JP2023025703A - Evaluation system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an evaluation system for effectively using information of a user.

SOLUTION: An evaluation system includes first to third systems. The first system includes: a first detector; a second detector; and a communication terminal that is communicable with the first detector and the second detector respectively. The second system includes a detector different from both of the first detector and the second detector. The third system is communicable with the first system and the second system. The third system converts data into reference information on the basis of first biological information acquired from the first detector and second biological information that is acquired from the second detector and whose kind is the same as that of the first biological information, and compares the second biological information acquired from the detector included in the second system with reference information.

SELECTED DRAWING: Figure 1

COPYRIGHT: (C)2023,JPO&INPIT

Description

Embodiments relate to rating systems.

Wearable devices that are worn on the body have been developed.

JP 2016-103275 A Japanese Patent No. 4759633

Embodiments provide an evaluation system that can make more effective use of user information.

The evaluation system of the embodiment includes first through third systems. The first system includes a first detector, a second detector, and a communication terminal capable of communicating with each of the first detector and the second detector. The second system includes a detector that is different from both the first detector and the second detector. The third system can communicate with the first system and the second system. The third system converts into reference information based on the first biological information obtained from the first detector and the second biological information of the same type as the first biological information obtained from the second detector, A second biometric information obtained from the detector included in the second system is compared with the reference information.

Embodiments can provide a rating system that can make better use of user information.

FIGS. 1(a) to 1(c) are schematic diagrams illustrating the evaluation system according to the first embodiment. It is a flowchart which shows the evaluation method of the evaluation system which concerns on 1st Embodiment. FIG. 4 is a schematic diagram illustrating evaluation results in the evaluation system according to the first embodiment; It is a schematic diagram which illustrates the evaluation system which concerns on 2nd Embodiment. It is a schematic diagram which illustrates the evaluation system which concerns on 2nd Embodiment. It is a flow chart which shows the evaluation method of the evaluation system concerning a 2nd embodiment. It is a flow chart which shows operation in an evaluation system concerning an embodiment. 1 is a schematic diagram illustrating an evaluation system according to an embodiment; FIG. FIGS. 9A to 9C are schematic diagrams illustrating information in the evaluation system according to the third embodiment. FIGS. 10A to 10C are schematic diagrams illustrating evaluation result information according to the third embodiment. It is a schematic diagram which illustrates the insurance which concerns on embodiment.

BEST MODE FOR CARRYING OUT THE INVENTION Embodiments of the present invention will be described below with reference to the drawings. In the present specification and each figure, the same reference numerals are given to the same elements as those described above with respect to the previous figures, and detailed description thereof will be omitted as appropriate.

(First embodiment)
An evaluation system according to the first embodiment will be described using examples shown in FIGS. 1(a) to 1(c).

(1 Overall Configuration of Evaluation System 110)
FIGS. 1(a) to 1(c) are schematic diagrams illustrating the evaluation system according to the first embodiment.
The evaluation system 110 includes a first user system 10 (first system), a second user system 20 (second system), and an arithmetic system 30 (third system).

The evaluation system 110 evaluates the user's condition based on biometric information and the like obtained from a plurality of user systems. For example, the evaluation system 110 acquires the user's biometric information while driving. The evaluation system 110 compares the biometric information with biometric information and the like previously obtained from a plurality of user systems, and evaluates the state of the user during driving. Specifically, the evaluation system 110 evaluates the state of the user who is driving at a certain timing, the state of the user who is driving at another timing, or the state of the user who is driving. Evaluate how different it is to the state of

This embodiment is not applied only when the user is driving. Embodiments are applicable when a user performs a series of tasks including one or more processes.

The first user system 10 includes a detector SN (eg sensor) and a communication terminal 11 . The detector SN includes, for example, first to n-th detectors (n is a natural number of 2 or more, and first to sixth detectors SN1 to SN6 in this embodiment). The number of detectors SN is arbitrary. The detector SN (the i-th detector SNi) detects at least one of the user's biometric information and information on the user's behavior. Examples of detectors SN (first to sixth detectors SN1 to SN6, etc.) will be described later.

For example, each of the detectors SN (eg, the i-th detector SNi) can communicate with the communication terminal 11 by wire or wirelessly. The first to sixth detectors SN1 to SN6 can temporarily store the obtained user's biometric information and behavioral information in a memory (not shown) within the first user system 10 .

The first user system 10 can communicate with the computing system 30 via the communication terminal 11 in a wired or wireless manner. For example, in response to a request from the computing system 30 , the first user system 10 transmits the acquired biometric information of the user and information about the behavior of the user to the computing system 30 via the communication terminal 11 .

Communication terminal 11 includes, for example, a computer. Communication terminal 11 includes, for example, a smartphone. Communication terminal 11 includes, for example, an electric circuit.

The second user system 20 is provided, for example, in a vehicle 28 (moving body). Vehicle 28 is, for example, an automobile. The second user system 20 includes a detector SN (the ith detector SNi, in this example the seventh detector SN7). A seventh detector SN7 is provided, for example, on the seat 27 of the vehicle 28 . For example, the seventh detector SN7 detects biometric information of the user 81 sitting on the seat 27 . The seventh detector SN7 may image the body of the user 81 (for example, the head such as the face). Information based on the imaging result may be at least part of the information about the user 81 . In this case, the seventh detector SN7 may be provided in the windshield portion of the vehicle 28. FIG.

In the example of FIG. 1A, the second user system 20 is provided with one detector SN (the i-th detector SNi, in this example, the seventh detector SN7). In embodiments, the number of detectors SN provided in the second user system 20 is arbitrary. In one example, the detector SN provided in the second user system 20 is different from any of the multiple detectors SN provided in the first user system 10 .

The second user system 20 may include, for example, a detector SN for detecting the alcohol concentration in the exhaled breath of the person driving the vehicle 28 .

The second user system 20 may include, for example, a detector that detects information about the vehicle 28 (information indicating movement/driving conditions of the vehicle 28). Second user system 20 may include, for example, a detector that detects the angular velocity of vehicle 28 .

The second user system 20 may include a communication section 21 and a server 22 . The second user system 20 may include, for example, a control unit (not shown). The second user system 20 can communicate with the computing system 30 by wire or wirelessly via the communication unit 21 . The control unit transmits the biological information of the user 81 acquired by the seventh detector SN7 to the computing system 30 via the communication unit 21 . Server 22 may include a computer. Server 22 may include memory. Server 22 includes, for example, electrical circuitry.

The first user system 10 and the second user system 20 acquire the biometric information of the user 81 and the like. The computing system 30 acquires biometric information of the user 81 from the first user system 10 and the second user system, and evaluates the user 81 while driving the vehicle 28 .

The computing system 30 includes, for example, a communication unit 31, a server 32, and a control unit 33. The communication unit 31 receives data from at least one of the first user system 10 and the second user system 20 . The data includes user's 81 biometric information or user's 81 behavior information. The communication unit 31 can communicate with the first user system 10 and the second user system 20 . For example, the communication unit 31 may transmit control signals and exchange data as necessary. The control unit 33 controls operations of the communication unit 31 and the server 32, for example. For example, the control unit 33 causes the server 32 or the like to execute an arithmetic program. The control unit 33 may set the calculation program in the RAM 34 provided in the calculation system 30, for example.

This computing program classifies the biometric information of the user 81 according to the state of the user 81 from the biometric information of the user 81 or the information on the behavior of the user 81 acquired from the first user system 10 . The computing program extracts the biological information of the user 81 corresponding to the state of the user 81 while driving the vehicle 28, for example. The computing program generates a reference distribution (or reference data) based on the extracted biological information of the user 81 . The computing program evaluates the user 81 by evaluating how the biological information of the user 81 acquired from the second user system 20 is relative to the reference distribution.

1(b) and 1(c) illustrate the detector SN.
As shown in FIG. 1(b), the first detector SN1 is provided on the bed 70, for example. Bed 70 includes, for example, bottom 71 and mattress 60 . The first detector SN1 is provided between the bottom 71 and the mattress 60, for example. A force (at least one of pressure and sound waves) by the user 81 is applied to the first detector SN1 via the mattress 60 . For example, the force-based signal detected by the first detector SN1 includes at least part of biometric information and behavioral information. Based on at least one of the magnitude of the signal (force) and the temporal change in the magnitude of the signal (force), the first detector SN1 detects biological information and behavioral information of the user 81 . For example, vibration according to the state of the user 81 is applied to the first detector SN1. The vibration corresponds to the body motion of the user 81, for example. Vibrations are detected at the first detector SN1. Vibration may include sound.

The first detector SN1 can detect, for example, at least one of heartbeat, pulse, respiration, body temperature, weight, sleep, wakefulness, falling asleep, getting out of bed, getting up, and edge sitting (for example, getting ready to get out of bed). For example, the state of the user 81 may be estimated based on at least one of the force detected by the first detector SN1 and the temporal change in the force.

As shown in FIG. 1(b), the second detector SN2 is attached to the body of the user 81 (the arm in this example). The second detector SN2 is, for example, a wristwatch-type wearable device. The second detector SN2 can acquire biological information of the user 81 including, for example, at least one of pulse, electromyogram (including electrocardiogram), and blood oxygen concentration. The second detector SN2 may be able to detect the heartbeat of the user 81, for example. The second detector SN2 may acquire behavior information including at least one of sleep, wakefulness, falling asleep, standing position, sitting position, lying position, walking, and exercise of the user 81, for example.

As shown in FIG. 1B, the third detector SN3 is provided on a pillow 72 or the like. A vibration according to the state of the user 81 is applied to the third detector SN3. The vibration corresponds to the body motion of the user 81, for example. The third detector SN3 detects, for example, heartbeat, pulse, respiration, body temperature, electromyogram (including electrocardiogram), electroencephalogram, blood pressure, and blood oxygen concentration (for example, percutaneous arterial blood oxygen saturation) of the user 81. (including degree) can be acquired.

As shown in FIG. 1(c), the fourth detector SN4 can be attached to the head of the user 81, for example. The fourth detector SN4 can acquire biological information including at least one of pulse, body temperature, and electroencephalogram of the user 81, for example. The fourth detector SN4 may acquire behavioral information of the user 81 including at least one of sleeping, waking, standing, sitting, lying, walking, and exercising.

As shown in FIG. 1(c), the fifth detector SN5 is, for example, an eyeglass-shaped wearable device. The user 81, for example, wears the fifth detector SN5 in the same manner as eyeglasses. The fifth detector SN5 can acquire biological information of the user 81 including at least one of pulse, body temperature and blood oxygen concentration, for example. The fifth detector SN5 may be capable of acquiring behavioral information including at least one of sleep, wakefulness, standing, sitting, lying, walking and exercise.

As shown in FIG. 1(c), the sixth detector SN6 is attached to the foot of the user 81. As shown in FIG. The sixth detector SN6 may be provided on the shoe of the user 81, for example. The sixth detector SN6 can acquire biological information of the user 81 including weight, for example. The sixth detector SN6 may acquire behavioral information of the user 81 including at least one of walking and exercise.

For example, the biological information of the user 81 includes heartbeat, pulse, respiration, body temperature, weight, pulse, electromyogram (including electrocardiogram), electroencephalogram, and blood oxygen concentration (for example, percutaneous arterial blood oxygen saturation). including).
For example, the behavior information of the user 81 can be classified into sleeping, waking, falling asleep, getting out of bed, getting up, sitting on the edge (for example, preparing to get out of bed), standing, sitting, lying down, walking, and exercising.
For example, the state of the user 81 includes sleeping, sitting, standing, walking, running, eating, after eating, excreting, and after excreting. state, during bathing, after bathing, during smoking, and after smoking.

(2 Evaluation method in the evaluation system)
The evaluation method in the evaluation system 110 will be described below with reference to FIG. In the following example, rating system 110 rates user 81 while driving vehicle 28 .

FIG. 2 is a flow chart showing the evaluation method of the evaluation system according to the first embodiment.
When evaluating the user 81 while driving the vehicle 28, it is evaluated whether there is a high risk of accidents or the like, for example. For the user 81 while driving, there are situations such as feeling unwell, being agitated, or being distracted. Under such circumstances, it is evaluated whether there is a high possibility of a risk such as an accident.

The evaluation system 110 acquires biometric information of the user 81 or information on the behavior of the user 81 from a plurality of detectors SN (first to sixth detectors SN1 to SN6). Evaluation system 110 evaluates user 81 based on this information. For example, a financial institution such as an automobile insurance company calculates an insurance premium, for example, based on the user's 81 evaluation result.

Each of the plurality of detectors SN (first to sixth detectors SN1 to SN6, etc.) has, for example, a portion that contacts the user 81 (and clothing, etc.). Biometric information may be obtained from different positions of the user 81 in each of the plurality of detectors SN (first to sixth detectors SN1 to SN6).

When the user 81 uses a plurality of detectors SN (first to sixth detectors SN1 to SN6, etc.), the types of biometric information that can be acquired may differ from each other. For example, one detector SN (eg the first detector SN1) can acquire the pulse. Other detectors SN (eg, sixth detector SN6) may not be able to acquire the pulse.

For example, the user 81 may not be using multiple detectors SN (first to sixth detectors SN1 to SN6, etc.) at the same time. For example, one detector SN is put on in the morning and taken off at night. Another detector SN may be worn at night. Accuracy of biometric information acquired by a plurality of detectors (first to sixth detectors SN1 to SN6, etc.) may differ from each other.

For example, when the biometric information of the user 81 driving the vehicle 28 is acquired by the detector SN7 and the user 81 is evaluated, a plurality of detectors SN (first to sixth detectors SN1 to SN6 etc.). For example, biometric information acquired by a plurality of detectors SN (first to sixth detectors SN1 to SN6, etc.) is used to create reference data (or reference distribution) that serves as a basis for calculating insurance premiums. This makes it possible, for example, to calculate an accurate insurance premium.

For example, during one period (evaluation period, timing) during which the user 81 is driving, the biological information of the user 81 is acquired by the detector SN7. On the other hand, for example, a plurality of detectors SN (first to sixth detectors SN1 to SN6, etc.) collect the biological information of the user 81 during a driving period (non-evaluation period) different from this evaluation period. to get For example, a plurality of detectors SN (first to sixth detectors SN1 to SN6, etc.) acquire biological information while the user 81 is driving during the non-evaluation period. For example, the evaluation system 110 extracts biometric information during the non-evaluation period from the biometric information acquired by the plurality of detectors SN (first to sixth detectors SN1 to SN6, etc.). Evaluation system 110 derives reference data (or reference distribution) based on the extracted biometric information.

A specific flow example of the evaluation method in the evaluation system 110 will be described below. The evaluation method in the evaluation system 110 may be realized by artificial intelligence (AI), for example.

For convenience of explanation, it is assumed that the pulse (biological information) of the user 81 can be acquired by the first detector SN1, the second detector SN2, the third detector SN3, and the seventh detector SN7. In the example below, the evaluation is based on the user's 81 pulse. In embodiments, the assessment may be based on other biometric information (eg, blood pressure, etc.). Before evaluation by the evaluation system 110, a plurality of detectors SN (for example, first to third detectors SN1 to SN3) acquire the pulse (biological information) of the user 81 and information on the behavior of the user 81. be done. The acquired information is already held on the server 32 of the rating system 110 . The user 81, for example, uses the second detector SN2 substantially all the time. The user 81, for example, uses the first detector SN1 and the third detector SN3 while sleeping.

(Step S1)
When starting the evaluation of the user 81 , the computing system 30 sends an instruction to start the evaluation to the second user system 20 . Specifically, the control unit 33 transmits the command CMD via the communication unit 31 . The control section of the second user system 20 receives this command CMD via the communication section 21 . The controller of the second user system 20 activates the seventh detector SN7.

(Step S2)
The second user system 20 acquires biometric information of the user 81, information on the behavior of the user 81, and other information with the seventh detector SN7. The second user system 20 holds this information in the server 22, for example.

(Step S3)
The second user system 20 transmits the information acquired in step S<b>2 to the arithmetic system 30 via the communication unit 21 . For example, the second user system 20 may transmit the detection result to the computing system 30 after detecting biological information or the like for a certain period of time. For example, the second user system 20 may transmit the detection result to the computing system 30 at any time while detecting biological information or the like. For example, data DATA1 containing these pieces of information is transmitted from the second user system 20 to the computing system 30 .

(Step S4)
The computing system 30 acquires the biological information of the user 81 (including, for example, pulse) acquired by the seventh detector SN7, information on the behavior of the user 81, and other information.

(Step S5)
The computing system 30 identifies the state of the user 81 from the information obtained in step S4. Specifically, from the information acquired in step S4, the control unit 33 detects, for example, a flag indicating that the seventh detector SN7 is a detector provided on the seat 27 (driver's seat) of the vehicle 28 (car). to read. The control unit 33 identifies, for example, that the user 81 is driving.

(Step S6)
The control unit 33 extracts from the server 32 data (biological information and information on behavior) when the user 81 is in the same state as the driving state under evaluation.

The server 32 holds biometric information and the like obtained from a plurality of detectors SN (first to third detectors SN1 to SN3). These biometric information are held in the server 32 together with the state of the user 81 . The state of the user 81 is, for example, whether the user 81 is lying down, sitting (sitting), standing (standing), walking, or running. whether during meals, after meals, during excretion, after excretion, during bathing, after bathing, during smoking, after smoking It is classified according to whether or not.

For example, the state St0 of the user 81 corresponds to the state where the user is sleeping. The state St1 of the user 81 corresponds to the state in which the user 81 is sitting. The state St2 of the user 81 corresponds to the state in which the user 81 is standing.

An example in which the control unit 33 extracts the data of the user 81 in the "same state" will be described. The control unit 33 determines which of the above categories stored in the server 32 corresponds to the state of the user 81 when the user 81 performs a certain action. The classifications stored on server 32 are finite. If the state of the user 81 does not correspond to the classification stored in the server 32, the control unit 33 selects a state similar to the state of the user 81 among the classifications stored in the server 32 as "same state". select. For example, the state classification of the user 81 stored in the server 32 does not include "driving". For example, the control unit 33 determines that the user 81 is sitting on the seat 27 of the vehicle 28 while the user 81 is driving, and the state St1 in which the user 81 is sitting is It is assumed to be in the "same state" as driving. As a result, the control unit 33 extracts the data of the state St1 of the user 81 in step S6.

The bed 70 shown in FIG. 1(b) may be provided with a weight sensor or an angle sensor. In this case, whether the user 81 is in state St0 or state St1 is specified based on the information acquired using these sensors. When the state of the user 81 cannot be recognized or is unknown based on the information obtained from the first to sixth detectors SN1 to SN6 and the other detectors SN, for example, the user 81 You may input your own status from a communication terminal or the like.

The control unit 33 determines that the “driving state” corresponds to the state St1 of the user 81, for example. Then, the control unit 33 extracts the pulse-related information corresponding to the state St1 of the user 81 from the pulse-related information held in the server 32 . The control unit 33 introduces the extracted pulse-related information into the RAM 34 .

(Step S7)
The control unit 33 classifies the “pulse information” corresponding to the state of the user 81 for each of the plurality of detector SNs. For example, the control unit 33 classifies which of the first to third detectors SN1 to SN3 the "pulse information" corresponding to the state of the user 81 was acquired. Then, the control unit 33 generates a pulse distribution for each of the first to third detectors SN1 to SN3. For example, the control unit 33 obtains the pulse distribution X[ _ei,t , _σi,t ] (where i is the number of the detector SN, t time, or a given time). The average of the pulse distribution is "ei,t". The variance of the pulse distribution is "σi,t".

(Step S8)
The control unit 33 generates a reference distribution (or reference data) of the pulse in consideration of the reliability of the first to third detectors SN1 to SN3. The control unit 33 generates a reference distribution C _S1t using weighting functions F _k,t (where k is the detector number) corresponding to the first to third detectors SN1 to SN3. The weighting function F _k,t is, for example, a natural number. Alternatively, the weighting function F _k,t may be some function. The reference distribution C _S1t is represented by the following first formula.

In this example, since the first to detectors SN1 to SN3 are used, 1 to 3 are input to i and k to obtain the following second and third equations.

The control unit 33 may recalculate each time the biological information and the like acquired by the first to third detectors SN1 to SN3 are updated.

Hereinafter, an example will be described in which after the control unit 33 generates the reference distribution C _S1,t1 for a period t1, the control unit 33 acquires biometric information for another period t2 to update the reference distribution.

At this time, the control unit 33 calculates the reference distribution C _S1,t2 in steps similar to those described above, based on the biological information of the period t2. Then, the control unit 33 updates the reference distribution for the period "t1+t2" using the weighting function G _ti (i is the period number). The reference distribution C _S1,t1+t2 is represented by the following fourth formula.

The weighting function G _ti is, for example, a function for weighting between multiple periods. This weighting function G _ti may be, for example, a natural number. The weighting function G _ti may be any function.

(Step S9)
The control unit 33 evaluates the user 81 . Specifically, the control unit 33 determines whether the pulse information acquired in step S4 belongs to, for example, the range of ±1σ or ±2σ of the reference distribution C _Sl,t1+t2. whether it belongs to the range of ±3σ, whether it does not belong to the range of ±3σ, and the like. Based on the result, the control unit 33 evaluates the risk of the user 81 driving the vehicle 28 .

(3 Calculation of automobile insurance premiums)
FIG. 3 is a schematic diagram illustrating evaluation results in the evaluation system according to the first embodiment. As shown in FIG. 3, for example, an automobile insurance premium value Iv1 is determined corresponding to the evaluation value Ev1 (1 to 9 in this example). The evaluation value Ev1 is a classification corresponding to the user's 81 risk.

The control unit 33 classifies and evaluates the user 81 according to the risk of the user 81, as shown in FIG. The control unit 33 calculates the insurance premium for the automobile insurance of the user 81 according to the classification.

As shown in FIG. 3, for example, an automobile insurance premium value Iv1 is determined corresponding to each of the plurality of evaluation values Ev1. For example, the premium value Iv1 when the evaluation value Ev1 is "1" is 11,000 yen. For example, the premium value Iv1 when the evaluation value Ev1 is "9" is 55,000 yen.

In the evaluation system 110 according to the present embodiment, the computing system 30 compares the biometric information of the user 81 who is driving and the biometric information of the user 81 in the same state as the user 81. do. Then, the evaluation system 110 evaluates the user 81 by evaluating the risk of the user 81 who is driving. Based on the evaluation result, the insurance premium value Iv1 is output. In the embodiment, the risk when the user 81 performs the work is based on the user's 81 information (for example, biometric information). Therefore, the user 81 can be evaluated accurately. By using the user's 81 evaluation, the user's 81 information can be used more effectively to calculate an appropriate automobile insurance premium.

The results of the user's 81 evaluation may include the user's 81 automobile insurance premium rank.

(Other examples)
In the example above, a user 81 driving a vehicle 28 is evaluated. In embodiments, the user 81 may be evaluated while performing other tasks.

The work may be the work of the user 81, for example. Work includes what the user 81 does for a living.

For example, when the control unit 33 determines that the work is performed standing, the control unit 33 extracts the biological information and the like corresponding to the state St2 of the user 81 in step S6. Control unit 33 evaluates user 81 based on the result. This makes it possible to evaluate the risk of the user 81 (for example, possible health impairment) while performing the work. When the work is work, for example, future health risks of the user 81 can be predicted. The results of these evaluations may include, for example, life and/or medical premium terms (eg, rank).

Thus, according to the embodiment, the future risk of the user 81 can be evaluated more accurately. Using this result, for example, by presenting insurance conditions, it is possible to determine more accurate insurance conditions.

Furthermore, the embodiment can predict the state when the user 81 performs "certain work" in the future. For example, it is possible to predict not only the state of the user 81 when "certain work" is actually performed, but also the state (symptoms) that the user 81 is likely to experience when the user 81 performs "certain work" in the future. This prediction may be included in the evaluation result information.

Based on this prediction, the user 81 can make appropriate changes to reduce future risks. Computing system 30 (and rating system 110 , etc.) can be used as a training system for user 81 .

In embodiments, activities may include, for example, driving a vehicle 28, working, or playing sports.

(Second embodiment)
FIG. 4 is a schematic diagram illustrating an evaluation system according to the second embodiment.
As shown in FIG. 4, the evaluation system 111 according to the second embodiment differs from the evaluation system 110 according to the first embodiment in that an output system 40 is further added, and other configurations are different from those of the first embodiment. It is the same. In the first embodiment, the control unit 33 calculates insurance premiums as shown in FIG. In the second embodiment, the control unit 33 determines which evaluation value Ev1 the user 81 corresponds to. In the second embodiment, the control unit 33 does not calculate insurance premiums, for example.

The output system 40 has a program for calculating insurance premiums as shown in FIG. The program causes the output system 40 to calculate the insurance premium for the user 81 .

The output system 40 includes, for example, an information terminal 41 and a server 42 . In this example, a control section 43 is further provided. Information terminal 41 is connected to server 42 . The control unit 43 is connected to the information terminal 41 and the server 42 . These connections are made by any method, wired or wireless. The information terminal 41, server 22 and control unit 43 may include computers. The information terminal 41, the server 22 and the control unit 43 may include memory. The information terminal 41, the server 22, and the control unit 43 include, for example, electric circuits. The information terminal 41 may include, for example, a smart phone.

The information terminal 41 communicates with the communication unit 31 of the computing system 30, for example. Communication includes sending and receiving data. For example, the evaluation result of the user 81 is sent from the communication unit 31 of the arithmetic system 30 to the information terminal 41 of the output system 40 . The information terminal 41 calculates insurance premiums for the user 81 based on the evaluation results. The server 42 stores evaluation results and insurance premiums for each user 81 . The control unit 43 controls operations of the information terminal 41 and the server 42, for example. The controller 43 may be omitted.

The information terminal 41 reads information (evaluation results) from the server 42 as necessary. The information terminal 41 can output evaluation result information.

In one example of output system 40, output system 40 is used, for example, by an insurance provider (eg, an insurance company). The insurance provider may set the terms of insurance for the user 81 based on the results of the user's 81 evaluation.

(Third embodiment)
The configuration of the evaluation system of the third embodiment differs from the configuration of the evaluation system of the first embodiment or the second embodiment in that the server 32 holds tables described below.

(1 Structure of evaluation system)
For example, when one kind of biological information (for example, pulse) is acquired by a plurality of detectors SNs, the acquisition accuracies of the plurality of detectors SNs are taken into consideration. The server 32 of the computing system 30 holds a table regarding the reliability of each of the multiple detectors SN for each of multiple pieces of biological information. The control unit 33 generates a reference distribution (or reference data) based on these tables.

FIG. 5 is a schematic diagram illustrating an evaluation system according to the third embodiment.
As shown in FIG. 5, server 32 maintains a table. In this table, for example, reliability levels CE for multiple detectors SN are defined for multiple pieces of biological information (eg, pulse). For example, when the value of the reliability CE is large, the reliability of the evaluation result (information) by the detector SN is high. FIG. 5 shows a table relating to pulse.

In this example, the confidence CE is 10 at the first detector SN1. The reliability CE is 3 at the second detector SN2. The reliability CE is 7 at the third detector SN3. The reliability CE is 5 at the fifth detector SN5. For other detectors SN, the confidence CE is zero. Any detector SNi has a confidence CEi.

(2 Evaluation method in the evaluation system)
The evaluation method of the evaluation system according to the third embodiment differs from the evaluation method of the first embodiment in that steps SS1 and SS2 are provided between steps S7 and S8. Other steps in the third embodiment are the same as in the first embodiment.

A specific evaluation method will be described with reference to FIGS. 5 and 6. FIG.

FIG. 6 is a flow chart showing the evaluation method of the evaluation system according to the third embodiment.
In the third embodiment, step SS1 is executed after executing step S7 described in relation to the first embodiment.

(Step SS1)
The control unit 33 loads the reliability CE table shown in FIG. 5 from the server 32 into the RAM. As described above, in step S7, the control unit 33 classifies the pulse information corresponding to the state St1 of the user 81 for each of the plurality of detectors SN. The control unit 33 extracts the reliability CE corresponding to this detector.

The control unit 33 determines whether the total reliability exceeds a predetermined value (for example, "11").

When the control unit 33 determines that the total reliability CE exceeds the predetermined value (step SS1, "Yes"), the process proceeds to step S8. In the third embodiment, first to third detectors SN1 to SN3 are used, and the total reliability CE of each detector SN is 20 (=10+3+7). Therefore, the control unit 33 performs processing to proceed to step S8.

(Step SS2)
When the control unit 33 determines that the total reliability CE does not exceed the desired value (step SS1, "No"), the process proceeds to step SS2.

For example, if pulse information cannot be obtained from the first detector SN1 and pulse information is obtained from the second detector SN2 and the third detector SN3, the total reliability CE is 10 (=3+7). be. At this time, the control unit 33 performs processing to proceed to step SS2.

At step SS2, the controller 33 estimates a reference distribution (or reference data).

Specifically, the control unit 33, for example, based on the correlation between the pulse distribution obtained by the first detector SN1 and the pulse distribution obtained by the second detector SN2 or the third detector SN3, , to calculate the reference distribution (or reference data).

An example will be described below based on the assumptions described in the evaluation method of the first embodiment. However, in the following example, the computing system 30 cannot obtain pulse information corresponding to the state St1 of the user 81 from the first detector SN1. In the following example, it is assumed that the computing system 30 has acquired information about the pulse corresponding to the state St0 of the user 81 from the second detector SN2 and the third detector SN3.

At this time, the control unit 33 calculates the correlation of the biological information acquired by the first to third detectors SN1 to SN3 in the state St0 of the user 81. FIG. Using the correlation, the control unit 33, from the pulse information obtained from the second detector SN2 and the third detector SN3, corresponding to the state St1 of the user 81, the pulse obtained by the first detector SN1 Estimate the pulse information that may be

Based on the estimated pulse information from the first detector SN1 and the pulse information from the second detector SN2 and the third detector SN3, the control unit 33 generates a reference distribution (or reference data). to generate

This is because the total reliability CE exceeds the desired value when the first to third detectors SN1 to SN3 are used.

In the third embodiment, the computing system 30 (for example, the control unit 33) evaluates the user 81 based on information (at least one of biological information and behavioral information) obtained from a plurality of detectors SN. . For example, multiple pieces of information (data) obtained from multiple detectors SN can be aligned in obtaining the results of the evaluation. A more accurate evaluation result of the user 81 can be output.

Such evaluation result information can be used, for example, as a basis for determining insurance conditions (for example, calculating insurance premiums). Insurance terms can be determined more accurately. It is possible to provide an evaluation device and an evaluation system that can use biological information more effectively.

FIG. 7 is a flowchart showing operations in the evaluation system according to the embodiment.
As shown in FIG. 7, for example, in step S9, the control unit 33 of the arithmetic system 30 (third system) evaluates the user 81. As shown in FIG. If the evaluation result of the user 81 is higher than the predetermined rank, the process ends. On the other hand, when the evaluation by the user 81 is equal to or lower than the predetermined rank, the control unit 33 issues an instruction to switch to automatic operation. A command CMD or the like for instructing switching to automatic operation is transmitted to, for example, the second user system 20 (second system). The second user system 20 starts automatic operation based on a command CMD or the like for instructing switching to automatic operation. For example, automatic driving of the vehicle 28 is started by the server 22 of the second user system 20 or the like.

When the evaluation result of the user 81 is below a predetermined rank, it can be determined that the user 81 who is driving is in an abnormal state. In such a case, for example, switching from manual operation to automatic operation is performed. For example, the vehicle 28 has a program for automatic driving. A command to execute this automatic driving program is provided from the computing system 30 to the second user system 20 via the server 22 of the second user system 20, for example.

FIG. 8 is a schematic diagram illustrating an evaluation system according to the embodiment;
FIG. 8 shows an example of the hardware configuration of the computing system 30. As shown in FIG. The arithmetic system 30 includes, for example, a communication unit 31 (communication interface), a server 32 (eg, an interface with an external memory, etc.), a control unit 33 (eg, CPU), a RAM 34a and a ROM 34b. The computing system 30 may further include, for example, an input section 35a and an output section 35b.

The processing (instruction) of the various information (data) described above is executed based on, for example, a program (software). For example, a computer stores this program and reads out this program to process the various information described above.

The processing of the various information described above can be performed by using a program that can be executed by a computer, such as a magnetic disk (flexible disk, hard disk, etc.), an optical disk (CD-ROM, CD-R, CD-RW, DVD-ROM, DVD±R). , DVD±RW, etc.), a semiconductor memory, or other recording media.

For example, information recorded on a recording medium can be read by a computer (or embedded system). Any recording format (storage format) can be used in the recording medium. For example, a computer reads a program from a recording medium and causes a CPU to execute instructions written in the program based on the program. Acquisition (or reading) of a program in a computer may be performed through a network.

At least part of the processing of the above information may be implemented in various software running on a computer based on a program installed from a recording medium to the computer (or embedded system). This software includes, for example, an OS (operating system). This software may include, for example, middleware that operates on a network.

The recording medium in the embodiment also includes a recording medium in which a program obtained by LAN or the Internet is downloaded and stored. The above processing may be performed based on a plurality of recording media.

A computer according to embodiments includes one or more devices (eg, personal computers, etc.). A computer according to an embodiment may include multiple devices connected by a network.

(Fourth embodiment)
In the fourth embodiment, the evaluation system 110 (see FIG. 1(a)) includes a computing system 30 (eg, a computing device, see FIG. 1(a)).

Evaluation system 110 (eg, computing system 30 ) obtains, for example, first information about user 81 and second information about user 81 . This first information includes at least one of biometric information and behavior information of the user 81 in the first state in which one work (first work) is being performed. On the other hand, the second information includes at least one of biometric information and behavior information of the user 81 in a second state in which the work (first work) is not performed. Then, the evaluation system 110 can output evaluation result information regarding the first task of the user 81 based on the first information and the second information. In this embodiment, the user 81 is an evaluation subject.

An example of evaluation by the user 81 in the evaluation system 110 will be described below.

In the example below, the work (first work) of the user 81 is driving the vehicle 28 . A user 81 is evaluated regarding the possibilities (eg, risks, etc.) associated with driving the vehicle 28 .

For example, the state of the user 81 is assessed by a first detector SN1 and a second detector SN2. As already explained, the first detector SN1 is provided on the bed 70 . The first detector SN1 detects the state of the user 81 while sleeping and before and after that. The first detector SN1 detects, for example, the heartbeat, pulse, respiration, sleep, wakefulness, etc. of the user 81 .

As already explained, the second detector SN2 is attached, for example, to the user's 81 body (eg, arm). The second detector SN2 may be able to detect the state of the user 81 continuously for one day or more, for example. The second detector SN2 can detect the user's 81 states such as heartbeat, pulse, sleep, wakefulness, falling asleep, standing, sitting, lying down, walking, and exercising, for example.

When the work (first work) of the user 81 is driving the vehicle 28, the state of the user 81 is detected by the seventh detector SN7. The seventh detector SN7 can detect the state of the user 81 while driving the vehicle 28 (while riding the vehicle 28). The seventh detector SN7 can detect, for example, heartbeat, pulse, respiration, sleep and wakefulness.

In the following, for the sake of simplification of explanation, the case of using pulse-related information among the various data obtained by these detectors SN will be explained. In the following, the information (for example, data) obtained by the detector SN is modeled for explanation.

FIGS. 9A to 9C are schematic diagrams illustrating information in the evaluation system according to the fourth embodiment.
The horizontal axis of these figures is time tm. 9A to 9C, the vertical axis represents the first signal SG1 and the second signal SG2 obtained by the first detector SN1, the second detector SN2 and the seventh detector SN7, respectively. and a seventh signal SG7. If pulses are detected, the pulse rate in one minute, for example, corresponds to the strength of these signals.

These signals provide at least part of the information about user 81 . Alternatively, information obtained by processing these signals becomes part of the information regarding the user 81 . These figures exemplify detection results (signals) from the first day D1 to the eighth day D8.

As shown in FIG. 9A, the intensity of the first signal SG1 obtained by the first detector SN1 increases while the user 81 is asleep and before and after that (ie, at night, for example). For example, during the daytime, detection by the first detector SN1 is not performed, so the first signal SG1 cannot be obtained. While the user 81 is sleeping, the pulse changes according to the sleep state of the user 81 . Accordingly, the intensity of the first signal SG1 changes.

As shown in FIG. 9B, the second detector SN2 detects the state of the user 81 substantially continuously from the first day D1 to the eighth day D8. The second signal SG2 is substantially continuous from the first day D1 to the eighth day D8. In this example, the intensity of the second signal SG2 is high during the daytime of each day, and the intensity of the second signal SG2 is low during the nighttime. The intensity of the second signal SG2 at night often corresponds to the intensity of the first signal SG1.

As shown in FIG. 9(c), the seventh signal SG7 corresponds to the pulse of the user 81 when the user 81 is on the vehicle 28. FIG. In this example, the user 81 is driving the vehicle 28 on the fifth day D5 and the seventh day D7.

In this example, the work to be evaluated (first work OP1) is driving the vehicle 28 . On the fifth day D5 and the seventh day D7, there is a first state ST1 in which the user 81 performs the first work OP1 (driving the vehicle 28). A period other than the first state ST1 is the second state ST2.

For example, during the first period (the period including the first time) of the fifth day D5, the user 81 is performing the first work OP1 (driving the vehicle 28). Other days (first day D1 to fourth day D4, sixth day D6 and eighth day D8 in this example) also have their first periods (periods including the first time).

In one example, the second signal SG2 in the first period (period including the first time) of the fifth day D5 and the other days (in this example, the first day D1 to the fourth day D4, the sixth day D6 and is compared with the second signal SG2 in the first period (the period including the first time) of the eighth day D8). In this case, a difference occurs in the second signal SG2 corresponding to the presence or absence of the first work OP1. For example, the intensity (pulse rate) of the second signal SG2 when the first task OP1 (driving) is performed and the intensity (pulse rate) of the second signal SG2 when the first task OP1 (driving) is not performed. There is a difference between and For example, the former is stronger than the latter.

Thus, the biometric information of the user 81 changes depending on whether or not the first work OP1 is performed. This change in biological information relates to the possibility (for example, the degree of risk) of the first work OP1 when the user 81 performs the first work OP1 (driving). The user 81 can be evaluated with respect to the first work OP1 by evaluating changes in the biological information of the user 81 based on whether or not the first work OP1 is performed.

In the fourth embodiment, first information including at least one of biometric information and behavior information of the user 81 in the first state ST1 performing the first work OP1, and second information not performing the first work OP1. The first task OP1 of the user 81 is evaluated based on the relative relationship (for example, the difference) between the second information including at least one of the biometric information and behavior information of the user 81 in the state ST2. will be

In this example, the information of the user 81 (in this example, the second signal SG2) on another day when the first work OP1 is not performed is used as the second information of the second state ST2. For example, the second information is information of another day corresponding to the same time (first period) as the time (first period) at which the first work OP1 is performed on the fifth day D5.

In the fourth embodiment, information (for example, the second signal SG2) at another time (period) on the fifth day D5 when the first work OP1 is performed may be used as the second information.

In this example, the user 81 is performing the first work OP1 (driving the vehicle 28) during the second period (the period including the second time) of the seventh day D7. The second signal SG2 when the first work OP1 is being performed on the fifth day D5 and the second signal SG2 when the first work OP1 is being performed on the seventh day D7 are used as the first information. May be used. From the signals for each of these two days, for example, an average value is derived. Information including average values may be used as the first information.

In the above example, one detector (second detector SN2) detects the above first information (the state when the first work OP1 is being performed) and the above second information (the state when the first work OP1 is being performed). The state when not in) and the state in is detected.

In the fourth embodiment, the first information and the second information may be obtained with different detectors. For example, the first information (state when performing the first operation OP1) may be obtained at the seventh detector SN7. On the other hand, the second information (the state when the first work OP1 is not performed) may be obtained by the first detector SN1. Based on the information obtained from these detectors, the user's 81 first work OP1 may be evaluated.

The first information and the second information may be obtained with different detectors SN. At this time, the relationship of derived data (information) is obtained between these detectors SN, and the results obtained when the state of the user 81 is the same may differ from each other. In this case, the results obtained with different detector SNs may be corrected appropriately.

In one example, the user 81 may be in a sleeping state in at least part of the second state ST2 in which the second information is obtained. The second information obtained in the sleep state has relatively small fluctuations. For example, relatively uniform second information is obtained even on different days. Therefore, the difference from the first information can be stably obtained.

For example, relatively stable data can be obtained when the first information and the second information include at least one of heartbeat, pulse, respiration and blood pressure of the user 81 .

For example, the first information is the user's 81 heartbeat, and the second information is also the user's 81 heartbeat. The first information is the user's 81 pulse, and the second information is also the user's 81 pulse. The first information is the user's 81 breathing, and the second information is also the user's 81 breathing. The first information is the body temperature of the user 81 and the second information is also the body temperature of the user 81 . The first information is the user's 81 weight, and the second information is also the user's 81 weight. The first information is the user's 81 electromyogram (including an electrocardiogram), and the second information is also the user's 81 electromyogram (including an electrocardiogram). The first information is the electroencephalogram of the user 81 and the second information is also the electroencephalogram of the user 81 . The first information is the user's 81 blood pressure, and the second information is also the user's 81 blood pressure. The first information is the blood oxygen concentration of the user 81 and the second information is also the blood oxygen concentration of the user 81 . The first information is the user's 81 exhaled gas, and the second information is also the user's 81 exhaled gas.

In the fourth embodiment, the first information and the second information may be different types of information (for example, biological information). For example, when the first information is the heartbeat of the user 81, the second information is the pulse, respiration, body temperature, weight, electromyogram (including electrocardiogram), electroencephalogram, blood pressure, blood oxygen concentration ( percutaneous arterial oxygen saturation) and/or exhaled gases. For example, different types of biometric information may be related to each other. For example, changes in pulse rate may be coupled with changes in heart rate. In such a case, for example, a pulse may be detected by the first detector SN1 and a heartbeat may be detected by the seventh detector SN7.

The type of information that can be acquired may be appropriately changed depending on the type of the first work. In one example, with one detector SN, a first type of first information and a first type of second information may be obtained. In another example, a first type of first information may be obtained with one detector SN and a first type of second information may be obtained with another detector SN. In another example, a first type of first information may be obtained with one detector SN and a second type of second information may be obtained with another detector SN. Furthermore, in another example, a first type of first information may be obtained and a second type of second information may be obtained with one detector SN.

As described above, in one embodiment, the evaluation results of the user 81 are the first information of the user 81 who is performing the first work OP1 and the first information of the user 81 who is not performing the first work OP1. is derived based on the relative relationship between the second information of A relative relationship includes, for example, the difference between the first information and the second information. Then, the evaluation result of the user 81 is output as evaluation result information.

For example, when the first work OP1 includes driving the vehicle 28, the evaluation result information is, for example, information regarding the driving risk of the user 81. For example, the pulse while driving may be excessively higher than the pulse at rest. In such a case, it can be estimated that the user 81 has difficulty driving calmly. In such a case, it can be estimated that the user 81 is highly likely to cause an accident or the like while driving. The estimated result becomes the evaluation result information.

According to the fourth embodiment, for example, it is possible to provide an evaluation system that can more effectively use information (at least one of biometric information and behavior information) of the user 81 .

An example of evaluation by the user 81 will be described below.
FIGS. 10A to 10C are schematic diagrams illustrating evaluation result information according to the fourth embodiment.
FIG. 10(a) illustrates one index xv1 of the user's 81 evaluation result. The index xv1 is, for example, first information (information when the user 81 is performing the first work OP1) and second information (information when the user 81 is not performing the first work OP1). is a value corresponding to the relative relationship (for example, the difference) between . For example, when the index xv1 is large, the difference between the first information and the second information is large. For example, when the index xv1 is large, the risk in the first work OP1 (for example, driving) is high.

For example, index xv1 relates to pulse. For example, the index xv1 may also relate to changes in pulse over time.

A plurality of ranges are defined according to the value of the index xv1. In this example, the range is defined by intervals of five. In one range, the difference in index xv1 is five. An evaluation value Ev1 is determined for each of the plurality of ranges. In this example, the larger the value of the index xv1, the larger the evaluation value Ev1.

For example, when the evaluation value Ev1 is large, it can be seen that the risk in the first task OP1 (for example, driving) is high. In the fourth embodiment, such an evaluation value Ev1 is at least part of the evaluation result information.

In this example, multiple ranges for the index xv1 are divided at equal intervals. In embodiments, it may be partitioned at non-equidistant intervals.

As shown in FIG. 10(b), in one example, the index xv1 ranges from 30 to 40 with fine widths. The range of the index xv1 can be appropriately divided according to the target first work OP1 and the type of information used.

As shown in FIG. 10(c), multiple ranges are defined according to the value of another index xv2. The index xv2 relates to blood pressure, for example. For example, the index xv2 may correspond to changes in blood pressure over time. In this example as well, an evaluation value Ev2 is determined for each of a plurality of ranges. In this example, the larger the value of the index xv2, the larger the evaluation value Ev2.

For example, the evaluation may be performed using at least one of the evaluation value Ev1 and the evaluation value Ev2. For example, the evaluation may be performed using the sum of the evaluation value Ev1 and the evaluation value Ev2. For example, the sum of the evaluation value Ev1 and the evaluation value Ev2 is output as the evaluation result information.

In this example, the range of evaluation value Ev2 is larger than the range of evaluation value Ev1. For example, the evaluation result of the index xv2 may more appropriately represent the change in biological information in the target first work OP1 (for example, driving) than the evaluation result of the index xv1. In such a case, the range of evaluation value Ev2 is made larger than the range of evaluation value Ev1. For example, weighting is performed. Thereby, more appropriate evaluation is performed when a plurality of evaluation values are used.

In embodiments, the above evaluation result information is output in any manner. For example, any method may be used, such as displaying on a display, printing, or providing an audio signal.

Thus, in the fourth embodiment, evaluation result information includes one of a plurality of sorted ranks. The plurality of classified ranks are, for example, the above evaluation value Ev1 or evaluation value Ev2. A plurality of classified ranks may be, for example, a processing result (eg, sum, product, weighted sum, etc.) of multiple evaluation values (eg, evaluation value Ev1 and evaluation value Ev2). For example, one of multiple ranks is output by any method.

The above fourth embodiment may be implemented by the evaluation system 111 (see FIG. 4).
In one example of an output system 40 (eg, an assessment results output system), the output system 40 is used, for example, by an insurance provider (eg, an insurance company). The insurance provider may determine the conditions of insurance for the user 81 based on the user's 81 evaluation result information.

For example, in one example, insurance is set for the user 81 driving the vehicle 28 (first task OP1). In this case, at least part of the insurance conditions of automobile insurance may be determined based on the evaluation result information of the user 81 regarding the first work OP1.

For example, an evaluation value Ev1 related to the index xv1, as illustrated in FIGS. 10A to 10C, is used. For example, insurance conditions may be set according to the evaluation value Ev1. The insurance conditions include, for example, at least one of insurance premiums and insurance claims.

As shown in FIG. 3, for example, an insurance premium value Iv1 is determined corresponding to each of the plurality of evaluation values Ev1. For example, the premium value Iv1 when the evaluation value Ev1 is "1" is 11,000 yen. For example, the premium value Iv1 when the evaluation value Ev1 is "9" is 55,000 yen.

In this way, the user 81 is evaluated with respect to the user 81 performing the first work OP1 (for example, driving). Based on the evaluation result, the insurance premium value Iv1 is output. Thereby, the insurance premium value Iv1 can be determined based on the risk when the user 81 performs the first work OP1 (for example, driving). In the fourth embodiment, the risk when the user 81 performs the first task OP1 (eg, driving) is based on information (eg, biometric information) of the user 81 . Therefore, the evaluation result of the user 81 is more accurate. The insurance premium value Iv1 can be determined more accurately based on the actual condition of the user 81 .

In this way, the evaluation result information may include the car insurance premium rank of the user 81 .

In the example above, the first work OP1 includes driving the vehicle 28 . In the embodiment, the first operation OP1 is optional.

The first work OP1 may be the work of the user 81, for example. Work includes what the user 81 does for a living. For example, the length of time that the user 81 is working is relatively long. Therefore, for example, the state of work performance is often related to the health state of the user 81 .

For example, the biometric information (such as blood pressure) of the user 81 may be significantly different during a period of work than during a period of non-work. In this case, for example, it is presumed that the user 81 has a heavy mental and physical burden due to the performance of the work. For example, if an excessive load is applied to the user 81, the health of the user 81 may be damaged.

For example, a job is set as the first job OP1. Then, perform the above evaluation. This makes it possible to evaluate the risk of the user 81 (for example, possible health impairment) while performing the work. For example, the future health risk of the user 81 can be predicted by performing the above evaluation with the first work OP1 as work. In such a case, the evaluation result information may include, for example, conditions (for example, rank) of at least one of life insurance and medical insurance premiums.

Thus, according to the fourth embodiment, the future risk of the user 81 can be evaluated more accurately. Using this result, for example, by presenting insurance conditions, it is possible to determine more accurate insurance conditions.

Furthermore, in the fourth embodiment, it is possible to output the state when the user 81 performs the first work OP1 in the future. For example, not only the state of the user 81 when actually performing the first work OP1, but also the state (symptom) that the user 81 is likely to experience when the user 81 performs the first work OP1 in the future can be predicted and output. This predicted output may be included in the evaluation result information.

Based on this predicted output, the user 81 can make appropriate changes to reduce future risks. For example, the evaluation result information can serve as advice to the user 81 . The user 81 can now perform the first work OP1 with less risk. For example, computing system 30 (and rating system 110 , etc.) can be used as a training system for user 81 .

In the embodiment, the first work OP1 may include, for example, driving the vehicle 28, working, or playing sports.

In the fourth embodiment, for example, based on the information (biological information and behavioral information) of the user 81 for a period of one day or longer, one of a plurality of classified ranks regarding the user 81 is included. Evaluation result information is output.

For example, regular health checkups are performed. In this case, the condition of the user 81 at the time (less than one day) when the medical examination is performed is detected. The health condition of the user 81 at that time is detected from the information obtained by the physical examination. However, it is difficult to evaluate the possibility (for example, risk) of the state when the user 81 performs the first task OP1 based on the health state detected in the health checkup.

For example, in health checkups, standard ranges are defined for evaluation items (for example, blood pressure). For example, a normal range for blood pressure is established, and exceeding that range is determined to be, for example, a disease state. That is, information (eg, biometric information) obtained from the user 81 is compared with a separately determined standard value (eg, normal range). The health condition of the user 81 and the like are determined based on the comparison result.

On the other hand, biometric information of a user 81 (for example, a patient or a cared person) is acquired in a hospital, nursing care facility, or the like. For example, a pulse or the like is detected as biological information. Then, the detected pulse is compared with a separately defined standard value (for example, normal range). Based on the result, for example, an alarm is generated and appropriate action is taken. However, the future possibility of the user 81 (for example, the risk of performing the first task OP1) is not evaluated from the results of biometric information acquisition in hospitals, nursing homes, or the like.

On the other hand, in the fourth embodiment, the future possibility of user 81 is evaluated based on the first information and second information about user 81 . For example, the evaluation result is output as one of a plurality of sorted ranks. Thereby, the future possibility (risk) of the user 81 can be known objectively.

In the fourth embodiment, the period for acquiring information on the user 81 may be, for example, two days or longer. For example, if the period is one day, one change of day and night conditions occurs. Therefore, the obtained information may vary widely. When the period for acquiring the information of the user 81 is two days or longer, the day and night conditions occur multiple times. Information obtained from multiple conditions is averaged, for example. Thereby, the user 81 can be evaluated with higher accuracy.

The period for acquiring the information of the user 81 may be, for example, 3 days or longer, or 5 days or longer. As a result, evaluation results with higher accuracy can be obtained.

In the fourth embodiment, for example, the second information in the second state ST2 in which the first work OP1 is not performed may be past corresponding information of the user 81 . For example, the state one month before the evaluation of the user 81 while performing the first work OP1 may be the second state ST2. For example, if the first information is the pulse while performing the first work OP1, the pulse data during sleep one month before may be used as the second information.

In the fourth embodiment, the evaluation result information of the user 81 includes insurance conditions (for example, at least one of insurance premiums and insurance money), deposit and savings conditions (for example, interest rates, etc.), and provision of various services. At least one of the conditions for receiving (for example, the content of the service) may be included.

(Fifth embodiment)
Also in the fifth embodiment, for example, an arithmetic system 30 (for example, an arithmetic device, see FIG. 1(a) or FIG. 4) is provided. The computing system 30 obtains first information including biometric information of the user 81 and second information including behavior information of the user 81 . These pieces of information are detected by the first detection system 10 or the second detection system 20, for example. For example, the second information may be input by the user 81 or the like.

The arithmetic system 30 can output evaluation result information regarding the user 81 based on the first information (biological information) and the second information (behavioral information).

For example, biometric information (for example, pulse rate) differs between sleeping and exercising. Based on the degree of difference and the like, it is possible to evaluate (estimate) the possibility of the future state of the user 81 (for example, the possibility of injury or disease). Evaluation results are output as evaluation result information.

For example, the sleep start time and end time of the user 81 are included in one piece of behavior information (second information). On the other hand, biometric information during sleep (pulse change, etc.) or daytime biometric information (pulse change, etc.) is acquired as the user's 81 biometric information (first information). Based on such first information and second information, it is possible to evaluate (estimate) the possibility of the future state of the user 81 (for example, the possibility of injury or illness). Evaluation results are output as evaluation result information.

Also in the fifth embodiment, it is possible to provide an evaluation system in which user information can be used more effectively.

Also in the fifth embodiment, the evaluation result information includes, for example, one of a plurality of classified ranks. Evaluation result information is output as one of a plurality of classifications. This clarifies the evaluation result of the user 81 . The evaluation result information may include, for example, conditions (for example, rank) of at least one of life insurance and medical insurance premiums.

(Sixth embodiment)
Also in the sixth embodiment, for example, an arithmetic system 30 (see FIG. 1(a) or FIG. 4) is provided. The computing system 30 obtains information including biometric information of the user 81 . This information is detected, for example, by the first detection system 10 or the second detection system 20 or the like.

Based on this information, the computing system 30 can output one of a plurality of sorted ranks regarding the user's 81 future possibilities (for example, risks). Furthermore, based on this evaluation result information, for example, at least one condition (for example, rank) of life insurance and medical insurance premiums may be output.

For example, biometric information (eg, blood pressure, etc.) of a user 81 (eg, a patient or a care recipient) is detected in a health checkup, hospital, or the like (including nursing care facilities). Then, the detected biological information is compared with a separately determined standard value (for example, normal range). Based on the result, for example, the health of the user 81 is diagnosed. Alternatively, an alarm is generated and appropriate action is taken. However, the future possibility (for example, risk) of the user 81 is not evaluated as a plurality of classified ranks from the result of biometric information obtained at a medical examination or hospital. Also, future possibilities (eg risks) are not output as one of a plurality of sorted ranks.

On the other hand, in the sixth embodiment, based on information including the biometric information of the user 81, the future possibility (for example, risk) of the user 81 is evaluated, and the evaluation results are classified. Output as one of multiple ranks. The future possibility (risk) of the user 81 can be known objectively.

In the sixth embodiment, the period for acquiring the biometric information of the user 81 is, for example, one day or longer. This period may be two days or longer. For example, if the period is one day, one change of day and night conditions occurs. Therefore, the obtained information may vary widely. When the period for acquiring the information of the user 81 is two days or longer, the day and night conditions occur multiple times. Information obtained from multiple conditions is averaged, for example. Thereby, the user 81 can be evaluated with higher accuracy. The period for acquiring the information of the user 81 may be, for example, 3 days or longer, or 5 days or longer. As a result, evaluation results with higher accuracy can be obtained.

Also in the sixth embodiment, it is possible to provide an evaluation system in which user information can be used more effectively.

The processing according to the fourth to sixth embodiments described above may be performed by the arithmetic system 30 (arithmetic device) described with reference to FIG.

FIG. 11 is a schematic diagram illustrating insurance according to the embodiment.
In an embodiment, insurance is a form of mutual aid in which participants fairly share a fixed premium corresponding to the predicted probability of occurrence of an accident against dangers that may occur in the future, and prepare for an accident. It is a system of mutual help born from the spirit. Insurance can be categorized based on the subject of risk into human insurance (e.g., insurance against death, injury, and illness), property insurance (e.g., property damage insurance), and money insurance (e.g., insurance against loss of income). ), and insurance related to things (for example, insurance against the burden of liability).

As shown in FIG. 11, human-money insurance includes, for example, life insurance and accident insurance. Examples of insurance include fire insurance, earthquake insurance, automobile insurance, compulsory automobile liability insurance, traffic accident insurance, ordinary accident insurance, overseas travel accident insurance, medical insurance, nursing care insurance, and annuity insurance. The present invention may also be applied to these insurances after appropriate design changes.

According to the embodiments, it is possible to provide an evaluation system that can use biometric information more effectively.

The embodiments of the present invention have been described above with reference to specific examples. However, the invention is not limited to these specific examples. For example, with respect to the specific configuration of each element such as an arithmetic unit, a communication unit, a server, a control unit, a detector, and a system included in the evaluation system, the present invention can be applied by appropriately selecting from a range known to those skilled in the art. It is included in the scope of the present invention as long as it can be implemented and the same effect can be obtained.

Any combination of two or more elements of each specific example within the technically possible range is also included in the scope of the present invention as long as it encompasses the gist of the present invention.

In addition, based on the evaluation system described above as an embodiment of the present invention, all evaluation systems that can be implemented by those skilled in the art by appropriately modifying the design also belong to the scope of the present invention as long as they include the gist of the present invention.

In addition, within the scope of the idea of the present invention, those skilled in the art can conceive of various modifications and modifications, and it is understood that these modifications and modifications also belong to the scope of the present invention. .

DESCRIPTION OF SYMBOLS 10... 1st user system, 11... Communication terminal, 20... 2nd user system, 21... Communication part, 22... Server, 27... Seat, 28... Vehicle, 30... Operation system, 31... Communication part, 32... Server, 33... Control part 34... RAM 34a... RAM 34b... ROM 35a... Input part 35b... Output part 40... Output system 41... Information terminal 42... Server 43... Control part 60... Mattress 70 Bed 71 Bottom 72 Pillow 81 User 110, 111 Evaluation system CE, CEi Reliability CMD Command D1 to D8 1st to 8th day DATA1 Data Ev1... Evaluation value Iv1... Value OP1... First work S1 to S8... Step SG1... First signal SG2... Second signal SG7... Seventh signal SN, SNi... Detector SN1 to SN7... 1st to 7th detectors ST1, ST2 state t1 to t5, tk period tm time xv1, xv2 index

Claims (4)

a first detector;
a second detector different from the first detector;
The user is in the first state or in the first state based on the first biological information acquired by the first detector during a first period in which the user is in the first state or in a state corresponding to the first state A control unit that evaluates the second biological information obtained by the second detector in a second period different from the first period in a state corresponding to
with
An electrically powered device that switches driving to automatic driving when the control unit evaluates that the user is in an abnormal state by evaluating the second biological information. 2. The electric device according to claim 1, wherein said first biological information and said second biological information are the same type of biological information. 3. The electric device according to claim 1, wherein said control unit calculates said user's insurance premium based on an evaluation result of said second biometric information. further comprising a third detector different from the first and second detectors;
Based on the third biological information acquired by the third detector in the first period, the first biological information, and the reliability of the first and third detectors, the control unit determines the 4. The evaluation system according to any one of claims 1 to 3, which evaluates 2 biological information.

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