measured.

[0040] The support device 100 (100B) shown in FIG. 6 includes a data acquisition unit 110 (110B), a tendency estimation unit 130 (130B), and a support information output unit 150 (150B).

[0041] The data acquiring section 110 (110B) shown in FIG. 6 includes a concentration level acquiring section 111 (111B) and an object identification information acquiring section 113.

[0042] The concentration level acquisition unit 111 (111B) has the same configuration as the concentration level acquisition unit 111 already described.

[0043] The subject identification information acquisition section 113 acquires subject identification information that identifies the subject to be measured. The object identification information acquisition unit 113 acquires object identification information received from, for example, the information source device 600 (600B). Alternatively, the target identification information acquisition unit 113 receives, for example, an operation by an operator of the support device 100 (100B) and generates and acquires target identification information based on the operation. In this case, for example, while the concentration level of the subject is being acquired, the operator of the support device 100 (100B) inputs information identifying the subject to be measured, thereby generating target identification information. This can improve the accuracy of estimating the concentration tendency for each individual.

[0044] The trend estimation unit 130 (130B) estimates the concentration trend based on the time-series concentration degree. The trend estimation unit 130 (130B) estimates the concentration trend by referring to the trend storage unit 170 (170B). Moreover, the tendency estimation section 130 (130B) further estimates the concentration tendency for each measurement subject identified by the subject identification information. The trend estimation unit 130 (130B) combines the object identification information and the concentration tendency and outputs the result as an estimation. The trend estimation unit 130 (130B) outputs the estimation result to the support information output unit 150 (150B) and the trend storage unit 170 (170B). For example, the trend estimation unit 130 (130B) outputs the concentration tendency at the current time to the support information output unit 150 (150B) and outputs the past concentration tendency to the trend storage unit 170 (170B). When outputting the past concentration tendency to the trend storage unit 170 (170B), the trend estimation unit 130 (130B) outputs the past time-series concentration tendency determined at intervals such as every day to the trend storage unit 170 (170B) to store it.

[0045] The trend storage unit 170 (170B) stores the past concentration trends estimated by the trend estimation unit 130B. Moreover, the tendency holding unit 170 (170B) updates and holds the concentration tendency by using the concentration tendency estimated by the tendency estimation unit 130B and the concentration tendency that has already been held. The tendency holding unit 170 (170B) can cause the tendency estimation unit 130B or the support information output unit 150 (150B) to refer to the concentration tendency that is held. The tendency holding unit 170 (170B) may be configured outside the support device 100 (100B). Furthermore, in the support device 100 (100B), the trend holding unit 170 (170B) can be realized in the form of a server. That is, the trend holding unit 170 (170B) in the support device 100 (100B) is configured as a server, and everything other than the trend holding unit 170 (170B) is configured as terminal devices. This enables the server including the trend holding unit 170 (170B) to collect concentration tendencies of many subjects from multiple terminal devices and perform analysis processing and learning processing. By using the results to estimate concentration tendencies, the accuracy of estimating past concentration tendencies and the accuracy of estimating concentration tendencies from the current time onwards can be improved.

[0046] The support information output unit 150 (150B) outputs support information based on the concentration tendency. The support information output unit 150 (150B) predicts the concentration tendency from the current time onwards using the past concentration tendency estimated by the tendency estimation unit 130 (130B), and generates and outputs support information based on the predicted concentration tendency from the current time onwards. The support information output unit 150 (150B) shown in FIG. 6 includes a tendency prediction unit 151 (151B), a plan generation unit 152 (152B), and an advice generation unit 153 (153B).

[0047] The trend prediction unit 151 (151B) predicts a concentration trend after the current time using the concentration trend at the current time and past concentration trends. The trend prediction unit 151 (151B) predicts a concentration trend after the current time using the concentration trend at the current time and by referring to past concentration trends in the trend storage unit 170 (170B). The support information output unit 150 (150B) generates and outputs support information using the concentration tendency after the current time, which is the result of prediction by the tendency prediction unit 151 (151B). Alternatively, the support information output unit 150 (150B) outputs support information including one or more of the concentration tendency of the subject to be measured up to the current time, the concentration tendency of the subject to be measured at the current time, or the concentration tendency of the subject to be measured after the current time.

[0048] The plan generating unit 152 (152B) generates a plan for the current time and thereafter based on the concentration tendency. The plan generating unit 152 (152B) generates a plan according to the concentration tendency from the current time onward, using the past concentration tendency.

[0049] The advice generation unit 153 (153B) generates advice using the concentration tendency. The advice generation unit 153 (153B) generates advice using the past concentration tendency, the concentration tendency at the current time, and the concentration tendency after the current time. The advice generation unit 153 (153B) is configured using, for example, a generation AI. The support information output unit 150 (150B) outputs support information including the advice generated by the advice generation unit 153 (153B).

[0050] Next, a processing example of the support device according to the second embodiment of the present disclosure will be described. FIG. 7 is a flowchart showing an example of processing by the support device 100 (100B) according to the second embodiment of the present disclosure. The process shown in FIG. 7 is a support method performed by the support device 100 (100B). For example, when the assistance device 100 (100B) shown in FIG. 6 is activated by receiving an operation from a user, or when a processing start condition is met, such as when a time-series concentration level is received, the assistance device 100 (100B) starts the processing shown in FIG. 7 ("Start").

[0051] The support device 100 (100B) then executes a data acquisition process (step ST2100). In the data acquisition process, the concentration degree acquisition unit 111 in the data acquisition unit 110 of the support device 100 (100B) acquires the concentration degrees in time series from, for example, an external device. The concentration degree acquisition unit 111 outputs the concentration degrees to the tendency estimation unit 130. Furthermore, in the data acquisition process, the target identification information acquisition unit 113 of the data acquisition unit 110 (110B) acquires target identification information. The target identification information acquisition unit 113 acquires target identification information received from, for example, the information source device 600 (600B). Alternatively, the target identification information acquisition unit 113 receives, for example, an operation by an operator of the support device 100 (100B) and generates and acquires target identification information based on the operation. In this case, for example, while a concentration level is being acquired from the subject to be measured, the operator of the support device 100 (100B) inputs information identifying the subject to be measured, thereby generating target identification information. The data acquiring section 110 (110B) outputs the concentration degree acquired by the concentration degree acquiring section 111 and the object identifying information acquired by the object identifying information acquiring section 113 to the tendency estimating section .

[0052] The support device 100 (100B) then executes a trend estimation process (step ST2200). In the trend estimation process, upon receiving the concentration level, the trend estimation unit 130 of the support device 100 (100B) estimates a concentration trend based on the time-series concentration level. The trend estimation unit 130 estimates the concentration trend for each trend type, such as the level of concentration, the duration of the level of concentration, the level of concentration by season, the level of concentration by month, the level of concentration by day of the week, the level of concentration by time period in a day, or the change point of the level of concentration. The trend estimation unit 130 outputs the concentration trend, which is the estimation result.

[0053] The support device 100 (100B) then executes an estimated trend holding process (step ST2400). In the estimated trend holding process, the trend holding unit 170 (170B) of the support device 100 (100B) holds the concentration trend estimated by the trend estimation unit 130B in combination with the target identification information.

[0054] The support device 100 (100B) then executes support information output processing (step ST2500). In the support information output processing, the support information output unit 150 (150B) of the support device 100 (100B) further outputs support information including a concentration tendency for each measurement subject from the current time onwards. The support information output unit 150 (150B) predicts a concentration tendency for each measurement subject from the current time onwards using the past concentration tendency and the concentration tendency at the current time, and outputs support information including advice based on the prediction result. Specifically, the support information output unit 150 (150B) predicts a concentration tendency for each measurement subject from the current time onwards using the past concentration tendency estimated by the tendency estimation unit 130B and stored in the tendency storage unit 170 (170B) and the concentration tendency at the current time estimated by the tendency estimation unit 130B, and outputs support information including advice based on the prediction result to the output destination device 700.

[0055] After outputting the support information to the output destination device 700, the support device 100 (100B) ends the process and goes into standby mode ("End").

[0056] Next, an example of data acquisition processing in the support device 100 (100B) will be described. FIG. 8 is a flowchart showing an example of a data acquisition process in the assistance device 100 (100B) according to the second embodiment of the present disclosure. For example, the data acquisition unit 110A in the support device 100A shown in FIG. 6 starts the process shown in FIG. 8 when it is activated by receiving an operation from a user or when it satisfies a processing start condition such as receiving a time-series concentration degree. ("Start")

[0057] The data acquiring unit 110 (110B) then executes a target identification information acquiring process (step ST2110). In the target identification information acquiring process, the target identification information acquiring unit 113 of the data acquiring unit 110 (110B) acquires target identification information. The target identification information acquiring unit 113 acquires target identification information received from, for example, the information source device 600 (600B). Alternatively, the target identification information acquiring unit 113 receives, for example, an operation by an operator of the assistance device 100 (100B) and generates and acquires target identification information based on the operation. In this case, for example, while a concentration level is being acquired from the subject to be measured, the operator of the assistance device 100 (100B) inputs information identifying the subject to be measured, thereby generating target identification information.

[0058] The data acquiring unit 110 (110B) then executes a concentration level acquiring process (step ST2120). In the concentration level acquiring process, the concentration level acquiring unit 111 in the data acquiring unit 110 (110B) acquires the concentration levels in time series from, for example, an external device. The concentration level acquiring unit 111 outputs the concentration levels to the tendency estimating unit 130.

[0059] The data acquisition unit 110 (110B) outputs the concentration level acquired by the concentration level acquisition unit 111 and the object identification information acquired by the object identification information acquisition unit 113 to the tendency estimation unit 130. ("End")

[0060] Next, an example of a trend estimation process according to the second embodiment of the present disclosure will be described. FIG. 9 is a flowchart showing an example of a part of the trend estimation process of the support device 100 (100B) according to the second embodiment of the present disclosure. The tendency estimation unit 130 (130B) of the support device 100 (100B) further executes the process shown in FIG. 9 in the tendency estimation process (“Start”).

[0061] The tendency estimation unit 130 (130B) then executes a tendency estimation process for each tendency type (step ST2310). In the tendency estimation process, the tendency estimation unit 130 (130B) outputs the concentration tendency and the target identification information as the estimation result to the support information output unit 150 and the tendency storage unit 170 (170B).

[0062] The tendency estimation unit 130 (130B) outputs the concentration tendency as the estimation result and the object identification information to the support information output unit 150 and the tendency storage unit 170 (170B), and then ends the processing shown in FIG. 9 ("End").

[0063] Next, an example of support information output processing according to the second embodiment of the present disclosure will be described. FIG. 10 is a flowchart showing an example of a support information output process in the support device 100 (100B) according to the second embodiment of the present disclosure. The support information output unit 150 (150B) in the support device 100 (100B) starts processing ("start") when it receives the concentration tendency at the current time as the estimation result from the tendency estimation unit 130 (130B).

[0064] The support information output unit 150 (150B) then executes a trend prediction process (step ST2510). In the trend prediction process, the trend prediction unit 151 (151B) of the support information output unit 150 (150B) refers to the trend holding unit 170 (170B) to acquire the concentration trend estimated by the trend estimation unit 130 (130B). The trend prediction unit 151 (151B) predicts the concentration trend from the current time onwards based on the past concentration trend estimated by the trend estimation unit 130 (130B) and the concentration trend at the current time. The trend prediction unit 151 (151B) outputs the concentration trend as the prediction result. The concentration trend as the prediction result is output together with time information such as the time.

[0065] The support information output unit 150 (150B) then executes a plan generation process (step ST2520). In the plan generation process, the plan generation unit 152 (152B) of the support information output unit 150 (150B) generates a plan for the current time and beyond based on the concentration tendency. The plan generation unit 152 (152B) uses the concentration tendency for each past schedule to generate a plan according to the concentration tendency for each schedule for the current time and beyond. For example, if the concentration tendency indicates that the concentration tendency is higher every Friday night, the plan generation unit 152 (152B) generates a study plan.

[0066] The support information output unit 150 (150B) then executes advice generation processing (step ST2530). In the advice generation processing, the advice generation unit 153 (153B) of the support information output unit 150 (150B) generates and outputs advice using the past concentration tendency, the concentration tendency at the current time, the concentration tendency after the current time, and the plan after the current time.

[0067] The support information output unit 150 (150B) of the support device 100B then proceeds to an end determination process ((step ST2540) ("End?")). In the termination determination process, the support information output unit 150 (150B) determines whether to terminate the processing of the support information output unit 150 (150B). The support information output unit 150 (150B) determines whether to terminate the processing of the support information output unit 150 (150B) in accordance with an external command or an execution program. For example, when support information different from the support information that has been output is requested, the support information output unit 150 (150B) determines not to terminate the processing. If the support information output unit 150 (150B) determines not to end the processing of the support information output unit 150 (150B) ("NO" in step ST2540), the process proceeds to step ST2530 and executes the process of step ST2530. When the support information output unit 150 (150B) determines that the processing of the support information output unit 150 (150B) is to be ended ("YES" in step ST2540), the support information output unit 150 (150B) ends the processing ("End").

[0068] The configuration of this embodiment makes it possible to provide the following support. Based on current vital data (time-series concentration level) and past concentration trends, support information including information such as study timing, study time, break time, study start time, and break start time can be output. Furthermore, it is possible to output the characteristics of an individual as support information based on the concentration tendency of each individual. For example, it is possible to show whether the individual is the type who can concentrate for a relatively long time compared to the average, or the type who is distracted. It is also possible to show, for example, that the individual is the type who can concentrate for a long time when they are interested in something or think they are good at it. It is also possible to show, for example, that the individual can quickly reach a state of high concentration, even if the period of time they can concentrate is short. Furthermore, specific examples of information that can be included in the support information will be described. - Support students by considering times when they tend to be most focused as the best time to study. "I tend to be most focused at 4 p.m. every day." "It's a good idea to plan your study time during this time." "Your concentration is low at this time of day." "It's a good idea to eat your meal earlier." - Help with timing of study by day of the week. "Every Tuesday, my overall concentration level drops." "There seems to be some underlying factor, so let's take a look at it." - Provides support according to the level of concentration over time. We will provide advice on how to allocate your study time based on your concentration tendencies at the start of the day. If the current time period tends to be more focused than usual, we will advise you on allocating your time in a slightly more stressful manner. - Support according to the duration of high and low concentration tendency "You can concentrate longer in the morning." "Your concentration level drops in the middle of the night, so it's best to switch to the morning." · Demonstrate and support individual characteristics. "You may have trouble concentrating for long periods of time." "Try setting a target time for yourself, such as taking a break every ○ minutes." For highly concentrated study, the system calculates the time allocation and time periods that make it easier to maintain a high level of concentration based on current vital data and time series data of past vital data, making it possible to determine the time when students can study with a high level of concentration and to suggest study times and timings that are tailored to each individual.

[0069] This embodiment further shows an example of an embodiment including the following configuration. [3] a subject identification information acquisition unit that acquires subject identification information that identifies the subject to be measured; Furthermore, the tendency estimation unit estimates a concentration tendency for each measurement subject identified by the subject identification information; 3. The support device according to claim 1 or 2. As a result, the present disclosure has an effect of providing an assistance device that enables more comprehensive assistance regarding concentration than the above-described embodiment. Furthermore, the present disclosure achieves the same effects as those described above by applying the above configuration to a system including the assistance device, the assistance method, or the program.

[0070] This embodiment further shows an example of an embodiment including the following configuration. [4] The support information output unit The concentration tendency after the current time is predicted using the past concentration tendency estimated by the tendency estimation unit, and support information based on the predicted concentration tendency after the current time is generated and output. 3. The support device according to claim 1 or 2. As a result, the present disclosure further has the effect of being able to provide an assistance device that enables more comprehensive assistance regarding concentration than the above-described embodiment. Furthermore, the present disclosure achieves the same effects as those described above by applying the above configuration to a system including the assistance device, the assistance method, or the program.

[0071] Embodiment 3 It is believed that the concentration tendency may vary depending on the activities that the measurement subjects plan to carry out. In the third embodiment, a configuration example in which the concentration tendency is estimated taking into account the schedule of the measurement subject will be described. In embodiment 3, among the components of embodiment 3, those components that are similar to the components of embodiment 1 or embodiment 2 already described will be given the same names and similar symbols, and duplicate explanations will be omitted as appropriate.

[0072] An example of the configuration of a support system including a support device according to a third embodiment of the present disclosure will be described. FIG. 11 is a diagram illustrating an example of the configuration of a support system 1 (1C) including a support device 100 (100C) according to the third embodiment of the present disclosure. The support system 1 (1C) includes a support device 100 (100C), an information source device 600 (600C), and an output destination device 700. The output destination device 700 has the same configuration as the output destination device 700 already described.

[0073] The information source device 600 (600C) has the same configuration as any of the already described information source devices 600. The information source device 600 (600C) may further have a configuration capable of outputting a schedule for each measurement subject.

[0074] The support device 100 (100C) outputs support information based on the concentration tendency taking into account the schedule of the subject to be measured. The support device 100 (100C) includes a data acquisition unit 110 (110C), a tendency estimation unit 130 (130C), and a support information output unit 150 (150C).

[0075] The data acquiring unit 110 (110C) includes a concentration level acquiring unit 111, an object identification information acquiring unit 113, and a schedule acquiring unit 114. The data acquisition unit 110 (110C) is configured to newly include a schedule acquisition unit 114 in addition to the components already described.

[0076] The schedule acquiring unit 114 acquires schedule information indicating the schedule of the subject. The schedule acquisition unit 114 acquires the schedule of the subject to be measured. A schedule is information (schedule information) that is expressed as a combination of scheduled content and scheduled time. Scheduled content is information that indicates scheduled content, such as extracurricular activities, events (concerts, sports days), menus, long vacations, etc. Scheduled time is information about the scheduled time, such as the month, day of the week, date, time zone, and time (start time, end time).

[0077] The trend estimation unit 130 (130C) has the same function as any of the trend estimation units 130 already described. The tendency estimation unit 130 (130C) also estimates the concentration tendency by taking into account the schedule of the measurement subject. The tendency estimation unit 130 (130C) further estimates the concentration tendency for each schedule indicated in the schedule information acquired by the schedule acquisition unit 114.

[0078] The trend holding unit 170 (170C) has the same function as the trend holding unit 170 already described. The tendency holding unit 170 (170C) further holds the concentration tendency for each schedule estimated by the tendency estimation unit 130C. Furthermore, the tendency holding unit 170 (170C) updates and holds the concentration tendency for each schedule, using the concentration tendency for each schedule estimated by the tendency estimating unit 130B and the concentration tendency for each schedule that has already been held. The tendency storage unit 170 (170C) causes the tendency estimation unit 130B or the support information output unit 150 (150C) to refer to the concentration tendency for each schedule stored therein. The tendency holding unit 170 (170C) may be configured outside the support device 100 (100C).

[0079] The support information output unit 150 (150C) has the same functions as any of the support information output units 150 already described. Furthermore, the support information output unit 150 (150C) further generates and outputs support information based on the concentration tendency for each schedule. Furthermore, the support information output unit 150 (150C) further generates a plan for the current time and thereafter using the concentration tendency for each schedule, and generates and outputs support information including advice based on the generated plan. The support information output unit 150 (150C) includes a tendency prediction unit 151 (151C), a plan generation unit 152 (152C), and an advice generation unit 153 (153C).

[0080] The trend prediction unit 151 (151C) predicts a concentration trend after the current time using the concentration trend at the current time and past concentration trends. The trend prediction unit 151 (151C) predicts a concentration trend after the current time using the concentration trend at the current time and by referring to past concentration trends stored in the trend storage unit 170 (170C).

[0081] The plan generation unit 152 (152C) generates a plan from the current time onwards based on the concentration tendency. The plan generation unit 152 (152C) generates a plan according to the concentration tendency for each schedule from the current time onwards, using the concentration tendency for each past schedule.

[0082] The advice generating unit 153 (153C) generates advice using the concentration tendency, similar to the already-described advice generating unit 153. The advice generating unit 153 (153C) generates advice using the concentration tendency for each past schedule, the concentration tendency at the current time, the concentration tendency for each schedule after the current time, and the concentration factor for each schedule.

[0083] Next, a processing example in the support device according to the third embodiment of the present disclosure will be described. First, an example of a part of the data acquisition process of the support device 100 (100C) according to the third embodiment of the present disclosure will be described. FIG. 12 is a flowchart showing an example of a part of the data acquisition process of the support device 100 (100C) according to the third embodiment of the present disclosure. For example, the data acquisition unit 110C in the support device 100C shown in FIG. 11 executes the process shown in FIG. 12 in the data acquisition process ("Start").

[0084] The data acquiring unit 110C then executes a schedule acquiring process (step ST3110). In the schedule acquiring process, the schedule acquiring unit 114 of the data acquiring unit 110 (110C) acquires the schedule of the subject to be measured. The schedule acquiring unit 114 acquires the schedule, for example, through an input operation by a user who is the operator of the support device 100 (100C). Alternatively, the schedule acquiring unit 114 acquires the schedule of the subject to be measured identified by the target identification information from an information source device 600 (600C) external to the support device 100 (100C), for example, using the target identification information acquired by the target identification information acquiring unit 113. The data acquiring unit 110 (110C) outputs the schedule of the subject to the tendency estimating unit 130 (130C).

[0085] Next, an example of a part of the trend estimation process of the support device 100 (100C) will be described. FIG. 13 is a flowchart showing an example of a part of the trend estimation process of the support device 100 (100C) according to the third embodiment of the present disclosure. The tendency estimation unit 130 (130C) of the support device 100 (100C) further executes the process shown in FIG. 13 in the tendency estimation process (“Start”).

[0086] The trend estimation unit 130 (130C) estimates a concentration trend for each schedule acquired by the schedule acquisition unit 114. The trend estimation unit 130 (130C) outputs the estimation result to the support information output unit 150 (150C) and the trend holding unit 170 (170C). The trend estimation unit 130 (130C), for example, outputs the concentration trend at the current time to the support information output unit 150 (150C) and outputs past concentration trends to the trend holding unit 170 (170C).

[0087] Next, an example of the support information output process of the support device 100 (100C) according to the third embodiment of the present disclosure will be described. FIG. 14 is a flowchart showing an example of a support information output process of the support device 100 (100C) according to the third embodiment of the present disclosure. The support information output unit 150 (150C) in the support device 100 (100C) starts processing ("start") when it receives the concentration tendency at the current time as the estimation result from the tendency estimation unit 130 (130C).

[0088] The support information output unit 150 (150C) then executes trend prediction processing taking into account the schedule (step ST3510). In the trend prediction processing taking into account the schedule, the trend prediction unit 151 (151C) of the support information output unit 150 (150C) refers to the trend holding unit 170 (170C) to acquire the concentration trend for each schedule estimated by the trend estimation unit 130 (130C). The trend prediction unit 151 (151C) predicts the concentration trend for each schedule from the current time onwards based on the past concentration trend estimated by the trend estimation unit 130 (130C) and the concentration trend at the current time. The trend prediction unit 151 (151C) outputs the concentration trend for each schedule, which is the prediction result.

[0089] The support information output unit 150 (150C) then executes a plan generation process that takes the schedule into consideration (step ST3520). In the plan generation process that takes the schedule into consideration, the plan generation unit 152 (152C) of the support information output unit 150 (150C) generates a plan for the current time and beyond based on the concentration tendency. The plan generation unit 152 (152C) uses the concentration tendency for each past schedule to generate a plan that corresponds to the concentration tendency for each schedule for the current time and beyond. For example, if the concentration tendency indicates that a positive state will occur after the cram school schedule every Friday, the plan generation unit 152 (152C) generates a plan that is suitable for a positive state.

[0090] The support information output unit 150 (150C) then executes advice generation processing that takes into account the schedule (step ST3530). In the advice generation processing that takes into account the schedule, the advice generation unit 153 (153C) of the support information output unit 150 (150C) generates advice using the concentration tendency. The advice generation unit 153 (153C) generates advice using the concentration tendency for each past schedule, the concentration tendency at the current time, and the concentration tendency for each schedule after the current time.

[0091] The support information output unit 150 (150C) of the support device 100C then proceeds to an end determination process ((step ST3540) ("end?")). In the termination determination process, the support information output unit 150 (150C) determines whether to terminate the processing of the support information output unit 150 (150C). The support information output unit 150 (150C) determines whether to terminate the processing of the support information output unit 150 (150C) in accordance with an external command or an execution program. For example, when support information different from the support information that has been output is requested, the support information output unit 150 (150C) determines not to terminate the processing. When the support information output unit 150 (150C) determines not to end the processing of the support information output unit 150 (150C) ("NO" in step ST3540), the process proceeds to step ST3530 and executes the process of step ST3530. When the support information output unit 150 (150C) determines that the processing of the support information output unit 150 (150C) is to be ended ("YES" in step ST3540), the support information output unit 150 (150C) ends the processing ("End").

[0092] Next, a specific example of the support information output by the support device 100 (100C) according to this embodiment will be described. FIG. 15 is a diagram illustrating an example of a concentration tendency and an example of a tendency type of the concentration tendency according to the third embodiment of the present disclosure. The support device 100 (100C) is configured to analyze the impact of a schedule when the schedule is input, and to derive trends and predictions. FIG. 15 shows, for example, the schedule for one day three days before the test superimposed on the concentration tendency. In FIG. 15, the schedule indicates that activity content 3010 is sleep time, activity content 3020 is club activities, activity content 3020 is attending cram school, and activity content 3030 is sleep time. In a case like that shown in FIG. 15, the following support information can be output. Taking into account periodic schedules such as club activities, it is possible to output support information including advice such as, "Your concentration level drops after you return home from cram school every Tuesday," or "How about keeping the content lighter or shorter?" Taking into account upcoming events such as tests, the system can output support information including advice such as, "You may be feeling anxious with three days until the test, but your concentration levels tend to be lower late at night," or "You tend to be more focused in the early morning, so try to make use of the morning hours." In addition, taking into account planned activities such as meals, it can output support information including advice such as, "Your concentration tends to drop after meals, so it might be a good idea to finish your weaker subjects before eating." In this way, the accuracy of the analysis can be improved by adding information about events such as extracurricular activities and cram schools that can affect fatigue and concentration, as well as information related to motivation for tests and presentations.

[0093] This embodiment shows an example of an embodiment including the following configuration. [5] a schedule acquisition unit that acquires schedule information indicating the schedule of the subject; Equipped with The tendency estimation unit further estimates a concentration tendency for each schedule indicated in the schedule information acquired by the schedule acquisition unit, The support information output unit further generates and outputs support information based on the concentration tendency for each schedule. 3. The support device according to claim 1 or 2. As a result, the present disclosure further has the effect of being able to provide a support device that takes into account schedules and enables enhanced support according to the concentration tendency of each schedule. Furthermore, the present disclosure achieves the same effects as those described above by applying the above configuration to a system including the assistance device, the assistance method, or the program.

[0094] This embodiment shows an example of an embodiment including the following configuration. [6] The support information output unit further generates a plan for the period after the current time using the concentration tendency for each schedule, and generates and outputs support information including advice based on the generated plan. 6. The support device according to claim 5. As a result, the present disclosure further has the effect of being able to provide a support device that takes into account schedules and enables enhanced support according to the concentration tendency of each schedule. Furthermore, the present disclosure achieves the same effects as those described above by applying the above configuration to a system including the assistance device, the assistance method, or the program.

[0095] Embodiment 4 In the above-described third embodiment, an example of a configuration that enables support that takes into account the schedule of the person to be measured has been described. Here, the scheduled implementation content may include multiple implementation types. In the fourth embodiment, a configuration example that enables support according to the implementation type in the implementation content will be further described. In embodiment 4, among the components of embodiment 4, those components that are similar to the components of embodiment 1, embodiment 2, or embodiment 3 already described will be given the same names and similar symbols, and duplicate explanations will be omitted as appropriate.

[0096] An example of the configuration of a support system including a support device according to a fourth embodiment of the present disclosure will be described. FIG. 16 is a diagram illustrating an example of the configuration of a support system 1 (1D) including a support device 100 (100D) according to the fourth embodiment of the present disclosure. The support system 1 (1D) shown in Fig. 16 includes a support device 100 (100D), an information source device 600 (600D), and an output destination device 700. The output destination device 700 has the same configuration as the output destination device 700 already described.

[0097] The information source device 600 (600D) is configured in the same manner as any of the information source devices 600 already described. The information source device 600 (600D) may further include a device having a function of outputting the implementation type of the implementation content of the measurement subject. The function of outputting the implementation type of the implementation content may be implemented, for example, by having the implementation type registered in advance by the user.

[0098] The support device 100 (100D) shown in FIG. 16 includes a data acquisition unit 110 (110D), a tendency estimation unit 130 (130D), a support information output unit 150 (150D), and a tendency storage unit 170 (170D).

[0099] The data acquisition unit 110 (110D) is configured to newly include an implementation type acquisition unit 115 in addition to the functions already described. The exercise type acquiring unit 115 acquires exercise type information indicating an exercise type, which is a type of exercise content performed by the subject to be measured. For example, the types of activities include, for learning-related activities, subjects, teaching materials, units, preparation and review, difficulty level, writing, seeing, listening, memorization, calculation, word problems, and composition. For extracurricular activities, there are types such as piano and abacus. For play-related activities, there are types such as puzzles, blocks, coloring books, and drawing.

[0100] The trend estimation unit 130 (130D) has a function of estimating a concentration trend for each implementation type indicated in the implementation type information, in addition to the functions already described.

[0101] The trend storage unit 170 (170D) stores the concentration trend taking into account the implementation type in addition to the functions already described. Furthermore, the support information output unit 150 (150D) has a function to generate and output support information based on the concentration tendency for each implementation type, in addition to the functions already described. In addition, the support information output unit 150 (150D) further has the function of generating a plan for the implementation content from the current time onwards using the concentration tendency for each implementation type, and generating and outputting support information including advice based on the generated implementation content plan. The trend prediction unit 151 (151D) predicts a concentration trend taking into account the implementation type. The plan generating unit 152 (152D) generates a plan taking into account the implementation type. The advice generating unit 153 (153D) generates advice taking into account the implementation type.

[0102] Next, a processing example of the support device according to the fourth embodiment of the present disclosure will be described. First, an example of a part of the data acquisition process of the support device 100 (100D) according to the fourth embodiment of the present disclosure will be described. FIG. 17 is a flowchart showing an example of a part of the data acquisition process of the support device 100 (100D) according to the fourth embodiment of the present disclosure. For example, the data acquisition unit 110D in the support device 100D shown in FIG. 16 executes the process shown in FIG. 17 in the data acquisition process ("Start").

[0103] The data acquiring unit 110D of the support device 100 (100D) executes an implementation type acquiring process (step ST4110). In the implementation type acquiring process, the implementation type acquiring unit 115 of the data acquiring unit 110D acquires implementation type information indicating an implementation type, which is a type of implementation content performed by the subject, for example, through an input operation by the operator of the support device 100 (100D).

[0104] Next, an example of a part of the trend estimation process of the support device 100 (100D) will be described. FIG. 18 is a flowchart showing an example of a part of the trend estimation process of the support device 100 (100D) according to the fourth embodiment of the present disclosure. The tendency estimation unit 130 (130D) of the support device 100 (100D) further executes the process shown in FIG. 18 in the tendency estimation process ("Start").

[0105] The trend estimation unit 130 (130D) further executes a trend estimation process taking into account the implementation type (step ST4310). In the trend estimation process taking into account the implementation type, the trend estimation unit 130 (130D) executes a trend estimation process for each implementation type. The tendency estimation unit 130 (130D) outputs the concentration tendency and the object identification information as the estimation result to the support information output unit 150 and the tendency storage unit 170 (170D).

[0106] Next, an example of support information output processing in the support device 100 (100D) according to the fourth embodiment of the present disclosure will be described. FIG. 19 is a flowchart showing an example of a support information output process in the support device 100 (100D) according to the fourth embodiment of the present disclosure. The support information output unit 150 (150D) in the support device 100 (100D) starts processing ("start") when it receives the concentration tendency at the current time as the estimation result from the tendency estimation unit 130 (130D).

[0107] The support information output unit 150 (150D) then executes trend prediction processing taking into account the implementation type (step ST4510). In the trend prediction processing taking into account the implementation type, the trend prediction unit 151 (151D) of the support information output unit 150 (150D) refers to the trend holding unit 170 (170D) to acquire the concentration trend estimated by the trend estimation unit 130 (130D). The trend prediction unit 151 (151D) predicts the concentration trend from the current time onwards based on the past concentration trend estimated by the trend estimation unit 130 (130D) and the concentration trend at the current time. The trend prediction unit 151 (151D) outputs the concentration trend as the prediction result. The concentration trend as the prediction result is output together with time information such as the time.

[0108] The support information output unit 150 (150D) then executes a plan generation process that takes into account the implementation type (step ST4520). In the plan generation process that takes into account the implementation type, the plan generation unit 152 (152D) of the support information output unit 150 (150D) generates a plan from the current time onwards based on the concentration tendency. The plan generation unit 152 (152D) uses the concentration tendency for each past schedule to generate a plan that corresponds to the concentration tendency for each schedule from the current time onwards.

[0109] The support information output unit 150 (150D) then executes advice generation processing taking into account the implementation type (step ST4530). In the advice generation processing taking into account the implementation type, the advice generation unit 153 (153D) of the support information output unit 150 (150D) generates and outputs advice using the past concentration tendency, the concentration tendency at the current time, the concentration tendency after the current time, and the plan after the current time.

[0110] The support information output unit 150 (150D) of the support device 100D then proceeds to an end determination process ((step ST4540) ("end?")). In the termination determination process, the support information output unit 150 (150D) determines whether to terminate the processing of the support information output unit 150 (150D). The support information output unit 150 (150D) determines whether to terminate the processing of the support information output unit 150 (150D) in accordance with an external command or an execution program. For example, when support information different from the support information that has been output is requested, the support information output unit 150 (150D) determines not to terminate the processing. If the support information output unit 150 (150D) determines not to end the processing of the support information output unit 150 (150D) ("NO" in step ST4540), the process proceeds to step ST4530 and executes the process of step ST4530. When the support information output unit 150 (150D) determines that the processing of the support information output unit 150 (150D) is to be ended ("YES" in step ST4540), the support information output unit 150 (150D) ends the processing ("End").

[0111] Next, a specific example of the support information output by the support device 100 (100D) according to this embodiment will be described. FIG. 20A is a first example of an image showing support information using concentration trends taking into account the implementation details, and FIG. 20B is a second example of an image showing support information using concentration trends taking into account the implementation details. The mobile terminal (smartphone) 4000 serving as the support device 100 (100D) can output an image (concentration graph) 4010, an image (implementation content) 4020, an image (planned implementation time) 4110, an image (planned rest time) 4120, and an image (advice) 4130 as support information. When the implementation details are input, the support device 100 (100D) also analyzes the influence of the implementation details and derives trends and predictions. (1) Example of the implementation details to be entered Subjects ·Educational materials Unit Write, listen, see Memorization / Calculation / Word Problems / Composition Preparation or review? Difficulty ·piano Abacus · Play (puzzle / block / drawing) / Coloring books etc. In response to this, it is possible to use the concentration tendency to output support information such as "Japanese language early" or "Math before bed." In addition, for calculation drills, support information such as taking a 5-minute break after 25 minutes can increase efficiency can be output. This allows the system to provide advice that reflects what to do and when, and how many minutes to take a break if you are doing this, in order to help you maintain a higher level of concentration. In addition, depending on the content, it is possible to analyze whether one is likely to be highly concentrated (like / good at), whether one is likely to maintain concentration (like / good at), or whether one is unlikely to maintain concentration (dislike / bad at). Furthermore, specific examples of advice are provided. When a subject is entered, the system can determine whether the student is good or bad at a certain subject based on factors such as how easily the student concentrates or how easily they can maintain their concentration, and then advise them to study the subject they are weak at at a time when they can concentrate better. It can also output support information including advice such as, "Japanese is your weak point, so why not try studying it at XX time when you can concentrate better?" Furthermore, it is possible to output support information including advice that takes into account the combination of implementation contents. Specifically, it can output support information including advice such as, "Since math tends to be more concentrated after English, why not try doing it with XX, who is more likely to have lower concentration?" It can also output support information including advice such as, "Your concentration level tends to be low when practicing the piano," or "Your concentration level was high only when you practiced the piano after doing puzzles, so why not try playing properly before practicing?" We can also help you find triggers for concentration. Specifically, it can output support information including advice such as, "Exercise tends to improve concentration, so it might be a good idea to do a little exercise before XX," or, "Concentration tends to improve after taking a shower, so it might be a good idea to take a shower XX hours before the actual exam."

[0112] This embodiment further shows an example of an embodiment including the following configuration. [7] an implementation type acquiring unit that acquires implementation type information indicating an implementation type, which is a type of implementation content performed by the measurement subject; Equipped with The tendency estimation unit further estimates a concentration tendency for each implementation type indicated in the implementation type information, The support information output unit further generates and outputs support information based on the concentration tendency for each implementation type. 3. The support device according to claim 1 or 2. As a result, the present disclosure further achieves the effect of being able to provide an assistance device that enables enhanced assistance with concentration. Furthermore, the present disclosure achieves the same effects as those described above by applying the above configuration to a system including the assistance device, the assistance method, or the program.

[0113] This embodiment further shows an example of an embodiment including the following configuration. [8] The support information output unit further generates a plan of the activity content from the current time onward using the concentration tendency for each activity type, and generates and outputs support information including advice based on the generated plan of the activity content. 8. The support device according to claim 7. As a result, the present disclosure further achieves the effect of being able to provide an assistance device that enables enhanced assistance with concentration. Furthermore, the present disclosure achieves the same effects as those described above by applying the above configuration to a system including the assistance device, the assistance method, or the program.

[0114] Embodiment 5 In the above-described fourth embodiment, a configuration example of a form in which support is provided using a concentration tendency that takes into account the implementation type has been described. In the fifth embodiment, a configuration example of a form that supports a goal based on a concentration tendency for each implementation type will be described. In embodiment 5, among the components of embodiment 5, those components that are similar to the components of embodiment 1, embodiment 2, embodiment 3, or embodiment 4 already described will be given the same names and similar symbols, and duplicate explanations will be omitted as appropriate.

[0115] An example of the configuration of a support system 1 (1E) including a support device according to a fifth embodiment of the present disclosure will be described. FIG. 21 is a diagram illustrating an example of the configuration of a support system 1 (1E) including a support device 100 (100E) according to the fifth embodiment of the present disclosure. The support system 1 (1E) shown in FIG. 20 includes a support device 100 (100E), an information source device 600 (600E), and an output destination device 700. The destination device 700 is configured in the same manner as the destination device 700 already described. The information source device 600 (600E) is configured to have the same functions as any of the information source devices 600 already described.

[0116] The support device 100 (100E) has the same functions as the already-described support device 100. The support device 100 (100E) further supports a goal based on a concentration tendency for each activity type. The support device 100 (100E) shown in FIG. 21 includes a data acquisition unit 110 (110E), a tendency estimation unit 130 (130E), and a support information output unit 150 (150E). The data acquisition unit 110 (110E) shown in FIG. 21 includes a target information acquisition unit 116 as a component for realizing a new function different from the functions already described. The practice type acquiring unit 115, like the practice type acquiring unit 115 already described, acquires practice type information indicating the practice type, which is the type of practice content performed by the subject to be measured. For example, the types of activities include, for learning-related activities, subjects, teaching materials, units, preparation and review, difficulty level, writing, seeing, listening, memorization, calculation, word problems, and composition. For extracurricular activities, there are types such as piano and abacus. For play-related activities, there are types such as puzzles, blocks, coloring books, and drawing. The target information acquisition unit 116 acquires, as target information, a target score that should be aimed for for each of the above-mentioned implementation types. The support information output unit 150 (150E) includes a score generation unit 154 and a goal generation unit 155 as components for realizing new functions different from the functions already described. The score generating unit 154 generates a score for each activity type using the activity time and concentration level for each activity type, which is the type of activity content performed by the measurement subject. The goal generation unit 155 uses the score generated by the score generation unit 154 and the goal score acquired by the goal information acquisition unit 116 to generate a goal including a target implementation time and a target concentration level that should be aimed for. The support information output unit 150 (150E) further generates advice based on the goal generated by the goal generating unit 155, and outputs support information including the generated advice.

[0117] Next, a processing example of the support device according to the fifth embodiment of the present disclosure will be described. First, an example of a part of the data acquisition process of the support device 100 (100E) according to the fifth embodiment of the present disclosure will be described. FIG. 22 is a flowchart showing an example of a part of the data acquisition process of the support device 100 (100E) according to the fifth embodiment of the present disclosure. For example, the data acquisition unit 110E in the support device 100E shown in FIG. 21 executes the process shown in FIG. 22 in the data acquisition process ("Start").

[0118] The data acquiring unit 110E of the support device 100 (100E) then executes a goal information acquiring process (step ST5110). In the goal information acquiring process, the goal information acquiring unit 116 of the data acquiring unit 110E acquires a goal score as goal information through an input operation by the operator of the support device 100 (100E). The data acquiring unit 110E outputs the target score as goal information to the tendency estimating unit 130 (130E) together with the implementation type information acquired by the implementation type acquiring unit 115.

[0119] Next, an example of a part of the trend estimation process of the support device 100 (100E) will be described. FIG. 23 is a flowchart showing an example of a part of the trend estimation process of the support device 100 (100E) according to the fifth embodiment of the present disclosure. The tendency estimation unit 130 (130E) of the support device 100 (100E) further executes the process shown in FIG. 23 in the tendency estimation process (“Start”).

[0120] The tendency estimation unit 130 (130E) then executes a tendency estimation process for the target (step ST5310). The tendency estimation unit 130 (130E) outputs the estimation result, for example, the concentration tendency at the current time to the support information output unit 150 (150C), and outputs the past concentration tendency to the tendency storage unit 170 (170E).

[0121] Next, an example of a part of the support information output process of the support device 100 (100E) will be explained. FIG. 24 is a flowchart showing an example of a part of the support information output process of the support device 100 (100E) according to the fifth embodiment of the present disclosure. The support information output unit 150 (150E) in the support device 100 (100E) starts processing ("start") when it receives the target score after the trend estimation process by the trend estimation unit 130 (130E).

[0122] The support information output unit 150 (150E) then executes a score generation process (step ST5810). In the score generation process, the score generation unit 154 of the support information output unit 150 (150E) generates a score for each activity type using the activity time and concentration level for each activity type, which is the type of activity content performed by the measurement subject.

[0123] The support information output unit 150 (150E) then executes a goal generation process (step ST5820). In the goal generation process, the goal generation unit 155 of the support information output unit 150 (150E) generates a goal including a target performance time and a target concentration level, which should be targeted, using the score generated by the score generation unit 154 and the target score acquired by the goal information acquisition unit 116.

[0124] The support information output unit 150 (150E) outputs the scores for each implementation type and the goal information indicating the goal generated by the goal generation unit 155 to the output destination device 700, and then ends the process and goes into standby mode ("End").

[0125] Next, an example of the support information output process in the support device 100 (100E) will be described. FIG. 25 is a flowchart showing an example of a support information output process in the support device 100 (100E) according to the fifth embodiment of the present disclosure. The support information output unit 150 (150E) in the support device 100 (100E) starts processing ("start") when it receives the concentration tendency at the current time as the estimation result from the tendency estimation unit 130 (130E).

[0126] Next, a trend prediction process for the goal is executed (step ST5510). In the trend prediction process for the goal, the trend prediction unit 151 (151E) of the support information output unit 150 (150E) refers to the trend holding unit 170 (170E) to acquire the concentration trend for each schedule estimated by the trend estimation unit 130 (130E). The trend prediction unit 151 (151E) predicts the concentration trend for each schedule from the current time onwards based on the past concentration trend estimated by the trend estimation unit 130 (130E) and the concentration trend at the current time. The trend prediction unit 151 (151E) outputs the concentration trend for the goal, which is the prediction result.

[0127] Next, a plan generation process for the goal is executed (step ST5520). In the plan generation process for the goal, the plan generation unit 152 (152E) of the support information output unit 150 (150E) generates a plan for the current time and thereafter based on the concentration tendency. The plan generation unit 152 (152E) uses the concentration tendency for each past schedule to generate a plan for the goal according to the concentration tendency for each schedule for the current time and thereafter.

[0128] Next, an advice generation process for the goal is executed (step ST5530). In the advice generation process for the goal, the advice generation unit 153 (153E) of the support information output unit 150 (150E) generates advice using the concentration tendency. The advice generation unit 153 (153E) generates advice for the goal using the concentration tendency for each past schedule, the concentration tendency at the current time, and the concentration tendency for each schedule after the current time.

[0129] The support information output unit 150 (150E) of the support device 100E then proceeds to an end determination process ((step ST5540) ("End?")). In the termination determination process, the support information output unit 150 (150E) determines whether to terminate the processing of the support information output unit 150 (150E). The support information output unit 150 (150E) determines whether to terminate the processing of the support information output unit 150 (150E) in accordance with an external command or an execution program. For example, when support information different from the support information that has been output is requested, the support information output unit 150 (150E) determines not to terminate the processing. When the support information output unit 150 (150E) determines not to end the processing of the support information output unit 150 (150E) ("NO" in step ST5540), the process proceeds to step ST5530 and executes the process of step ST5530. When the support information output unit 150 (150E) determines that the processing of the support information output unit 150 (150E) is to be ended ("YES" in step ST5540), the support information output unit 150 (150E) ends the processing ("End").

[0130] Next, a specific example of the support information output by the support device 100 (100E) according to this embodiment will be described. FIG. 26 is a diagram illustrating an example of support information output by the support device 100 (100E) according to the fifth embodiment of the present disclosure. When the implementation content (implementation type) 5010 is input, the assistance device 100 (100E) scores the specific content as shown in FIG. 26 and gives advice to improve the score. (1) Scoring time x concentration It is possible to grasp the strengths and weaknesses of the subject being measured. (2) Providing target scores for weak areas (subjects) (3) Suggestions (advice) to improve scores in weak areas Gradually increase your concentration time Increase your study time Take breaks to improve your concentration Regarding (3), specifically, it is possible to output support information including advice such as, "Social studies take up a lot of time, so try to increase your concentration by taking breaks," and "Try increasing the time you spend on English by two hours per week." This will help students improve their concentration and increase the amount of time they spend studying even if they are not good at or dislike the subject.Also, by having students check their scores, they can be notified that they are using their time efficiently and with high concentration, even if the study time is short, which will give them peace of mind.

[0131] This embodiment further shows an example of an embodiment including the following configuration. [9] a score generation unit that generates a score for each activity type using an activity time and a concentration level for each activity type, which is a type of activity content performed by the measurement subject; a target information acquisition unit that acquires a target score that should be a target for each implementation type; a goal generation unit that generates a goal including a goal performance time and a goal concentration level that should be targeted using the score generated by the score generation unit and the goal score acquired by the goal information acquisition unit; Equipped with The support information output unit further generates advice based on the goal generated by the goal generation unit, and outputs support information including the generated advice. 3. The support device according to claim 1 or 2. As a result, the present disclosure further achieves the effect of being able to provide an assistance device that enables enhanced assistance with concentration. Furthermore, the present disclosure achieves the same effects as those described above by applying the above configuration to a system including the assistance device, the assistance method, or the program.

[0132] Embodiment 6 In the sixth embodiment, a configuration example will be described in which the user can set the time period during which they tend to concentrate, and assistance is provided based on their tendency to concentrate at any given time period. In the sixth embodiment, among the components of the sixth embodiment, those components and their functions that are similar to those of the components of the first, second, third, fourth, or fifth embodiment already described are indicated by the same component names and the same symbols, and redundant explanations of those components are omitted as appropriate.

[0133] A configuration example of a support device and a system including the support device according to a sixth embodiment of the present disclosure will be described. FIG. 27 is a diagram illustrating an example of the configuration of a support system 1 (1F) including a support device 100 (100F) according to the sixth embodiment of the present disclosure. The data acquisition unit 110 (110F) in the support device 100 (100F) is configured to include a new set time acquisition unit 118. The set time acquisition unit 118 accepts and acquires the set time. The set time is a time that can be arbitrarily set by the operator, who is the user. The tendency estimation unit 130 (130F) in the support device 100 (100F) further estimates the concentration tendency for the set time acquired by the set time acquisition unit 118. The support information output unit 150 (150F) in the support device 100 (100F) further generates advice using the past concentration tendency during the set time, and outputs support information including the generated advice.

[0134] Next, a processing example of the support device according to the sixth embodiment of the present disclosure will be described. First, an example of a part of the data acquisition process of the support device 100 (100F) according to the sixth embodiment of the present disclosure will be described. FIG. 28 is a flowchart showing an example of a part of the data acquisition process of the support device 100 (100F) according to the sixth embodiment of the present disclosure. For example, the data acquisition unit 110F in the support device 100E shown in FIG. 27 executes the process shown in FIG. 28 in the data acquisition process ("Start").

[0135] The data acquiring unit 110 (110F) then executes a set time acquiring process (step ST6110). In the set time acquiring process, the set time acquiring unit 118 of the data acquiring unit 110 (110F) accepts and acquires the set time through a user input operation. The data acquiring unit 110 (110F) outputs the set time to the tendency estimating unit 130 (130F).

[0136] Next, an example of a part of the trend estimation process of the support device 100 (100F) according to the sixth embodiment of the present disclosure will be described. FIG. 29 is a flowchart showing an example of a part of the trend estimation process of the support device 100 (100F) according to the sixth embodiment of the present disclosure. The tendency estimation unit 130 (130F) of the support device 100 (100F) further executes the process shown in FIG. 29 in the tendency estimation process (“Start”).

[0137] The trend estimation unit 130 (130F) then executes trend estimation processing for the set time (step ST6310). In the trend estimation processing, the trend estimation unit 130 (130F) further estimates the concentration trend for the set time acquired by the set time acquisition unit 118. For example, if the set time is set to 90 minutes, the trend estimation unit 130 (130F) estimates the concentration trend for 90 minutes from the start command.

[0138] Next, an example of support information output processing in the support device 100 (100F) according to the sixth embodiment of the present disclosure will be described. FIG. 30 is a flowchart showing an example of a support information output process in the support device 100 (100F) according to the sixth embodiment of the present disclosure. The support information output unit 150 (150F) in the support device 100 (100F) starts processing ("start") when it receives the concentration tendency for the current set time as an estimation result from the tendency estimation unit 130 (130F).

[0139] The support information output unit 150 (150F) then executes a process of acquiring a trend at the set time (step ST6510). In this process, the plan generation unit 152 (152F) of the support information output unit 150 (150F) acquires the past concentration trend at the set time by referring to the trend storage unit 170 (170C).

[0140] The support information output unit 150 (150F) then executes a plan generation process using the trend at the set time (step ST6520). In the plan generation process, the plan generation unit 152 (152F) of the support information output unit 150 (150F) generates a plan for the set time using the current concentration trend at the set time and the past concentration trend at the set time.

[0141] The support information output unit 150 (150F) then executes advice generation processing for the set time (step ST6530). In the advice generation processing, the advice generation unit 153 (153F) of the support information output unit 150 (150F) generates advice for the set time by taking into account the past concentration tendency at the set time and the current concentration tendency at the set time.

[0142] The support information output unit 150 (150F) of the support device 100F then proceeds to an end determination process ((step ST6540) ("End?")). In the termination determination process, the support information output unit 150 (150F) determines whether to terminate the processing of the support information output unit 150 (150F). The support information output unit 150 (150F) determines whether to terminate the processing of the support information output unit 150 (150F) in accordance with an external command or an execution program. For example, if support information different from the support information that has been output is requested, the support information output unit 150 (150F) determines not to terminate the processing. When the support information output unit 150 (150F) determines not to end the processing of the support information output unit 150 (150F) ("NO" in step ST6540), the process proceeds to step ST6530 and executes the process of step ST6530. When the support information output unit 150 (150F) determines that the processing of the support information output unit 150 (150F) is to be ended ("YES" in step ST6540), the support information output unit 150 (150F) ends the processing ("End").

[0143] Next, a specific example of the support information output by the support device 100 (100F) according to this embodiment will be described. FIG. 31 is a diagram illustrating an example of support information output by the support device 100 (100F) according to the sixth embodiment of the present disclosure. When a specific time such as the actual test time is registered, the support device 100 (100F) can analyze the concentration during that time and provide support to help maintain high concentration during the actual test. FIG. 31 shows the time series transition of the concentration level over multiple times for the same set time length. FIG. 31 shows a time series transition of concentration level at a set time (first time) 6010, a time series transition of concentration level at a set time (second time) 6020, and a time series transition of concentration level at a set time (third time) 6030. The advice generation unit of the support information output unit 150 (150F) performs the following analysis on such data. (1) Check the changes within the set time (2) Compare the history within the same set time and provide advice based on trends and changes in concentration. (3) Enter the subject and content to see trends that include them. A specific example of support information for case (2) is shown below. "I was able to maintain my concentration in the first half. As a result, I had a lot of dips along the way, but I was able to stay focused towards the end. Did you ever lose focus along the way and run out of time?" A specific example of support information for case (3) is shown below. "I can concentrate at the beginning of math, but my concentration is always low in the first half of Japanese. It would be good to try to come up with some way to help me concentrate before the class starts." In this way, for example, it can lead to ideas for maintaining high concentration during the actual exam. Also, by comparing it with the content of the questions, it can help to grasp trends such as whether changes in concentration level are due to the influence of time or the influence of the questions.

[0144] This embodiment further shows an example of an embodiment including the following configuration.

[10] a set time acquisition unit that accepts and acquires a set time; Equipped with The tendency estimation unit further estimates a concentration tendency during the set time acquired by the set time acquisition unit, The support information output unit further generates advice using the past concentration tendency for the set time period, and outputs the support information including the generated advice. 3. The support device according to claim 1 or 2. As a result, the present disclosure further has the effect of being able to provide an assistance device that makes it possible to enhance assistance regarding concentration based on concentration tendencies for each set time period. Furthermore, the present disclosure achieves the same effects as those described above by applying the above configuration to a system including the assistance device, the assistance method, or the program.

[0145] Embodiment 7 The concentration tendency of the subject is thought to be related to the subject's state of fatigue. In the seventh embodiment, a configuration example will be described in which support information based on the concentration tendency of the measurement subject, taking into account the fatigue tendency of the measurement subject, is output. In the seventh embodiment, among the components of the seventh embodiment, those components and their functions that are similar to those of the components of the first, second, third, fourth, fifth, or sixth embodiment already described are indicated by the same component names and the same symbols, and redundant explanations of those components are omitted as appropriate.

[0146] A configuration example of a support device and a system including the support device according to a seventh embodiment of the present disclosure will be described. FIG. 32 is a diagram illustrating an example of the configuration of a support system 1 (1G) including a support device 100 (100G) according to the seventh embodiment of the present disclosure. The data acquisition unit 110 (110G) in the support device 100 (100G) is configured to newly include a fatigue level acquisition unit 119. The fatigue level acquiring section 119 acquires the fatigue level of the subject in chronological order. The tendency estimation unit 130 (130G) estimates the concentration tendency taking into account the fatigue tendency. The tendency estimation unit 130 (130G) includes a fatigue tendency estimation unit 131. The fatigue tendency estimation unit 131 estimates a fatigue tendency for each type of tendency using the time-series fatigue level acquired by the fatigue level acquisition unit 119. The tendency estimation unit 130 (130G) further uses the fatigue tendency estimated by the fatigue tendency estimation unit 131 to estimate a concentration tendency taking into account the fatigue tendency. The support information output unit 150 (150G) further generates and outputs support information using the concentration tendency taking into account the fatigue tendency.

[0147] Next, a processing example of the support device according to the seventh embodiment of the present disclosure will be described. First, an example of a part of the data acquisition process of the support device 100 (100G) according to the seventh embodiment of the present disclosure will be described. FIG. 33 is a flowchart showing an example of a part of the data acquisition process of the support device 100 (100G) according to the seventh embodiment of the present disclosure. For example, the data acquisition unit 110G in the support device 100G shown in FIG. 32 executes the process shown in FIG. 33 in the data acquisition process (“Start”).

[0148] The data acquiring unit 110G then executes a fatigue level acquiring process (step ST7110). In the fatigue level acquiring process, the fatigue level acquiring unit 119 of the data acquiring unit 110G acquires the fatigue level of the measurement subject in time series. The data acquiring unit 110G outputs the time series fatigue level to the tendency estimating unit 130 (130G).

[0149] Next, an example of a part of the trend estimation process of the support device 100 (100G) according to the seventh embodiment of the present disclosure will be described. FIG. 34 is a flowchart showing an example of a part of the trend estimation process of the support device 100 (100G) according to the seventh embodiment of the present disclosure. The tendency estimation unit 130 (130G) of the support device 100 (100G) further executes the process shown in FIG. 34 in the tendency estimation process (“Start”).

[0150] The trend estimation unit 130 (130G) then executes a fatigue trend estimation process (step ST7310). In the fatigue trend estimation process, the fatigue trend estimation unit 131 of the trend estimation unit 130 (130G) estimates a fatigue trend for each trend type using the time-series fatigue level acquired by the fatigue level acquisition unit 119.

[0151] The tendency estimation unit 130 (130G) then executes a concentration tendency estimation process that takes the fatigue tendency into account (step ST7320). In the concentration tendency estimation process, the tendency estimation unit 130 (130G) outputs the concentration tendency estimated with the fatigue tendency taken into account.

[0152] Next, an example of support information output processing in the support device 100 (100G) according to the seventh embodiment of the present disclosure will be described. FIG. 35 is a flowchart showing an example of a support information output process in the support device 100 (100G) according to the seventh embodiment of the present disclosure. The support information output unit 150 (150G) in the support device 100 (100G) starts processing ("start") when it receives the concentration tendency at the current time as the estimation result from the tendency estimation unit 130 (130G).

[0153] The support information output unit 150 (150C) then executes trend prediction processing that takes into account the fatigue trend (step ST7510). In the trend prediction processing, the trend prediction unit 151 (151G) of the support information output unit 150 (150G) refers to the trend storage unit 170 (170G) to acquire the concentration trend that takes into account the fatigue trend estimated by the trend estimation unit 130 (130G). The trend prediction unit 151 (151G) predicts the concentration trend for each schedule from the current time onwards based on the past concentration trends estimated by the trend estimation unit 130 (130G) and the concentration trend at the current time. The trend prediction unit 151 (151G) outputs the concentration trend that takes into account the fatigue trend, which is the prediction result.

[0154] The support information output unit 150 (150G) then executes a plan generation process that takes into account the fatigue tendency (step ST7520). In the plan generation process, the plan generation unit 152 (152G) of the support information output unit 150 (150G) generates a plan for the current time and thereafter based on the concentration tendency. The plan generation unit 152 (152G) uses the concentration tendency for each past schedule to generate a plan according to the concentration tendency that takes into account the fatigue tendency for the current time and thereafter.

[0155] The support information output unit 150 (150G) then executes advice generation processing that takes fatigue tendency into consideration (step ST7530). In the advice generation processing, the advice generation unit 153 (153G) of the support information output unit 150 (150G) generates advice using the concentration tendency. The advice generation unit 153 (153G) generates advice that takes fatigue tendency into consideration by using the concentration tendency for each past schedule, the concentration tendency at the current time, and the concentration tendency for each schedule after the current time.

[0156] The support information output unit 150 (150G) of the support device 100G then proceeds to an end determination process ((step ST7540) ("End?")). In the termination determination process, the support information output unit 150 (150G) determines whether to terminate the processing of the support information output unit 150 (150G). The support information output unit 150 (150G) determines whether to terminate the processing of the support information output unit 150 (150G) in accordance with an external command or an execution program. For example, if support information different from the support information that has been output is requested, the support information output unit 150 (150G) determines not to terminate the processing. When the support information output unit 150 (150G) determines not to end the processing of the support information output unit 150 (150G) ("NO" in step ST7540), the process proceeds to step ST7530 and executes the process of step ST7530. If the support information output unit 150 (150G) determines that the processing of the support information output unit 150 (150G) is to be ended ("YES" in step ST7540), the support information output unit 150 (150G) ends the processing ("End").

[0157] Next, a specific example of the support information output by the support device 100 (100G) according to this embodiment will be described. FIG. 36 is a diagram illustrating an example of a fatigue tendency and an example of a tendency type of the fatigue tendency used in the assistance device 100 (100G) according to the seventh embodiment of the present disclosure. FIG. 37 is a diagram for explaining an example of concentration tendency taking into account fatigue tendency in the assistance device 100 (100G) according to the seventh embodiment of the present disclosure. As shown in FIG. 37, the support device 100 (100G) analyzes the current data and past time-series data of the fatigue level, including the fatigue level, and finds a trend. Convert fatigue (=exhaustion) data into time series data alone. The analysis is done by comparing concentration levels and fatigue levels. Based on time series data on fatigue levels, (1) When fatigue level is high (low) (2) When high concentration continues for a long period of time (3) Seasons and months (4) Day of the week (5) Time period (6) Continuous fatigue time (blue painted area) This is analyzed against concentration data, and predictions are made based on trends. A specific example of the support information will be described. As a specific example of the above case (2), support information including the following advice can be output. Although your concentration level is high, your fatigue level is also quite high. In this case, your concentration level tends to drop sharply in the afternoon. It may be a good idea to take frequent breaks for the rest of your plans for today. Since you are focused and not too tired, let's get some heavy studying done early today. You don't seem too tired yet, but you're not concentrating as well today. Let's change your mood and start with something that you can concentrate on. Your concentration is low and you're feeling fatigued. Taking a rest is one option. In this way, by analyzing fatigue level as one of the factors that affect concentration level, it is possible to improve accuracy and provide more advice.

[0158] This embodiment further shows an example of an embodiment including the following configuration.

[11] a fatigue level acquiring unit that acquires the fatigue level of the subject in time series; a fatigue tendency estimation unit that estimates a fatigue tendency for each type of tendency using the time-series fatigue degree acquired by the fatigue degree acquisition unit; Equipped with The tendency estimation unit further uses the fatigue tendency estimated by the fatigue tendency estimation unit to estimate a concentration tendency taking the fatigue tendency into consideration. The support information output unit further generates and outputs support information using the concentration tendency taking into account the fatigue tendency. 3. The support device according to claim 1 or 2. As a result, the present disclosure further achieves the effect of being able to provide an assistance device that enables enhanced assistance with concentration. Furthermore, the present disclosure achieves the same effects as those described above by applying the above configuration to a system including the assistance device, the assistance method, or the program.

[0159] Embodiment 8 In the eighth embodiment, a configuration example will be described in which support information based on the concentration tendency of the subject taking into account the subject's tendency to become sleepy will be output. In the eighth embodiment, among the components of the eighth embodiment, those components and their functions that are similar to those of the components of the first, second, third, fourth, fifth, or sixth embodiment already described are indicated by the same component names and the same symbols, and redundant explanations of those components are omitted as appropriate.

[0160] An example configuration of a support device and a system including the support device according to an eighth embodiment of the present disclosure will be described. FIG. 38 is a diagram illustrating an example of the configuration of a support system 1 (1H) including a support device 100 (100H) according to the eighth embodiment of the present disclosure. The data acquisition unit 110 (110H) in the support device 100 (100H) is configured to newly include a drowsiness level acquisition unit 120. The drowsiness level acquiring section 120 acquires the drowsiness level of the subject in time series. The tendency estimation unit 130 (130H) is configured to newly include a drowsiness tendency estimation unit 133. The drowsiness tendency estimation unit 133 estimates the drowsiness tendency for each type of tendency using the time-series drowsiness level acquired by the drowsiness level acquisition unit. The tendency estimation unit 130 (130H) further uses the sleepiness tendency estimated by the sleepiness tendency estimation unit to estimate a concentration tendency taking the sleepiness tendency into consideration. The support information output unit 150 (150H) The support information output unit further generates and outputs support information using the concentration tendency taking into account the drowsiness tendency estimated by the tendency estimation unit.

[0161] Next, a processing example of the support device according to the eighth embodiment of the present disclosure will be described. First, an example of a part of the data acquisition process of the support device 100 (100H) according to the eighth embodiment of the present disclosure will be described. FIG. 39 is a flowchart showing an example of a part of the data acquisition process of the support device 100 (100H) according to the eighth embodiment of the present disclosure. For example, the data acquisition unit 110H in the support device 100H shown in FIG. 32 executes the process shown in FIG. 33 in the data acquisition process (“Start”).

[0162] The data acquiring unit 110H then executes a drowsiness level acquiring process (step ST8110). In the drowsiness level acquiring process, the drowsiness level acquiring unit 120 of the data acquiring unit 110H estimates the drowsiness tendency for each tendency type using the time-series drowsiness level acquired by the drowsiness level acquiring unit. Next, an example of a part of the trend estimation process of the support device 100 (100H) according to the eighth embodiment of the present disclosure will be described. FIG. 40 is a flowchart showing an example of a part of the trend estimation process of the support device 100 (100H) according to the eighth embodiment of the present disclosure. The tendency estimation unit 130 (130H) of the support device 100 (100H) further executes the process shown in FIG. 40 in the tendency estimation process ("Start").

[0163] The tendency estimation unit 130 (130H) then executes a drowsiness tendency estimation process (step ST8310). In the drowsiness tendency estimation process, the drowsiness tendency estimation unit 133 of the tendency estimation unit 130 (130H) estimates a drowsiness tendency for each tendency type using the time-series drowsiness degree acquired by the drowsiness degree acquisition unit.

[0164] The tendency estimation unit 130 (130H) then executes a tendency estimation process taking into account the drowsiness tendency (step ST8320). In the tendency estimation process, the tendency estimation unit 130 (130H) further uses the drowsiness tendency estimated by the drowsiness tendency estimation unit to estimate a concentration tendency taking into account the drowsiness tendency.

[0165] Next, an example of support information output processing in the support device 100 (100H) according to the eighth embodiment of the present disclosure will be described. FIG. 41 is a flowchart showing an example of a support information output process in the support device 100 (100H) according to the eighth embodiment of the present disclosure. The support information output unit 150 (150H) in the support device 100 (100H) starts processing ("start") when it receives the concentration tendency at the current time as the estimation result from the tendency estimation unit 130 (130H).

[0166] The support information output unit 150 (150H) then executes a concentration tendency prediction process taking into account the drowsiness tendency (step ST8510). In the concentration tendency prediction process taking into account the drowsiness tendency, the tendency prediction unit 151 (151H) of the support information output unit 150 (150H) refers to the tendency storage unit 170 (170H) to acquire the concentration tendency taking into account the fatigue tendency estimated by the tendency estimation unit 130 (130H). The tendency prediction unit 151 (151H) predicts the concentration tendency for each schedule from the current time onwards based on the past concentration tendency estimated by the tendency estimation unit 130 (130H) and the concentration tendency at the current time. The tendency prediction unit 151 (151H) outputs the concentration tendency taking into account the fatigue tendency, which is the prediction result.

[0167] The support information output unit 150 (150H) then executes a plan generation process that takes into account the drowsiness tendency (step ST8520). In the plan generation process that takes into account the drowsiness tendency, the plan generation unit 152 (152H) of the support information output unit 150 (150H) generates a plan for the current time and thereafter based on the concentration tendency. The plan generation unit 152 (152H) uses the concentration tendency of each past schedule to generate a plan according to the concentration tendency that takes into account the fatigue tendency for the current time and thereafter.

[0168] The support information output unit 150 (150H) then executes advice generation processing that takes into account the drowsiness tendency (step ST8530). In the advice generation processing that takes into account the drowsiness tendency, the advice generation unit 153 (153H) of the support information output unit 150 (150H) generates advice using the concentration tendency. The advice generation unit 153 (153H) generates advice that takes into account the fatigue tendency using the concentration tendency for each past schedule, the concentration tendency at the current time, and the concentration tendency for each schedule after the current time.

[0169] The support information output unit 150 (150H) of the support device 100H then proceeds to an end determination process ((step ST8540) ("End?")). In the termination determination process, the support information output unit 150 (150H) determines whether to terminate the processing of the support information output unit 150 (150H). The support information output unit 150 (150H) determines whether to terminate the processing of the support information output unit 150 (150H) in accordance with an external command or an execution program. For example, if support information different from the support information that has been output is requested, the support information output unit 150 (150H) determines not to terminate the processing. When the support information output unit 150 (150H) determines not to end the processing of the support information output unit 150 (150H) ("NO" in step ST8540), the process proceeds to step ST8530 and executes the process of step ST8530. When the support information output unit 150 (150H) determines that the processing of the support information output unit 150 (150H) is to be ended ("YES" in step ST8540), the support information output unit 150 (150E) ends the processing ("End").

[0170] This embodiment further shows an example of an embodiment including the following configuration.

[12] a drowsiness level acquisition unit that acquires the drowsiness level of the subject in time series; a drowsiness tendency estimation unit that estimates a drowsiness tendency for each type of tendency by using the time-series drowsiness degree acquired by the drowsiness degree acquisition unit; Equipped with The tendency estimation unit further uses the sleepiness tendency estimated by the sleepiness tendency estimation unit to estimate a concentration tendency taking the sleepiness tendency into consideration. The support information output unit further generates and outputs support information using the concentration tendency taking into account the drowsiness tendency estimated by the tendency estimation unit. 3. The support device according to claim 1 or 2. As a result, the present disclosure further achieves the effect of being able to provide an assistance device that enables enhanced assistance with concentration. Furthermore, the present disclosure achieves the same effects as those described above by applying the above configuration to a system including the assistance device, the assistance method, or the program.

[0171] Embodiment 9 The tendency for measurement subjects to concentrate may be related to the environmental conditions around the measurement subjects. In the ninth embodiment, a configuration example will be described in which support for concentration is provided taking into consideration the environmental conditions around the subject to be measured. In the ninth embodiment, among the components of the ninth embodiment, those components and their functions that are similar to those of the components of the first, second, third, fourth, fifth, or sixth embodiment already described are indicated by the same component names and the same symbols, and redundant explanations of those components are omitted as appropriate.

[0172] A configuration example of a support device and a system including the support device according to a ninth embodiment of the present disclosure will be described. FIG. 42 is a diagram illustrating an example of the configuration of a support system 1 (1J) including a support device 100 (100J) according to the ninth embodiment of the present disclosure. The support system 1 (1J) shown in Fig. 41 includes a support device 100 (100J), an information source device 600 (600J), and an output destination device 700. The output destination device 700 functions in the same manner as the output destination device 700 already described.

[0173] The information source device 600 (600J) further outputs subject surroundings information relating to the surroundings of the subject to be measured. The information source device 600 (600J) is configured to have the same functions as the information source device 600 already described.

[0174] The support device 100 (100J) shown in FIG. 41 includes a data acquisition unit 110 (110J), a tendency estimation unit 130 (130J), a support information output unit 150 (150J), and a tendency holding unit 170 (170F).

[0175] The data acquisition unit 110 (110J) is configured to have the same functions as the data acquisition unit 110 already described. The data acquisition unit 110 (110J) further acquires target surroundings information. The data acquisition unit 110 (110J) includes an object surrounding information acquisition unit 118. The subject surroundings information acquisition unit 118 acquires subject surroundings information indicating the environmental conditions of the subject to be measured. The subject surroundings information is information about the environment and weather around the subject. The environment includes, for example, information on lighting, air flow, indoor temperature, indoor humidity, etc. The weather includes, for example, information on air pressure, outdoor temperature, outdoor humidity, and weather.

[0176] The trend estimation unit 130 (130J) has the functions of the trend estimation unit 130 already described. The tendency estimation unit 130 (130J) shown in FIG. 41 further estimates a concentration tendency for each environmental situation indicated in the object surrounding information acquired by the object surrounding information acquisition unit 118.

[0177] The support information output unit 150 (150J) has the same functions as the support information output unit 150 already described. The support information output unit 150 (150J) further generates and outputs support information using the concentration tendency for each environmental situation estimated by the tendency estimation unit 130 (130J). The support information output unit 150 (150J) shown in FIG. 25 includes a tendency prediction unit 151 (151J), a plan generation unit 152 (152J), and an advice generation unit 153 (153J).

[0178] The trend prediction unit 151 (151J) of the support information output unit 150 (150J) has the same function as any of the already-described trend prediction units 151. The trend prediction unit 151 (151J) further has a function of predicting a concentration trend from the current time onwards, using a past concentration trend that takes into account the environmental situation.

[0179] The plan generation unit 152 (152J) of the support information output unit 150 (150J) has the same function as any of the already-described trend prediction units 151. The plan generation unit 152 (152J) further has a function of generating a plan using a concentration trend that takes into account the environmental situation.

[0180] The advice generating unit 153 (153J) of the support information output unit 150 (150J) has the same function as any of the already-described trend predicting units 151. The advice generating unit 153 (153J) further has a function of generating advice using a concentration trend that takes into account the environmental situation.

[0181] The trend holding unit 170 (170J) has the same function as the trend holding unit 170 already described. The trend holding unit 170 (170J) further holds the concentration trend for each environmental situation estimated by the trend estimation unit 130 (130J). Furthermore, the trend holding unit 170 (170J) further updates and holds the concentration tendency for each environmental situation by using the concentration tendency for each environmental situation estimated by the trend estimation unit 130 (130J) and the concentration tendency for each environmental situation that has already been held.

[0182] Next, a processing example of the support device according to the ninth embodiment of the present disclosure will be described. First, an example of a part of the data acquisition process of the support device 100 (100J) according to the ninth embodiment of the present disclosure will be described. FIG. 43 is a flowchart showing an example of a part of the data acquisition process of the support device 100 (100J) according to the ninth embodiment of the present disclosure.

[0183] The data acquisition unit 110 (110F) of the support device 100 (100J) further executes a target peripheral information acquisition process in the data acquisition process (step ST9110). In the concentration tendency prediction process, the target peripheral information acquisition unit 118 of the data acquisition unit 110 (110J) acquires target peripheral information indicating the environmental condition of the measured subject. The target peripheral information acquisition unit 118 acquires the target peripheral information from, for example, the information source device 600 (600J).

[0184] Next, an example of a part of the trend estimation process of the support device 100 (100J) according to the ninth embodiment of the present disclosure will be described. FIG. 44 is a flowchart showing an example of a part of the trend estimation process of the support device 100 (100J) according to the ninth embodiment of the present disclosure.

[0185] The trend estimation unit 130 (130J) further executes the trend estimation process taking into account the target surrounding information (step ST9310). In the trend estimation process, the trend estimation unit 130 (130J) estimates a concentration trend for each environmental situation indicated in the target surrounding information acquired by the target surrounding information acquisition unit 118.

[0186] Next, an example of support information output processing in the support device 100 (100J) according to the ninth embodiment of the present disclosure will be described. FIG. 45 is a flowchart showing an example of a support information output process in the support device 100 (100J) according to the ninth embodiment of the present disclosure. The support information output unit 150 (150J) of the support device 100 (100J) starts processing when it receives the concentration tendency estimated by the tendency estimation unit 130 (130F). ("Start")

[0187] A trend prediction process taking into account the target peripheral information is executed (step ST9510). In the trend prediction process taking into account the target peripheral information, the trend prediction unit 151 (151J) of the support information output unit 150 (150J) predicts the concentration trend from the current time onwards based on the concentration trend to which the target peripheral information has been added by the trend estimation unit 130 (130J). The trend prediction unit 151 (151J) refers to the trend storage unit 170 (170J) to acquire the past concentration trend estimated by the trend estimation unit 130 (130J). The trend prediction unit 151 (151J) predicts the concentration trend from the current time onwards based on the past concentration trend estimated by the trend estimation unit 130 (130J) and the concentration trend at the current time. The trend prediction unit 151 (151J) outputs the concentration trend as a prediction result.

[0188] A plan generation process taking into account the target peripheral information is executed (step ST9520). In the plan generation process taking into account the target peripheral information, the plan generation unit 152 (152J) of the support information output unit 150 (150J) generates a plan for the current time and beyond based on the concentration trend to which the target peripheral information has been added by the trend estimation unit 130 (130J). The plan generation unit 152 (152J) generates a plan for the current time and beyond using the past concentration trend estimated by the trend estimation unit 130 (130J), the concentration trend at the current time, and the concentration trend for the current time and beyond predicted by the trend prediction unit 151 (151J).

[0189] An advice generation process taking into account the target peripheral information is executed (step ST9530). In the advice generation process taking into account the target peripheral information, the advice generation unit 153 (153J) of the support information output unit 150 (150J) generates advice based on the concentration tendency to which the target peripheral information has been added by the tendency estimation unit 130 (130J). The advice generation unit 153 (153J) generates and outputs advice using the past concentration tendency estimated by the tendency estimation unit 130 (130J), the concentration tendency at the current time, the concentration tendency from the current time onwards predicted by the tendency prediction unit 151 (151J), and the plan generated by the plan generation unit 152 (152J).

[0190] The support information output unit 150 (150J) of the support device 100J then proceeds to an end determination process ((step ST9540) ("End?")). In the termination determination process, the support information output unit 150 (150J) determines whether to terminate the processing of the support information output unit 150 (150J). The support information output unit 150 (150J) determines whether to terminate the processing of the support information output unit 150 (150J) in accordance with an external command or an execution program. For example, if support information different from the support information that has been output is requested, the support information output unit 150 (150J) determines not to terminate the processing. If the support information output unit 150 (150J) determines not to end the processing of the support information output unit 150 (150J) ("NO" in step ST9540), the process proceeds to step ST9530 and executes the process of step ST9530. When the support information output unit 150 (150J) determines that the processing of the support information output unit 150 (150J) is to be ended (step ST9540 "YES"), the support information output unit 150 (150E) ends the processing ("end").

[0191] Next, a specific example of the support information output by the support device 100 (100J) according to this embodiment will be described.

[0192] This embodiment further shows an example of an embodiment including the following configuration.

[10] a target surrounding information acquisition unit that acquires target surrounding information indicating the environmental condition of the person to be measured; Equipped with The tendency estimation unit further estimates a concentration tendency for each environmental situation indicated in the target surrounding information acquired by the target surrounding information acquisition unit, The support information output unit further generates and outputs support information using the concentration tendency for each environmental situation estimated by the tendency estimation unit. 3. The support device according to claim 1 or 2. As a result, the present disclosure further achieves the effect of being able to provide an assistance device that enables enhanced assistance with concentration. Furthermore, the present disclosure achieves the same effects as those described above by applying the above configuration to a system including the assistance device, the assistance method, or the program.

[0193] Here, a hardware configuration for realizing the functions of the present disclosure will be described. FIG. 46 is a diagram illustrating a first example of a hardware configuration for realizing the functions according to the configuration of the present disclosure. FIG. 47 is a diagram illustrating a second example of a hardware configuration for realizing the functions according to the configuration of the present disclosure. Each of the assistance devices 100, 100A, 100B, 100C, 100D, 100E, 100F, 100G, 100H, and 100J of the present disclosure is realized by hardware such as that shown in FIG. 46 or FIG. 47.

[0194] As shown in FIG. 46, each of the support devices 100, 100A, 100B, 100C, 100D, 100E, 100F, 100G, 100H, and 100J is configured, for example, with a processor 10001, a memory 10002, an input / output interface 10003, and a communication circuit 10004. The processor 10001 and the memory 10002 are, for example, installed in a computer. The memory 10002 stores the computer, data acquisition units 110, 110A, 110B, 110C, 110D, 110E, 110F, 110G, 110H, 110J, concentration level acquisition units 111, 111A, 111B, biological information acquisition unit 112, object identification information acquisition unit 113, schedule acquisition unit 114, implementation type acquisition unit 115, goal information acquisition unit 116, set time acceptance unit 117, set time acquisition unit 118, fatigue level acquisition unit 119, drowsiness level acquisition unit 120, object surrounding information acquisition unit 121, tendency estimation units 130, 130A, 130B, 130C, 130D, 130E, 130F,G,H,J, fatigue tendency estimation unit 131, drowsiness tendency estimation unit 133, support information output units 150, 150A, 150B, 150C, 150D, 150E, 150F,G,H,J, 50B, 150C, 150D, 150E, 150F, 150G, 150H, 150J, trend prediction section 151, 151B, 151C, 151D, 151E, 151F , 151G, 151H, 151J, plan generation unit 152, 152B, 152C, 152D, 152E, 152F, 152G, 152H, 152J, advice generation unit 153 , 153B, 153C, 153D, 153E, 153F, 153G, 153H, 153J, score generation unit 154, goal generation unit 155, trend holding units 170, 170B, 170C, 170D, 170E, 170F, 170G, 170H, 170J, and programs for functioning as a control unit not shown are stored.The processor 10001 reads out and executes the program stored in the memory 10002, whereby the data acquisition units 110, 110A, 110B, 110C, 110D, 110E, 110F, 110G, 110H, 110J, concentration level acquisition units 111, 111A, 111B, biological information acquisition unit 112, target identification information acquisition unit 113, schedule acquisition unit 114, implementation type acquisition unit 115, goal information acquisition unit 116, set time acceptance unit 117, set time acquisition unit 118, fatigue level acquisition unit 119, drowsiness level acquisition unit 120, target surrounding information acquisition unit 121, tendency estimation units 130, 130A, 130B, 130C, 130D, 130E, 130F,G,H,J, fatigue tendency estimation unit 131, drowsiness tendency estimation unit 132, and the like are provided. 33, Support information output section 150, 150A, 150B, 150C, 150D, 150E, 150F, 150G, 150H, 150J, Trend prediction section 151, 151B, 1 51C, 151D, 151E, 151F, 151G, 151H, 151J, plan generation part 152, 152B, 152C, 152D, 152E, 152F, 152G, 1 52H, 152J, advice generation units 153, 153B, 153C, 153D, 153E, 153F, 153G, 153H, 153J, score generation unit 154, goal generation unit 155, trend holding units 170, 170B, 170C, 170D, 170E, 170F, 170G, 170H, 170J, and a control unit not shown are realized. Furthermore, a storage unit (not shown) is realized by the memory 10002 or another memory (not shown). Furthermore, the communication circuit 10004 realizes a communication unit (not shown).

[0195] The processor 10001 is, for example, a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor, a microcontroller, or a digital signal processor (DSP). Memory 10002 may be a non-volatile or volatile semiconductor memory such as RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable Read Only Memory) or flash memory, or a magnetic disk such as a hard disk or flexible disk, or an optical disk such as a CD (Compact Disc) or DVD (Digital Versatile Disc), or a magneto-optical disk. The processor 10001 and the memory 10002 or the communication circuit 10004 are connected in a state where they can transmit data to each other. The processor 10001, the memory 10002, and the communication circuit 10004 are also connected in a state where they can transmit data to other hardware via the input / output interface 10003.

[0196] Or, in the support devices 100, 100A, 100B, 100C, 100D, 100E, 100F, 100G, 100H, and 100J, the data acquisition units 110, 110A, 110B, 110C, 110D, 110E, 110F, 110G, 110H, and 110J, the concentration degree acquisition units 111, 111A, and 111B, the biometric information acquisition unit 112, and the target identification information acquisition unit 113 , schedule acquisition unit 114, implementation type acquisition unit 115, goal information acquisition unit 116, set time reception unit 117, set time acquisition unit 118, fatigue level acquisition unit 119, drowsiness level acquisition unit 120, target surrounding information acquisition unit 121, tendency estimation units 130, 130A, 130B, 130C, 130D, 130E, 130F, G, H, J, fatigue tendency estimation unit 131, drowsiness tendency estimation unit 133, support information output unit 150,150A,150B,150C,150D,150E,150F,150G,150H,150J, Trend prediction section 151,151B,151C,151D,151E,15 1F, 151G, 151H, 151J, plan generation section 152, 152B, 152C, 152D, 152E, 152F, 152G, 152H, 152J, advice generation section 153, 153B , 153C, 153D, 153E, 153F, 153G, 153H, 153J, score generation unit 154, goal generation unit 155, trend holding units 170, 170B, 170C, 170D, 170E, 170F, 170G, 170H, 170J, and the functions of a control unit not shown may be realized by a dedicated processing circuit 20001, as shown in FIG. 47.

[0197] The processing circuit 20001 may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), an FPGA (Field-Programmable Gate Array), an SoC (System-on-a-Chip), or a system LSI (Large-Scale Integration). Furthermore, the memory 20002 or another memory not shown implements a storage unit not shown. Memory 20002 may be a non-volatile or volatile semiconductor memory such as RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable Read Only Memory) or flash memory, or a magnetic disk such as a hard disk or flexible disk, or an optical disk such as a CD (Compact Disc) or DVD (Digital Versatile Disc), or a magneto-optical disk. Furthermore, the communication circuit 20004 realizes a communication unit (not shown). The processing circuit 20001 and the memory 20002 or the communication circuit 20004 are connected in a state where they can transmit data to each other. In addition, the processing circuit 20001, the memory 20002, and the communication circuit 20004 are connected in a state where they can transmit data to each other and to other hardware via the input / output interface 20003. In addition, in the support devices 100, 100A, 100B, 100C, 100D, 100E, 100F, 100G, 100H, and 100J, the data acquisition units 110, 110A, 110B, 110C, 110D, 110E, 110F, 110G, 110H, and 110J, the concentration degree acquisition units 111, 111A, and 111B, the biometric information acquisition unit 112, and the target identification information acquisition unit 113 , schedule acquisition unit 114, implementation type acquisition unit 115, goal information acquisition unit 116, set time reception unit 117, set time acquisition unit 118, fatigue level acquisition unit 119, drowsiness level acquisition unit 120, target surrounding information acquisition unit 121, tendency estimation units 130, 130A, 130B, 130C, 130D, 130E, 130F, G, H, J, fatigue tendency estimation unit 131, drowsiness tendency estimation unit 133, support information output unit Power section 150, 150A, 150B, 150C, 150D, 150E, 150F, 150G, 150H, 150J, trend prediction section 151, 151B, 151C, 151D, 151E ,151F,151G,151H,151J, Plan generation unit 152,152B,152C,152D,152E,152F,152G,152H,152J, Advice generation unit 153,1 The functions of 53B, 153C, 153D, 153E, 153F, 153G, 153H, 153J, score generation unit 154, target generation unit 155, trend holding units 170, 170B, 170C, 170D, 170E, 170F, 170G, 170H, 170J, and a control unit (not shown) may be realized by separate processing circuits, or may be realized together by a processing circuit.

[0198] Or, in the support devices 100, 100A, 100B, 100C, 100D, 100E, 100F, 100G, 100H, and 100J, the data acquisition units 110, 110A, 110B, 110C, 110D, 110E, 110F, 110G, 110H, and 110J, the concentration degree acquisition units 111, 111A, and 111B, the biometric information acquisition unit 112, the target identification information acquisition unit 113, and the schedule acquisition unit 11 4, implementation type acquisition unit 115, goal information acquisition unit 116, set time acceptance unit 117, set time acquisition unit 118, fatigue level acquisition unit 119, drowsiness level acquisition unit 120, target surrounding information acquisition unit 121, tendency estimation units 130, 130A, 130B, 130C, 130D, 130E, 130F, G, H, J, fatigue tendency estimation unit 131, drowsiness tendency estimation unit 133, support information output unit 150, 150A, 150B, 150C, 150D, 150E, 150F, G, H, J, 50C,150D,150E,150F,150G,150H,150J, Trend prediction section 151,151B,151C,151D,151E,151F,151G,151H,151J,Total Image generation unit 152, 152B, 152C, 152D, 152E, 152F, 152G, 152H, 152J, advice generation unit 153, 153B, 153C, 153D, 153E, 153F, 15 3G, 153H, 153J, score generation unit 154, target generation unit 155, trend holding units 170, 170B, 170C, 170D, 170E, 170F, 170G, 170H, 170J, and some of the functions of a control unit not shown may be realized by processor 10001 and memory 10002, and the remaining functions may be realized by processing circuit 20001.

[0199] It should be noted that, within the scope of this disclosure, the embodiments may be freely combined, any component of each embodiment may be modified, or any component of each embodiment may be omitted.

[0200] The present disclosure can provide more comprehensive support for concentration than ever before, and is therefore suitable for use in, for example, a support device that provides support for the concentration of a subject to be measured. [Explanation of symbols]

[0201] 1, 1A, 1B, 1C, 1D, 1E, 1F, 1G, 1H, 1J Support system, 100, 100A, 100B, 100C, 100D, 100E, 100F, 100G, 100H, 100J Support device, 110, 110A, 110B, 110C, 110D, 110E, 110F, 110G, 110H, 110J Data acquisition unit, 111, 111A, 111B Concentration level acquisition unit, 112 Biometric information acquisition unit, 113 Target identification information acquisition unit, 114 Schedule acquisition unit, 115 Implementation type acquisition unit, 116 Goal information acquisition unit, 117 Set time acceptance unit, 118 Set time acquisition unit, 119 Fatigue level acquisition unit, 120 Sleepiness level acquisition unit, 121 Target peripheral information acquisition unit, 130,130A,130B,130C,130D,130E,130F,130G,130H,130J Trend estimation unit, 131 Fatigue tendency estimation unit, 133 Sleepiness tendency estimation unit, 150,150A,150B,150C,150D,150E,150F,150G,150H,150J Support information output unit, 151,151B,151C,151D,151E,151F,151G,151H,151J Trend prediction section, 152,152B,152C,152D,152E,152F,152G,152H,152J Plan generation section, 153,153B,153C,153D,153E,153F,153G,153H,153J Advice generation section, 154 Score generation section, 155 Goal generation unit, 170, 170B, 170C, 170D, 170E, 170F, 170G, 170H, 170J Trend holding unit, 600, 600A, 600B, 600C, 600D, 600E, 600F, 600G, 600H, 600J Information source device, 700 Output destination device, 3010 Schedule (sleep), 3020 Schedule (club activities), 3030 Schedule (cram school), 3030 Schedule (sleep), 4000 Mobile terminal (smartphone), 4010 Image (concentration graph), 4020 Image (implementation content), 4110 Image (planned implementation time), 4120 Image (planned break time), 4130 Image (advice), 5010 Implementation content (implementation type), 6010 Time series change in concentration (1st time), 6020 Time series change in concentration (2nd time), 6030 Time series change in concentration (3rd time), 10001 Processor, 10002 Memory, 10003 Input / output interface, 10004 Communication circuit, 20001 Processing circuit, 20002 Memory, 20003 Input / output interface, 20004 Communication circuit.

Claims

1. a concentration level acquisition unit that acquires the concentration level of the person to be measured in time series; a tendency estimation unit that estimates a concentration tendency, which is a tendency related to concentration, for each tendency type using the time-series concentration degree acquired by the concentration degree acquisition unit; a support information output unit that outputs support information related to concentration based on the estimation result by the tendency estimation unit; An assistive device comprising:

2. The support information is The information includes one or more of the following: a time series of high and low levels of concentration; a duration of a high and low level of concentration; an accumulated amount of concentration for each duration of a high and low level of concentration; a high and low level of concentration by season; a high and low level of concentration by month; a high and low level of concentration by day of the week; or a high and low level by time period in a day.

2. The support device according to claim 1.

3. a subject identification information acquisition unit that acquires subject identification information that identifies the subject to be measured; Furthermore, the tendency estimation unit estimates a concentration tendency for each measurement subject identified by the subject identification information; 3. The support device according to claim 1 or 2.

4. The support information output unit predicting a concentration tendency from the current time onward using the past concentration tendency estimated by the tendency estimation unit, and generating and outputting support information based on the predicted concentration tendency from the current time onward.

3. The support device according to claim 1 or 2.

5. a schedule acquisition unit that acquires schedule information indicating the schedule of the subject; Equipped with The tendency estimation unit further estimates a concentration tendency for each schedule indicated in the schedule information acquired by the schedule acquisition unit, The support information output unit further generates and outputs support information based on the concentration tendency for each schedule.

3. The support device according to claim 1 or 2.

6. The support information output unit further generates a plan for the period after the current time using the concentration tendency for each schedule, and generates and outputs support information including advice based on the generated plan.

6. The support device according to claim 5.

7. an implementation type acquiring unit that acquires implementation type information indicating an implementation type, which is a type of implementation content performed by the measurement subject; Equipped with The tendency estimation unit further estimates a concentration tendency for each implementation type indicated in the implementation type information, The support information output unit further generates and outputs support information based on the concentration tendency for each implementation type.

3. The support device according to claim 1 or 2.

8. The support information output unit further generates a plan of the implementation content from the current time onward using the concentration tendency for each implementation type, and generates and outputs support information including advice based on the generated implementation content plan.

8. The support device according to claim 7.

9. a score generating unit that generates a score for each activity type using an activity time and a concentration level for each activity type, which is a type of activity content performed by the subject; a target information acquisition unit that acquires a target score that should be a target for each implementation type; a goal generation unit that generates a goal including a goal performance time and a goal concentration level that should be targeted using the score generated by the score generation unit and the goal score acquired by the goal information acquisition unit; Equipped with The support information output unit further generates advice based on the goal generated by the goal generation unit, and outputs support information including the generated advice.

3. The support device according to claim 1 or 2.

10. a set time acquisition unit that accepts and acquires a set time; Equipped with The tendency estimation unit further estimates a concentration tendency during the set time acquired by the set time acquisition unit, The support information output unit further generates advice using the past concentration tendency for the set time period, and outputs the support information including the generated advice.

3. The support device according to claim 1 or 2.

11. a fatigue level acquiring unit that acquires the fatigue level of the subject in time series; a fatigue tendency estimation unit that estimates a fatigue tendency for each type of tendency using the time-series fatigue degree acquired by the fatigue degree acquisition unit; Equipped with The tendency estimation unit further uses the fatigue tendency estimated by the fatigue tendency estimation unit to estimate a concentration tendency taking the fatigue tendency into consideration. The support information output unit further generates and outputs support information using the concentration tendency taking into account the fatigue tendency.

3. The support device according to claim 1 or 2.

12. a drowsiness level acquisition unit that acquires the drowsiness level of the subject in time series; a drowsiness tendency estimation unit that estimates a drowsiness tendency for each type of tendency by using the time-series drowsiness degree acquired by the drowsiness degree acquisition unit; Equipped with The tendency estimation unit further uses the sleepiness tendency estimated by the sleepiness tendency estimation unit to estimate a concentration tendency taking the sleepiness tendency into consideration. The support information output unit further generates and outputs support information using the concentration tendency taking into account the drowsiness tendency estimated by the tendency estimation unit.

3. The support device according to claim 1 or 2.

13. a target surrounding information acquisition unit that acquires target surrounding information indicating the environmental condition of the person to be measured; Equipped with The tendency estimation unit further estimates a concentration tendency for each environmental situation indicated in the target surrounding information acquired by the target surrounding information acquisition unit, the support information output unit further generates and outputs support information using the concentration tendency for each environmental situation estimated by the tendency estimation unit.

3. The support device according to claim 1 or 2.

14. A support method using a support device, a concentration level acquisition unit of the support device acquires the concentration level of the measurement subject in chronological order; a tendency estimation unit of the assistance device estimating a concentration tendency, which is a tendency related to concentration, for each tendency type using the time-series concentration degree acquired by the concentration degree acquisition unit; an assistance information output unit of the assistance device outputs assistance information related to concentration based on the estimation result by the tendency estimation unit; A support method characterized by:

15. Computer, a concentration level acquisition unit that acquires the concentration level of the person to be measured in time series; a tendency estimation unit that estimates a concentration tendency, which is a tendency related to concentration, for each tendency type using the time-series concentration degree acquired by the concentration degree acquisition unit; a support information output unit that outputs support information related to concentration based on the estimation result by the tendency estimation unit; an assist device comprising: A program characterized by operating as

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